intro_to_pandas1.ipynb

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      "name": "intro_to_pandas.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/annLiu310/TensorFlowCrashCourse/blob/master/intro_to_pandas1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "hMqWDc_m6rUC",
        "cellView": "both",
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      "cell_type": "code",
      "source": [
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "rHLcriKWLRe4"
      },
      "cell_type": "markdown",
      "source": [
        " # Pandas \n",
        " "
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "s_JOISVgmn9v"
      },
      "cell_type": "markdown",
      "source": [
        " ## Basic Concepts"
      ]
    },
    {
      "metadata": {
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        "id": "aSRYu62xUi3g",
        "outputId": "74082c2b-2106-40cf-b482-4df6c5610708",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "# import pandas\n",
        "import pandas as pd\n",
        "pd.__version__"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "u'0.22.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "daQreKXIUslr"
      },
      "cell_type": "markdown",
      "source": [
        "DataFrame, which you can imagine as a relational data table, with rows and named columns.\n",
        "\n",
        "Series, which is a single column. A DataFrame contains one or more Series and a name for each Series.\n",
        "\n",
        "**One way to create a Series is to construct a Series object. **\n",
        "\n",
        "pd.Series\n",
        "\n",
        "Series -> DataFrame:  pd.DataFrame\n",
        "\n",
        "(dataframe).hist"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "DFZ42Uq7UFDj",
        "outputId": "2ff0fe74-aefc-4923-aed8-021768c914b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "# create and init a new series\n",
        "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n",
        "#pd.Series(['1', '2', '3'])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    3\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
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      "cell_type": "code",
      "source": [
        "# define init a new series \n",
        "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n",
        "population = pd.Series([852469, 1015785, 485199])\n",
        "\n",
        "\n",
        "# Conbine different series to a DataFrame and print dataframe\n",
        "pd.DataFrame({ 'City name': city_names, 'Population': population })\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City name</th>\n",
              "      <th>Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramento</td>\n",
              "      <td>485199</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "       City name  Population\n",
              "0  San Francisco      852469\n",
              "1       San Jose     1015785\n",
              "2     Sacramento      485199"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
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      "cell_type": "code",
      "source": [
        "# add Sun_height\n",
        "sun_height = pd.Series([300, 400, 0])\n",
        "# print and init a dataframe\n",
        "pd.DataFrame({'City name': city_names, 'Population': population, 'Sun Height': sun_height})"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Sun Height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramento</td>\n",
              "      <td>485199</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Sun Height\n",
              "0  San Francisco      852469         300\n",
              "1       San Jose     1015785         400\n",
              "2     Sacramento      485199           0"
            ]
          },
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            "tags": []
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          "execution_count": 18
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      "cell_type": "code",
      "source": [
        "# Pd Read CSV\n",
        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.cn/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
        "california_housing_dataframe.describe()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.562108</td>\n",
              "      <td>35.625225</td>\n",
              "      <td>28.589353</td>\n",
              "      <td>2643.664412</td>\n",
              "      <td>539.410824</td>\n",
              "      <td>1429.573941</td>\n",
              "      <td>501.221941</td>\n",
              "      <td>3.883578</td>\n",
              "      <td>207300.912353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.005166</td>\n",
              "      <td>2.137340</td>\n",
              "      <td>12.586937</td>\n",
              "      <td>2179.947071</td>\n",
              "      <td>421.499452</td>\n",
              "      <td>1147.852959</td>\n",
              "      <td>384.520841</td>\n",
              "      <td>1.908157</td>\n",
              "      <td>115983.764387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.790000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>790.000000</td>\n",
              "      <td>282.000000</td>\n",
              "      <td>2.566375</td>\n",
              "      <td>119400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>1167.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.544600</td>\n",
              "      <td>180400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.000000</td>\n",
              "      <td>37.720000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3151.250000</td>\n",
              "      <td>648.250000</td>\n",
              "      <td>1721.000000</td>\n",
              "      <td>605.250000</td>\n",
              "      <td>4.767000</td>\n",
