schulz_lucie_SP2024_codebook_uganda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22b4fedc",
   "metadata": {},
   "source": [
    "## FinSim Uganda Data Codebook"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f230d21e",
   "metadata": {},
   "source": [
    "### Goal of this notebook\n",
    "\n",
    "This jupyter notebook is used to create new data frames for the analysis part of my master's thesis and includes all the graphs/ visuals used within the thesis.\n",
    "\n",
    "### Information about the thesis\n",
    "Title of the thesis: Game Changer: Mobile Apps Leveling Up Field Experiments\n",
    "Student: Lucie Schulz\n",
    "Advisor: Andrew Hobbs\n",
    "\n",
    "Department of Economics University of San Francisco \n",
    "2130 Fulton St. \n",
    "San Francisco, CA 94117\n",
    "\n",
    "Thesis Submission for the Master of Science Degree \n",
    "in Applied Economics \n",
    "\n",
    "If you have any questions please e-mail: lschulz@dons.usfca.edu\n",
    "\n",
    "### Overview:\n",
    "\n",
    "1. Reading in Raw Data and Overview of Data Set\n",
    "2. Renaming of Variables\n",
    "3. Error Counts - Calculated Fields\n",
    "4. Create sub dataframes for regression models\n",
    "5. Session Length - Calculated Field\n",
    "6. Distribution of Decisions Made per User\n",
    "7. Histogram Survey Question Baseline: Digital Learning Tools\n",
    "8. Error Distribution Type 1 and 2\n",
    "9. Decisions Speed Overview\n",
    "10. Overview Decisions based on ROI and decision made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "187f303e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Packages used for analysis\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163a9e72",
   "metadata": {},
   "source": [
    "## 1. Reading in Raw Data and Overview of Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0013d7ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import cleaned dataset - You might have to change that based on the path to the cleaned raw data\n",
    "file_path = \"/Users/lucieschulz/Desktop/Thesis/Data Analysis/cleaned_datasets/schulz_lucie_SP2024_dataset_uganda.csv\"\n",
    "\n",
    "# Read the Excel file and access sheet 0 (the first sheet)\n",
    "UG_raw_data = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d35fe014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117870"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overall observations from the game after the cleaning that will be used in the analytics part\n",
    "len(UG_raw_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9eb7b079",
   "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>uid</th>\n",
       "      <th>user_created_on</th>\n",
       "      <th>session_id</th>\n",
       "      <th>session_started_on</th>\n",
       "      <th>session_completed_on</th>\n",
       "      <th>session_status</th>\n",
       "      <th>level_id</th>\n",
       "      <th>level_started_on</th>\n",
       "      <th>level_completed_on</th>\n",
       "      <th>level_status</th>\n",
       "      <th>level_number</th>\n",
       "      <th>played_periods</th>\n",
       "      <th>period_number</th>\n",
       "      <th>cash_at_start</th>\n",
       "      <th>cash_at_end</th>\n",
       "      <th>decision</th>\n",
       "      <th>income_per_period</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>price</th>\n",
       "      <th>risk_level</th>\n",
       "      <th>asset_type</th>\n",
       "      <th>country</th>\n",
       "      <th>treatment_group</th>\n",
       "      <th>level_date</th>\n",
       "      <th>decision_buycash</th>\n",
       "      <th>decision_dontbuy</th>\n",
       "      <th>decision_loan</th>\n",
       "      <th>expected_income_from_asset</th>\n",
       "      <th>actual_price_asset</th>\n",
       "      <th>roi_from_asset</th>\n",
       "      <th>should_buy_cash</th>\n",
       "      <th>can_afford_in_cash</th>\n",
       "      <th>can_afford_via_loan</th>\n",
       "      <th>should_borrow</th>\n",
       "      <th>branch</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>number_of_dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>income_source1</th>\n",
       "      <th>digital_lit1</th>\n",
       "      <th>fin_training</th>\n",
       "      <th>type_one_error</th>\n",
       "      <th>type_two_error</th>\n",
       "      <th>correct_decision</th>\n",
       "      <th>session</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>aRrqSip8ZsNIe4tH...</td>\n",
       "      <td>7/8/23 17:46</td>\n",
       "      <td>7/15/23 7:47</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>FxYg8oCcuhVMqMnK...</td>\n",
       "      <td>7/8/23 18:28</td>\n",
       "      <td>7/8/23 18:48</td>\n",
       "      <td>won</td>\n",
       "      <td>3</td>\n",
       "      <td>139</td>\n",
       "      <td>92</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>7/8/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Not very familia...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>RWzKToUNUfDJ12HI...</td>\n",
       "      <td>7/15/23 17:47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>active</td>\n",
       "      <td>OFBBm88wq61hZTjI...</td>\n",
       "      <td>7/15/23 18:28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not_completed</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>7/15/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Not very familia...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>jmqHB14yopLLywxF...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/24/23 22:58</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>IaB7ROpU70m5djRH...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>won</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/14/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Not very familia...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>jmqHB14yopLLywxF...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/24/23 22:58</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>ULZxNZEaM6jLa2PJ...</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>6/25/23 8:58</td>\n",
