4_data_analysis_course_prep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CADT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164, 460)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cadt = pd.read_csv('./course_prep_dataset/progress_dse_cadt.csv')\n",
    "df_cadt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164, 460)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cadt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = df_cadt.columns\n",
    "for col in columns:\n",
    "    if('Unnamed' in col):\n",
    "        # print(col)\n",
    "        df_cadt = df_cadt.drop(columns=[col], axis=1)\n",
    "\n",
    "\n",
    "new_name = 0\n",
    "columns = df_cadt.columns\n",
    "for col in columns:\n",
    "    df_cadt.rename(columns={col: new_name}, inplace=True)\n",
    "    new_name += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_name = 0\n",
    "# columns = df_cadt.columns\n",
    "# for col in columns:\n",
    "#     df_cadt.rename(columns={col: new_name}, inplace=True)\n",
    "#     new_name += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace True/False with Yes/No\n",
    "df_cadt = df_cadt.replace({'Not completed': 0, 'Completed': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cadt = df_cadt.dropna(axis=1, how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     1    2    3    4    5    6    7    8    9    10   ...  220  221  222  \\\n",
       "0      0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
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       "..   ...  ...  ...  ...  ...  ...  ...  \n",
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       "[164 rows x 229 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cadt = df_cadt.rename({0:'email'}, axis=1)\n",
    "df_cadt = df_cadt.drop(columns=['email'])\n",
    "df_cadt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m column_sum \u001b[38;5;241m=\u001b[39m \u001b[43mdf_cadt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m column_sum \u001b[38;5;241m=\u001b[39m column_sum\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m      3\u001b[0m height \u001b[38;5;241m=\u001b[39m column_sum\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/generic.py:11512\u001b[0m, in \u001b[0;36mNDFrame._add_numeric_operations.<locals>.sum\u001b[0;34m(self, axis, skipna, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m  11493\u001b[0m \u001b[38;5;129m@doc\u001b[39m(  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m  11494\u001b[0m     _num_doc,\n\u001b[1;32m  11495\u001b[0m     desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn the sum of the values over the requested axis.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  11510\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m  11511\u001b[0m ):\n\u001b[0;32m> 11512\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_count\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/generic.py:11280\u001b[0m, in \u001b[0;36mNDFrame.sum\u001b[0;34m(self, axis, skipna, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m  11272\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msum\u001b[39m(\n\u001b[1;32m  11273\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  11274\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  11278\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m  11279\u001b[0m ):\n\u001b[0;32m> 11280\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_min_count_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m  11281\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnansum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_count\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m  11282\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/generic.py:11263\u001b[0m, in \u001b[0;36mNDFrame._min_count_stat_function\u001b[0;34m(self, name, func, axis, skipna, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m  11260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m  11261\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stat_axis_number\n\u001b[0;32m> 11263\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m  11264\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11265\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11266\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmin_count\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmin_count\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  11270\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/frame.py:10519\u001b[0m, in \u001b[0;36mDataFrame._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m  10515\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m  10517\u001b[0m \u001b[38;5;66;03m# After possibly _get_data and transposing, we are now in the\u001b[39;00m\n\u001b[1;32m  10518\u001b[0m \u001b[38;5;66;03m#  simple case where we can use BlockManager.reduce\u001b[39;00m\n\u001b[0;32m> 10519\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblk_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m  10520\u001b[0m out \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39m_constructor(res)\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m  10521\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/internals/managers.py:1534\u001b[0m, in \u001b[0;36mBlockManager.reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m   1532\u001b[0m res_blocks: \u001b[38;5;28mlist\u001b[39m[Block] \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m   1533\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks:\n\u001b[0;32m-> 1534\u001b[0m     nbs \u001b[38;5;241m=\u001b[39m \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1535\u001b[0m     res_blocks\u001b[38;5;241m.\u001b[39mextend(nbs)\n\u001b[1;32m   1537\u001b[0m index \u001b[38;5;241m=\u001b[39m Index([\u001b[38;5;28;01mNone\u001b[39;00m])  \u001b[38;5;66;03m# placeholder\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/internals/blocks.py:339\u001b[0m, in \u001b[0;36mBlock.reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreduce\u001b[39m(\u001b[38;5;28mself\u001b[39m, func) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[1;32m    335\u001b[0m     \u001b[38;5;66;03m# We will apply the function and reshape the result into a single-row\u001b[39;00m\n\u001b[1;32m    336\u001b[0m     \u001b[38;5;66;03m#  Block with the same mgr_locs; squeezing will be done at a higher level\u001b[39;00m\n\u001b[1;32m    337\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m--> 339\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    341\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    342\u001b[0m         \u001b[38;5;66;03m# TODO(EA2D): special case not needed with 2D EAs\u001b[39;00m\n\u001b[1;32m    343\u001b[0m         res_values \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([[result]])\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/frame.py:10482\u001b[0m, in \u001b[0;36mDataFrame._reduce.<locals>.blk_func\u001b[0;34m(values, axis)\u001b[0m\n\u001b[1;32m  10480\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39m_reduce(name, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m  10481\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m> 10482\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/nanops.py:96\u001b[0m, in \u001b[0;36mdisallow.__call__.<locals>._f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     95\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(invalid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m---> 96\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     98\u001b[0m     \u001b[38;5;66;03m# we want to transform an object array\u001b[39;00m\n\u001b[1;32m     99\u001b[0m     \u001b[38;5;66;03m# ValueError message to the more typical TypeError\u001b[39;00m\n\u001b[1;32m    100\u001b[0m     \u001b[38;5;66;03m# e.g. this is normally a disallowed function on\u001b[39;00m\n\u001b[1;32m    101\u001b[0m     \u001b[38;5;66;03m# object arrays that contain strings\u001b[39;00m\n\u001b[1;32m    102\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_object_dtype(args[\u001b[38;5;241m0\u001b[39m]):\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/nanops.py:421\u001b[0m, in \u001b[0;36m_datetimelike_compat.<locals>.new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    419\u001b[0m     mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 421\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    423\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m    424\u001b[0m     result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/nanops.py:494\u001b[0m, in \u001b[0;36mmaybe_operate_rowwise.<locals>.newfunc\u001b[0;34m(values, axis, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m         results \u001b[38;5;241m=\u001b[39m [func(x, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrs]\n\u001b[1;32m    492\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(results)\n\u001b[0;32m--> 494\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/pandas/core/nanops.py:652\u001b[0m, in \u001b[0;36mnansum\u001b[0;34m(values, axis, skipna, min_count, mask)\u001b[0m\n\u001b[1;32m    649\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_timedelta64_dtype(dtype):\n\u001b[1;32m    650\u001b[0m     dtype_sum \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[0;32m--> 652\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m \u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype_sum\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    653\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m _maybe_null_out(the_sum, axis, mask, values\u001b[38;5;241m.\u001b[39mshape, min_count\u001b[38;5;241m=\u001b[39mmin_count)\n\u001b[1;32m    655\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m the_sum\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/dse_project/lib/python3.9/site-packages/numpy/core/_methods.py:49\u001b[0m, in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_sum\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     48\u001b[0m          initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_sum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