              "      <td>265000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>37937.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
              "count  17000.000000  17000.000000        17000.000000  17000.000000   \n",
              "mean    -119.562108     35.625225           28.589353   2643.664412   \n",
              "std        2.005166      2.137340           12.586937   2179.947071   \n",
              "min     -124.350000     32.540000            1.000000      2.000000   \n",
              "25%     -121.790000     33.930000           18.000000   1462.000000   \n",
              "50%     -118.490000     34.250000           29.000000   2127.000000   \n",
              "75%     -118.000000     37.720000           37.000000   3151.250000   \n",
              "max     -114.310000     41.950000           52.000000  37937.000000   \n",
              "\n",
              "       total_bedrooms    population    households  median_income  \\\n",
              "count    17000.000000  17000.000000  17000.000000   17000.000000   \n",
              "mean       539.410824   1429.573941    501.221941       3.883578   \n",
              "std        421.499452   1147.852959    384.520841       1.908157   \n",
              "min          1.000000      3.000000      1.000000       0.499900   \n",
              "25%        297.000000    790.000000    282.000000       2.566375   \n",
              "50%        434.000000   1167.000000    409.000000       3.544600   \n",
              "75%        648.250000   1721.000000    605.250000       4.767000   \n",
              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
              "\n",
              "       median_house_value  \n",
              "count        17000.000000  \n",
              "mean        207300.912353  \n",
              "std         115983.764387  \n",
              "min          14999.000000  \n",
              "25%         119400.000000  \n",
              "50%         180400.000000  \n",
              "75%         265000.000000  \n",
              "max         500001.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "s3ND3bgOkB5k",
        "outputId": "6a982f7e-d41b-481c-c217-e0296d5ec1a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# Print dataframe tope 5 lines\n",
        "california_housing_dataframe.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2    -114.56     33.69                17.0        720.0           174.0   \n",
              "3    -114.57     33.64                14.0       1501.0           337.0   \n",
              "4    -114.57     33.57                20.0       1454.0           326.0   \n",
              "\n",
              "   population  households  median_income  median_house_value  \n",
              "0      1015.0       472.0         1.4936             66900.0  \n",
              "1      1129.0       463.0         1.8200             80100.0  \n",
              "2       333.0       117.0         1.6509             85700.0  \n",
              "3       515.0       226.0         3.1917             73400.0  \n",
              "4       624.0       262.0         1.9250             65500.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "g72rHgxJ9SHP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "a9455d01-bd28-47c0-f4be-02d22c795bf5"
      },
      "cell_type": "code",
      "source": [
        "# Print dataframe tope 25 lines\n",
        "california_housing_dataframe.head(6)"
      ],
      "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 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>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-114.58</td>\n",
              "      <td>33.63</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>671.0</td>\n",
              "      <td>239.0</td>\n",
              "      <td>3.3438</td>\n",
              "      <td>74000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2    -114.56     33.69                17.0        720.0           174.0   \n",
              "3    -114.57     33.64                14.0       1501.0           337.0   \n",
              "4    -114.57     33.57                20.0       1454.0           326.0   \n",
              "5    -114.58     33.63                29.0       1387.0           236.0   \n",
              "\n",
              "   population  households  median_income  median_house_value  \n",
              "0      1015.0       472.0         1.4936             66900.0  \n",
              "1      1129.0       463.0         1.8200             80100.0  \n",
              "2       333.0       117.0         1.6509             85700.0  \n",
              "3       515.0       226.0         3.1917             73400.0  \n",
              "4       624.0       262.0         1.9250             65500.0  \n",
              "5       671.0       239.0         3.3438             74000.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "nqndFVXVlbPN",
        "outputId": "874dc6f7-b210-493f-df30-df0f47145d12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        }
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe.hist('housing_median_age')\n",
        "california_housing_dataframe.hist('total_rooms')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1101537950>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wQNnZ2ZKkvLw89evXryGOCQCAn4Uaz6g//PBDFRcXa8aMGb5to0aN0owZM9SmTRuFhIRo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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f11016b9e10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KzMyUz+dTQ0ODkpKS5Ha7FR0drcmTJ8vv98vn8ykrK0uSlJaWJr/fr56eHjU1NSk5OXnA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\nkwiEYQN5UiwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1101516050>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "XtYZ7114n3b-"
      },
      "cell_type": "markdown",
      "source": [
        " ## Accessing Data\n",
