Pandas_handson.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Pandas-handson.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPD3QgEVSeE9RowsxRYD9dh"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1lcsDzDO7oC"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2NQBqeBRCGL",
        "outputId": "0886c5e9-2164-42fe-fe0b-73e42f18be3d"
      },
      "source": [
        "s = pd.Series([2,-1,3,5])\n",
        "s"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2\n",
              "1   -1\n",
              "2    3\n",
              "3    5\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-WsfGcHRPpS",
        "outputId": "3013ba23-dced-4eb3-979a-607a5b08583c"
      },
      "source": [
        "import numpy as np\n",
        "np.exp(s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      7.389056\n",
              "1      0.367879\n",
              "2     20.085537\n",
              "3    148.413159\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BiAIbPz-RTYr",
        "outputId": "44dd4598-3c2a-4d5c-e6d9-c799c8e5933a"
      },
      "source": [
        "s + [1000,2000,3000,4000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1002\n",
              "1    1999\n",
              "2    3003\n",
              "3    4005\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjFRd7vsRX0P",
        "outputId": "10bc52fe-93ab-4eb3-dac4-1c7f7ec0d34b"
      },
      "source": [
        "s + 1000"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1002\n",
              "1     999\n",
              "2    1003\n",
              "3    1005\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYa5yV5KRaZc",
        "outputId": "d34d624a-1eb9-4de1-e1bb-72e78a385eca"
      },
      "source": [
        "s < 0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    False\n",
              "1     True\n",
              "2    False\n",
              "3    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iq64VoXmRcH7",
        "outputId": "d47542d7-8ef3-406c-a220-7d7fbde2778b"
      },
      "source": [
        "s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
        "s2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice       68\n",
              "bob         83\n",
              "charles    112\n",
              "darwin      68\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxxQvoccRu9-",
        "outputId": "0eb26568-70ed-491c-a340-647debe8a73f"
      },
      "source": [
        "s2[\"bob\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FquuXtUoRx3c",
        "outputId": "fa923509-15e2-4fa6-b613-edd1a51208c4"
      },
      "source": [
        "s2[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWPkjbjwRzxw",
        "outputId": "1f9181da-10be-4a05-b9b9-a18e2127e75d"
      },
      "source": [
        "s2.loc[\"bob\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9kKCjydR7Jb",
        "outputId": "c64e137d-8b78-423f-de28-8b20dabe05e9"
      },
      "source": [
        "s2.iloc[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hr9Up23yR8ve",
        "outputId": "ac96bf62-2bcd-4abb-e490-a19e3a67e7b5"
      },
      "source": [
        "s2.iloc[1:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bob         83\n",
              "charles    112\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVgjuECkSABg",
        "outputId": "99c4fc8e-4804-42f5-98cc-2e7c68c69c62"
      },
      "source": [
        "surprise = pd.Series([1000, 1001, 1002, 1003])\n",
        "surprise"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1000\n",
              "1    1001\n",
              "2    1002\n",
              "3    1003\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrbNW1BASGgR",
        "outputId": "d2f587d3-52e8-471c-9b40-b5733f3dac76"
      },
      "source": [
        "surprise_slice = surprise[2:]\n",
        "surprise_slice"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    1002\n",
              "3    1003\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqiopidTSKDn",
        "outputId": "5e7e74ce-2659-45f4-dec7-d5b020b0e9aa"
      },
      "source": [
        "try:\n",
        "    surprise_slice[0]\n",
        "except KeyError as e:\n",
        "    print(\"Key error:\", e)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Key error: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYlpiqowSTiT",
        "outputId": "f5830d4a-7224-4989-e92f-6f87146952c7"
      },
      "source": [
        "surprise_slice.iloc[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OGfGT3xSXvE",
        "outputId": "3dc7def8-0dd1-464d-b0ae-fe0a77c35309"
      },
      "source": [
        "#init from dict\n",
        "\n",
        "weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
        "s3 = pd.Series(weights)\n",
        "s3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice     68\n",
              "bob       83\n",
              "colin     86\n",
              "darwin    68\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIs0_D2MSeF9",
        "outputId": "bc7ccb92-03e8-4566-ddd8-34e8d299e68c"
      },
      "source": [
        "s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
        "s4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "colin    86\n",
              "alice    68\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTnVtP5YSnOK",
        "outputId": "3b422f30-f5f2-476e-c11b-a41c1b241db5"
      },
      "source": [
        "#automatic alignment\n",
        "\n",
        "print(s2.keys())\n",
        "print(s3.keys())\n",
        "\n",
        "s2 + s3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['alice', 'bob', 'charles', 'darwin'], dtype='object')\n",
            "Index(['alice', 'bob', 'colin', 'darwin'], dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice      136.0\n",
              "bob        166.0\n",
              "charles      NaN\n",
              "colin        NaN\n",
              "darwin     136.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfW-PcmKSzfi",
        "outputId": "0f3cca5e-45ea-4651-8334-b88313acdb99"
      },
      "source": [
        "s5 = pd.Series([1000,1000,1000,1000])\n",
        "print(\"s2 =\", s2.values)\n",
        "print(\"s5 =\", s5.values)\n",
        "\n",
        "s2 + s5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "s2 = [ 68  83 112  68]\n",
            "s5 = [1000 1000 1000 1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice     NaN\n",
              "bob       NaN\n",
              "charles   NaN\n",
              "darwin    NaN\n",
              "0         NaN\n",
              "1         NaN\n",
              "2         NaN\n",
              "3         NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHjaxbgqTEiW"
      },
      "source": [
        "#Pandas could not align the Series, since their labels do not match at all, hence the full NaN result."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waurEf3cTNoZ",
        "outputId": "aa107b4f-4338-4615-8f64-d3daa4d8e0d1"
      },
      "source": [
        "#Init with a scalar\n",
        "\n",
        "meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
        "meaning"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "life          42\n",
              "universe      42\n",
              "everything    42\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PiWtchpKTTpX",
        "outputId": "1762d0e9-a817-4de1-faf4-ad2079667fa8"
      },
      "source": [
        "#Series name\n",
        "\n",
        "s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
        "s6"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bob      83\n",
              "alice    68\n",
              "Name: weights, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "CXbgqDp4TcLA",
        "outputId": "e8079577-e5ed-4edd-ba1c-4d160557a18f"
      },
      "source": [
        "#plotting a series\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
        "s7 = pd.Series(temperatures, name=\"Temperature\")\n",
        "s7.plot()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78E4lrNfT1j1"
      },
      "source": [
        "**Handling time**\n",
        "\n",
        "\n",
        "Many datasets have timestamps, and pandas is awesome at manipulating such data:\n",
        "\n",
        "it can represent periods (such as 2016Q3) and frequencies (such as \"monthly\"),\n",
        "\n",
        "it can convert periods to actual timestamps, and vice versa,\n",
        "\n",
        "it can resample data and aggregate values any way you like,\n",
        "\n",
        "it can handle timezones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsBM_w4jTldT",
        "outputId": "e5c96642-4bd4-430c-bec0-39da6da06298"
      },
      "source": [
        "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')\n",
        "dates"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',\n",
              "               '2016-10-29 19:30:00', '2016-10-29 20:30:00',\n",
              "               '2016-10-29 21:30:00', '2016-10-29 22:30:00',\n",
              "               '2016-10-29 23:30:00', '2016-10-30 00:30:00',\n",
              "               '2016-10-30 01:30:00', '2016-10-30 02:30:00',\n",
              "               '2016-10-30 03:30:00', '2016-10-30 04:30:00'],\n",
              "              dtype='datetime64[ns]', freq='H')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Z5J35DNUGMB",
        "outputId": "a8c4f6d3-d162-40f3-de40-1d1726e5e4a3"
      },
      "source": [
        "temp_series = pd.Series(temperatures, dates)\n",
        "temp_series"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 17:30:00    4.4\n",
              "2016-10-29 18:30:00    5.1\n",
              "2016-10-29 19:30:00    6.1\n",
              "2016-10-29 20:30:00    6.2\n",
              "2016-10-29 21:30:00    6.1\n",
              "2016-10-29 22:30:00    6.1\n",
              "2016-10-29 23:30:00    5.7\n",
              "2016-10-30 00:30:00    5.2\n",
              "2016-10-30 01:30:00    4.7\n",
              "2016-10-30 02:30:00    4.1\n",
              "2016-10-30 03:30:00    3.9\n",
              "2016-10-30 04:30:00    3.5\n",
              "Freq: H, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "gfKRjn3LULhd",
        "outputId": "f73f4f4d-5913-4d4f-f597-dfeecc9d9701"
      },
      "source": [
        "temp_series.plot(kind=\"bar\")\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vw3pOzRPUUeA",
        "outputId": "f0ca40e2-8276-4a8e-853a-b37e41c677ff"
      },
      "source": [
        "#Resampling\n",
        "temp_series_freq_2H = temp_series.resample(\"2H\")\n",
        "temp_series_freq_2H"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.resample.DatetimeIndexResampler object at 0x7f056e889f50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPm_RPGrUqI1"
      },
      "source": [
        "temp_series_freq_2H = temp_series_freq_2H.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "thOv-JvDVBpZ",
        "outputId": "7668770e-3bff-45a8-a0a5-43e3ad1404ec"
      },
      "source": [
        "temp_series_freq_2H.plot(kind=\"bar\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4TRYjo8VExD",
        "outputId": "3bd98f34-e888-4116-a858-03a4640a8788"
      },
      "source": [
        "temp_series_freq_2H = temp_series.resample(\"2H\").min()\n",
        "temp_series_freq_2H"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 16:00:00    4.4\n",
              "2016-10-29 18:00:00    5.1\n",
              "2016-10-29 20:00:00    6.1\n",
              "2016-10-29 22:00:00    5.7\n",
              "2016-10-30 00:00:00    4.7\n",
              "2016-10-30 02:00:00    3.9\n",
              "2016-10-30 04:00:00    3.5\n",
              "Freq: 2H, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPYsNaPjVTuU",
        "outputId": "01ae6b8f-13b6-469e-9bdc-5a9aa56b3d68"
      },
      "source": [
        "temp_series_freq_2H = temp_series.resample(\"2H\").apply(np.min)\n",
        "temp_series_freq_2H"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 16:00:00    4.4\n",
              "2016-10-29 18:00:00    5.1\n",
              "2016-10-29 20:00:00    6.1\n",
              "2016-10-29 22:00:00    5.7\n",
              "2016-10-30 00:00:00    4.7\n",
              "2016-10-30 02:00:00    3.9\n",
              "2016-10-30 04:00:00    3.5\n",
              "Freq: 2H, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAGbEilfVXsT",
        "outputId": "6bd043e5-6469-42fc-dcd8-9115772e0472"
      },
      "source": [
        "#Unsampling and interpolation\n",
        "\n",
        "temp_series_freq_15min = temp_series.resample(\"15Min\").mean()\n",
        "temp_series_freq_15min.head(n=10) # `head` displays the top n values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 17:30:00    4.4\n",
              "2016-10-29 17:45:00    NaN\n",
              "2016-10-29 18:00:00    NaN\n",
              "2016-10-29 18:15:00    NaN\n",
              "2016-10-29 18:30:00    5.1\n",
              "2016-10-29 18:45:00    NaN\n",
              "2016-10-29 19:00:00    NaN\n",
              "2016-10-29 19:15:00    NaN\n",
              "2016-10-29 19:30:00    6.1\n",
              "2016-10-29 19:45:00    NaN\n",
              "Freq: 15T, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66ey8irjk4y2"
      },
      "source": [
        "One solution is to fill the gaps by interpolating. We just call the interpolate() method. The default is to use linear interpolation, but we can also select another method, such as cubic interpolation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skWSVSkyk0zL",
        "outputId": "6684d4a4-1aaa-4699-edbd-81a4bf41ab4c"
      },
      "source": [
        "temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"cubic\")\n",
        "temp_series_freq_15min.head(n=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 17:30:00    4.400000\n",
              "2016-10-29 17:45:00    4.452911\n",
              "2016-10-29 18:00:00    4.605113\n",
              "2016-10-29 18:15:00    4.829758\n",
              "2016-10-29 18:30:00    5.100000\n",
              "2016-10-29 18:45:00    5.388992\n",
              "2016-10-29 19:00:00    5.669887\n",
              "2016-10-29 19:15:00    5.915839\n",
              "2016-10-29 19:30:00    6.100000\n",
              "2016-10-29 19:45:00    6.203621\n",
              "Freq: 15T, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "gupe_61Wk9Ab",
        "outputId": "bbc501ad-e265-4c22-b70e-1bf465140cf1"
      },
      "source": [
        "temp_series.plot(label=\"Period: 1 hour\")\n",
