Day_8_Pandas_Part_1.ipynb

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      "source": [
        "<H1><B>PANDAS</B></H1>\n",
        "Welcome to the lesson on Pandas.Pandas is a package for data manipulation and analysis in Python. The name Pandas is derived from the econometrics term Panel Data. Pandas incorporates two additional data structures into Python, namely <b>Pandas Series</b> and <b>Pandas DataFrame</b>. These data structures allow us to work with labeled and relational data in an easy and intuitive manner. These lessons are intended as a basic overview of Pandas and introduces some of its most important features."
      ]
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      "source": [
        "## **Why Use Pandas?**\n",
        "The recent success of machine learning algorithms is partly due to the huge amounts of data that we have available to train our algorithms on. However, when it comes to data, quantity is not the only thing that matters, the quality of your data is just as important.\n",
        "\n",
        "More often than not, large datasets will often have missing values, outliers, incorrect values, etc… Having data with a lot of missing or bad values, for example, is not going to allow your machine learning algorithms to perform well.\n",
        "\n",
        "his is where Pandas come in. Pandas Series and DataFrames are designed for fast data analysis and manipulation, as well as being flexible and easy to use. Below are just a few features that makes Pandas an excellent package for data analysis:\n",
        "\n",
        "- Allows the use of labels for rows and columns\n",
        "- Can calculate rolling statistics on time series data\n",
        "- Easy handling of NaN values\n",
        "- Is able to load data of different formats into DataFrames\n",
        "- Can join and merge different datasets together\n",
        "- It integrates with NumPy and Matplotlib\n",
        "\n",
        "For these and other reasons, Pandas DataFrames have become one of the most commonly used Pandas object for data analysis in Python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## **PANDAS SERIES:**\n",
        "A Pandas series is a one-dimensional array-like object that can hold many data types, such as numbers or strings. One of the main differences between Pandas Series and NumPy ndarrays is that you can assign an index label to each element in the Pandas Series. In other words, you can name the indices of your Pandas Series anything you want. Another big difference between Pandas Series and NumPy ndarrays is that Pandas Series can hold data of different data types.\n",
        "\n",
        "```\n",
        "`# This is formatted as code`\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jS_d4Ig2B9N"
      },
      "source": [
        "# We first have to import pandas using import statement in python\n",
        "\n",
        "import pandas as pd  "
      ],
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
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      "source": [
        "Let's create a pandas series.  For that we just write <B>pd.Series()</b>\n",
        "\n",
        "```\n",
        "items = pd.Series(data, index)    # For now we just names the series object in a variable named item\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "\n",
        "\n",
        "So while creating series we can pass arguments for <u>data</u> and <u>index</u>"
      ]
    },
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      "source": [
        "item = pd.Series(data=[15, 5, 'No'], index=['chocolates', 'chips', 'milk'])\n",
        "item"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chocolates    15\n",
              "chips          5\n",
              "milk          No\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
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      "source": [
        "As we see the series is displayed as indicies in the first column ans data in the second column.\n",
        "\n",
        "<br><br>\n",
        "## **Attributes of Panda Series:**\n",
        "\n",
        "Let's see some of the attributes of the pandas series that helps us to understand our series"
      ]
    },
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      "source": [
        "item.shape   # gives us the  sizes of each dimension of the data"
      ],
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            "text/plain": [
              "(3,)"
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      "source": [
        "item.ndim   # gives us the number of dimensions of the data"
      ],
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            "text/plain": [
              "1"
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      "source": [
        "item.size   # gives us the total number of items in the array "
      ],
      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
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    {
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      "source": [
