5. Data Exploration .ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **Exploratory Data Analysis Lab**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **30** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "In this module you get to work with the cleaned dataset from the previous module.\n\nIn this assignment you will perform the task of exploratory data analysis.\nYou will find out the distribution of data, presence of outliers and also determine the correlation between different columns in the dataset.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "In this lab you will perform the following:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "-   Identify the distribution of data in the dataset.\n\n-   Identify outliers in the dataset.\n\n-   Remove outliers from the dataset.\n\n-   Identify correlation between features in the dataset.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "* * *\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Hands on Lab\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Import the pandas module.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "import pandas as pd"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Load the dataset into a dataframe.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m2_survey_data.csv\")"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Distribution\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Determine how the data is distributed\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The column `ConvertedComp` contains Salary converted to annual USD salaries using the exchange rate on 2019-02-01.\n\nThis assumes 12 working months and 50 working weeks.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Plot the distribution curve for the column `ConvertedComp`.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "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>Respondent</th>\n      <th>MainBranch</th>\n      <th>Hobbyist</th>\n      <th>OpenSourcer</th>\n      <th>OpenSource</th>\n      <th>Employment</th>\n      <th>Country</th>\n      <th>Student</th>\n      <th>EdLevel</th>\n      <th>UndergradMajor</th>\n      <th>...</th>\n      <th>WelcomeChange</th>\n      <th>SONewContent</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Trans</th>\n      <th>Sexuality</th>\n      <th>Ethnicity</th>\n      <th>Dependents</th>\n      <th>SurveyLength</th>\n      <th>SurveyEase</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n      <td>I am a developer by profession</td>\n      <td>No</td>\n      <td>Never</td>\n      <td>The quality of OSS and closed source software ...</td>\n      <td>Employed full-time</td>\n      <td>United States</td>\n      <td>No</td>\n      <td>Bachelor\u2019s degree (BA, BS, B.Eng., etc.)</td>\n      <td>Computer science, computer engineering, or sof...</td>\n      <td>...</td>\n      <td>Just as welcome now as I felt last year</td>\n      <td>Tech articles written by other developers;Indu...</td>\n      <td>22.0</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Straight / Heterosexual</td>\n      <td>White or of European descent</td>\n      <td>No</td>\n      <td>Appropriate in length</td>\n      <td>Easy</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9</td>\n      <td>I am a developer by profession</td>\n      <td>Yes</td>\n      <td>Once a month or more often</td>\n      <td>The quality of OSS and closed source software ...</td>\n      <td>Employed full-time</td>\n      <td>New Zealand</td>\n      <td>No</td>\n      <td>Some college/university study without earning ...</td>\n      <td>Computer science, computer engineering, or sof...</td>\n      <td>...