Numpy,_Pandas,_Matplotlib_and_Saeborn_Task.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Augusta02/Data-Preprocessing-Visualization-Projects/blob/main/Numpy%2C_Pandas%2C_Matplotlib_and_Saeborn_Task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is my second SheCode Africa task. The focus is on different Python libraries Numpy, Pandas, Matplotlib and Seaborn. The datasets used for Pandas and Matlpotlib are different.\n"
      ],
      "metadata": {
        "id": "2wxTtWdmjFw9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **NUMPY**"
      ],
      "metadata": {
        "id": "CnFHe_h_jjyS"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppFVtzc1qiyP"
      },
      "source": [
        "Exercise 1: Create a 5X2 integer array from a range between 100 to 200 such that the difference between each element is 10\n",
        "Expected Output:\n",
        "\n",
        "Creating 5X2 array using numpy.arange\n",
        "[[100 110]\n",
        " [120 130]\n",
        " [140 150]\n",
        " [160 170]\n",
        " [180 190]]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_-t1FVwqbOL",
        "outputId": "98933f5d-e7e5-421b-b952-9b61e5cb6d46"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[100, 110],\n",
              "       [120, 130],\n",
              "       [140, 150],\n",
              "       [160, 170],\n",
              "       [180, 190]])"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# import numpy library \n",
        "import numpy as np\n",
        "# arange() is used to create a range of values \n",
        "# reshape() is used to reshape the array\n",
        "arr = np.arange(100,200,10).reshape(5,2)\n",
        "arr"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EgP-HxfzrcA-"
      },
      "source": [
        "Exercise 2: Following is the provided numPy array. return array of items in the third column from all rows\n",
        "import numpy\n",
        "sampleArray = numpy.array([[11 ,22, 33], [44, 55, 66], [77, 88, 99]])\n",
        "Expected Output:\n",
        "\n",
        "Printing Input Array\n",
        "[[11 22 33]\n",
        " [44 55 66]\n",
        " [77 88 99]]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pt-f1psmZDH7",
        "outputId": "69016149-5f51-497b-fd12-a0773eb8c45b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([33, 66, 99])"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sampleArray= np.array([[11,22,33], [44,55,66], [77,88,99]])\n",
        "# [:] returns all the elements in the rows and [2] returns the elements in the column second index\n",
        "# using the comma single brackets [:,2]\n",
        "newArray= sampleArray[:, 2]\n",
        "newArray"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQAKKE7HWkBe"
      },
      "source": [
        "Add the following two NumPy arrays and Modify a result array by calculating the square of each element\n",
        "import numpy\n",
        "\n",
        "arrayOne = numpy.array([[5, 6, 9], [21 ,18, 27]])\n",
        "arrayTwo = numpy.array([[15 ,33, 24], [4 ,7, 1]])\n",
        "Expected Output:\n",
        "\n",
        "addition of two arrays is \n",
        "\n",
        "[[20 39 33]\n",
        " [25 25 28]]\n",
        "\n",
        "Result array after calculating the square root of all elements\n",
        "\n",
        "[[ 400 1521 1089]\n",
        " [ 625  625  784]]\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLnF3PlIWmYe",
        "outputId": "6c67ee05-f7f7-43db-cb9e-9472aa7febf3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[20 39 33]\n",
            " [25 25 28]]\n",
            "[[ 400 1521 1089]\n",
            " [ 625  625  784]]\n"
          ]
        }
      ],
      "source": [
        "arrayOne= np.array([[5,6,9], [21,18,27]])\n",
        "arrayTwo= np.array([[15,33,24], [4,7,1]])\n",
        "\n",
        "# add arrayOne and arrayTwo and assign to new_arr\n",
        "new_arr= arrayOne + arrayTwo\n",
        "# print new_arr\n",
        "print(new_arr)\n",
        "\n",
        "# new_arr ** 2 returns the square of the elements in the array\n",
        "sqr_new_arr= new_arr ** 2\n",
        "# print sqr_new_arr\n",
        "print(sqr_new_arr)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **PANDAS**"
      ],
      "metadata": {
        "id": "fAGRsCYmQFfr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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          "height": 74
        },
        "id": "FAvxWU2ggcZs",
        "outputId": "e3995e59-c7d1-4c71-c19e-7ab384b25545"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-efb51b57-b763-49c1-b4fd-ab9ff23459a2\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-efb51b57-b763-49c1-b4fd-ab9ff23459a2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Automobile_data.csv to Automobile_data.csv\n"
          ]
        }
      ],
      "source": [
        "# first import the necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "# then import the data\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 1**: From given data set print first and last five rows\n",
        "\n"
      ],
      "metadata": {
        "id": "4WFdf5mvP27P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# read the csv file\n",
        "df= pd.read_csv('Automobile_data.csv')\n",
