AR-M3ExploratoryDataAnalysis-lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork928-2023-01-01\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Exploratory Data Analysis Lab**\n",
    "## by: Andrei Ramani"
   ]
  },
  {
   "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",
    "\n",
    "In this assignment you will perform the task of exploratory data analysis.\n",
    "You 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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset into a dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\")\n",
    "df=pd.read_csv(\"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",
    "\n",
    "This 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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ConvertedComp', ylabel='Density'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "#sns.distplot(df.ConvertedComp)\n",
    "\n",
    "sns.histplot(df[\"ConvertedComp\"], kde=True, stat=\"density\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([9659.,  238.,  115.,  125.,   99.,  131.,   34.,   15.,   15.,\n",
       "         151.]),\n",
       " array([      0.,  200000.,  400000.,  600000.,  800000., 1000000.,\n",
       "        1200000., 1400000., 1600000., 1800000., 2000000.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Pt6vawcAAO2L30Pn008/1erVq5WYmKg//vGPevLJJzVr1iy98sorkqSqqipJUmxsrM/jYmNjnfuqqqoUFhamzp07NzsTExPT5PljYmKcmSvNnz9fXq/XuVVUVNzcYgEAQLsW4u8dNjY2qm/fvsrJyZEk9enTR/v379fq1av193//985cUFCQz+OMMU22XenKmavNN7cfl8sll8t13WsBAACBze9XdLp166ZevXr5bOvZs6eOHTsmSfJ4PJLU5KpLdXW1c5XH4/GooaFBNTU1zc6cPHmyyfOfOnWqydUiAADw/eT30Bk4cKAOHz7ss+2jjz7S7bffLklKSEiQx+NRcXGxc39DQ4NKSko0YMAASVJKSopCQ0N9ZiorK7Vv3z5nJi0tTV6vV7t373Zmdu3aJa/X68wAAIDvN7//6OqnP/2pBgwYoJycHI0bN067d+/W2rVrtXbtWknf/LgpOztbOTk5SkxMVGJionJyctSxY0dlZmZKktxutyZNmqTZs2crOjpaUVFRmjNnjnr37q3hw4dL+uYq0ahRozR58mStWbNGkjRlyhRlZGRc9RNXAADg+8fvodOvXz8VFBRo/vz5euaZZ5SQkKCVK1dqwoQJzszcuXNVV1enadOmqaamRqmpqSoqKlJERIQzs2LFCoWEhGjcuHGqq6vTsGHDlJ+fr+DgYGdm/fr1mjVrlvPprDFjxigvL8/fSwIAAAHK779HJ5C05HP4N4LfowMAgP+16e/RAQAAaC8IHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYK1WD53c3FwFBQUpOzvb2WaM0YIFCxQXF6fw8HANHTpU+/fv93lcfX29Zs6cqS5duqhTp04aM2aMjh8/7jNTU1OjrKwsud1uud1uZWVl6cyZM629JAAAECBaNXT27NmjtWvX6oc//KHP9iVLlmj58uXKy8vTnj175PF4NGLECJ09e9aZyc7OVkFBgTZt2qTt27fr3LlzysjI0KVLl5yZzMxMlZeXq7CwUIWFhSovL1dWVlZrLgkAAASQVgudc+fOacKECXrxxRfVuXNnZ7sxRitXrtTPfvYz/c3f/I2Sk5P18ssv66uvvtKGDRskSV6vV+vWrdOyZcs0fPhw9enTR7/5zW/04Ycf6r//+78lSQcPHlRhYaH+/d//XWlpaUpLS9OLL76o3//+9zp8+PBVj6m+vl61tbU+NwAAYK9WC53p06dr9OjRGj58uM/2I0eOqKqqSunp6c42l8ulIUOGaMeOHZKksrIyXbhwwWcmLi5OycnJzszOnTvldruVmprqzPTv319ut9uZuVJubq7zYy632634+Hi/rRcAALQ/rRI6mzZt0vvvv6/c3Nwm91VVVUmSYmNjfbbHxsY691VVVSksLMznStDVZmJiYprsPyYmxpm50vz58+X1ep1bRUVFyxcHAAACRoi/d1hRUaGf/OQnKioqUocOHb51LigoyOdrY0yTbVe6cuZq883tx+VyyeVyNfscAADAHn6/olNWVqbq6mqlpKQoJCREISEhKikp0a9//WuFhIQ4V3KuvOpSXV3t3OfxeNTQ0KCamppmZ06ePNnk+U+dOtXkahEAAPh+8nvoDBs2TB9++KHKy8udW9++fTVhwgSVl5frzjvvlMfjUXFxsfOYhoYGlZSUaMCAAZKklJQUhYaG+sxUVlZq3759zkxaWpq8Xq92797tzOzatUter9eZAQAA329+/9FVRESEkpOTfbZ16tRJ0dHRzvbs7Gzl5OQoMTFRiYmJysnJUceOHZWZmSlJcrvdmjRpkmbPnq3o6GhFRUVpzpw56t27t/Pm5p49e2rUqFGaPHmy1qxZI0maMmWKMjIylJSU5O9lAQCAAOT30Lkec+fOVV1dnaZNm6aamhqlpqaqqKhIERERzsyKFSsUEhKicePGqa6uTsOGDVN+fr6Cg4OdmfXr12vWrFnOp7PGjBmjvLy873w9AACgfQoyxpi2Poi2UltbK7fbLa/Xq8jISL/v/455b/t9n63ts0Wj2/oQAABoVku+f/NvXQEAAGsROgAAwFqEDgAAsBahAwAArEXoAAAAaxE6AADAWoQOAACwFqEDAACsRegAAABrEToAAMBahA4AALAWoQMAAKxF6AAAAGsROgAAwFqEDgAAsBahAwAArEXoAAAAaxE6AADAWoQOAACwFqEDAACsRegAAABrEToAAMBahA4AALAWoQMAAKxF6AAAAGsROgAAwFqEDgAAsBahAwAArEXoAAAAaxE6AADAWoQOAACwFqEDAACsRegAAABrEToAAMBahA4AALAWoQMAAKxF6AAAAGsROgAAwFqEDgAAsBahAwAArEXoAAAAaxE6AADAWoQOAACwFqEDAACsRegAAABrEToAAMBahA4AALAWoQMAAKxF6AAAAGsROgAAwFqEDgAAsBahAwAArEXoAAAAaxE6AADAWoQOAACwFqEDAACsRegAAABrEToAAMBahA4AALAWoQMAAKxF6AAAAGsROgAAwFp+D53c3Fz169dPERERiomJ0dixY3X48GGfGWOMFixYoLi4OIWHh2vo0KHav3+/z0x9fb1mzpypLl26qFOnThozZoyOHz/uM1NTU6OsrCy53W653W5lZWXpzJkz/l4SAAAIUH4PnZKSEk2fPl2lpaUqLi7WxYsXlZ6ervPnzzszS5Ys0fLly5WXl6c9e/bI4/FoxIgROnv2rDOTnZ2tgoICbdq0Sdu3b9e5c+eUkZGhS5cuOTOZmZkqLy9XYWGhCgsLVV5erqysLH8vCQAABKggY4xpzSc4deqUYmJiVFJSovvuu0/GGMXFxSk7O1tPP/20pG+u3sTGxmrx4sWaOnWqvF6vunbtqldffVXjx4+XJJ04cULx8fHasmWLRo4cqYMHD6pXr14qLS1VamqqJKm0tFRpaWk6dOiQkpKSmhxLfX296uvrna9ra2sVHx8vr9eryMhIv6/9jnlv+32fre2zRaPb+hAAAGhWbW2t3G73dX3/bvX36Hi9XklSVFSUJOnIkSOqqqpSenq6M+NyuTRkyBDt2LFDklRWVqYLFy74zMTFxSk5OdmZ2blzp9xutxM5ktS/f3+53W5n5kq5ubnOj7ncbrfi4+P9u1gAANCutGroGGP01FNPadCgQUpOTpYkVVVVSZJiY2N9ZmNjY537qqqqFBYWps6dOzc7ExMT0+Q5Y2JinJkrzZ8/X16v17lVVFTc3AIBAEC7FtKaO58xY4Y++OADbd++vcl9QUFBPl8bY5psu9KVM1ebb24/LpdLLpfreg4dAABYoNWu6MycOVObN2/W1q1bddtttznbPR6PJDW56lJdXe1c5fF4PGpoaFBNTU2zMydPnmzyvKdOnWpytQgAAHw/+T10jDGaMWOG3nzzTb3zzjtKSEjwuT8hIUEej0fFxcXOtoaGBpWUlGjAgAGSpJSUFIWGhvrMVFZWat++fc5MWlqavF6vdu/e7czs2rVLXq/XmQEAAN9vfv/R1fTp07Vhwwb97ne/U0REhHPlxu12Kzw8XEFBQcrOzlZOTo4SExOVmJionJwcdezYUZmZmc7spEmTNHv2bEVHRysqKkpz5sxR7969NXz4cElSz549NWrUKE2ePFlr1qyRJE2ZMkUZGRlX/cQVAAD4/vF76KxevVqSNHToUJ/tL730kh577DFJ0ty5c1VXV6dp06appqZGqampKioqUkREhDO/YsUKhYSEaNy4caqrq9OwYcOUn5+v4OBgZ2b9+vWaNWuW8+msMWPGKC8vz99LAgAAAarVf49Oe9aSz+HfCH6PDgAA/teufo8OAABAWyF0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFiL0AEAANYidAAAgLUIHQAAYC1CBwAAWIvQAQAA1iJ0AACAtQgdAABgLUIHAABYi9ABAADWInQAAIC1CB0AAGAtQgcAAFgrpK0PAO3LHfPebutDaLHPFo1u60MAALRTXNEBAADWInQAAIC1CB0AAGAt3qMDwFq85ww2CcS/z1Lb/50O+NB5/vnn9eyzz6qyslL33HOPVq5cqcGDB7f1YeE7FKgnf6Bp6/9YAcCNCOjQee2115Sdna3nn39eAwcO1Jo1a/TAAw/owIED6t69e1sfHmAVghJAIAro9+gsX75ckyZN0hNPPKGePXtq5cqVio+P1+rVq9v60AAAQDsQsFd0GhoaVFZWpnnz5vlsT09P144dO676mPr6etXX1ztfe71eSVJtbW2rHGNj/Vetsl8A9ur+09fb+hBabN/CkW19CN8Lgfo9pTW+x17epzHmmrMBGzqnT5/WpUuXFBsb67M9NjZWVVVVV31Mbm6uFi5c2GR7fHx8qxwjAHwfuFe29RGgPWvNvx9nz56V2+1udiZgQ+eyoKAgn6+NMU22XTZ//nw99dRTzteNjY368ssvFR0d/a2PuVG1tbWKj49XRUWFIiMj/brv9oD1BT7b18j6Ap/ta7R9fVLrrdEYo7NnzyouLu6aswEbOl26dFFwcHCTqzfV1dVNrvJc5nK55HK5fLbdeuutrXWIkqTIyEhr/wJLrM8Gtq+R9QU+29do+/qk1lnjta7kXBawb0YOCwtTSkqKiouLfbYXFxdrwIABbXRUAACgPQnYKzqS9NRTTykrK0t9+/ZVWlqa1q5dq2PHjunJJ59s60MDAADtQECHzvjx4/XFF1/omWeeUWVlpZKTk7VlyxbdfvvtbX1ocrlc+sUvftHkR2W2YH2Bz/Y1sr7AZ/sabV+f1D7WGGSu57NZAAAAAShg36MDAABwLYQOAACwFqEDAACsRegAAABrEToAAMBahM51ev7555WQkKAOHTooJSVFf/rTn5qdLykpUUpKijp06KA777xTL7zwQpOZN954Q7169ZLL5VKvXr1UUFDQWod/TS1Z35tvvqkRI0aoa9euioyMVFpamv74xz/6zOTn5ysoKKjJ7euvv27tpXyrlqzx3XffverxHzp0yGcuUF/Dxx577Krru+eee5yZ9vQabtu2TQ899JDi4uIUFBSk3/72t9d8TKCdgy1dY6Cdhy1dX6Cdgy1dX6Cdg7m5uerXr58iIiIUExOjsWPH6vDhw9d8XHs4Dwmd6/Daa68pOztbP/vZz7R3714NHjxYDzzwgI4dO3bV+SNHjujBBx/U4MGDtXfvXv3zP/+zZs2apTfeeMOZ2blzp8aPH6+srCz9+c9/VlZWlsaNG6ddu3Z9V8tytHR927Zt04gRI7RlyxaVlZXp/vvv10MPPaS9e/f6zEVGRqqystLn1qFDh+9iSU20dI2XHT582Of4ExMTnfsC+TX8t3/7N591VVRUKCoqSo888ojPXHt5Dc+fP697771XeXl51zUfaOeg1PI1Btp52NL1XRYo52BL1xdo52BJSYmmT5+u0tJSFRcX6+LFi0pPT9f58+e/9THt5jw0uKa//uu/Nk8++aTPth49eph58+ZddX7u3LmmR48ePtumTp1q+vfv73w9btw4M2rUKJ+ZkSNHmh//+Md+Ourr19L1XU2vXr3MwoULna9feukl43a7/XWIN62la9y6dauRZGpqar51nza9hgUFBSYoKMh89tlnzrb29hpeJskUFBQ0OxNo5+CVrmeNV9Pez8PLrmd9gXYO/l838voF0jlojDHV1dVGkikpKfnWmfZyHnJF5xoaGhpUVlam9PR0n+3p6enasWPHVR+zc+fOJvMjR47U//7v/+rChQvNznzbPlvLjazvSo2NjTp79qyioqJ8tp87d0633367brvtNmVkZDT5P83vys2ssU+fPurWrZuGDRumrVu3+txn02u4bt06DR8+vMlvFW8vr2FLBdI56C/t/Ty8UYFwDvpDoJ2DXq9Xkpr8ffu/2st5SOhcw+nTp3Xp0qUm/yJ6bGxsk385/bKqqqqrzl+8eFGnT59udubb9tlabmR9V1q2bJnOnz+vcePGOdt69Oih/Px8bd68WRs3blSHDh00cOBA/eUvf/Hr8V+PG1ljt27dtHbtWr3xxht68803lZSUpGHDhmnbtm3OjC2vYWVlpf7whz/oiSee8Nnenl7Dlgqkc9Bf2vt52FKBdA7erEA7B40xeuqppzRo0CAlJyd/61x7OQ8D+t+6+i4FBQX5fG2MabLtWvNXbm/pPlvTjR7Lxo0btWDBAv3ud79TTEyMs71///7q37+/8/XAgQP1ox/9SKtWrdKvf/1r/x14C7RkjUlJSUpKSnK+TktLU0VFhZYuXar77rvvhvbZ2m70WPLz83Xrrbdq7NixPtvb42vYEoF2Dt6MQDoPr1cgnoM3KtDOwRkzZuiDDz7Q9u3brznbHs5DruhcQ5cuXRQcHNykLqurq5tU6GUej+eq8yEhIYqOjm525tv22VpuZH2Xvfbaa5o0aZL+8z//U8OHD2929pZbblG/fv3a5P9EbmaN/1f//v19jt+G19AYo//4j/9QVlaWwsLCmp1ty9ewpQLpHLxZgXIe+kN7PQdvRqCdgzNnztTmzZu1detW3Xbbbc3OtpfzkNC5hrCwMKWkpKi4uNhne3FxsQYMGHDVx6SlpTWZLyoqUt++fRUaGtrszLfts7XcyPqkb/4P8rHHHtOGDRs0evToaz6PMUbl5eXq1q3bTR9zS93oGq+0d+9en+MP9NdQ+uaTFB9//LEmTZp0zedpy9ewpQLpHLwZgXQe+kN7PQdvRqCcg8YYzZgxQ2+++abeeecdJSQkXPMx7eY89Nvbmi22adMmExoaatatW2cOHDhgsrOzTadOnZx3x8+bN89kZWU5859++qnp2LGj+elPf2oOHDhg1q1bZ0JDQ81//dd/OTPvvfeeCQ4ONosWLTIHDx40ixYtMiEhIaa0tLTdr2/Dhg0mJCTEPPfcc6aystK5nTlzxplZsGCBKSwsNJ988onZu3evefzxx01ISIjZtWvXd74+Y1q+xhUrVpiCggLz0UcfmX379pl58+YZSeaNN95wZgL5Nbzs0UcfNampqVfdZ3t6Dc+ePWv27t1r9u7daySZ5cuXm71795qjR48aYwL/HDSm5WsMtPOwpesLtHOwpeu7LFDOwX/8x380brfbvPvuuz5/37766itnpr2eh4TOdXruuefM7bffbsLCwsyPfvQjn4/UTZw40QwZMsRn/t133zV9+vQxYWFh5o477jCrV69uss/XX3/dJCUlmdDQUNOjRw+fE/i71pL1DRkyxEhqcps4caIzk52dbbp3727CwsJM165dTXp6utmxY8d3uKKmWrLGxYsXm7vuust06NDBdO7c2QwaNMi8/fbbTfYZqK+hMcacOXPGhIeHm7Vr1151f+3pNbz8UeNv+ztnwznY0jUG2nnY0vUF2jl4I39HA+kcvNraJJmXXnrJmWmv52HQ/18AAACAdXiPDgAAsBahAwAArEXoAAAAaxE6AADAWoQOAACwFqEDAACsRegAAABrEToAAMCvtm3bpoceekhxcXEKCgrSb3/72xbvwxijpUuX6u6775bL5VJ8fLxycnJavB/+9XIAAOBX58+f17333qvHH39cDz/88A3t4yc/+YmKioq0dOlS9e7dW16vV6dPn27xfvjNyAAAoNUEBQWpoKBAY8eOdbY1NDTo5z//udavX68zZ84oOTlZixcv1tChQyVJBw8e1A9/+EPt27dPSUlJN/X8/OgKAAB8px5//HG999572rRpkz744AM98sgjGjVqlP7yl79Ikt566y3deeed+v3vf6+EhATdcccdeuKJJ/Tll1+2+LkIHQAA8J355JNPtHHjRr3++usaPHiw7rrrLs2ZM0eDBg3SSy+9JEn69NNPdfToUb3++ut65ZVXlJ+fr7KyMv3t3/5ti5+P9+gAAIDvzPvvvy9jjO6++26f7fX19YqOjpYkNTY2qr6+Xq+88oozt27dOqWkpOjw4cMt+nEWoQMAAL4zjY2NCg4OVllZmYKDg33u+8EPfiBJ6tatm0JCQnxiqGfPnpKkY8eOEToAAKB96tOnjy5duqTq6moNHjz4qjMDBw7UxYsX9cknn+iuu+6SJH300UeSpNtvv71Fz8enrgAAgF+dO3dOH3/8saRvwmb58uW6//77FRUVpe7du+vRRx/Ve++9p2XLlqlPnz46ffq03nnnHfXu3VsPPvigGhsb1a9fP/3gBz/QypUr1djYqOnTpysyMlJFRUUtOhZCBwAA+NW7776r+++/v8n2iRMnKj8/XxcuXNAvf/lLvfLKK/r8888VHR2ttLQ0LVy4UL1795YknThxQjNnzlRRUZE6deqkBx54QMuWLVNUVFSLjoXQAQAA1uLj5QAAwFqEDgAAsBahAwAArEXoAAAAaxE6AADAWoQOAACwFqEDAACsRegAAABrEToAAMBahA4AALAWoQMAAKz1/wC9BVNHkdf1VAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "plt.hist(df.ConvertedComp)"
   ]
  },
  {
   "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\n",
    "df.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\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.Gender.value_counts()"
   ]
  },
  {
   "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": [
       "57708.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.ConvertedComp[df.Gender == 'Woman'].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",
    "<!--\n",
    "min,q1,median,q3,max of a column are its five number summary.\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.Age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of the column `Age`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.Age.plot(kind = 'hist',bins = 20, xticks = [10,20,30,40,50,60,70,80,90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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’s 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’s 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’s 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’s 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 × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                      MainBranch Hobbyist  \\\n",
       "0           4  I am a developer by profession       No   \n",
       "1           9  I am a developer by profession      Yes   \n",
       "2          13  I am a developer by profession      Yes   \n",
       "3          16  I am a developer by profession      Yes   \n",
       "4          17  I am a developer by profession      Yes   \n",
       "\n",
       "                                         OpenSourcer  \\\n",
       "0                                              Never   \n",
       "1                         Once a month or more often   \n",
       "2  Less than once a month but more than once per ...   \n",
       "3                                              Never   \n",
