Assignment_2_DS_BasicDataUnderstanding.ipynb

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        "<a href=\"https://colab.research.google.com/github/sp1999/SHALA2020/blob/master/Assignment_2_DS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\n",
        "# **Assignment - 2: Basic Data Understanding**\n",
        "\n",
        "---\n",
        "\n",
        "This assignment will get you familiarized with Python libraries and  functions required for data visualization."
      ]
    },
    {
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      "metadata": {
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      "source": [
        "---\n",
        "## Part 1 - Loading data \n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7W6I-fVIoqp",
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      "source": [
        "###Import the following libraries:  \n",
        "\n",
        "* ```numpy``` with an alias name ```np```, \n",
        "* ```pandas``` with an alias name ```pd```, \n",
        "* ```matplotlib.pyplot``` with an alias name ```plt```, and \n",
        "* ```seaborn``` with an alias name ```sns```. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NihF3MwIGI4m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the four libraries with their aliases \n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dsLj-TSQJgMb",
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      },
      "source": [
        "### Using the files ```train.csv``` and ```moviesData.csv```,  peform the following:\n",
        "\n",
        "* Load these file as ```pandas``` dataframes and store it in variables named ```df``` and ```movies``` respectively. \n",
        "* Print the first ten rows of ```df```. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AjvT40AGIq6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the file as a dataframe \n",
        "df = pd.read_csv('/content/drive/My Drive/SHALA 2020 for Instructors and Organizers/Teaching/Module - Data Science/DS 2 Basic_Data_Understanding/train.csv')\n",
        "movies = pd.read_csv('/content/drive/My Drive/SHALA 2020 for Instructors and Organizers/Teaching/Module - Data Science/DS 2 Basic_Data_Understanding/moviesData.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "outputId": "62c0ab19-b065-4fb2-beca-74f01d16e9c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "aafcdf6c-38fd-4ba1-ae06-087c04b72439",
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      "source": [
        "# Print the first ten rows of df\n",
        "df.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>Gender</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>OverTime</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1102</td>\n",
              "      <td>Sales</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>4</td>\n",
              "      <td>Single</td>\n",
              "      <td>5993</td>\n",
              "      <td>19479</td>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>279</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1373</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>92</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>3</td>\n",
              "      <td>Single</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
              "      <td>Yes</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>1392</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>56</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>3</td>\n",
              "      <td>Married</td>\n",
              "      <td>2909</td>\n",
              "      <td>23159</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>591</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>3468</td>\n",
              "      <td>16632</td>\n",
              "      <td>9</td>\n",
              "      <td>No</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>1005</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>79</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>4</td>\n",
              "      <td>Single</td>\n",
              "      <td>3068</td>\n",
              "      <td>11864</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>59</td>\n",
              "      <td>0</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1324</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>81</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>1</td>\n",
              "      <td>Married</td>\n",
              "      <td>2670</td>\n",
              "      <td>9964</td>\n",
              "      <td>4</td>\n",
              "      <td>Yes</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1358</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>67</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>3</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>2693</td>\n",
              "      <td>13335</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>216</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>Manufacturing Director</td>\n",
              "      <td>3</td>\n",
              "      <td>Single</td>\n",
              "      <td>9526</td>\n",
              "      <td>8787</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1299</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>27</td>\n",
              "      <td>3</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Healthcare Representative</td>\n",
              "      <td>3</td>\n",