        "# head() returns the first 5 rows of a dataframe\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aLWD-IQGCbW_",
        "outputId": "b2afade0-fda8-4c83-f2db-8f57b636e754"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        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>index</th>\n",
              "      <th>company</th>\n",
              "      <th>body-style</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>average-mileage</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>convertible</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</td>\n",
              "      <td>111</td>\n",
              "      <td>21</td>\n",
              "      <td>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>convertible</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</td>\n",
              "      <td>111</td>\n",
              "      <td>21</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>94.5</td>\n",
              "      <td>171.2</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</td>\n",
              "      <td>154</td>\n",
              "      <td>19</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>audi</td>\n",
              "      <td>sedan</td>\n",
              "      <td>99.8</td>\n",
              "      <td>176.6</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>102</td>\n",
              "      <td>24</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>audi</td>\n",
              "      <td>sedan</td>\n",
              "      <td>99.4</td>\n",
              "      <td>176.6</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>115</td>\n",
              "      <td>18</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77cc6254-492c-4b84-a533-2212f7b14f90')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-77cc6254-492c-4b84-a533-2212f7b14f90 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-77cc6254-492c-4b84-a533-2212f7b14f90');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   index      company   body-style  ...  horsepower  average-mileage    price\n",
              "0      0  alfa-romero  convertible  ...         111               21  13495.0\n",
              "1      1  alfa-romero  convertible  ...         111               21  16500.0\n",
              "2      2  alfa-romero    hatchback  ...         154               19  16500.0\n",
              "3      3         audi        sedan  ...         102               24  13950.0\n",
              "4      4         audi        sedan  ...         115               18  17450.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 2: \n",
        "Clean data and update the CSV file\n",
        "Replace all column values which contain ‘?’ and n.a with NaN.\n",
        "\n"
      ],
      "metadata": {
        "id": "_lCX6FHmQmpq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use replace() to replace values\n",
        "replace_value= df.replace(to_replace=\" \", value=\"np.NaN\", inplace=True)\n",
        "replace_value"
      ],
      "metadata": {
        "id": "vVih36LuCeTR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking if the NaN value is inplace\n",
        "row_index= df.loc[23]\n",
        "row_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1h7_b3BCndo",
        "outputId": "e271e3c6-4133-4912-93b2-a99302c8d689"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "index                  32\n",
              "company             isuzu\n",
              "body-style          sedan\n",
              "wheel-base           94.5\n",
              "length              155.9\n",
              "engine-type           ohc\n",
              "num-of-cylinders     four\n",
              "horsepower             70\n",
              "average-mileage        38\n",
              "price                 NaN\n",
              "Name: 23, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Question 3:**\n",
        "\n",
        " Find the most expensive car company name\n",
        "Print the most expensive car’s company name and price.\n"
      ],
      "metadata": {
        "id": "-J2DIT5hQ43-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# max() returns the highest/expensive car in the dataframe\n",
        "expensive_car= df.max()\n",
        "expensive_car[['company','price']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcaMUFe2DwFh",
        "outputId": "78221771-5b81-4941-a51d-13231c62654a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "company    volvo\n",
              "price      45400\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop the index since it was confusing and not helpful to access data points\n",
        "df.drop('index', axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "vRbY0qkBG67j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "PREC25l2IIiC",
        "outputId": "a84c782c-270a-45db-d976-3c096f5bfc1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-00fbd6d9-fe7c-4923-bf90-ba60a8969524\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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>company</th>\n",
              "      <th>body-style</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>average-mileage</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>convertible</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</td>\n",
              "      <td>111</td>\n",