        "item.index   # gives us the list indeices of the series "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['chocolates', 'chips', 'milk'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yykuZM3r7NpH",
        "outputId": "31f37654-e700-4d2e-c697-439a6a2c76a3",
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      },
      "source": [
        "item.values    #gives us the data of the series"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15, 5, 'No'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6slr6osd7BR"
      },
      "source": [
        "If you are dealing with a very large Pandas Series and if you are not sure whether an index label exists, you can check by using the ```in``` command"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DkcMhcod_is",
        "outputId": "007923f1-315c-4193-af94-0e5a7c43ce3c",
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      "source": [
        "# We check whether bananas is a food item (an index) in Groceries\n",
        "x = 'bananas' in item\n",
        "\n",
        "# We check whether bread is a food item (an index) in Groceries\n",
        "y = 'chips' in item\n",
        "\n",
        "# We print the results\n",
        "print('Is bananas an index label in item:', x)\n",
        "print('Is chips an index label in item:', y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Is bananas an index label in item: False\n",
            "Is chips an index label in item: True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ech4F_TSeWAM"
      },
      "source": [
        "## **Accessing and Deleting Elements in Pandas Series:**\n",
        "Now let's look at how we can access or modify elements in a Pandas Series. One great advantage of Pandas Series is that it allows us to access data in many different ways. Elements can be accessed using index labels or numerical indices inside square brackets, [ ], similar to how we access elements in NumPy ndarrays. Since we can use numerical indices, we can use both positive and negative integers to access data from the beginning or from the end of the Series, respectively. "
      ]
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    {
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      "source": [
        "# One by their index labels\n",
        "item['chocolates']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
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    {
      "cell_type": "code",
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      },
      "source": [
        "item[['chocolates', 'milk']]   #a list of indices is passed "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chocolates    15\n",
              "milk          No\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 34
        }
      },
      "source": [
        "item[0]  #first element"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuNX_ZJ7B0oC",
        "outputId": "e2414a29-362b-4b46-b425-936bd54edcd6",
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      },
      "source": [
        "item[-1]  #last element"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'No'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0dIwJRHCDX-",
        "outputId": "ec706f52-c9d9-4760-c2cf-db9f903ac856",
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      },
      "source": [
        "item[[0,1]]    # list of numerical indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chocolates    15\n",
              "chips          5\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F7sccmsCCMRB"
      },
      "source": [
        "In order to remove any ambiguity from whether we are reffering to the labeled index or the numerical index, panda series have two attributes <b>loc</b> and <b>iloc</b>.\n",
        "\n",
        "* The attribute loc stands for the location and is used to explicitly state that we are using our labeled index.\n",
        "* The attribute iloc stands for integer location and is used to explicitly state that we are using a numerical index"
      ]
    },
    {
      "cell_type": "code",
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      },
      "source": [
        "item.loc[['milk', 'chips']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "milk     No\n",
              "chips     5\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xeHhH-kbDik7",
        "outputId": "20d2a33a-34d8-448e-bd05-cbe271b5981e",
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          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "item.iloc[[1,2]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chips     5\n",
              "milk     No\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPGLIKM0DuC3",
        "outputId": "82faab3c-89f3-4abc-9594-bfe2dc1d0b40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "item #let's see the series again"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chocolates    15\n",
              "chips          5\n",
              "milk          No\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yO8aYkvUfTUC"
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      "source": [
        "Pandas Series are also mutable like NumPy ndarrays, which means we can change the elements of a Pandas Series after it has been created."