        "temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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06BAVK1bkwIEDtG/fnoiICG677bY/bXe1fz0iIoI6depQvrw1fqNq1ar8+uuvfyrojRs3JjQ0FICoqCji4uJuWNAzs3z5cn755fc28Pnz57l48SItWrTgiSeeYNCgQfTt2/faMXIjWwXdGBMLXP9WOSXDegP8JdfRFAbGwLf/JD2gEn871oae9SpQytXmZylkqoaU4PPRzRk8fR39Fl5kVrf3afj9fTCrLwz9BorZPBd2NlvSec3dps+tWNF6861atSpt27Zl8+bNmRb0q1PrOhyOP0yz63A4SE1NveH2YJ14vbrNzabczSg9PZ21a9f+qQU+btw4unXrxtdff02LFi349ttvqVmzZnbTzZR+zi9oB36A37bxU+iDnLnixZDm4XZHpICKJYvy6chmhJcuzoAliaxr+hYkHIRP+sOVS3aH5zKuTp97tR958+bNWe5zdfpcINPpc4FcT5975swZkpOtUUqnTp1i9erV1K6dv8OAr065CzB//vxry/39/f/QH96xY0cmT5587fnVN8n9+/cTERHBP/7xDxo1asSuXbtyHZMW9IK27j1MsdK8EFebhmFB1K0YaHdEyinE35e5I5oSGVqSe7/zYXX9V+DoRvh0MKResTs8lzB+/HhSUlKIjIykTp06jB8/Pst9Hn74YdLT04mIiOCee+75w/S5Fy9epFatWjzzzDN/mj43syGMGzZsIDQ0lM8//5yRI0dSp45184+dO3cSHR1NvXr1aNeuHePGjcv3gj5hwgQee+wxoqOj8fL6fRK9Hj16sGDBgmsnRd98801iYmKIjIykdu3a10YLTZo0ibp16xIZGYmPjw9dunTJdUw6fW5BSjgAbzYgrs7DtN3YgjfvrU/PehXsjkpdJ/FKKiM/3siqvaeY1WA3LX95zroxd99pBTZbo06fq+DWp8/VFnpBWjcVHF68frYVZfx96VK3nN0RqUwUK+LN9Aei6VSnLPdtqsHq8L/C9nmw9B/WORClXJQW9IKSdB42z+LCbT1YvD+dQU3C8NGhii7L19uLtwc24K4GoQza1Yw1Ze+F9VNh5St2h6bUDel86AUl9hO4coG5jm74eAn3NqmU9T7KVt5eDl7tF4m/nzf3/tydeeXOEP3Di9a9Sxvcb3d4Sv2JFvSCkJ4O698jrUI0b+wKoFtEGcr45/4iApX/HA5hQo/aBPh5c8/3g1hS6hS1vhyDBFWBKq3y9djGmPy9X6xyaTk5v6mf+QvCvu8g4QCrS/fjYnIqD+hQRbciIjzRsQbjutZlQMJIjjvKYz4bDKf359sx/fz8OH36dI7+qZX7M8Zw+vTpW756VFvoBWHtuxj/CrxwoDr1Qv2oXznI7ohUDjzUuqrV/bLgCZb4TaD47P54PbQciub97zM0NJQjR46gs5IWXn5+frd89agW9Px2chccWMGhek+wZ10yr/fP3ZVgyl4DGlemuG9nRn52ho8SXiR97gP43D8fvP58c+vc8PHxoUqVKnn6msrzaZdLfls3Bbx8mXSmOaVLFKFbpN73w931qFeBEYMH80zacHwOrSRx0Vgdzqhcghb0/JSYAFvmcrFGXxbtvcK9jSvj6+2V9X7K5bWrWYbeQ//OB6YnxbbO4PT3k7PeSal8pgU9P236CFIvM0e64iXCoCZhdkek8lCTqsE0evANfiCakqsm8Ov63N1UQanc0oKeX9JSYcN00iq3ZPIOXzrVLUe5QB2q6GkiKpcidPhs9ktlSn49gkM7N9odkirEtKDnl91fwblfWV26H+eTUnVWRQ9WLbQcxR/4nCv44v3ZvZyO13u7KHtoQc8v66ZiSlbm//aFU7t8ANFhOlTRk1UMv52E7h8Qkn6aY1P733R+bKXyixb0/HB6Pxz6icNV7mHnyUSGNA/XK/4KgerRHdjV+D9EpGwl5t3hpKel2x2SKmS0oOeH2NkgDt5JaERQMR96RukUuYVFZLdRbK48hJbnlvDDrP/YHY4qZLSg57X0NIidQ1JYWz7fk8o9jSrj56NDFQuTqAde4xf/FrQ+8Dqrln5mdziqENGCntcOrIALx1jqY90J/b6megPowka8vKk2ag7HfCoTueZxtsRusDskVUhoQc9rm2djigbx4r5w7qxdltCgYnZHpGxQpHggJYfNxzi8CFw4mENHjtodkioEtKDnpctnYNdX7CvbhZOX0VkVC7mACtVI6jOTipzk5IcDOXsx0e6QlIfTgp6Xts2DtGQmJzSmRll/mlUNtjsiZbNyke050uJFGqXFsvadkVxJ1ZEvKv9kq6CLSJyIbBORWBH5052dRaStiJxzro8VkWfyPlQ3EDubxKCaLD4Zwv3Nw3SoogKgyp2j2FdtCJ0TF7Nk+vM6x7nKN7fSQm9njIm60d2mgVXO9VHGmOfzIji3cuIXOLaZr707EODnQ5/6Fe2OSLmQagNf50BQS3odf4PFCz6xOxzlobTLJa/EzsY4vHn5aCT9oytRrIhONa8ycHhRZeQnnPQLo+2WsfywerXdESkPlN2CboBlIrJRREbcYJtmIrJFRL4RkTp5FJ97SEuBrZ+yt2QrThl/7m8WbndEygWJXyDBwxeAw4fwZcPYuveg3SEpD5Pdgt7SGNMA6AL8RURaX7d+ExBmjKkHTAYWZvYiIjJCRGJEJMajbq21dxlciueds01oX6MMlYN1qKLKnG9IFRgwiwpyiuRP7uPX+HN2h6Q8SLYKujHmqPPrSWAB0Pi69eeNMRed338N+IhI6UxeZ6oxJtoYEx0SEpLr4F3G5tkk+ZZmSWIdHaqoshRYozVnOrxGI7OdrdMe4vzlK3aHpDxElgVdRIqLiP/V74GOwPbrtiknziEdItLY+bqn8z5cF3TxJOz9lq8dbQgLCaBltT+9jyn1J2VbDeHXOqPpduVblrz3DKk6kZfKA9lpoZcFfhKRLcB64CtjzFIRGSUio5zb9AO2O7d5ExhgCsvYrK2fQnoqb59tygPNwnE4dKiiyp5Kd73Ir2U7MODMFD6ZNU2HM6pcE7v+iKKjo01MzJ+GtLsXY+CdZsRdgO6Xn2PtPztQwldHt6hbcOUSx99oT8DFg3zXdCa9u3SxOyLl4kRk442Gj+uwxdw4tgnid/L+peb0axiqxVzduiLFKTtiAcneATRdO5pVMVvsjki5MS3ouRH7CakOXxamNOP+ZnoDaJUzjsAKFBv6BQGOJIKX3M8vccfsDkm5KS3oOZWajNk2j/+ZRtS/PYyqISXsjki5Mb/QSK70fp8acpiEmYP57cwlu0NSbkgLek7tWYoknWV2cguGNNfWucq9kvW6cbLF87Q0MaybMpJLyal2h6TcjBb0nNoylwRHKX4t2Zi2t5exOxrlIcrf+QiHbx9Cr+QlLJz6LGnpOvJFZZ8W9Jy4dAqzZxmfXWnOoGZVdaiiylOVB7zO4ZC2DDj1FvPmTLc7HOVGtKDnxLZ5iEnla2nD3dGV7I5GeRqHF5Uf+oQTxW6n+55/8c2yb+yOSLkJLeg5kLr5E3aYcCIaNCOwqI/d4ShPVKQ4ZUYu5LJ3SaJXj2T95k12R6TcgBb0W3VyJ94ntjAvtRVDdN4WlY+8S1ag6NAF+DnSCFk4kP2HDtsdknJxWtBvUfrmT0jFwW+Vu1O9rL/d4SgPVzy0Dkn9ZlNBTpE4sx+nzpy1OyTlwrSg34r0NK5snssPafXo0zLK7mhUIRFSpy3H279JnbQ97Ht3AEnJOjujypwW9FtxcCV+SSdZWbQDHWqVtTsaVYiEtx7IrqinaHplDeveeYh0nZ1RZUIL+i04t+YjzplihDW9Cy8dqqgKWO0+/yC20mDanFvITx+Ntzsc5YK0oGdX8gWK7v+ab0xz+jWtZnc0qpCqN/QNYgM70PrQW6xfNMXucJSL0YKeTYmxX1DEJJNQrS8lixWxOxxVSInDi9qjZ7OjSCT1N/2TXasW2B2SciFa0LPpzJqPOJhelnZ3dLc7FFXIFfErSujohcR5VSbsfyM5tv1Hu0NSLkILejakJRyi4tkY1gd0pFaFQLvDUYrAoGD8hi4kniCKzxvI+cPb7A5JuQAt6Nlw8PsPASjT8gGbI1Hqd5UqhXPmrs9IMt6kzOjNlVNxdoekbKYFPSvGUHzX52ySOrRq1NDuaJT6g3oR9djR/kO80xI5O7U75mK83SEpG2lBz8Kv21ZSPvUIZ6vfhbeX/riU62nfph1LI9/EP/kE8VN6QPIFu0NSNtEKlYWjP3xAkvEhqpN2tyjX1b9vP2aGPkepC7s5Nb0fpCTZHZKygRb0m9h+8Di1Ty9jd3B7SgWXtjscpW5IRBgyZCRvBz5J6fi1nJk9BNLT7A5LFTAt6DeQlm5YPv89AuQy1Tr/xe5wlMqSn48X9434G2/6DCMo7hsuLngcjN7xqDDRgn4Dc9YfpsX5r7hYIpzi1VvbHY5S2RJcwpeuDz3P+/SixLaPSPrfi3aHpApQtgq6iMSJyDYRiRWRmEzWi4i8KSL7RGSriDTI+1ALzqmLycxfupxGjj0UbzoMROdtUe6jWhl/ag56jc/T2uD30yukrdfb2BUWt9JCb2eMiTLGRGeyrgtQ3fkYAbybF8HZ5aVvdtEjbTnG4YNEDbQ7HKVuWYvqIaR1m8TytPrI12MxOxbaHZIqAHnV5dIL+MhY1gIlRaR8Hr12gVp/MIHFGw8ywHc1UrMrlAixOySlcmRA06psbvxfNqVXJ33ecDi4yu6QVD7LbkE3wDIR2SgiIzJZXxH4NcPzI85lfyAiI0QkRkRi4uNd7wKIlLR0xi/czgD/LRRLPQcNdKiicm9PdotiVpWX2Z9WhtRPBsDxrXaHpPJRdgt6S2NMA6yulb+ISI7OEhpjphpjoo0x0SEhrtfynflzHLtPXOCxoJ+hZGWo2s7ukJTKFYdDeHFQa14s9SLxV3xJ+agvJBy0OyyVT7JV0I0xR51fTwILgMbXbXIUqJTheahzmds4fu4y//1uDwNuSyX45Fqofz84dBCQcn/Finjz8rDOjCkygcTLl0n9+C5ITLA7LJUPsqxYIlJcRPyvfg90BLZft9li4H7naJemwDljzPE8jzYfvfDlTlLTDf8svwHEAXoyVHmQsgF+PDO0D39N/xvmzCHSPh0MqXpvUk+TnSZoWeAnEdkCrAe+MsYsFZFRIjLKuc3XwAFgHzANeDhfos0nK/fE89W24zzaJpyAXZ9B9Y4Q+KdTAEq5tdoVAhg68F7GpQzH69BPpH/1hF545GG8s9rAGHMAqJfJ8ikZvjeAW15OmZSSxoRF26lSujgjKuyF1Sf0ZKjyWO1rluVQ11FM/uY3Htn8MZS+HVo8andYKo8U+k7iqT8eIO50Is/3qoNP7MfgX95qoSvloYY0D+dU9JN8mdYE890zsPNLu0NSeaRQF/RDpy/x1op9dIssT6syybBvOUQNAq8sP7go5bZEhPE96rIwbDxb0quSNm84HIu1OyyVBwptQTfG8OziHfg4hPHdasPmWWDSocFgu0NTKt95ezn4731NeSlwAidSi5M6uz+cP2Z3WCqXCm1B/3bHCVbsjmfMnbdTzt8HNn0MVdtCULjNkSlVMPz9fJg47E6e8H6KK5fOkTqrP1y5ZHdYKhcKZUFPvJLK80t2ULOcP0Oah8P+FXD+iJ4MVYVOaFAxxg3px+NpjyInd5C24GEd+eLGCmVBf/N/+zh2LokXete1biu3/j0oVhpqdrM7NKUKXFSlkvTuP4xXU/rjtXMh6eun2R2SyqFCV9D3nrjA9FUHuLthKNHhpay5LfYugyajwNvX7vCUskXXiPIE3jGW79OiSF/6Tzi6ye6QVA4UqoJujOHphdsp7uvNuC41rYU//ReK+EPj4fYGp5TNRrWtxo91/s2J9AAuzR4Ml8/aHZK6RYWqoC+KPca6gwn8o3NNgkv4wun98MtCaDQMigbZHZ5SthIR/tmvJe+FPE2RS8dJmPOQ9qe7mUJT0M9dTuGFr3ZSr1JJBjRyziO2ehI4fKCpW17kqlSeK+Lt4Mlhg5nm9wClDi/j9P/esDskdQsKTUF/fdluEi4l80KvujgcYo25jZ0D9e8D/7J2h6eUywgs5kPX4f9mBY0I/Ol5Luz72e6QVDYVioK+/eg5Pl57iMFNw4gIDbQWrnnbusTAbe8AABrPSURBVJBI57FQ6k/CQ0oQMGAax00pkuY8wJULp+0OSWWDxxf09HTDvxZup1RxX57oWMNamJgAMR9CRD+9kEipG2hYswr72kwmMPU0e9+7D5OeZndIKgseX9DnbviVLb+e5V/dahJY1MdauO49SLkELcfYG5xSLq5d+y78VHUMdS7+zLo5L9gdjsqCRxf00xeTeXnpLppUKUXvKOf85skXYd0UqNEVytSyN0Cl3EC7wf8itlhz6u+ZzJ4dG+0OR92ERxf0l77ZxaXkVF7oXRcRsRZunAFJZ6HlE7bGppS7EIeD8AfeI0l8SZ0/mstJeqcjV+WxBT0mLoHPNx5heKuqVC/rby1MTYY1b0F4K6jUyN4AlXIjJctW5njz56idvpuVH02wOxx1Ax5Z0FPT0nl64XYqBPrxaIdqv6/YMgcuHIdW2jpX6lbVvPNBdpdsTbuj01izbrXd4ahMeGRBn/FzHLt+u8AzPepQrIjzZhVpqfDTJKhQH6q2szdApdyRCOFD3iPZ4UeJbx7j5DmdatfVeFxB/+1cEv/9bg/taoTQqU6GC4a2z4czB62+86v96UqpW+JbsgKX2r9IBHtZ8eEzGJ0awKV4XEH/91e/kJpueK5nhhOhF36DpeOgfBTU7G5vgEq5ufItB3O4TDt6n5nJou9W2B2OysCjCvqqvfF8tfU4D7etRuXgYtZCY2DRXyElEfpOBYdHpaxUwROh0uAppHj5UWX1WPYcP2N3RMrJY6pbcmoazyzaQXhwMUa2qfr7ig3TYd93cOe/IaSGfQEq5UHEvxzpnV+lnuxn1cwJJKfqVaSuINsFXUS8RGSziHyZybohIhIvIrHOR4FPLj515QEOnrrE873q4ufjZS08tReWjYfbOkDjhwo6JKU8WkCjAZwM7ch9l2fz4YKldoejuLUW+mPAzpus/9QYE+V8TM9lXLfk14RE3lqxj24R5Wl9e4i1MC0FvngIfPyg19t6IlSpvCZCmQFvk+pdnEbbJrBq9292R1ToZaugi0go0A0o0EKdHcYYJizegZdDeLp7hkv5V74MxzZDjzchoLx9ASrlyUqUwafbyzR07GXdpy+TcEmvIrVTdlvok4C/A+k32eYuEdkqIvNEpFLuQ8ue7345wfe7TjLmjtspH1jUWnh4Hax6DaIGQe2eBRWKUoVSkfoDuFCpHQ+nzebVuct0KKONsizoItIdOGmMudmsPEuAcGNMJPAdMPMGrzVCRGJEJCY+Pj5HAWeUeCWV55b8Qo2y/gxpEW4tTL4AC0ZAYCh0finXx1BKZUEE/7sm4+3tRZe4l/h0/WG7Iyq0stNCbwH0FJE4YC7QXkRmZdzAGHPaGJPsfDodaJjZCxljphpjoo0x0SEhIbkI2zL5+30cPXuZF/rUxcfLmcrSp+DsYegzFfwCcn0MpVQ2lKyEd8fnaO21jdivpnAg/qLdERVKWRZ0Y8xTxphQY0w4MAD43hhzX8ZtRCRjJ3VPbn7yNE/sO3mB6asO0K9hKI3CS8GlU/Dtv2Dzx9DicQhrlt8hKKUycDQazpUKjXnKMZNn56wgJe1mPbQqP+R4HLqIPC8iVzuoHxWRHSKyBXgUGJIXwd2IMYbxC3dQ1MeLp1oHw7KnYVIErH0H6g2Etk/l5+GVUplxOCjS9x1KOFLoHz+ZScv32B1RoSN2ncCIjo42MTExOdp3UexR/j13JR/VXEPto/MgNQki7obWf4PS1fM4UqXULflxInz/b0amjGHYg4/QpGqw3RF5FBHZaIyJzmydd0EHk1sXTh3hwqJxrPZbRpFDKRDR31nIq2W9s1Iq/7V4jLTtC/hP/AwGzm3A52O6/H77R5Wv3O7S/x1rljIg/SsSq/dA/hoDfd/TYq6UK/Hywav32wRzngcvf8DTC7frUMYC4nYFvWm3YRwb/BNBg96H4NvsDkcplZkKUUjzv3KP1wrObPuWhbFH7Y6oUHC7go7DQeVqdeyOQimVlbZPYUrfzht+U3l14Tp+TUi0OyKP534FXSnlHnyKIn2nUopzPM10Hv80llQdypivtKArpfJPhfpI23F0lZ+p+OuXvPPDfrsj8mha0JVS+avFGAhtzEt+M/jsf2vZdFhviJFftKArpfKXlzf0fY+i3vCG3xTGzNnExeRUu6PySFrQlVL5r1RVpPNLNEzfzp0XvuDZxTvsjsgjaUFXShWM+oOhRlfG+XzG1k1r+Grrcbsj8jha0JVSBUMEeryJV7FA3is2hWe+2Mixs5ftjsqjaEFXShWcEiFIz7eoknaQ0eZTnvxsC+npehVpXtGCrpQqWDU6Q8MhPChLKBL3PdNWHbA7Io+hBV0pVfA6vQhl6zDFdzJLli1j+9FzdkfkEbSgK6UKXpHiyMDP8C0ewPs+r/LcJ8u5fCXN7qjcnhZ0pZQ9AiviGPQ5pb2TmHDheV5dkrP7I6jfaUFXStmnfCRe/WdQ23GY5rH/YPn2Y3ZH5Na0oCul7HV7R9I6v8wdXpuJn/8EJy8k2R2R29KCrpSynU/TEZypN4J7zTcse/9ZvSFGDmlBV0q5hKBeL3O4THsGnpnC9ws/tDsct6QFXSnlGhwOKg2fRZzv7TSLHcfCT6Zw6mKy3VG5FS3oSimXIUWKEzT8C076hdN7zz9Y8MqDPLswVu92lE1a0JVSLiWoTCjhf1vFuTr385BjCZ02jeauiQsZ82ksu3+7YHd4Lk0LulLK9Xj7Enj3ZOjzHk2KHGR58fGc2LGSTpN+5MEZG9h4KMHuCF1Stgu6iHiJyGYR+TKTdb4i8qmI7BORdSISnpdBKqUKqXoDcAz/joAS/sz2fp4ZtTex6VACd727hv5T1rBi10kdEZPBrbTQHwN23mDdg8AZY0w14L/Ay7kNTCmlACgXASN+QKp3pO2BiWyoMZtX7ijJkTOJDJ2xgS5vrGJR7FG9ATXZLOgiEgp0A6bfYJNewEzn9/OADiIiuQ9PKaWAoiXhntnQYQLee76m/889WVVjHlO6BJCabnhsbiztXvuBj9ceIiml8M4Jk90W+iTg78CN3gIrAr8CGGNSgXNA8PUbicgIEYkRkZj4+PgchKuUKrQcDmj1BDwWC42G4/XLAjqv6MF3oR/ySY/iBBf3ZfzC7bR8+XveXrGPc5dT7I64wGVZ0EWkO3DSGLMxtwczxkw1xkQbY6JDQkJy+3JKqcIoMBS6vAyPb4OWjyP7ltP8u14sCHqDL3s6qF0+gFe/3U3Ll77npW92FaqpBCSrEwoi8n/AYCAV8AMCgC+MMfdl2OZb4FljzBoR8QZ+A0LMTV48OjraxMTo7GpKqVy6fAbWT4O171jfB1TkVKWOzDoXyVv7Q3B4edOvYSgjW1clLLi43dHmmohsNMZEZ7ruVs4Qi0hbYKwxpvt1y/8CRBhjRonIAKCvMab/zV5LC7pSKk8lX4SdS2DXl7BvOaQmkVY0mI1Fm/PeyTr8lFabjpGVGdWmKnUqBNodbY7drKB75+JFnwdijDGLgfeBj0VkH5AADMjp6yqlVI74loCoe61H8kXYtxyvnUtovOdbGnsvIcm3OD/simDatgZcqdKB+zvUp0mVUnjS+I1baqHnJW2hK6UKREoSHPgBdn9F+u6lOC6dJA0HMem3s9u/GdVa3U3TRs1weLnHdZZ51uWSl7SgK6UKXHo6HN9M6s6vOb9lCaUu7AbgsFdlgtuOpnij+8AvwOYgb04LulJKZSI14TBbvv8Un21ziJT9pPsUx1FvADR+CMrUsju8TN2soLvHZwyllMoH3qUq07Df30h84DsGmBdZmt4Ys3kWvNMUPuwGOxZAmvuMZ9cWulJKAVuPnOWBD9YT7LjInIZ7Cdk9C84ehuDq0PtdqNTI7hABbaErpVSWIkNL8tnIZlxwBHDnhvrE9vnBmm4gNQk+6AjfPWOdYHVhWtCVUsqpell/5o1qToCfD4Pe38DPRZrC6J+h/mBY/QZMbQNHN9kd5g1pQVdKqQwqlSrG56OaUTGoKEM+3MDyA5eh55tw33xIOg/T74DvX4DUK3aH+ida0JVS6jplA/z4dEQzapbzZ9SsjSyKPQrV7oCH10C9AfDjqzCtHZzYYXeof6AFXSmlMhFUvAizhzehYVgQj38ay6y1h6xpfHu/A/fOhUvx8GFXOPGL3aFeowVdKaVuwN/Ph5nDGtOuRhmeXridd3/Yb62o0QUeXAY+ReHj3pBwwN5AnbSgK6XUTfj5ePHe4Ib0qFeBl5fu4pWlu6zb3gWFw+CF1jj1j3rB+WN2h6oFXSmlsuLj5WDSPVEMbFKZd37YzzOLdpCebqBMTetkaeIZ+Kg3XDpta5xa0JVSKhu8HMJ/etdlZJuqfLz2EE9+voWUtHSo2AAGzoWzh2BWX2skjE20oCulVDaJCOM61+RvnWqwYPNRRs/aZN3DNLwl9P8ITmyHOQMg5bIt8WlBV0qpWyAi/KVdNZ7vVYflO08wbMYGLiWnwu2doM97cOhn+OwBW+aA0YKulFI5cH+zcF7vX491BxMYNH0dZxOvQEQ/6P467P0Wlj5V4DFpQVdKqRzq2yCUdwY14Jdj5xkwda11Q+roYdD0YdgwDQ6vK9B4tKArpVQudKpTjg+GNOJwQiL9p6zhyJlEaPcvCAiFLx8v0K4XLehKKZVLLauX5uMHm5Bw6Qp3T1nDvnNAt4lw8hf4eXKBxaEFXSml8kDDsCA+HdmMlDRD//fWsL1Ec6jVA1a+DAkHCyQGLehKKZVHapUP4PNRzSjq48W9U9cSW/ef4PCBr56AAriZkBZ0pZTKQ1VKF+fzUc0I8fdlwNxD7I0YA/u/h+3z8/3YWtCVUiqPVShZlM9GNaNq6RJ0X1uDs0ERsHQcXD6Tr8fNsqCLiJ+IrBeRLSKyQ0Sey2SbISISLyKxzsfw/AlXKaXcQ+kSvswZ0ZSI0FIMOnEv6YkJsPzZfD1mdlroyUB7Y0w9IAroLCJNM9nuU2NMlPMxPU+jVEopNxRY1IePHmxMqduimZ7SGTbOgENr8u14WRZ0Y7nofOrjfOR/775SSnmAYkW8mf5ANL/c/jBHTGlOfzoak5qcL8fKVh+6iHiJSCxwEvjOGJPZ5U93ichWEZknIpXyNEqllHJjvt5eTBzUnG/DxhKceJDVH03Il+Nkq6AbY9KMMVFAKNBYROpet8kSINwYEwl8B8zM7HVEZISIxIhITHx8fG7iVkopt+Lt5WDokFGsLT+YcnXb5csxxNzi2EgReQZINMZMvMF6LyDBGBN4s9eJjo42MTExt3RspZQq7ERkozEmOrN12RnlEiIiJZ3fFwXuBHZdt035DE97AjtzHq5SSqmc8M7GNuWBmc6WtwP4zBjzpYg8D8QYYxYDj4pITyAVSACG5FfASimlMnfLXS55RbtclFLq1uWqy0UppZR70IKulFIeQgu6Ukp5CC3oSinlIWw7KSoi8cChAj5saeBUAR+zIHlyfpqb+/Lk/OzILcwYE5LZCtsKuh1EJOZGZ4c9gSfnp7m5L0/Oz9Vy0y4XpZTyEFrQlVLKQxS2gj7V7gDymSfnp7m5L0/Oz6VyK1R96Eop5ckKWwtdKaU8lkcVdBEJFZGbTtvrzkQk06FKnkBEgu2OIT85Zyr1SCLia3cM+enq/52IiN2xZMUjCrqIFBOR14BvsWaGHOxc7vK/gOxw3qj7XWCFiDwvIu2dy93+9yciJUTkv8BXIvKCiOTPzP82ceb3FjBdRDp7UoMjw+/ubRHpKiIBdseU10RkFLBVRCKMMcbV/+dcOrhbMB4IMcbUAT4CHgLrfqi2RpV3hgFlgDbAQeADEfEzxqTbG1buiMjtwAIgDSvHeOCftgaV9yYBRYAvgHuBcfaGkzdEpCOwBkgCfgKGA11sDSoPZWgM+gFngH8BuPr/nFsXdBHxFhE/oCiw0Lm4LLD06k03XP0d9UZEpMh1i9YYY04bYz7E+kd60bmd230KyZDbJWCqMWasMeYX4GvguIiE2hdd7l39nYhIaaAC8IQxZj7wOlBeRB6yM77cyPD3dgGYaIx5yhgzA9gN1LhuG3fmcN4DIggYDQSJyEC4dlc2l+R2xU5EaojIKwDGmFRjTBLWO2hXEVkD/A0oBax3fkxKd6c/MBGpLiIfAK+JSBPnYl+sS4yv+hvQR0Ruc34MdIv8MsntGLAwQ/zFgJrGmCO2BZkLIlJTRKZg3fAlwBhzCkjH+YkR605fC4DuIlLKrjhzIpPc1gBzRMTHuckuIBjc85Px1fxE5DER8XfeRzkNKAEEAO8Ao0QkHKsB6ZLcqqCLSDesj65jRSTjR9cXgQnAESDSGDMW+ACYCO7zByYio4FFwEbgBPCIiNQHPge6iUgdAGfBW4Sze8Id8sskt78ArY0xKRniL4XV0nM7IlIFmAXsB+oB74pIFPAq0ElEgowxycBWrG6zBrYFe4syye0tEWlijLlijElxbtYS2GNXjLmRSX5vi0gz5+oiwApjzCKsT1uxQG1X/eTvkkHdxAlgEHA78A8R8XcuT+X3SXISncumAKkiUrzAo8y5E8Bjxpi3gf/DukXgbc4CvgQr57LObZdS8JOb5cb1ufliFfCMH2FrAzucywY6+9jdRU3glDHmVWAk1htTb6w+5i3AUwDGmINAOFZ3k7u4Prd9WA2McABnKz0IWOF83uTqfYjdRGb5dReRMsBvwHwR2Yb1KeQIsNFV+9LdqqAbY2KAXcaYfVgF7V3ncoNVCFpjtWr7AnOBDcYYd/rHWQL8ICJFnB/3TmKdDAV4Buvk4QQRGQ68jHX/VndxfW4ncObmfA5WKy9ERBZgvXGnZPpKrmk7kCQiNZ2t1m+wupBux7qasLeI9BWRpkAI4BbdZE7X5/Y1Vm6tneuv3pu4oYgsA4baEGNuZJafL9AdOId1vuBBY0x3rBPAf7Mt0iy4ZEEXkaCMH2mu6yNOdn4difUu2gis/nTgYaz+rkeBd4wxzxZMxLfmRvk5ux9SjDFXnKvq4GyxOvN7CuuN7A7gFWPMWwUbedZuMbddGbbzw2qhtwXmGGO6OVuzLuX6YYcZ/jZ9gZ1Yb0oYYzYAx4Gqxpj9wN+BxsA04F1jzM8FFnQ23UJuMVgt1TDn+oZAX6ybw39gjBlljDlbEDHfilvM7xjWOYGPjTH9jDHrnduONca8VEAh3zpjjEs9sIYH7QJeA0bfYBsv59fxWP1bAJ0BH7vjz6P8HEB54CvAy/m8iavnl8PcBGjiXNfH7hyyyO85YC/WOZsBzmXeGdYPxzpv08z5vCmw3e648zG3bc7viwNP2p1DPuS3NcN6L7tzyM7DpVroItIfaIFVvN4CHheRVplsmg5gjPk30EpELgAdAS9XHvGR3fyM1T8XiNX/eg8QA7RzvoZL5peL3DYCd4iIlzFmQQGGfEtEpDvQCusTxI/AJOcoo9QMn0iWYXUljReRElh95etEpJgNIWdbLnJbLyLFjTGXjDGv2RB6tuQivw1Xf3fm925Bl+ad9Sb5S0RKGGMuOp+WA5YbY84B50RkI/AfEbnHGHP86j7GGCPWGN//A7YBfzXGrC7w4LMhJ/k5tQX6YJ3wfdwY82OBBZ1NnpwbgIiUNL93HfhgnQw7Chx1Dr98Cbjb+SaFMeawiEzEOjcwC6gGDDPGJGby8rbKw9xc8hyVJ//ubsa2FrqIBIvI21iX6t/rfCdMACJFpJVY80PEY518aercJ2O8Z4EZxpj6rljM8yC/74FRxph7XK3geXJuACJSypnfHBF50DnaIQ3rhO3Vi6Kexhq+1tm5jzdcO0H/d+AhY0xd83vfq0vw5NzA8/PLii0FXUTuxBridAKYDXTAOqHyCdZY0JHAWqzW91vAKPjjZbfGuqjI5Qo55Fl+e4wxLjXXMnh2bgAi0gnrxPMZrDHkHYA7sUatRADt4Vo+k3GOeDDWSWuc3xtjTHzBRp41T84NPD+/7LCry+UU8JoxZiaAiIQBoca6qvMFrLPLfsaYIyISjdXPiog4jIuO/7yOJ+fnybmBddHPEGNNRYCI3A/EG2NSxJqIaqyI7DDG/AqsBGqKNRTzyk1e01V4cm7g+fllKd8LuoiI86PMNcaYzSKyJ8M/eTwQ5VxnRCTBWSBqY52VXupc53IFwZPz8+Tc4Ib57XGuC8G6zqEhUEREahtjJolIJNa1ABuAAcAmVywInpwbeH5+OZWvXS7Odz+T4fm1ERrGOjN+9Z88EuuS6Iz7tgbmA0uNMa/nZ5w55cn5eXJucPP8nM4BM40xVbDemOqIyDCsoZmzsMYsLzDGPFlQMWeXJ+cGnp9frpj8G/f5CNa8B88DPZ3LBHBk2ObqOOQZQFPnsjuBklhzKPjmV3yaX+HMLbv5ZbLPy8DgDM9vuK3mpvnZ9ciXFrqItAHuw5rj+hessZ0tjCXduU0NY43t9MG6FDpKRL4D+gNirIl/km9wCFt5cn6enBtkO7/br9unEdZl7kevLjOu2YXksbmB5+eXF/K0Dz1Dv2pprI/bm4BNIhKE1acVKSIVsa4k9BGREUAlrInxfbAu13fli0s8Nj9Pzg1ylN9IrKlT3wP8gZeNMd/bFP5NeXJu4Pn55ancNvGx3hTGApUyLOuH85L8DMu2AAOB5sC/rlv3mN0fVQpjfp6cWx7mN8DuPApbboUhv3z7ueXyhx4BbMIakzznunW7+GO/VXfgy+u2KWL3D6Cw5ufJueVRfi47b44n51YY8svPR2770E8Bb2LNJxwu1n0Gr3oCeEGsWfTAmr1st4j4iIjDOezI1YcMeXJ+npwb5D4/V56615NzA8/PL9+I8x0t5y8gUtQYc9nZb3WvMaZthnUzsKa7XQ7cDZwzxrjV/RQ9OT9Pzg08Oz9Pzg08P7/8kuuCfu2FRIpi3S/xa2PMm85lgVhjPocCO40x4/PkYDbw5Pw8OTfw7Pw8OTfw/PzyXB73fXUC1mXoBwtxfu/S/a2an2fn5un5eXJuhSG/vHzk6Th0Y8y3wBkRScaa2tbhXO7q/a3Z4sn5eXJu4Nn5eXJu4Pn55aU8G4cu1vSozwO1sOYnn5ZXr+0KPDk/T84NPDs/T84NPD+/vJZnfegAItIF+N646FWCueXJ+XlybuDZ+XlybuD5+eWlPC3oSiml7ONS9xRVSimVc1rQlVLKQ2hBV0opD6EFXSmlPIQWdKWU8hBa0JVSykNoQVdKKQ+hBV0ppTzE/wMVLYwEYA69SQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvXKg24Uk_jk",
        "outputId": "33eaa9d0-e47e-42a0-80b6-b6f5bde91871"
      },
      "source": [
        "#Timezones\n",
        "\n",
        "temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
        "temp_series_ny\n",
        "\n",
        "#Note that -04:00 is now appended to all the datetimes. This means that these datetimes refer to UTC - 4 hours."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 17:30:00-04:00    4.4\n",
              "2016-10-29 18:30:00-04:00    5.1\n",
              "2016-10-29 19:30:00-04:00    6.1\n",
              "2016-10-29 20:30:00-04:00    6.2\n",
              "2016-10-29 21:30:00-04:00    6.1\n",
              "2016-10-29 22:30:00-04:00    6.1\n",
              "2016-10-29 23:30:00-04:00    5.7\n",
              "2016-10-30 00:30:00-04:00    5.2\n",
              "2016-10-30 01:30:00-04:00    4.7\n",
              "2016-10-30 02:30:00-04:00    4.1\n",
              "2016-10-30 03:30:00-04:00    3.9\n",
              "2016-10-30 04:30:00-04:00    3.5\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmGHHFEhlKWm",
        "outputId": "55ed6318-7008-423c-b58e-f2575b1cca56"
      },
      "source": [
        "temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
        "temp_series_paris"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 23:30:00+02:00    4.4\n",
              "2016-10-30 00:30:00+02:00    5.1\n",
              "2016-10-30 01:30:00+02:00    6.1\n",
              "2016-10-30 02:30:00+02:00    6.2\n",
              "2016-10-30 02:30:00+01:00    6.1\n",
              "2016-10-30 03:30:00+01:00    6.1\n",
              "2016-10-30 04:30:00+01:00    5.7\n",
              "2016-10-30 05:30:00+01:00    5.2\n",
              "2016-10-30 06:30:00+01:00    4.7\n",
              "2016-10-30 07:30:00+01:00    4.1\n",
              "2016-10-30 08:30:00+01:00    3.9\n",
              "2016-10-30 09:30:00+01:00    3.5\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hAqSKXrXlTLN",
        "outputId": "110f1775-9040-4512-ee95-8dd4e0d68f43"
      },
      "source": [
        "temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
        "temp_series_paris_naive"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 23:30:00    4.4\n",
              "2016-10-30 00:30:00    5.1\n",
              "2016-10-30 01:30:00    6.1\n",
              "2016-10-30 02:30:00    6.2\n",
              "2016-10-30 02:30:00    6.1\n",
              "2016-10-30 03:30:00    6.1\n",
              "2016-10-30 04:30:00    5.7\n",
              "2016-10-30 05:30:00    5.2\n",
              "2016-10-30 06:30:00    4.7\n",
              "2016-10-30 07:30:00    4.1\n",
              "2016-10-30 08:30:00    3.9\n",
              "2016-10-30 09:30:00    3.5\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwfMP3yollG9",
        "outputId": "ede8c221-2599-45c4-f876-1ca0633b7e05"
      },
      "source": [
        "try:\n",
        "    temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
        "except Exception as e:\n",
        "    print(type(e))\n",
        "    print(e)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pytz.exceptions.AmbiguousTimeError'>\n",
            "Cannot infer dst time from 2016-10-30 02:30:00, try using the 'ambiguous' argument\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwTwaXntmC7T",
        "outputId": "3051825b-e187-44a4-83fe-74e6f0caded9"
      },
      "source": [
        "temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-10-29 23:30:00+02:00    4.4\n",