              "      <td>21</td>\n",
              "      <td>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>convertible</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>dohc</td>\n",
              "      <td>four</td>\n",
              "      <td>111</td>\n",
              "      <td>21</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>94.5</td>\n",
              "      <td>171.2</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>six</td>\n",
              "      <td>154</td>\n",
              "      <td>19</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>audi</td>\n",
              "      <td>sedan</td>\n",
              "      <td>99.8</td>\n",
              "      <td>176.6</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>102</td>\n",
              "      <td>24</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>audi</td>\n",
              "      <td>sedan</td>\n",
              "      <td>99.4</td>\n",
              "      <td>176.6</td>\n",
              "      <td>ohc</td>\n",
              "      <td>five</td>\n",
              "      <td>115</td>\n",
              "      <td>18</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>volkswagen</td>\n",
              "      <td>sedan</td>\n",
              "      <td>97.3</td>\n",
              "      <td>171.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>85</td>\n",
              "      <td>27</td>\n",
              "      <td>7975.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>volkswagen</td>\n",
              "      <td>sedan</td>\n",
              "      <td>97.3</td>\n",
              "      <td>171.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>52</td>\n",
              "      <td>37</td>\n",
              "      <td>7995.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>volkswagen</td>\n",
              "      <td>sedan</td>\n",
              "      <td>97.3</td>\n",
              "      <td>171.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>100</td>\n",
              "      <td>26</td>\n",
              "      <td>9995.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>volvo</td>\n",
              "      <td>sedan</td>\n",
              "      <td>104.3</td>\n",
              "      <td>188.8</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>114</td>\n",
              "      <td>23</td>\n",
              "      <td>12940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>volvo</td>\n",
              "      <td>wagon</td>\n",
              "      <td>104.3</td>\n",
              "      <td>188.8</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>114</td>\n",
              "      <td>23</td>\n",
              "      <td>13415.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00fbd6d9-fe7c-4923-bf90-ba60a8969524')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-00fbd6d9-fe7c-4923-bf90-ba60a8969524 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-00fbd6d9-fe7c-4923-bf90-ba60a8969524');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        company   body-style  wheel-base  ...  horsepower average-mileage    price\n",
              "0   alfa-romero  convertible        88.6  ...         111              21  13495.0\n",
              "1   alfa-romero  convertible        88.6  ...         111              21  16500.0\n",
              "2   alfa-romero    hatchback        94.5  ...         154              19  16500.0\n",
              "3          audi        sedan        99.8  ...         102              24  13950.0\n",
              "4          audi        sedan        99.4  ...         115              18  17450.0\n",
              "..          ...          ...         ...  ...         ...             ...      ...\n",
              "56   volkswagen        sedan        97.3  ...          85              27   7975.0\n",
              "57   volkswagen        sedan        97.3  ...          52              37   7995.0\n",
              "58   volkswagen        sedan        97.3  ...         100              26   9995.0\n",
              "59        volvo        sedan       104.3  ...         114              23  12940.0\n",
              "60        volvo        wagon       104.3  ...         114              23  13415.0\n",
              "\n",
              "[61 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Question 4:**\n",
        " Print All Toyota Cars details\n"
      ],
      "metadata": {
        "id": "3RW7x5W4RsSl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# use the slicing index notatation to get all toyota cars\n",
        "toy= df[48:55]\n",
        "toy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Zzf7ltpEJaJb",
        "outputId": "359f45cf-88e2-48f0-c4e2-c701b4e181f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-44a8c889-430d-4654-81de-3ef93ac0383b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>company</th>\n",
              "      <th>body-style</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>num-of-cylinders</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>average-mileage</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>toyota</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>95.7</td>\n",
              "      <td>158.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>62</td>\n",
              "      <td>35</td>\n",