     ]
    }
   ],
   "source": [
    "column_sum = df_cadt.sum()\n",
    "column_sum = column_sum.tolist()\n",
    "height = column_sum\n",
    "print('Height:', height)\n",
    "\n",
    "label = []\n",
    "number = 1\n",
    "for h in height:\n",
    "    label.append(number)\n",
    "    number = number + 1\n",
    "\n",
    "print('Label', label)\n",
    "print(height)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (11, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(label, height)\n",
    " \n",
    "\n",
    "plt.ylim(top=len(df_cadt))\n",
    "plt.xlabel(\"Slide and Videos lessons\")\n",
    "plt.ylabel(\"No. of examinee study pre-course\")\n",
    "plt.title(\"Number of examinees who completed slides and video lesson\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.plot(label, height)\n",
    " \n",
    "\n",
    "plt.ylim(top=len(df_cadt))\n",
    "plt.xlabel(\"Slide and Video Lessons\")\n",
    "plt.ylabel(\"No. of examinee study pre-course\")\n",
    "plt.title(\"Number of examinees who completed slides and video lesson, CADT\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.set(rc={'figure.figsize': (10, 5)})\n",
    "# sns.lineplot(x=label, y=height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dt/90w7dnxx5xzfd7px48yrrpt00000gn/T/ipykernel_17344/3891607973.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",
      "  df_cadt['num_completed_lesson'] = df_cadt.sum(axis=1)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>0.330435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 231 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  2  3  4  5  6  7  8  9  10  ...  222  223  224  225  226  227  228  229  \\\n",
       "0  0  0  0  0  0  0  0  0  0   0  ...    0    0    0    0    0    0    0    0   \n",
       "1  1  0  0  1  0  0  0  0  0   0  ...    0    0    0    0    0    0    0    0   \n",
       "2  0  0  0  0  0  0  0  0  0   0  ...    0    0    0    0    0    0    0    0   \n",
       "3  1  1  1  1  1  1  1  1  1   1  ...    0    0    0    0    0    0    0    0   \n",
       "4  0  0  0  0  0  0  0  0  0   0  ...    0    0    0    0    0    0    0    0   \n",
       "\n",
       "   num_completed_lesson  completed_rate  \n",
       "0                     0        0.000000  \n",
       "1                     9        0.039130  \n",
       "2                     0        0.000000  \n",
       "3                    76        0.330435  \n",
       "4                     2        0.008696  \n",
       "\n",
       "[5 rows x 231 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cadt['num_completed_lesson'] = df_cadt.sum(axis=1)\n",
    "df_cadt['completed_rate'] = df_cadt['num_completed_lesson']/len(df_cadt.columns)\n",
    "df_cadt.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Completed Lessons: 37.15243902439025\n",
      "Max: 227\n",
      "Median: 3.0\n",
      "Min: 0\n",
      "Standard Deviation: 64.11086969965831\n"
     ]
    }
   ],
   "source": [
    "average = df_cadt['num_completed_lesson'].mean()\n",
    "print(\"Average Completed Lessons:\", average)\n",
    "\n",
    "max = df_cadt['num_completed_lesson'].max()\n",
    "print(\"Max:\", max)\n",
    "\n",
    "median = df_cadt['num_completed_lesson'].median()\n",
    "print(\"Median:\", median)\n",
    "\n",
    "min = df_cadt['num_completed_lesson'].min()\n",
    "print(\"Min:\", min)\n",
    "\n",
    "std = df_cadt['num_completed_lesson'].std()\n",
    "print(\"Standard Deviation:\", std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1615323435843054"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average = df_cadt['completed_rate'].mean()\n",
    "average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "completed_rate = average\n",
    "not_completed_rate = 1-average\n",
    "\n",
    "# Data for the pie chart\n",
    "labels = 'Completed Rate', 'Not Completed Rate'\n",
    "sizes = [completed_rate, not_completed_rate]\n",
    "\n",
    "# Create the pie chart\n",
    "plt.pie(sizes, labels=labels, autopct='%1.0f%%')\n",
    "\n",
    "# Set the legend\n",
    "plt.legend(title=\"Rate\", loc='upper left')\n",
    "\n",
    "plt.title('Lessons Completed Rate of CADT')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# corrmat = df_cadt.corr()\n",
    "\n",
    "# plt.figure(figsize=(13, 6))\n",
    "# sns.heatmap(corrmat, vmax=1, annot=True, linewidths=.5)\n",
    "# plt.xticks(rotation=30, horizontalalignment='right')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 385)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_b018 = pd.read_csv('./course_prep_dataset/progress_dse_b018_v2.csv')\n",
    "df_b018.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = df_b018.columns\n",
    "for col in columns:\n",
    "    if('Column' in col or 'Unnamed:' in col):\n",
    "        df_b018 = df_b018.drop(columns=[col], axis=1)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_name = 0\n",
    "columns = df_b018.columns\n",
    "for col in columns:\n",
    "    df_b018.rename(columns={col: new_name}, inplace=True)\n",
    "    new_name += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace True/False with Yes/No\n",
    "df_b018 = df_b018.replace({'Not completed': 0, 'Completed': 1})\n",
    "# df_b018\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_b018 = df_b018.dropna(axis=1, how='all') # drop null values\n",
    "df_b018 = df_b018.rename({0:'email'}, axis=1) # rename first column to 'email'\n",
    "df_b018 = df_b018.drop(columns=['email'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Height: [13, 12, 16, 10, 16, 19, 10, 10, 10, 10, 8, 20, 13, 10, 10, 9, 9, 9, 16, 13, 9, 7, 7, 7, 6, 7, 6, 5, 5, 5, 5, 5, 5, 5, 12, 11, 9, 9, 6, 5, 5, 5, 5, 7, 12, 13, 10, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 4, 4, 3, 4, 3, 4, 8, 8, 8, 8, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 7, 7, 5, 5, 5, 5, 3, 2, 2, 2, 1, 1, 7, 2, 2, 2, 3, 2, 2, 2, 3, 12, 5, 2, 2, 2, 2, 3, 2, 3, 8, 7, 6, 6, 6, 6, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 3, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0]\n",
      "Label [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191]\n"
     ]
    }
   ],
   "source": [
    "column_sum = df_b018.sum()\n",
    "column_sum = column_sum.tolist()\n",
    "height = column_sum\n",
    "print('Height:', height )\n",
    "\n",
    "label = []\n",
    "number = 1\n",
    "for h in height:\n",
    "    label.append(number)\n",
    "    number +=1 \n",
    "\n",
    "print('Label', label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(label, height)\n",
    " \n",
    "\n",
    "plt.ylim(top=len(df_b018))\n",
    "plt.xlabel(\"Slide and Videos lessons\")\n",
    "plt.ylabel(\"No. of examinee study pre-course\")\n",
    "plt.title(\"Number of examinees who completed slides and video lesson\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.plot(label, height)\n",
    " \n",
    "\n",
    "plt.ylim(top=len(df_b018))\n",
    "plt.xlabel(\"Slide and Video Lessons\")\n",
    "plt.ylabel(\"No. of examinee study pre-course\")\n",
    "plt.title(\"Number of examinees who completed slides and video lesson, B018\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>num_completed_lesson</th>\n",
       "      <th>completed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>0.640625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.020833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  2  3  4  5  6  7  8  9  10  ...  184  185  186  187  188  189  190  191  \\\n",
       "0  1  1  1  1  1  1  1  1  1   1  ...    0    0    0    0    0    0    0    0   \n",
       "1  0  0  1  0  1  1  0  0  0   0  ...    0    0    0    0    0    0    0    0   \n",
       "2  0  0  0  0  0  0  0  1  0   0  ...    0    0    0    0    0    0    0    0   \n",
       "\n",
       "   num_completed_lesson  completed_rate  \n",
       "0                   123        0.640625  \n",
       "1                    16        0.083333  \n",
       "2                     4        0.020833  \n",
       "\n",
       "[3 rows x 193 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_b018['num_completed_lesson'] = df_b018.sum(axis=1)\n",
    "df_b018['completed_rate'] = df_b018['num_completed_lesson']/len(df_b018.columns)\n",
    "df_b018.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Completed Lessons: 26.06451612903226\n",
      "Max: 161\n",
      "Median: 13.0\n",
      "Min: 0\n",
      "Standard Deviation: 38.66776045913095\n"
     ]
    }
   ],
   "source": [
    "average = df_b018['num_completed_lesson'].mean()\n",
    "print(\"Average Completed Lessons:\", average)\n",
    "\n",
    "max = df_b018['num_completed_lesson'].max()\n",
    "print(\"Max:\", max)\n",
    "\n",
    "median = df_b018['num_completed_lesson'].median()\n",
    "print(\"Median:\", median)\n",
    "\n",
    "min = df_b018['num_completed_lesson'].min()\n",
    "print(\"Min:\", min)\n",
    "\n",
    "std = df_b018['num_completed_lesson'].std()\n",
    "print(\"Standard Deviation:\", std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.135752688172043"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average = df_b018['completed_rate'].mean()\n",
    "average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "completed_rate = average\n",
    "not_completed_rate = 1-average\n",
    "\n",
    "# Data for the pie chart\n",
    "labels = 'Completed Rate', 'Not Completed Rate'\n",
    "sizes = [completed_rate, not_completed_rate]\n",
    "\n",
    "# Create the pie chart\n",
