Working_with_Pandas.ipynb

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      "cell_type": "markdown",
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        "id": "NgayMd7cK3U6"
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      "source": [
        "---\n",
        "# **Working with Pandas**\n",
        "---\n",
        "- Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures.\n",
        "\n",
        "### **Self Learning Resource**\n",
        "1. Pandas in 10 Minutes: <a href=\"https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html\"> Click Here </a>\n",
        "2. Video1: <a href=\"https://www.youtube.com/watch?v=QUClKFFn1Vk\"> Click Here </a>\n",
        "3. Video2: <a href=\"https://www.youtube.com/watch?v=tW1BWtQRZ2M\"> Click Here </a>\n",
        "4. Video3: <a href=\"https://www.youtube.com/watch?v=xx4Vkc5RrWY\"> Click Here </a>\n",
        "    \n",
        "\n",
        "\n",
        "###**Note:**\n",
        "1. First Clean the Evironment (Go to \"Kernel\" Menu --> \"Restart & Clean Output\"\n",
        "2. To execute the code --> Click on a cell and press cntrl + enter key\n",
        "\n",
        "\n",
        "### **Key Features of Pandas**\n",
        "- Fast and efficient DataFrame object with default and customized indexing.\n",
        "- Tools for loading data into in-memory data objects from different file formats.\n",
        "- Data alignment and integrated handling of missing data.\n",
        "- Reshaping and pivoting of date sets.\n",
        "- Label-based slicing, indexing and subsetting of large data sets.\n",
        "- Columns from a data structure can be deleted or inserted.\n",
        "- Group by data for aggregation and transformations.\n",
        "- High performance merging and joining of data.\n",
        "- Time Series functionality."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Import pandas library**\n",
        "---"
      ],
      "metadata": {
        "id": "JT3kqhZja3dI"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4JFo1WM8ZON"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOp1wf9hlyC2"
      },
      "source": [
        "---\n",
        "# **1. Working with Series**\n",
        "- Series is a one-dimensional labeled array\n",
        "\n",
        "### **1.1 Create a series using list**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8b01dd2-ef92-482a-ee43-baf2436faebd"
      },
      "source": [
        "a1 = [1, 3, 5, 7, 9, 2, 4, 6, 8]\n",
        "a2 = pd.Series(a1)\n",
        "\n",
        "print(a2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1\n",
            "1    3\n",
            "2    5\n",
            "3    7\n",
            "4    9\n",
            "5    2\n",
            "6    4\n",
            "7    6\n",
            "8    8\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **1.2 A series has been created with data along with it's Index**\n",
        "---"
      ],
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        "id": "Zbx4VUzJInQ-"
      }
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    {
      "cell_type": "code",
      "source": [
        "a1 = [1, 3, 5, 7, 9, 2, 4, 6, 8]\n",
        "a2 = ['a','b','c','d','e','f','g','h','i']\n",
        "a3 = pd.Series(a1, a2)\n",
        "#a3 = pd.Series(a2, a1)\n",
        "print(a3)"
      ],
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        "outputId": "e50e439f-f01b-4818-8467-05021eb2b5d1"
      },
      "execution_count": null,
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a    1\n",
            "b    3\n",
            "c    5\n",
            "d    7\n",
            "e    9\n",
            "f    2\n",
            "g    4\n",
            "h    6\n",
            "i    8\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **Access the data in series**\n",
        "---"
      ],
      "metadata": {
        "id": "CfyA7TpFIwSU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"a3['b'] -->\",a3['b'])\n",
        "print (\"a3['i'] -->\",a3['i'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YPvwXcL4Iwtf",
        "outputId": "bf10fd7c-e9bf-4231-bf77-c153d124f0e2"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "a3['b'] --> 3\n",
            "a3['i'] --> 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **1.3 Creating a series using dictionary**\n",
        "---"
      ],
      "metadata": {
        "id": "YixzR8BiI8yJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "d1 = {'Oranges':3, 'Apples':4, 'Mangoes':2, 'Banana':12}\n",
        "d2 = pd.Series(d1)\n",
        "\n",
        "print (d2)\n",
        "print (type(d2))"
      ],
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        "id": "e8zJaGYPI9In",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Oranges     3\n",
            "Apples      4\n",
            "Mangoes     2\n",
            "Banana     12\n",
            "dtype: int64\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **1.4 Creating a series using nested list**\n",
        "---"
      ],
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      }
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    {
      "cell_type": "code",
      "source": [
        "a1 = [[1,3,5],[2,4,6]]\n",
        "a2 = pd.Series(a1)\n",
        "\n",
        "print (a2)"
      ],
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        },
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          "text": [
            "0    [1, 3, 5]\n",
            "1    [2, 4, 6]\n",
            "dtype: object\n"
          ]
        }
      ]
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      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **2. DataFrames**\n",
        "- DataFrames are 2 dimensional data structure which has rows and columns.\n",
        "\n",
        "### **2.1 Creating a data frame using dictionary**\n",
        "---"
      ],
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        "d1 = {'Age':[23,33,12,45],'Name':['Rahul','John','Robert','Sneha']}\n",
        "d2 = pd.DataFrame(d1)\n",
        "\n",
        "d2\n"
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              "   Age    Name\n",
              "0   23   Rahul\n",
              "1   33    John\n",
              "2   12  Robert\n",
              "3   45   Sneha"
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        "---\n",
        "### **2.2 Creating a data frame using nested list**\n",
        "---"
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        "d1 = [[4,1900],[3,1600],[2,1100],[1,850]]\n",
        "d2 = pd.DataFrame(d1, columns = ['Bedrooms','Area'])\n",
        "\n",
        "d2"
      ],
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              "   Bedrooms  Area\n",
              "0         4  1900\n",
              "1         3  1600\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-58357ed5-3fc7-4be3-96c5-027d3e9fbb65 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-58357ed5-3fc7-4be3-96c5-027d3e9fbb65');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.3 Assigning indexes within a data frame**\n",
        "---"
      ],
      "metadata": {
        "id": "3ftroLp6JxTA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d1 = {'Name':['Ankit','Rishitha','Karthik','Vishnu'],'Marks':[78,67,98,56]}\n",
        "d2 = pd.DataFrame(d1,index = ['Rank 2','Rank 3','Rank 1','Rank 4'])\n",
        "\n",
        "d2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "Xc67N90_JxdY",
        "outputId": "3da00244-c749-4642-9a7a-2b03bc34f90d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Name  Marks\n",
              "Rank 2     Ankit     78\n",
              "Rank 3  Rishitha     67\n",
              "Rank 1   Karthik     98\n",
              "Rank 4    Vishnu     56"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Marks</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Rank 2</th>\n",
              "      <td>Ankit</td>\n",
              "      <td>78</td>\n",
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              "    <tr>\n",
              "      <th>Rank 3</th>\n",
              "      <td>Rishitha</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank 1</th>\n",
              "      <td>Karthik</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank 4</th>\n",
              "      <td>Vishnu</td>\n",