              "      <td>5348.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>toyota</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>95.7</td>\n",
              "      <td>158.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>62</td>\n",
              "      <td>31</td>\n",
              "      <td>6338.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>toyota</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>95.7</td>\n",
              "      <td>158.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>62</td>\n",
              "      <td>31</td>\n",
              "      <td>6488.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>toyota</td>\n",
              "      <td>wagon</td>\n",
              "      <td>95.7</td>\n",
              "      <td>169.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>62</td>\n",
              "      <td>31</td>\n",
              "      <td>6918.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>toyota</td>\n",
              "      <td>wagon</td>\n",
              "      <td>95.7</td>\n",
              "      <td>169.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>62</td>\n",
              "      <td>27</td>\n",
              "      <td>7898.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>toyota</td>\n",
              "      <td>wagon</td>\n",
              "      <td>95.7</td>\n",
              "      <td>169.7</td>\n",
              "      <td>ohc</td>\n",
              "      <td>four</td>\n",
              "      <td>62</td>\n",
              "      <td>27</td>\n",
              "      <td>8778.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>toyota</td>\n",
              "      <td>wagon</td>\n",
              "      <td>104.5</td>\n",
              "      <td>187.8</td>\n",
              "      <td>dohc</td>\n",
              "      <td>six</td>\n",
              "      <td>156</td>\n",
              "      <td>19</td>\n",
              "      <td>15750.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44a8c889-430d-4654-81de-3ef93ac0383b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-44a8c889-430d-4654-81de-3ef93ac0383b button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-44a8c889-430d-4654-81de-3ef93ac0383b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   company body-style  wheel-base  ...  horsepower average-mileage    price\n",
              "48  toyota  hatchback        95.7  ...          62              35   5348.0\n",
              "49  toyota  hatchback        95.7  ...          62              31   6338.0\n",
              "50  toyota  hatchback        95.7  ...          62              31   6488.0\n",
              "51  toyota      wagon        95.7  ...          62              31   6918.0\n",
              "52  toyota      wagon        95.7  ...          62              27   7898.0\n",
              "53  toyota      wagon        95.7  ...          62              27   8778.0\n",
              "54  toyota      wagon       104.5  ...         156              19  15750.0\n",
              "\n",
              "[7 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# **Question 5:**\n",
        " Count total cars per company"
      ],
      "metadata": {
        "id": "hpbflA3oR45t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# value.counts() returns the frequency or count of the cars in each company\n",
        "tot_count= df['company'].value_counts()\n",
        "tot_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsCaPKhAKZA0",
        "outputId": "70fa0b9c-99df-446f-bc72-9c8603393466"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "toyota           7\n",
              "bmw              6\n",
              "mazda            5\n",
              "nissan           5\n",
              "volkswagen       4\n",
              "audi             4\n",
              "mitsubishi       4\n",
              "mercedes-benz    4\n",
              "isuzu            3\n",
              "jaguar           3\n",
              "porsche          3\n",
              "chevrolet        3\n",
              "honda            3\n",
              "alfa-romero      3\n",
              "dodge            2\n",
              "volvo            2\n",
              "Name: company, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 6: \n",
        "Find each company’s Highest price car"
      ],
      "metadata": {
        "id": "745lHTODSObx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# groupby() allows us to group elements in the row based on the column the elements are located\n",
        "# max() returns the highest element in the dataframe\n",
        "max_comp= df.groupby('company').max()\n",
        "\n",
        "\n",
        "# max_comp['price'] returns the column maximum price of each car\n",
        "max_price= max_comp['price']\n",
        "max_price\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QT-5BVI5K6g6",
        "outputId": "cf1e1f13-c092-41f8-bb62-fddeb35735c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "company\n",
              "alfa-romero      16500.0\n",
              "audi             18920.0\n",
              "bmw              41315.0\n",
              "chevrolet         6575.0\n",
              "dodge             6377.0\n",
              "honda            12945.0\n",
              "isuzu             6785.0\n",