        " \n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "_TFm7-looBFF",
        "outputId": "6a274eac-60f3-4d14-ac07-8019f619b6f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "# define and init dataframe\n",
        "cities = pd.DataFrame({ 'City name': city_names, 'Population': population, 'Sun Height': sun_height })\n",
        "#print(type(cities['City name']))\n",
        "# --> <class 'pandas.core.series.Series'>\n",
        "cities['City name']\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    San Francisco\n",
              "1         San Jose\n",
              "2       Sacramento\n",
              "Name: City name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "GRfvh8-u-jiR",
        "colab_type": "code",
        "outputId": "cd66e10f-31cf-4a93-aaa3-63e4196a35aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "cities['Population']"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     852469\n",
              "1    1015785\n",
              "2     485199\n",
              "Name: Population, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "V5L6xacLoxyv",
        "outputId": "69489cf3-203d-4130-dbc3-a0bc56841ce3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#print(type(cities['City name'][1]))\n",
        "\n",
        "# Print second\n",
        "cities['City name'][1]\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'San Jose'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "dIZ79WIiBkFs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "261d6fc8-dad9-4cfe-87c3-8dc667828fe9"
      },
      "cell_type": "code",
      "source": [
        "# Print element start from index 1 to 2\n",
        "cities['City name'][1:2]"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    San Jose\n",
              "Name: City name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "metadata": {
        "id": "fNlc0kTmCKR9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "2a75bdd6-9c49-4dbe-94a5-01d6e7ae14e3"
      },
      "cell_type": "code",
      "source": [
        "# Print element start from index 0 to 3\n",
        "cities['City name'][0:2]"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    San Francisco\n",
              "1         San Jose\n",
              "Name: City name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "gcYX1tBPugZl",
        "outputId": "2a1b8104-c4de-40d1-9ea4-dd990dc212ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "cell_type": "code",
      "source": [
        "print(type(cities[0:2]))\n",
        "\n",
        "# print 2 element start from 0\n",
        "print(cities[1:2])\n",
        "\n",
        "cities[1:2]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "  City name  Population  Sun Height\n",
            "1  San Jose     1015785         400\n"
          ],
          "name": "stdout"
        },
        {
          "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Sun Height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  City name  Population  Sun Height\n",
              "1  San Jose     1015785         400"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "XWmyCFJ5bOv-",
        "outputId": "bdd43705-6f6c-4293-ae45-4cd68258ba87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "print(cities['Population'][:2])\n",
        "# calculate whole column\n",
        "population / 1000."
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0     852469\n",
            "1    1015785\n",
            "Name: Population, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     852.469\n",
              "1    1015.785\n",
              "2     485.199\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ko6pLK6JmkYP",
        "outputId": "3e0e2598-f502-45a2-8c95-035cc7bcd7e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "# import np for Mathmatic Calculation\n",
        "import numpy as np\n",
        "\n",
        "# calculate Log\n",
        "np.log(population)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    13.655892\n",
              "1    13.831172\n",
              "2    13.092314\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Fc1DvPAbstjI",
        "outputId": "cd3590ea-56f3-4e42-d565-9a2be1f8c3e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "population.apply(lambda val: val > 1000000)\n",
        "sun_height.apply(lambda x: x > 1)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     True\n",
              "1     True\n",
              "2    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "0gCEX99Hb8LR",
        "outputId": "c9631fec-59ad-4375-ce59-61b2476a6e2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "cell_type": "code",
      "source": [
        "# modify DataFrame add new series/ Column\n",
        "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n",
        "cities['Population density'] = cities['Population'] / cities['Area square miles']\n",
        "cities "
      ],
      "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",
              "        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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Sun Height</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>300</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>400</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramento</td>\n",
              "      <td>485199</td>\n",
              "      <td>0</td>\n",
              "      <td>97.92</td>\n",
              "      <td>4955.055147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Sun Height  Area square miles  \\\n",
              "0  San Francisco      852469         300              46.87   \n",
              "1       San Jose     1015785         400             176.53   \n",
              "2     Sacramento      485199           0              97.92   \n",
              "\n",
              "   Population density  \n",
              "0        18187.945381  \n",
              "1         5754.177760  \n",