</td>\n      <td>Just as welcome now as I felt last year</td>\n      <td>NaN</td>\n      <td>23.0</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Bisexual</td>\n      <td>White or of European descent</td>\n      <td>No</td>\n      <td>Appropriate in length</td>\n      <td>Neither easy nor difficult</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>13</td>\n      <td>I am a developer by profession</td>\n      <td>Yes</td>\n      <td>Less than once a month but more than once per ...</td>\n      <td>OSS is, on average, of HIGHER quality than pro...</td>\n      <td>Employed full-time</td>\n      <td>United States</td>\n      <td>No</td>\n      <td>Master\u2019s degree (MA, MS, M.Eng., MBA, etc.)</td>\n      <td>Computer science, computer engineering, or sof...</td>\n      <td>...</td>\n      <td>Somewhat more welcome now than last year</td>\n      <td>Tech articles written by other developers;Cour...</td>\n      <td>28.0</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Straight / Heterosexual</td>\n      <td>White or of European descent</td>\n      <td>Yes</td>\n      <td>Appropriate in length</td>\n      <td>Easy</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>16</td>\n      <td>I am a developer by profession</td>\n      <td>Yes</td>\n      <td>Never</td>\n      <td>The quality of OSS and closed source software ...</td>\n      <td>Employed full-time</td>\n      <td>United Kingdom</td>\n      <td>No</td>\n      <td>Master\u2019s degree (MA, MS, M.Eng., MBA, etc.)</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>Just as welcome now as I felt last year</td>\n      <td>Tech articles written by other developers;Indu...</td>\n      <td>26.0</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Straight / Heterosexual</td>\n      <td>White or of European descent</td>\n      <td>No</td>\n      <td>Appropriate in length</td>\n      <td>Neither easy nor difficult</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17</td>\n      <td>I am a developer by profession</td>\n      <td>Yes</td>\n      <td>Less than once a month but more than once per ...</td>\n      <td>The quality of OSS and closed source software ...</td>\n      <td>Employed full-time</td>\n      <td>Australia</td>\n      <td>No</td>\n      <td>Bachelor\u2019s degree (BA, BS, B.Eng., etc.)</td>\n      <td>Computer science, computer engineering, or sof...</td>\n      <td>...</td>\n      <td>Just as welcome now as I felt last year</td>\n      <td>Tech articles written by other developers;Indu...</td>\n      <td>29.0</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Straight / Heterosexual</td>\n      <td>Hispanic or Latino/Latina;Multiracial</td>\n      <td>No</td>\n      <td>Appropriate in length</td>\n      <td>Easy</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 85 columns</p>\n</div>",
                        "text/plain": "   Respondent                      MainBranch Hobbyist  \\\n0           4  I am a developer by profession       No   \n1           9  I am a developer by profession      Yes   \n2          13  I am a developer by profession      Yes   \n3          16  I am a developer by profession      Yes   \n4          17  I am a developer by profession      Yes   \n\n                                         OpenSourcer  \\\n0                                              Never   \n1                         Once a month or more often   \n2  Less than once a month but more than once per ...   \n3                                              Never   \n4  Less than once a month but more than once per ...   \n\n                                          OpenSource          Employment  \\\n0  The quality of OSS and closed source software ...  Employed full-time   \n1  The quality of OSS and closed source software ...  