      ]
    },
    {
      "cell_type": "code",
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        "id": "b1eca2q9D1zS",
        "outputId": "fa78f341-1b66-4189-ae22-321b18b4860d",
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      },
      "source": [
        "# we can change the data by reassigning the value\n",
        "item['milk'] = 'Yes'\n",
        "item"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chocolates     15\n",
              "chips           5\n",
              "milk          Yes\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chWD7SGcEFag"
      },
      "source": [
        "We can see that our series is now modified\n",
        "<br><br>\n",
        "\n",
        "## **Deleting values from Pandas Series:**\n",
        "\n",
        "We can also delete items from a Pandas Series by using the ```.drop()``` method. The ```Series.drop(label)``` method removes the given label from the given Series."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIg1bACpEPQ0",
        "outputId": "c2b606f3-64d2-44e9-9e12-2abc0a1c7dac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "# Also we can delete items from the pandas series using drop method\n",
        "item.drop('chocolates')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chips      5\n",
              "milk     Yes\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymR0EeqwEcrR"
      },
      "source": [
        "As we see the chocolate item is no longer is present in the series returned by the method.\n",
        "\n",
        "><B>Note: </B>However, this drops elements from the series out of place meaning the code above just returned the modified series and actually didn't change the actual series."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-og9vwoElH-",
        "outputId": "f9929f15-6e0c-4f19-b409-645791a37c7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "item   # THE CHOCOLATES ITEM MUST BE PRESENT HERE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chocolates     15\n",
              "chips           5\n",
              "milk          Yes\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9umykXg7FTpF"
      },
      "source": [
        "If you want to modify the actual series, we can use either of the two methods\n",
        "* overwrite the returned series on the original one\n",
        "\n",
        "```\n",
        "item = item.drop('chocolates')\n",
        "```\n",
        "\n",
        "* use inplace agrgument and set it to True\n",
        "\n",
        "```\n",
        "item.drop('chocolates', inplace=True)\n",
        "```\n",
        "Let's try the second one :p"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZdjDZaWgGC1a",
        "outputId": "bf875f8b-0973-4b59-baed-23afcd34e185",
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          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "item.drop('chocolates', inplace= True)\n",
        "item"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chips      5\n",
              "milk     Yes\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-hhKDQqGLY5"
      },
      "source": [
        "As you see the item is dropped from the original series."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BT--7ENOGSUP"
      },
      "source": [
        "<br>\n",
        "\n",
        "##  **Arithmatic operations on Pandas Series:**\n",
        "Just like with NumPy ndarrays, we can perform element-wise arithmetic operations on Pandas Series.\n",
        "We will look at the arithematic operations between pandas series and single numbers.<br>\n",
        "Let's first make a new series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxOmwI6k5OQi",
        "outputId": "3015b89a-f60c-4074-cb68-c288e8847d96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "sweets = pd.Series(data=[10, 5, 7], index=['candies', 'donuts', 'ladoos'])\n",
        "sweets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "candies    10\n",
              "donuts      5\n",
              "ladoos      7\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mEmiMxBBjUCd"
      },
      "source": [
        "We can now modify the data in sweets by performing basic arithmetic operations. Let's see some examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-nFUQwe6qpo",
        "outputId": "25e814a3-854d-4eb4-8e17-14138e19557f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "sweets+2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "candies    12\n",
              "donuts      7\n",
              "ladoos      9\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQsbjeqC6xx_",
        "outputId": "85a4717a-8209-4208-da8d-c6f5e5931c6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "sweets-2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "candies    8\n",
              "donuts     3\n",
              "ladoos     5\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oD8kOyMS60HR",
        "outputId": "f4f418cd-ca26-4414-cb27-77b5c9680030",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "sweets*2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "candies    20\n",
              "donuts     10\n",
              "ladoos     14\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bQA4AUa62-J",
        "outputId": "46c32d54-fb22-4f13-9063-8aef8a8eb542",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "sweets/2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "candies    5.0\n",
              "donuts     2.5\n",
              "ladoos     3.5\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "giVgcGZU64RZ",
        "outputId": "8dad46b4-fe83-4c79-be35-185bf823ed93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "# WE CAN ALSO APPLY MATHEMATICAL FUNCTIONS FROM NUPY SUCH AS SQAURE ROOT\n",