              "      <td>Married</td>\n",
              "      <td>5237</td>\n",
              "      <td>16577</td>\n",
              "      <td>6</td>\n",
              "      <td>No</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Attrition  ... YearsWithCurrManager  ID\n",
              "0   41          1  ...                    5   0\n",
              "1   49          0  ...                    7   1\n",
              "2   37          1  ...                    0   2\n",
              "3   33          0  ...                    0   3\n",
              "4   27          0  ...                    2   4\n",
              "5   32          0  ...                    6   5\n",
              "6   59          0  ...                    0   6\n",
              "7   30          0  ...                    0   7\n",
              "8   38          0  ...                    8   8\n",
              "9   36          0  ...                    7   9\n",
              "\n",
              "[10 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
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    },
    {
      "cell_type": "markdown",
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      "source": [
        "### Using the dataframe ```df```, perform the following: \n",
        "\n",
        "* Print the first five rows of the column ```MonthlyRate```. \n",
        "* Find out the details of the column ```MonthlyRate``` like mean, maximum value, minimum value, etc. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QS5LttI-GT2f",
        "colab_type": "code",
        "outputId": "47de1d03-ff77-4592-c05a-54bcdd579083",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Print the first five rows of MonthlyRate\n",
        "df['MonthlyRate'].head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    19479\n",
              "1    24907\n",
              "2     2396\n",
              "3    23159\n",
              "4    16632\n",
              "Name: MonthlyRate, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzQ1a1M0GURm",
        "colab_type": "code",
        "outputId": "917c23d7-c7cc-44ca-abc2-842e8a611108",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "# Find the details of MonthlyRate \n",
        "df['MonthlyRate'].describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     1028.000000\n",
              "mean     14243.531128\n",
              "std       7048.768076\n",
              "min       2094.000000\n",
              "25%       8259.750000\n",
              "50%      14074.500000\n",
              "75%      20342.250000\n",
              "max      26999.000000\n",
              "Name: MonthlyRate, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3h-YOTvPQI48",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Part 2 - Cleaning and manipulating data \n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtLzBibsQfXu",
        "colab_type": "text"
      },
      "source": [
        "### Using the dataframe ```df```, peform the following:\n",
        "\n",
        "* Check whether there are any missing values in ```df```. \n",
        "* If yes, drop those values and print the size of ```df``` after dropping these. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XG-UK53fRDRZ",
        "colab_type": "code",
        "outputId": "c328fd40-05e1-46b4-f70c-be878537f0fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "# Check for missing values \n",
        "print(df.isnull().sum())\n",
        "\n",
        "# Drop the missing values \n",
        "df = df.dropna()\n",
        "print(df)\n",
        "\n",
        "# Print the size of df after dropping \n",
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Age                         0\n",
            "Attrition                   0\n",
            "BusinessTravel              0\n",
            "DailyRate                   0\n",
            "Department                  0\n",
            "DistanceFromHome            0\n",
            "Education                   0\n",
            "EducationField              0\n",
            "EmployeeCount               0\n",
            "EmployeeNumber              0\n",
            "EnvironmentSatisfaction     0\n",
            "Gender                      0\n",
            "HourlyRate                  0\n",
            "JobInvolvement              0\n",
            "JobLevel                    0\n",
            "JobRole                     0\n",
            "JobSatisfaction             0\n",
            "MaritalStatus               0\n",
            "MonthlyIncome               0\n",
            "MonthlyRate                 0\n",
            "NumCompaniesWorked          0\n",
            "OverTime                    0\n",
            "PercentSalaryHike           0\n",
            "PerformanceRating           0\n",
            "RelationshipSatisfaction    0\n",
            "StockOptionLevel            0\n",
            "TotalWorkingYears           0\n",
            "TrainingTimesLastYear       0\n",
            "WorkLifeBalance             0\n",
            "YearsAtCompany              0\n",
            "YearsInCurrentRole          0\n",
            "YearsSinceLastPromotion     0\n",
            "YearsWithCurrManager        0\n",
            "ID                          0\n",
            "dtype: int64\n",
            "      Age  Attrition  ... YearsWithCurrManager    ID\n",
            "0      41          1  ...                    5     0\n",