              "jaguar           36000.0\n",
              "mazda            18344.0\n",
              "mercedes-benz    45400.0\n",
              "mitsubishi        8189.0\n",
              "nissan           13499.0\n",
              "porsche          37028.0\n",
              "toyota           15750.0\n",
              "volkswagen        9995.0\n",
              "volvo            13415.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Question 7:**\n",
        "\n",
        " Find the average mileage of each car making company"
      ],
      "metadata": {
        "id": "mfySiBCJTaHM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# groupby() allows us to group elements in the row based on the column the elements are located\n",
        "# mean() returns the mean vaule of each company\n",
        "avg_mile= df.groupby('company').mean('average-mileage')\n",
        "avg_mile\n",
        "\n",
        "# avg_mile['average-mileage'] returns the column mean average-mileage of each company\n",
        "\n",
        "mean_avg= avg_mile['average-mileage']\n",
        "mean_avg\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1PA2cR2NM7h4",
        "outputId": "c41a9e0e-943b-41d3-d6f6-76b9bd3f3b05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "company\n",
              "alfa-romero      20.333333\n",
              "audi             20.000000\n",
              "bmw              19.000000\n",
              "chevrolet        41.000000\n",
              "dodge            31.000000\n",
              "honda            26.333333\n",
              "isuzu            33.333333\n",
              "jaguar           14.333333\n",
              "mazda            28.000000\n",
              "mercedes-benz    18.000000\n",
              "mitsubishi       29.500000\n",
              "nissan           31.400000\n",
              "porsche          17.000000\n",
              "toyota           28.714286\n",
              "volkswagen       31.750000\n",
              "volvo            23.000000\n",
              "Name: average-mileage, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Question 8:**\n",
        " Sort all cars by Price column"
      ],
      "metadata": {
        "id": "2FSsggKgUEnF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# sort_values() allows us arrange the values from smallest to the largest\n",
        "sort_car= df.sort_values('price')\n",
        "sort_car\n",
        "\n",
        "# sort_car['prices'] returns the sorted values of prices\n",
        "sorted_cars = sort_car['price']\n",
        "sorted_cars"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YiIEhbKN7g-",
        "outputId": "658cdfa3-76ab-41d8-d62c-03cee0c382a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13     5151.0\n",
              "27     5195.0\n",
              "48     5348.0\n",
              "36     5389.0\n",
              "28     6095.0\n",
              "       ...   \n",
              "11    41315.0\n",
              "35    45400.0\n",
              "22        NaN\n",
              "23        NaN\n",
              "47        NaN\n",
              "Name: price, Length: 61, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Question 9:**\n",
        "\n",
        " Concatenate two data frames using the following conditions\n",
        "Create two data frames using the following two Dicts, Concatenate those two data frames and create a key for each data frame.\n",
        "\n",
        "GermanCars = {'Company': ['Ford', 'Mercedes', 'BMV', 'Audi'], 'Price': [23845, 171995, 135925 , 71400]}\n",
        "japaneseCars = {'Company': ['Toyota', 'Honda', 'Nissan', 'Mitsubishi '], 'Price': [29995, 23600, 61500 , 58900]}\n"
      ],
      "metadata": {
        "id": "aKaNLsrcOoh9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "GermanCars = {'Company': ['Ford', 'Mercedes', 'BMV', 'Audi'], 'Price': [23845, 171995, 135925 , 71400]}\n",
        "japaneseCars = {'Company': ['Toyota', 'Honda', 'Nissan', 'Mitsubishi '], 'Price': [29995, 23600, 61500 , 58900]}\n",
        "\n",
        "# change GermanCars to a DataFrame\n",
        "GC= pd.DataFrame(GermanCars)\n",
        "\n",
        "# change japaneseCars to a DataFrame\n",
        "JC= pd.DataFrame(japaneseCars)\n",
        "\n",
        "# concat() allows us glue GC and JC to become one DataFrame\n",
        "# the arguement Keys help us to identify which are GermanCars and which are Japanese Cars\n",
        "GC_JC= pd.concat([GC,JC],keys=['GC','JC'])\n",
        "GC_JC\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_AnhS3EjOsZV",
        "outputId": "b959812a-9b5f-4dc2-a076-deec25d0a312"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-39a71e91-c257-45d2-83e4-b319ac6e6eb3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">GC</th>\n",
              "      <th>0</th>\n",
              "      <td>Ford</td>\n",
              "      <td>23845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mercedes</td>\n",
              "      <td>171995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMV</td>\n",
              "      <td>135925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Audi</td>\n",
              "      <td>71400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">JC</th>\n",