       "4  Less than once a month but more than once per ...   \n",
       "\n",
       "                                          OpenSource          Employment  \\\n",
       "0  The quality of OSS and closed source software ...  Employed full-time   \n",
       "1  The quality of OSS and closed source software ...  Employed full-time   \n",
       "2  OSS is, on average, of HIGHER quality than pro...  Employed full-time   \n",
       "3  The quality of OSS and closed source software ...  Employed full-time   \n",
       "4  The quality of OSS and closed source software ...  Employed full-time   \n",
       "\n",
       "          Country Student                                            EdLevel  \\\n",
       "0   United States      No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "1     New Zealand      No  Some college/university study without earning ...   \n",
       "2   United States      No        Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
       "3  United Kingdom      No        Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
       "4       Australia      No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "\n",
       "                                      UndergradMajor  ...  \\\n",
       "0  Computer science, computer engineering, or sof...  ...   \n",
       "1  Computer science, computer engineering, or sof...  ...   \n",
       "2  Computer science, computer engineering, or sof...  ...   \n",
       "3                                                NaN  ...   \n",
       "4  Computer science, computer engineering, or sof...  ...   \n",
       "\n",
       "                              WelcomeChange  \\\n",
       "0   Just as welcome now as I felt last year   \n",
       "1   Just as welcome now as I felt last year   \n",
       "2  Somewhat more welcome now than last year   \n",
       "3   Just as welcome now as I felt last year   \n",
       "4   Just as welcome now as I felt last year   \n",
       "\n",
       "                                        SONewContent   Age Gender Trans  \\\n",
       "0  Tech articles written by other developers;Indu...  22.0    Man    No   \n",
       "1                                                NaN  23.0    Man    No   \n",
       "2  Tech articles written by other developers;Cour...  28.0    Man    No   \n",
       "3  Tech articles written by other developers;Indu...  26.0    Man    No   \n",
       "4  Tech articles written by other developers;Indu...  29.0    Man    No   \n",
       "\n",
       "                 Sexuality                              Ethnicity Dependents  \\\n",
       "0  Straight / Heterosexual           White or of European descent         No   \n",
       "1                 Bisexual           White or of European descent         No   \n",
       "2  Straight / Heterosexual           White or of European descent        Yes   \n",