              "      <td>56</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-67a5f0db-72bc-4072-9e63-c564257a1e35 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-67a5f0db-72bc-4072-9e63-c564257a1e35');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.4 Creating data frame using list of dictionaries**\n",
        "---"
      ],
      "metadata": {
        "id": "uhZTBR77J4v7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "d1 = [{'A':65,'B':66,'C':67},{'A':97,'B':98,'C':99}]\n",
        "d2 = pd.DataFrame(d1)\n",
        "\n",
        "d2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "JfaSCMIXJ46j",
        "outputId": "6fc71fba-e0a9-4297-e48c-887a9b61f106"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    A   B   C\n",
              "0  65  66  67\n",
              "1  97  98  99"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7483d5f-a9c6-491e-88f8-21dc67912418\">\n",
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              "\n",
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              "      <th>0</th>\n",
              "      <td>65</td>\n",
              "      <td>66</td>\n",
              "      <td>67</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>97</td>\n",
              "      <td>98</td>\n",
              "      <td>99</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e7483d5f-a9c6-491e-88f8-21dc67912418');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.5 Creating random data frame**\n",
        "---"
      ],
      "metadata": {
        "id": "lpH0_0okJ_I-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random as r\n",
        "import string as s\n",
        "\n",
        "df = pd.DataFrame({'A': [r.randint(1, 9)     for x in range(5)],\n",
        "                   'B': [r.randint(1, 9)*10  for x in range(5)],\n",
        "                   'C': [r.randint(1, 9)*100 for x in range(5)],\n",
        "                   'D': [ \"\".join(r.sample(s.ascii_letters,5)) for x in range(5)],\n",
        "                   'E': 'Hello',\n",
        "                   'F': 1})\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "c22wO2JRJ_Sv",
        "outputId": "37cc9a48-23f2-4d64-c6a5-b995b45fb633"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   A   B    C      D      E  F\n",
              "0  3  30  100  ZVKDl  Hello  1\n",
              "1  5  90  100  lLICZ  Hello  1\n",
              "2  2  60  600  wncto  Hello  1\n",
              "3  8  20  800  VLIYG  Hello  1\n",
              "4  6  70  800  dXDQm  Hello  1"
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-a9587423-243a-41a6-9aae-82383c08524d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9587423-243a-41a6-9aae-82383c08524d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.6 Describe the data frame**\n",
        "---"
      ],
      "metadata": {
        "id": "jFftLNZrKFre"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()  # Only numeric columns are selected"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "rDzQ-BE_KF7z",
        "outputId": "453f619b-fa61-477c-d6e1-4b2958eab9dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              A          B           C    F\n",
              "count  5.000000   5.000000    5.000000  5.0\n",
              "mean   4.800000  54.000000  480.000000  1.0\n",
              "std    2.387467  28.809721  356.370594  0.0\n",
              "min    2.000000  20.000000  100.000000  1.0\n",
              "25%    3.000000  30.000000  100.000000  1.0\n",
              "50%    5.000000  60.000000  600.000000  1.0\n",
              "75%    6.000000  70.000000  800.000000  1.0\n",
              "max    8.000000  90.000000  800.000000  1.0"
            ],
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>F</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.800000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.387467</td>\n",
              "      <td>28.809721</td>\n",
              "      <td>356.370594</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff813ed2-beef-4909-905e-5440e729b887')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ff813ed2-beef-4909-905e-5440e729b887 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ff813ed2-beef-4909-905e-5440e729b887');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.7 Date as index**\n",
        "---"
      ],
      "metadata": {
        "id": "fAJGd0p3KK54"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "dates = pd.date_range('20130101', periods=6)\n",
        "dates\n",
        "\n",
        "df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "yrubsAedKLEC",
        "outputId": "9c77e8e2-d7ac-4652-f3f8-f13c2dcae1c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   A         B         C         D\n",
              "2013-01-01 -1.237065 -1.160801 -0.209130 -1.043257\n",
              "2013-01-02  0.142390  0.241717 -0.143741  0.330071\n",
              "2013-01-03  0.838439 -0.033407  0.094696 -0.961578\n",
              "2013-01-04  0.066227  0.885609  0.648316  0.042513\n",
              "2013-01-05 -0.273377  1.748682  0.132514 -0.916096\n",
              "2013-01-06 -0.135437 -0.733914 -0.174702 -1.207022"
            ],
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              "\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-01-01</th>\n",
              "      <td>-1.237065</td>\n",
              "      <td>-1.160801</td>\n",
              "      <td>-0.209130</td>\n",
              "      <td>-1.043257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-02</th>\n",
              "      <td>0.142390</td>\n",
              "      <td>0.241717</td>\n",
              "      <td>-0.143741</td>\n",
              "      <td>0.330071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-03</th>\n",
              "      <td>0.838439</td>\n",
              "      <td>-0.033407</td>\n",
              "      <td>0.094696</td>\n",
              "      <td>-0.961578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-04</th>\n",
              "      <td>0.066227</td>\n",
              "      <td>0.885609</td>\n",
              "      <td>0.648316</td>\n",
              "      <td>0.042513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-05</th>\n",
              "      <td>-0.273377</td>\n",
              "      <td>1.748682</td>\n",
              "      <td>0.132514</td>\n",
              "      <td>-0.916096</td>\n",
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              "    <tr>\n",
              "      <th>2013-01-06</th>\n",
              "      <td>-0.135437</td>\n",
              "      <td>-0.733914</td>\n",
              "      <td>-0.174702</td>\n",
              "      <td>-1.207022</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc3a830e-0f48-4e73-86bf-7538f8aa3a7f')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dc3a830e-0f48-4e73-86bf-7538f8aa3a7f button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dc3a830e-0f48-4e73-86bf-7538f8aa3a7f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.8 List the column names**\n",
        "---"
      ],
      "metadata": {
        "id": "IIC6xSQnKSDv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xOTz47aGKSN8",
        "outputId": "04127aea-cbcf-4d30-8232-bd1ca073da30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['A', 'B', 'C', 'D'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.9 List the index names**\n",
        "---"
      ],
      "metadata": {
        "id": "CVHzrL57Kakx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YqkAl8BnKawr",
        "outputId": "94bc1b17-494b-4af3-9cd2-bfa6cb45d44e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
              "               '2013-01-05', '2013-01-06'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.10 Sorting by an axis**\n",
        "---"
      ],
      "metadata": {
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        "df.sort_index(axis=1, ascending=False)"
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              "                   D         C         B         A\n",
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              "2013-01-03 -0.961578  0.094696 -0.033407  0.838439\n",
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              "2013-01-05 -0.916096  0.132514  1.748682 -0.273377\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.11 Sorting wrt to column name**\n",
        "---"
      ],