              "      <th>0</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>29995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Honda</td>\n",
              "      <td>23600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nissan</td>\n",
              "      <td>61500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mitsubishi</td>\n",
              "      <td>58900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39a71e91-c257-45d2-83e4-b319ac6e6eb3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-39a71e91-c257-45d2-83e4-b319ac6e6eb3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-39a71e91-c257-45d2-83e4-b319ac6e6eb3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          Company   Price\n",
              "GC 0         Ford   23845\n",
              "   1     Mercedes  171995\n",
              "   2          BMV  135925\n",
              "   3         Audi   71400\n",
              "JC 0       Toyota   29995\n",
              "   1        Honda   23600\n",
              "   2       Nissan   61500\n",
              "   3  Mitsubishi    58900"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Question 10:**\n",
        "\n",
        " Merge two data frames using the following condition\n",
        "Create two data frames using the following two Dicts, Merge two data frames, and append the second data frame as a new column to the first data frame.\n",
        "\n",
        "Car_Price = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'Price': [23845, 17995, 135925 , 71400]}\n",
        "car_Horsepower = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'horsepower': [141, 80, 182 , 160]}\n"
      ],
      "metadata": {
        "id": "7akNrFxEXQQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Car_Price = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'Price': [23845, 17995, 135925 , 71400]} \n",
        "car_Horsepower = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'horsepower': [141, 80, 182 , 160]}\n",
        "\n",
        "# make Car_Price a DataFrame\n",
        "CP= pd.DataFrame(Car_Price)\n",
        "\n",
        "# make car_HorsePower a DataFrame\n",
        "C_HP= pd.DataFrame(car_Horsepower)\n",
        "\n",
        "CH_P= CP.merge(C_HP, left_on='Company', right_on='Company')\n",
        "CH_P"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "Pk20I-BSYE_O",
        "outputId": "8ed999f8-db11-451b-c976-513eb8cf36f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-331a910a-69d5-4731-bbb1-dae8559eafa5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Price</th>\n",
              "      <th>horsepower</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>23845</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Honda</td>\n",
              "      <td>17995</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMV</td>\n",
              "      <td>135925</td>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Audi</td>\n",
              "      <td>71400</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-331a910a-69d5-4731-bbb1-dae8559eafa5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-331a910a-69d5-4731-bbb1-dae8559eafa5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-331a910a-69d5-4731-bbb1-dae8559eafa5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Company   Price  horsepower\n",
              "0  Toyota   23845         141\n",
              "1   Honda   17995          80\n",
              "2     BMV  135925         182\n",
              "3    Audi   71400         160"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MATPLOTLIB & SEABORN**"
      ],
      "metadata": {
        "id": "bCu51ojcjskp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "dvlOuCEBj0sz",
        "outputId": "1db80601-21ee-4e3c-f924-a1bd9d21fea2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4ec888a4-292b-4662-8032-82dcf909c0ce\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-4ec888a4-292b-4662-8032-82dcf909c0ce\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving company_sales_data.csv to company_sales_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "D6obkIYgymR-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "import pandas as pd\n",
        "df= pd.read_csv(io.BytesIO(uploaded['company_sales_data.csv']))\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "xFeQvlZHkybX",
        "outputId": "2159f35c-4fd3-4881-e5c5-023a94e9a23e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d7f55087-1256-4708-93c2-2260da4eee70\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>month_number</th>\n",
              "      <th>facecream</th>\n",
              "      <th>facewash</th>\n",
              "      <th>toothpaste</th>\n",
              "      <th>bathingsoap</th>\n",
              "      <th>shampoo</th>\n",
              "      <th>moisturizer</th>\n",
              "      <th>total_units</th>\n",
              "      <th>total_profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2500</td>\n",
              "      <td>1500</td>\n",
              "      <td>5200</td>\n",
              "      <td>9200</td>\n",
              "      <td>1200</td>\n",
              "      <td>1500</td>\n",
              "      <td>21100</td>\n",
              "      <td>211000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2630</td>\n",
              "      <td>1200</td>\n",
              "      <td>5100</td>\n",