       "3  Straight / Heterosexual           White or of European descent         No   \n",
       "4  Straight / Heterosexual  Hispanic or Latino/Latina;Multiracial         No   \n",
       "\n",
       "            SurveyLength                  SurveyEase  \n",
       "0  Appropriate in length                        Easy  \n",
       "1  Appropriate in length  Neither easy nor difficult  \n",
       "2  Appropriate in length                        Easy  \n",
       "3  Appropriate in length  Neither easy nor difficult  \n",
       "4  Appropriate in length                        Easy  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent       12484.0\n",
      "CompTotal        65000.0\n",
      "ConvertedComp    57745.0\n",
      "WorkWeekHrs         40.0\n",
      "CodeRevHrs           4.0\n",
      "Age                 29.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\temp\\ipykernel_12608\\1286936816.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df.median())\n"
     ]
    }
   ],
   "source": [
    "print(df.median()) "
   ]
  },
  {
   "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": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.dropna()\n",
    "df.ConvertedComp.plot(kind= 'box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the Inter Quartile Range for the column `ConvertedComp`.\n"
   ]
  },
  {
   "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": [
    "# your code goes here\n",
    "df.ConvertedComp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73132.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr = df.ConvertedComp.describe()[6] - df.ConvertedComp.describe()[4]\n",
    "iqr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the upper and lower bounds.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper bound: 209698.0,  Lower bound: -82830.0\n"
     ]
    }
   ],
   "source": [
    "# your code goes here\n",
    "whiskers = 1.5*iqr\n",
    "\n",
    "lower = df.ConvertedComp.describe()[4] - whiskers\n",
    "upper = df.ConvertedComp.describe()[6] + whiskers\n",
    "\n",
    "print(\"Upper bound: {},  Lower bound: {}\".format(upper, lower))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify how many outliers are there in the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(879, 85)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df[(df['ConvertedComp'] > upper) | (df ['ConvertedComp'] < lower)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      9703.000000\n",
       "mean      59883.208389\n",
       "std       43394.336755\n",
       "min           0.000000\n",
       "25%       24060.000000\n",
       "50%       52704.000000\n",
       "75%       85574.500000\n",
       "max      209356.000000\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df_clean_m3 = df[df.ConvertedComp <= upper]\n",
    "df_clean_m3.ConvertedComp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent       12571.0\n",