            "1      49          0  ...                    7     1\n",
            "2      37          1  ...                    0     2\n",
            "3      33          0  ...                    0     3\n",
            "4      27          0  ...                    2     4\n",
            "...   ...        ...  ...                  ...   ...\n",
            "1023   56          0  ...                    0  1023\n",
            "1024   47          0  ...                    6  1024\n",
            "1025   24          0  ...                    3  1025\n",
            "1026   32          0  ...                    3  1026\n",
            "1027   34          0  ...                    2  1027\n",
            "\n",
            "[1028 rows x 34 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1028, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtoGcl2XRWnS",
        "colab_type": "text"
      },
      "source": [
        "### Using the dataframe ```df```, peform the following:\n",
        "\n",
        "* Add another column named ```MonthRateNew``` in ```df``` by subtracting the mean from ```MonthlyRate``` and dividing it by standard deviation. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDMncSUKR12P",
        "colab_type": "code",
        "outputId": "e2212a58-1b87-4ec8-8a30-8e648ca94df9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Add a column named MonthRateNew \n",
        "df['MonthRateNew'] = (df['MonthlyRate']-df['MonthlyRate'].mean())/df['MonthlyRate'].std()\n",
        "print(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      Age  Attrition  ...    ID  MonthRateNew\n",
            "0      41          1  ...     0      0.742749\n",
            "1      49          0  ...     1      1.512813\n",
            "2      37          1  ...     2     -1.680795\n",
            "3      33          0  ...     3      1.264827\n",
            "4      27          0  ...     4      0.338849\n",
            "...   ...        ...  ...   ...           ...\n",
            "1023   56          0  ...  1023     -0.531941\n",
            "1024   47          0  ...  1024      1.767609\n",
            "1025   24          0  ...  1025      0.137254\n",
            "1026   32          0  ...  1026      1.290930\n",
            "1027   34          0  ...  1027     -1.494663\n",
            "\n",
            "[1028 rows x 35 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcbN7jep13og",
        "colab_type": "text"
      },
      "source": [
        "### Using the dataframe ```movies```, perform the following: \n",
        "\n",
        "* Check whether there are any missing values in ```movies```. \n",
        "* Find out the number of observations/rows having any of their features/columns missing. \n",
        "* Drop the missing values and print the size of ```movies``` after dropping these. \n",
        "* Instead of dropping the missing values, replace the missing values by their mean (or some suitable value). \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNszBx8A14ai",
        "colab_type": "code",
        "outputId": "b79725ca-8742-4d73-9e99-7962d8456b46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Check for missing values \n",
        "print(movies.isna().sum())\n",
        "\n",
        "# Drop the missing values \n",
        "movies1 = movies.dropna()\n",
        "print(movies1.shape)\n",
        "\n",
        "# Replace the missing values\n",
        "movies2 = movies.fillna(movies.mean())\n",
        "print(movies2.isna().sum())\n",
        "print(movies2.shape)\n",
        "\n",
        "# You can use SimpleImputer of sklearn for this\n",
        "from sklearn.impute import SimpleImputer\n",
        "imp_mode = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
        "imp_mode.fit(movies)\n",
        "movies3 = imp_mode.transform(movies)\n",
        "movies3 = pd.DataFrame(movies3)\n",
        "print(movies3.isna().sum())\n",
        "print(movies3.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unnamed: 0           0\n",
            "title                0\n",
            "title_type           0\n",
            "genre                0\n",
            "runtime              1\n",
            "mpaa_rating          0\n",
            "studio               5\n",
            "thtr_rel_year        0\n",
            "thtr_rel_month       0\n",
            "thtr_rel_day         0\n",
            "dvd_rel_year         7\n",
            "dvd_rel_month        7\n",
            "dvd_rel_day          7\n",
            "imdb_rating          0\n",
            "imdb_num_votes       0\n",
            "critics_rating       0\n",
            "critics_score        0\n",
            "audience_rating      0\n",
            "audience_score       0\n",
            "best_pic_nom         0\n",
            "best_pic_win         0\n",
            "best_actor_win       0\n",
            "best_actress_win     0\n",
            "best_dir_win         0\n",
            "top200_box           0\n",
            "director             2\n",
            "actor1               2\n",
            "actor2               7\n",
            "actor3               9\n",
            "actor4              12\n",
            "actor5              14\n",
            "dtype: int64\n",
            "(572, 31)\n",
            "Unnamed: 0           0\n",
            "title                0\n",
            "title_type           0\n",
            "genre                0\n",
            "runtime              0\n",
