M3ExploratoryDataAnalysis-lab.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# **Exploratory Data Analysis Lab**\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Estimated time needed: **30** minutes\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Objectives\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "In this lab you will perform the following:\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "* * *\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Hands on Lab\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Import the pandas module.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np",
            "execution_count": 90,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Load the dataset into a dataframe.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m2_survey_data.csv\")",
            "execution_count": 91,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df.shape",
            "execution_count": 130,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 130,
                    "data": {
                        "text/plain": "(11398, 85)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Distribution\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Determine how the data is distributed\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Plot the distribution curve for the column `ConvertedComp`.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\ndf.ConvertedComp.plot.density(color='green')\nplt.title('Visualization of Converted Compensation')\nplt.show()",
            "execution_count": 92,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Plot the histogram for the column `ConvertedComp`.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\ndf.hist(column='ConvertedComp', bins=5)",
            "execution_count": 93,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 93,
                    "data": {
                        "text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2bd637ca90>]],\n      dtype=object)"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": "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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "What is the median of the column `ConvertedComp`?\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\ndf['ConvertedComp'].median()",
            "execution_count": 94,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 94,
                    "data": {
                        "text/plain": "57745.0"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "\nHow many responders identified themselves only as a **Man**?\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\ndf1=df.loc[df['Gender']=='Man']\ndf1['Gender'].count()",
            "execution_count": 95,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 95,
                    "data": {
                        "text/plain": "10480"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df['Gender'].value_counts() #Control of calculated values",
            "execution_count": 96,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 96,
                    "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"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Find out the  median ConvertedComp of responders identified themselves only as a **Woman**?\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\ndf2=df[['ConvertedComp','Gender']]\ndf3=df2.loc[df2['Gender']=='Woman']\ndf3.median()",
            "execution_count": 97,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 97,
                    "data": {
                        "text/plain": "ConvertedComp    57708.0\ndtype: float64"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Give the five number summary for the column `Age`?\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "**Double click here for hint**.\n\n<!--\nmin,q1,median,q3,max of a column are its five number summary.\n-->\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nmin=df['Age'].min()\nmax=df['Age'].max()\nmedian=df['Age'].median()\nq1=df['Age'].quantile(q=0.25)\nq3=df['Age'].quantile(q=0.75)\nprint('Age Distribution')\nprint('Minimum age: ', min)\nprint('Maximum age: ', max)\nprint()\nprint('Quartile 1 (25%)is: ', q1)\nprint('Quartile 3 (75%)is: ', q3)\nprint()\nprint('The median is: ', median)\ndf['Age'].describe() #Control of values",
            "execution_count": 98,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Age Distribution\nMinimum age:  16.0\nMaximum age:  99.0\n\nQuartile 1 (25%)is:  25.0\nQuartile 3 (75%)is:  35.0\n\nThe median is:  29.0\n",
                    "name": "stdout"
                },
                {
                    "output_type": "execute_result",
                    "execution_count": 98,
                    "data": {
                        "text/plain": "count    11111.000000\nmean        30.778895\nstd          7.393686\nmin         16.000000\n25%         25.000000\n50%         29.000000\n75%         35.000000\nmax         99.000000\nName: Age, dtype: float64"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Plot a histogram of the column `Age`.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\ndf.hist(column='Age', figsize=(8,4))",
            "execution_count": 100,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 100,
                    "data": {
                        "text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2bd6314d10>]],\n      dtype=object)"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 576x288 with 1 Axes>",
                        "image/png": "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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df.boxplot(column='Age', figsize=(10,10))",
            "execution_count": 145,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 145,
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f2bd639b150>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 720x720 with 1 Axes>",
                        "image/png": "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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Outliers\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Finding outliers\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Find out if outliers exist in the column `ConvertedComp` using a box plot?\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nquantile=df['ConvertedComp'].quantile([0, 1.0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])\nprint(quantile)\ndf.boxplot(column='ConvertedComp', figsize=(8,10))",