       "      <td>not_completed</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/14/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Not very familia...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>D60e8Qi21WGvpoU4...</td>\n",
       "      <td>6/25/23 9:14</td>\n",
       "      <td>6/25/23 9:27</td>\n",
       "      <td>won</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>goat</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Not very familia...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uid user_created_on           session_id session_started_on  \\\n",
       "0  UGGM001    6/14/23 4:25  aRrqSip8ZsNIe4tH...       7/8/23 17:46   \n",
       "1  UGGM001    6/14/23 4:25  RWzKToUNUfDJ12HI...      7/15/23 17:47   \n",
       "2  UGGM001    6/14/23 4:25  jmqHB14yopLLywxF...      6/14/23 14:25   \n",
       "3  UGGM001    6/14/23 4:25  jmqHB14yopLLywxF...      6/14/23 14:25   \n",
       "4  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "\n",
       "  session_completed_on session_status             level_id level_started_on  \\\n",
       "0         7/15/23 7:47      abandoned  FxYg8oCcuhVMqMnK...     7/8/23 18:28   \n",
       "1                  NaN         active  OFBBm88wq61hZTjI...    7/15/23 18:28   \n",
       "2        6/24/23 22:58      abandoned  IaB7ROpU70m5djRH...    6/14/23 14:25   \n",
       "3        6/24/23 22:58      abandoned  ULZxNZEaM6jLa2PJ...    6/14/23 15:00   \n",
       "4          7/1/23 7:30      abandoned  D60e8Qi21WGvpoU4...     6/25/23 9:14   \n",
       "\n",
       "  level_completed_on   level_status  level_number  played_periods  \\\n",
       "0       7/8/23 18:48            won             3             139   \n",
       "1                NaN  not_completed             4              30   \n",
       "2      6/14/23 15:00            won             1              20   \n",
       "3       6/25/23 8:58  not_completed             2              21   \n",
       "4       6/25/23 9:27            won             2              32   \n",
       "\n",
       "   period_number  cash_at_start  cash_at_end decision  income_per_period  \\\n",
       "0             92           33.4         33.4  dontBuy                  1   \n",
       "1             30           38.0         41.0  dontBuy                  1   \n",
       "2              1           50.0         50.0  dontBuy                  1   \n",
       "3             10           15.0         22.0  dontBuy                  1   \n",
       "4             32           44.0         50.0  dontBuy                  2   \n",
       "\n",
       "   life_expectancy  price  risk_level asset_type country treatment_group  \\\n",
       "0                6     12         0.0    chicken      UG            UGGM   \n",
       "1                6     12         0.0    chicken      UG            UGGM   \n",
       "2                6     11         0.0    chicken      UG            UGGM   \n",
       "3                6     11         0.0    chicken      UG            UGGM   \n",
       "4                5     15         0.0       goat      UG            UGGM   \n",
       "\n",
       "  level_date  decision_buycash  decision_dontbuy  decision_loan  \\\n",
       "0     7/8/23                 0                 1              0   \n",
       "1    7/15/23                 0                 1              0   \n",
       "2    6/14/23                 0                 1              0   \n",
       "3    6/14/23                 0                 1              0   \n",
       "4    6/25/23                 0                 1              0   \n",
       "\n",
       "   expected_income_from_asset  actual_price_asset  roi_from_asset  \\\n",
       "0                  6.0                       12.0            -6.0   \n",
       "1                  6.0                       12.0            -6.0   \n",
       "2                  6.0                       11.0            -5.0   \n",
       "3                  6.0                       11.0            -5.0   \n",
       "4                 10.0                       15.0            -5.0   \n",
       "\n",
       "   should_buy_cash  can_afford_in_cash  can_afford_via_loan  should_borrow  \\\n",
       "0            False                True                 True          False   \n",
       "1            False                True                 True          False   \n",
       "2            False                True                 True          False   \n",
       "3            False                True                 True          False   \n",
       "4            False                True                 True          False   \n",
       "\n",
       "   branch                group       id  gender    age            education  \\\n",
       "0  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "1  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "2  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "3  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "4  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "\n",
       "   number_of_dependents income   income_source1         digital_lit1  \\\n",
       "0                    1     Yes  Self-employment  Not very familia...   \n",
       "1                    1     Yes  Self-employment  Not very familia...   \n",
       "2                    1     Yes  Self-employment  Not very familia...   \n",
       "3                    1     Yes  Self-employment  Not very familia...   \n",
       "4                    1     Yes  Self-employment  Not very familia...   \n",
       "\n",
       "  fin_training  type_one_error  type_two_error  correct_decision  session  \n",
       "0          Yes           False           False              True        4  \n",
       "1          Yes           False           False              True        5  \n",