            "mpaa_rating          0\n",
            "studio               5\n",
            "thtr_rel_year        0\n",
            "thtr_rel_month       0\n",
            "thtr_rel_day         0\n",
            "dvd_rel_year         0\n",
            "dvd_rel_month        0\n",
            "dvd_rel_day          0\n",
            "imdb_rating          0\n",
            "imdb_num_votes       0\n",
            "critics_rating       0\n",
            "critics_score        0\n",
            "audience_rating      0\n",
            "audience_score       0\n",
            "best_pic_nom         0\n",
            "best_pic_win         0\n",
            "best_actor_win       0\n",
            "best_actress_win     0\n",
            "best_dir_win         0\n",
            "top200_box           0\n",
            "director             2\n",
            "actor1               2\n",
            "actor2               7\n",
            "actor3               9\n",
            "actor4              12\n",
            "actor5              14\n",
            "dtype: int64\n",
            "(600, 31)\n",
            "0     0\n",
            "1     0\n",
            "2     0\n",
            "3     0\n",
            "4     0\n",
            "5     0\n",
            "6     0\n",
            "7     0\n",
            "8     0\n",
            "9     0\n",
            "10    0\n",
            "11    0\n",
            "12    0\n",
            "13    0\n",
            "14    0\n",
            "15    0\n",
            "16    0\n",
            "17    0\n",
            "18    0\n",
            "19    0\n",
            "20    0\n",
            "21    0\n",
            "22    0\n",
            "23    0\n",
            "24    0\n",
            "25    0\n",
            "26    0\n",
            "27    0\n",
            "28    0\n",
            "29    0\n",
            "30    0\n",
            "dtype: int64\n",
            "(600, 31)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDr11sjlBk_W",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Part 3 - Visualizing data \n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wC_w1zNCBw2G",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the ```df``` by drawing the following plots:\n",
        "\n",
        "* Plot a histogram of ```Age``` and find the range in which most people are there. \n",
        "* Modify the histogram of ```Age``` by adding 30 bins. \n",
        "* Draw a scatter plot between ```Age``` and ```Attrition``` and suitable labels to the axes. Find out whether people more than 50 years are more likely to leave the company. (```Attrition``` = 1 means people have left the company). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "645EwAsoH63i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "outputId": "a8c2dc22-c230-4e8d-b791-16e296ff6e66"
      },
      "source": [
        "# Plot and modify the histogram of Age\n",
        "plt.hist(df[\"Age\"], color = \"yellow\")\n",
        "plt.title(\"Histogram of Age\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.show()\n",
        "\n",
        "plt.hist(df[\"Age\"], bins = 30, color = \"orange\")\n",
        "plt.title(\"Histogram of Age\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.show()\n",
        "\n",
        "# Draw a scatter plot between Age and Attrition\n",
        "plt.scatter(df[\"Age\"],df[\"Attrition\"], c = \"red\")\n",
        "plt.title(\"Scatter plot of Age vs Attrition\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Attrition\")\n",
        "plt.show()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eez_pkZ-HDKP",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the ```df``` by following the steps given below:\n",
        "\n",
        "* Get a series containing counts of unique values of ```Attrition```.\n",
        "* Draw a countplot for ```Attrition``` using ```sns.countplot()```. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tp8LnxLWIPfk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "2bd590b4-1007-402d-f96e-4ceaf48f3d26"
      },
      "source": [
        "# Get a series of counts of values of Attrition\n",
        "print(pd.Series(df[\"Attrition\"]).value_counts())\n",
        "\n",
        "# Draw a countplot for Attrition \n",
        "# You may use countplot of seaborn for this \n",
        "sns.set(style=\"darkgrid\")\n",
        "ax = sns.countplot(x = \"Attrition\", data = df)\n",
        "plt.ylim(0, 1000)\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    856\n",
            "1    172\n",
            "Name: Attrition, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vi01m9MBHaJD",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the ```df``` by following the steps given below:\n",
        "\n",
        "* Draw a cross tabulation of ```Attrition``` and ```BusinessTravel``` as bar charts. Find which value of ```BusinessTravel``` has highest number of people."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OHAeOqeIQVM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "outputId": "d2b3b755-6b72-4969-861f-87cf65609d9b"
      },
      "source": [
        "# Draw a cross tab of Attritiona and BusinessTravel \n",
        "# You may use crosstab of pandas for this \n",
        "ct = pd.crosstab(df[\"Attrition\"],df[\"BusinessTravel\"])\n",
        "print(ct)\n",
        "ct.plot.bar()\n",
        "plt.legend(title = \"Business Travel\")\n",
        "plt.show()\n",