              "2016-10-30 00:30:00+02:00    5.1\n",
              "2016-10-30 01:30:00+02:00    6.1\n",
              "2016-10-30 02:30:00+02:00    6.2\n",
              "2016-10-30 02:30:00+01:00    6.1\n",
              "2016-10-30 03:30:00+01:00    6.1\n",
              "2016-10-30 04:30:00+01:00    5.7\n",
              "2016-10-30 05:30:00+01:00    5.2\n",
              "2016-10-30 06:30:00+01:00    4.7\n",
              "2016-10-30 07:30:00+01:00    4.1\n",
              "2016-10-30 08:30:00+01:00    3.9\n",
              "2016-10-30 09:30:00+01:00    3.5\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swU8wFC3mJ7e",
        "outputId": "9e0ebd8b-87d7-446b-c271-24777d9de378"
      },
      "source": [
        "#Period - The pd.period_range() function returns a PeriodIndex instead of a DatetimeIndex.\n",
        "\n",
        "quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
        "quarters"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n",
              "             '2017Q3', '2017Q4'],\n",
              "            dtype='period[Q-DEC]', freq='Q-DEC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZlITOshmcBv",
        "outputId": "67c6c386-a241-4ac1-bcf0-bc27b93d2d53"
      },
      "source": [
        "quarters + 3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n",
              "             '2018Q2', '2018Q3'],\n",
              "            dtype='period[Q-DEC]', freq='Q-DEC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNxSdleomgrV",
        "outputId": "82c571c1-c110-4db8-bcc1-697d5da914fc"
      },
      "source": [
        "quarters.asfreq(\"M\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n",
              "             '2017-09', '2017-12'],\n",
              "            dtype='period[M]', freq='M')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGYwAdZcmn6M",
        "outputId": "1270765f-5451-4bc0-cb54-59b2894f6d15"
      },
      "source": [
        "quarters.asfreq(\"M\", how=\"start\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n",
              "             '2017-07', '2017-10'],\n",
              "            dtype='period[M]', freq='M')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TET8q_LBmuq4",
        "outputId": "a7a09686-7e7d-4ddb-e78b-d4de9a80adb0"
      },
      "source": [
        "quarters.asfreq(\"A\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rtsVLNymxYh",
        "outputId": "07328970-4632-469b-8143-6bb0289c09c7"
      },
      "source": [
        "quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index = quarters)\n",
        "quarterly_revenue"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016Q1    300\n",
              "2016Q2    320\n",
              "2016Q3    290\n",
              "2016Q4    390\n",
              "2017Q1    320\n",
              "2017Q2    360\n",
              "2017Q3    310\n",
              "2017Q4    410\n",
              "Freq: Q-DEC, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "v9bLEMTXm03q",
        "outputId": "c3816e2d-5363-4dfb-bfb3-9885b0bd9930"
      },
      "source": [
        "quarterly_revenue.plot(kind=\"line\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPqgBdQzm5UP",
        "outputId": "1b8d3671-8e62-4cff-8b29-7d345891191b"
      },
      "source": [
        "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"H\")\n",
        "last_hours"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-03-31 23:59:59.999999999    300\n",
              "2016-06-30 23:59:59.999999999    320\n",
              "2016-09-30 23:59:59.999999999    290\n",
              "2016-12-31 23:59:59.999999999    390\n",
              "2017-03-31 23:59:59.999999999    320\n",
              "2017-06-30 23:59:59.999999999    360\n",
              "2017-09-30 23:59:59.999999999    310\n",
              "2017-12-31 23:59:59.999999999    410\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_O_03SC8nA1P",
        "outputId": "2801dc16-a665-44d6-a2f1-dd3a281c7eab"
      },
      "source": [
        "last_hours.to_period()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016Q1    300\n",
              "2016Q2    320\n",
              "2016Q3    290\n",
              "2016Q4    390\n",
              "2017Q1    320\n",
              "2017Q2    360\n",
              "2017Q3    310\n",
              "2017Q4    410\n",
              "Freq: Q-DEC, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dqo7IqernEuq",
        "outputId": "af6dd715-e25c-4cf0-f76a-6846574165c0"
      },
      "source": [
        "months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
        "one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
        "last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
        "last_bdays.to_period(\"H\") + 9"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',\n",
              "             '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n",
              "             '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n",
              "             '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n",
              "            dtype='period[H]', freq='H')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNR1jrI7nMJL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "0e230967-cc18-44f5-b3bc-4f45ff0634b3"
      },
      "source": [
        "#Creating a dataframe\n",
        "\n",
        "people_dict = {\n",
        "    \"weight\": pd.Series([68, 83, 112], index=[\"alice\", \"bob\", \"charles\"]),\n",
        "    \"birthyear\": pd.Series([1984, 1985, 1992], index=[\"bob\", \"alice\", \"charles\"], name=\"year\"),\n",
        "    \"children\": pd.Series([0, 3], index=[\"charles\", \"bob\"]),\n",
        "    \"hobby\": pd.Series([\"Biking\", \"Dancing\"], index=[\"alice\", \"bob\"]),\n",
        "}\n",
        "people = pd.DataFrame(people_dict)\n",
        "people"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>weight</th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>68</td>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>83</td>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>112</td>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         weight  birthyear  children    hobby\n",
              "alice        68       1985       NaN   Biking\n",
              "bob          83       1984       3.0  Dancing\n",
              "charles     112       1992       0.0      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43eFkEaa8o52",
        "outputId": "4a4a2c2d-0227-41f2-bed4-fcdb2cda01fb"
      },
      "source": [
        "people[\"birthyear\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice      1985\n",
              "bob        1984\n",
              "charles    1992\n",
              "Name: birthyear, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "pENHN3w28x57",
        "outputId": "009e571a-6cd0-4974-eb10-9b9ebb37c4e4"
      },
      "source": [
        "people[[\"birthyear\", \"hobby\"]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear    hobby\n",
              "alice         1985   Biking\n",
              "bob           1984  Dancing\n",
              "charles       1992      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "TqIWKsbA8z2_",
        "outputId": "c7f97d42-b672-4edd-c697-07397d3ea5f7"
      },
      "source": [
        "d2 = pd.DataFrame(\n",
        "        people_dict,\n",
        "        columns=[\"birthyear\", \"weight\", \"height\"],\n",
        "        index=[\"bob\", \"alice\", \"eugene\"]\n",
        "     )\n",
        "d2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>weight</th>\n",
              "      <th>height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>eugene</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        birthyear  weight height\n",
              "bob        1984.0    83.0    NaN\n",
              "alice      1985.0    68.0    NaN\n",
              "eugene        NaN     NaN    NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "GFbCfpP983Cp",
        "outputId": "fcb4eae2-1e8d-4fa3-c39d-06e5b287beee"
      },
      "source": [
        "values = [\n",
        "            [1985, np.nan, \"Biking\",   68],\n",
        "            [1984, 3,      \"Dancing\",  83],\n",
        "            [1992, 0,      np.nan,    112]\n",
        "         ]\n",
        "d3 = pd.DataFrame(\n",
        "        values,\n",
        "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
        "        index=[\"alice\", \"bob\", \"charles\"]\n",
        "     )\n",
        "d3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear  children    hobby  weight\n",
              "alice         1985       NaN   Biking      68\n",
              "bob           1984       3.0  Dancing      83\n",
              "charles       1992       0.0      NaN     112"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "eCw-UdMS85is",
        "outputId": "4cd7c316-73d7-4f22-c0de-f7154decd9c1"
      },
      "source": [
        "masked_array = np.ma.asarray(values, dtype=np.object)\n",
        "masked_array[(0, 2), (1, 2)] = np.ma.masked\n",
        "d3 = pd.DataFrame(\n",
        "        masked_array,\n",
        "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
        "        index=[\"alice\", \"bob\", \"charles\"]\n",
        "     )\n",
        "d3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        birthyear children    hobby weight\n",
              "alice        1985      NaN   Biking     68\n",
              "bob          1984        3  Dancing     83\n",
              "charles      1992        0      NaN    112"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "q48wULwZ9TcN",
        "outputId": "e424bb0f-5beb-4ef4-8878-0a6c7e8961af"
      },
      "source": [
        "d4 = pd.DataFrame(\n",
        "         d3,\n",
        "         columns=[\"hobby\", \"children\"],\n",
        "         index=[\"alice\", \"bob\"]\n",
        "     )\n",
        "d4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         hobby children\n",
              "alice   Biking      NaN\n",
              "bob    Dancing        3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "NTJnbUa29Yvt",
        "outputId": "8960d456-790a-445b-88ba-c9230da3fd5b"
      },
      "source": [
        "people = pd.DataFrame({\n",
        "    \"birthyear\": {\"alice\":1985, \"bob\": 1984, \"charles\": 1992},\n",
        "    \"hobby\": {\"alice\":\"Biking\", \"bob\": \"Dancing\"},\n",
        "    \"weight\": {\"alice\":68, \"bob\": 83, \"charles\": 112},\n",
        "    \"children\": {\"bob\": 3, \"charles\": 0}\n",
        "})\n",
        "people"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear    hobby  weight  children\n",
              "alice         1985   Biking      68       NaN\n",
              "bob           1984  Dancing      83       3.0\n",
              "charles       1992      NaN     112       0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "KLZcuVao9dZJ",
        "outputId": "daff008f-575a-4dd6-92e0-e209fafa457c"
      },
      "source": [
        "#Multi indexing\n",
        "\n",
        "d5 = pd.DataFrame(\n",
        "  {\n",
        "    (\"public\", \"birthyear\"):\n",
        "        {(\"Paris\",\"alice\"):1985, (\"Paris\",\"bob\"): 1984, (\"London\",\"charles\"): 1992},\n",
        "    (\"public\", \"hobby\"):\n",
        "        {(\"Paris\",\"alice\"):\"Biking\", (\"Paris\",\"bob\"): \"Dancing\"},\n",
        "    (\"private\", \"weight\"):\n",
        "        {(\"Paris\",\"alice\"):68, (\"Paris\",\"bob\"): 83, (\"London\",\"charles\"): 112},\n",
        "    (\"private\", \"children\"):\n",
        "        {(\"Paris\", \"alice\"):np.nan, (\"Paris\",\"bob\"): 3, (\"London\",\"charles\"): 0}\n",
        "  }\n",
        ")\n",
        "d5"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">public</th>\n",
              "      <th colspan=\"2\" halign=\"left\">private</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>London</th>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  public          private         \n",
              "               birthyear    hobby  weight children\n",
              "Paris  alice        1985   Biking      68      NaN\n",
              "       bob          1984  Dancing      83      3.0\n",
              "London charles      1992      NaN     112      0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "0rlHz4cp9jGz",
        "outputId": "83c0eeaa-6743-4909-c312-f00b4b9a0706"
      },
      "source": [
        "d5[\"public\"]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>London</th>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                birthyear    hobby\n",
              "Paris  alice         1985   Biking\n",
              "       bob           1984  Dancing\n",
              "London charles       1992      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_sdntil9p3F",
        "outputId": "a361a823-764c-41ba-df7b-2e02a4057cf1"
      },
      "source": [
        "d5[\"public\", \"hobby\"]  # Same result as d5[\"public\"][\"hobby\"]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Paris   alice       Biking\n",
              "        bob        Dancing\n",
              "London  charles        NaN\n",
              "Name: (public, hobby), dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "gwrbkYpF9twj",
        "outputId": "5b9851b3-028b-45ca-b28c-0a57ce0172ce"
      },
      "source": [
        "#Dropping a level\n",
        "\n",
        "d5"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">public</th>\n",
              "      <th colspan=\"2\" halign=\"left\">private</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>London</th>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  public          private         \n",
              "               birthyear    hobby  weight children\n",
              "Paris  alice        1985   Biking      68      NaN\n",
              "       bob          1984  Dancing      83      3.0\n",
              "London charles      1992      NaN     112      0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "oiUNeTUU9xLB",
        "outputId": "7aa98523-2792-4514-a46d-a28b381e54eb"
      },
      "source": [
        "d5.columns = d5.columns.droplevel(level = 0)\n",
        "d5"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>London</th>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                birthyear    hobby  weight  children\n",
              "Paris  alice         1985   Biking      68       NaN\n",
              "       bob           1984  Dancing      83       3.0\n",
              "London charles       1992      NaN     112       0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "XLpbAOd390XW",
        "outputId": "2fe7090c-eb58-4b15-efe1-b368476a2606"
      },
      "source": [
        "#Transposing\n",
        "\n",
        "d6 = d5.T\n",
        "d6"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">Paris</th>\n",
              "      <th>London</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "      <th>charles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>birthyear</th>\n",
              "      <td>1985</td>\n",
              "      <td>1984</td>\n",
              "      <td>1992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hobby</th>\n",
              "      <td>Biking</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>68</td>\n",
              "      <td>83</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Paris           London\n",
              "            alice      bob charles\n",
              "birthyear    1985     1984    1992\n",
              "hobby      Biking  Dancing     NaN\n",
              "weight         68       83     112\n",
              "children      NaN        3       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "7JHfInnh99Hh",
        "outputId": "08dc71d1-768d-4061-dba4-a09eafa3afbc"
      },
      "source": [
        "#Stacking and unstacking levels\n",
        "\n",
        "d7 = d6.stack()\n",
        "d7"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>London</th>\n",
              "      <th>Paris</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">birthyear</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">hobby</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">weight</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>112</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">children</th>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  London    Paris\n",
              "birthyear alice      NaN     1985\n",
              "          bob        NaN     1984\n",
              "          charles   1992      NaN\n",
              "hobby     alice      NaN   Biking\n",
              "          bob        NaN  Dancing\n",