              "      <td>6100</td>\n",
              "      <td>2100</td>\n",
              "      <td>1200</td>\n",
              "      <td>18330</td>\n",
              "      <td>183300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2140</td>\n",
              "      <td>1340</td>\n",
              "      <td>4550</td>\n",
              "      <td>9550</td>\n",
              "      <td>3550</td>\n",
              "      <td>1340</td>\n",
              "      <td>22470</td>\n",
              "      <td>224700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3400</td>\n",
              "      <td>1130</td>\n",
              "      <td>5870</td>\n",
              "      <td>8870</td>\n",
              "      <td>1870</td>\n",
              "      <td>1130</td>\n",
              "      <td>22270</td>\n",
              "      <td>222700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3600</td>\n",
              "      <td>1740</td>\n",
              "      <td>4560</td>\n",
              "      <td>7760</td>\n",
              "      <td>1560</td>\n",
              "      <td>1740</td>\n",
              "      <td>20960</td>\n",
              "      <td>209600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>2760</td>\n",
              "      <td>1555</td>\n",
              "      <td>4890</td>\n",
              "      <td>7490</td>\n",
              "      <td>1890</td>\n",
              "      <td>1555</td>\n",
              "      <td>20140</td>\n",
              "      <td>201400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>2980</td>\n",
              "      <td>1120</td>\n",
              "      <td>4780</td>\n",
              "      <td>8980</td>\n",
              "      <td>1780</td>\n",
              "      <td>1120</td>\n",
              "      <td>29550</td>\n",
              "      <td>295500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>3700</td>\n",
              "      <td>1400</td>\n",
              "      <td>5860</td>\n",
              "      <td>9960</td>\n",
              "      <td>2860</td>\n",
              "      <td>1400</td>\n",
              "      <td>36140</td>\n",
              "      <td>361400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>3540</td>\n",
              "      <td>1780</td>\n",
              "      <td>6100</td>\n",
              "      <td>8100</td>\n",
              "      <td>2100</td>\n",
              "      <td>1780</td>\n",
              "      <td>23400</td>\n",
              "      <td>234000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1990</td>\n",
              "      <td>1890</td>\n",
              "      <td>8300</td>\n",
              "      <td>10300</td>\n",
              "      <td>2300</td>\n",
              "      <td>1890</td>\n",
              "      <td>26670</td>\n",
              "      <td>266700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>2340</td>\n",
              "      <td>2100</td>\n",
              "      <td>7300</td>\n",
              "      <td>13300</td>\n",
              "      <td>2400</td>\n",
              "      <td>2100</td>\n",
              "      <td>41280</td>\n",
              "      <td>412800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>2900</td>\n",
              "      <td>1760</td>\n",
              "      <td>7400</td>\n",
              "      <td>14400</td>\n",
              "      <td>1800</td>\n",
              "      <td>1760</td>\n",
              "      <td>30020</td>\n",
              "      <td>300200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7f55087-1256-4708-93c2-2260da4eee70')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d7f55087-1256-4708-93c2-2260da4eee70 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d7f55087-1256-4708-93c2-2260da4eee70');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    month_number  facecream  facewash  ...  moisturizer  total_units  total_profit\n",
              "0              1       2500      1500  ...         1500        21100        211000\n",
              "1              2       2630      1200  ...         1200        18330        183300\n",
              "2              3       2140      1340  ...         1340        22470        224700\n",
              "3              4       3400      1130  ...         1130        22270        222700\n",
              "4              5       3600      1740  ...         1740        20960        209600\n",
              "5              6       2760      1555  ...         1555        20140        201400\n",
              "6              7       2980      1120  ...         1120        29550        295500\n",
              "7              8       3700      1400  ...         1400        36140        361400\n",
              "8              9       3540      1780  ...         1780        23400        234000\n",
              "9             10       1990      1890  ...         1890        26670        266700\n",
              "10            11       2340      2100  ...         2100        41280        412800\n",
              "11            12       2900      1760  ...         1760        30020        300200\n",
              "\n",
              "[12 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exercise 1:** \n",
        "\n",
        "Read Total profit of all months and show it using a line plot\n",