      "metadata": {
        "id": "jO8eCRauKq6W"
      }
    },
    {
      "cell_type": "code",
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        "df.sort_values(by='B', ascending=True)"
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        "id": "xBdZCWmDKrIo",
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              "                   A         B         C         D\n",
              "2013-01-01 -1.237065 -1.160801 -0.209130 -1.043257\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **2.12 Selecting Data**\n",
        "\n",
        "##### **Example 1**\n",
        "---"
      ],
      "metadata": {
        "id": "ZmJWlLvOKyp7"
      }
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      "cell_type": "code",
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        "df[df > 0]     # Put NaN where condition is false"
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              "                   A         B         C         D\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-fd8cc91a-137c-4a90-80aa-dc3706375d2f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fd8cc91a-137c-4a90-80aa-dc3706375d2f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **Example 2**\n",
        "---"
      ],
      "metadata": {
        "id": "JyEaQurMK5fF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['A'] > 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "93gEnc7gK5qK",
        "outputId": "d2c65968-f951-424a-c5d3-0254f61de65b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   A         B         C         D\n",
              "2013-01-02  0.142390  0.241717 -0.143741  0.330071\n",
              "2013-01-03  0.838439 -0.033407  0.094696 -0.961578\n",
              "2013-01-04  0.066227  0.885609  0.648316  0.042513"
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              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
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              "      <th>D</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-01-02</th>\n",
              "      <td>0.142390</td>\n",
              "      <td>0.241717</td>\n",
              "      <td>-0.143741</td>\n",
              "      <td>0.330071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-03</th>\n",
              "      <td>0.838439</td>\n",
              "      <td>-0.033407</td>\n",
              "      <td>0.094696</td>\n",
              "      <td>-0.961578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-04</th>\n",
              "      <td>0.066227</td>\n",
              "      <td>0.885609</td>\n",
              "      <td>0.648316</td>\n",
              "      <td>0.042513</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
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              "      </button>\n",
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              "          const element = document.querySelector('#df-b321a26d-f5d3-4ed8-8fff-51d736cd46fa');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **3. Operations on DataFrames**\n",
        "- Select\n",
        "- Append\n",
        "- Concatinate\n",
        "- Join\n",
        "- Merge\n",
        "\n",
        "---\n",
        "### **3.1 Accessing data from data frame - Part 1**\n",
        "\n",
        "- **Syntax**\n",
        "  - #### df [ Row] [ Col ]\n",
        "  - #### df [ Row_Range ] [ Col_Names_in_List ]\n",
        "---"
      ],
      "metadata": {
        "id": "fatlVjv4K91b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Un-comment each row and run\n",
        "\n",
        "#df                       # Show all records\n",
        "#df[:]                    # Show all records\n",
        "#df[1:4]                  # Show 1,2,3 rows of all Columns\n",
        "#df[:]['A']               # Show all rows of Column A\n",
        "#df[:]['A','B']           # Error; Column names must be in the list\n",
        "#df[:][['A','B']]         # Show all rows of Column A, B\n",
        "#df[1:3][['A','B','C']]   # Show 1,2 rows of Column A, B, C"
      ],
      "metadata": {
        "id": "akE04bhwK9_j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.2 Accessing data from data frame using column names (using loc) - Part 2** \n",
        "- #### **Syntax**\n",
        "  - #### df.loc [ Row_Range , Col_Names_in_List ]\n",
        "---"
      ],
      "metadata": {
        "id": "_7Lbo9mwLhI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Un-comment each row and run\n",
        "\n",
        "#df.loc                       # Print the memory location\n",
        "#df.loc[1:4]                  # Show 1,2,3 rows of all Columns\n",
        "#df.loc[:]                    # Show all records\n",
        "#df.loc[:, ]                  # Show all records\n",
        "#df.loc[:, :]                 # Show all records\n",
        "#df.loc[: , 'A','B']          # Error; Column names must be in the list\n",
        "#df.loc[: , ['A','B']]        # Show all rows of Column A, B\n",
        "#df.loc[2: , ['A','B','D']]   # Show rows from 2 to end of A, B, D columns\n",
        "#df.loc[2: , ]                # Show rows from 2 to end of all columns\n",
        "#df.loc[1:3, ['A','B','D']]   # Show rows from 1 to 3 of A, B, D columns\n",
        "#df.loc[:, []]                # Show all index but no columns value\n",
        "#df.loc[:-1, ]                # Show column names"
      ],
      "metadata": {
        "id": "Ag5VV3T_LhW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.3 Accessing data from data frame using column index (using iloc) - Part 3**\n",
        "- #### **Syntax**\n",
        "  - #### df.iloc [ Row_Range, Col_Index_in List ]\n",
        "---"
      ],
      "metadata": {
        "id": "SCLLkp6eMxZA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Un-comment each row and run\n",
        "\n",
        "#df.iloc[,]                    # Error\n",
        "#df.iloc[:,]                   # Show all rows of all columns\n",
        "#df.iloc[:,1,2,3]              # Error\n",
        "#df.iloc[:,[1,2,3]]            # Show all rows of 1, 2, 3 columns\n",
        "#df.iloc[2:,]                  # Show rows from 2 to end of all columns\n",
        "#df.iloc[2:, [0,1,3]]          # Show rows from 2 to end of 0, 1, 3 columns\n",
        "#df.iloc[1:3, ]                # Show rows from 1 to 3 of all columns\n",
        "#df.iloc[1:3, [0,1]]           # Show rows from 1 to 3 of 0, 1 columns\n",
        "#df.iloc[1:3, 1:5]             # Show rows from 1 to 3 of 1-4 columns\n",
        "#df.iloc[1:3, :5]              # Show rows from 1 to 3 of 0-4 columns\n",
        "#df.iloc[1:3, :-2]             # Show rows from 1 to 3 of 0 to 2nd last columns\n",
        "#df.iloc[:-2, 1:3]             # Show rows from 0 to 2nd last of 1-2 columns\n"
      ],
      "metadata": {
        "id": "EkP5drRDMxkf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.4 Concatenate data frames**\n",
        "---"
      ],
      "metadata": {
        "id": "OyKpWgviNAxY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "\n",
        "df1 = pd.DataFrame(np.random.randn(5, 4))\n",
        "df1\n",
        "\n",
        "df2 = pd.DataFrame(np.random.randn(4, 3))\n",
        "df2\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "dv5_kISlNA73",
        "outputId": "f92c6fac-0247-49eb-f541-b4722af0aa4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2\n",
              "0 -0.685572 -1.112475 -0.538389\n",
              "1 -0.085461  0.827874  0.549924\n",
              "2  0.021902  1.323436  0.538332\n",
              "3  0.742470  1.546152  0.078092"
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              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
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              "      <th>0</th>\n",
              "      <td>-0.685572</td>\n",
              "      <td>-1.112475</td>\n",
              "      <td>-0.538389</td>\n",
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              "      <th>1</th>\n",
              "      <td>-0.085461</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.538332</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c7d6a364-23a8-44f5-81bf-b856e6f009a6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **axis = 1 (Merge column wise)**\n",
        "---"
      ],
      "metadata": {
