3. Pie and Bar chart.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SKawsar/Data_Visualization_with_Python_s2/blob/main/Lecture_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ws2KB6e1urpT"
      },
      "source": [
        "# Lecture 03: Data Visualization: Pie Chart, Bar plot\n",
        "\n",
        "Instructor:\n",
        "<br>**Md Shahidullah Kawsar**\n",
        "<br>Data Scientist, IDARE, Houston, TX, USA\n",
        "\n",
        "#### Objectives:\n",
        "- Familiarity with matplotlib and seaborn\n",
        "- Data Analysis using NumPy and Pandas\n",
        "- How to find the number of unique values present in the DataFrame?\n",
        "- How to rename a column name is pandas DataFrame?\n",
        "- How to round the numeric values in a pandas column?\n",
        "- How to change the index of a DataFrame?\n",
        "- Data Visualization: Pie Chart, Bar plot\n",
        "- How to set a title, xlabel and ylabel of a plot?\n",
        "- How to change the range of x and y axis?\n",
        "- How to rotate the x and y tick labels?\n",
        "\n",
        "#### References:\n",
        "[1] How to count unique values in a column: https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html\n",
        "<br>[2] How to change a column name: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html\n",
        "<br>[3] How to change index of a pandas DataFrame: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reset_index.html\n",
        "<br>[4] How to round values in a DataFrame: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.round.html\n",
        "<br>[5] Subplot in matplotlib: https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subplots_demo.html\n",
        "<br>[6] How to add annotation in matplotlib Barplot: https://stackoverflow.com/questions/30228069/how-to-display-the-value-of-the-bar-on-each-bar-with-pyplot-barh\n",
        "<br>[7] Seaborn Barplot: https://seaborn.pydata.org/generated/seaborn.barplot.html\n",
        "<br>[8] How to change context of an image: https://seaborn.pydata.org/generated/seaborn.set_context.html\n",
        "<br>[9] How to add annotation in Seaborn Barplot: https://datavizpyr.com/how-to-annotate-bars-in-barplot-with-matplotlib-in-python/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bosmZEHgurpW"
      },
      "source": [
        "#### Import required libraries and packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qkvum6aYurpX"
      },
      "outputs": [],
      "source": [
        "# for data analysis\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# for data visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfA6a-t7urpX"
      },
      "source": [
        "#### Read a csv file as pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ttXFEapOurpY",
        "outputId": "3d7b5c98-41b3-43b1-d4ff-b073878a65f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        },
        {
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>year</th>\n",
              "      <th>price</th>\n",
              "      <th>transmission</th>\n",
              "      <th>mileage</th>\n",
              "      <th>fuelType</th>\n",
              "      <th>mpg</th>\n",
              "      <th>engineSize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5 Series</td>\n",
              "      <td>2014</td>\n",
              "      <td>11200</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>67068</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>57.6</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6 Series</td>\n",
              "      <td>2018</td>\n",
              "      <td>27000</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>14827</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>42.8</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5 Series</td>\n",
              "      <td>2016</td>\n",
              "      <td>16000</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>62794</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>51.4</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1 Series</td>\n",
              "      <td>2017</td>\n",
              "      <td>12750</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>26676</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>72.4</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7 Series</td>\n",
              "      <td>2014</td>\n",
              "      <td>14500</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>39554</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>50.4</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       model  year  price transmission  mileage fuelType   mpg  engineSize\n",
              "0   5 Series  2014  11200    Automatic    67068   Diesel  57.6         2.0\n",
              "1   6 Series  2018  27000    Automatic    14827   Petrol  42.8         2.0\n",
              "2   5 Series  2016  16000    Automatic    62794   Diesel  51.4         3.0\n",
              "3   1 Series  2017  12750    Automatic    26676   Diesel  72.4         1.5\n",
              "4   7 Series  2014  14500    Automatic    39554   Diesel  50.4         3.0"
            ]
          },
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          "output_type": "display_data"
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          "data": {
            "text/html": [
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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>model</th>\n",
              "      <th>year</th>\n",
