Learn_Data_Analysis_With_Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0381cdb3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'column' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-ac6100f23474>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mDegrees\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrades\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbsdegrees\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsdegrees\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphddegrees\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Names'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Grades'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'BS'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'MS'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'PhD'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDegrees\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'column' is not defined"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['Bob', 'Jessica', 'Mary', 'John', 'Mel']\n",
    "grades=[76, 95, 77, 78, 99]\n",
    "bsdegrees=[1, 1, 0, 0, 1]\n",
    "msdegrees=[2, 1, 0, 0, 0]\n",
    "phddegrees=[0, 1, 0, 0, 0]\n",
    "\n",
    "Degrees=zip(names, grades, bsdegrees, msdegrees, phddegrees)\n",
    "columns=['Names', 'Grades', 'BS', 'MS', 'PhD']\n",
    "df=pd.DataFrame(data=Degrees, columns=column)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09190713",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0034da43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "names = ['Bob','Jessica','Mary','John','Mel']\n",
    "grades = [76,95,77,78,99]\n",
    "GradeList = zip(names,grades)\n",
    "df = pd.DataFrame(data = GradeList,\n",
    "columns=['Names','Grades'])\n",
    "writer = pd.ExcelWriter('dataframe.xlsx', engine='xlsxwriter')\n",
    "df.to_excel(writer, sheet_name='Sheet1')\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2dd910",
   "metadata": {},
   "source": [
    "# Random Numbers and Creating Random Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e901be4",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-713030cdf9b2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0mBabyDataSet2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbirths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mBabyDataSet2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Names'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Births'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    580\u001b[0m                             \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mibase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefault_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 582\u001b[1;33m                     \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    583\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    584\u001b[0m                     \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minit_ndarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype, verify_integrity)\u001b[0m\n\u001b[0;32m     93\u001b[0m     \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcreate_block_manager_from_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_arrays\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m   1699\u001b[0m     \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCPandasArray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1700\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1701\u001b[1;33m         \u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_form_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1702\u001b[0m         \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblocks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1703\u001b[0m         \u001b[0mmgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_form_blocks\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m   1769\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1770\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"IntBlock\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1771\u001b[1;33m         \u001b[0mint_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_multi_blockify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"IntBlock\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1772\u001b[0m         \u001b[0mblocks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1773\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_multi_blockify\u001b[1;34m(tuples, dtype)\u001b[0m\n\u001b[0;32m   1850\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup_block\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1851\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1852\u001b[1;33m         \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_stack_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup_block\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1853\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1854\u001b[0m         \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplacement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_stack_arrays\u001b[1;34m(tuples, dtype)\u001b[0m\n\u001b[0;32m   1880\u001b[0m     \u001b[0mstacked\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1881\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1882\u001b[1;33m         \u001b[0mstacked\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_asarray_compat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1884\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mstacked\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from numpy import random\n",
    "from numpy.random import randint\n",
    "names=['Bob', 'Jessica', 'Mary', 'john', 'Mel']\n",
    "random.seed(500)\n",
    "\n",
    "randnames=[]\n",
    "randint(low=0, high=len(names))\n",
    "\n",
    "for i in range(1000):\n",
    "    name=names[randint(low=0, high=len(names))]\n",
    "    randnames.append(name)\n",
    "\n",
    "    births=[]\n",
    "    for i in range(1000):\n",
    "        births.append(randint(low=0, high=1000))\n",
    "        \n",
    "        BabyDataSet2=list(zip(randnames, births))\n",
    "        df=pd.DataFrame(data=BabyDataSet2, columns=['Names', 'Births'])\n",
    "df\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0716cf",
   "metadata": {},
   "source": [