        "id": "QryvuuFtPbX3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.concat([df1,df2], axis=1)\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "PDWOH9bfPbh8",
        "outputId": "b833467d-c66b-4d8c-91a9-51b4121dff92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2         3         0         1         2\n",
              "0  0.368711  0.766437  0.222318 -0.259218 -0.685572 -1.112475 -0.538389\n",
              "1 -0.413142 -0.897481  0.939320  1.385967 -0.085461  0.827874  0.549924\n",
              "2 -1.777963  0.418620 -1.716197 -0.027161  0.021902  1.323436  0.538332\n",
              "3  0.549390  0.780766 -1.293229 -0.326161  0.742470  1.546152  0.078092\n",
              "4  1.315857 -0.434598  0.517609  1.442737       NaN       NaN       NaN"
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              "</style>\n",
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              "      <th>0</th>\n",
              "      <td>0.368711</td>\n",
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              "      <td>-0.259218</td>\n",
              "      <td>-0.685572</td>\n",
              "      <td>-1.112475</td>\n",
              "      <td>-0.538389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.413142</td>\n",
              "      <td>-0.897481</td>\n",
              "      <td>0.939320</td>\n",
              "      <td>1.385967</td>\n",
              "      <td>-0.085461</td>\n",
              "      <td>0.827874</td>\n",
              "      <td>0.549924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.777963</td>\n",
              "      <td>0.418620</td>\n",
              "      <td>-1.716197</td>\n",
              "      <td>-0.027161</td>\n",
              "      <td>0.021902</td>\n",
              "      <td>1.323436</td>\n",
              "      <td>0.538332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.549390</td>\n",
              "      <td>0.780766</td>\n",
              "      <td>-1.293229</td>\n",
              "      <td>-0.326161</td>\n",
              "      <td>0.742470</td>\n",
              "      <td>1.546152</td>\n",
              "      <td>0.078092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.315857</td>\n",
              "      <td>-0.434598</td>\n",
              "      <td>0.517609</td>\n",
              "      <td>1.442737</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be27d10f-18ee-4f21-ae97-427b5ffb669a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-be27d10f-18ee-4f21-ae97-427b5ffb669a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-be27d10f-18ee-4f21-ae97-427b5ffb669a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **axis = 0 (Merge row wise)**\n",
        "---"
      ],
      "metadata": {
        "id": "PBBx1i0PP9y7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.concat([df1,df2], axis=0)\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "F-kapV04P-CT",
        "outputId": "7c3ed2f0-de9a-47ba-b65a-12fb9297281f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2         3\n",
              "0  0.368711  0.766437  0.222318 -0.259218\n",
              "1 -0.413142 -0.897481  0.939320  1.385967\n",
              "2 -1.777963  0.418620 -1.716197 -0.027161\n",
              "3  0.549390  0.780766 -1.293229 -0.326161\n",
              "4  1.315857 -0.434598  0.517609  1.442737\n",
              "0 -0.685572 -1.112475 -0.538389       NaN\n",
              "1 -0.085461  0.827874  0.549924       NaN\n",
              "2  0.021902  1.323436  0.538332       NaN\n",
              "3  0.742470  1.546152  0.078092       NaN"
            ],
            "text/html": [
              "\n",
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              "      <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",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.939320</td>\n",
              "      <td>1.385967</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>-1.777963</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.549390</td>\n",
              "      <td>0.780766</td>\n",
              "      <td>-1.293229</td>\n",
              "      <td>-0.326161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.315857</td>\n",
              "      <td>-0.434598</td>\n",
              "      <td>0.517609</td>\n",
              "      <td>1.442737</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.685572</td>\n",
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              "      <td>-0.538389</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.085461</td>\n",
              "      <td>0.827874</td>\n",
              "      <td>0.549924</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.538332</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.742470</td>\n",
              "      <td>1.546152</td>\n",
              "      <td>0.078092</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a999e3d4-0fa1-4781-b5fa-23754363c339')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a999e3d4-0fa1-4781-b5fa-23754363c339 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a999e3d4-0fa1-4781-b5fa-23754363c339');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **Change index using reset_index()**\n",
        "---"
      ],
      "metadata": {
        "id": "sGAEVAlQQHDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3.reset_index(drop=True, inplace=True)\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "jSI3Ysa_QHN0",
        "outputId": "fff15132-da2a-4bec-8788-da57df616058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          0         1         2         3\n",
              "0  0.368711  0.766437  0.222318 -0.259218\n",
              "1 -0.413142 -0.897481  0.939320  1.385967\n",
              "2 -1.777963  0.418620 -1.716197 -0.027161\n",
              "3  0.549390  0.780766 -1.293229 -0.326161\n",
              "4  1.315857 -0.434598  0.517609  1.442737\n",
              "5 -0.685572 -1.112475 -0.538389       NaN\n",
              "6 -0.085461  0.827874  0.549924       NaN\n",
              "7  0.021902  1.323436  0.538332       NaN\n",
              "8  0.742470  1.546152  0.078092       NaN"
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              "      <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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.368711</td>\n",
              "      <td>0.766437</td>\n",
              "      <td>0.222318</td>\n",
              "      <td>-0.259218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.413142</td>\n",
              "      <td>-0.897481</td>\n",
              "      <td>0.939320</td>\n",
              "      <td>1.385967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.777963</td>\n",
              "      <td>0.418620</td>\n",
              "      <td>-1.716197</td>\n",
              "      <td>-0.027161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.549390</td>\n",
              "      <td>0.780766</td>\n",
              "      <td>-1.293229</td>\n",
              "      <td>-0.326161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.315857</td>\n",
              "      <td>-0.434598</td>\n",
              "      <td>0.517609</td>\n",
              "      <td>1.442737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.685572</td>\n",
              "      <td>-1.112475</td>\n",
              "      <td>-0.538389</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.085461</td>\n",
              "      <td>0.827874</td>\n",
              "      <td>0.549924</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.021902</td>\n",
              "      <td>1.323436</td>\n",
              "      <td>0.538332</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.742470</td>\n",
              "      <td>1.546152</td>\n",
              "      <td>0.078092</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad80567a-62db-46a7-a39b-8bafe45ecdf6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-ad80567a-62db-46a7-a39b-8bafe45ecdf6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ad80567a-62db-46a7-a39b-8bafe45ecdf6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **ignore_index=True**\n",
        "---"
      ],
      "metadata": {
        "id": "9TiCFVfnQO_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.concat([df1,df2], ignore_index=True)\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "_dckMcojQPI1",