    "plt.pie(sizes, labels=labels, autopct='%1.0f%%')\n",
    "\n",
    "# Set the legend\n",
    "plt.legend(title=\"Rate\", loc='upper left')\n",
    "\n",
    "plt.title('Lessons Completed Rate of B018')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# corrmat = df_b018.corr()\n",
    "\n",
    "# plt.figure(figsize=(13, 6))\n",
    "# sns.heatmap(corrmat, vmax=1, annot=True, linewidths=.5)\n",
    "# plt.xticks(rotation=30, horizontalalignment='right')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions - ALL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 98 files.\n",
      "Reading files and combine ...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dt/90w7dnxx5xzfd7px48yrrpt00000gn/T/ipykernel_17344/342820352.py:30: 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",
      "  df['completed_lesson'] = df.iloc[:, 2:-1].sum(axis=1)\n",
      "/var/folders/dt/90w7dnxx5xzfd7px48yrrpt00000gn/T/ipykernel_17344/342820352.py:30: 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",
      "  df['completed_lesson'] = df.iloc[:, 2:-1].sum(axis=1)\n",
      "/var/folders/dt/90w7dnxx5xzfd7px48yrrpt00000gn/T/ipykernel_17344/342820352.py:30: 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",
      "  df['completed_lesson'] = df.iloc[:, 2:-1].sum(axis=1)\n",
      "/var/folders/dt/90w7dnxx5xzfd7px48yrrpt00000gn/T/ipykernel_17344/342820352.py:30: 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",
      "  df['completed_lesson'] = df.iloc[:, 2:-1].sum(axis=1)\n",
      "/var/folders/dt/90w7dnxx5xzfd7px48yrrpt00000gn/T/ipykernel_17344/342820352.py:30: 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",
      "  df['completed_lesson'] = df.iloc[:, 2:-1].sum(axis=1)\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>email</th>\n",
       "      <th>total_lesson</th>\n",
       "      <th>completed_lesson</th>\n",
       "      <th>completed_rate</th>\n",
       "      <th>completed_rate_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>se vanak</td>\n",
       "      <td>vanak.se@cadt.edu.kh</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ប៊ូ គិមហុក</td>\n",
       "      <td>bou.kimhok168@gmail.com</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ប៊ុត គីឌែន</td>\n",
       "      <td>kidaine-buth@mptc.gov.kh</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ធាម ង៉ែត</td>\n",
       "      <td>theamnget09@gmail.com</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>0.439024</td>\n",
       "      <td>43.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>មន​ ច័ន្ទនិកា</td>\n",
       "      <td>monchannika@gmail.com</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>92.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4361</th>\n",
       "      <td>សយ សុវណ្ណធីនាថ</td>\n",
       "      <td>tneathsoy@gmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>97</td>\n",
       "      <td>0.782258</td>\n",
       "      <td>78.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4362</th>\n",
       "      <td>កុយ សុវណ្ណា</td>\n",
       "      <td>koysovanna@gmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>98</td>\n",
       "      <td>0.790323</td>\n",
       "      <td>79.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4363</th>\n",
       "      <td>ញាន សុសិត្យា</td>\n",
       "      <td>sosithya.nhean@gmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>5</td>\n",
       "      <td>0.040323</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>កែវ សោភារក្ស</td>\n",
       "      <td>keosorphearak@gmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4365</th>\n",
       "      <td>ទិត្យ ឡង់ឌី</td>\n",
       "      <td>londy_tth@hotmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>6</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4366 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name                     email  total_lesson  \\\n",
       "0           se vanak      vanak.se@cadt.edu.kh            41   \n",
       "1         ប៊ូ គិមហុក   bou.kimhok168@gmail.com            41   \n",
       "2         ប៊ុត គីឌែន  kidaine-buth@mptc.gov.kh            41   \n",
       "3           ធាម ង៉ែត     theamnget09@gmail.com            41   \n",
       "4      មន​ ច័ន្ទនិកា     monchannika@gmail.com            41   \n",
       "...              ...                       ...           ...   \n",
       "4361  សយ សុវណ្ណធីនាថ       tneathsoy@gmail.com           124   \n",
       "4362     កុយ សុវណ្ណា      koysovanna@gmail.com           124   \n",
       "4363    ញាន សុសិត្យា  sosithya.nhean@gmail.com           124   \n",
       "4364    កែវ សោភារក្ស   keosorphearak@gmail.com           124   \n",
       "4365     ទិត្យ ឡង់ឌី     londy_tth@hotmail.com           124   \n",
       "\n",
       "      completed_lesson  completed_rate  completed_rate_percentage  \n",
       "0                    0        0.000000                       0.00  \n",
       "1                   41        1.000000                     100.00  \n",
       "2                   41        1.000000                     100.00  \n",
       "3                   18        0.439024                      43.90  \n",
       "4                   38        0.926829                      92.68  \n",
       "...                ...             ...                        ...  \n",
       "4361                97        0.782258                      78.23  \n",
       "4362                98        0.790323                      79.03  \n",
       "4363                 5        0.040323                       4.03  \n",
       "4364                 0        0.000000                       0.00  \n",
       "4365                 6        0.048387                       4.84  \n",
       "\n",
       "[4366 rows x 6 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "act_completion_files = glob.glob('./course_prep_dataset/all_activity_completion_excel/*')\n",
    "print(\"There are\", len(act_completion_files), \"files.\")\n",
    "\n",
    "print(\"Reading files and combine ...\\n\")\n",
    "combine_act_completion_df = pd.DataFrame()\n",
    "for file in act_completion_files:\n",
    "    # print(file)\n",
    "\n",
    "    df = pd.read_excel(file)\n",
    "    df.rename(columns={df.columns[0]: 'name'}, inplace=True)\n",
    "\n",
    "    columns = df.columns\n",
    "    for col in columns:\n",
    "        if('Unnamed' in col):\n",
    "            df = df.drop(columns=[col], axis=1)\n",
    "\n",
    "    new_name = 0\n",
    "    columns = df.columns\n",
    "    for col in columns:\n",
    "        df.rename(columns={col: new_name}, inplace=True)\n",
    "        new_name += 1\n",
    "\n",
    "    df.rename(columns={df.columns[0]: 'name'}, inplace=True)\n",
    "    df.rename(columns={df.columns[1]: 'email'}, inplace=True)\n",
    "\n",
    "    df = df.replace({'Not completed': 0, 'Completed': 1})\n",
    "\n",
    "    df['total_lesson'] = len(df.iloc[:, 2:].columns)\n",
    "\n",
    "    df['completed_lesson'] = df.iloc[:, 2:-1].sum(axis=1)\n",
    "    df['completed_rate'] = df['completed_lesson']/df['total_lesson']\n",
    "    df['completed_rate_percentage'] = (df['completed_rate'] * 100).round(2)\n",
    "\n",
    "    df = df[['name', 'email', 'total_lesson', 'completed_lesson', 'completed_rate', 'completed_rate_percentage']]\n",
    "    # print(df)\n",
    "\n",
    "    # combine dataset - all_pretest\n",
    "    combine_act_completion_df = pd.concat([combine_act_completion_df, df], ignore_index=True)\n",
    "\n",
    "combine_act_completion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5798647242455774"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['completed_rate'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find Duplicated Rows\n",
    "# duplicated_rows = combine_act_completion_df[combine_act_completion_df.duplicated(keep=False)]\n",
    "# print(\"Double Duplicated Rows :\", len(duplicated_rows))\n",
    "# print(\"Unique Duplicated :\", len(duplicated_rows['name'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame without Duplicates:\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>email</th>\n",
       "      <th>total_lesson</th>\n",
       "      <th>completed_lesson</th>\n",
       "      <th>completed_rate</th>\n",
       "      <th>completed_rate_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>se vanak</td>\n",
       "      <td>vanak.se@cadt.edu.kh</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ប៊ូ គិមហុក</td>\n",
       "      <td>bou.kimhok168@gmail.com</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ប៊ុត គីឌែន</td>\n",
       "      <td>kidaine-buth@mptc.gov.kh</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ធាម ង៉ែត</td>\n",
       "      <td>theamnget09@gmail.com</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>0.439024</td>\n",
       "      <td>43.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>មន​ ច័ន្ទនិកា</td>\n",
       "      <td>monchannika@gmail.com</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>92.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4361</th>\n",
       "      <td>សយ សុវណ្ណធីនាថ</td>\n",
       "      <td>tneathsoy@gmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>97</td>\n",
       "      <td>0.782258</td>\n",
       "      <td>78.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4362</th>\n",
       "      <td>កុយ សុវណ្ណា</td>\n",
       "      <td>koysovanna@gmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>98</td>\n",
       "      <td>0.790323</td>\n",
       "      <td>79.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4363</th>\n",
       "      <td>ញាន សុសិត្យា</td>\n",
       "      <td>sosithya.nhean@gmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>5</td>\n",
       "      <td>0.040323</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>កែវ សោភារក្ស</td>\n",