    "# Preparing Data is Half the Battle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9121a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2. Interquartile Range (IQR): The IQR is the\\ndifference between the 25 percent quantile and the\\n75 percent quantile. Any values that are either lower\\nthan Q1 - 1.5 x IQR or greater than Q3 + 1.5 x IQR are\\ntreated as outliers and removed'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating and removing outliers\n",
    "#There are two ways:\n",
    "\"\"\"1. Standard Deviations: If the data is normally\n",
    "distributed, then 95 percent of the data is within 1.96\n",
    "standard deviations of the mean. So we can drop the\n",
    "values either above or below that range\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "#Interquartile Range (IQR)\n",
    "\"\"\"2. Interquartile Range (IQR): The IQR is the\n",
    "difference between the 25 percent quantile and the\n",
    "75 percent quantile. Any values that are either lower\n",
    "than Q1 - 1.5 x IQR or greater than Q3 + 1.5 x IQR are\n",
    "treated as outliers and removed\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d4f0b39",
   "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>Names</th>\n",
       "      <th>Births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bob</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bob</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mary</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>john</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mel</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>john</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mary</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>john</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bob</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bob</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mel</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mel</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bob</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Mel</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bob</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>john</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mary</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Mary</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mary</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mary</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>john</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Names  Births\n",
       "0   Jessica     286\n",
       "1   Jessica     243\n",
       "2       Bob     319\n",
       "3       Bob     215\n",
       "4       Mel     980\n",
       "5      Mary     679\n",
       "6      john     956\n",
       "7       Mel      89\n",
       "8      john     462\n",
       "9      Mary     353\n",
       "10     john     785\n",
       "11  Jessica     128\n",
       "12      Bob      95\n",
       "13      Bob     145\n",
       "14      Mel     252\n",
       "15      Mel     245\n",
       "16      Bob     123\n",
       "17  Jessica     540\n",
       "18      Mel     768\n",
       "19      Bob     561\n",
       "20     john     484\n",
       "21     Mary     516\n",
       "22     Mary       6\n",
       "23     Mary     864\n",
       "24     Mary      75\n",
       "25     john     434"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a748110",
   "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>Names</th>\n",
       "      <th>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Grades\n",
       "0      Bob      76\n",
       "1  Jessica      -2\n",
       "2     Mary      77\n",
       "3     John      78\n",
       "4      Mel     101"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating DataSet\n",
    "\n",
    "import pandas as pd\n",
    "names=['Bob', 'Jessica', 'Mary', 'John', 'Mel']\n",
    "grades=[76, -2, 77, 78, 101]\n",
    "\n",
    "GradeList=zip(names, grades)\n",
    "df=pd.DataFrame(data=GradeList, columns=['Names', 'Grades'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eb4c0868",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-24-609afe8019dc>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-24-609afe8019dc>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    df.loc[df['Grades'] <=100 or <0]\u001b[0m\n\u001b[1;37m                                 ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#Filtering out impossible grades\n",
    "df.loc[df['Grades'] <=100 or <0]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d3be2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding Duplicate Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d2b9263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
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       "      <th>2</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jan</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jon</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mel</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mel</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Names  Grades\n",
       "0   Jan      95\n",
       "1  John      78\n",
       "2   Bob      76\n",
       "3   Jan      95\n",
       "4  Mary      77\n",
       "5   Jon      78\n",
       "6   Mel      99\n",
       "7   Mel     100"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['Jan', 'John', 'Bob', 'Jan', 'Mary', 'Jon', 'Mel', 'Mel']\n",
    "\n",
    "grades=[95, 78, 76, 95, 77, 78, 99, 100]\n",
    "\n",
    "GradeList=zip(names, grades)\n",
    "df=pd.DataFrame(data = GradeList, columns=['Names', 'Grades'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cf9a6d65",
   "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>Names</th>\n",
       "      <th>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jon</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mel</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mel</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Names  Grades\n",
       "0   Jan      95\n",
       "1  John      78\n",
       "2   Bob      76\n",
       "4  Mary      77\n",
       "5   Jon      78\n",
       "6   Mel      99\n",
       "7   Mel     100"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a333851",
   "metadata": {},
   "source": [
    "# Standardizing Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0821ab71",
   "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>Names</th>\n",
       "      <th>Grades</th>\n",
       "      <th>BS</th>\n",
       "      <th>MS</th>\n",
       "      <th>PhD</th>\n",