        "import numpy as np\n",
        "\n",
        "np.sqrt(sweets)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "candies    3.162278\n",
              "donuts     2.236068\n",
              "ladoos     2.645751\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5ZvlMOujiXf"
      },
      "source": [
        "Pandas also allows us to only apply arithmetic operations on selected items in our sweets list. Let's see some examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Awbqevhr7VHT",
        "outputId": "8457c201-175f-4e8c-e551-05cec7b242fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "np.power(sweets, 4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "candies    10000\n",
              "donuts       625\n",
              "ladoos      2401\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pxvw5wfP7L0i",
        "outputId": "f5e1a4e1-fc5d-4511-c6bf-6278162f86b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "np.exp(sweets)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "candies    22026.465795\n",
              "donuts       148.413159\n",
              "ladoos      1096.633158\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Es1VSsrl7TXh"
      },
      "source": [
        "We can also apply arithemeatic operations on specific elements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7RMy2Y3DG4f",
        "outputId": "195ecee5-6b7b-485d-a705-44ea68cc19bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sweets['candies']-4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CSLGDr5DNl8",
        "outputId": "d0c12677-73d4-4bac-edca-f39b706fba45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "sweets[['donuts', 'ladoos']]*3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "donuts    15\n",
              "ladoos    21\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbxXiVEZDV3V"
      },
      "source": [
        "><b>Note:</b> We can also apply arithematic operations on a pandas series of mixed data types, provided the operations are defines on all the datatypes.\n",
        "\n",
        "* Let's use the item series we made earlier "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9lmMLf0Dran",
        "outputId": "6ce42c49-5152-4e01-d40e-b5a5449d1458",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "item = pd.Series(data=[15, 5, 'No'], index=['chocolates', 'chips', 'milk'])\n",
        "item"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chocolates    15\n",
              "chips          5\n",
              "milk          No\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6JyhdtIEK9m",
        "outputId": "7139d236-8552-49a0-c45e-0f322f04f864",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "item * 2  #SINCE MULTIPLICATION OPERATION IS DEFINED FOR BOTH STRINGS AND INTEGERS, THE CODE DOESN'T RETURN ANY ERROR"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chocolates      30\n",
              "chips           10\n",
              "milk          NoNo\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdojuZPDEZa2",
        "outputId": "2187f43f-1bfe-4919-ac33-b1e1ab422750",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "item/2 #SINCE THE DIVISION OPERATION IS DEFINED ONLY FOR NUMBERS AND NOT FOR STRING, THE CODE WILL RETURN AN ERROR"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mna_arithmetic_op\u001b[0;34m(left, right, op, str_rep)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(op, op_str, a, b, use_numexpr)\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0muse_numexpr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'int'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-63fa23154c8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mitem\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;31m#SINCE THE DIVISION OPERATION IS DEFINED ONLY FOR NUMBERS AND NOT FOR STRING, THE CODE WILL RETURN AN ERROR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m    501\u001b[0m         \u001b[0mlvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op, str_rep)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m             \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mna_arithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mna_arithmetic_op\u001b[0;34m(left, right, op, str_rep)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmasked_arith_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_fill_zeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mmasked_arith_op\u001b[0;34m(x, y, op)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m                 \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_upcast_putmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'int'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okRYknjbEpEi"
      },
      "source": [
        "<br><br><hr>\n",
        "<H3><B>2. PANDAS DATAFRAME</B></H3>\n",
        "\n",
        "Dataframe is a two dimensional object which holds rows and columns and can hold values of different data types.\n",
        "\n",
        "* We can create a dataframe manually or by loading data from a file.\n",
        "\n",
        "<br>\n",
        "Let's first ceate a dataframe manually:<<br>\n",
        "\n",
        "*  First let's create a dictionary of pandas series and pass it into pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pZPkbrnFgYV"
      },
      "source": [
        "item = {'Shaurya': pd.Series([250, 15, 70, 100], index=['watch', 'toys', 'glasses', 'shirt']),\n",
        "        'Ashish': pd.Series([120, 50, 90], index=['pants', 'books', 'toys' ])}\n",
        "\n",