        "outputId": "c9c23c52-3d4d-4c8d-dbca-cd5a90d77b76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "5 -0.685572 -1.112475 -0.538389       NaN\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4624ba9f-02fd-4980-a125-844719498b32');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.5 Shuffle data frames (row wise)**\n",
        "---"
      ],
      "metadata": {
        "id": "aamauROsQVwj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3.sample(frac=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "HEqZKvUtQV7N",
        "outputId": "fc042352-6109-4911-f9b8-54f340763c3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          0         1         2         3\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.6 Concatenate data frames using join = 'inner' (intersection)**\n",
        "---"
      ],
      "metadata": {
        "id": "4B3SiifTQeZy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "\n",
        "df1 = pd.DataFrame(np.random.randn(5, 4))\n",
        "df1\n",
        "\n",
        "df2 = pd.DataFrame(np.random.randn(4, 3))\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "01OppCTjQejD",
        "outputId": "7d84f1cd-e371-433a-bbcb-22ebf739d138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2\n",
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              "1 -0.722930 -0.798202  0.133940\n",
              "2 -0.141516 -0.142613 -1.290755\n",
              "3  1.459465 -0.431091  0.411425"
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-2f02b2a2-5ec7-4f40-b3a5-294abcc1b117');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **join = 'inner' and axis = 0 (intersection of dataframe columnwise)**\n",
        "---"
      ],
      "metadata": {
        "id": "o0X3gdQnQnCo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.concat([df1,df2],axis=0, join=\"inner\")\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "_-waSNylQnYX",
        "outputId": "748fd7b7-e8f6-4115-c18f-fe90eda08e93"
      },
      "execution_count": null,
      "outputs": [
        {
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              "          0         1         2\n",
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              "2  2.100804 -0.161368  0.856475\n",
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              "0  0.627724 -0.219055  0.149851\n",
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              "      <th>3</th>\n",
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              "      <td>-0.183668</td>\n",
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              "      <td>-1.290755</td>\n",
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              "      <td>-0.431091</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **join = 'inner' and axis = 1 (intersection of dataframe row wise; using index)**\n",
        "---"
      ],
      "metadata": {
        "id": "G6gt8pnNQtKJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.concat([df1,df2],axis=1, join=\"inner\")\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "38WqIZ5pQtWM",
        "outputId": "33b1be9e-0e3a-469c-c281-28ef170ecc9f"
      },
      "execution_count": null,
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              "          0         1         2         3         0         1         2\n",
              "0  1.047034  0.482128 -0.312174  0.002897  0.627724 -0.219055  0.149851\n",
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              "      <td>0.627724</td>\n",
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              "      <td>0.856475</td>\n",
              "      <td>2.117462</td>\n",
              "      <td>-0.141516</td>\n",
              "      <td>-0.142613</td>\n",
              "      <td>-1.290755</td>\n",
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              "      <td>-1.736147</td>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.7 Concatenate data frames using join = 'outer' (union)**\n",
        "---"
      ],
      "metadata": {
        "id": "TtzDR54-Qz8B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "\n",
        "df1 = pd.DataFrame(np.random.randn(5, 4))\n",
        "df1\n",
        "\n",
        "df2 = pd.DataFrame(np.random.randn(4, 3))\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "egFbPm6tQ0H9",
        "outputId": "2c24af3b-673a-44ff-ccf7-56f10b99b652"
      },
      "execution_count": null,
      "outputs": [
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **join = 'outer' and axis = 0 (union of dataframe columnwise)**\n",
        "---"
      ],
      "metadata": {
        "id": "y8tO0EWoQ8x2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.concat([df1,df2],axis=0, join=\"outer\")\n",
        "df3"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
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        "id": "l2adhE3VQ87G",
        "outputId": "ec3c445d-03fb-4426-d592-d6dcf60f2017"
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      "execution_count": null,
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bddada54-ef41-4975-a04c-ca3778017be0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bddada54-ef41-4975-a04c-ca3778017be0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##### **join = 'outer' and axis = 1 (union of dataframe row wise; using index)**\n",
        "---"
      ],
      "metadata": {
        "id": "eBAAlSkQRE-D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.concat([df1,df2],axis=1, join=\"outer\")\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "yPMcnOsIRFIh",
        "outputId": "69b4a7a7-6b21-4c99-9189-7bdd4d7fe173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2         3         0         1         2\n",
              "0 -1.387719  1.452732  0.949633 -1.926266 -1.050112 -0.147377 -0.050098\n",
              "1  0.363050  0.607562 -1.060620  0.060494 -0.110932 -1.049295  1.086415\n",
              "2 -1.294838  0.308216 -1.476482 -0.403704 -0.045651  0.796760 -0.160929\n",
              "3 -1.080472  1.290611  0.448061 -0.272063  0.986609 -0.791687  1.166034\n",
              "4  0.762238  0.281243 -1.147357  1.554115       NaN       NaN       NaN"
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              "      <td>-0.050098</td>\n",
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              "      <td>0.363050</td>\n",
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              "      <td>-1.049295</td>\n",
              "      <td>1.086415</td>\n",
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              "      <td>-1.294838</td>\n",
              "      <td>0.308216</td>\n",
              "      <td>-1.476482</td>\n",
              "      <td>-0.403704</td>\n",
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              "      <td>0.796760</td>\n",
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              "      <td>-0.272063</td>\n",
              "      <td>0.986609</td>\n",
              "      <td>-0.791687</td>\n",
              "      <td>1.166034</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.762238</td>\n",
              "      <td>0.281243</td>\n",
              "      <td>-1.147357</td>\n",
              "      <td>1.554115</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      </button>\n",
              "      \n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Join Data frames using merge()**\n",
        "- Pandas have options for high-performance in-memory merging and joining.\n",
        "- When we need to combine very large DataFrames, joins serve as a powerful way to perform these operations swiftly.\n",
        "- Joins can only be done on two DataFrames at a time, denoted as left and right tables. \n",
        "- The key is the common column that the two DataFrames will be joined on. \n",
        "- It’s a good practice to use keys which have unique values throughout the column to avoid unintended duplication of row values. \n",
        "- Pandas provide a single function, merge(), as the entry point for all standard database join operations between DataFrame objects. \n",
        "- There are four basic ways to handle the join (inner, left, right, and outer), depending on which rows must retain their data\n",
        "---"
      ],
      "metadata": {
        "id": "xBXrdQQARNhv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.8 Merge 1: Merging a dataframe with one unique key combination**\n",