              "      <th>price</th>\n",
              "      <th>transmission</th>\n",
              "      <th>mileage</th>\n",
              "      <th>fuelType</th>\n",
              "      <th>mpg</th>\n",
              "      <th>engineSize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10776</th>\n",
              "      <td>X3</td>\n",
              "      <td>2016</td>\n",
              "      <td>19000</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>40818</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10777</th>\n",
              "      <td>5 Series</td>\n",
              "      <td>2016</td>\n",
              "      <td>14600</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>42947</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>60.1</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10778</th>\n",
              "      <td>3 Series</td>\n",
              "      <td>2017</td>\n",
              "      <td>13100</td>\n",
              "      <td>Manual</td>\n",
              "      <td>25468</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>42.8</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10779</th>\n",
              "      <td>1 Series</td>\n",
              "      <td>2014</td>\n",
              "      <td>9930</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>45000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>64.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10780</th>\n",
              "      <td>X1</td>\n",
              "      <td>2017</td>\n",
              "      <td>15981</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>59432</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>57.6</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           model  year  price transmission  mileage fuelType   mpg  engineSize\n",
              "10776         X3  2016  19000    Automatic    40818   Diesel  54.3         2.0\n",
              "10777   5 Series  2016  14600    Automatic    42947   Diesel  60.1         2.0\n",
              "10778   3 Series  2017  13100       Manual    25468   Petrol  42.8         2.0\n",
              "10779   1 Series  2014   9930    Automatic    45000   Diesel  64.2         2.0\n",
              "10780         X1  2017  15981    Automatic    59432   Diesel  57.6         2.0"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = pd.read_csv(\"bmw.csv\")\n",
        "\n",
        "# bmw.xlsx\n",
        "# df = pd.read_excel(\"bmw.xlsx\")\n",
        "\n",
        "print(type(df))\n",
        "display(df.head())\n",
        "display(df.tail())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBbdvhogurpY"
      },
      "source": [
        "#### Check for missing values, data types of the columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kcc-lH-HurpY",
        "outputId": "edc5bb4f-f836-4112-cf8d-23cf5b7c8426"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10781 entries, 0 to 10780\n",
            "Data columns (total 8 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   model         10781 non-null  object \n",
            " 1   year          10781 non-null  int64  \n",
            " 2   price         10781 non-null  int64  \n",
            " 3   transmission  10781 non-null  object \n",
            " 4   mileage       10781 non-null  int64  \n",
            " 5   fuelType      10781 non-null  object \n",
            " 6   mpg           10781 non-null  float64\n",
            " 7   engineSize    10781 non-null  float64\n",
            "dtypes: float64(2), int64(3), object(3)\n",
            "memory usage: 673.9+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()\n",
        "\n",
        "# Nan - not a number"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dq2Og7lpA0Ap",
        "outputId": "85bb90b0-cd36-448e-8af1-6f68760eca73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(10781, 8)\n",
            "number of rows =  10781\n",
            "number of columns =  8\n"
          ]
        }
      ],
      "source": [
        "print(df.shape)\n",
        "\n",
        "print(\"number of rows = \", df.shape[0])\n",
        "print(\"number of columns = \", df.shape[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKKv7r4DurpZ"
      },
      "source": [
        "#### Count the number of unique values present in the 'year' column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Po05cr07urpZ",
        "outputId": "06ed6a36-7fca-4c06-94b5-8239c2def774"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2014 2018 2016 2017 2015 2019 2013 2020 2002 2004 2007 2008 2011 2012\n",
            " 2009 2006 2010 2003 2001 2005 2000 1999 1996 1997 1998]\n",
            "<class 'numpy.ndarray'>\n",
            "25\n",
            "2019    3485\n",
            "2016    1882\n",
            "2017    1721\n",
            "2015     922\n",
            "2018     848\n",
            "2020     733\n",
            "2014     501\n",
            "2013     357\n",
            "2012     119\n",
            "2011      51\n",
            "2010      41\n",
            "2009      30\n",
            "2008      23\n",
            "2007      16\n",
            "2006      14\n",
            "2004      12\n",
            "2002       6\n",
            "2005       6\n",
            "1999       4\n",
            "2001       3\n",
            "2003       2\n",
            "2000       2\n",
            "1996       1\n",
            "1997       1\n",
            "1998       1\n",
            "Name: year, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df[\"year\"].unique())\n",