              "weight    alice      NaN       68\n",
              "          bob        NaN       83\n",
              "          charles    112      NaN\n",
              "children  bob        NaN        3\n",
              "          charles      0      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "bP4fqCo3-Gnz",
        "outputId": "27d1d923-f558-4a34-ec20-344521838e6a"
      },
      "source": [
        "d8 = d7.unstack()\n",
        "d8"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">London</th>\n",
              "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "      <th>charles</th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "      <th>charles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>birthyear</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1992</td>\n",
              "      <td>1985</td>\n",
              "      <td>1984</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hobby</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>68</td>\n",
              "      <td>83</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          London                Paris                 \n",
              "           alice  bob charles   alice      bob charles\n",
              "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
              "children     NaN  NaN       0     NaN        3     NaN\n",
              "hobby        NaN  NaN     NaN  Biking  Dancing     NaN\n",
              "weight       NaN  NaN     112      68       83     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJ_aZMc--L6Z",
        "outputId": "ada7977a-6f57-4753-b26f-fdfc2fd04109"
      },
      "source": [
        "d9 = d8.unstack()\n",
        "d9"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "London  alice    birthyear        NaN\n",
              "                 children         NaN\n",
              "                 hobby            NaN\n",
              "                 weight           NaN\n",
              "        bob      birthyear        NaN\n",
              "                 children         NaN\n",
              "                 hobby            NaN\n",
              "                 weight           NaN\n",
              "        charles  birthyear       1992\n",
              "                 children           0\n",
              "                 hobby            NaN\n",
              "                 weight           112\n",
              "Paris   alice    birthyear       1985\n",
              "                 children         NaN\n",
              "                 hobby         Biking\n",
              "                 weight            68\n",
              "        bob      birthyear       1984\n",
              "                 children           3\n",
              "                 hobby        Dancing\n",
              "                 weight            83\n",
              "        charles  birthyear        NaN\n",
              "                 children         NaN\n",
              "                 hobby            NaN\n",
              "                 weight           NaN\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Ie7ojhoO-PJv",
        "outputId": "3e8648f6-7b86-49ec-de07-7ca27445f0f0"
      },
      "source": [
        "#the stack() and unstack() methods let you select the level to stack/unstack. You can even stack/unstack multiple levels at once:\n",
        "\n",
        "d10 = d9.unstack(level = (0,1))\n",
        "d10"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">London</th>\n",
              "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "      <th>charles</th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "      <th>charles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>birthyear</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1992</td>\n",
              "      <td>1985</td>\n",
              "      <td>1984</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hobby</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>68</td>\n",
              "      <td>83</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          London                Paris                 \n",
              "           alice  bob charles   alice      bob charles\n",
              "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
              "children     NaN  NaN       0     NaN        3     NaN\n",
              "hobby        NaN  NaN     NaN  Biking  Dancing     NaN\n",
              "weight       NaN  NaN     112      68       83     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "mXnifNwx-Y00",
        "outputId": "92b0f13e-e6c5-4820-a2ce-3d6c2f2e5e24"
      },
      "source": [
        "#Most methods return modified copies\n",
        "#accessing rows\n",
        "\n",
        "people"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear    hobby  weight  children\n",
              "alice         1985   Biking      68       NaN\n",
              "bob           1984  Dancing      83       3.0\n",
              "charles       1992      NaN     112       0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMTBLO9N-htx",
        "outputId": "9ae983d3-866a-4877-ff99-16b30de40019"
      },
      "source": [
        "people.loc[\"charles\"]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "birthyear    1992\n",
              "hobby         NaN\n",
              "weight        112\n",
              "children        0\n",
              "Name: charles, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvXBFGZb-kYF",
        "outputId": "eef1a4a7-db39-4621-e67d-52886c96bd62"
      },
      "source": [
        "people.iloc[2]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "birthyear    1992\n",
              "hobby         NaN\n",
              "weight        112\n",
              "children        0\n",
              "Name: charles, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "zMODOEQa-nBE",
        "outputId": "a106dd97-c4eb-47c9-cc4e-9c32cf07fa3a"
      },
      "source": [
        "people.iloc[1:3]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear    hobby  weight  children\n",
              "bob           1984  Dancing      83       3.0\n",
              "charles       1992      NaN     112       0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 111
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        "id": "SBY9PTDP-pGg",
        "outputId": "7b4a12de-19d3-4468-f5f9-841f9891b291"
      },
      "source": [
        "people[np.array([True, False, True])]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear   hobby  weight  children\n",
              "alice         1985  Biking      68       NaN\n",
              "charles       1992     NaN     112       0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "1jw_iEph-ria",
        "outputId": "b7aa5b9e-0f9c-4dfd-ae3f-42a81a0cfaea"
      },
      "source": [
        "people[people[\"birthyear\"] < 1990]"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       birthyear    hobby  weight  children\n",
              "alice       1985   Biking      68       NaN\n",
              "bob         1984  Dancing      83       3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "ZQnBamxW-uGO",
        "outputId": "fffc7f66-aa25-4c70-ca25-683e4ebd368a"
      },
      "source": [
        "#Adding and removing columns\n",
        "\n",
        "people"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear    hobby  weight  children\n",
              "alice         1985   Biking      68       NaN\n",
              "bob           1984  Dancing      83       3.0\n",
              "charles       1992      NaN     112       0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "wLSl33Lc-8mF",
        "outputId": "3f30ac1a-248d-4611-e08f-194114559fe7"
      },
      "source": [
        "people[\"age\"] = 2018 - people[\"birthyear\"]  # adds a new column \"age\"\n",
        "people[\"over 30\"] = people[\"age\"] > 30      # adds another column \"over 30\"\n",
        "birthyears = people.pop(\"birthyear\")\n",
        "del people[\"children\"]\n",
        "\n",
        "people"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  weight  age  over 30\n",
              "alice     Biking      68   33     True\n",
              "bob      Dancing      83   34     True\n",
              "charles      NaN     112   26    False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QHMgmdsJ_BIJ",
        "outputId": "49917475-3a3e-4725-841e-ac027a4d6bd9"
      },
      "source": [
        "birthyears"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice      1985\n",
              "bob        1984\n",
              "charles    1992\n",
              "Name: birthyear, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "xscBizun_EfT",
        "outputId": "f552d16f-6f56-4f1d-f609-aed22556e7ce"
      },
      "source": [
        "people[\"pets\"] = pd.Series({\"bob\": 0, \"charles\": 5, \"eugene\":1})  # alice is missing, eugene is ignored\n",
        "people"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  weight  age  over 30  pets\n",
              "alice     Biking      68   33     True   NaN\n",
              "bob      Dancing      83   34     True   0.0\n",
              "charles      NaN     112   26    False   5.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "OS4HkRue_HAa",
        "outputId": "2cb8e09f-32bc-4d08-b5f3-2bea092c1363"
      },
      "source": [
        "people.insert(1, \"height\", [172, 181, 185])\n",
        "people"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets\n",
              "alice     Biking     172      68   33     True   NaN\n",
              "bob      Dancing     181      83   34     True   0.0\n",
              "charles      NaN     185     112   26    False   5.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "1KSeBMiv_KPU",
        "outputId": "6e16c100-c34c-48bc-d7ea-2b5fcfb32558"
      },
      "source": [
        "#Assigning new columns\n",
        "\n",
        "people.assign(\n",
        "    body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
        "    has_pets = people[\"pets\"] > 0\n",
        ")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>has_pets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets  body_mass_index  has_pets\n",
              "alice     Biking     172      68   33     True   NaN        22.985398     False\n",
              "bob      Dancing     181      83   34     True   0.0        25.335002     False\n",
              "charles      NaN     185     112   26    False   5.0        32.724617      True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sl9YqzjM_PWu",
        "outputId": "7d3466cc-5cf3-4638-c2b5-0aa4faaf9fb4"
      },
      "source": [
        "#Note that you cannot access columns created within the same assignment:\n",
        "\n",
        "try:\n",
        "    people.assign(\n",
        "        body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
        "        overweight = people[\"body_mass_index\"] > 25\n",
        "    )\n",
        "except KeyError as e:\n",
        "    print(\"Key error:\", e)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Key error: 'body_mass_index'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nMOkJdO_bBK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "289d06a7-4a06-4309-d677-b4bf289224a7"
      },
      "source": [
        "d6 = people.assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
        "d6.assign(overweight = d6[\"body_mass_index\"] > 25)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  ...  pets  body_mass_index  overweight\n",
              "alice     Biking     172      68  ...   NaN        22.985398       False\n",
              "bob      Dancing     181      83  ...   0.0        25.335002        True\n",
              "charles      NaN     185     112  ...   5.0        32.724617        True\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFw77W_z_ee6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "e47134a4-9877-4190-b5ba-540961da1b63"
      },
      "source": [
        "d6 = people.assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
        "d6.assign(overweight = d6[\"body_mass_index\"] > 25)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  ...  pets  body_mass_index  overweight\n",
              "alice     Biking     172      68  ...   NaN        22.985398       False\n",
              "bob      Dancing     181      83  ...   0.0        25.335002        True\n",
              "charles      NaN     185     112  ...   5.0        32.724617        True\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lw4dhaFo_KpF",
        "outputId": "19b4c732-0acf-42c9-b9d5-22834a8355b6"
      },
      "source": [
        "try:\n",
        "    (people\n",
        "         .assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
        "         .assign(overweight = people[\"body_mass_index\"] > 25)\n",
        "    )\n",
        "except KeyError as e:\n",
        "    print(\"Key error:\", e)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Key error: 'body_mass_index'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "n9bWv7KS_y4q",
        "outputId": "59e18fdf-2a97-4280-e433-1deabcfba66b"
      },
      "source": [
        "(people\n",
        "     .assign(body_mass_index = lambda df: df[\"weight\"] / (df[\"height\"] / 100) ** 2)\n",
        "     .assign(overweight = lambda df: df[\"body_mass_index\"] > 25)\n",
        ")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  ...  pets  body_mass_index  overweight\n",
              "alice     Biking     172      68  ...   NaN        22.985398       False\n",
              "bob      Dancing     181      83  ...   0.0        25.335002        True\n",
              "charles      NaN     185     112  ...   5.0        32.724617        True\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyiVvdZu_2JZ",
        "outputId": "42d26cd1-7a26-4228-c980-ccc1242f51af"
      },
      "source": [
        "#Evaluating an expression\n",
        "\n",
        "people.eval(\"weight / (height/100) ** 2 > 25\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice      False\n",
              "bob         True\n",
              "charles     True\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "UVL6f9uM_8Ka",
        "outputId": "f2540dd8-d1c3-4000-9b5f-c3a637aaaca4"
      },
      "source": [
        "people.eval(\"body_mass_index = weight / (height/100) ** 2\", inplace=True)\n",
        "people"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets  body_mass_index\n",
              "alice     Biking     172      68   33     True   NaN        22.985398\n",
              "bob      Dancing     181      83   34     True   0.0        25.335002\n",
              "charles      NaN     185     112   26    False   5.0        32.724617"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "jy46g9c8_-xE",
        "outputId": "bd48fb96-5c3c-481c-f0c7-8711e1dbb2d9"
      },
      "source": [
        "overweight_threshold = 30\n",
        "people.eval(\"overweight = body_mass_index > @overweight_threshold\", inplace=True)\n",
        "people"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  ...  pets  body_mass_index  overweight\n",
              "alice     Biking     172      68  ...   NaN        22.985398       False\n",
              "bob      Dancing     181      83  ...   0.0        25.335002       False\n",
              "charles      NaN     185     112  ...   5.0        32.724617        True\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "ACnmXwtAACa7",
        "outputId": "47190dcd-1ec7-468d-b166-82f0343ee073"
      },
      "source": [
        "#Querying a dataframe\n",
        "\n",
        "people.query(\"age > 30 and pets == 0\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       hobby  height  weight  age  over 30  pets  body_mass_index  overweight\n",
              "bob  Dancing     181      83   34     True   0.0        25.335002       False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "Ka9xnUy2AH7Y",
        "outputId": "7d8c2b21-820e-4855-d966-3c8b661aeb5a"
      },
      "source": [
        "#sorting a dataframe\n",
        "\n",
        "people.sort_index(ascending=False)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  ...  pets  body_mass_index  overweight\n",
              "charles      NaN     185     112  ...   5.0        32.724617        True\n",
              "bob      Dancing     181      83  ...   0.0        25.335002       False\n",
              "alice     Biking     172      68  ...   NaN        22.985398       False\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "BBPIy25tAMOq",
        "outputId": "025a927c-3980-4009-bb6e-cf334bc034de"
      },