        "---"
      ],
      "metadata": {
        "id": "erybee8GReCv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        " \n",
        "# Define a dictionary containing employee data \n",
        "df1 = pd.DataFrame({'Key': ['K0', 'K1', 'K2', 'K3'],\n",
        "         'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
        "        'Age':[27, 24, 22, 32],})\n",
        "df1\n",
        "\n",
        "df2 = pd.DataFrame({'Key': ['K0', 'K1', 'K2', 'K3'],\n",
        "         'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
        "        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']})\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "T_y7QFl2RNuG",
        "outputId": "a9fa980e-3da4-4104-b8a3-a5fbc70280d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Key    Address Qualification\n",
              "0  K0     Nagpur         Btech\n",
              "1  K1     Kanpur           B.A\n",
              "2  K2  Allahabad          Bcom\n",
              "3  K3    Kannuaj        B.hons"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bfbbe037-5000-41a6-852f-27e78091ad0f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Key</th>\n",
              "      <th>Address</th>\n",
              "      <th>Qualification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>Nagpur</td>\n",
              "      <td>Btech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>Kanpur</td>\n",
              "      <td>B.A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>Allahabad</td>\n",
              "      <td>Bcom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
              "      <td>Kannuaj</td>\n",
              "      <td>B.hons</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bfbbe037-5000-41a6-852f-27e78091ad0f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bfbbe037-5000-41a6-852f-27e78091ad0f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### **Merge with one unique key combination**\n",
        "---"
      ],
      "metadata": {
        "id": "nTStJyi4Rn7p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.merge(df1, df2, on='Key')\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ykhbTYKqRoFi",
        "outputId": "ae227e8e-3182-4d36-daf7-0264eb4f7b23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Key    Name  Age    Address Qualification\n",
              "0  K0     Jai   27     Nagpur         Btech\n",
              "1  K1  Princi   24     Kanpur           B.A\n",
              "2  K2  Gaurav   22  Allahabad          Bcom\n",
              "3  K3    Anuj   32    Kannuaj        B.hons"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>Key</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K0</td>\n",
              "      <td>Jai</td>\n",
              "      <td>27</td>\n",
              "      <td>Nagpur</td>\n",
              "      <td>Btech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>Princi</td>\n",
              "      <td>24</td>\n",
              "      <td>Kanpur</td>\n",
              "      <td>B.A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>Gaurav</td>\n",
              "      <td>22</td>\n",
              "      <td>Allahabad</td>\n",
              "      <td>Bcom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
              "      <td>Anuj</td>\n",
              "      <td>32</td>\n",
              "      <td>Kannuaj</td>\n",
              "      <td>B.hons</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.9 Merge 2: Merging dataframe using multiple join keys**\n",
        "---"
      ],
      "metadata": {
        "id": "Ui2XE69WRzCs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.DataFrame({'Key1': ['K0', 'K1', 'K2', 'K3'],\n",
        "         'Key2': ['K0', 'K1', 'K0', 'K1'],\n",
        "         'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
        "        'Age':[27, 24, 22, 32],})\n",
        "df1\n",
        "\n",
        "df2 = pd.DataFrame({'Key1': ['K0', 'K1', 'K2', 'K3'],\n",
        "         'Key2': ['K0', 'K0', 'K0', 'K0'],\n",
        "         'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
        "        'Qualification':['BTech', 'BA', 'BCom', 'BE']})\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
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        "id": "VH5OryweRzMl",
        "outputId": "4fefb057-e9ed-474d-910d-ca372d989091"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Key1 Key2    Address Qualification\n",
              "0   K0   K0     Nagpur         BTech\n",
              "1   K1   K0     Kanpur            BA\n",
              "2   K2   K0  Allahabad          BCom\n",
              "3   K3   K0    Kannuaj            BE"
            ],
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              "\n",
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              "      <th>0</th>\n",
              "      <td>K0</td>\n",
              "      <td>K0</td>\n",
              "      <td>Nagpur</td>\n",
              "      <td>BTech</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>K0</td>\n",
              "      <td>Kanpur</td>\n",
              "      <td>BA</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>K0</td>\n",
              "      <td>Allahabad</td>\n",
              "      <td>BCom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
              "      <td>K0</td>\n",
              "      <td>Kannuaj</td>\n",
              "      <td>BE</td>\n",
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              "  </tbody>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### **Merge dataframe using multiple keys**\n",
        "---"
      ],
      "metadata": {
        "id": "Z3bnRFu_R5ol"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.merge(df1, df2, on=['Key1', 'Key2'])\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "7nDhRxOMR50T",
        "outputId": "0ab38f80-bf2f-4b59-be7a-bd591417aa3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Key1 Key2    Name  Age    Address Qualification\n",
              "0   K0   K0     Jai   27     Nagpur         BTech\n",
              "1   K2   K0  Gaurav   22  Allahabad          BCom"
            ],
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              "\n",
              "  <div id=\"df-4d7d2e94-4f3b-4e41-81d8-c42f83eedbdf\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>Key1</th>\n",
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              "      <th>Name</th>\n",
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              "      <td>K0</td>\n",
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              "      <td>Nagpur</td>\n",
              "      <td>BTech</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K2</td>\n",
              "      <td>K0</td>\n",
              "      <td>Gaurav</td>\n",
              "      <td>22</td>\n",
              "      <td>Allahabad</td>\n",
              "      <td>BCom</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4d7d2e94-4f3b-4e41-81d8-c42f83eedbdf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.10 Merge 3: Merging dataframe using \"how\" in an argument**\n",
        "---\n",
        "- We use how argument to merge specifies how to determine which keys are to be included in the resulting table. \n",
        "- If a key combination does not appear in either the left or right tables, the values in the joined table will be NA. \n",
        "- Here is a summary of the how options and their SQL equivalent names:"
      ],
      "metadata": {
        "id": "gTQKcv91S04U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "9f3h5TULSJ6R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### **Merge using keys from left data frame**\n",
        "---"
      ],
      "metadata": {
        "id": "v4x7plu6TDHu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.merge(df1, df2, on=['Key1', 'Key2'],how='left')\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "1Y1k5LljSKDO",
        "outputId": "fccb4b64-249e-4deb-bff9-43685019fd28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Key1 Key2    Name  Age    Address Qualification\n",
              "0   K0   K0     Jai   27     Nagpur         BTech\n",
              "1   K1   K1  Princi   24        NaN           NaN\n",
              "2   K2   K0  Gaurav   22  Allahabad          BCom\n",