        "# item is a dictionary for two people containing some items and the cost of the item "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hpp_vkzI_s8",
        "outputId": "9c54011c-d3d3-4323-838d-4a8e9a410fc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "# WE CAN CREATE A DATA FRAME BY PASSINF THE DICTIONARY TO THE DataFrame FUNCTION\n",
        "\n",
        "cart = pd.DataFrame(item)\n",
        "cart"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Shaurya</th>\n",
              "      <th>Ashish</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>books</th>\n",
              "      <td>NaN</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>glasses</th>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pants</th>\n",
              "      <td>NaN</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shirt</th>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>toys</th>\n",
              "      <td>15.0</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>watch</th>\n",
              "      <td>250.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Shaurya  Ashish\n",
              "books        NaN    50.0\n",
              "glasses     70.0     NaN\n",
              "pants        NaN   120.0\n",
              "shirt      100.0     NaN\n",
              "toys        15.0    90.0\n",
              "watch      250.0     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u_ohRPM6KGnh"
      },
      "source": [
        "* Make sure to capitalize the **D** and **F** while calling the dataframe function.\n",
        "* The dataframe is displayed in the tabular form\n",
        "* The row labels for the dataframe are built from the union of the index labels we provided in the series and the column labels for the dataframe is taken from the keys of the dictionaries.\n",
        "* The dataframe has NaN values because for Shaurya we have no item like books and pants in the dicrionary we provided and similarly we have NaN values  for column Ashish."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhKDZeCtLBD_",
        "outputId": "5a353fed-a361-4216-81b9-b1de70b32256",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "# IN ABOVE EXAMPLE WE PROVIDED THE DICTIONARIES THAT CLEARLY DEFINED THE INDEX LABLES, HOWEVER IF WE DON'T PROVIDE THE INDEX LABELS, \n",
        "# THEN THE DATAFRAME WOULD USE THE NUMERICAL INDEX VALUES\n",
        "# LET'S CREATE THE SAME DICIONARY WITHOUT THE INDEXED LABELS\n",
        "new_item = {'Shaurya': pd.Series([250, 15, 70, 100]),\n",
        "        'Ashish': pd.Series([120, 50, 90])}\n",
        "\n",
        "\n",
        "# NOW MAKE THE DATAFRAME USING THE NEW DICTIONARIES \n",
        "df = pd.DataFrame(new_item)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Shaurya</th>\n",
              "      <th>Ashish</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>250</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Shaurya  Ashish\n",
              "0      250   120.0\n",
              "1       15    50.0\n",
              "2       70    90.0\n",
              "3      100     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9r7zyiMQsGmH"
      },
      "source": [
        "The dataframe uses the numerical indices."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xapVa3PLsaIA"
      },
      "source": [
        "<br>\n",
        "<hr>\n",
        "<h4><b>2.a Attributes</b></h4>\n",
        "\n",
        "Like we did in pandas series, we can also extract information from pandas dataframe using some attributes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIkSyf4Qs0cj",
        "outputId": "c3b2a92d-71ac-4b17-af22-2a5694d05c66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cart.index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['books', 'glasses', 'pants', 'shirt', 'toys', 'watch'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj_1jt9Ts2Yf",
        "outputId": "3592dd7f-51fa-421a-8136-e5211e7b55bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cart.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Shaurya', 'Ashish'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
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        "outputId": "f30e6394-4192-476f-9cfd-ae8cd90b37a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "cart.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ nan,  50.],\n",
              "       [ 70.,  nan],\n",
              "       [ nan, 120.],\n",
              "       [100.,  nan],\n",
              "       [ 15.,  90.],\n",
              "       [250.,  nan]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0FyyZWIs6tc",
        "outputId": "b0ad67d7-7ec1-469b-cc0a-2f0293c21394",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "source": [
        "cart.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbA4-QHhs_D6",
        "outputId": "3afd3ce6-6023-409e-f29e-caee64d3e4ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "source": [
        "cart.ndim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "cnePLBSUtAtA",
        "outputId": "470f2adc-4c62-4887-924f-7bda82891a5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "source": [
        "cart.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqigt8XStBrk"
      },
      "source": [
        "><b>NOTE:</B> While creating the cart dataframe, we passed the whole dictionary to the dataframe function. However, there might be cases when we are only interested in some specific subset of the whole data. Pandas let's us select which data we want to put into the DataFrame, with the keywords **columns** and **index**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvnC4DodtzJV",