       "      <td>keosorphearak@gmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4365</th>\n",
       "      <td>ទិត្យ ឡង់ឌី</td>\n",
       "      <td>londy_tth@hotmail.com</td>\n",
       "      <td>124</td>\n",
       "      <td>6</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4305 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name                     email  total_lesson  \\\n",
       "0           se vanak      vanak.se@cadt.edu.kh            41   \n",
       "1         ប៊ូ គិមហុក   bou.kimhok168@gmail.com            41   \n",
       "2         ប៊ុត គីឌែន  kidaine-buth@mptc.gov.kh            41   \n",
       "3           ធាម ង៉ែត     theamnget09@gmail.com            41   \n",
       "4      មន​ ច័ន្ទនិកា     monchannika@gmail.com            41   \n",
       "...              ...                       ...           ...   \n",
       "4361  សយ សុវណ្ណធីនាថ       tneathsoy@gmail.com           124   \n",
       "4362     កុយ សុវណ្ណា      koysovanna@gmail.com           124   \n",
       "4363    ញាន សុសិត្យា  sosithya.nhean@gmail.com           124   \n",
       "4364    កែវ សោភារក្ស   keosorphearak@gmail.com           124   \n",
       "4365     ទិត្យ ឡង់ឌី     londy_tth@hotmail.com           124   \n",
       "\n",
       "      completed_lesson  completed_rate  completed_rate_percentage  \n",
       "0                    0        0.000000                       0.00  \n",
       "1                   41        1.000000                     100.00  \n",
       "2                   41        1.000000                     100.00  \n",
       "3                   18        0.439024                      43.90  \n",
       "4                   38        0.926829                      92.68  \n",
       "...                ...             ...                        ...  \n",
       "4361                97        0.782258                      78.23  \n",
       "4362                98        0.790323                      79.03  \n",
       "4363                 5        0.040323                       4.03  \n",
       "4364                 0        0.000000                       0.00  \n",
       "4365                 6        0.048387                       4.84  \n",
       "\n",
       "[4305 rows x 6 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove duplicated rows\n",
    "df_no_duplicates = combine_act_completion_df.drop_duplicates(keep='first')\n",
    "print(\"DataFrame without Duplicates:\")\n",
    "\n",
    "df = df_no_duplicates\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>name</th>\n",
       "      <th>Email address</th>\n",
       "      <th>pre_test_score</th>\n",
       "      <th>Grade/100.00</th>\n",
       "      <th>diff</th>\n",
       "      <th>improvement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NHET TOU</td>\n",
       "      <td>nhettou.math@gmail.com</td>\n",
       "      <td>53.65</td>\n",
       "      <td>57.71</td>\n",
       "      <td>4.06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>កង ចន្នី</td>\n",
       "      <td>dp23410@DSEPROVINCE23.com</td>\n",
       "      <td>34.94</td>\n",
       "      <td>48.18</td>\n",
       "      <td>13.24</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>កាំង សុផាន់ណា</td>\n",
       "      <td>D220129@DSEERA22.com</td>\n",
       "      <td>40.75</td>\n",
       "      <td>54.32</td>\n",
       "      <td>13.57</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>កឹម តុលា</td>\n",
       "      <td>D220570@DSEERA22.com</td>\n",
       "      <td>20.52</td>\n",
       "      <td>62.52</td>\n",
       "      <td>42.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>កឹម សៀករតនៈ</td>\n",
       "      <td>dp23411@DSEPROVINCE23.com</td>\n",
       "      <td>39.63</td>\n",
       "      <td>72.72</td>\n",
       "      <td>33.09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>អ៊ុំ សុធា</td>\n",
       "      <td>dp23467@DSEPROVINCE23.com</td>\n",
       "      <td>48.42</td>\n",
       "      <td>74.66</td>\n",
       "      <td>26.24</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>អ៊ួង សារុន</td>\n",
       "      <td>D220729@DSEERA22.com</td>\n",
       "      <td>11.25</td>\n",
       "      <td>34.54</td>\n",
       "      <td>23.29</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>ឯក ដាលីស</td>\n",
       "      <td>D220357@DSEERA22.com</td>\n",
       "      <td>34.04</td>\n",
       "      <td>45.52</td>\n",
       "      <td>11.48</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>ឯកសារន្ត ផៅសិរីសិរីបុត្រ</td>\n",
       "      <td>D220558@DSEERA22.com</td>\n",
       "      <td>60.75</td>\n",
       "      <td>81.30</td>\n",
       "      <td>20.55</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>ឯម វណ្ណវ៉ារីណែត</td>\n",
       "      <td>D220717@DSEERA22.com</td>\n",
       "      <td>23.17</td>\n",
       "      <td>35.76</td>\n",
       "      <td>12.59</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>697 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name              Email address  pre_test_score  \\\n",
       "0                    NHET TOU     nhettou.math@gmail.com           53.65   \n",
       "1                    កង ចន្នី  dp23410@DSEPROVINCE23.com           34.94   \n",
       "2               កាំង សុផាន់ណា       D220129@DSEERA22.com           40.75   \n",
       "3                    កឹម តុលា       D220570@DSEERA22.com           20.52   \n",
       "4                 កឹម សៀករតនៈ  dp23411@DSEPROVINCE23.com           39.63   \n",
       "..                        ...                        ...             ...   \n",
       "692                 អ៊ុំ សុធា  dp23467@DSEPROVINCE23.com           48.42   \n",
       "693                អ៊ួង សារុន       D220729@DSEERA22.com           11.25   \n",
       "694                  ឯក ដាលីស       D220357@DSEERA22.com           34.04   \n",
       "695  ឯកសារន្ត ផៅសិរីសិរីបុត្រ       D220558@DSEERA22.com           60.75   \n",
       "696           ឯម វណ្ណវ៉ារីណែត       D220717@DSEERA22.com           23.17   \n",
       "\n",
       "     Grade/100.00   diff  improvement  \n",
       "0           57.71   4.06         True  \n",
       "1           48.18  13.24         True  \n",
       "2           54.32  13.57         True  \n",
       "3           62.52  42.00         True  \n",
       "4           72.72  33.09         True  \n",
       "..            ...    ...          ...  \n",
       "692         74.66  26.24         True  \n",
       "693         34.54  23.29         True  \n",
       "694         45.52  11.48         True  \n",
       "695         81.30  20.55         True  \n",
       "696         35.76  12.59         True  \n",
       "\n",
       "[697 rows x 6 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mapped = pd.read_excel('./course_prep_dataset/improvement_list_mapped.xlsx')\n",
    "df_mapped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dt/90w7dnxx5xzfd7px48yrrpt00000gn/T/ipykernel_17344/2130224212.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['pre_test_score'] = None\n",
      "/var/folders/dt/90w7dnxx5xzfd7px48yrrpt00000gn/T/ipykernel_17344/2130224212.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['certified_score'] = None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>name</th>\n",
       "      <th>email</th>\n",
       "      <th>total_lesson</th>\n",
       "      <th>completed_lesson</th>\n",
       "      <th>completed_rate</th>\n",
       "      <th>completed_rate_percentage</th>\n",
       "      <th>pre_test_score</th>\n",
       "      <th>certified_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>នោ ដារ៉ូ</td>\n",
       "      <td>knowdaro@gmail.com</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ខឹម ណាលីម</td>\n",
       "      <td>098677739@DSEmowa23.com</td>\n",
       "      <td>192</td>\n",
       "      <td>186</td>\n",
       "      <td>0.968750</td>\n",
       "      <td>96.88</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ភឿក សារិទ្ធ</td>\n",
       "      <td>016566806@DSEmowa23.com</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>សរ សេរីសិទ្ធី</td>\n",
       "      <td>012670019@DSEmowa23.com</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>វុត្ថា កញ្ញាតា</td>\n",
       "      <td>vkanhatah12n29@era.gov.kh</td>\n",
       "      <td>192</td>\n",
       "      <td>94</td>\n",
       "      <td>0.489583</td>\n",
       "      <td>48.96</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>ទុំ សារឿន</td>\n",
       "      <td>011444105@DSEPROVINCE23.com</td>\n",
       "      <td>192</td>\n",
       "      <td>104</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>54.17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4210</th>\n",
       "      <td>ជា សុខា</td>\n",
       "      <td>012976146@DSEPROVINCE23.com</td>\n",
       "      <td>192</td>\n",
       "      <td>24</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>12.50</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4211</th>\n",
       "      <td>លឹម សុគន្ធា</td>\n",
       "      <td>limsokunthea111@gmail.com</td>\n",
       "      <td>192</td>\n",
       "      <td>31</td>\n",
       "      <td>0.161458</td>\n",
       "      <td>16.15</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212</th>\n",
       "      <td>អ៊ុំ សុធា</td>\n",
       "      <td>017300021@DSEPROVINCE23.com</td>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4214</th>\n",
       "      <td>សែម សូដា</td>\n",
       "      <td>010736210@DSEPROVINCE23.com</td>\n",
       "      <td>192</td>\n",
       "      <td>23</td>\n",
       "      <td>0.119792</td>\n",
       "      <td>11.98</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>613 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name                        email  total_lesson  \\\n",