              "2         4955.055147  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "eIoj_zU36Q_T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "outputId": "94279b3c-0e0e-4c0e-c396-6d73b1a94e23"
      },
      "cell_type": "code",
      "source": [
        "# delete column \n",
        "#cities.drop(columns = ['Sun Height']) \n",
        "\n",
        "#delete row\n",
        "#cities.drop(2,axis=0,inplace=True)  \n",
        "cities"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m\u001b[0m",
            "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
            "\u001b[0;32m<ipython-input-76-a2af356232d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcities\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   2528\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[1;32m   2529\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2530\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2532\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   2560\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2561\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2562\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2563\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2564\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/indexes/base.pyc\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   3742\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3743\u001b[0m                 raise ValueError('labels %s not contained in axis' %\n\u001b[0;32m-> 3744\u001b[0;31m                                  labels[mask])\n\u001b[0m\u001b[1;32m   3745\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3746\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: labels [3] not contained in axis"
          ]
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "6qh63m-ayb-c"
      },
      "cell_type": "markdown",
      "source": [
        "## Exercise #1\n",
        "\n",
        "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n",
        "\n",
        "  * The city is named after a saint.   (san ***)\n",
        "  * The city has an area greater than 50 square miles.\n",
        "\n",
        "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n",
        "\n",
        "**Hint:** \"San\" in Spanish means \"saint.\"\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "zCOn8ftSyddH",
        "outputId": "f9d39177-adcd-494e-cd1c-5d4481cf8b57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "cell_type": "code",
      "source": [
        "#Solution\n",
        "cities['Is named after a saint and area greater than 50'] = (cities['Area square miles'] >50) & (cities['City name'].apply(lambda name: name.startswith('San')))\n",
        "cities"
      ],
      "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Sun Height</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "      <th>Is named after a saint and area greater than 50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>300</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>400</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Sun Height  Area square miles  \\\n",
              "0  San Francisco      852469         300              46.87   \n",
              "1       San Jose     1015785         400             176.53   \n",
              "\n",
              "   Population density  Is named after a saint and area greater than 50  \n",
              "0        18187.945381                                            False  \n",
              "1         5754.177760                                             True  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "metadata": {
        "id": "TsV72qc5GBKe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "8e2ee02e-82eb-4f79-f35c-9b12885b5279"
      },
      "cell_type": "code",
      "source": [
        "#Is named after a saint and sun height less equal than 350\n",
        "\n",
        "cities['Is named after a saint and sun height less than 350'] = (cities['Sun Height'] <= 350) & (cities['City name'].apply(lambda name: name.startswith('San')))\n",
        "cities"
      ],
      "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Sun Height</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "      <th>Is named after a saint and area greater than 50</th>\n",
              "      <th>Is named after a saint and sun height less than 350</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>300</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>400</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Sun Height  Area square miles  \\\n",
              "0  San Francisco      852469         300              46.87   \n",
              "1       San Jose     1015785         400             176.53   \n",
              "\n",
              "   Population density  Is named after a saint and area greater than 50  \\\n",
              "0        18187.945381                                            False   \n",
              "1         5754.177760                                             True   \n",
              "\n",
              "   Is named after a saint and sun height less than 350  \n",
              "0                                               True    \n",
              "1                                              False    "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "f-xAOJeMiXFB"
      },
      "cell_type": "markdown",
      "source": [
        " ## Index 索引\n",
        " \n",
        " Both Series and DataFrame objects also define an index property that assigns an identifier value to each Series item or DataFrame row.\n",
        "\n",
        "By default, at construction, pandas assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered.\n",
        "\n",
        "\n",
        "`Series` 和 `DataFrame` 对象也定义了 `index` 属性，该属性会向每个 `Series` 项或 `DataFrame` 行赋一个标识符值。\n",
        "\n",