        "outputId": "60c1cc85-cf91-4b40-b385-011cf10315c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "ashish_cart = pd.DataFrame(item, columns=['Ashish'])\n",
        "ashish_cart"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ashish</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>pants</th>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>books</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>toys</th>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Ashish\n",
              "pants     120\n",
              "books      50\n",
              "toys       90"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "UmuK0nW8uAkk",
        "outputId": "e92617a6-da49-43d1-c0b5-33ad96077370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "selected_item = pd.DataFrame(item, index=['pants', 'toys'])\n",
        "selected_item"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Shaurya</th>\n",
              "      <th>Ashish</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>pants</th>\n",
              "      <td>NaN</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>toys</th>\n",
              "      <td>15.0</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Shaurya  Ashish\n",
              "pants      NaN     120\n",
              "toys      15.0      90"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "A_cmATJCuWMn",
        "outputId": "f4223f24-f2c1-4a92-c500-382e46c98a52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "ashish_selected_item = pd.DataFrame(item, columns=['Ashish'], index=['pants', 'toys'])\n",
        "ashish_selected_item"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ashish</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>pants</th>\n",
              "      <td>120</td>\n",
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              "      <td>90</td>\n",
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              "       Ashish\n",
              "pants     120\n",
              "toys       90"
            ]
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          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VBuq8aPuhJ8"
      },
      "source": [
        "We can also create a dataframe from a dictionary of lists or arrays. The procedure is same as before, we start by creating the dictionary and then pass it into the dataframe function. In this case however all the list or arrays in the dictionary must be of the same length."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "# Here's the dictionary of the integers and the floats.\n",
        "data = {'Integers':[1,2,3],\n",
        "         'Floats':[1.1, 2.2, 3.3]}\n",
        "\n",
        "df = pd.DataFrame(data, index=['label1', 'label2', 'label3'])    ## IF WE DON'T PASS THE INDICES, DATAFRAME WILL AUTOMATICALLY USE NUMERICAL INDICES\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Integers</th>\n",
              "      <th>Floats</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>label1</th>\n",
              "      <td>1</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label2</th>\n",
              "      <td>2</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label3</th>\n",
              "      <td>3</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Integers  Floats\n",
              "label1         1     1.1\n",
              "label2         2     2.2\n",
              "label3         3     3.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3XQ4k6f7fhf",
        "outputId": "4db3514c-c12f-47a4-8af8-5f467409efae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "# Creating DataFrame using a list of python dictionaries \n",
        "ListOfDict = [{'apple':20, 'banana':15, 'orange':30},{'apple':10, 'tomato':17, 'grapes': 35}]\n",
        "\n",
        "df = pd.DataFrame(ListOfDict)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>orange</th>\n",
              "      <th>tomato</th>\n",
              "      <th>grapes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   apple  banana  orange  tomato  grapes\n",
              "0     20    15.0    30.0     NaN     NaN\n",
              "1     10     NaN     NaN    17.0    35.0"
            ]
          },
          "metadata": {
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PD8KdII8p5F"
      },
      "source": [
        "DataFrame used the numerical row indices.\n",
        "If we want to assign the row indices some values, we can use:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vcNwfj_8u-D",
        "outputId": "25e75a4f-7879-4eee-a01c-e3cc6b449ffd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "df.index = ['personA', 'personB']\n",
        "df  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>orange</th>\n",
              "      <th>tomato</th>\n",
              "      <th>grapes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  orange  tomato  grapes\n",
              "personA     20    15.0    30.0     NaN     NaN\n",
              "personB     10     NaN     NaN    17.0    35.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dsYmgRM9OHQ"
      },
      "source": [
        "**Accessing the values in a DataFrame**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MV7BPb7v9lFb",
        "outputId": "f42f8d59-970d-4b74-ef1a-83d084a1d250",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "df[['apple']]     ##accessing a column"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "         apple\n",
              "personA     20\n",
              "personB     10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYDVCdUu9p3o",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "df[['banana','tomato']]     ##accessing by passing a list of columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>banana</th>\n",