      "source": [
        "people.sort_index(axis=1, inplace=True)\n",
        "people"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>height</th>\n",
              "      <th>hobby</th>\n",
              "      <th>over 30</th>\n",
              "      <th>overweight</th>\n",
              "      <th>pets</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>33</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>172</td>\n",
              "      <td>Biking</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>34</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>181</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>26</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>185</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>5.0</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         age  body_mass_index  height  ... overweight  pets  weight\n",
              "alice     33        22.985398     172  ...      False   NaN      68\n",
              "bob       34        25.335002     181  ...      False   0.0      83\n",
              "charles   26        32.724617     185  ...       True   5.0     112\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "kqWQ5oZHAQxB",
        "outputId": "ac77d553-c454-4b0a-b4bd-ba2ccd125e63"
      },
      "source": [
        "people.sort_values(by=\"age\", inplace=True)\n",
        "people"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>height</th>\n",
              "      <th>hobby</th>\n",
              "      <th>over 30</th>\n",
              "      <th>overweight</th>\n",
              "      <th>pets</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>26</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>185</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>5.0</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>33</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>172</td>\n",
              "      <td>Biking</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>34</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>181</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         age  body_mass_index  height  ... overweight  pets  weight\n",
              "charles   26        32.724617     185  ...       True   5.0     112\n",
              "alice     33        22.985398     172  ...      False   NaN      68\n",
              "bob       34        25.335002     181  ...      False   0.0      83\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "mx3QophrAUIx",
        "outputId": "d9b5ce71-e86e-4434-b07d-dae0decb13c4"
      },
      "source": [
        "#Plotting a dataframe\n",
        "\n",
        "people.plot(kind = \"line\", x = \"body_mass_index\", y = [\"height\", \"weight\"])\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-b0ee534e612b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"line\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"body_mass_index\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"height\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"weight\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hXp/CXUR6hsoy2LYiaJW/B1uXQ2WwkdzAkTDur32rfNh0SM+KbV3bgcJdRMKp4ogP8M1LfVdLUSFUlwMGJ4yFU6/3QT5sBqRkxrq27U7hLiLhUFYCW5ZFboBu/whqqsB6wZAJwUiWGX6IYnL/WNe2wyncRaR7Kt0fufG5+T1/M9TVQK94yJoE0+/wYZ4zBZLSYl3bTqdwF5Hu4dCuqJEsS2D3el8el+Sn8p/5jz7Ms0+DhOTY1rULULiLSNdUUnTsVP59n/ny+L6QezqM+yYMO8O30uMSY1vXLkjhLiKx55yfuh89lb94sz+WmA7DpsGk632YD8mH3vGxrW83oHAXkc7nHOz91Id47YqJh7b7Y8kD/CiWqbf5bpYTxsZ8Kn93pHAXkY5XU+P7yDcviXSzlO71x1JO8CFeO/tz4KguN5W/O1K4i0j7q66CnauPncpfVuyPpefCyV+LhHn/E7vd7M/uoMlwN7OFwCXAbufcuKCsP/BfQB6wCbjKOXfAzAx4FLgYKAVucM592DFVF5Euo6rCjyuvm8q/DCoO+WP9T4JTvgF5Z/julozc2Na1h2hOy30R8BjwdFTZPcD/Oefmmdk9wfN/Ai4CRgQ/pwO/Cv4UkTCpPOpnfNZOGNr6AVQd9ccyT4H8q3zLPHc6pA2JbV17qCbD3Tn3jpnl1SueBZwdPP4N8DY+3GcBTzvnHPC+mWWY2RDn3I72qrCIxED5Ydi6LNLNsm0FVFcABoPHw+QbgjCfBn0Hxrq2Quv73E+ICuydwAnB4yxga9R5RUHZV8LdzOYAcwByc/U1TaRLOVrsV0ms7WbZvhJcNVhvGDoRTv+O72bJOR36ZMS6ttKANt9Qdc45M3OteN0TwBMABQUFLX69iLSjI/uibn6+BzujNnLOmgxn3OX7y3NOh8SUWNdWmqG14b6rtrvFzIYAu4PybUBO1HnZQZmIdCWHdkaGJG5e4nccgmAj59Pg7B/4MM8ugPg+sa2rtEprw/0VYDYwL/jz5ajyvzOz5/A3UkvU3y7SBRRvicz83LwE9n/hyxNS/VT+/Kv9sMShp3b6Rs7SMZozFPJZ/M3TgWZWBNyHD/XnzexmYDNwVXD6H/DDIDfih0Le2AF1FpHjcc6Hd3TLvCS4FZaU4VvkBTdHNnLurekuYdSc0TLXNnLovAbOdcDctlZKRFrAOd+tUhfmS+HwTn+sb6YP8el3+D8HjdHszx5Cv7JFupuaati1NrJi4pa/QOk+fyx1KAyfGewwdAYMHKHZnz2Uwl2kq6uuhB2rIotsbXkfykv8sYxhMPLCyHZx/fIU5gIo3EW6nqryYCPnIMy3LofKI/7YgBEw7vKojZyzY1tX6bIU7iKxVlEKRR9Ewrzog2AjZ2DQWJh4XWSRrZRBsa2rdBsKd5HOVnbQt8ZrZ39u+xBqKv1GzoPz4bRbIlP5e8BGztIxFO4iHa10fzCVP1jLfOfqYCPnOBg6CabNDabyT4Gk9FjXVkJC4S7S3g7vjowv37wUdq3DT+VP9Js3z/yeb5lnnwYJfWNdWwkphbtIW5Vsi6zJsnmp3z4OID7Zr8Vyzg99mA+dBPFJsa2r9BgKd5GWcM5v3Bw9lf/AJn8sMQ1yp8LEb/luliETtJGzxIzCXeR4nIN9G4+dyn8wWAuvTz8/gmXK3wZT+cdrI2fpMhTuItFqamDPhqiW+VI4Eix62ndQZEjisBmQOVpT+aXLUrhLz1ZdBbvWBGG+FLYshaMH/LG0bDjpnEiYDzhJsz+l21C4S89SVQE7Vka6Wba8H7WR84kw+q/8mizDpkO/YbGtq0gbKNwl3CrLYFthpJul6AOoLPXHBo6C/CsjU/nThsa2riLtSOEu4VJxJNjIeakP9G2FkY2cTxgHk77tgzx3OqRkxrq2Ih1G4S7dW1lJ1OzPJb7LpabKb+Q8ZAKc/re+ZZ471Y9uEekhFO7SvRzZ52961q5lvmttMJU/3m/kPOPvozZyTo11bUViRuEuXduhXZGZn5uW+GGKAHFJfvr+md+PTOXXRs4idRTu0rUUbz12Kv++jb48IcW3xsdf4Wd/Dj0V4hJjW1eRLkzhLrFTu5Fz3SJbS6B4iz+WlO5vek6a7VvmgydoI2eRFtC/Fuk8zsGeTyJBvnkpHNrhjyUP9H3lU+f6MB80RlP5e6jKykqKioooKyuLdVW6jKSkJLKzs4mPb/5aRQr3nqD8sB/b3dm7+NTURDZyru1mqdvIeUhkfHneGTBwpGZ/CgBFRUWkpqaSl5eH6f8JnHPs27ePoqIihg8f3uzXKdzDqrIMNv4J1rwIn/4vnPot+KtfdOxnVldFNnLevAS2/MUPVQTIyIURF/hAz5sB/YYrzKVBZWVlCvYoZsaAAQPYs2dPi16ncA+T6kr44s+w9kXY8KqfVp88EE79G5hwTft/XlU5bP8oMpV/6zKoOOyPDTgZxsyKTOXPyGn/z5fQUrAfqzV/H20KdzO7C7gFcMAa4EZgCPAcMABYAVzvnKtoy+fIcdTU+HHfa16E9S/D0f2QmA5jZ8G4v4a8M9vvRmRF6Ven8lcF/aKDxvhfILVdLamD2+czRaRVWv2v3syygDuAMc65o2b2PHANcDHwS+fcc2b2OHAz8Kt2qa14zvlNldcuhnUv+ZuS8ckw6iIYdwWcfF77DBMsP+Rb47UrJm5bEbWR83gouCmY/TkN+g5o++eJdBGbNm3ikksuYe3atc06//HHHyc5OZlvf/vbjZ6zaNEiCgsLeeyxx75y7Gc/+xn33ntvq+vbkLY26eKAPmZWCSQDO4BzgeuC478B7kfh3nbOwe71PtDXLva7//ROgJPPh/F/DSMvbPt+nEcP+Kn8td0sO1aBqw42cj4Vpt3uu1lyT9dGziJRvvOd77Tp9V0q3J1z28xsPrAFOAr8Ed8NU+ycqwpOKwKy2lzLnmzf55FA3/OxXzPlxLP8zMzRfwV9Mlr/3of3RE3lX+JHtuD8L43s02Dm3b5lnn0aJKa02yWJNNe//Pc61m8/2K7vOWZoGvd9Y2yT51VXV3PrrbeydOlSsrKyePnll9m+fTtz585lz549JCcn8+STTzJ69Gjuv/9+UlJS+N73vscHH3zAzTffTK9evTj//PN5/fXX674BbN++nQsvvJDPP/+cyy+/nIceeoh77rmHo0ePMnHiRMaOHcszzzzTLtfZlm6ZfsAsYDhQDLwAXNiC188B5gDk5ua2thrhVFIEa1/ygb5jpS/Lne5Hu5wyq/WrGR7cERnJsmkJ7P3El8f1gZwpcM69vr88q0AbOUuP99lnn/Hss8/y5JNPctVVV7F48WKeeuopHn/8cUaMGMGyZcu4/fbbefPNN4953Y033siTTz7JtGnTuOeee445tnLlSj766CMSExMZNWoU3/3ud5k3bx6PPfYYK1eubNf6t6Vb5mvAl865PQBm9hIwA8gws7ig9Z4NbGvoxc65J4AnAAoKClwb6hEOh/fA+t/7QN/yF1829FS44Kcw9nJIb+EXIOf8bM/oMD/wpT+WkBps5Hytb5kPmQhxCe17PSLtoDkt7I4yfPhwJk6cCMDkyZPZtGkTS5cu5corr6w7p7y8/JjXFBcXc+jQIaZNmwbAddddx6uvvlp3/LzzziM93Xdpjhkzhs2bN5OT0zEjydoS7luAqWaWjO+WOQ8oBN4CrsCPmJkNvNzWSobW0WL4+FU/0uXLP/vVDTNHwzn/DOO+6bd1ay7nfBdO9CJbB4v8sT79fMt/yq2+ZX7CeE3lF2lCYmJkUELv3r3ZtWsXGRkZbWph13/Pqqqq45zdNm3pc19mZi8CHwJVwEf4lvhrwHNm9kBQtqA9KhoaFUfgk9d9C33jG34jiX55cMbdfujiCWOa9z41Nb4PPnoq/+Fd/ljfzGCy0J0+zDNP0UbOIm2UlpbG8OHDeeGFF7jyyitxzrF69WomTJhQd05GRgapqaksW7aM008/neeee65Z7x0fH09lZWWLlhdoSpuab865+4D76hV/AUxpy/uGTlW5D/I1L8Kn/+OXAkgdClPm+Bb60ElNz9asqYada6IW2Vrqx7QDpGXB8LMiU/kHnKzZnyId4JlnnuG2227jgQceoLKykmuuueaYcAdYsGABt956K7169eKss86q64Y5njlz5pCfn8+kSZPa7YaqORf77u6CggJXWFgY62q0r+oq39WydrGfLVpeAn36w9jL/Fj03GnHb01XV8L2lVFT+d+H8mDUQL+8yMzPvBmQMUxhLqGxYcMGTjnllFhXo9UOHz5MSoofXTZv3jx27NjBo48+2ub3bejvxcxWOOcKGjpfHa/tqaYGtr4fmS1auhcS02D0JX4s+vCzoHcjX7sqy/wkodpFtrYuj9rIeaTvsqmd/dnSm6si0mlee+01fv7zn1NVVcWwYcNYtGhRTOqhcG8r5/z6KmsX++GLh7b7oYWjLgxmi36t4WGFFUd8gNd2sxQVQnU5fiPnsXDq9T7Ih83QRs4i3cjVV1/N1VdfHetqKNxbbfeGyOSi/V/4PTxP/hpc8BM/W7T+pJ+yg5GNnDcv8b8Qaqr8VP4hE4KRLMFGzsn9Y3NNIhIaCveW2P9FZHLR7vU+mIefCWfcBad8ww85rFW6349Xr11ka+fqqI2cJ8H07/p+85wpkJQWu2sSkVBSuDfl4HZY9zvfj779Q1+WMxUuetjfHK3dAOPwbn9e7SJbu9f58rqNnP8xMpU/ITk21yIiPYbCvSFH9gazRV/yQY3zXSfn/xjGftOvTV5SBF+8HVlka99n/rXxff3CWuMu9y3zrEnayFlEOp3CvVZZCXz8mm+hf/G2Xw1x4Eg4+wd+pEqv3r575a2fBRs5b/avS0yHYdNg0vU+zIfkNz4iRkRC7ZZbbuHuu+9mzJjGJyPecMMNXHLJJVxxxRXHlNcub3Ddddc18sqW6dnhXlHqJxWtXQyf/dHPFs3I9f3hQ/L98gBb/gK/+YYfBQOQPCDYyPk2381ywlht5CwiAPz6179u9Ws3bdrEb3/7W4V7q1WVw+dv+hb6J69D5RHoOwhyTvebNlcdhY/+E5bs9eennBDZ93PYDBg4SlP5RTrL6/f4mdntafB4uGjecU95+OGHSUxM5I477uCuu+5i1apVvPnmm7z55pssWLCA2bNnc99991FeXs5JJ53EU089RUpKCmeffTbz58+noKCABQsW8OCDD5KRkcGECRNITEys26jjnXfe4ZFHHmHnzp089NBDXHHFFdxzzz1s2LCBiRMnMnv2bO666642XWbPCPfqKtj0brC36H9HNm0Gv0JiVbk/DpCe64c01oZ5/xM1+1Okh5k5cya/+MUvuOOOOygsLKS8vJzKykreffdd8vPzeeCBB3jjjTfo27cvDz74II888gg/+tGP6l6/fft2fvKTn/Dhhx+SmprKueeee8wyBTt27OC9997j448/5tJLL+WKK65g3rx5zJ8//5hVJNsivOFeUwNFy4PZor+HI43sHJ4yKLImy7DpvltGRLqGJlrYHWXy5MmsWLGCgwcPkpiYyKRJkygsLOTdd9/l0ksvZf369cyYMQOAioqKuiV+ay1fvpyzzjqL/v39nJUrr7ySTz/9tO74ZZddRq9evRgzZgy7du3qkGsIV7g757eGW/sirP1dZMnbaJmjI90sudMhbUjn11NEurT4+HiGDx/OokWLmD59Ovn5+bz11lts3LiR4cOHc/755/Pss8+2+v2jl/7tqPW9whHuez7xLfS1i2H/51EHzPev1YX5NOg7MGbVFJHuY+bMmcyfP5+FCxcyfvx47r77biZPnszUqVOZO3cuGzdu5OSTT+bIkSNs27aNkSNH1r32tNNO48477+TAgQOkpqayePFixo8ff9zPS01N5dChQ+1W/+4d7gd3wG+v8rM/we8vmjU5CPMz/E3StuwxKiI91syZM/npT3/KtGnT6Nu3L0lJScycOZPMzEwWLVrEtddeW7cT0wMPPHBMuGdlZXHvvfcyZcoU+vfvz+jRo5tc+jc/P5/evXszYcIEbrjhhjbfUO3eS/4e3A6v/5Mfjz5sug9zbeQs0q119yV/a9Uu/VtVVcXll1/OTTfdxOWXX97q9+tZS/6mDYWr/yPWtRAR+Yr777+fN954gzH7heoAAAgFSURBVLKyMi644AIuu+yyTv387h3uIiJd1Pz582P6+ZqNIyJdTlfoLu5KWvP3oXAXkS4lKSmJffv2KeADzjn27dtHUlIDm/4ch7plRKRLyc7OpqioiD17Gpl42AMlJSWRnZ3dotco3EWkS6mdQCRto24ZEZEQUriLiISQwl1EJIS6xAxVM9sDbI51PVppILA31pWIkZ567T31uqHnXntXve5hzrnMhg50iXDvzsyssLHpv2HXU6+9p1439Nxr747XrW4ZEZEQUriLiISQwr3tnoh1BWKop157T71u6LnX3u2uW33uIiIhpJa7iEgIKdxFREJI4d4CZpZjZm+Z2XozW2dmf1/v+D+YmTOzUG3UerzrNrPvmtnHQflDsaxnR2js2s1sopm9b2YrzazQzKbEuq7tycySzGy5ma0KrvtfgvLhZrbMzDaa2X+ZWUKs69rejnPtz5jZJ2a21swWmll8rOt6XM45/TTzBxgCTAoepwKfAmOC5znA/+InYw2MdV0747qBc4A3gMTg2KBY17UTr/2PwEVB+cXA27GuaztftwEpweN4YBkwFXgeuCYofxy4LdZ17cRrvzg4ZsCzXf3a1XJvAefcDufch8HjQ8AGICs4/Evg+0Do7lAf57pvA+Y558qDY7tjV8uOcZxrd0BacFo6sD02NewYzjscPI0PfhxwLvBiUP4boHP3jusEjV27c+4PwTEHLAdatgZvJ1O4t5KZ5QGnAsvMbBawzTm3KqaV6gTR1w2MBGYGX9P/bGanxbJuHa3etd8JPGxmW4H5wA9iV7OOYWa9zWwlsBv4E/A5UOycqwpOKSLSuAmV+tfunFsWdSweuB74n1jVrzkU7q1gZinAYvw/8CrgXuBHMa1UJ4i+bufcQfx+AP3xX1n/EXjezCyGVewwDVz7bcBdzrkc4C5gQSzr1xGcc9XOuYn4FuoUYHSMq9Rp6l+7mY2LOvzvwDvOuXdjU7vmUbi3UPBbezHwjHPuJeAkYDiwysw24f9n+NDMBseulu2vgesG33J7KfimuhyowS+wFCqNXPtsoPbxC/jwCyXnXDHwFjANyDCz2k1+soFtMatYJ4i69gsBzOw+IBO4O5b1ag6FewsErdIFwAbn3CMAzrk1zrlBzrk851wePvAmOed2xrCq7aqh6w78Hn9TFTMbCSTQNVfOa7XjXPt24Kzg8bnAZ51dt45kZplmlhE87gOcj7/f8BZwRXDabODl2NSw4zRy7R+b2S3A14FrnXM1saxjc2iGaguY2RnAu8AafCsV4F7n3B+iztkEFDjnQhNyjV03fqTMQmAiUAF8zzn3Zkwq2UGOc+0HgUfxXVNlwO3OuRUxqWQHMLN8/A3T3vhG4PPOuR+b2YnAc/juuI+Av6m9oR4Wx7n2KvxouEPBqS85534co2o2SeEuIhJC6pYREQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl06nZnlmdnaVr72bDN7tb3r1JHMrMDM/rWFr7nfzL7XUXWS8Itr+hQRaQvnXCFQGOt6SM+ilrvESlyw+cEGM3vRzJLN7Dwz+8jM1gSbISQCmNmFwYYgHwLfDMp6mdlnZpYZ9Xxj7fP6zGyRmf0q2GDji+AbwMLg8xdFnferYPONuk0agvJ5wYYdq81sflB2ZbBxwyoze6exC43+thG0yBea2dtBPe6IOu+HZvapmb0HjIoqP8nM/sfMVpjZu2Y22szizOwDMzs7OOfnZvbTlv9nkNCK9YLy+ul5P0Aefm3wGcHzhcA/A1uBkUHZ0/hVN5OC8hH4TRKeB14NzrkPv0ojwAXA4uN85iL8tHkDZuGXDxiPb+CsACYG5/UP/uwNvA3kAwOAT4jM6M4I/lwDZEWXNfLZZ0fV+X5gKZCIX2RtH3698MnB+yXj14nfiF/OAeD/gBHB49OBN4PHY/HrvXwNvxRAQqz/2+qn6/yo5S6xstU5tyR4/J/AecCXzrlPg7LfAGfil5n90jn3mXPOBefWWgh8O3h8E/BUE5/538F7rAF2Ob/oWw2wDv8LB+Cq4BvCR/jwHAOU4NePWWBm3wRKg3OXAIvM7Fb8L4Pmes05V+78+kO7gROAmcDvnHOlzi8p/ArULTU8HXghWF/8/+F3h8I5tw74D+BV4CbnXEUL6iAhp3CXWKm/qFFxi9/Aua3ALjM7F7/k7utNvKR2gauaqMe1z+PMbDjwPeA851w+8BqQ5PzmFFPwOxBdQrBJg3PuO/hvHDnACjMb0MyqR392Nce/99ULv0HGxKifU6KOj8f/3Q1q5mdLD6Fwl1jJNbNpwePr8Dcc88zs5KDseuDPwMdB+UlB+bX13ufX+Nb8C8656jbWKQ04ApSY2QnARVDXek53fvXPu4AJQflJzrllzrkfAXvwId9a7wCXmVkfM0sFvgEQtOK/NLMrg880M6v9/G/iV2c8E/i32mVqRUDhLrHzCTDXzDYA/fB70N6I736oXV73cedcGTAHeC3oLqm/T+srQApNd8k0yfltEj/C/0L5Lb7bBfzG2K+a2WrgPSIbNTwc3Pxdi+9Hb/U2i87v0/pfwXu8DnwQdfhbwM1mtgrfhTTLzAYC84Bbgq6sx/BLEIsAWvJXujkzKwB+6ZybGeu6iHQlGucu3ZaZ3YPfy/Rbsa6LSFejlruEipn9ELiyXvELzrkOHwNuZl8HHqxX/KVz7vKO/myR+hTuIiIhpBuqIiIhpHAXEQkhhbuISAgp3EVEQuj/A6y/m6qL6lQYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "YE6gdilmAZxi",
        "outputId": "5b702621-1509-4ff4-9f4e-f48df408a2ef"
      },
      "source": [
        "people.plot(kind = \"scatter\", x = \"height\", y = \"weight\", s=[40, 120, 200])\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-84b2719816fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"scatter\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"height\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"weight\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m120\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "7hPGYqVTAdrQ",
        "outputId": "24f2a31e-9f69-49af-e51f-7e6d5a709785"
      },
      "source": [
        "#Operations on dataframe\n",
        "\n",
        "grades_array = np.array([[8,8,9],[10,9,9],[4, 8, 2], [9, 10, 10]])\n",
        "grades = pd.DataFrame(grades_array, columns=[\"sep\", \"oct\", \"nov\"], index=[\"alice\",\"bob\",\"charles\",\"darwin\"])\n",
        "grades"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sep  oct  nov\n",
              "alice      8    8    9\n",
              "bob       10    9    9\n",
              "charles    4    8    2\n",
              "darwin     9   10   10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "obwAYTVZAjyD",
        "outputId": "c972a351-372a-4cb8-d581-0ef31ab56758"
      },
      "source": [
        "np.sqrt(grades)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>2.828427</td>\n",
              "      <td>2.828427</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>3.162278</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.828427</td>\n",
              "      <td>1.414214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.162278</td>\n",
              "      <td>3.162278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              sep       oct       nov\n",
              "alice    2.828427  2.828427  3.000000\n",
              "bob      3.162278  3.000000  3.000000\n",
              "charles  2.000000  2.828427  1.414214\n",
              "darwin   3.000000  3.162278  3.162278"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "tfRQymzBAmvY",
        "outputId": "e2e3f4d9-d1c9-4be0-94e3-3123594a9531"
      },
      "source": [
        "grades + 1"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sep  oct  nov\n",
              "alice      9    9   10\n",
              "bob       11   10   10\n",
              "charles    5    9    3\n",
              "darwin    10   11   11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "quwQCN1tApQ0",
        "outputId": "6093b361-c617-4e67-b538-9a9044247b37"
      },
      "source": [
        "grades >= 5"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           sep   oct    nov\n",
              "alice     True  True   True\n",
              "bob       True  True   True\n",
              "charles  False  True  False\n",
              "darwin    True  True   True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdgPMTTIArxd",
        "outputId": "88370083-fb52-41a1-aee5-68935e632fb6"
      },
      "source": [
        "grades.mean()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sep    7.75\n",
              "oct    8.75\n",
              "nov    7.50\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U00rDlrwAuN8",
        "outputId": "eae6fb8f-d449-47f5-b49c-54e08d4f97d0"
      },
      "source": [
        "(grades > 5).all()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sep    False\n",
              "oct     True\n",
              "nov    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1jv_yAlAwjP",
        "outputId": "1bdd33ff-2e39-43c0-92b5-71617cea3837"
      },
      "source": [
        "(grades > 5).all(axis = 1)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice       True\n",
              "bob         True\n",
              "charles    False\n",
              "darwin      True\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOP1Z0ajAy_f",
        "outputId": "c0895ba8-01d7-4062-8aef-34b08a79aade"
      },
      "source": [
        "(grades == 10).any(axis = 1)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alice      False\n",
              "bob         True\n",
              "charles    False\n",
              "darwin      True\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "L0srMxjmA1nM",
        "outputId": "76d94568-df46-4396-ac23-3c4de5f3a157"
      },
      "source": [
        "grades - grades.mean()  # equivalent to: grades - [7.75, 8.75, 7.50]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>0.25</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>2.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>-3.75</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>1.25</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct  nov\n",