       "8           នោ ដារ៉ូ           knowdaro@gmail.com            41   \n",
       "46         ខឹម ណាលីម      098677739@DSEmowa23.com           192   \n",
       "56       ភឿក សារិទ្ធ      016566806@DSEmowa23.com           192   \n",
       "63     សរ សេរីសិទ្ធី      012670019@DSEmowa23.com           192   \n",
       "120   វុត្ថា កញ្ញាតា    vkanhatah12n29@era.gov.kh           192   \n",
       "...              ...                          ...           ...   \n",
       "4208       ទុំ សារឿន  011444105@DSEPROVINCE23.com           192   \n",
       "4210         ជា សុខា  012976146@DSEPROVINCE23.com           192   \n",
       "4211     លឹម សុគន្ធា    limsokunthea111@gmail.com           192   \n",
       "4212       អ៊ុំ សុធា  017300021@DSEPROVINCE23.com           192   \n",
       "4214        សែម សូដា  010736210@DSEPROVINCE23.com           192   \n",
       "\n",
       "      completed_lesson  completed_rate  completed_rate_percentage  \\\n",
       "8                   41        1.000000                     100.00   \n",
       "46                 186        0.968750                      96.88   \n",
       "56                   0        0.000000                       0.00   \n",
       "63                   0        0.000000                       0.00   \n",
       "120                 94        0.489583                      48.96   \n",
       "...                ...             ...                        ...   \n",
       "4208               104        0.541667                      54.17   \n",
       "4210                24        0.125000                      12.50   \n",
       "4211                31        0.161458                      16.15   \n",
       "4212               192        1.000000                     100.00   \n",
       "4214                23        0.119792                      11.98   \n",
       "\n",
       "     pre_test_score certified_score  \n",
       "8              None            None  \n",
       "46             None            None  \n",
       "56             None            None  \n",
       "63             None            None  \n",
       "120            None            None  \n",
       "...             ...             ...  \n",
       "4208           None            None  \n",
       "4210           None            None  \n",
       "4211           None            None  \n",
       "4212           None            None  \n",
       "4214           None            None  \n",
       "\n",
       "[613 rows x 8 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapped_name = np.array(df_mapped['name'])\n",
    "df = df[df['name'].isin(mapped_name)]\n",
    "df['pre_test_score'] = None\n",
    "df['certified_score'] = None\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in np.array(df.name):\n",
    "    pre_test_score = df_mapped[df_mapped['name'] == name]['pre_test_score'].values[0]\n",
    "    certified_score = df_mapped[df_mapped['name'] == name]['Grade/100.00'].values[0]\n",
    "\n",
    "    # print(pre_test_score)\n",
    "    # print(certified_score)\n",
    "    # print()\n",
    "\n",
    "    df.loc[df['name'] == name, 'pre_test_score'] = pre_test_score\n",
    "    df.loc[df['name'] == name, 'certified_score'] = certified_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>name</th>\n",
       "      <th>email</th>\n",
       "      <th>total_lesson</th>\n",
       "      <th>completed_lesson</th>\n",
       "      <th>completed_rate</th>\n",
       "      <th>completed_rate_percentage</th>\n",
       "      <th>pre_test_score</th>\n",
       "      <th>certified_score</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>NHET TOU</td>\n",
       "      <td>nhettou.math@gmail.com</td>\n",
       "      <td>192</td>\n",
       "      <td>18</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>9.38</td>\n",
       "      <td>53.65</td>\n",
       "      <td>57.71</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>កាំង សុផាន់ណា</td>\n",
       "      <td>ksophannapg2n140@era.gov.kh</td>\n",
       "      <td>192</td>\n",
       "      <td>38</td>\n",
       "      <td>0.197917</td>\n",
       "      <td>19.79</td>\n",
       "      <td>40.75</td>\n",
       "      <td>54.32</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>កឺម វណ្ណហ្វីដែល</td>\n",
       "      <td>kvanfidelp22n2@era.gov.kh</td>\n",
       "      <td>192</td>\n",
       "      <td>13</td>\n",
       "      <td>0.067708</td>\n",
       "      <td>6.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.83</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>កុយ ថារី</td>\n",
       "      <td>ktharypg2n114@era.gov.kh</td>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>38.46</td>\n",
       "      <td>60.35</td>\n",
       "      <td>Passed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3204</th>\n",
       "      <td>កុយ វិច្ឆានី</td>\n",
       "      <td>dse_cm014@gmail.com</td>\n",
       "      <td>192</td>\n",
       "      <td>4</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>2.08</td>\n",
       "      <td>32.56</td>\n",
       "      <td>40.75</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212</th>\n",
       "      <td>អ៊ុំ សុធា</td>\n",
       "      <td>017300021@DSEPROVINCE23.com</td>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>48.42</td>\n",
       "      <td>74.66</td>\n",
       "      <td>Passed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>អ៊ួង សារុន</td>\n",
       "      <td>usarunp22n77@era.gov.kh</td>\n",
       "      <td>192</td>\n",
       "      <td>10</td>\n",
       "      <td>0.052083</td>\n",
       "      <td>5.21</td>\n",
       "      <td>11.25</td>\n",
       "      <td>34.54</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>ឯក ដាលីស</td>\n",
       "      <td>edalispg2n78@era.gov.kh</td>\n",
       "      <td>192</td>\n",
       "      <td>189</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>98.44</td>\n",
       "      <td>34.04</td>\n",
       "      <td>45.52</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>ឯកសារន្ត ផៅសិរីសិរីបុត្រ</td>\n",
       "      <td>epserybothh12n34@era.gov.kh</td>\n",
       "      <td>192</td>\n",
       "      <td>17</td>\n",
       "      <td>0.088542</td>\n",
       "      <td>8.85</td>\n",
       "      <td>60.75</td>\n",
       "      <td>81.3</td>\n",
       "      <td>Passed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>ឯម វណ្ណវ៉ារីណែត</td>\n",
       "      <td>evanvarinethpg1n36@era.gov.kh</td>\n",
       "      <td>192</td>\n",
       "      <td>2</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>1.04</td>\n",
       "      <td>23.17</td>\n",
       "      <td>35.76</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>613 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name                          email  total_lesson  \\\n",
       "482                   NHET TOU         nhettou.math@gmail.com           192   \n",
       "2128             កាំង សុផាន់ណា    ksophannapg2n140@era.gov.kh           192   \n",
       "581            កឺម វណ្ណហ្វីដែល      kvanfidelp22n2@era.gov.kh           192   \n",
       "2116                  កុយ ថារី       ktharypg2n114@era.gov.kh           192   \n",
       "3204              កុយ វិច្ឆានី            dse_cm014@gmail.com           192   \n",
       "...                        ...                            ...           ...   \n",
       "4212                 អ៊ុំ សុធា    017300021@DSEPROVINCE23.com           192   \n",
       "603                 អ៊ួង សារុន        usarunp22n77@era.gov.kh           192   \n",
       "3531                  ឯក ដាលីស        edalispg2n78@era.gov.kh           192   \n",
       "992   ឯកសារន្ត ផៅសិរីសិរីបុត្រ    epserybothh12n34@era.gov.kh           192   \n",
       "580            ឯម វណ្ណវ៉ារីណែត  evanvarinethpg1n36@era.gov.kh           192   \n",
       "\n",
       "      completed_lesson  completed_rate  completed_rate_percentage  \\\n",
       "482                 18        0.093750                       9.38   \n",
       "2128                38        0.197917                      19.79   \n",
       "581                 13        0.067708                       6.77   \n",
       "2116               192        1.000000                     100.00   \n",
       "3204                 4        0.020833                       2.08   \n",
       "...                ...             ...                        ...   \n",
       "4212               192        1.000000                     100.00   \n",
       "603                 10        0.052083                       5.21   \n",
       "3531               189        0.984375                      98.44   \n",
       "992                 17        0.088542                       8.85   \n",
       "580                  2        0.010417                       1.04   \n",
       "\n",
       "     pre_test_score certified_score  result  \n",
       "482           53.65           57.71  Failed  \n",
       "2128          40.75           54.32  Failed  \n",
       "581             0.0           52.83  Failed  \n",
       "2116          38.46           60.35  Passed  \n",
       "3204          32.56           40.75  Failed  \n",
       "...             ...             ...     ...  \n",
       "4212          48.42           74.66  Passed  \n",
       "603           11.25           34.54  Failed  \n",
       "3531          34.04           45.52  Failed  \n",
       "992           60.75            81.3  Passed  \n",
       "580           23.17           35.76  Failed  \n",
       "\n",
       "[613 rows x 9 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df  = df.sort_values(by='name')\n",
    "df.loc[df['certified_score'] >= float(60), 'result'] = 'Passed'\n",
    "df.loc[df['certified_score'] < float(60), 'result'] = 'Failed'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# # Count the number of 'Passed' and 'Failed' items\n",