              "      <th>tomato</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         banana  tomato\n",
              "personA    15.0     NaN\n",
              "personB     NaN    17.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJxeEXQh9txH",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "df.loc[['personA']]    ##accessing a row"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>orange</th>\n",
              "      <th>tomato</th>\n",
              "      <th>grapes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  orange  tomato  grapes\n",
              "personA     20    15.0    30.0     NaN     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_p4fCJZu91Ys",
        "outputId": "0e28a3fc-639d-4722-9c4c-ce4f6278226b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['grapes']['personB']    ##accessing a specific value"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbcXSNA_-MIK"
      },
      "source": [
        "><b>NOTE:</B> While accessing the specific element, the column label always comes the first then the row label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzhwA-ux-edY",
        "outputId": "f69b3037-8582-43d8-97c6-a86e30fbb0bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "## IF WE WANT TO ADD A NEW COLUMN TO OUR DATAFRAME,  WE CAN ADD LIKE THIS:\n",
        "\n",
        "df['corn'] = [5, 7]\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>orange</th>\n",
              "      <th>tomato</th>\n",
              "      <th>grapes</th>\n",
              "      <th>corn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  orange  tomato  grapes  corn\n",
              "personA     20    15.0    30.0     NaN     NaN     5\n",
              "personB     10     NaN     NaN    17.0    35.0     7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wc14TKil-z4_"
      },
      "source": [
        "We can also add new columns using the arithematic operations on the other columns of our DataFrame.<br>\n",
        "For eg: we can add a new column vegies by adding the values for corn and tomato\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BO542bPm_HJ9",
        "outputId": "c15c2ffc-98c8-412e-ffe3-f598c5b7f77f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "df['vegies'] = df['tomato'] + df['corn']\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>orange</th>\n",
              "      <th>tomato</th>\n",
              "      <th>grapes</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>7</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  orange  tomato  grapes  corn  vegies\n",
              "personA     20    15.0    30.0     NaN     NaN     5     NaN\n",
              "personB     10     NaN     NaN    17.0    35.0     7    24.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0DVZL82_7nG"
      },
      "source": [
        ">**NOTE:** IF YOU WANT TO ADD THE VALUES FOR ONE MORE PERSON, MEANS YOU WANT TO ADD A NEW ROW. WE FIRST HAVE TO CREATE A NEW DATAFRAME WITH THOSE ROWS AND THEN APPEND IT TO THE ORIGINAL DATAFRAME\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJEVR-5TAg0t",
        "outputId": "5ac907d1-0763-4865-e357-2ac657c22514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "new_person = [{'apple':15, 'banana': 17, 'corn': 3, 'orange':5}]\n",
        "new_df = pd.DataFrame(new_person, index=['personC'])\n",
        "new_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>corn</th>\n",
              "      <th>orange</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personC</th>\n",
              "      <td>15</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  corn  orange\n",
              "personC     15      17     3       5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIeNHuzxA-ft",
        "outputId": "0abacedd-f071-474f-d096-b0562234d332",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "## WE CAN NOW ADD THE NEW ROW TO THE ORIGINAL DATAFRAME\n",
        "\n",
        "df = df.append(new_df)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>orange</th>\n",
              "      <th>tomato</th>\n",
              "      <th>grapes</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>7</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personC</th>\n",
              "      <td>15</td>\n",
              "      <td>17.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  orange  tomato  grapes  corn  vegies\n",
              "personA     20    15.0    30.0     NaN     NaN     5     NaN\n",
              "personB     10     NaN     NaN    17.0    35.0     7    24.0\n",
              "personC     15    17.0     5.0     NaN     NaN     3     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4Dn0yQZczZg"
      },
      "source": [
        "It is also possible to insert columns in the DataFrame using **insert method** to **any location we want**.\n",
        "The insert method allows us to specify:\n",
        "* the location,\n",
        "* label, and\n",
        "* the data \n",
        "\n",
        "of the column that we want to add."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sgr6NIVNiIoD",
        "outputId": "de3f47f7-3f67-475f-d4be-1c4898f9725d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "df.insert(5, 'ginger', [7,11,13])\n",
        "# THE FIRST ARGUMENT IS LOCATION OF THE COLUMN\n",
        "# THE SECOND ARGUMENT IS THE LABEL FOR THE COLUMN\n",
        "# AND THE LAST ONE IS THE DATA FOR THE COLUMN\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>orange</th>\n",
              "      <th>tomato</th>\n",
              "      <th>grapes</th>\n",
              "      <th>ginger</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personC</th>\n",