              "alice    0.25 -0.75  1.5\n",
              "bob      2.25  0.25  1.5\n",
              "charles -3.75 -0.75 -5.5\n",
              "darwin   1.25  1.25  2.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "uxUV9jRMA8Z6",
        "outputId": "5d0ab08c-2f7f-408b-a137-71ba8eb20af2"
      },
      "source": [
        "pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>7.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>7.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>7.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>7.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct  nov\n",
              "alice    7.75  8.75  7.5\n",
              "bob      7.75  8.75  7.5\n",
              "charles  7.75  8.75  7.5\n",
              "darwin   7.75  8.75  7.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "inuGJnJiA-i4",
        "outputId": "31cd616a-be4a-43c1-b3eb-149b06d0eb15"
      },
      "source": [
        "grades - grades.values.mean() # substracts the global mean (8.00) from all grades"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>-4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sep  oct  nov\n",
              "alice    0.0  0.0  1.0\n",
              "bob      2.0  1.0  1.0\n",
              "charles -4.0  0.0 -6.0\n",
              "darwin   1.0  2.0  2.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "9iiOsjIEBBnR",
        "outputId": "59420aea-3a76-44ec-aca0-ce1f6ee1de28"
      },
      "source": [
        "#Automatic alignmen\n",
        "\n",
        "bonus_array = np.array([[0,np.nan,2],[np.nan,1,0],[0, 1, 0], [3, 3, 0]])\n",
        "bonus_points = pd.DataFrame(bonus_array, columns=[\"oct\", \"nov\", \"dec\"], index=[\"bob\",\"colin\", \"darwin\", \"charles\"])\n",
        "bonus_points"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         oct  nov  dec\n",
              "bob      0.0  NaN  2.0\n",
              "colin    NaN  1.0  0.0\n",
              "darwin   0.0  1.0  0.0\n",
              "charles  3.0  3.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "HwcTk3KjBHUm",
        "outputId": "5cb4d82b-b372-4382-8357-ff301b08b6fd"
      },
      "source": [
        "grades + bonus_points"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dec</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         dec   nov   oct  sep\n",
              "alice    NaN   NaN   NaN  NaN\n",
              "bob      NaN   NaN   9.0  NaN\n",
              "charles  NaN   5.0  11.0  NaN\n",
              "colin    NaN   NaN   NaN  NaN\n",
              "darwin   NaN  11.0  10.0  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "V5WbbJ3gBRIN",
        "outputId": "3bbe793c-daa7-4102-cac1-65fc5fd00642"
      },
      "source": [
        "#Handling missing data\n",
        "\n",
        "(grades + bonus_points).fillna(0)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dec</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         dec   nov   oct  sep\n",
              "alice    0.0   0.0   0.0  0.0\n",
              "bob      0.0   0.0   9.0  0.0\n",
              "charles  0.0   5.0  11.0  0.0\n",
              "colin    0.0   0.0   0.0  0.0\n",
              "darwin   0.0  11.0  10.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "N8nal9K8Ba1j",
        "outputId": "f50d9cf9-98ab-4fdc-8279-26b2bbfe14dd"
      },
      "source": [
        "fixed_bonus_points = bonus_points.fillna(0)\n",
        "fixed_bonus_points.insert(0, \"sep\", 0)\n",
        "fixed_bonus_points.loc[\"alice\"] = 0\n",
        "grades + fixed_bonus_points"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dec</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         dec   nov   oct   sep\n",
              "alice    NaN   9.0   8.0   8.0\n",
              "bob      NaN   9.0   9.0  10.0\n",
              "charles  NaN   5.0  11.0   4.0\n",
              "colin    NaN   NaN   NaN   NaN\n",
              "darwin   NaN  11.0  10.0   9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "oBWZ1whTBeNx",
        "outputId": "a344d373-2c1b-4aff-a310-b9017636d15b"
      },
      "source": [
        "bonus_points"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         oct  nov  dec\n",
              "bob      0.0  NaN  2.0\n",
              "colin    NaN  1.0  0.0\n",
              "darwin   0.0  1.0  0.0\n",
              "charles  3.0  3.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "Nevr47tsBk-6",
        "outputId": "8c2ad68f-1561-46ac-ce3d-20bce28014f7"
      },
      "source": [
        "bonus_points.interpolate(axis=1)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         oct  nov  dec\n",
              "bob      0.0  1.0  2.0\n",
              "colin    NaN  1.0  0.0\n",
              "darwin   0.0  1.0  0.0\n",
              "charles  3.0  3.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "sQ9Svd16Bm7x",
        "outputId": "e34152eb-9716-4b1a-c39a-69b924547f98"
      },
      "source": [
        "better_bonus_points = bonus_points.copy()\n",
        "better_bonus_points.insert(0, \"sep\", 0)\n",
        "better_bonus_points.loc[\"alice\"] = 0\n",
        "better_bonus_points = better_bonus_points.interpolate(axis=1)\n",
        "better_bonus_points"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sep  oct  nov  dec\n",
              "bob      0.0  0.0  1.0  2.0\n",
              "colin    0.0  0.5  1.0  0.0\n",
              "darwin   0.0  0.0  1.0  0.0\n",
              "charles  0.0  3.0  3.0  0.0\n",
              "alice    0.0  0.0  0.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "nnCJ0C7jB0U-",
        "outputId": "72a14e9c-c13e-4412-9d76-4a033d8c6af5"
      },
      "source": [
        "grades + better_bonus_points"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dec</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         dec   nov   oct   sep\n",
              "alice    NaN   9.0   8.0   8.0\n",
              "bob      NaN  10.0   9.0  10.0\n",
              "charles  NaN   5.0  11.0   4.0\n",
              "colin    NaN   NaN   NaN   NaN\n",
              "darwin   NaN  11.0  10.0   9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "f1kAbnvDB3Yk",
        "outputId": "a8dfbe1b-dea0-473b-951c-14b426c18786"
      },
      "source": [
        "grades[\"dec\"] = np.nan\n",
        "final_grades = grades + better_bonus_points\n",
        "final_grades"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct   nov  dec\n",
              "alice     8.0   8.0   9.0  NaN\n",
              "bob      10.0   9.0  10.0  NaN\n",
              "charles   4.0  11.0   5.0  NaN\n",
              "colin     NaN   NaN   NaN  NaN\n",
              "darwin    9.0  10.0  11.0  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "iYOt7RyEB6Yw",
        "outputId": "631a3617-0a7b-4056-c3d4-eafcece52cf7"
      },
      "source": [
        "final_grades_clean = final_grades.dropna(how=\"all\")\n",
        "final_grades_clean"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct   nov  dec\n",
              "alice     8.0   8.0   9.0  NaN\n",
              "bob      10.0   9.0  10.0  NaN\n",
              "charles   4.0  11.0   5.0  NaN\n",
              "darwin    9.0  10.0  11.0  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "11gx47AuB-m9",
        "outputId": "281b9310-73d8-44cd-a95e-90a0b211eefd"
      },
      "source": [
        "final_grades_clean = final_grades_clean.dropna(axis=1, how=\"all\")\n",
        "final_grades_clean"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct   nov\n",
              "alice     8.0   8.0   9.0\n",
              "bob      10.0   9.0  10.0\n",
              "charles   4.0  11.0   5.0\n",
              "darwin    9.0  10.0  11.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "17ahlx3ZCBeD",
        "outputId": "a6205a97-3ec2-4bf2-b480-012d3d40d0f1"
      },
      "source": [
        "#Aggregating with groupby\n",
        "\n",
        "final_grades[\"hobby\"] = [\"Biking\", \"Dancing\", np.nan, \"Dancing\", \"Biking\"]\n",
        "final_grades"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct   nov  dec    hobby\n",
              "alice     8.0   8.0   9.0  NaN   Biking\n",
              "bob      10.0   9.0  10.0  NaN  Dancing\n",
              "charles   4.0  11.0   5.0  NaN      NaN\n",
              "colin     NaN   NaN   NaN  NaN  Dancing\n",
              "darwin    9.0  10.0  11.0  NaN   Biking"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dI-4J3olCI3O",
        "outputId": "3588640e-5b22-40b2-ff4f-150ba32c9083"
      },
      "source": [
        "grouped_grades = final_grades.groupby(\"hobby\")\n",
        "grouped_grades"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f28a09e1d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "vqaDPnIeCNOD",
        "outputId": "7f675ffa-aecd-470c-f1fd-f5aff291f09f"
      },
      "source": [
        "grouped_grades.mean()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hobby</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Biking</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dancing</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep  oct   nov  dec\n",
              "hobby                        \n",
              "Biking    8.5  9.0  10.0  NaN\n",
              "Dancing  10.0  9.0  10.0  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "X8L4fyfFCPj9",
        "outputId": "25f94684-61e1-452e-fe22-30d3dfc4d83c"
      },
      "source": [
        "#Pivot tables\n",
        "\n",
        "bonus_points"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         oct  nov  dec\n",
              "bob      0.0  NaN  2.0\n",
              "colin    NaN  1.0  0.0\n",
              "darwin   0.0  1.0  0.0\n",
              "charles  3.0  3.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "d5651kW2CTSq",
        "outputId": "be15d931-dba4-4252-b407-67ddb6e21467"
      },
      "source": [
        "more_grades = final_grades_clean.stack().reset_index()\n",
        "more_grades.columns = [\"name\", \"month\", \"grade\"]\n",
        "more_grades[\"bonus\"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]\n",
        "more_grades"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>month</th>\n",
              "      <th>grade</th>\n",
              "      <th>bonus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>alice</td>\n",
              "      <td>sep</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>alice</td>\n",
              "      <td>oct</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alice</td>\n",
              "      <td>nov</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bob</td>\n",
              "      <td>sep</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bob</td>\n",
              "      <td>oct</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>bob</td>\n",
              "      <td>nov</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>charles</td>\n",
              "      <td>sep</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>charles</td>\n",
              "      <td>oct</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>charles</td>\n",
              "      <td>nov</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>darwin</td>\n",
              "      <td>sep</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>darwin</td>\n",
              "      <td>oct</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>darwin</td>\n",
              "      <td>nov</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       name month  grade  bonus\n",
              "0     alice   sep    8.0    NaN\n",
              "1     alice   oct    8.0    NaN\n",
              "2     alice   nov    9.0    NaN\n",
              "3       bob   sep   10.0    0.0\n",
              "4       bob   oct    9.0    NaN\n",
              "5       bob   nov   10.0    2.0\n",
              "6   charles   sep    4.0    3.0\n",
              "7   charles   oct   11.0    3.0\n",
              "8   charles   nov    5.0    0.0\n",
              "9    darwin   sep    9.0    0.0\n",
              "10   darwin   oct   10.0    1.0\n",
              "11   darwin   nov   11.0    0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "cw2phLHICWRk",
        "outputId": "4237ca2e-be8c-4cdd-a5f9-4dc21f4ae2b9"
      },
      "source": [
        "pd.pivot_table(more_grades, index=\"name\")"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bonus</th>\n",
              "      <th>grade</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            bonus      grade\n",
              "name                        \n",
              "alice         NaN   8.333333\n",
              "bob      1.000000   9.666667\n",
              "charles  2.000000   6.666667\n",
              "darwin   0.333333  10.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "b-zY7I8SCY6C",
        "outputId": "921f94f8-c4f5-42b3-a067-18415b3a7ddb"
      },
      "source": [
        "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\",\"bonus\"], aggfunc=np.max)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bonus</th>\n",
              "      <th>grade</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         bonus  grade\n",
              "name                 \n",
              "alice      NaN    9.0\n",
              "bob        2.0   10.0\n",
              "charles    3.0   11.0\n",
              "darwin     1.0   11.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "EQbbICwGCbEl",
        "outputId": "f5720b5b-8d79-4d65-cf1b-80b12c173152"
      },
      "source": [
        "pd.pivot_table(more_grades, index=\"name\", values=\"grade\", columns=\"month\", margins=True)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>month</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>9.00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.00</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>9.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>5.00</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>11.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.00</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>8.75</td>\n",
              "      <td>9.5</td>\n",
              "      <td>7.75</td>\n",
              "      <td>8.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "month      nov   oct    sep        All\n",
              "name                                  \n",
              "alice     9.00   8.0   8.00   8.333333\n",
              "bob      10.00   9.0  10.00   9.666667\n",
              "charles   5.00  11.0   4.00   6.666667\n",
              "darwin   11.00  10.0   9.00  10.000000\n",
              "All       8.75   9.5   7.75   8.666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "wzaej4hbCg_N",
        "outputId": "c28c6644-54d8-44f1-a4de-1d16f1a5e10c"
      },
      "source": [
        "pd.pivot_table(more_grades, index=(\"name\", \"month\"), margins=True)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>bonus</th>\n",
              "      <th>grade</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th>month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">alice</th>\n",
              "      <th>nov</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oct</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sep</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">bob</th>\n",
              "      <th>nov</th>\n",
              "      <td>2.000</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oct</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sep</th>\n",
              "      <td>0.000</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">charles</th>\n",
              "      <th>nov</th>\n",
              "      <td>0.000</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oct</th>\n",
              "      <td>3.000</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sep</th>\n",
              "      <td>3.000</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">darwin</th>\n",
              "      <th>nov</th>\n",
              "      <td>0.000</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oct</th>\n",
              "      <td>1.000</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sep</th>\n",
              "      <td>0.000</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <th></th>\n",
              "      <td>1.125</td>\n",
              "      <td>8.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               bonus  grade\n",
              "name    month              \n",
              "alice   nov      NaN   9.00\n",
              "        oct      NaN   8.00\n",
              "        sep      NaN   8.00\n",
              "bob     nov    2.000  10.00\n",