        "默认情况下，在构造时，*pandas* 会赋可反映源数据顺序的索引值。索引值在创建后是稳定的；也就是说，它们不会因为数据重新排序而发生改变。"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "2684gsWNinq9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a6a5009f-dec0-452b-9d26-9b5ca41fece0"
      },
      "cell_type": "code",
      "source": [
        "city_names.index"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=3, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "F_qPe2TBjfWd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "19a41059-843f-4e90-ab04-fb88a619273e"
      },
      "cell_type": "code",
      "source": [
        "print(cities.index)\n",
        "cities"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Int64Index([0, 1], dtype='int64')\n"
          ],
          "name": "stdout"
        },
        {
          "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Sun Height</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "      <th>Is named after a saint and area greater than 50</th>\n",
              "      <th>Is named after a saint and sun height less than 350</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>300</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>400</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Sun Height  Area square miles  \\\n",
              "0  San Francisco      852469         300              46.87   \n",
              "1       San Jose     1015785         400             176.53   \n",
              "\n",
              "   Population density  Is named after a saint and area greater than 50  \\\n",
              "0        18187.945381                                            False   \n",
              "1         5754.177760                                             True   \n",
              "\n",
              "   Is named after a saint and sun height less than 350  \n",
              "0                                               True    \n",
              "1                                              False    "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "hp2oWY9Slo_h"
      },
      "cell_type": "markdown",
      "source": [
        "Call DataFrame.reindex to manually reorder the rows. For example, the following has the same effect as sorting by city name:\n",
        "\n",
        "调用 `DataFrame.reindex` 以手动重新排列各行的顺序。例如，以下方式与按城市名称排序具有相同的效果："
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "sN0zUzSAj-U1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "1508a4e0-1b4c-4a12-d3f1-1913a94699ec"
      },
      "cell_type": "code",
      "source": [
        "cities.reindex([2, 0, 1])\n",
        "\n",
        "#If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:"
      ],
      "execution_count": 100,
      "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Sun Height</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "      <th>Is named after a saint and area greater than 50</th>\n",
              "      <th>Is named after a saint and sun height less than 350</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>300</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>400</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Sun Height  Area square miles  \\\n",
              "0  San Francisco      852469         300              46.87   \n",
              "1       San Jose     1015785         400             176.53   \n",
              "\n",
              "   Population density  Is named after a saint and area greater than 50  \\\n",
              "0        18187.945381                                            False   \n",
              "1         5754.177760                                             True   \n",
              "\n",
              "   Is named after a saint and sun height less than 350  \n",
              "0                                               True    \n",
              "1                                              False    "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "-GQFz8NZuS06"
      },
      "cell_type": "markdown",
      "source": [
        " Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n",
        "Try running the following cell multiple times!\n",
        " \n",
        " \n",
        " 重建索引是一种随机排列 `DataFrame` 的绝佳方式。在下面的示例中，我们会取用类似数组的索引，然后将其传递至 NumPy 的 `random.permutation` 函数，该函数会随机排列其值的位置。如果使用此重新随机排列的数组调用 `reindex`，会导致 `DataFrame` 行以同样的方式随机排列。\n",
        "尝试多次运行以下单元格！"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "mF8GC0k8uYhz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "8c4caf92-6da8-47d6-8dba-05a1737f4ddd"
      },
      "cell_type": "code",
      "source": [
        " cities.reindex(np.random.permutation(cities.index)) \n"
      ],
      "execution_count": 101,
      "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Sun Height</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "      <th>Is named after a saint and area greater than 50</th>\n",
              "      <th>Is named after a saint and sun height less than 350</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>400</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>300</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Sun Height  Area square miles  \\\n",
              "1       San Jose     1015785         400             176.53   \n",
              "0  San Francisco      852469         300              46.87   \n",
              "\n",
              "   Population density  Is named after a saint and area greater than 50  \\\n",
              "1         5754.177760                                             True   \n",
              "0        18187.945381                                            False   \n",
              "\n",
              "   Is named after a saint and sun height less than 350  \n",
              "1                                              False    \n",
              "0                                               True    "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    }
  ]
}