              "      <td>15</td>\n",
              "      <td>17.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  orange  tomato  grapes  ginger  corn  vegies\n",
              "personA     20    15.0    30.0     NaN     NaN       7     5     NaN\n",
              "personB     10     NaN     NaN    17.0    35.0      11     7    24.0\n",
              "personC     15    17.0     5.0     NaN     NaN      13     3     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYH4G-Nzii2X"
      },
      "source": [
        "We can also delete the columns and rows using two methods:\n",
        "* pop\n",
        "* drop\n",
        "\n",
        "> **NOTE:** The pop method allows us to delete columns while the drop method allows us to delete both rows and columns by using the access keyword."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpFVxHj7i5Ms",
        "outputId": "53616799-314c-4162-c36e-cbde1badfdf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "df.pop('ginger')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>orange</th>\n",
              "      <th>tomato</th>\n",
              "      <th>grapes</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>7</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personC</th>\n",
              "      <td>15</td>\n",
              "      <td>17.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  orange  tomato  grapes  corn  vegies\n",
              "personA     20    15.0    30.0     NaN     NaN     5     NaN\n",
              "personB     10     NaN     NaN    17.0    35.0     7    24.0\n",
              "personC     15    17.0     5.0     NaN     NaN     3     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgA-bxpbjXdQ",
        "outputId": "679996a5-ffad-4e61-d1c7-ca37f155847c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "df = df.drop(['grapes', 'orange'], axis=1)   # AXIS = 1 MEANS WE ARE REMOVING COLUMNS\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>tomato</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personB</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>7</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>personC</th>\n",
              "      <td>15</td>\n",
              "      <td>17.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  tomato  corn  vegies\n",
              "personA     20    15.0     NaN     5     NaN\n",
              "personB     10     NaN    17.0     7    24.0\n",
              "personC     15    17.0     NaN     3     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0i5z_WGjlwp",
        "outputId": "01ba7f35-7056-45c9-f942-c50d821a9ca1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "df = df.drop(['personB', 'personC'], axis=0)   # AXIS = 0 MEANS WE ARE REMOVING ROWS\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>banana</th>\n",
              "      <th>tomato</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  banana  tomato  corn  vegies\n",
              "personA     20    15.0     NaN     5     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-skNMSCBj-ct"
      },
      "source": [
        "To change the name of any row or any column,  we use the rename method\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbFi9UlCkIZT",
        "outputId": "fe6716ec-959a-4451-cd9b-bcd121d4916f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "df  = df.rename(columns={'banana':'pineapple'})\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>pineapple</th>\n",
              "      <th>tomato</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>personA</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         apple  pineapple  tomato  corn  vegies\n",
              "personA     20       15.0     NaN     5     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gl6gWUYpkWg_"
      },
      "source": [
        "Now let's change the name of the row"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXccXPQEkhnW",
        "outputId": "015ba460-45d3-474b-c26f-0878a45997ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "df  = df.rename(index={'personA':'A'})\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>pineapple</th>\n",
              "      <th>tomato</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   apple  pineapple  tomato  corn  vegies\n",
              "A     20       15.0     NaN     5     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cgEIw-iekpID"
      },
      "source": [
        "We  can also set index to be one of the existing columns of the DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDrcI63Wk7jU",
        "outputId": "33510d2a-6f71-4a76-89dd-ff893341a45b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "df = df.set_index('tomato')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apple</th>\n",
              "      <th>pineapple</th>\n",
              "      <th>corn</th>\n",
              "      <th>vegies</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tomato</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        apple  pineapple  corn  vegies\n",
              "tomato                                \n",
              "NaN        20       15.0     5     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOMhypUElCFR"
      },
      "source": [
        "Now we have the column tomato as our index label\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkwJJS6nlKZ9"
      },
      "source": [
        "<br>\n",
        "<hr>\n",
        "<h4><b>2.b Cleaning Data</b></h4>\n",
        "\n",
        "Before using the data for making predictions or using it for analysis, we first need to clean the data. By cleaning data, it means to detect and correct thr bad data\n"
      ]
    }
  ]
}