              "        oct      NaN   9.00\n",
              "        sep    0.000  10.00\n",
              "charles nov    0.000   5.00\n",
              "        oct    3.000  11.00\n",
              "        sep    3.000   4.00\n",
              "darwin  nov    0.000  11.00\n",
              "        oct    1.000  10.00\n",
              "        sep    0.000   9.00\n",
              "All            1.125   8.75"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "IrfmvfTACkD-",
        "outputId": "fa5ee3fe-a4a1-4c9b-f234-5a6433538d72"
      },
      "source": [
        "#Overview functions\n",
        "\n",
        "much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
        "large_df = pd.DataFrame(much_data, columns=list(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"))\n",
        "large_df[large_df % 16 == 0] = np.nan\n",
        "large_df.insert(3,\"some_text\", \"Blabla\")\n",
        "large_df"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>some_text</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "      <th>G</th>\n",
              "      <th>H</th>\n",
              "      <th>I</th>\n",
              "      <th>J</th>\n",
              "      <th>K</th>\n",
              "      <th>L</th>\n",
              "      <th>M</th>\n",
              "      <th>N</th>\n",
              "      <th>O</th>\n",
              "      <th>P</th>\n",
              "      <th>Q</th>\n",
              "      <th>R</th>\n",
              "      <th>S</th>\n",
              "      <th>T</th>\n",
              "      <th>U</th>\n",
              "      <th>V</th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>88.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>132.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
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              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>9997</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
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              "      <td>66.0</td>\n",
              "      <td>Blabla</td>\n",
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              "      <td>110.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>11.0</td>\n",
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              "      <td>66.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>55.0</td>\n",
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              "      <td>132.0</td>\n",
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              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 27 columns</p>\n",
              "</div>"
            ],
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              "         A     B     C some_text      D  ...      V      W     X      Y      Z\n",
              "0      NaN  11.0  44.0    Blabla   99.0  ...    NaN   88.0  22.0  165.0  143.0\n",
              "1     11.0  22.0  55.0    Blabla  110.0  ...    NaN   99.0  33.0    NaN  154.0\n",
              "2     22.0  33.0  66.0    Blabla  121.0  ...   11.0  110.0  44.0    NaN  165.0\n",
              "3     33.0  44.0  77.0    Blabla  132.0  ...   22.0  121.0  55.0   11.0    NaN\n",
              "4     44.0  55.0  88.0    Blabla  143.0  ...   33.0  132.0  66.0   22.0    NaN\n",
              "...    ...   ...   ...       ...    ...  ...    ...    ...   ...    ...    ...\n",
              "9995   NaN   NaN  33.0    Blabla   88.0  ...  165.0   77.0  11.0  154.0  132.0\n",
              "9996   NaN  11.0  44.0    Blabla   99.0  ...    NaN   88.0  22.0  165.0  143.0\n",
              "9997  11.0  22.0  55.0    Blabla  110.0  ...    NaN   99.0  33.0    NaN  154.0\n",
              "9998  22.0  33.0  66.0    Blabla  121.0  ...   11.0  110.0  44.0    NaN  165.0\n",
              "9999  33.0  44.0  77.0    Blabla  132.0  ...   22.0  121.0  55.0   11.0    NaN\n",
              "\n",
              "[10000 rows x 27 columns]"
            ]
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          "execution_count": 81
        }
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    },
    {
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      "outputs": [
        {
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              "      A     B     C some_text      D  ...     V      W     X      Y      Z\n",
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              "2  22.0  33.0  66.0    Blabla  121.0  ...  11.0  110.0  44.0    NaN  165.0\n",
              "3  33.0  44.0  77.0    Blabla  132.0  ...  22.0  121.0  55.0   11.0    NaN\n",
              "4  44.0  55.0  88.0    Blabla  143.0  ...  33.0  132.0  66.0   22.0    NaN\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
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          "execution_count": 82
        }
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    {
      "cell_type": "code",
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          "height": 131
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        "id": "3G-wvjaxHc1I",
        "outputId": "bd61df2b-5345-441d-dd8d-6d09abcae62b"
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      "source": [
        "large_df.tail(n=2)"
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      "execution_count": 83,
      "outputs": [
        {
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          "data": {
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              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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              "         A     B     C some_text      D  ...     V      W     X     Y      Z\n",
              "9998  22.0  33.0  66.0    Blabla  121.0  ...  11.0  110.0  44.0   NaN  165.0\n",
              "9999  33.0  44.0  77.0    Blabla  132.0  ...  22.0  121.0  55.0  11.0    NaN\n",
              "\n",
              "[2 rows x 27 columns]"
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        "id": "aoBT_WP_HesJ",
        "outputId": "71dac3c4-8923-477e-c6bc-f6b02404ccc2"
      },
      "source": [
        "large_df.info()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 27 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   A          8823 non-null   float64\n",
            " 1   B          8824 non-null   float64\n",
            " 2   C          8824 non-null   float64\n",
            " 3   some_text  10000 non-null  object \n",
            " 4   D          8824 non-null   float64\n",
            " 5   E          8822 non-null   float64\n",
            " 6   F          8824 non-null   float64\n",
            " 7   G          8824 non-null   float64\n",
            " 8   H          8822 non-null   float64\n",
            " 9   I          8823 non-null   float64\n",
            " 10  J          8823 non-null   float64\n",
            " 11  K          8822 non-null   float64\n",
            " 12  L          8824 non-null   float64\n",
            " 13  M          8824 non-null   float64\n",
            " 14  N          8822 non-null   float64\n",
            " 15  O          8824 non-null   float64\n",
            " 16  P          8824 non-null   float64\n",
            " 17  Q          8824 non-null   float64\n",
            " 18  R          8823 non-null   float64\n",
            " 19  S          8824 non-null   float64\n",
            " 20  T          8824 non-null   float64\n",
            " 21  U          8824 non-null   float64\n",
            " 22  V          8822 non-null   float64\n",
            " 23  W          8824 non-null   float64\n",
            " 24  X          8824 non-null   float64\n",
            " 25  Y          8822 non-null   float64\n",
            " 26  Z          8823 non-null   float64\n",
            "dtypes: float64(26), object(1)\n",
            "memory usage: 2.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "sbDkWwhfHhak",
        "outputId": "eea33075-0f1e-4465-9aa7-3d9e30602b11"
      },
      "source": [
        "large_df.describe()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "      <th>G</th>\n",
              "      <th>H</th>\n",
              "      <th>I</th>\n",
              "      <th>J</th>\n",
              "      <th>K</th>\n",
              "      <th>L</th>\n",
              "      <th>M</th>\n",
              "      <th>N</th>\n",
              "      <th>O</th>\n",
              "      <th>P</th>\n",
              "      <th>Q</th>\n",
              "      <th>R</th>\n",
              "      <th>S</th>\n",
              "      <th>T</th>\n",
              "      <th>U</th>\n",
              "      <th>V</th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8823.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8823.000000</td>\n",
              "      <td>8823.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8823.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8823.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>87.977559</td>\n",
              "      <td>87.972575</td>\n",
              "      <td>87.987534</td>\n",
              "      <td>88.012466</td>\n",
              "      <td>87.983791</td>\n",
              "      <td>88.007480</td>\n",
              "      <td>87.977561</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.022441</td>\n",
              "      <td>88.022441</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>87.977561</td>\n",
              "      <td>88.007480</td>\n",
              "      <td>87.983791</td>\n",
              "      <td>88.012466</td>\n",
              "      <td>87.987534</td>\n",
              "      <td>87.972575</td>\n",
              "      <td>87.977559</td>\n",
              "      <td>87.972575</td>\n",
              "      <td>87.987534</td>\n",
              "      <td>88.012466</td>\n",
              "      <td>87.983791</td>\n",
              "      <td>88.007480</td>\n",
              "      <td>87.977561</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.022441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47.535911</td>\n",
              "      <td>47.535523</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.535001</td>\n",
              "      <td>47.519371</td>\n",
              "      <td>47.529755</td>\n",
              "      <td>47.536879</td>\n",
              "      <td>47.535911</td>\n",
              "      <td>47.535911</td>\n",
              "      <td>47.536879</td>\n",
              "      <td>47.529755</td>\n",
              "      <td>47.519371</td>\n",
              "      <td>47.535001</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.535523</td>\n",
              "      <td>47.535911</td>\n",
              "      <td>47.535523</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.535001</td>\n",
              "      <td>47.519371</td>\n",
              "      <td>47.529755</td>\n",
              "      <td>47.536879</td>\n",
              "      <td>47.535911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 A            B  ...            Y            Z\n",
              "count  8823.000000  8824.000000  ...  8822.000000  8823.000000\n",
              "mean     87.977559    87.972575  ...    88.000000    88.022441\n",
              "std      47.535911    47.535523  ...    47.536879    47.535911\n",
              "min      11.000000    11.000000  ...    11.000000    11.000000\n",
              "25%      44.000000    44.000000  ...    44.000000    44.000000\n",
              "50%      88.000000    88.000000  ...    88.000000    88.000000\n",
              "75%     132.000000   132.000000  ...   132.000000   132.000000\n",
              "max     165.000000   165.000000  ...   165.000000   165.000000\n",
              "\n",
              "[8 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "NWH9GJYbHlrP",
        "outputId": "3c2314a1-d18b-4445-b4fb-aecf069dc11e"
      },
      "source": [
        "#Saving and loading\n",
        "\n",
        "my_df = pd.DataFrame(\n",
        "    [[\"Biking\", 68.5, 1985, np.nan], [\"Dancing\", 83.1, 1984, 3]], \n",
        "    columns=[\"hobby\",\"weight\",\"birthyear\",\"children\"],\n",
        "    index=[\"alice\", \"bob\"]\n",
        ")\n",
        "my_df"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>68.5</td>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>83.1</td>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         hobby  weight  birthyear  children\n",
              "alice   Biking    68.5       1985       NaN\n",
              "bob    Dancing    83.1       1984       3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qwqlgg3gHqO1"
      },
      "source": [
        "#Saving\n",
        "\n",
        "my_df.to_csv(\"my_df.csv\")\n",
        "my_df.to_html(\"my_df.html\")\n",
        "my_df.to_json(\"my_df.json\")"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJlUkMGfHxnq",
        "outputId": "eda87076-85f4-4f3d-c296-7c321c03344c"
      },
      "source": [
        "for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
        "    print(\"#\", filename)\n",
        "    with open(filename, \"rt\") as f:\n",
        "        print(f.read())\n",
        "        print()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# my_df.csv\n",
            ",hobby,weight,birthyear,children\n",
            "alice,Biking,68.5,1985,\n",
            "bob,Dancing,83.1,1984,3.0\n",
            "\n",
            "\n",
            "# my_df.html\n",
            "<table border=\"1\" class=\"dataframe\">\n",
            "  <thead>\n",
            "    <tr style=\"text-align: right;\">\n",
            "      <th></th>\n",
            "      <th>hobby</th>\n",
            "      <th>weight</th>\n",
            "      <th>birthyear</th>\n",
            "      <th>children</th>\n",
            "    </tr>\n",
            "  </thead>\n",
            "  <tbody>\n",
            "    <tr>\n",
            "      <th>alice</th>\n",
            "      <td>Biking</td>\n",
            "      <td>68.5</td>\n",
            "      <td>1985</td>\n",
            "      <td>NaN</td>\n",
            "    </tr>\n",
            "    <tr>\n",
            "      <th>bob</th>\n",
            "      <td>Dancing</td>\n",
            "      <td>83.1</td>\n",
            "      <td>1984</td>\n",
            "      <td>3.0</td>\n",
            "    </tr>\n",
            "  </tbody>\n",
            "</table>\n",
            "\n",
            "# my_df.json\n",
            "{\"hobby\":{\"alice\":\"Biking\",\"bob\":\"Dancing\"},\"weight\":{\"alice\":68.5,\"bob\":83.1},\"birthyear\":{\"alice\":1985,\"bob\":1984},\"children\":{\"alice\":null,\"bob\":3.0}}\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aPIA_hJH1hu"
      },
      "source": [
        "try:\n",
        "    my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
        "except ImportError as e:\n",
        "    print(e)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "osSjBcBOH69T",
        "outputId": "8bac514e-b7b5-4228-a917-02b5ee6f60ee"
      },
      "source": [
        "#Loading\n",
        "\n",
        "my_df_loaded = pd.read_csv(\"my_df.csv\", index_col=0)\n",
        "my_df_loaded"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>68.5</td>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>83.1</td>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         hobby  weight  birthyear  children\n",
              "alice   Biking    68.5       1985       NaN\n",
              "bob    Dancing    83.1       1984       3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPBN4gkEH_Ap",
        "outputId": "6b8d7cfb-6e2f-448b-d073-4f846d378dde"
      },
      "source": [
        "us_cities = None\n",
        "try:\n",
        "    csv_url = \"http://simplemaps.com/files/cities.csv\"\n",
        "    us_cities = pd.read_csv(csv_url, index_col=0)\n",
        "    us_cities = us_cities.head()\n",
        "except IOError as e:\n",
        "    print(e)\n",
        "us_cities"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "HTTP Error 403: Forbidden\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "eZ5Rl9D1ICYl",
        "outputId": "9f14de01-d553-4cca-932a-eacc0e94bc4a"
      },
      "source": [
        "#Combining dataframes\n",
        "#SQL-like joins\n",
        "\n",
        "city_loc = pd.DataFrame(\n",
        "    [\n",
        "        [\"CA\", \"San Francisco\", 37.781334, -122.416728],\n",
        "        [\"NY\", \"New York\", 40.705649, -74.008344],\n",
        "        [\"FL\", \"Miami\", 25.791100, -80.320733],\n",
        "        [\"OH\", \"Cleveland\", 41.473508, -81.739791],\n",
        "        [\"UT\", \"Salt Lake City\", 40.755851, -111.896657]\n",
        "    ], columns=[\"state\", \"city\", \"lat\", \"lng\"])\n",