    "# status_counts = df['result'].value_counts()\n",
    "\n",
    "# # Plot the bar chart\n",
    "# plt.bar(status_counts.index, status_counts.values, color=['blue', 'orange'])\n",
    "\n",
    "# # Add labels and title\n",
    "# plt.xlabel('Result')\n",
    "# plt.ylabel('Count')\n",
    "# plt.title('Passed vs. Failed Items')\n",
    "\n",
    "# # Display the plot\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_excel('./course_prep_dataset/lesson_engagement_pre_and_test.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Passed': 135, 'Failed': 84})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_g_50 = df[df['completed_rate_percentage'] >= 60]\n",
    "result = Counter(df_g_50.result)\n",
    "print(result)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Count the number of 'Passed' and 'Failed' items\n",
    "status_counts = df_g_50['result'].value_counts()\n",
    "\n",
    "# Plot the bar chart\n",
    "# plt.bar(status_counts.index, status_counts.values, color=['blue', 'orange'])\n",
    "plt.pie(status_counts, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Passed vs. Failed Items MORE than 60')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Failed': 247, 'Passed': 147})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_l_50 = df[df['completed_rate_percentage'] < 60]\n",
    "result = Counter(df_l_50.result)\n",
    "print(result)\n",
    "\n",
    "# Count the number of 'Passed' and 'Failed' items\n",
    "status_counts = df_l_50['result'].value_counts()\n",
    "\n",
    "# Plot the bar chart\n",
    "# plt.bar(status_counts.index, status_counts.values, color=['blue', 'orange'])\n",
    "plt.pie(status_counts, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Passed vs. Failed Items')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# file_path = \"./course_prep_dataset/progressa1f1.xlsx\"\n",
    "\n",
    "# df = pd.read_excel(file_path)\n",
    "# df.rename(columns={df.columns[0]: 'name'}, inplace=True)\n",
    "\n",
    "# columns = df.columns\n",
    "# for col in columns:\n",
    "#     if('Unnamed' in col):\n",
    "#         df = df.drop(columns=[col], axis=1)\n",
    "\n",
    "# new_name = 0\n",
    "# columns = df.columns\n",
    "# for col in columns:\n",
    "#     df.rename(columns={col: new_name}, inplace=True)\n",
    "#     new_name += 1\n",
    "\n",
    "# df.rename(columns={df.columns[0]: 'name'}, inplace=True)\n",
    "# df.rename(columns={df.columns[1]: 'email'}, inplace=True)\n",
    "\n",
    "# df = df.replace({'Not completed': 0, 'Completed': 1})\n",
    "\n",
    "# df['total_lesson'] = len(df.iloc[:, 2:].columns)\n",
    "\n",
    "# df['completed_lesson'] = df.iloc[:, 2:-1].sum(axis=1) #df.sum(axis=1)\n",
    "# df['completed_rate'] = df['completed_lesson']/df['total_lesson']\n",
    "# df['completed_rate_percentage'] = (df['completed_rate'] * 100).round(2)\n",
    "\n",
    "# df = df[['name', 'email', 'total_lesson', 'completed_lesson', 'completed_rate', 'completed_rate_percentage']]\n",
    "# # df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "average = df['completed_rate'].mean()\n",
    "\n",
    "completed_rate = average\n",
    "not_completed_rate = 1-average\n",
    "\n",
    "# Data for the pie chart\n",
    "labels = 'Completed Rate in Average', 'Not Completed Rate'\n",
    "sizes = [completed_rate, not_completed_rate]\n",
    "\n",
    "# Create the pie chart\n",
    "plt.pie(sizes, labels=labels, autopct='%1.0f%%')\n",
    "\n",
    "# Set the legend\n",
    "plt.legend(title=\"Rate\", loc='upper left')\n",
    "\n",
    "plt.title('Overall Lessons Completed Rate')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Passed': 126, 'Failed': 80})\n"
     ]
    },
    {
     "data": {
      "image/png": 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aat++vVS5cmXJyspKqlmzpvTuu+9KaWlpWvuLj4+X3nrrLcnHx0eytLSUPD09pS5dukhr1qzRWu7evXtS3759JVtbW8nV1VWaNm2adODAgRJd4ueRw4cPSwAkmUwmRUVFab2WmJgovfXWW1KdOnUkOzs7ycnJSWrZsqXWZYUkSZLGjBmjuUzPszy6xM+2bduKfT03N1eaMWOG5OXlJdnY2Eht27aV/vrrL6lDhw5al68pySV+HtmxY4fUrl07yc7OTrKzs5Pq1KkjvfXWW9KtW7e0llu5cqVUo0YNycrKSmrWrJl06tSpJ/b7NP+9xI8kSdLZs2elpk2bSkql8onL/YSHh0ujR4+WPD09JUtLS6lKlSrSSy+9JG3fvl2zzNO+Nx+9z/9eSmrMmDGSnZ2d5uPt27dL3bp1k9zd3SWlUilVq1ZNmjRpkhQbG/vc90NETyeTpP/MbRARERERPQePiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcWogMQEVUUqdn5SM8pRHpuQdEjpxAZuQXIyC16LiO3ENn5KqjVElSSBLUkoY9jODrlHAZkCkD+70OmACysAFsXwMYFsK387+Pfv9u4AAr++CYisfhTiIioBApVasSl5yImJQcxqTn/+/Pfvz9Iy0FugVrn7b5Q8wYQ85uOa8kAK8d/S6ULYOcOuPgBrrUA1wCgsj/g4KFzFiIiXbBEEhH9R0JGLkJjM3AzNh2hsekIjc3A3cRMFKgk0dH+JQF5aUWPlIjiF7FyeqxU/vunqz/gUhOwUBo2LhFVSCyRRGTW4tNzcT4iGdeiU3EzLgOhselIzMwXHavs8tKAmMtFj8fJLQDPIKBaa8CnZdGfHLUkolJgiSQisxKVnI3zEcm4EJGE8xHJuJeULTqSYakLgQd/Fz3OrSx6rlINoFqrfx+ti0YtZTKxOYnI6LFEElGFFpuWg5O3Hv5bHJMRk5ojOpLxSYkoegT/e2ymjUvRKGX1NkBAD8AtQGw+IjJKMkmSjOUgHyIivbgZl47D1+NxODQe12LSYMw/5b6qeQUDYpaKjvFsrgFAnd5AnT5AlSYcpSQiAByJJKIKQKWWcDEyGYeux+NIaDzuJ5vZFHV5SwwD/gwD/vwKcPAG6vQqKpW+LwAKS9HpiEgQlkgiMlnn7ybh9ysxOHQjDinZBaLjmIeMB8DFH4se1k5F0911egO1XgSUtqLTEZEBsUQSkUmJSc3BjsvR2HEl2vxOijE2uWnAP1uKHkoHIGgw0Gwc4NVQdDIiMgCWSCIyerkFKuwPicX2y9E4G55k1Mc4mq38DODy+qKHd2Og6biiUqm0E52MiMoJSyQRGa1bcRnYcDYCe4NjkZFXKDoOldSjSwgd/ABoMKSoUHo1EJ2KiPSMJZKIjM6psIf44fRdnL6dKDoKlUV+BnBpXdHDuwnQdCxHJ4kqEJZIIjIK+YVq7Loag7WnI3ArPkN0HNK3B1eKHofmAi0mAK0nF933m4hMFkskEQmVkpWPTefuYdO5e3iYkSc6DpW3vDTg9DLg/Gqg+Xig9RTA3k10KiIqBZZIIhIiKTMP358Mx8/n7iOnQCU6DhlafiZwZjlw4YeiYybbTuM9vIlMDEskERlUSlY+Vp+6i5/+ikR2Psuj2SvIBs6tAC6tBZqMAdpNBxy9RaciohJgiSQig8jKK8SPpyPww+m7yOSZ1vRfhbnAhdXA5Q1A45eBdm8Dzj6iUxHRM7BEElG5yi9U45fz97Di+B0kZuaLjkPGTpVXNCr59yag5etAh1mAlYPoVERUDJZIIio3h2/EY+HeG7yXNelOlQ+c/Qb4Zyvw4gKg4TDRiYjoP1giiUjv7idlY/7/XcfRmwmio5Cpy4wDdr5WdCecnp/zouVERoQlkoj0JrdAhVUnwvH9yXDkFapFx6GK5P5fwJoORWdyd/6Q15gkMgIskUSkF0duxGMBp66pPEnqouMlr+8sKpJNxwFyuehURGaLJZKIyiQqORsf7+HUNRlQTjLwxztFZ3L3WgpUayk6EZFZ4n/hiKjUfrtwHz2+PsUCSWLE/QOs7wEceB8oyBWdhsjscCSSiHSWkJ6L93b8g+O3HoqOQuZOUhddrPzOYWDA90CVpqITEZkNjkQSkU7+L/gBun19igWSjEtiGPDji8DRhYCqQHQaIrPAkUgiKpHU7Hx8uCsEe/+JFR2FqHiSCji9tGhUctA6wLWW6EREFRpHIonouU6GPUT3r0+xQJJpiA0GVrcHLm8UnYSoQmOJJKKnUqslLD14C2PXX0B8ep7oOEQlV5AF/N9UYOtoICdFdBqiCoklkoiKlZyVjzHrL+C743cgSaLTEJXSjd3AqnZAzGXRSYgqHJZIInpCcFQq+nz7J07fThQdhajs0qOB9b2K7sNNRHrDEklEWn4+dw9Dvv8LMak5oqMQ6U9hLvD7RODwPEDNW3IS6QPPziYiAEX3vX5/5zX8fiVGdBSi8nNmOZBwExj0I2DtKDoNkUnjSCQRISE9F4NWnWWBJPNw+yDwY1cgKVx0EiKTxhJJZObC4jMwYOVZXH+QLjoKkeEk3gJ+6AyEHxedhMhksUQSmbG/wpMweNVZHv9I5ik3Ffh5EHBulegkRCaJJZLITO2+GoMx6y4gPbdQdBQicSQVcGA2sGcqoFaJTkNkUnhiDZEZWnH8DpYeusXrPxI9cmUjkJcODPwBUFiKTkNkElgiicyISi1h7u4Q/Hr+vugoRMbn+k6gIAcY+hNgYSU6DZHR43Q2kZkoVKkx5bcrLJBEzxJ2APhlCJCfJToJkdFjiSQyAwUqNSb/+jf2XYsTHYXI+EWcBDYNBHJ5xQKiZ2GJJKrgClRqvPXLFRy4zgJJVGJR54CNfYDsZNFJiIwWSyRRBZZfqMYbP1/GoRvxoqMQmZ7Yq8CG3kBmgugkREaJJZKogsorVOH1ny/jSCh/ARKVWsINYH1PII13cyL6L5ZIogoor1CFSZsu49hNFkiiMku6U1QkM3hICNHjWCKJKphClRpv/HwFJ249FB2FqOJIvQf8PJgn2xA9hiWSqIKZ/fs1jkASlYf4a8DmkUBhvugkREaBJZKoAll68Ba2X44WHYOo4oo8Dex8DbzdExFLJFGFsencPXx3/I7oGEQV3/WdRffbJjJzLJFEFcCBkDh8tDtEdAwi83H+e+DPr0SnIBKKJZLIxF2MTMa0zX9Dzdk1IsM6Mh+4+pvoFETCsEQSmbDb8RmYsPES8grVoqMQmSEJ2DMZuHNEdBAiIVgiiUxUWnYBxm+8hLScAtFRiMyXuhDYOgZ4cFV0EiKDY4kkMkFqtYQpm//G/eRs0VGIKD8T2PIK77NNZoclksgELTt8C6fCeDFxIqORdh/YOYmX/iGzwhJJZGIOXo/DyhPhomMQ0X/dPgScWio6BZHBsEQSmZA7CZmYsTWYgx1ExurEYiD8uOgURAbBEklkIjLzCjFp0yVk5hWKjkJETyOpgR0TgLQY0UmIyh1LJJEJkCQJ72y5ivCHWaKjENHzZCcC28YCKl45gSo2lkgiE7D+TCQO3YgXHYOISir6AnBorugUROWKJZLIyN2MS8dnB26KjkFEujq/Cgj5XXQKonLDEklkxHILVJj629/I5x1piEzTnqlAEq+mQBUTSySREfts/02ExWeKjkFEpZWfAeyZwutHUoXEEklkpM7cScTGvyJFxyCisrp3Brj4o+gURHrHEklkhNJzC/DuNl4PkqjCOPIxkHpfdAoivWKJJDJC8/fcwIO0XNExiEhf8jOB/5smOgWRXrFEEhmZ47cSsONKtOgYRKRv4ceAK5tEpyDSG5ZIIiOSW6DCvN0homMQUXk59AGQESc6BZFesEQSGZHvjt1BVHKO6BhEVF5y04C9b4tOQaQXLJFERiL8YSbWnLorOgYRlbdb+4Br20WnICozlkgiIzF3VwjyVbyoOJFZ2D8LyEoUnYKoTFgiiYzA7qsxOBueJDoGERlKdhJwbKHoFERlwhJJJFh6bgEW7g0VHYOIDO3KJiDhpugURKXGEkkk2LKDt5CYmSc6BhEZmqQCjnwkOgVRqbFEEgl092EmfjnPu1gQma2wA0DEadEpiEqFJZJIoKWHbqFQzXsbEpm1Qx+C9zglU8QSSSTIP9Gp2B/Ciw4Tmb3Yq7zkD5kklkgiQZYcuMnBByIqcnQBUMhjo8m0sEQSCfDn7UScucNL+hDRv9LuA+e/F52CSCcskUQGJkkSPj/Iy3oQ0X+cXgZkJ4tOQVRiLJFEBrbvWhz+iU4THYOIjE1uGnDqC9EpiEqMJZLIgNRqCcsO3xIdg4iM1aX1vB0imQyWSCIDOnA9DncfZomOQUTGqjAHuPCD6BREJcISSWRAq0/dFR2BiIzdxR+AghzRKYieiyWSyEDO3U1CcFSq6BhEZOyyk4Crv4hOQfRcLJFEBrKGo5BEVFJ/rQDUatEpKrQNGzbA2dlZ2P5lMhl27dolbP/6wBJJZABh8Rk4fitBdAwiMhXJd4Gbe/W2ubFjx0Imk+Gzzz7Ten7Xrl2QyWQ6bcvX1xdff/11iZb9+++/MWTIEHh4eMDa2hr+/v6YOHEiwsLCdNqnsTBk8Xv0NZPJZLC0tESNGjUwa9Ys5Obm6rSdjh07Yvr06eWSkSWSyADWnLrLu9MQkW7OfqPXzVlbW2PJkiVISUnR63afZu/evWjVqhXy8vLwyy+/IDQ0FD///DOcnJwwd+5cg2QwdT169EBsbCzu3r2Lr776CqtXr8ZHH30kOpYGSyRROYtPz8Weqw9ExyAiUxN9Ebh/Tm+b69q1Kzw9PfHpp58+c7kdO3agXr16sLKygq+vL5YtW6Z5rWPHjrh37x7efvttzShZcbKzszFu3Dj06tULe/bsQdeuXVGjRg20bNkSS5cuxerVqzXLnjx5Ei1atICVlRW8vLwwe/ZsFBYWau1zypQpmD59OipVqgQPDw/88MMPyMrKwrhx4+Dg4IBatWph//79mnVOnDgBmUyGP/74Aw0aNIC1tTVatWqFkJCQZ7733bt3o0mTJrC2toafnx/mz5+vyeLr6wsAGDBgAGQymebj560HALdv30b79u1hbW2NwMBAHD58+Jk5HrGysoKnpyd8fHzQv39/dO3aVWvdpKQkjBgxAlWqVIGtrS2CgoLw22+/aV4fO3YsTp48ieXLl2u+XpGRkQCAkJAQ9OzZE/b29vDw8MArr7yCxETdLi/FEklUztafiUS+isc2EVEpnNHfaKRCocDixYvx7bffIjo6uthlLl++jKFDh2L48OG4du0aPv74Y8ydOxcbNmwAAPz++++oWrUqFixYgNjYWMTGxha7nYMHDyIxMRGzZs0q9vVHxyLGxMSgV69eaN68OYKDg7Fq1SqsXbsWn3zyidbyGzduhKurKy5cuIApU6bgjTfewJAhQ9CmTRtcuXIF3bp1wyuvvILs7Gyt9d59910sW7YMFy9ehJubG/r06YOCgoJiM50+fRqjR4/GtGnTcOPGDaxevRobNmzAokWLAAAXL14EAKxfvx6xsbGaj5+3nlqtxsCBA6FUKnH+/Hl8//33eO+994rN8CwhISE4e/YslEql5rnc3Fw0bdoUf/zxB0JCQvDaa6/hlVdewYULFwAAy5cvR+vWrTFx4kTN18vHxwepqano3LkzGjdujEuXLuHAgQOIj4/H0KFDdcrEEklUjgpUamy7FCU6BhGZqlv7gMQ7etvcgAED0KhRo6dOiX755Zfo0qUL5s6di4CAAIwdOxaTJ0/GF18U3UnHxcUFCoUCDg4O8PT0hKenZ7HbuX37NgCgTp06z8yzcuVK+Pj44LvvvkOdOnXQv39/zJ8/H8uWLYP6sROLGjZsiA8//BD+/v6YM2cOrK2t4erqiokTJ8Lf3x/z5s1DUlIS/vnnH63tf/TRR3jxxRcRFBSEjRs3Ij4+Hjt37iw2y/z58zF79myMGTMGfn5+