      "CompTotal        63000.0\n",
      "ConvertedComp    52704.0\n",
      "WorkWeekHrs         40.0\n",
      "CodeRevHrs           4.0\n",
      "Age                 29.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\temp\\ipykernel_12608\\1959820674.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_clean_m3.median())\n"
     ]
    }
   ],
   "source": [
    "print(df_clean_m3.median()) "
   ]
  },
  {
   "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": 26,
   "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>-0.019354</td>\n",
       "      <td>0.010878</td>\n",
       "      <td>-0.016221</td>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.002180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompTotal</th>\n",
       "      <td>-0.019354</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.063561</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>0.017007</td>\n",
       "      <td>0.006337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvertedComp</th>\n",
       "      <td>0.010878</td>\n",
       "      <td>-0.063561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034351</td>\n",
       "      <td>-0.088934</td>\n",
       "      <td>0.401821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <td>-0.016221</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>0.034351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038935</td>\n",
       "      <td>0.032032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.017007</td>\n",
       "      <td>-0.088934</td>\n",
       "      <td>0.038935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.002180</td>\n",
       "      <td>0.006337</td>\n",
       "      <td>0.401821</td>\n",
       "      <td>0.032032</td>\n",
       "      <td>-0.012878</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  -0.019354       0.010878    -0.016221    0.005293   \n",
       "CompTotal       -0.019354   1.000000      -0.063561     0.004910    0.017007   \n",
       "ConvertedComp    0.010878  -0.063561       1.000000     0.034351   -0.088934   \n",
       "WorkWeekHrs     -0.016221   0.004910       0.034351     1.000000    0.038935   \n",
       "CodeRevHrs       0.005293   0.017007      -0.088934     0.038935    1.000000   \n",
       "Age              0.002180   0.006337       0.401821     0.032032   -0.012878   \n",
       "\n",
       "                    Age  \n",
       "Respondent     0.002180  \n",
       "CompTotal      0.006337  \n",
       "ConvertedComp  0.401821  \n",
       "WorkWeekHrs    0.032032  \n",
       "CodeRevHrs    -0.012878  \n",
       "Age            1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df_clean_m3.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 © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork928-2023-01-01&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"
   ]
  }
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