        "print(type(df[\"year\"].unique()))\n",
        "print(len(df[\"year\"].unique()))\n",
        "\n",
        "# print(df[\"year\"].values)\n",
        "print(df[\"year\"].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mW-mfyDVurpZ"
      },
      "source": [
        "#### Count the number of unique values present in the 'model' column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nAYqdWUEurpa",
        "outputId": "68d7318c-8c74-441e-dc32-5755c49f26d4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " 3 Series    2443\n",
            " 1 Series    1969\n",
            " 2 Series    1229\n",
            " 5 Series    1056\n",
            " 4 Series     995\n",
            " X1           804\n",
            " X3           551\n",
            " X5           468\n",
            " X2           288\n",
            " X4           179\n",
            " M4           125\n",
            " 6 Series     108\n",
            " Z4           108\n",
            " 7 Series     106\n",
            " X6           106\n",
            " X7            55\n",
            " i3            43\n",
            " 8 Series      39\n",
            " M5            29\n",
            " M3            27\n",
            " M2            21\n",
            " i8            17\n",
            " M6             8\n",
            " Z3             7\n",
            "Name: model, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df[\"model\"].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ztjr_zuturpa"
      },
      "source": [
        "#### Count the number of unique values present in the 'transmission' column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1dJ0JQkCurpa",
        "outputId": "db6edf3f-3b71-40f1-9d13-3234e7d2b271"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Semi-Auto    4666\n",
            "Automatic    3588\n",
            "Manual       2527\n",
            "Name: transmission, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df[\"transmission\"].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oppa4Rc7urpb"
      },
      "source": [
        "#### Count the number of unique values present in the 'fuelType' column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a4TWc6ynurpb",
        "outputId": "bdd17cfa-c2d3-44fb-83ef-3ef1877c32cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Diesel      7027\n",
            "Petrol      3417\n",
            "Hybrid       298\n",
            "Other         36\n",
            "Electric       3\n",
            "Name: fuelType, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df[\"fuelType\"].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3poKJKK-urpb"
      },
      "source": [
        "#### Pie Chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L_VXEJqRurpb",
        "outputId": "1afd8e07-5fe6-4bad-d551-a2a4276f7644"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df[\"transmission\"].value_counts().plot(kind=\"pie\", \n",
        "                                       autopct='%1.1f%%', \n",
        "                                       startangle=180)\n",
        "plt.ylabel(\"\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMyDiU0Qurpb"
      },
      "source": [
        "#### Problems with pie Chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mH5zOiDaurpc",
        "outputId": "26caf94e-f772-4c07-e1e8-97df634425d6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df[\"fuelType\"].value_counts().plot(kind=\"pie\", \n",
        "                                       autopct='%1.1f%%', \n",
        "                                       startangle=180)\n",
        "plt.ylabel(\"\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7kH6H-lurpc"
      },
      "source": [
        "#### Find the percentage of unique values present in the 'fuelType' column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BgYHwf5durpc",
        "outputId": "19dc48fd-b46a-461a-9f63-6be3a6d59aee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Diesel      7027\n",
            "Petrol      3417\n",
            "Hybrid       298\n",
            "Other         36\n",
            "Electric       3\n",
            "Name: fuelType, dtype: int64\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        },
        {
          "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>fuelType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Diesel</th>\n",
              "      <td>7027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Petrol</th>\n",
              "      <td>3417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hybrid</th>\n",
              "      <td>298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electric</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          fuelType\n",
              "Diesel        7027\n",
              "Petrol        3417\n",
              "Hybrid         298\n",
              "Other           36\n",
              "Electric         3"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Diesel', 'Petrol', 'Hybrid', 'Other', 'Electric'], dtype='object')\n",
            "Index(['fuelType'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(df[\"fuelType\"].value_counts())\n",
        "\n",
        "print(type(df[\"fuelType\"].value_counts()))\n",
        "\n",
        "df_fuelType = pd.DataFrame(df[\"fuelType\"].value_counts())\n",
        "\n",
        "display(df_fuelType.head())\n",
        "print(df_fuelType.index)\n",
        "print(df_fuelType.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cl3ErPevA0Au",