        "Total profit data provided for each month. Generated line plot must include the following properties: –\n",
        "X label name = Month Number\n",
        "Y label name = Total profit\n"
      ],
      "metadata": {
        "id": "Z05An-LRzATB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define x and y axis\n",
        "x= \"month_number\"\n",
        "y= \"total_profit\" \n",
        "\n",
        "# plot a simple line plot\n",
        "plt.plot(x,y, data= df)\n",
        "plt.xlabel('Month Number')\n",
        "plt.ylabel('Total Porfit')\n",
        "plt.title('Company Profit Per Month')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "NhVft222y_Sb",
        "outputId": "4294cd67-6cfa-42a9-8943-145561f9c1cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Company Profit Per Month')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exercise Question 2:**\n",
        "\n",
        " Read all product sales data and show it  using a multi line plot\n",
        "Display the number of units sold per month for each product using multi line plots. (i.e., Separate Plotline for each product ).\n",
        "\n"
      ],
      "metadata": {
        "id": "jZu2dad33zcW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define the figure size of the plot\n",
        "fig= plt.figure(figsize=(12,6))\n",
        "\n",
        "# convert series to list\n",
        "monthList= df['month_number'].tolist()\n",
        "FaceCreamSalesData= df['facecream'].tolist()\n",
        "FaceWashSalesData= df['facewash'].tolist()\n",
        "ToothPasteSalesData= df['toothpaste'].tolist()\n",
        "BathingSoapSalesData= df['bathingsoap'].tolist()\n",
        "ShampooSalesData= df['shampoo'].tolist()\n",
        "MoisturizerSalesData= df['moisturizer'].tolist()\n",
        "\n",
        "# plot line for each product, use marker to define there data points\n",
        "plt.plot(monthList, FaceCreamSalesData, label='FaceCreamSalesData', marker='o', linewidth=3)\n",
        "plt.plot(monthList, FaceWashSalesData, label='FaceWashSalesData', marker= 'o', linewidth= 3)\n",
        "plt.plot(monthList, ToothPasteSalesData, label='ToothPasteSalesData', marker='o', linewidth=3)\n",
        "plt.plot(monthList, BathingSoapSalesData, label='BathingSoapSalesData', marker= 'o', linewidth=3)\n",
        "plt.plot(monthList, ShampooSalesData, label='ShampooSalesData', marker='o', linewidth=3)\n",
        "plt.plot(monthList, MoisturizerSalesData, label='MoisturizerSalesData', marker='o', linewidth=3)\n",
        "\n",
        "# use legend to show the different products\n",
        "plt.legend(loc=0)\n",
        "# assign the x label\n",
        "plt.xlabel('Month Number')\n",
        "# assign the y label\n",
        "plt.ylabel('Sales Units In Number')\n",
        "# assign the title of the plot\n",
        "plt.title('Sales Data')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "dzUtNBv26zXm",
        "outputId": "1358a9c6-31ba-4373-d793-8fb61200fccf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Sales Data')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exercise Question 3:**\n",
        "\n",
        " Read toothpaste sales data of each month and show it using a scatter plot\n",
        "Also, add a grid in the plot. gridline style should “–“.\n"
      ],
      "metadata": {
        "id": "rLEDXKnxDXMi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define x and y axis\n",
        "x= monthList\n",
        "y= ToothPasteSalesData\n",
        "\n",
        "# plot a scatter plot\n",
        "plt.scatter(x, y, label='ToothPasteSalesData')\n",
        "\n",
        "# add grid lines\n",
        "plt.grid(True, linewidth=2, linestyle='--')\n",
        "\n",
        "# assign the x label\n",
        "plt.xlabel('Month Number')\n",
        "# assign the y label\n",
        "plt.ylabel('Number of Units Sold')\n",
        "# assign the title of the plot\n",
        "plt.title('Toothpaste Sales Data')\n",
        "plt.legend(loc=0)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "uaOjCsLnDcSI",
        "outputId": "bdefdf1f-c380-4a93-938d-b441c5735936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7b090db890>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exercise 5**\n",
        "\n",
        " Calculate total sale data for last year for each product and show it using a Pie chart\n"
      ],
      "metadata": {
        "id": "HLLiiCeuGJdp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "totalSales= [df['facecream'].sum(), df['facewash'].sum(), df['toothpaste'].sum(), df['bathingsoap'].sum(), df['shampoo'].sum(), df['moisturizer'].sum()]\n",
        "labels=['Face Cream', 'Face Wash', 'Tooth Paste', 'Bathing Soap', 'Shampoo', 'Moisturizer']\n",
        "plt.axis('equal')\n",
        "plt.pie(totalSales, labels=labels,autopct='%1.1f%%')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Sales Data')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "cgCyLIkSHpkJ",
        "outputId": "62850bd6-8b6d-4090-f0d9-89725cac9fcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Sales Data')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Numpy, Pandas, Matplotlib and Saeborn Task.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPMMRMD5Ib6hFjrEHSlYUqY",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}