Employed full-time   \n2  OSS is, on average, of HIGHER quality than pro...  Employed full-time   \n3  The quality of OSS and closed source software ...  Employed full-time   \n4  The quality of OSS and closed source software ...  Employed full-time   \n\n          Country Student                                            EdLevel  \\\n0   United States      No           Bachelor\u2019s degree (BA, BS, B.Eng., etc.)   \n1     New Zealand      No  Some college/university study without earning ...   \n2   United States      No        Master\u2019s degree (MA, MS, M.Eng., MBA, etc.)   \n3  United Kingdom      No        Master\u2019s degree (MA, MS, M.Eng., MBA, etc.)   \n4       Australia      No           Bachelor\u2019s degree (BA, BS, B.Eng., etc.)   \n\n                                      UndergradMajor  ...  \\\n0  Computer science, computer engineering, or sof...  ...   \n1  Computer science, computer engineering, or sof...  ...   \n2  Computer science, computer engineering, or sof...  ...   \n3                                                NaN  ...   \n4  Computer science, computer engineering, or sof...  ...   \n\n                              WelcomeChange  \\\n0   Just as welcome now as I felt last year   \n1   Just as welcome now as I felt last year   \n2  Somewhat more welcome now than last year   \n3   Just as welcome now as I felt last year   \n4   Just as welcome now as I felt last year   \n\n                                        SONewContent   Age Gender Trans  \\\n0  Tech articles written by other developers;Indu...  22.0    Man    No   \n1                                                NaN  23.0    Man    No   \n2  Tech articles written by other developers;Cour...  28.0    Man    No   \n3  Tech articles written by other developers;Indu...  26.0    Man    No   \n4  Tech articles written by other developers;Indu...  29.0    Man    No   \n\n                 Sexuality                              Ethnicity Dependents  \\\n0  Straight / Heterosexual           White or of European descent         No   \n1                 Bisexual           White or of European descent         No   \n2  Straight / Heterosexual           White or of European descent        Yes   \n3  Straight / Heterosexual           White or of European descent         No   \n4  Straight / Heterosexual  Hispanic or Latino/Latina;Multiracial         No   \n\n            SurveyLength                  SurveyEase  \n0  Appropriate in length                        Easy  \n1  Appropriate in length  Neither easy nor difficult  \n2  Appropriate in length                        Easy  \n3  Appropriate in length  Neither easy nor difficult  \n4  Appropriate in length                        Easy  \n\n[5 rows x 85 columns]"
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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\n",
                        "text/plain": "<Figure size 1584x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# your code goes here\n\nimport seaborn as sns \nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nplt.figure(figsize=(22,6))\nsns.distplot(df['ConvertedComp'])\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Plot the histogram for the column `ConvertedComp`.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1584x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# your code goes here\nplt.figure(figsize=(22,6))\nsns.distplot(df['ConvertedComp'],kde  = False)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "What is the median of the column `ConvertedComp`?\n"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "57745.0"