        "outputId": "8fc3eaff-a4fa-4d49-ccfc-79c593afa879"
      },
      "outputs": [
        {
          "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>fuelType</th>\n",
              "      <th>no_of_cars</th>\n",
              "      <th>% of cars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Diesel</td>\n",
              "      <td>7027</td>\n",
              "      <td>65.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Petrol</td>\n",
              "      <td>3417</td>\n",
              "      <td>31.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hybrid</td>\n",
              "      <td>298</td>\n",
              "      <td>2.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Other</td>\n",
              "      <td>36</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Electric</td>\n",
              "      <td>3</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fuelType  no_of_cars  % of cars\n",
              "0    Diesel        7027      65.18\n",
              "1    Petrol        3417      31.69\n",
              "2    Hybrid         298       2.76\n",
              "3     Other          36       0.33\n",
              "4  Electric           3       0.03"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_fuelType = pd.DataFrame(df[\"fuelType\"].value_counts())\n",
        "df_fuelType = df_fuelType.reset_index()\n",
        "df_fuelType = df_fuelType.rename(columns={\"index\":\"fuelType\",\n",
        "                                          \"fuelType\":\"no_of_cars\"})\n",
        "\n",
        "df_fuelType[\"% of cars\"] = (df_fuelType[\"no_of_cars\"]/df.shape[0])*100\n",
        "\n",
        "df_fuelType = df_fuelType.round(2)\n",
        "\n",
        "# df_fuelType = np.round(df_fuelType, 2)\n",
        "\n",
        "display(df_fuelType)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2iMqPV4urpc"
      },
      "source": [
        "#### Create a Barplot for the 'fuelType' column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pp_RZ1dJurpc",
        "outputId": "57675b47-7d14-4ed3-d3fc-e3cb1a613ce6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(x=\"fuelType\", \n",
        "            y=\"% of cars\", \n",
        "            data=df_fuelType, \n",
        "            color=\"red\",\n",
        "            alpha=0.8)\n",
        "\n",
        "plt.xlabel(\"Types of fuel\")\n",
        "plt.ylabel(\"% of cars\")\n",
        "plt.title(\"Percentage of cars present in each fuelType\")\n",
        "\n",
        "plt.yticks(np.arange(0,101,10))\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X0IojTuUA0Au",
        "outputId": "5428d7c9-59d6-4318-b0c2-6b0ead4ada56"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([  0,  10,  20,  30,  40,  50,  60,  70,  80,  90, 100])"
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.arange(0,101,10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e9opv3zourpd"
      },
      "source": [
        "#### Find the percentage of unique values present in the 'transmission' column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2RN3zOprurpd",
        "outputId": "ed6f7751-fd8e-49b2-e003-f8c66fb54c90"
      },
      "outputs": [
        {
          "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>transmission</th>\n",
              "      <th>no_of_cars</th>\n",
              "      <th>% of cars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Semi-Auto</td>\n",
              "      <td>4666</td>\n",
              "      <td>43.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Automatic</td>\n",
              "      <td>3588</td>\n",
              "      <td>33.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Manual</td>\n",
              "      <td>2527</td>\n",
              "      <td>23.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  transmission  no_of_cars  % of cars\n",
              "0    Semi-Auto        4666      43.28\n",
              "1    Automatic        3588      33.28\n",
              "2       Manual        2527      23.44"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_transmission = pd.DataFrame(df[\"transmission\"].value_counts())\n",
        "df_transmission = df_transmission.reset_index()\n",
        "df_transmission = df_transmission.rename(columns={\"index\":\"transmission\",\n",
        "                                          \"transmission\":\"no_of_cars\"})\n",
        "\n",
        "df_transmission[\"% of cars\"] = (df_transmission[\"no_of_cars\"]/df.shape[0])*100\n",
        "\n",
        "df_transmission = df_transmission.round(2)\n",
        "\n",
        "display(df_transmission)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDFAveUSurpd"
      },
      "source": [
        "#### Create a Barplot for the 'transmission' column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SDV561NJurpd",
        "outputId": "ce532eaa-7114-493b-dd9c-f709e16ef56f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(x=\"transmission\", \n",
        "            y=\"% of cars\", \n",
        "            data=df_transmission, \n",
        "            color=\"g\",\n",
        "            alpha=0.8)\n",
        "\n",
        "plt.xlabel(\"Types of transmission\")\n",
        "plt.ylabel(\"% of cars\")\n",
        "plt.title(\"Percentage of cars present in each transmission\")\n",
        "\n",
        "plt.yticks(np.arange(0,51,10))\n",
        "\n",
        "# integer\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Lecture_3.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}