        "ct1 = pd.crosstab(df[\"BusinessTravel\"],df[\"Attrition\"])\n",
        "print(ct1)\n",
        "ct1.plot.bar()\n",
        "plt.legend(title = \"Attrition\")\n",
        "plt.show()"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BusinessTravel  Non-Travel  Travel_Frequently  Travel_Rarely\n",
            "Attrition                                                   \n",
            "0                       92                150            614\n",
            "1                       10                 47            115\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Attrition            0    1\n",
            "BusinessTravel             \n",
            "Non-Travel          92   10\n",
            "Travel_Frequently  150   47\n",
            "Travel_Rarely      614  115\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FCc9ZkUHfqL",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the ```df``` by drawing the following plot:\n",
        "\n",
        "* Draw a stacked bar chart between ```Attrition``` and ```Gender``` columns. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58ELSAXBIQST",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "10b4299b-3d86-4024-81aa-02a8ac70bf2d"
      },
      "source": [
        "# Draw a stacked bar chart between Attrition and Gender \n",
        "cross_tab=pd.crosstab(df.Gender,df.Attrition)\n",
        "cross_tab.plot.bar(stacked=True)\n",
        "plt.ylabel('Attrition count')\n",
        "plt.show()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZzsi8_QLEdq",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the ```df``` by drawing the following histogram:\n",
        "\n",
        "* Draw a histogram of ```TotalWorkingYears``` with 30 bins. \n",
        "* Draw a histogram of ```YearsAtCompany``` with 30 bins and find whether the values in ```YearsAtCompany``` are skewed. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvSlfj3FLUvV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "9dee1404-c2a2-4f2f-8142-aebf885d25be"
      },
      "source": [
        "# Draw a histogram of TotalWorkingYears with 30 bins\n",
        "plt.hist(df[\"TotalWorkingYears\"], bins = 30)\n",
        "plt.title(\"Histogram of TotalWorkingYears\")\n",
        "plt.xlabel(\"TotalWorkingYears\")\n",
        "plt.show()\n",
        "\n",
        "# Draw a histogram of YearsAtCompany\n",
        "plt.hist(df[\"YearsAtCompany\"], bins = 30)\n",
        "plt.title(\"Histogram of YearsAtCompany\")\n",
        "plt.xlabel(\"YearsAtCompany\")\n",
        "plt.show()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBcsFqz-Moja",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the ```df``` by drawing the following boxplot:\n",
        "\n",
        "* Draw a boxplot of ```MonthlyIncome``` for each ```Department``` and report whether there is/are outlier(s). \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6zsSE65NED9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "1b83e13c-bdb4-4414-da16-81cd87960699"
      },
      "source": [
        "# Draw a boxplot of MonthlyIncome for each Department and report outliers \n",
        "sns.boxplot(data = df, x = df[\"Department\"], y = df[\"MonthlyIncome\"])"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f23ce1f6518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NRPgJjp-NX07",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the ```df``` by drawing the following piechart:\n",
        "\n",
        "* Create a pie chart of the values in ```JobRole``` with suitable label and report which role has highest number of persons. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pr2HVVGNlaV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "outputId": "aee0e30e-7de5-43e8-f0f4-271d06e82050"
      },
      "source": [
        "# Create a piechart of JobRole \n",
        "# You will need to find the counts of unique values in JobRole. \n",
        "plt.pie(pd.Series(df[\"JobRole\"]).value_counts(), labels = pd.Series(df[\"JobRole\"]).unique())"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7f23cadcfc88>,\n",
              "  <matplotlib.patches.Wedge at 0x7f23cab7d208>,\n",
              "  <matplotlib.patches.Wedge at 0x7f23cab7d710>,\n",
              "  <matplotlib.patches.Wedge at 0x7f23cab7dc18>,\n",
              "  <matplotlib.patches.Wedge at 0x7f23cab61160>,\n",
              "  <matplotlib.patches.Wedge at 0x7f23cab61668>,\n",
              "  <matplotlib.patches.Wedge at 0x7f23cab61b70>,\n",
              "  <matplotlib.patches.Wedge at 0x7f23cab6c0b8>,\n",
              "  <matplotlib.patches.Wedge at 0x7f23cab6c5c0>],\n",
              " [Text(0.8542961340812947, 0.6929488547459725, 'Sales Executive'),\n",
              "  Text(-0.45945693362420414, 0.9994495115535571, 'Research Scientist'),\n",
              "  Text(-1.0981462091349679, -0.06383496974620964, 'Laboratory Technician'),\n",
              "  Text(-0.6503653604759383, -0.8871442373667334, 'Manufacturing Director'),\n",
              "  Text(-0.05712202505487643, -1.0985158507065933, 'Healthcare Representative'),\n",
              "  Text(0.47770465248811617, -0.9908573383647155, 'Manager'),\n",
              "  Text(0.841447151537986, -0.708496077031207, 'Sales Representative'),\n",
              "  Text(1.03505987357499, -0.37235877606808926, 'Research Director'),\n",
              "  Text(1.09597534812125, -0.0940108308148859, 'Human Resources')])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}