              "3   K3   K1    Anuj   32        NaN           NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44493091-dac3-41c3-afc5-b73029475196\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Key1</th>\n",
              "      <th>Key2</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Address</th>\n",
              "      <th>Qualification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K0</td>\n",
              "      <td>K0</td>\n",
              "      <td>Jai</td>\n",
              "      <td>27</td>\n",
              "      <td>Nagpur</td>\n",
              "      <td>BTech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>K1</td>\n",
              "      <td>Princi</td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>K0</td>\n",
              "      <td>Gaurav</td>\n",
              "      <td>22</td>\n",
              "      <td>Allahabad</td>\n",
              "      <td>BCom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
              "      <td>K1</td>\n",
              "      <td>Anuj</td>\n",
              "      <td>32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44493091-dac3-41c3-afc5-b73029475196')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-44493091-dac3-41c3-afc5-b73029475196 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-44493091-dac3-41c3-afc5-b73029475196');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### **Merge using keys from right data frame**\n",
        "---"
      ],
      "metadata": {
        "id": "0Ki2iRNcTJNW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.merge(df1, df2, on=['Key1', 'Key2'],how='right')\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "i8T61C3ITJXi",
        "outputId": "6ab5cdbd-6c56-4614-ac17-9f68dbb8f5c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Key1 Key2    Name   Age    Address Qualification\n",
              "0   K0   K0     Jai  27.0     Nagpur         BTech\n",
              "1   K1   K0     NaN   NaN     Kanpur            BA\n",
              "2   K2   K0  Gaurav  22.0  Allahabad          BCom\n",
              "3   K3   K0     NaN   NaN    Kannuaj            BE"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9712fb07-4724-4da6-94fd-e91b6860d75f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      <th></th>\n",
              "      <th>Key1</th>\n",
              "      <th>Key2</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Address</th>\n",
              "      <th>Qualification</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K0</td>\n",
              "      <td>K0</td>\n",
              "      <td>Jai</td>\n",
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              "      <td>Nagpur</td>\n",
              "      <td>BTech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>K0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kanpur</td>\n",
              "      <td>BA</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>K0</td>\n",
              "      <td>Gaurav</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Allahabad</td>\n",
              "      <td>BCom</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
              "      <td>K0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kannuaj</td>\n",
              "      <td>BE</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### **Merge using keys from outer data frame**\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "8jgv_WekTPj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.merge(df1, df2, on=['Key1', 'Key2'],how='outer')\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "eqVeZezoTPuK",
        "outputId": "0acc4e8a-03ae-4212-ca65-63fb7db6fb0f"
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      "execution_count": null,
      "outputs": [
        {
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              "  Key1 Key2    Name   Age    Address Qualification\n",
              "0   K0   K0     Jai  27.0     Nagpur         BTech\n",
              "1   K1   K1  Princi  24.0        NaN           NaN\n",
              "2   K2   K0  Gaurav  22.0  Allahabad          BCom\n",
              "3   K3   K1    Anuj  32.0        NaN           NaN\n",
              "4   K1   K0     NaN   NaN     Kanpur            BA\n",
              "5   K3   K0     NaN   NaN    Kannuaj            BE"
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              "          document.querySelector('#df-8a55a090-84ba-4e22-b54a-9a8a92c71ae2 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-8a55a090-84ba-4e22-b54a-9a8a92c71ae2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### **Merge using keys from inner data frame**\n",
        "---"
      ],
      "metadata": {
        "id": "KKaCaq4BTWFw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.merge(df1, df2, on=['Key1', 'Key2'],how='inner')\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "fxNV2s7nTWRX",
        "outputId": "4fa520ab-7572-4f4f-d716-d4fb2dedbb4e"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "  Key1 Key2    Name  Age    Address Qualification\n",
              "0   K0   K0     Jai   27     Nagpur         BTech\n",
              "1   K2   K0  Gaurav   22  Allahabad          BCom"
            ],
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **3.11  Concatenating DataFrame using .append()**\n",
        "---"
      ],
      "metadata": {
        "id": "P_OWgXmTTet7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.DataFrame({'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
        "        'Age':[27, 24, 22, 32], \n",
        "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
        "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}, index=[0, 1, 2, 3])\n",
        "df1\n",
        "\n",
        "df2 = pd.DataFrame({'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'], \n",
        "        'Age':[17, 14, 12, 52], \n",
        "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
        "        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}, index=[4, 5, 6, 7])\n",
        "df2\n",
        "\n",
        "df3 = df1.append(df2)\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "miX1_C0FTe29",
        "outputId": "07a6cb34-1e7d-42c1-bbf3-337a213db82c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Name  Age    Address Qualification\n",
              "0     Jai   27     Nagpur           Msc\n",
              "1  Princi   24     Kanpur            MA\n",
              "2  Gaurav   22  Allahabad           MCA\n",
              "3    Anuj   32    Kannuaj           Phd\n",
              "4    Abhi   17     Nagpur         Btech\n",
              "5  Ayushi   14     Kanpur           B.A\n",
              "6  Dhiraj   12  Allahabad          Bcom\n",
              "7  Hitesh   52    Kannuaj        B.hons"
            ],
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              "      <th>0</th>\n",
              "      <td>Jai</td>\n",
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              "      <td>Nagpur</td>\n",
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              "      <th>1</th>\n",
              "      <td>Princi</td>\n",
              "      <td>24</td>\n",
              "      <td>Kanpur</td>\n",
              "      <td>MA</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gaurav</td>\n",
              "      <td>22</td>\n",
              "      <td>Allahabad</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Hitesh</td>\n",
              "      <td>52</td>\n",
              "      <td>Kannuaj</td>\n",
              "      <td>B.hons</td>\n",
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              "          const element = document.querySelector('#df-d461eda1-73a4-46da-8b9e-e1e8e19141f1');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **4. Working with Date**\n",
        "### **4.1 Generate dates for a period (days)**\n",
        "#### **Example 1**\n",
        "---"
      ],
      "metadata": {
        "id": "QkqR7CDUToHP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range('20130101', periods = 6)\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnL0zD46ToSM",
        "outputId": "c4188490-3ec6-4c0e-dd11-0b12e5942c28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
              "               '2013-01-05', '2013-01-06'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### **Example 2**\n",