       "      <th>bdates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1/1/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10/21/76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3/3/90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>04/30/1901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1963-09-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Grades  BS  MS  PhD      bdates\n",
       "0      Bob      76   1   2    0    1/1/1945\n",
       "1  Jessica      95   1   1    1    10/21/76\n",
       "2     Mary      77   0   0    0      3/3/90\n",
       "3     John      78   0   0    0  04/30/1901\n",
       "4      Mel      99   1   0    0  1963-09-01"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['Bob', 'Jessica', 'Mary', 'John', 'Mel']\n",
    "grades=[76, 95, 77, 78, 99]\n",
    "bsdegrees=[1, 1, 0, 0, 1]\n",
    "msdegrees=[2, 1, 0, 0, 0]\n",
    "phddegress=[0, 1, 0, 0, 0]\n",
    "bdates=['1/1/1945', '10/21/76', '3/3/90', '04/30/1901', '1963-09-01']\n",
    "\n",
    "GradeList=zip(names, grades, bsdegrees, msdegrees, phddegrees, bdates)\n",
    "\n",
    "columns=['Names', 'Grades', 'BS', 'MS', 'PhD', 'bdates']\n",
    "df=pd.DataFrame(data=GradeList, columns=columns)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "33fdf6d1",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "default 'except:' must be last (<ipython-input-33-d67b79a08376>, line 24)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-33-d67b79a08376>\"\u001b[1;36m, line \u001b[1;32m24\u001b[0m\n\u001b[1;33m    formatted_date=str(datetime.strptime(thedate, '%m/%d/%y')\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m default 'except:' must be last\n"
     ]
    }
   ],
   "source": [
    "#Function to standardize Dates\n",
    "\n",
    "from time import strftime\n",
    "from datetime import datetime\n",
    "\n",
    "def standardize_date(thedate):\n",
    "    formatted_date=\"\"\n",
    "    thedate=str(thedate)\n",
    "    if not thedate or thedate.lower() ==\"missing\" or thedate ==\"nan\":\n",
    "        formatted_date=\"MISSING\"\n",
    "        if the_date.lower().find('x') !=-1:\n",
    "            formatted_date=\"Incomplete\"\n",
    "    if the_date[0:2]==\"00\":\n",
    "        formatted_date=thedate.replace(\"00\", \"19\")\n",
    "    \n",
    "    try:\n",
    "        formatted_date=str(datetime.strptime(thedate, '%m/%d/%y')\n",
    "                          \n",
    "    .strftime('%m/%d/%y'))\n",
    "        \n",
    "    except:\n",
    "        pass\n",
    "    try:\n",
    "        formatted_date=str(datetime.strptime(thedate, '%m/%d/%y')\n",
    "    .strftime('%m/%d/%y'))\n",
    "        \n",
    "    except:\n",
    "        if int(the_date[0:4]) < 1900:\n",
    "            formatted_date=\"Incomplete\"\n",
    "        else:\n",
    "            formatted_date=str(datetime.strptime(thedate, '%Y-%m-%d')\n",
    "                              \n",
    "         .strftime('%m/%d/%y'))\n",
    "    except:\n",
    "                pass\n",
    "    return formatted_date\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d1647e",
   "metadata": {},
   "source": [
    "# Standardizing Text like SSNs, Phone Numbers and Zip Codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0f9f2667",
   "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>Names</th>\n",
       "      <th>Grades</th>\n",
       "      <th>BS</th>\n",
       "      <th>MS</th>\n",
       "      <th>PhD</th>\n",
       "      <th>ssn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>867-53-0909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>333-12-4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>777-93-9311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Grades  BS  MS  PhD          ssn\n",
       "0      Bob      76   1   2    0  867-53-0909\n",
       "1  Jessica      95   1   1    1  333-12-4444\n",
       "2     Mary      77   0   0    0  123-12-1234\n",
       "3     John      78   0   0    0  777-93-9311\n",
       "4      Mel      99   1   0    0  123-12-1423"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating DataFrame with SSNs\n",
    "\n",
    "import pandas as pd\n",
    "names=['Bob', 'Jessica', 'Mary', 'John', 'Mel']\n",
    "\n",
    "grades=[76, 95, 77, 78, 99]\n",
    "bsdegrees=[1, 1, 0, 0, 1]\n",
    "msdegrees=[2, 1, 0, 0, 0]\n",
    "phddegrees=[0, 1, 0, 0, 0]\n",
    "\n",
    "ssns=['867-53-0909', '333-12-4444', '123-12-1234', '777-93-9311', '123-12-1423']\n",
    "\n",
    "GradeList=zip(names, grades, bsdegrees, msdegrees, phddegrees, ssns)\n",
    "columns=['Names', 'Grades', 'BS', 'MS', 'PhD', \"ssn\"]\n",
    "\n",
    "df=pd.DataFrame(data=GradeList, columns=columns)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee03ddca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grades    85.0\n",
       "BS         0.6\n",
       "MS         0.6\n",
       "PhD        0.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f844c61f",
   "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>Grades</th>\n",
       "      <th>BS</th>\n",
       "      <th>MS</th>\n",
       "      <th>PhD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.067972</td>\n",
       "      <td>0.547723</td>\n",
       "      <td>0.894427</td>\n",
       "      <td>0.447214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Grades        BS        MS       PhD\n",
       "count   5.000000  5.000000  5.000000  5.000000\n",
       "mean   85.000000  0.600000  0.600000  0.200000\n",
       "std    11.067972  0.547723  0.894427  0.447214\n",
       "min    76.000000  0.000000  0.000000  0.000000\n",
       "25%    77.000000  0.000000  0.000000  0.000000\n",
       "50%    78.000000  1.000000  0.000000  0.000000\n",
       "75%    95.000000  1.000000  1.000000  0.000000\n",
       "max    99.000000  1.000000  2.000000  1.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "58b7c093",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-41-ad176d256669>, line 15)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-41-ad176d256669>\"\u001b[1;36m, line \u001b[1;32m15\u001b[0m\n\u001b[1;33m    def.ssn=df.ssn.apply(standardize_ssn)\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def rights(s, amount):\n",
    "    return s[-amount]\n",
    "\n",
    "def standardize_ssn(ssn):\n",
    "    try:\n",
    "        ssn=ssn.replace(\"-\", \"\")\n",
    "        ssn=\"\".join(ssn.split())\n",
    "        if len(ssn)<9 and ssn !='Missing':\n",
    "            ssn=\"000000000\" + ssn\n",