ePHFF7Fw4ULNqKmbmxuAogLs6emp+fh56x05cgQ3b97ETz/9hIYNG6J9+/ZYvHjxMz8vj+zduxf29vawtrZGUFAQEhIS8O6772per1KlCmbOnIlGjRrBz88PU6ZMQY8ePbB161YAgJOTE5RKJWxtbTVfL4VCge+++w6NGzfG4sWLUadOHTRu3Bjr1q3D8ePHdTpe1aLESxKRzo7ciEdSVr7oGERksqSi60b2XKK3LS5ZsgSdO3fGzJkzn3gtNDQU/fr103qubdu2+Prrr6FSqaBQKEq0D6mEB4GHhoaidevWWtPibdu2RWZmJqKjo1GtWjUAQIMGDTSvKxQKVK5cGUFBQZrnPDw8AAAJCdonMLZu3VrzdxcXF9SuXRuhoaHFZgkODsaZM2c0I4gAoFKpkJubi+zsbNja2pZqvdDQUPj4+MDb27vYXM/SqVMnrFq1CllZWfjqq69gYWGBQYMGae1n8eLF2Lp1K2JiYpCfn4+8vLynZn088/Hjx2Fvb//Ea+Hh4QgICChRPpZIonK0haOQRFRW/2wFXlwIWCifv2wJtG/fHt27d8ecOXMwduxYvWzzvx6VkJs3b5a4MD2LpaWl1sePzlh+/GMAWqOXusrMzMT8+fMxcODAJ16ztrbW+3olYWdnh1q1agEA1q1bh4YNG2Lt2rUYP348AOCLL77A8uXL8fXXXyMoKAh2dnaYPn068vOfPXiRmZmJPn36YMmSJ/9z4uXlVeJ8LJFE5SQ2LQenwh6KjkFEpi4nuWhau15/vW3ys88+Q6NGjVC7dm2t5+vWrYszZ85oPXfmzBkEBARoRiGVSiVUKtUzt9+tWze4urri888/L3b6ODU1Fc7Ozqhbty527NgBSZI0RfDMmTNwcHBA1apVy/IWAQDnzp3TjGampKQgLCwMdevWLXbZJk2a4NatW5rSVhxLS8sn3vvz1qtbty6ioqIQGxurKWjnzul+wpRcLsf777+Pd955ByNHjoSNjQ3OnDmDfv364eWXXwZQVKLDwsIQGBioWa+4r1eTJk2wY8cO+Pr6wsKi9FWQx0QSlZNtl6Kh5mV9iEgf9HwHm6CgIIwaNQrffKN94s6MGTNw9OhRLFy4EGFhYdi4cSO+++47ralvX19fnDp1CjExMU89m9fOzg4//vgj/vjjD/Tt2xdHjhxBZGQkLl26hFmzZuH1118HALz55puIiorClClTcPPmTezevRsfffQR3nnnHcjlZa8oCxYswNGjRxESEoKxY8fC1dUV/fv3L3bZefPm4aeffsL8+fNx/fp1hIaGYvPmzfjwww+13vvRo0cRFxenuVTS89br2rUrAgICMGbMGAQHB+P06dP44IMPSvV+hgwZAoVCgRUrVgAA/P39cfjwYZw9exahoaGYNGkS4uPjtdbx9fXF+fPnERkZicTERKjVarz11ltITk7GiBEjcPHiRYSHh+PgwYMYN27cc/+D8DiWSKJyIEkStl3mVDYR6cmdo0B68WdCl9aCBQuemP5t0qQJtm7dis2bN6N+/fqYN28eFixYoDXtvWDBAkRGRqJmzZqak0uK069fP5w9exaWlpYYOXIk6tSpgxEjRiAtLU1z9nWVKlWwb98+XLhwAQ0bNsTrr7+O8ePHaxW3svjss88wbdo0NG3aFHFxcfi///s/rbObH9e9e3fs3bsXhw4dQvPmzdGqVSt89dVXqF69umaZZcuW4fDhw/Dx8UHjxo1LtJ5cLsfOnTuRk5ODFi1aYMKECVrHT+rCwsICkydPxueff46srCx8+OGHaNKkCbp3746OHTvC09PziZI8c+ZMKBQKBAYGws3NDffv34e3tzfOnDkDlUqFbt26ISgoCNOnT4ezs7NO5V0mlfToVyIqsT9vJ+LltedFxyAT8FXNKxgQs1R0DDIFXecD7aaLTmESTpw4gU6dOiElJUXorQ0rOo5EEpUDnlBDRHp3bZvoBERaWCKJ9CwnX4UjN+KfvyARkS7iQ4CE4i9PQyQCSySRnp0MS0BOQckPTCYiKjGORpZIx44dIUkSp7LLGUskkZ4dCIkTHYGIKiqWSDIiLJFEelSgUuPozYTnL0hEVBqp94GoC6JTEAFgiSTSq7PhScjILRQdg4gqslv7RScgAsASSaRXnMomonJ354joBEQAWCKJ9EatlnCYZ2UTUXmLuwZk8rAZEo8lkkhPLt1LQWJmnugYRFThSUV3sCESjCWSSE+OhHIUkogMhFPaZARYIon05MydRNERiMhc3D0O/Oe+10SGxhJJpAdp2QUIjU0XHYOIzEV2EvDgb9EpyMyxRBLpwbmIJKgl0SmIyKxwSpsEY4kk0oO/wpNERyAic8MSSYKxRBLpAUskERlczGUgJ0V0CjJjLJFEZZSYmYewhAzRMYhMzqqL+WiwKhOOn6bD8dN0tF6bhf23CzSvx2Wq8crOHHguzYDd4nQ0WZ2JHTcKnrFFQKWWMPdYLmosz4DNonTU/CYDC0/mQZL+d7zJ0rN5cP8iA+5fZGDZWe3Lcp2PLkTTNZkoNIXjUyQVcPek6BRkxixEByAydefuJkEygd83RMamqqMMn3W1gr+LHBKAjVcL0G9zDv6eJEc9dwVG78xBaq6EPSNs4Worw6/XCjB0ew4uTZSjsZei2G0uOZOPVZcKsLG/Neq5K3DpgQrjdufAyRqY2tIK/8SrMO94HvaOtIUkAS/9lo1uNS0Q5KFAoVrC63/kYs1LNrCQywz7ySit6ItAvf6iU5CZ4kgkURlxKpuodPrUtkQvf0v4V1YgoLICi7pYw14JnItWAQDORqkwpYUSLaoo4FdJjg/bW8HZWobLsaqnbvNslAr9alugd4AlfJ3lGBxoiW41LXAhpuhyODcT1WjgoUDnGhbo4meBBh5y3Ewseu2LM/loX80CzasUX1CNUmyw6ARkxlgiicroyv1U0RGITJ5KLWFzSAGyCoDWPkUlro2PAluuFyI5R4JaKno9t1BCR9+nT6K18VHgaEQhwpKKimZwnAp/3lehZ62idYLc5QhLUuF+mhr3UtUIS1Kjvrsc4clqrL9agE86W5X/m9Wn2GBwKoRE4XQ2URnkF6pxh8dDEpXatXgVWq/NQm4hYK8Edg6zQaBbUYncOsQWw7Zno/LnGbCQA7aWwM5htqjl8vTxj9ntlEjPk1Dnuywo5IBKDSzqbIVRDSwBAHXdFFjcxRovbsoGAHzaxRp13RTo+lMWPn/RCgfDC/HxiTxYKoDlPazRvrqR/5rMSweSwgHXWqKTkBky8n8dRMYtLD4DBSqOAhCVVm1XOa6+bo+0XAnbbxRgzK5cnBwrR6CbAnOP5SI1V8KRV4qOidx1sxBDt2Xj9Dg7BHkUP+W89XohfrlWgF8H2aCemxxX41SYfjAP3g4yjGmkBAC83kyJ15spNetsvJoPBysZWldVoPZ3mbg40Q7R6RKGb89BxDR7WFkY+fGRsVdZIkkIlkiiMrj+IE10BCKTplTIUMulqKQ19Vbg4gMVlp/Lx6y2VvjuYgFC3rBDPfeiwtjQU4HT9wux4mI+vn/JptjtvXs4F7PbWmF4/aKRxyAPBe6lSfj0z3xNiXxcYrYa80/m4dQ4O5yPUSGgshz+lRXwrwwUqIGwJPVTC6vRePA3EDRYdAoyQzwmkqgMQmJ4q0MifVJLQJ4KyC4oGuH/70nSCjmeeXeo7IJi1pE9fZ23D+bh7VZWqOooh0pdVBwfKVRLMImJhgdXRScgM8WRSKIy4EgkUenNOZKLnv4WqOYkR0aehF+vFeBEpAoHX7ZCHVc5arnIMWlvLpZ2s0ZlGxl23SzA4XAV9o7834hil5+yMKCOJSa3KHquT4AFFp3OQzUnGeq5K/B3rApfnsvHq40sn9j/4fCiE3A29rcGADSvosDNRDX23y5AVLoEhUyG2pVNYKwl7p+ik2tkRj7tThUOSyRRKanVEm7G8aQaotJKyJIwemcOYjMlOFnJ0MBDjoMv2+LFmkW/mvaNtMHso3no81s2MvMl1HKRY2N/a/Ty/18hDE9WIzH7f8OH3/a0xtzjeXhzXy4SsiR4O8gwqakl5nXQPus6p0DC5P252DLYBvJ/y1dVRzm+7WmNcbtzYWUBbOxvDRtLEyhmPLmGBJFJEq8NQFQadxIy0fVL3i2CyuarmlcwIGap6Bhk6gb+CDQYIjoFmRkTGKcnMk43Ynk8JBEZidirohOQGWKJJCql8IRM0RGIiIokR4hOQGaIJZKolO4lZYmOQERUJO2+6ARkhlgiiUopMilbdAQioiKpUaITkBliiSQqJY5EEpHRyE0FcnmcNhkWSyRRKWTmFSIlu0B0DCKi/0njaCQZFkskUSnEpOSIjkBEpI1T2mRgLJFEpRCTyuMhicjIpPLkGjIslkiiUuBIJBEZHZ6hTQbGEklUCg/SckVHICLSxulsMjCWSKJSSMnKFx2BiEgbT6whA2OJJCqFVJ6ZTUTGhiORZGAskUSlkJrDkUgiMjI5KaITkJlhiSQqhbScQtERiIi0qQuAwjzRKciMsEQSlUJaNkciicgI5fNOWmQ4LJFEpZCaw2MiicgI5WeKTkBmhCWSSEf5hWpk56tExyAielI+b4RAhsMSSaQjnlRDREaL09lkQCyRRDrKzOVJNURkpDidTQbEEkmkI5VaEh2BiKh4HIkkA2KJJNIROyQRGa0CHhNJhsMSSaQjjkQSkdHidDYZEEskkY7UEkskERkpTmeTAbFEEumIHZKIjJaalx8jw2GJJNKRii2S9OhgenVIlnaiY1BFYWEtOgGZEZZIIh1xOpv06cDDyljh8h4kGX8ckx5YWIlOQGaEP7WIdKTmiTWkZ0vv1cLxqm+IjkEVAUskGRBLJJGO5HKZ6AhUAb16uy3u+gwUHYNMHUskGRBLJJGO7JQWoiNQBdX77gCkebQSHYNMGY+JJANiiSTSkZ2VQnQEqqByVAr0SXgNBU5+oqOQqeJIJBkQSySRjuytOBJJ5ed+jjXGF8yE2tpZdBQyRQqWSDIclkgiHdmxRFI5O5XsjEV2cyDJLUVHIVPD6WwyIJZIIh1ZKuRQWvCfDpWvtTE+2OH5jugYZGo4nU0GxN+ERKXAKW0yhJl3GyLY5xXRMciUcCSSDIglkqgUeHINGcrAO92R4N1FdAwyFUre/YgMhyWSqBR4mR8yFJUkR8/o0cipXE90FDJ2MgVg7y46BZkRlkiiUnCxU4qOQGYkKd8SwzKmQWXnIToKGTN7D0DOWRIyHJZIolLwdORxR2RY/6Tb412LOZAsbUVHIWPl6C06AZkZlkiiUvByZokkw/s93h0/VJ4FCbz1JhXD0Ut0AjIzLJFEpeDpZCM6ApmpxZEBOO3zuugYZIwcOBJJhsUSSVQKXpzOJoFG334B96r2FR2DjA2ns8nAWCKJSoHT2SRar4ihSHdvLjoGGROWSDIwlkiiUvDidDYJlqWSo+/DN1Dg5Cs6ChkLBx4TSYbFEklUCi52Slhb8p8PiRWZY42JBe9CsnISHYWMAUciycD4W5ColDgaScbgRHIlfOYwB5KcF8A3eyyRZGAskUSl5FuZ1+sj47A6uhp2e08XHYNEsq0MWPI/tmRYLJFEpRTg6SA6ApHG9DtNEOIzUnQMEsU9UHQCMkMskUSlVNuDJZKMS787vZDo3VF0DBLBM0h0AjJDLJFEpRTAEklGRiXJ0StmHHJd6oiOQobGEkkCsEQSlVItd3so5Lz9HBmXhDxLjMx6B2pbN9FRyJA86otOQGaIp/MRlZK1pQLVXWxxNzFLdJTnKsxIROqJDci5exlSYR4snL1Qudd0WHn5AwCyb51FxtX9yI+7A3VuBrzGfgOlh98zt5lx9QCyrh9DwcN7AAClZy04tx8NK+/ammXSzv+O9As7AABOLQfBscVAzWt5D24h+dBKeI7+EjK5Qt9v2axdSbPHe55z8Hn++5AV5oqOQ+VNbgm4cfSZDI8jkURlYApT2qrcTMT9PAuQW8B9yMfwGr8SlTqPh9zaXrOMuiAXVlUD4dxxbIm3mxt1DXZ1O8BjxKfwfGUpFA5uiN86D4UZiQCA/IQIpP35C1z7zoJrn3eRevpn5D+MBABIahWSDq6AS/e3WCDLybY4T6x3excSOFpe4bnVBiyUolOQGeJIJFEZBHg64MD1ONExnin93HZYOLrCtfd0zXOWzp5ay9jX7wwAKEyLL/F23fq8q/Vx5Z5TkB12Brn3gmFfvwsKkqJh6eYLm+oNi/bp5ouCpGgo3XyRfn4HrH3qwcoroJTvikpiQURd1PGfiDZRa0RHofLEqWwShCWSqAzqmsBlfnLunId1jSZ4uOtT5EaFQGFfGQ6Ne8GhUQ+97kcqyAPUKsitiz4nSjdfFKbEoDA9AZCAwuQYKF2royAlFpnXjsBrzNd63T8Vb+TtjjhdKwY+0X+IjkLlxZMlksRgiSQqg8bVKomO8FwFqXEo+HsfHJv3h0frociLvY2Uo2sgU1jCPqiL3vaTcnIDFPYusPFtBACwdPWBc/vRiN8yFwDg3GEMLF19EL/5A1TqOA45EVeQduZXQG4Bl66vwdqHvwjLS4/I4TjvHQf7hMuio1B54JnZJAhLJFEZeDpZo2olG0Sn5IiO8nSSBCvPWqjUYQwAQOlREwWJ95BxdZ/eSmTauW3IDj0FjxGfQvbYsVkOjXvBoXEvzceZ145CprSBVZU6iPnhdXiN/hKqjCQk7vkcVSathczCUi95SFtWoQL9Et/EQcf5sEi/LzoO6ZsHSySJwRNriMqoua+L6AjPpLCvBEvXalrPWVb2gSr9oV62n3b+d6Sd2w73oQuhdK/x1OVU2WlIO/MrXLq+jrwHYbB08YalSxVYV28ASVWIgpQYveSh4oVn2+AN9SxIVsZ/CAbpwMEbsKssOgWZKZZIojJq5mvcU9pWVQJRkByt9VxBcgwsHN3LvO2089uRdnYzPIbM11wu6GlSjv0Ih+b9YeHoCkgqSCrV/15UqwC1usx56NkOJ7pgmeMcSDKeEV9hVG8jOgGZMZZIojJqYeQjkY7N+yHvwS2k/bUVBSkPkHXjBDKDD8C+SW/NMqqcDOTH30VBYtFUZ0FyNPLj70KVmaJZJnHvMqSc3KD5OO3cdqSe/hmVe02DhZMHVJkpUGWmQJ3/5NR+TsTfKEiOgcO/+1R6BqAwORo54ZeQcfUAIFfAwqVKOX0G6HHfRfliX5WpomOQvvi2E52AzJhMkiRJdAgiUyZJEposPIyU7ALRUZ4q+84FpJ7ciIKUB7Bw8oBj8/5aZ2dnXjuCpH1fP7GeU9sRcG43CgAQ9+tsWDh5wLX32wCA6FWvQpWe8Mx1AEBdkIfYDVPh1vc9rQuYZwQfROrpTZApLOHS7U3Y1myur7dLJXDAfzfqRG0RHYPKavJlwLWW6BRkplgiifRgwsaLOBL6ZKEiMlaWcgnnq38Pl9jToqNQaTl4ATNuik5BZozT2UR60MzIp7SJ/qtALUOvB+ORV6n28xcm48SpbBKMJZJID1r78exIMj1xeUqMyn4HahtX0VGoNGp0EJ2AzBxLJJEeNKjqBFd7K9ExiHR2Kc0BH1rPgaTg96/JqaW/mwUQlQZLJJEeyGQydK7jJjoGUan8GuuFn91nio5BunAPBBy9RacgM8cSSaQnnet4iI5AVGpzI+rhvM8E0TGopGp2Fp2AiCWSSF/aB7hCacF/UmS6ht/phJgqPUXHoJKo1VV0AiKWSCJ9sVVaoBVPsCETJkky9Lg3AllujURHoWextOOdasgosEQS6VHXumW/lSCRSBmFFhiQPAWFDryDkNGq0wuw4IlQJB5LJJEedanL4yLJ9IVl2WAyZkNS2ouOQsUJGiI6AREAlkgivaribIM6ng6iYxCV2YGHlbHceTYkGX9NGBWbSjyphowGfzoQ6dlLDbxERyDSi6/v++FQlSmiY9DjAvsBCkvRKYgAsEQS6V2/RjyWjCqOSXdaIsyH06dGg1PZZERYIon0zMfFFk2qOYuOQaQ3fcL7IcWzregY5FgFqMazssl4VKgSGRkZCZlMhqtXrwrZf8eOHTF9+nQh+y4N0Z+viqx/Y45GUsWRp5bjpbgJyHeuJTqKeas3AJBXqF/bZOJ0/m6Mi4vDlClT4OfnBysrK/j4+KBPnz44evRoeeQrd4Ysfhs2bIBMJoNMJoNcLoeXlxeGDRuG+/fv67Sdjz/+GI0aNSpzHh8fH8TGxqJ+/fpl3hYATJo0CQqFAtu2bdPL9kxZnwbeUCr4w54qjphcK4zJmwG1jYvoKOYraLDoBERadPotFxkZiaZNm+LYsWP44osvcO3aNRw4cACdOnXCW2+9VV4ZKxRHR0fExsYiJiYGO3bswK1btzBkiJhjXBQKBTw9PWFhYVHmbWVnZ2Pz5s2YNWsW1q1bp4d0z5afn1/u+yiLSnZKdK7Da0ZSxfJXihM+tnkfkkIpOor5qVwL8G4sOgWRFp1K5JtvvgmZTIYLFy5g0KBBCAgIQL169fDOO+/g3LlzmuXu37+Pfv36wd7eHo6Ojhg6dCji4+M1rz8aSVu3bh2qVasGe3t7vPnmm1CpVPj888/h6ekJd3d3LFq0SGv/MpkMq1atQs+ePWFjYwM/Pz9s3779mZlDQkLQs2dP2Nvbw8PDA6+88goSExMBAGPHjsXJkyexfPlyzQhhZGTkc9cDgKysLIwePRr29vbw8vLCsmXLSvQ5lMlk8PT0hJeXF9q0aYPx48fjwoULSE9P1yzz3nvvISAgALa2tvDz88PcuXNRUFAAoGg0c/78+QgODtZk3rBhAwAgNTUVEyZMgJubGxwdHdG5c2cEBwc/Nct/p7NPnDgBmUyGo0ePolmzZrC1tUWbNm1w69at576vbdu2ITAwELNnz8apU6cQFRUFAEhPT4eNjQ3279+vtfzOnTvh4OCA7OxsAEBUVBSGDh0KZ2dnuLi4oF+/fpqvBVD0terfvz8WLVoEb29v1K5dGwCwadMmNGvWDA4ODvD09MTIkSORkJCgta89e/bA398f1tbW6NSpEzZu3AiZTIbU1FTNMn/++SdeeOEF2NjYwMfHB1OnTkVWVtZz3/ezDG5atUzrExmjnx54Y7PHDNExzE99jkKS8SlxiUxOTsaBAwfw1ltvwc7O7onXnZ2dAQBqtRr9+vVDcnIyTp48icOHD+Pu3bsYNmyY1vLh4eHYv38/Dhw4gN9++w1r165F7969ER0djZMnT2LJkiX48MMPcf78ea315s6di0GDBiE4OBijRo3C8OHDERoaWmzm1NRUdO7cGY0bN8alS5dw4MABxMfHY+jQoQCA5cuXo3Xr1pg4cSJiY2MRGxsLHx+f564HAO+++y5OnjyJ3bt349ChQzhx4gSuXLlS0k8nACAhIQE7d+6EQqGAQqHQPO/g4IANGzbgxo0bWL58OX744Qd89dVXAIBhw4ZhxowZqFevnibzo8/tkCFDkJCQgP379+Py5cto0qQJunTpguTkZJ1yffDBB1i2bBkuXboECwsLvPrqq89dZ+3atXj55Zfh5OSEnj17aoqto6MjXnrpJfz6669ay//yyy/o378/bG1tUVBQgO7du8PBwQGnT5/GmTNnYG9vjx49emiNOB49ehS3bt3C4cOHsXfvXgBAQUEBFi5ciODgYOzatQuRkZEYO3asZp2IiAgMHjwY/fv3R3BwMCZNmoQPPvhAK0t4eDh69OiBQYMG4Z9//sGWLVvw559/YvLkyTp93v6rY203uNrzrhJU8cy5G4TLPuNExzAfMjnQcLjoFERPKPE85p07dyBJEurUqfPM5Y4ePYpr164hIiICPj4+AICffvoJ9erVw8WLF9G8eXMARWVz3bp1cHBwQGBgIDp16oRbt25h3759kMvlqF27NpYsWYLjx4+jZcuWmu0PGTIEEyZMAAAsXLgQhw8fxrfffouVK1c+keW7775D48aNsXjxYs1z69atg4+PD8LCwhAQEAClUglbW1t4enqWeD1vb2+sXbsWP//8M7p06QIA2LhxI6pWff7IU1paGuzt7SFJkmYUburUqVrF/MMPP9T83dfXFzNnztRMFdvY2MDe3h4WFhZamf/8809cuHABCQkJsLIqKi5Lly7Frl27sH37drz22mvPzfbIokWL0KFDBwDA7Nmz0bt3b+Tm5sLa2rrY5W/fvo1z587h999/BwC8/PLLeOedd/Dhhx9CJpNh1KhReOWVV5CdnQ1bW1ukp6fjjz/+wM6dOwEAW7ZsgVqtxo8//giZTAYAWL9+PZydnXHixAl069YNAGBnZ4cff/wRSuX/ptIeL7h+fn745ptv0Lx5c2RmZsLe3h6rV69G7dq18cUXXwAAateujZCQEK1R7k8//RSjRo3SHBvr7++Pb775Bh06dMCqVaue+r6fx0Ihx9BmVbHyRHip1icyZoPvdMVffjHwjDkkOkrFF9ADcKkhOgXRE0o8EilJUomWCw0NhY+Pj6ZAAkBgYCCcnZ21Rgx9fX3h4PC/O3t4eHggMDAQ8sfOPPPw8HhiarJ169ZPfPy0kcjg4GAcP34c9vb2msejEhwe/vRf7M9bLzw8HPn5+Vrl1sXFRTPF+iwODg64evUqLl26hGXLlqFJkyZPTNtv2bIFbdu2haenJ+zt7fHhhx8+9+Sb4OBgZGZmonLlylq5IyIinvlei9OgQQPN3728ii6c/d+vw+PWrVuH7t27w9XVFQDQq1cvpKWl4dixY5qPLS0tsWfPHgDAjh074OjoiK5du2qy37lzBw4ODprcLi4uyM3N1coeFBSkVSAB4PLly+jTpw+qVasGBwcHTfl99Pm6deuW5j8uj7Ro0ULr4+DgYGzYsEHr89a9e3eo1WpERESU8LNWvNGtfWEhl5VpG0TGSJJk6Hn/ZWS7Nnj+wlQ2rd4QnYCoWCUeifT394dMJsPNmzf1smNLS+0r7stksmKfU6vVpd5HZmYm+vTpgyVLljzx2qNyVJr17ty5U+pMcrkctWoVXSajbt26CA8PxxtvvIFNmzYBAP766y+MGjUK8+fPR/fu3eHk5ITNmzc/95jLzMxMeHl54cSJE0+89uhQg5J6/OvwaGTwaV8HlUqFjRs3Ii4uTusEHZVKhXXr1qFLly5QKpUYPHgwfv31VwwfPhy//vorhg0bplk+MzMTTZs2xS+//PLE9t3c3DR//+9hFFlZWejevTu6d++OX375BW5ubrh//z66d++u04k3mZmZmDRpEqZOnfrEa9WqVSvxdorj6WSNnkFe+L/gB2XaDpExSimwwKDUqdhrPw+KTH6PlwuPIKBGe9EpiIpV4hLp4uKC7t27Y8WKFU9MvwJFxx86Ozujbt26iIqKQlRUlGY08saNG0hNTUVgYGCZA587dw6jR4/W+rhx4+LPWGvSpAl27NgBX1/fp56BrFQqoVKpdFqvZs2asLS0xPnz5zUlIyUlBWFhYZqRsJKaPXs2atasibfffhtNmjTB2bNnUb16da3j9u7du1eizI+KnK+vr04ZymLfvn3IyMjA33//rXVcZ0hICMaNG6f5vhg1ahRefPFFXL9+HceOHcMnn3yilX3Lli1wd3eHo6Njifd98+ZNJCUl4bPPPtN8r126dElrmdq1a2Pfvn1az128eFHr4yZNmuDGjRuacq9v49r6skRShRWaaYvptu/hG8v3ISso28loVIxWr4tOQPRUOp2dvWLFCqhUKrRo0QI7duzA7du3ERoaim+++UYzzdy1a1cEBQVh1KhRuHLlCi5cuIDRo0ejQ4cOaNasWZkDb9u2DevWrUNYWBg++ugjXLhw4aknQLz11ltITk7GiBEjcPHiRYSHh+PgwYMYN26cpoT5+vri/PnziIyMRGJiItRq9XPXs7e3x/jx4/Huu+/i2LFjCAkJwdixY7Wm4kvKx8cHAwYMwLx58wAUjfjev38fmzdvRnh4OL755hvNsYOP+Pr6IiIiAlevXkViYiLy8vLQtWtXtG7dGv3798ehQ4cQGRmJs2fP4oMPPniiWOnToxOiGjZsiPr162sej860fjS62L59e3h6emLUqFGoUaOG1qEAo0aNgqurK/r164fTp08jIiICJ06cwNSpUxEdHf3UfVerVg1KpRLffvst7t69iz179mDhwoVay0yaNAk3b97Ee++9h7CwMGzdulVz0s+jUdb33nsPZ8+exeTJk3H16lXcvn0bu3fvLvOJNY80qVYJjXyc9bItImP0fwluWOHyHiQZr42qV3ZuvM0hGTWd/sX7+fnhypUr6NSpE2bMmIH69evjxRdfxNGjR7Fq1SoARb+Yd+/ejUqVKqF9+/bo2rUr/Pz8sGXLFr0Enj9/PjZv3owGDRrgp59+wm+//fbUEU5vb2+cOXMGKpUK3bp1Q1BQEKZPnw5nZ2dN4Zs5cyYUCgUCAwM106ElWe+LL77ACy+8gD59+qBr165o164dmjZtWqr39Pbbb+OPP/7AhQsX0LdvX7z99tuYPHkyGjVqhLNnz2Lu3Llayw8aNAg9evRAp06d4Obmht9++w0ymQz79u1D+/btMW7cOAQEBGD48OG4d+8ePDw8SpXreeLj4/HHH39g0KBBT7wml8sxYMAArF27FkDR98WIESM0Z9U/ztbWFqdOnUK1atUwcOBA1K1bF+PHj0dubu4zRybd3NywYcMGzeWFPvvsMyxdulRrmRo1amD79u34/fff0aBBA6xatUozyvvoBKQGDRrg5MmTCAsLwwsvvIDGjRtj3rx58Pb2LtPn53GvtuNB8VSxLb1XC8eqvik6RsXS7FXAgld4IOMlk0p6xowRkMlk2LlzJ/r37y86CpmwRYsW4fvvv9dcy9IQClVqtFtyHHHpuQbbJ5EIx/y3wy/qd9ExTJ9CCbx9HbDnTQvIeHHugSq8lStX4uLFi7h79y42bdqEL774AmPGjDFoBguFHK+0rm7QfRKJ0PvuAKR5tBIdw/TVG8gCSUaPJZIqvNu3b6Nfv34IDAzEwoULMWPGDHz88ccGzzGqZTXYW5X9FpNExixHpUDv+EnId+IhHGXCy/qQCTCp6WwiU7f04C18d7z0l4giMhXtXNLwk/p9yHNTREcxPb4vAGP3ik5B9FwciSQyoIkv+MHBmqORVPH9meyET+xmQ5JbPn9h0tb5w+cvQ2QEWCKJDMjJ1hLjeaY2mYl1MT7Y4fmO6BimJaAHUI3HlJJpYIkkMrDx7WrAyYajM2QeZt5tiGCfV0THMA0yOdBlnugURCXGEklkYA7Wlpj4AkcjyXwMvNMdCd5dRMcwfkFDAI96olMQlRhLJJEA49rWgIudUnQMIoNQSXL0jB6NnMr1RUcxXgol0Ol90SmIdMISSSSAnZUFXmvvJzoGkcEk5VtiaPo0qOw8RUcxTk3HApV8Racg0glLJJEgY1r7wsvJWnQMIoO5lmGHGRazIVnaio5iXJT2QPtZolMQ6YwlkkgQG6UCc3rVFR2DyKB2xbtjtcssSJCJjmI8Wr0B2LuJTkGkM5ZIIoH6NvRGyxouomMQGdRn9wJwyud10TGMg40L0Gaq6BREpcISSSTYx33rQSHnqAyZlzG3X0Bk1X6iY4jXYRZg7Sg6BVGpsEQSCVbXyxEjW1QTHYPI4HpHDEG6RwvRMcTxagS0eE10CqJSY4kkMgIzugWgki0vQE7mJUslR9+E11Hg5Cs6iuHJFEDfbwC5QnQSolJjiSQyAs62SszoVlt0DCKDi8yxxsSCdyFZOYmOYlit3gC8GopOQVQmLJFERmJki2qo581jo8j8nEiuhE8d5kCSW4iOYhjO1XhhcaoQWCKJjIRcLsOSQQ1gwZNsyAytia6G3d7TRccwjN5fAko70SmIyowlksiI1K/ihDc71hQdg0iI6Xea4JrPKNExylf9QYD/i6JTEOkFSySRkZnSxR91vTitTeap/52eeOjdSXSM8mHtDPRYIjoFkd6wRBIZGUuFHEuHNIClgtPaZH5Ukhy9osci16UC3s2p20LemYYqFJZIIiNUz9sJb3WqJToGkRAP8y0xMuttqOzcRUfRn+rtgCajRacg0iuWSCIjNblTLZ6tTWbrSpo93rOcA8nCWnSUsrN2AvqvFJ2CSO9YIomMlIVCjmVDG0Kp4D9TMk/b4zywzm0WJJj4oR39VgCVqotOQaR3/O1EZMTqeDpiZvcA0TGIhFkYUQdnfUz41oAtJgF1+4hOQVQuWCKJjNzEF/zQta6H6BhEwoy63QFRVV8SHUN3Xo2Abp+ITkFUblgiiYycTCbDsqEN4eNiIzoKkTA9Iochw72Z6BglZ+UIDFkPWChFJyEqNyyRRCbAycYSK0c2hdKC/2TJPGUVKtA/8Q0UOlYTHaVk+iwHXPxEpyAqV/yNRGQigqo6Yd5LgaJjEAkTnm2DSapZkKwcREd5tmavAvUHik5BVO5YIolMyMutqqNfI2/RMYiEOZrkgqWOcyDJFKKjFM8jCOj+qegURAbBEklkYj4dGIRa7vaiYxAJsyLKF39UmSY6xpOU9sCQDYBlBbi2JVEJsEQSmRhbpQW+f7kpHK0tREchEmbynWYI9RkuOsb/yBTAoB8BV95piswHSySRCarlbo/vX27K+2uTWesb3gdJXu1FxyjScwlQu6foFEQGxRJJZKLa1HLFogFBomMQCVOglqH3g1eRV6m22CCtJwMtJorNQCQASySRCRvazAdvdaopOgaRMHF5SozKfgdqW1cxAQL78YLiZLZYIolM3MxutdGnIc/YJvN1Kc0B71vNgaSwMuyOfVoCA9YAMh5WQuaJJZLIxMlkMiwd0gDNqlcSHYVImM2xXtjk/q7hdujiBwz/jWdik1ljiSSqAKwsFPhhdDP4VrYVHYVImHkRgTjnM6H8d2TjAozaDthVLv99ERkxlkiiCqKSnRKbxreElxNHRsh8Db/dGdFVe5XfDiysgRGbgco8FpmIJZKoAvFxscWvE1vB3cHAx4YRGZGekcOR5dZI/xuWWwADfwCqtdT/tolMEEskUQVTw9UOv05sCVd7pegoREJkFFpgQPIUFDpU1d9G5ZbA4HVAYF/9bZPIxLFEElVAtdwd8POElqhkayk6CpEQYVk2mIz3ICn1cItQuWXR7QwD+5V9W0QVCEskUQVVx9MRm8a35O0RyWwdeFgZXzvPhiRTlH4jCiUwbBNQ9yX9BSOqIFgiiSqw+lWcsPHVFrC3YpEk87T8vh8OVplcupUVVsCwX3g7Q6KnkEmSJIkOQUTl6/K9ZIxdfxEZuYWioxAJcdB/F2pHbS35ChbWwPBfgFpdyy8UkYljiSQyE9cfpGHMugtIzMwXHYXI4KzkapyrtgqV4s48f2ELG2DEr0DNzuUfjMiEcTqbyEzU83bCttfboIqzjegoRAaXp5ajV9xE5Ffyf/aClrbAyC0skEQlwBJJZEZquNph+xut4e+uhzNWiUxMbK4Sr+TMgNrmKXeasXYquhONXwfDBiMyUSyRRGbGy8kG219vg+a+vNc2mZ/zqY74yGY2JMV/rqPqVA149RDg21ZMMCITxBJJZIacbC2xaXxLdK/nIToKkcFtelAFv3nM+N8T3o2BiUcB9zriQhGZIJ5YQ2TG1GoJn/wRinVnIkRHITK47QFH0MwmDhi0FlDaio5DZHJYIokI2y5F4YNdIcgvVIuOQmQwr71QA+/3rAPIOSlHVBoskUQEALgalYrXN11GXHqu6ChE5UqpkGPRgPoY0sxHdBQik8YSSUQaCRm5eOPnK7h8L0V0FKJy4WqvxPcvN0UzXxfRUYhMHkskEWnJL1Tjoz0h+O1ClOgoRHrVwtcF34xoDE8na9FRiCoElkgiKtamc/ew4P+uo0DFHxFk2mQy4M2ONfHOi7WhkMtExyGqMFgiieipgqNSMW3z34hMyhYdhahUXO2V+HJoI7QPcBMdhajCYYkkomfKyivEx3uuY9vlaNFRiHTSys8F3wxvDHdHTl8TlQeWSCIqkX3XYjHn92tIyykQHYXomeQyYHJnf0zr4s/pa6JyxBJJRCUWm5aDd7YE46+7SaKjEBXLx8UGSwY1QJuarqKjEFV4LJFEpBO1WsLqU3fx5eFbPOmGjIZMBoxp7YtZPWrDVmkhOg6RWWCJJKJSufEgHXN2XkNwVKroKGTm/FztsGRwAzTntR+JDIolkohKTa2WsOncPSw9eAsZeYWi45CZUchlGN+uBt55MQDWlgrRcYjMDkskEZVZXFouPt5zHQeux4mOQmYiwMMenw9uiEY+zqKjEJktlkgi0psjN+Ixb3cIHqTx/ttUPuytLPBmp5qY0M4PSgu56DhEZo0lkoj0KiuvEF8eDsPGs5EoVPPHC+mHXAYMbloVM7vXhrsDr/tIZAxYIomoXIQ/zMTnB27i4PV40VHIxDX3rYSP+tRD/SpOoqMQ0WNYIomoXF2+l4zF+27i8r0U0VHIxFRxtsGcXnXwUgNv0VGIqBgskURkEAdC4vD5wZu4+zBLdBQyco7WFnitvR8mvODHs66JjBhLJBEZTKFKjc0Xo/D1kdtIzMwTHYeMjJONJca3q4GxbX3haG0pOg4RPQdLJBEZXHZ+IX67EIW1p+/yTG6Cs60lxrctKo8OLI9EJoMlkoiEKVCpsfPvGKw+GY5wTnObnUq2lpjwgh/GtPGFvRVvVUhkalgiiUg4SZJw8Ho8Vp24g+DoNNFxqJx5OVnjldbVMbo1yyORKWOJJCKjcvZOIladDMefdxLBn04VS4saLhjbxhfdAj1goeCFwolMHUskERmliMQs/HbhPnZcjkZSVr7oOFRKNpYK9G/sjdGtfVHXy1F0HCLSI5ZIIjJq+YVqHLweh98u3Mdfd5M4OmkifFxs8Eqr6hjWrBqcbHmyDFFFxBJJRCYjIjELmy/cx3aOTholR2sL9G7ghX6NqqBlDRfIZDLRkYioHLFEEpHJyS9U4/Tth/jjWiwO34hHRm6h6Ehmy8pCji513dGvURV0qu0OpQWPdSQyFyyRRGTS8gvVOHMnUVMo03IKREeq8OQyoHXNyujXqAp61PfkhcGJzBRLJBFVGAUqNf68k4j912Jx6EY8UrNZKPXFxU6J9v6u6FjbHR0C3FDJTik6EhEJxhJJRBWSWi0hODoVp8IScfr2Q1yNSkWhmj/uSkomA+p7O6FTbTd0rOOORlWdIZfzGEci+h+WSCIyCxm5BTh/Nxl/3U3CX+FJCI1L55ne/+Fb2RbNfV3Q0q8yOgS4wc3BSnQkIjJiLJFEZJZSs/Nx5X4K/olOw7XoNFyLSUNCRp7oWAZjbSlHfW8nNPJxRpPqldDMtxLcHaxFxyIiE8ISSUT0r/j0XFyLTsM/MWm4Fp2KazHpSMw0/WLp7WSNmu728Hd3gL+HPYKqOKGOpwPvGkNEZcISSUT0DGk5BbiXlIV7Sdm4l5SFyH//vJeUbVQjl47WFvBwtEb1yrao5e6AWu728He3R013e96fmojKBUskEVEpZecXIio5B0mZeUjKykdKdj6Ss/KRkpX/2McFSM8pQL5KjUKVGoUqCQXqoj//e6KPTAZYWyhgo1TAxrLoT1ulAtaWRX862VjCw9Ea7g5W8HC0/vdR9HdrS4WgzwIRmSuWSCIigQr+LZZA0XGKvMsLEZkKlkgiIiIi0hmPqiYiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItIZSyQRERER6YwlkoiIiIh0xhJJRERERDpjiSQiIiIinbFEEhEREZHOWCKJiIiISGcskURERESkM5ZIIiIiItLZ/wON3l4RHmwftwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_g_50 = df[df['completed_rate_percentage'] >= 70]\n",
    "result = Counter(df_g_50.result)\n",
    "print(result)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Count the number of 'Passed' and 'Failed' items\n",
    "status_counts = df_g_50['result'].value_counts()\n",
    "\n",
    "# Plot the bar chart\n",
    "# plt.bar(status_counts.index, status_counts.values, color=['blue', 'orange'])\n",
    "plt.pie(status_counts, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Passed vs. Failed Items')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Passed': 119, 'Failed': 77})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_g_50 = df[df['completed_rate_percentage'] >= 80]\n",
    "result = Counter(df_g_50.result)\n",
    "print(result)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Count the number of 'Passed' and 'Failed' items\n",
    "status_counts = df_g_50['result'].value_counts()\n",
    "\n",
    "# Plot the bar chart\n",
    "# plt.bar(status_counts.index, status_counts.values, color=['blue', 'orange'])\n",
    "plt.pie(status_counts, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Passed vs. Failed Items')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Passed': 108, 'Failed': 65})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_g_50 = df[df['completed_rate_percentage'] >= 90]\n",
    "result = Counter(df_g_50.result)\n",
    "print(result)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Count the number of 'Passed' and 'Failed' items\n",
    "status_counts = df_g_50['result'].value_counts()\n",
    "\n",
    "# Plot the bar chart\n",
    "# plt.bar(status_counts.index, status_counts.values, color=['blue', 'orange'])\n",
    "plt.pie(status_counts, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Passed vs. Failed Items')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.18"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}