        "---"
      ],
      "metadata": {
        "id": "ISS7Hi9fT4j0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(start='1/1/2018', periods = 10)\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xk3IWDEyT4tz",
        "outputId": "ac2e24cd-54ac-491e-83cd-58ce31c448f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
              "               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',\n",
              "               '2018-01-09', '2018-01-10'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.2 Generate dates within range**\n",
        "#### **Example 1**\n",
        "---\n"
      ],
      "metadata": {
        "id": "NZ-bLFckT-TA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(start='1/1/2018', end='1/15/2018')\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYxM0nD7T-cz",
        "outputId": "fd1a63ec-e527-4e95-82d2-b9d5ce75bab9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
              "               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',\n",
              "               '2018-01-09', '2018-01-10', '2018-01-11', '2018-01-12',\n",
              "               '2018-01-13', '2018-01-14', '2018-01-15'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "#### **Example 2**\n",
        "---"
      ],
      "metadata": {
        "id": "UpYTZYVrUHGs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(start='1-1-2018', end='1/15/2018')\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hyYlse-bUHQH",
        "outputId": "634553b0-c04a-4400-f9fc-2deb912304cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
              "               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',\n",
              "               '2018-01-09', '2018-01-10', '2018-01-11', '2018-01-12',\n",
              "               '2018-01-13', '2018-01-14', '2018-01-15'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.3 Generate dates till end date**\n",
        "---"
      ],
      "metadata": {
        "id": "meY6NasVUNS3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(end='1/1/2018', periods=12)\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GD8Fs6Z5UNdZ",
        "outputId": "cc605c50-588f-4fb3-a820-c8e0db437f9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2017-12-21', '2017-12-22', '2017-12-23', '2017-12-24',\n",
              "               '2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',\n",
              "               '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.4 Generate dates with days frequency with multiple GAP**\n",
        "---"
      ],
      "metadata": {
        "id": "yXs6h_C4UThd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(start='1/1/2018', periods=12, freq='2D')  # D = Days\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGDYNEMdUTrX",
        "outputId": "8a0ba7b3-537d-4ea4-fd0c-29a1e3a3e029"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05', '2018-01-07',\n",
              "               '2018-01-09', '2018-01-11', '2018-01-13', '2018-01-15',\n",
              "               '2018-01-17', '2018-01-19', '2018-01-21', '2018-01-23'],\n",
              "              dtype='datetime64[ns]', freq='2D')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.5 Generate dates with month frequency**\n",
        "---"
      ],
      "metadata": {
        "id": "uGz_HBymUZsQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(start='1/1/2018', periods=5, freq='M')   # M = Months\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZg0jojNUZ1s",
        "outputId": "e22a32c3-fbbc-4bcb-da80-1fdc5bad3003"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',\n",
              "               '2018-05-31'],\n",
              "              dtype='datetime64[ns]', freq='M')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.6 Generate dates with month frequency with multiple GAP**\n",
        "---"
      ],
      "metadata": {
        "id": "yoDcHus2UfYb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(start='1/1/2018', periods=5, freq='3M')    # M = Months\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMnJ1V9-Ufg-",
        "outputId": "860ebaf9-1cbf-440d-bdde-852dcbbae26f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',\n",
              "               '2019-01-31'],\n",
              "              dtype='datetime64[ns]', freq='3M')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.7 Generate dates with month frequency with END OF MONTH**\n",
        "---"
      ],
      "metadata": {
        "id": "EWuYXFRuUlOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(start='2/25/2018', periods=5, freq=pd.offsets.MonthEnd(3))\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nu59jtCYUlXA",
        "outputId": "5aa0912a-1c1f-4317-d5bc-779af7654981"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-02-28', '2018-05-31', '2018-08-31', '2018-11-30',\n",
              "               '2019-02-28'],\n",
              "              dtype='datetime64[ns]', freq='3M')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.8 Generate dates with month frequency with START OF MONTH**\n",
        "---"
      ],
      "metadata": {
        "id": "V-_30V8_UrTg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(start='1/10/2018', periods=5, freq=pd.offsets.MonthBegin(1))\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7pyziqYUrcz",
        "outputId": "73a17ad5-71e6-4ccd-d2d6-f070b26bfd5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-02-01', '2018-03-01', '2018-04-01', '2018-05-01',\n",
              "               '2018-06-01'],\n",
              "              dtype='datetime64[ns]', freq='MS')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.9 Generate random data frame with date as index**\n",
        "---"
      ],
      "metadata": {
        "id": "EU7VYzmnUw5e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range('20130101', periods=6)\n",
        "dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxM4y93HUxD9",
        "outputId": "fb888ff9-9140-4177-e902-98127329ba64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
              "               '2013-01-05', '2013-01-06'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### **4.10 Generate a random matrix of 6 by 4**\n",
        "---"
      ],
      "metadata": {
        "id": "bMlJ0TfFU9de"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "I-wuKJKkH5gB",
        "outputId": "6918cf70-ce2c-481c-a2ff-db28ec9aa087"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   A         B         C         D\n",
              "2013-01-01 -0.471538 -0.792470 -0.810719 -1.282932\n",
              "2013-01-02  1.250880 -0.728161  0.619933  0.731551\n",
              "2013-01-03 -0.278422 -0.035555  0.042509  0.157727\n",
              "2013-01-04 -0.020300  1.170164 -0.180335 -0.981219\n",
              "2013-01-05 -1.171100  1.098006 -0.786329 -1.320562\n",
              "2013-01-06 -1.419400  1.349781  0.911876  0.683887"
            ],
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              "    <tr>\n",
              "      <th>2013-01-01</th>\n",
              "      <td>-0.471538</td>\n",
              "      <td>-0.792470</td>\n",
              "      <td>-0.810719</td>\n",
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              "    <tr>\n",
              "      <th>2013-01-02</th>\n",
              "      <td>1.250880</td>\n",
              "      <td>-0.728161</td>\n",
              "      <td>0.619933</td>\n",
              "      <td>0.731551</td>\n",
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              "      <td>-0.278422</td>\n",
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              "      <td>0.042509</td>\n",
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              "      <td>-0.981219</td>\n",
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              "      <th>2013-01-05</th>\n",
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              "      <th>2013-01-06</th>\n",
              "      <td>-1.419400</td>\n",
              "      <td>1.349781</td>\n",
              "      <td>0.911876</td>\n",
              "      <td>0.683887</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-a99ce660-73e6-4547-a8ea-61122e0a6c83');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 55
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}