Pandas/Enrich-Pivot-Table.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "179f0596",
   "metadata": {},
   "source": [
    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)\n",
    "\n",
    "# A Simple Trick to Make The Most Out of Pivot Tables in Pandas\n",
    "\n",
    "Post Link: [Substack](https://avichawla.substack.com/p/a-simple-trick-to-make-the-most-out)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7035194488901840896/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84e6eada",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c71dac",
   "metadata": {},
   "source": [
    "### Create data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "169ece19",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_list = [\"New York\", \"Munich\", \"Bombay\", \n",
    "             \"Sydeny\", \"London\", \"Moscow\", \"Dubai\", \"Tokyo\"]\n",
    "\n",
    "job_list = [\"Software Development Engineer\", \"Research Engineer\", \n",
    "            \"Test Engineer\", \"Software Development Engineer-II\", \n",
    "            \"Python Developer\", \"Back End Developer\", \n",
    "            \"Front End Developer\", \"Data Scientist\", \n",
    "            \"IOS Developer\", \"Android Developer\"]\n",
    "\n",
    "cmp_list = [\"Amazon\", \"Google\", \"Microsoft\", \n",
    "            \"Uber\", \"IBM\", \"Apple\"]\n",
    "\n",
    "data = []\n",
    "for i in range(1000):\n",
    "  \n",
    "    company = random.choice(cmp_list)\n",
    "    job = random.choice(job_list)\n",
    "    city = random.choice(city_list)\n",
    "    salary = int(round(np.random.rand(), 3)*10**6)\n",
    "    employment = random.choices([\"Full Time\", \"Intern\"], weights=(80, 20))[0]\n",
    "    rating = round((np.random.rand()*5), 1)\n",
    "    \n",
    "    data.append([company, job, city, salary, employment, rating])\n",
    "    \n",
    "data = pd.DataFrame(data, columns=[\"Company Name\", \"Employee Job Title\",\n",
    "                                   \"Employee Work Location\",  \"Employee Salary\", \n",
    "                                   \"Employment Status\", \"Employee Rating\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f8d3c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(33.0, 0.5, 'Company')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_pivot = pd.pivot_table(data, index = \"Company Name\", columns = \"Employee Work Location\", values = \"Employee Rating\", aggfunc=\"count\" )\n",
    "ticks=np.arange(15, 36, 2)\n",
    "plot_ = sns.heatmap(result_pivot, annot=True, cmap = \"Blues\", fmt='d', cbar_kws={\"ticks\":ticks})\n",
    "plot_.collections[0].colorbar.set_label(\"Count\")\n",
    "fig = plot_.get_figure()\n",
    "plt.yticks(rotation=0)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Location\", weight='bold')\n",
    "plt.ylabel(\"Company\", weight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "410ae3e8",
   "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>Employee Work Location</th>\n",
       "      <th>Bombay</th>\n",
       "      <th>Dubai</th>\n",
       "      <th>London</th>\n",
       "      <th>Moscow</th>\n",
       "      <th>Munich</th>\n",
       "      <th>New York</th>\n",
       "      <th>Sydeny</th>\n",
       "      <th>Tokyo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amazon</th>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Google</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microsoft</th>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uber</th>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Employee Work Location  Bombay  Dubai  London  Moscow  Munich  New York  \\\n",
       "Company Name                                                              \n",
       "Amazon                      24     13      25      24      18        21   \n",
       "Apple                       18     21      22      11      22        20   \n",
       "Google                      18     19      19      21      22        30   \n",
       "IBM                         22     21      23      26      21        14   \n",
       "Microsoft                   27     22      23      18      24        25   \n",
       "Uber                        15     19      21      17      20        18   \n",
       "\n",
       "Employee Work Location  Sydeny  Tokyo  \n",
       "Company Name                           \n",
       "Amazon                      19     16  \n",
       "Apple                       14     17  \n",
       "Google                      28     27  \n",
       "IBM                         27     17  \n",
       "Microsoft                   16     22  \n",
       "Uber                        25     28  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(data, index = \"Company Name\", columns = \"Employee Work Location\", values = \"Employee Rating\", aggfunc=\"count\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df684557",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}