    "            ssn=right(ssn, 9)\n",
    "            \n",
    "    except:\n",
    "        pass\n",
    "    return ssn\n",
    "def.ssn=df.ssn.apply(standardize_ssn)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d33c929c",
   "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>Names</th>\n",
       "      <th>Grades</th>\n",
       "      <th>BS</th>\n",
       "      <th>MS</th>\n",
       "      <th>PhD</th>\n",
       "      <th>ssn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>867-53-0909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>333-12-4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>777-93-9311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Grades  BS  MS  PhD          ssn\n",
       "0      Bob      76   1   2    0  867-53-0909\n",
       "1  Jessica      95   1   1    1  333-12-4444\n",
       "2     Mary      77   0   0    0  123-12-1234\n",
       "3     John      78   0   0    0  777-93-9311\n",
       "4      Mel      99   1   0    0  123-12-1423"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d8899c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3\n",
       "0    2\n",
       "Name: BS, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a new variables\n",
    "pd.value_counts(df['BS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "20ae6311",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'grade'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'grade'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-47-e4fb02bea6ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m60\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m70\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m80\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m90\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mgroub_names\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'F'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'D'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'C'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'B'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'A'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'letterGrades'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'grade'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgroub_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3022\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3023\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3024\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3025\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3080\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3082\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3084\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'grade'"
     ]
    }
   ],
   "source": [
    "bins=[0, 60, 70, 80, 90, 100]\n",
    "group_names=['F', 'D', 'C', 'B', 'A']\n",
    "df['letterGrades']=pd.cut(df['grade'], bins, labels=groub_names)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "310bec99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Grades</th>\n",
       "      <th>BS</th>\n",
       "      <th>MS</th>\n",
       "      <th>PhD</th>\n",
       "      <th>ssn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>867-53-0909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>333-12-4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>777-93-9311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Grades  BS  MS  PhD          ssn\n",
       "0      Bob      76   1   2    0  867-53-0909\n",
       "1  Jessica      95   1   1    1  333-12-4444\n",
       "2     Mary      77   0   0    0  123-12-1234\n",
       "3     John      78   0   0    0  777-93-9311\n",
       "4      Mel      99   1   0    0  123-12-1423"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de5652d",
   "metadata": {},
   "source": [
    "# Ranking Rows of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "66ae56d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Grades</th>\n",
       "      <th>BS</th>\n",
       "      <th>MS</th>\n",
       "      <th>PhD</th>\n",
       "      <th>ssn</th>\n",
       "      <th>graderanked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>867-53-0909</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>333-12-4444</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1234</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>777-93-9311</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1423</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Grades  BS  MS  PhD          ssn  graderanked\n",
       "0      Bob      76   1   2    0  867-53-0909          1.0\n",
       "1  Jessica      95   1   1    1  333-12-4444          4.0\n",
       "2     Mary      77   0   0    0  123-12-1234          2.0\n",
       "3     John      78   0   0    0  777-93-9311          3.0\n",
       "4      Mel      99   1   0    0  123-12-1423          5.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['graderanked']=df['Grades'].rank(ascending=1)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "212e2587",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Grades</th>\n",
       "      <th>BS</th>\n",
       "      <th>MS</th>\n",
       "      <th>PhD</th>\n",
       "      <th>ssn</th>\n",
       "      <th>graderanked</th>\n",
       "      <th>isFailing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>867-53-0909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>333-12-4444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1234</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>777-93-9311</td>\n",
       "      <td>3.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123-12-1423</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Grades  BS  MS  PhD          ssn  graderanked isFailing\n",
       "0      Bob      76   1   2    0  867-53-0909          1.0        no\n",
       "1  Jessica      95   1   1    1  333-12-4444          4.0        no\n",
       "2     Mary      77   0   0    0  123-12-1234          2.0        no\n",
       "3     John      78   0   0    0  777-93-9311          3.0        no\n",
       "4      Mel      99   1   0    0  123-12-1423          5.0        no"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df['isFailing']=np.where(df['Grades']<70, 'yes', 'no')\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db182448",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb5afb7e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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