        "city_loc"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state            city        lat         lng\n",
              "0    CA   San Francisco  37.781334 -122.416728\n",
              "1    NY        New York  40.705649  -74.008344\n",
              "2    FL           Miami  25.791100  -80.320733\n",
              "3    OH       Cleveland  41.473508  -81.739791\n",
              "4    UT  Salt Lake City  40.755851 -111.896657"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "0jYXplsOILn8",
        "outputId": "fe58a91c-3d20-48f0-8a5c-25fb6bdef550"
      },
      "source": [
        "city_pop = pd.DataFrame(\n",
        "    [\n",
        "        [808976, \"San Francisco\", \"California\"],\n",
        "        [8363710, \"New York\", \"New-York\"],\n",
        "        [413201, \"Miami\", \"Florida\"],\n",
        "        [2242193, \"Houston\", \"Texas\"]\n",
        "    ], index=[3,4,5,6], columns=[\"population\", \"city\", \"state\"])\n",
        "city_pop"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2242193</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   population           city       state\n",
              "3      808976  San Francisco  California\n",
              "4     8363710       New York    New-York\n",
              "5      413201          Miami     Florida\n",
              "6     2242193        Houston       Texas"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "v13hPNXbIRKa",
        "outputId": "c1819878-bab9-4924-814b-10d2bf0ec15e"
      },
      "source": [
        "pd.merge(left=city_loc, right=city_pop, on=\"city\")"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_x</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state_x           city        lat         lng  population     state_y\n",
              "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
              "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
              "2      FL          Miami  25.791100  -80.320733      413201     Florida"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "gBU5qvTaIT5D",
        "outputId": "a5b8d6f9-a5c4-4103-9994-e25c9c8b6d12"
      },
      "source": [
        "all_cities = pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"outer\")\n",
        "all_cities"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_x</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state_x            city        lat         lng  population     state_y\n",
              "0      CA   San Francisco  37.781334 -122.416728    808976.0  California\n",
              "1      NY        New York  40.705649  -74.008344   8363710.0    New-York\n",
              "2      FL           Miami  25.791100  -80.320733    413201.0     Florida\n",
              "3      OH       Cleveland  41.473508  -81.739791         NaN         NaN\n",
              "4      UT  Salt Lake City  40.755851 -111.896657         NaN         NaN\n",
              "5     NaN         Houston        NaN         NaN   2242193.0       Texas"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "HYKhYk9tIdCC",
        "outputId": "380fe866-efbf-4e15-eced-68c6c753feb8"
      },
      "source": [
        "pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"right\")"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_x</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state_x           city        lat         lng  population     state_y\n",
              "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
              "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
              "2      FL          Miami  25.791100  -80.320733      413201     Florida\n",
              "3     NaN        Houston        NaN         NaN     2242193       Texas"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "EDcY8S28IgTt",
        "outputId": "3aad833d-ceac-4441-f926-d71019212fa9"
      },
      "source": [
        "city_pop2 = city_pop.copy()\n",
        "city_pop2.columns = [\"population\", \"name\", \"state\"]\n",
        "pd.merge(left=city_loc, right=city_pop2, left_on=\"city\", right_on=\"name\")"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_x</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>name</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state_x           city        lat  ...  population           name     state_y\n",
              "0      CA  San Francisco  37.781334  ...      808976  San Francisco  California\n",
              "1      NY       New York  40.705649  ...     8363710       New York    New-York\n",
              "2      FL          Miami  25.791100  ...      413201          Miami     Florida\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "Uq2T8kyDImC2",
        "outputId": "a2fcd7b6-041e-426b-c45c-d85bed6ab9d8"
      },
      "source": [
        "#Concatenation\n",
        "\n",
        "result_concat = pd.concat([city_loc, city_pop])\n",
        "result_concat"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808976.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>New-York</td>\n",
              "      <td>New York</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8363710.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Florida</td>\n",
              "      <td>Miami</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>413201.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Texas</td>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state            city        lat         lng  population\n",
              "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
              "1          NY        New York  40.705649  -74.008344         NaN\n",
              "2          FL           Miami  25.791100  -80.320733         NaN\n",
              "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
              "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
              "3  California   San Francisco        NaN         NaN    808976.0\n",
              "4    New-York        New York        NaN         NaN   8363710.0\n",
              "5     Florida           Miami        NaN         NaN    413201.0\n",
              "6       Texas         Houston        NaN         NaN   2242193.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "LbrICfPBIq0C",
        "outputId": "f4767bf4-5922-4f2b-9171-0286ba6a2178"
      },
      "source": [
        "result_concat.loc[3]"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808976.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state           city        lat        lng  population\n",
              "3          OH      Cleveland  41.473508 -81.739791         NaN\n",
              "3  California  San Francisco        NaN        NaN    808976.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "MtbwmlSxItT1",
        "outputId": "ea8c4caf-3547-4a1b-b086-1bf307d749b0"
      },
      "source": [
        "pd.concat([city_loc, city_pop], ignore_index=True)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>California</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808976.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>New-York</td>\n",
              "      <td>New York</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8363710.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Florida</td>\n",
              "      <td>Miami</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>413201.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Texas</td>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state            city        lat         lng  population\n",
              "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
              "1          NY        New York  40.705649  -74.008344         NaN\n",
              "2          FL           Miami  25.791100  -80.320733         NaN\n",
              "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
              "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
              "5  California   San Francisco        NaN         NaN    808976.0\n",
              "6    New-York        New York        NaN         NaN   8363710.0\n",
              "7     Florida           Miami        NaN         NaN    413201.0\n",
              "8       Texas         Houston        NaN         NaN   2242193.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "h5UX-vYLIv6I",
        "outputId": "bb99eabd-5b9b-41b7-b1b1-d837856da5cf"
      },
      "source": [
        "pd.concat([city_loc, city_pop], join=\"inner\")"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>New-York</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Florida</td>\n",
              "      <td>Miami</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Texas</td>\n",
              "      <td>Houston</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state            city\n",
              "0          CA   San Francisco\n",
              "1          NY        New York\n",
              "2          FL           Miami\n",
              "3          OH       Cleveland\n",
              "4          UT  Salt Lake City\n",
              "3  California   San Francisco\n",
              "4    New-York        New York\n",
              "5     Florida           Miami\n",
              "6       Texas         Houston"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "ep5e4_cbI0J-",
        "outputId": "35433ef2-6c83-4a7d-dbba-828b33099714"
      },
      "source": [
        "pd.concat([city_loc, city_pop], axis=1)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state            city        lat  ...  population           city       state\n",
              "0    CA   San Francisco  37.781334  ...         NaN            NaN         NaN\n",
              "1    NY        New York  40.705649  ...         NaN            NaN         NaN\n",
              "2    FL           Miami  25.791100  ...         NaN            NaN         NaN\n",
              "3    OH       Cleveland  41.473508  ...    808976.0  San Francisco  California\n",
              "4    UT  Salt Lake City  40.755851  ...   8363710.0       New York    New-York\n",
              "5   NaN             NaN        NaN  ...    413201.0          Miami     Florida\n",
              "6   NaN             NaN        NaN  ...   2242193.0        Houston       Texas\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "TXXR1GDsI2eM",
        "outputId": "b19641dc-861d-4917-c5b9-802d8403a46c"
      },
      "source": [
        "pd.concat([city_loc.set_index(\"city\"), city_pop.set_index(\"city\")], axis=1)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>state</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>San Francisco</th>\n",
              "      <td>CA</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New York</th>\n",
              "      <td>NY</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miami</th>\n",
              "      <td>FL</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cleveland</th>\n",
              "      <td>OH</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Salt Lake City</th>\n",
              "      <td>UT</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Houston</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               state        lat         lng  population       state\n",
              "San Francisco     CA  37.781334 -122.416728    808976.0  California\n",
              "New York          NY  40.705649  -74.008344   8363710.0    New-York\n",
              "Miami             FL  25.791100  -80.320733    413201.0     Florida\n",
              "Cleveland         OH  41.473508  -81.739791         NaN         NaN\n",
              "Salt Lake City    UT  40.755851 -111.896657         NaN         NaN\n",
              "Houston          NaN        NaN         NaN   2242193.0       Texas"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "KHK3qnl3I4_Y",
        "outputId": "965383be-1ace-42c3-e690-61c4c5b220aa"
      },
      "source": [
        "city_loc.append(city_pop)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808976.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>New-York</td>\n",
              "      <td>New York</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8363710.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Florida</td>\n",
              "      <td>Miami</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>413201.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Texas</td>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state            city        lat         lng  population\n",
              "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
              "1          NY        New York  40.705649  -74.008344         NaN\n",
              "2          FL           Miami  25.791100  -80.320733         NaN\n",
              "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
              "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
              "3  California   San Francisco        NaN         NaN    808976.0\n",
              "4    New-York        New York        NaN         NaN   8363710.0\n",
              "5     Florida           Miami        NaN         NaN    413201.0\n",
              "6       Texas         Houston        NaN         NaN   2242193.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "9iIGQHLAI7TF",
        "outputId": "c5a61210-7337-4bd7-f7c1-6e8d22fd790c"
      },
      "source": [
        "#Categories\n",
        "\n",
        "city_eco = city_pop.copy()\n",
        "city_eco[\"eco_code\"] = [17, 17, 34, 20]\n",
        "city_eco"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>eco_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2242193</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   population           city       state  eco_code\n",
              "3      808976  San Francisco  California        17\n",
              "4     8363710       New York    New-York        17\n",
              "5      413201          Miami     Florida        34\n",
              "6     2242193        Houston       Texas        20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVLPvxicI_Ql",
        "outputId": "8ac05faa-3b2e-438f-ea87-031b548ba469"
      },
      "source": [
        "city_eco[\"economy\"] = city_eco[\"eco_code\"].astype('category')\n",
        "city_eco[\"economy\"].cat.categories"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([17, 20, 34], dtype='int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "YsHOwMWWJCA8",
        "outputId": "cca0aa39-7bdd-4f5d-dffd-afaa3d3e2e77"
      },
      "source": [
        "city_eco[\"economy\"].cat.categories = [\"Finance\", \"Energy\", \"Tourism\"]\n",
        "city_eco"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>eco_code</th>\n",
              "      <th>economy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>17</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "      <td>17</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "      <td>34</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2242193</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>20</td>\n",
              "      <td>Energy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   population           city       state  eco_code  economy\n",
              "3      808976  San Francisco  California        17  Finance\n",
              "4     8363710       New York    New-York        17  Finance\n",
              "5      413201          Miami     Florida        34  Tourism\n",
              "6     2242193        Houston       Texas        20   Energy"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "QTB299-vJEi9",
        "outputId": "717467a8-cea7-467f-8668-2980eb8ea0b0"
      },
      "source": [
        "city_eco.sort_values(by=\"economy\", ascending=False)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>eco_code</th>\n",
              "      <th>economy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "      <td>34</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2242193</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>20</td>\n",
              "      <td>Energy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>17</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "      <td>17</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   population           city       state  eco_code  economy\n",
              "5      413201          Miami     Florida        34  Tourism\n",
              "6     2242193        Houston       Texas        20   Energy\n",
              "3      808976  San Francisco  California        17  Finance\n",
              "4     8363710       New York    New-York        17  Finance"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFHhabxvJGoc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}