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\ndf['ConvertedComp'].median()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "How many responders identified themselves only as a **Man**?\n"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Man                                                            10480\nWoman                                                            731\nNon-binary, genderqueer, or gender non-conforming                 63\nMan;Non-binary, genderqueer, or gender non-conforming             26\nWoman;Non-binary, genderqueer, or gender non-conforming           14\nWoman;Man                                                          9\nWoman;Man;Non-binary, genderqueer, or gender non-conforming        2\nName: Gender, dtype: int64"
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\ndf['Gender'].value_counts()\n\n#man : 10480"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find out the  median ConvertedComp of responders identified themselves only as a **Woman**?\n"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "ConvertedComp    57708.0\ndtype: float64"
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\n# df.groupby('Gender')['ConvertedComp'].median()\ndf.loc[df['Gender'] == 'Woman', ['ConvertedComp']].median()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Give the five number summary for the column `Age`?\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**Double click here for hint**.\n\n<!--\nmin,q1,median,q3,max of a column are its five number summary.\n-->\n"
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "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>Age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>min</th>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>25.0</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>29.0</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>99.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "      Age\nmin  16.0\n25%  25.0\n50%  29.0\n75%  35.0\nmax  99.0"
                    },
                    "execution_count": 38,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\ndfage = df[['Age']].describe()\ndfage.drop(['count','mean','std'],axis = 0)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Plot a histogram of the column `Age`.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fb34098d7d0>"
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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TVXV5kkyvT82sv2Nm++1Jnpjj+wEAAACAyYofxtHdX6uqx6vqNd39aJLrkzw8/dyS5D3T633TJoeSHKiqn8/iwzh2J3lwnskDXCjW8oE1N1+7c832DQAAwPqZ96m7/ybJh6cn7n4lyQ9l8SzBg1V1a5LjSd6WJN19tKoOZjEEPpPkdk/cBQAAAIDVMVfo6+7PJ9mzxKLrl1l/f5L983wnAAAAAHCmee7RBwAAAABsEkIfAAAAAAxA6AMAAACAAQh9AAAAADAAoQ8AAAAABiD0AQAAAMAAhD4AAAAAGIDQBwAAAAADEPoAAAAAYABCHwAAAAAMQOgDAAAAgAEIfQAAAAAwAKEPAAAAAAawsNETAGBjHTh8fE32e/O1O9dkvwAAACzNGX0AAAAAMAChDwAAAAAGIPQBAAAAwACEPgAAAAAYgNAHAAAAAAMQ+gAAAABgAEIfAAAAAAxA6AMAAACAAQh9AAAAADAAoQ8AAAAABiD0AQAAAMAAhD4AAAAAGIDQBwAAAAADEPoAAAAAYABCHwAAAAAMQOgDAAAAgAEIfQAAAAAwAKEPAAAAAAYg9AEAAADAAOYOfVW1pao+V1Ufnz6/qqrur6ovT68Xz6x7Z1Udq6pHq+qGeb8bAAAAAFi0Gmf0vSvJIzOf70jyQHfvTvLA9DlVdWWSfUmuSnJjkruqassqfD8AAAAAXPDmCn1VtT3J9yd538zw3iT3TO/vSXLTzPi93f10dz+W5FiSa+b5fgAAAABg0bxn9P1Ckp9K8lczY5d198kkmV4vnca3JXl8Zr0T09gZquq2qjpSVUdOnTo15xQBAAAAYHwrDn1V9eYkT3X3Z17oJkuM9VIrdvfd3b2nu/ds3bp1pVMEAAAAgAvGwhzbvjHJW6rqnyT55iSvqKpfSfJkVV3e3Ser6vIkT03rn0iyY2b77UmemOP7ATadA4ePb/QUAAAAuECt+Iy+7r6zu7d3964sPmTjt7r7HUkOJbllWu2WJPdN7w8l2VdVF1XVFUl2J3lwxTMHAAAAAP7GPGf0Lec9SQ5W1a1Jjid5W5J099GqOpjk4STPJLm9u59dg+8HAAAAgAvOqoS+7v6dJL8zvf+/Sa5fZr39SfavxncCAAAAAM+Z96m7AAAAAMAmIPQBAAAAwACEPgAAAAAYgNAHAAAAAAMQ+gAAAABgAEIfAAAAAAxA6AMAAACAAQh9AAAAADAAoQ8AAAAABiD0AQAAAMAAhD4AAAAAGIDQBwAAAAADEPoAAAAAYABCHwAAAAAMQOgDAAAAgAEsbPQEAOBcHDh8fM32ffO1O9ds3wAAAGvNGX0AAAAAMAChDwAAAAAGIPQBAAAAwACEPgAAAAAYgNAHAAAAAAMQ+gAAAABgAEIfAAAAAAxA6AMAAACAAQh9AAAAADAAoQ8AAAAABiD0AQAAAMAAhD4AAAAAGIDQBwAAAAADEPoAAAAAYABCHwAAAAAMQOgDAAAAgAGsOPRV1Y6q+u2qeqSqjlbVu6bxV1XV/VX15en14plt7qyqY1X1aFXdsBq/AAAAAAAw3xl9zyT5ie7+jiTXJbm9qq5MckeSB7p7d5IHps+Zlu1LclWSG5PcVVVb5pk8AAAAALBoxaGvu09292en999I8kiSbUn2JrlnWu2eJDdN7/cmube7n+7ux5IcS3LNSr8fAAAAAHjOqtyjr6p2JXltksNJLuvuk8liDExy6bTatiSPz2x2YhoDAAAAAOY0d+irqpcn+dUkP9bdf/p8qy4x1svs87aqOlJVR06dOjXvFAEAAABgeAvzbFxVL8pi5Ptwd39sGn6yqi7v7pNVdXmSp6bxE0l2zGy+PckTS+23u+9OcneS7NmzZ8kYCACr7cDh42uy35uv3bkm+wUAAJg1z1N3K8n7kzzS3T8/s+hQklum97ckuW9mfF9VXVRVVyTZneTBlX4/AAAAAPCcec7oe2OSf5nki1X1+WnsZ5K8J8nBqro1yfEkb0uS7j5aVQeTPJzFJ/be3t3PzvH9AGxia3V2HAAAAEtbcejr7t/N0vfdS5Lrl9lmf5L9K/1OAAAAAGBpq/LUXQAAAABgYwl9AAAAADAAoQ8AAAAABiD0AQAAAMAAhD4AAAAAGIDQBwAAAAADEPoAAAAAYABCHwAAAAAMQOgDAAAAgAEsbPQEAICVOXD4+Jrt++Zrd67ZvgEAgLUh9AEAZ1iriCggAgDA2nHpLgAAAAAMQOgDAAAAgAEIfQAAAAAwAPfoG9Ra3qAdAAAAgM3HGX0AAAAAMAChDwAAAAAGIPQBAAAAwACEPgAAAAAYgIdxAMAa84AkAABgPTijDwAAAAAGIPQBAAAAwABcugsAnPfW8vLom6/duWb7BgCA1eSMPgAAAAAYgNAHAAAAAANw6S4AsG7OxycQr9WcXRIMAMBqc0YfAAAAAAxA6AMAAACAAQh9AAAAADAAoQ8AAAAABiD0AQAAAMAAPHUXAGAga/lkY08KBgDY3IQ+AABekLWKiAIiAMDqWPdLd6vqxqp6tKqOVdUd6/39AAAAADCidT2jr6q2JPnPSb43yYkkv19Vh7r74fWcBwDARlvLS2xZ5AxEAOBCs96X7l6T5Fh3fyVJqureJHuTXLChzx/yAQDOL+6DCABsVusd+rYleXzm84kk167zHAAA2ET8w+dzzrf/FudjmHSmJwAjW+/QV0uM9RkrVd2W5Lbp459V1aNrOitW2yVJ/mijJwGcNxwzgHPhmLGJ/MBGT2AT8d9i03LMAM7F+XLM+NblFqx36DuRZMfM5+1Jnjh9pe6+O8nd6zUpVldVHenuPRs9D+D84JgBnAvHDOBcOGYA52KEY8Z6P3X395PsrqorqurFSfYlObTOcwAAAACA4azrGX3d/UxV/eskv5FkS5IPdPfR9ZwDAAAAAIxovS/dTXf/epJfX+/vZV257Bo4F44ZwLlwzADOhWMGcC7O+2NGdZ/xLAwAAAAA4Dyz3vfoAwAAAADWgNDHXKpqR1X9dlU9UlVHq+pd0/irqur+qvry9HrxRs8V2ByqaktVfa6qPj59drwAllRVr6yqj1bVl6Y/a7zBMQNYTlX9+PR3koeq6iNV9c2OGcCsqvpAVT1VVQ/NjC17nKiqO6vqWFU9WlU3bMysz43Qx7yeSfIT3f0dSa5LcntVXZnkjiQPdPfuJA9MnwGS5F1JHpn57HgBLOcXk3yyu789yT/M4rHDMQM4Q1VtS/KjSfZ099VZfPjjvjhmAH/bB5PceNrYkseJqW3sS3LVtM1dVbVl/aa6MkIfc+nuk9392en9N7L4B/BtSfYmuWda7Z4kN23MDIHNpKq2J/n+JO+bGXa8AM5QVa9I8l1J3p8k3f0X3f3HccwAlreQ5CVVtZDkpUmeiGMGMKO7P5Xk66cNL3ec2Jvk3u5+ursfS3IsyTXrMtE5CH2smqraleS1SQ4nuay7TyaLMTDJpRs3M2AT+YUkP5Xkr2bGHC+ApXxbklNJfnm63P99VfWyOGYAS+juryb5uSTHk5xM8ifd/ZtxzADObrnjxLYkj8+sd2Ia29SEPlZFVb08ya8m+bHu/tONng+w+VTVm5M81d2f2ei5AOeFhSSvS/Le7n5tkj+PS+6AZUz31Nqb5Iokr07ysqp6x8bOCjjP1RJjve6zOEdCH3OrqhdlMfJ9uLs/Ng0/WVWXT8svT/LURs0P2DTemOQtVfWHSe5N8t1V9StxvACWdiLJie4+PH3+aBbDn2MGsJTvSfJYd5/q7r9M8rEk3xnHDODsljtOnEiyY2a97Vm8JcCmJvQxl6qqLN4755Hu/vmZRYeS3DK9vyXJfes9N2Bz6e47u3t7d+/K4k1tf6u73xHHC2AJ3f21JI9X1WumoeuTPBzHDGBpx5NcV1Uvnf6Ocn0W7x/umAGczXLHiUNJ9lXVRVV1RZLdSR7cgPmdk+re9GcdsolV1T9O8j+TfDHP3XPrZ7J4n76DSXZm8X+6b+vu0294CVygqupNSX6yu99cVd8SxwtgCVX1j7L48J4XJ/lKkh/K4j9UO2YAZ6iqf5vkXyR5JsnnkvxIkpfHMQOYVNVHkrwpySVJnkzy7iT/LcscJ6rqZ5P8cBaPKz/W3Z/YgGmfE6EPAAAAAAbg0l0AAAAAGIDQBwAAAAADEPoAAAAAYABCHwAAAAAMQOgDAAAAgAEIfQAAJEmq6p9WVVfVt2/0XAAAOHdCHwAAf+3tSX43yb6NnggAAOdO6AMAIFX18iRvTHJrptBXVd9UVXdV1dGq+nhV/XpVvXVa9vqq+h9V9Zmq+o2qunwDpw8AQIQ+AAAW3ZTkk939B0m+XlWvS/LPkuxK8veT/EiSNyRJVb0oyX9K8tbufn2SDyTZvxGTBgDgOQsbPQEAADaFtyf5hen9vdPnFyX5r939V0m+VlW/PS1/TZKrk9xfVUmyJcnJ9Z0uAACnE/oAAC5wVfUtSb47ydVV1VkMd53k15bbJMnR7n7DOk0RAIAXwKW7AAC8NcmHuvtbu3tXd+9I8liSP0ryz6d79V2W5E3T+o8m2VpVf3Mpb1VdtRETBwDgOUIfAABvz5ln7/1qklcnOZHkoST/JcnhJH/S3X+RxTj476vqC0k+n+Q712+6AAAspbp7o+cAAMAmVVUv7+4/my7vfTDJG7v7axs9LwAAzuQefQAAPJ+PV9Urk7w4yb8T+QAANi9n9AEAAADAANyjDwAAAAAGIPQBAAAAwACEPgAAAAAYgNAHAAAAAAMQ+gAAAABgAEIfAAAAAAzg/wPgaLINVq4LUQAAAABJRU5ErkJggg==\n",
                        "text/plain": "<Figure size 1584x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# your code goes here\nplt.figure(figsize=(22,6))\nsns.distplot(df['Age'],kde = False)"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0"
