EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis Lab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset into a dataframe\n",
    "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",
    "#### Determining how data is being distributed\n",
    "\n",
    "The column <b>ConvertedComp</b> contains Salary converted to annual USD salaries using the exchange rate on 2019-02-01, which assumes 12 working months, and 50 working weeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.distplot( a = df[\"ConvertedComp\"], bins = 20, hist = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57745.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Media of the column \"ConvertedComp\"\n",
    "df[\"ConvertedComp\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Man                                                            10480\n",
       "Woman                                                            731\n",
       "Non-binary, genderqueer, or gender non-conforming                 63\n",
       "Man;Non-binary, genderqueer, or gender non-conforming             26\n",
       "Woman;Non-binary, genderqueer, or gender non-conforming           14\n",
       "Woman;Man                                                          9\n",
       "Woman;Man;Non-binary, genderqueer, or gender non-conforming        2\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many responders identified themselves only as a Man\n",
    "df[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57708.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median of \"ConvertedComp\" responders identified only as \"Woman\"\n",
    "woman = df[df[\"Gender\"] == \"Woman\"]\n",
    "woman[\"ConvertedComp\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11111.000000\n",
       "mean        30.778895\n",
       "std          7.393686\n",
       "min         16.000000\n",
       "25%         25.000000\n",
       "50%         29.000000\n",
       "75%         35.000000\n",
       "max         99.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age summary\n",
    "df[\"Age\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of the column \"Age\"\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.displot(a = df[\"Age\"], bins = 20, kde = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Finding Outliers </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding out if outliers exist in the column \"ConvertedComp\" using\n",
    "# box plot\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x = df.ConvertedComp, data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.058200e+04\n",
       "mean     1.315967e+05\n",
       "std      2.947865e+05\n",
       "min      0.000000e+00\n",
       "25%      2.686800e+04\n",
       "50%      5.774500e+04\n",
       "75%      1.000000e+05\n",
       "max      2.000000e+06\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding out the Inter Quartile Range for the column \"ConvertedComp\"\n",
    "df[\"ConvertedComp\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73132.0\n"
     ]
    }
   ],
   "source": [
    "# Finding out the upper and lower bounds\n",
    "Q1 = df[\"ConvertedComp\"].quantile(0.25)\n",
    "Q3 = df[\"ConvertedComp\"].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    10519\n",
       "True       879\n",
       "Name: ConvertedComp, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying how many outliers are there in the \"ConvertedComp\" column.\n",
    "outliers = (df[\"ConvertedComp\"] < (Q1 - 1.5 * IQR)) | (df[\"ConvertedComp\"] > (Q3 + 1.5 * IQR))\n",
    "outliers.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    11398\n",
       "Name: ConvertedComp, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less = (df[\"ConvertedComp\"] < (Q1 - 1.5 * IQR))\n",
    "less.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    10519\n",
       "True       879\n",
       "Name: ConvertedComp, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more = (df[\"ConvertedComp\"] > (Q3 + 1.5 * IQR))\n",
    "more.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59883.20838915799"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new Dataframe by removing the outliers from the \"ConvertedComp\" column\n",
    "RemoveConvertedComp = df[~(df[\"ConvertedComp\"] > (Q3 + 1.5 * IQR))]\n",
    "RemoveConvertedComp.head()\n",
    "RemoveConvertedComp[\"ConvertedComp\"].median()\n",
    "RemoveConvertedComp[\"ConvertedComp\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Correlation </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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>"
      ],
      "text/plain": [
       "               Respondent  CompTotal  ConvertedComp  WorkWeekHrs  CodeRevHrs  \\\n",
       "Respondent       1.000000  -0.013490       0.002181    -0.015314    0.004621   \n",
       "CompTotal       -0.013490   1.000000       0.001037     0.003510    0.007063   \n",
       "ConvertedComp    0.002181   0.001037       1.000000     0.021143   -0.033865   \n",
       "WorkWeekHrs     -0.015314   0.003510       0.021143     1.000000    0.026517   \n",
       "CodeRevHrs       0.004621   0.007063      -0.033865     0.026517    1.000000   \n",
       "Age              0.004041   0.006970       0.105386     0.036518   -0.020469   \n",
       "\n",
       "                    Age  \n",
       "Respondent     0.004041  \n",
       "CompTotal      0.006970  \n",
       "ConvertedComp  0.105386  \n",
       "WorkWeekHrs    0.036518  \n",
       "CodeRevHrs    -0.020469  \n",
       "Age            1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Correlation between \"Age\" and, all ofther numerical columns\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result - Data Visualization & Dashboard link will be provided in in \"ReadME.md\" file.\n",
    "\n",
    "Screenshots of the visualization provided in the folder \"graphs\".\n",
    "Visit the link for an interactive experience."
   ]
  }
 ],
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