            "execution_count": 108,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "0.0          0.0\n1.0    2000000.0\n0.2      20628.0\n0.3      32842.2\n0.4      45797.0\n0.5      57745.0\n0.6      70998.0\n0.7      88000.0\n0.8     113967.4\n0.9     170000.0\n1.0    2000000.0\nName: ConvertedComp, dtype: float64\n",
                    "name": "stdout"
                },
                {
                    "output_type": "execute_result",
                    "execution_count": 108,
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f2bd735f1d0>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 576x720 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Find out the Inter Quartile Range for the column `ConvertedComp`.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nq1=df['ConvertedComp'].quantile(q=0.25)\nq3=df['ConvertedComp'].quantile(q=0.75)\niqr=q3-q1\nprint('The Inter Quartile Range for the column ConvertedComp is: ', iqr)",
            "execution_count": 113,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "The Inter Quartile Range for the column ConvertedComp is:  73132.0\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Find out the upper and lower bounds.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\nmin2=df['ConvertedComp'].min()\nmax2=df['ConvertedComp'].max()\n\nprint('The lower boundary is: ', min2)\nprint('The upper boundary is: ', max2)",
            "execution_count": 114,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "The lower boundary is:  0.0\nThe upper boundary is:  2000000.0\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Identify how many outliers are there in the `ConvertedComp` column.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\noutliers=((df['ConvertedComp'] < (q1 - 1.5 * iqr)) | (df['ConvertedComp'] > (q3 + 1.5 * iqr))).sum()\nprint('There are ' ,outliers, ' in the column ConvertedComp.')",
            "execution_count": 125,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "There are  879  in the column ConvertedComp.\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\ndel_convertedcomp=df[~(df['ConvertedComp'] > (q3 + 1.5 * iqr))]\nremoved=df.shape[0]- del_convertedcomp.shape[0]\noriginal_df=df.shape[0]\nnew_df=del_convertedcomp.shape[0]\nprint(removed, 'outliers has been removed from the original dataframe.')\nprint('The original dataframe contained ',original_df,' entries.')\nprint('The new dataframe contains ',new_df,' entries.')",
            "execution_count": 142,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "879 outliers has been removed from the original dataframe.\nThe original dataframe contained  11398  entries.\nThe new dataframe contains  10519  entries.\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "del_median=del_convertedcomp['ConvertedComp'].median()\nprint('The median after removing the outliers is: ', del_median)",
            "execution_count": 143,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "The median after removing the outliers is:  52704.0\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "del_mean=del_convertedcomp['ConvertedComp'].mean()\nprint('The mean after removing the outliers is: ', del_mean)",
            "execution_count": 144,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "The mean after removing the outliers is:  59883.20838915799\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Correlation\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Finding correlation\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Find the correlation between `Age` and all other numerical columns.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# your code goes here\ndf.corr()",
            "execution_count": 148,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 148,
                    "data": {
                        "text/plain": "               Respondent  CompTotal  ConvertedComp  WorkWeekHrs  CodeRevHrs  \\\nRespondent       1.000000  -0.013490       0.002181    -0.015314    0.004621   \nCompTotal       -0.013490   1.000000       0.001037     0.003510    0.007063   \nConvertedComp    0.002181   0.001037       1.000000     0.021143   -0.033865   \nWorkWeekHrs     -0.015314   0.003510       0.021143     1.000000    0.026517   \nCodeRevHrs       0.004621   0.007063      -0.033865     0.026517    1.000000   \nAge              0.004041   0.006970       0.105386     0.036518   -0.020469   \n\n                    Age  \nRespondent     0.004041  \nCompTotal      0.006970  \nConvertedComp  0.105386  \nWorkWeekHrs    0.036518  \nCodeRevHrs    -0.020469  \nAge            1.000000  ",
                        "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>CompTotal</th>\n      <th>ConvertedComp</th>\n      <th>WorkWeekHrs</th>\n      <th>CodeRevHrs</th>\n      <th>Age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Respondent</th>\n      <td>1.000000</td>\n      <td>-0.013490</td>\n      <td>0.002181</td>\n      <td>-0.015314</td>\n      <td>0.004621</td>\n      <td>0.004041</td>\n    </tr>\n    <tr>\n      <th>CompTotal</th>\n      <td>-0.013490</td>\n      <td>1.000000</td>\n      <td>0.001037</td>\n      <td>0.003510</td>\n      <td>0.007063</td>\n      <td>0.006970</td>\n    </tr>\n    <tr>\n      <th>ConvertedComp</th>\n      <td>0.002181</td>\n      <td>0.001037</td>\n      <td>1.000000</td>\n      <td>0.021143</td>\n      <td>-0.033865</td>\n      <td>0.105386</td>\n    </tr>\n    <tr>\n      <th>WorkWeekHrs</th>\n      <td>-0.015314</td>\n      <td>0.003510</td>\n      <td>0.021143</td>\n      <td>1.000000</td>\n      <td>0.026517</td>\n      <td>0.036518</td>\n    </tr>\n    <tr>\n      <th>CodeRevHrs</th>\n      <td>0.004621</td>\n      <td>0.007063</td>\n      <td>-0.033865</td>\n      <td>0.026517</td>\n      <td>1.000000</td>\n      <td>-0.020469</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>0.004041</td>\n      <td>0.006970</td>\n      <td>0.105386</td>\n      <td>0.036518</td>\n      <td>-0.020469</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Authors\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Ramesh Sannareddy\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Other Contributors\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Rav Ahuja\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Change Log\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
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