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 864x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.figure(figsize=(12,8))\nsns.boxplot(data = df['Age'],orient = 'v')\ndf[(df['Age'] <= 15)].Age.count()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Outliers\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Finding outliers\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find out if outliers exist in the column `ConvertedComp` using a box plot?\n"
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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CNsyqkdVau2KV83+Z5C9PcW5fkn0/22gAAM9e7vgOjIKbkQIbTWQBg+dmpEAfRBYweG5GCvRBZAGD52akQB9EFjB4bkYK9EFkAYM3Ozu7bCfLbRyAjSCygME78eajbkYKbASRBQzeRz/60WXrG264oadJgDERWcDg/fVf//WytXcXAhtBZAEAdEBkAQB0QGQBg/fmN7952dq7C4GNILKAwfvd3/3dZevf/u3f7mkSYExEFjAKx3ez7GIBG6Vaa33P8FOmp6fb3Nxc32MAAKyqqg601qZPPG4nCxiF+fn5XHXVVZmfn+97FGAkRBYwCnv27Mndd9/tHlnAhhFZwODNz8/nlltuSWstt9xyi90sYEOILGDw9uzZ89QHRD/xxBN2s4ANIbKAwdu/f3+Ov8mntZbbb7+954mAMRBZwOCde+65K64BuiCygMH77ne/u+IaoAsiCxi87du3p6qSJFWVN77xjT1PBIyByAIGb3Z2NpOTk0mSyclJd30HNoTIAgZvamoq5513XpLkvPPOy9TUVM8TAWMgsoDBm5+fz+HDh5Mk3/72t90nC9gQIgsYPPfJAvogsoDB279//7K1+2QBG0FkAYN34muwzj777J4mAcZEZAGD953vfGfZ+sEHH+xpEmBMRBYAQAdEFjB4v/7rv75svW3btp4mAcZEZAGD99a3vjWbNh37c7dp06bs2rWr54mAMRBZwOBNTU09tXu1fft2NyMFNsRk3wMAbIS3vvWt+ed//me7WMCGsZMFjMJ73/ve3HXXXbn66qv7HgUYCZEFjMJ9992XJLn77rt7ngQYC5EFDN7b3/72Zesrr7yyp0mAMRFZwOAd38U6zm4WsBFEFgBAB0QWAEAHRBYweC9/+cuXrV/xilf0NAkwJiILGLyPfOQjy9a7d+/uaRJgTEQWMArHd7PsYgEbxR3fgVE4cTcLoGt2sgAAOiCyAAA6ILIAADogsgAAOiCyAAA6ILKAUfj0pz+drVu35jOf+UzfowAjIbKAUfjTP/3TJMmf/Mmf9DwJMBYiCxi8T3/601lYWEiSLCws2M0CNsSqkVVVN1TVQ1V1zynOv7mq7lr8+lJVXbzk3KGquruqvlpVc+s5OMBaHd/FOs5uFrAR1rKT9bEkMyuc/2aS17fWXpnk/0xy/Qnn39Bae1VrbfpnGxHgmTm+i3WqNUAXVo2s1trnkzy8wvkvtdYeWVx+Jcn56zQbwLqYmJhYcQ3QhfV+TdZbktyyZN2S3F5VB6pq10oPrKpdVTVXVXNHjhxZ57GAMXvVq161bP0rv/IrPU0CjMm6fUB0Vb0hxyLrtUsOv6a19mBVvSjJ/qr6+uLO2E9prV2fxacap6en23rNBXDfffctW3/ta1/raRJgTNZlJ6uqXpnkPyfZ0VqbP368tfbg4n8fSvKpJJesx78H8HRs27btqacIJyYmsn379p4nAsbgGUdWVf2rJH+b5Ldaa/+05Pjzq+qFx79P8sYkJ32HIkCXZmdns2nTsT93mzZtys6dO3ueCBiDVZ8urKqPJ9ma5OyqOpzkfUmekyStteuS/HGSqSR/VVVJsrD4TsJzk3xq8dhkkr9prd3awe8AsKKpqak897nPzRNPPJHnPve5mZqa6nskYARWjazW2hWrnP+dJL9zkuP3J7n4px8BsLEOHjyYxx57LEny2GOP5eDBg9myZUvPUwFD547vwOB94AMfWHEN0AWRBQzeoUOHVlwDdEFkAYO3efPmFdcAXRBZwOC9973vXXEN0AWRBQzeiS9y96J3YCOILGDw/uzP/mzZ+i/+4i96mgQYE5EFDN4tt9yybH3zzTf3NAkwJiILAKADIgsAoAMiCxi8Sy+9dNn6sssu62kSYExEFjB4f/RHf7Rs/Qd/8Ac9TQKMicgCBm9ubm7Z+sCBAz1NAoyJyAIG7+qrr162ft/73tfPIMCoiCxg8B577LEV1wBdEFkAAB0QWQAAHRBZwOBNTk6uuAbogsgCBu+Vr3zlsvXFF1/c0yTAmIgsYPC+/vWvL1vfd999PU0CjInIAgbPThbQB5EFDN7dd9+9bH3XXXf1NAkwJiILGLwf/vCHK64BuiCygMF7wQtesOIaoAsiCxi8Ez9W5/3vf38/gwCjIrKAwbvggguWrTdv3tzPIMCoiCxg8Pbs2ZNNm479udu0aVP27t3b80TAGIgsYPDuuOOOPPnkk0mSJ598Mvv37+95ImAMRBYweNu2bVu23r59e0+TAGMisoDBe93rXrfiGqALIgsYvA996EPL1tdcc01PkwBjIrKAwXvggQdWXAN0QWQBAHRAZAGD9/rXv37ZeuvWrf0MAoyKyAIG76qrrlq2vvLKK3uaBBgTkQUM3q233rps7T5ZwEYQWcDgffSjH122vu6663qaBBgTkQUA0AGRBQDQAZEFDN7LX/7yZeuLLrqop0mAMRFZwOB961vfWrY+dOhQP4MAoyKygMHzAdFAH0QWMHizs7PL1jt37uxpEmBMRBYweI888siKa4AuiCxg8D7wgQ+suAbogsgCBu/EF7p74TuwEUQWMHibN29ecQ3QBZEFDN573/veFdcAXRBZwOBt2bIl559/fpLk/PPPz5YtW3qeCBgDkQWMwvGwEljARhFZwODNz8/ni1/8YpLkS1/6Uubn53ueCBgDkQUM3p49e7KwsJAkeeKJJ7J3796eJwLGQGQBg7d///601pIkrbXcfvvtPU8EjIHIAgbv3HPPXXEN0IVVI6uqbqiqh6rqnlOcr6q6tqoOVtVdVfXqJedmquobi+fes56DA6zVd7/73RXXAF1Yy07Wx5LMrHD+0iQXLn7tSvKRJKmqiSQfXjx/UZIrquqiZzIswM/i8ccfX3EN0IVVI6u19vkkD69wyY4ke9sxX0ny81X14iSXJDnYWru/tfaTJJ9YvBYAYPDW4zVZ5yV5YMn68OKxUx0/qaraVVVzVTV35MiRdRgLAKA/6xFZdZJjbYXjJ9Vau761Nt1amz7nnHPWYSwAgP5MrsPPOJzkJUvW5yd5MMkZpzgOADB467GTdVOSnYvvMvzVJN9vrX0nyZ1JLqyqC6rqjCSXL14LADB4q+5kVdXHk2xNcnZVHU7yviTPSZLW2nVJ9iV5U5KDSX6U5D8tnluoqnckuS3JRJIbWmv3dvA7AACcdlaNrNbaFaucb0l+7xTn9uVYhAH0ZnJy8qmP1Tm+BuiaO74Dg/fkk0+uuAbogsgCBk9kAX0QWQAAHRBZAAAdEFkAAB0QWQAAHRBZwOCdeMuG5zznOT1NAoyJyAIGb+k9spLkiSee6GkSYExEFgBAB0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYAQAdEFgBAB0QWMHgTExMrrgG6ILKAwdu2bduKa4AuiCxg8Hbt2rXiGqALIgsYvEceeWTFNUAXRBYweO9///tXXAN0QWQBg/fAAw+suAbogsgCAOiAyAIGb9OmTSuuAbrgLw0weGedddaKa4AuiCxg8H74wx+uuAbogsgCBs9OFtAHkQUM3uOPP77iGqALIgsAoAMiCwCgAyILAKADIgsAoAMiCwCgAyILAKADIgsAoAMiCwCgAyILAKADIgsAoAMiCwCgAyILAKADIgsAoAMiCxi8iYmJFdcAXRBZwOAdPXp0xTVAF0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYAQAfWFFlVNVNV36iqg1X1npOc/8Oq+uri1z1VdbSqfmHx3KGqunvx3Nx6/wIAAKejydUuqKqJJB9Osj3J4SR3VtVNrbWvHb+mtfbnSf588frLkvx+a+3hJT/mDa21763r5AAAp7G17GRdkuRga+3+1tpPknwiyY4Vrr8iycfXYzgAgGertUTWeUkeWLI+vHjsp1TV85LMJPnkksMtye1VdaCqdp3qH6mqXVU1V1VzR44cWcNYAACnr7VEVp3kWDvFtZcl+eIJTxW+prX26iSXJvm9qnrdyR7YWru+tTbdWps+55xz1jAWAMDpay2RdTjJS5asz0/y4CmuvTwnPFXYWntw8b8PJflUjj39CAAwaGuJrDuTXFhVF1TVGTkWUjedeFFV/Yskr09y45Jjz6+qFx7/Pskbk9yzHoMDAJzOVn13YWttoarekeS2JBNJbmit3VtVb1s8f93ipf8hye2ttR8uefi5ST5VVcf/rb9prd26nr8AwGrOPPPM/PjHP162BujaqpGVJK21fUn2nXDsuhPWH0vysROO3Z/k4mc0IcAztDSwTrYG6II7vgMAdEBkAQB0QGQBAHRAZAGDNzExseIaoAsiCxi8o0ePrrgG6ILIAgDogMgCAOiAyAIA6IDIAgDogMgCAOiAyAIA6IDIAgbvla985bL1q1/96p4mAcZEZAGD9+ijjy5bP/zwwz1NAoyJyAIG79ChQyuuAbogsoDBO/PMM5etzzrrrJ4mAcZEZAGD9+Mf/3jZ+vHHH+9pEmBMRBYAQAdEFgBAB0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYweGefffay9Yte9KKeJgHGRGQBg/e9731v2fqhhx7qaRJgTEQWAEAHRBYAQAdEFgBAB0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYAQAdEFgBAB0QWAEAHRBYweJs2bVpxDdAFf2mAwZuYmFhxDdAFkQUM3hNPPLHiGqALIgsAoAMiCwCgAyILGLwXvvCFy9Y/93M/19MkwJiILGDwfvCDHyxbP/rooz1NAoyJyAIA6IDIAgDogMgCAOiAyAIA6IDIAgbvxS9+8bL1L/7iL/Y0CTAmIgsYvJe97GXL1i996Ut7mgQYkzVFVlXNVNU3qupgVb3nJOe3VtX3q+qri19/vNbHAnTty1/+8oprgC5MrnZBVU0k+XCS7UkOJ7mzqm5qrX3thEu/0Fr7n37GxwJ0ZmFhYcU1QBfWspN1SZKDrbX7W2s/SfKJJDvW+POfyWMB1sXRo0dXXAN0YS2RdV6SB5asDy8eO9G/q6p/rKpbqurfPM3Hpqp2VdVcVc0dOXJkDWMBAJy+1hJZdZJj7YT1PyT5pdbaxUl2J/m7p/HYYwdbu761Nt1amz7nnHPWMBYAwOlrLZF1OMlLlqzPT/Lg0gtaa4+21h5b/H5fkudU1dlreSwAwBCtJbLuTHJhVV1QVWckuTzJTUsvqKp/WVW1+P0liz93fi2PBQAYolXfXdhaW6iqdyS5LclEkhtaa/dW1dsWz1+X5D8meXtVLSR5PMnlrbWW5KSP7eh3AQA4bawaWclTTwHuO+HYdUu+/8skf7nWxwIADJ07vgMAdEBkAQB0QGQBAHRAZAEAdEBkAQB0QGQBAHRAZAEAdEBkAQB0QGQBAHRAZAEAdEBkAYO3efPmZetf/uVf7mcQYFREFjB4hw4dWra+//77+xkEGBWRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQgTVFVlXNVNU3qupgVb3nJOffXFV3LX59qaouXnLuUFXdXVVfraq59RweAOB0NbnaBVU1keTDSbYnOZzkzqq6qbX2tSWXfTPJ61trj1TVpUmuT/I/LDn/htba99ZxbgCA09padrIuSXKwtXZ/a+0nST6RZMfSC1prX2qtPbK4/EqS89d3TACAZ5e1RNZ5SR5Ysj68eOxU3pLkliXrluT2qjpQVbtO9aCq2lVVc1U1d+TIkTWMBQBw+lr16cIkdZJj7aQXVr0hxyLrtUsOv6a19mBVvSjJ/qr6emvt8z/1A1u7PseeZsz09PRJfz4AwLPFWnayDid5yZL1+UkePPGiqnplkv+cZEdrbf748dbag4v/fSjJp3Ls6UcAgEFbS2TdmeTCqrqgqr4FqpkAAAXQSURBVM5IcnmSm5ZeUFX/KsnfJvmt1to/LTn+/Kp64fHvk7wxyT3rNTwAwOlq1acLW2sLVfWOJLclmUhyQ2vt3qp62+L565L8cZKpJH9VVUmy0FqbTnJukk8tHptM8jettVs7+U0AAE4ja3lNVlpr+5LsO+HYdUu+/50kv3OSx92f5OITjwMADJ07vgMAdEBkAQB0QGQBAHRgTa/JApLdu3fn4MGDfY/BOnnnO9/Z9wg8DVu2bMmVV17Z9xjwtNjJAgDoQLV2+t1cfXp6us3NzfU9BjAQW7du/aljn/3sZzd8DmCYqurA4q2rlrGTBQzey1/+8mXrV7ziFT1NAoyJyAIG7yMf+ciy9e7du3uaBBgTkQWMwplnnpnELhawcby7EBiFl73sZUmSD33oQz1PAoyFnSwAgA6ILACADogsAIAOiCwAgA6ILACADogsAIAOiCwAgA6ILACADogsAIAOiCwAgA6ILACADogsAIAOiCwAgA6ILACADogsAIAOiCwAgA6ILACADogsAIAOiCwAgA6ILACADkz2PcCY7N69OwcPHux7DBil4/+79853vrPnSWB8tmzZkiuvvLLvMTacyNpABw8ezFfvuS9Hn/cLfY8Co7PpJy1JcuD+7/Y8CYzLxI8e7nuE3oisDXb0eb+Qx//1m/oeAwA2xFlf39f3CL3xmiwAgA6ILACADogsAIAOiCwAgA6ILACADogsAIAOiCwAgA6ILACADrgZ6Qb69re/nYkffX/UN2YDYFwmfjSfb397oe8xemEnCwCgA3ayNtB5552Xf/7/Jn2sDgCjcdbX9+W8887te4xe2MkCAOiAyAIA6IDIAgDogMgCAOiAF75vsIkfPewWDtCDTT9+NEny5Jk/1/MkMC4TP3o4yThf+C6yNtCWLVv6HgFG6+DBHyRJtvzyOP/YQ3/OHe3//RNZG+jKK6/sewQYrXe+851Jkg996EM9TwKMhddkAQB0QGQBAHRgTZFVVTNV9Y2qOlhV7znJ+aqqaxfP31VVr17rYwEAhmjVyKqqiSQfTnJpkouSXFFVF51w2aVJLlz82pXkI0/jsQAAg7OWF75fkuRga+3+JKmqTyTZkeRrS67ZkWRva60l+UpV/XxVvTjJ5jU8Fp4Vdu/enYMHD/Y9Bj+j4/+zO/4CeJ5dtmzZ4s1DPOus5enC85I8sGR9ePHYWq5Zy2OTJFW1q6rmqmruyJEjaxgLYO3OOuusnHXWWX2PAYzIWnay6iTH2hqvWctjjx1s7fok1yfJ9PT0Sa+BPvn/ogF4OtYSWYeTvGTJ+vwkD67xmjPW8FgAgMFZy9OFdya5sKouqKozklye5KYTrrkpyc7Fdxn+apLvt9a+s8bHAgAMzqo7Wa21hap6R5LbkkwkuaG1dm9VvW3x/HVJ9iV5U5KDSX6U5D+t9NhOfhMAgNNIHXtD4Ollenq6zc3N9T0GAMCqqupAa236xOPu+A4A0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0IFqrfU9w0+pqiNJvtX3HMDgnJ3ke30PAQzOL7XWzjnx4GkZWQBdqKq51tp033MA4+DpQgCADogsAIAOiCxgTK7vewBgPLwmCwCgA3ayAAA6ILIAADogsoDBq6qZqvpGVR2sqvf0PQ8wDl6TBQxaVU0k+ack25McTnJnkitaa1/rdTBg8OxkAUN3SZKDrbX7W2s/SfKJJDt6ngkYAZEFDN15SR5Ysj68eAygUyILGLo6yTGvkwA6J7KAoTuc5CVL1ucnebCnWYAREVnA0N2Z5MKquqCqzkhyeZKbep4JGIHJvgcA6FJrbaGq3pHktiQTSW5ord3b81jACLiFAwBABzxdCADQAZEFANABkQUA0AGRBQDQAZEFANABkQUA0AGRBQDQgf8fqnQfFNgjbtMAAAAASUVORK5CYII=\n",
                        "text/plain": "<Figure size 720x720 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# your code goes here\nplt.figure(figsize=(10,10))\nax = sns.boxplot(data = df['ConvertedComp'],orient = 'v')\n\n# Yes, there are outliers and that too a lot of."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find out the Inter Quartile Range for the column `ConvertedComp`.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "IQR : 73132.0\n"
                }
            ],
            "source": "# your code goes here\nQ1 = df['ConvertedComp'].quantile(0.25)\nQ3 = df['ConvertedComp'].quantile(0.75)\nIQR = Q3 - Q1\nprint('IQR :',IQR)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find out the upper and lower bounds.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "lower bounds: -82830.0\nupper bounds: 209698.0\n"
                }
            ],
            "source": "# your code goes here\nlower_limit = Q1 - 1.5 * IQR\nupper_limit = Q3 + 1.5 * IQR\n\nprint('lower bounds: ' + str(lower_limit))\nprint('upper bounds: ' + str(upper_limit))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Identify how many outliers are there in the `ConvertedComp` column.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "879"
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\ndf[(df['ConvertedComp'] < lower_limit) | (df['ConvertedComp'] > upper_limit)].ConvertedComp.count()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "count     10582.000000\nmean      72327.661406\nstd       58619.926538\nmin           0.000000\n25%       26868.000000\n50%       57745.000000\n75%      100000.000000\nmax      209698.000000\nName: ConvertedComp, dtype: float64"
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# your code goes here\ndf_n = df['ConvertedComp'].clip(lower=lower_limit, upper=upper_limit, axis=0)\ndf_n.describe()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Correlation\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Finding correlation\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find the correlation between `Age` and all other numerical columns.\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": "# your code goes here\ndf.corr()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Ramesh Sannareddy\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Other Contributors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Rav Ahuja\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n| ----------------- | ------- | ----------------- | ---------------------------------- |\n| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": " Copyright \u00a9 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.7",
            "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",
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