       "2          Yes           False           False              True        1  \n",
       "3          Yes           False           False              True        1  \n",
       "4          Yes           False           False              True        2  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UG_raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eddab5c0",
   "metadata": {},
   "source": [
    "## 2. Renaming of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e4b82c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>level</th>\n",
       "      <th>status</th>\n",
       "      <th>started_on</th>\n",
       "      <th>completed_on</th>\n",
       "      <th>session</th>\n",
       "      <th>periods_played</th>\n",
       "      <th>period_number</th>\n",
       "      <th>decision</th>\n",
       "      <th>income_per_period</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>price</th>\n",
       "      <th>risk</th>\n",
       "      <th>asset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>buyCash</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>won</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>buyCash</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         uid  level status     started_on   completed_on  session  \\\n",
       "2    UGGM001      1    won  6/14/23 14:25  6/14/23 15:00        1   \n",
       "479  UGGM001      1    won  6/14/23 14:25  6/14/23 15:00        1   \n",
       "180  UGGM001      1    won  6/14/23 14:25  6/14/23 15:00        1   \n",
       "205  UGGM001      1    won  6/14/23 14:25  6/14/23 15:00        1   \n",
       "714  UGGM001      1    won  6/14/23 14:25  6/14/23 15:00        1   \n",
       "\n",
       "     periods_played  period_number decision  income_per_period  \\\n",
       "2                20              1  dontBuy                  1   \n",
       "479              20              2  buyCash                  2   \n",
       "180              20              3  dontBuy                  3   \n",
       "205              20              4  dontBuy                  4   \n",
       "714              20              5  buyCash                  5   \n",
       "\n",
       "     life_expectancy  price  risk    asset  \n",
       "2                  6     11   0.0  chicken  \n",
       "479                6     10   0.0  chicken  \n",
       "180                4     13   0.0      cow  \n",
       "205                4     16   0.0      cow  \n",
       "714                4     13   0.0      cow  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting specific columns\n",
    "selected_columns = ['uid', 'level_number','level_status','level_started_on', 'level_completed_on','session','played_periods','period_number', 'decision', 'income_per_period','life_expectancy', 'price', 'risk_level', 'asset_type']\n",
    "\n",
    "# Filter grouped_df to only include selected columns\n",
    "raw_data_filtered = UG_raw_data[selected_columns]\n",
    "\n",
    "# Sort the raw_data_filtered DataFrame\n",
    "sorted_data = raw_data_filtered.sort_values(by=['uid', 'session', 'level_number', 'period_number'])\n",
    "pd.set_option('display.max_colwidth', 20)  # Adjust the number to your preference\n",
    "\n",
    "# Rename columns in the sorted_data DataFrame\n",
    "sorted_data = sorted_data.rename(columns={\n",
    "    'level_number': 'level',  # Rename 'level_number' to 'level'\n",
    "    'level_status': 'status',  # Rename 'level_status' to 'status'\n",
    "    'level_started_on': 'started_on',  # Rename 'level_started_on' to 'started_on'\n",
    "    'level_completed_on': 'completed_on',  # Rename 'level_completed_on' to 'completed_on'\n",
    "    'played_periods': 'periods_played',  # Rename 'played_periods' to 'periods_played'  # Rename 'decision' to 'user_decision'\n",
    "# Rename 'price' to 'current_price'\n",
    "    'risk_level': 'risk',  # Rename 'risk_level' to 'risk'\n",
    "    'asset_type': 'asset'  # Rename 'asset_type' to 'asset'\n",
    "})\n",
    "\n",
    "\n",
    "# Display the sorted DataFrame\n",
    "sorted_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c0059fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making it easier to read the answers from the baseline survey answers\n",
    "\n",
    "# Define a dictionary mapping the old strings to the new strings\n",
    "rename_mapping = {\n",
    "    \"Not familiar at all and never used\": \"Never used\",\n",
    "    \"Moderately familiar and occasionally used\": \"Occasionally used\",\n",
    "    \"Not very familiar and rarely used\": \"Rarely used\",\n",
    "    \"Quite familiar and frequently used\": \"Frequently used\",\n",
    "    \"Very familiar and extensively used\": \"Extensively used\",\n",
    "    # Add more mappings as needed\n",
    "}\n",
    "\n",
    "# Use the replace method to rename the strings in the column\n",
    "UG_raw_data['digital_lit1'] = UG_raw_data['digital_lit1'].replace(rename_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de8e4387",
   "metadata": {},
   "source": [
    "## 3. Error Counts Calculated Fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d7d191f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_counts_per_uid = UG_raw_data['uid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "27f130bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_counts_per_dig_lit = UG_raw_data['digital_lit1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4b0b294c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new field to only look at type2 error when the ROI was above $3\n",
    "UG_raw_data['type_two_error_adjusted'] = (UG_raw_data['type_two_error'] == True) & (UG_raw_data['roi_from_asset'] >= 3.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4f4f4923",
   "metadata": {},
   "outputs": [],
   "source": [
    "# counting errors combining type 1 and type 2 error\n",
    "UG_raw_data['error_count'] = UG_raw_data['type_one_error'] + UG_raw_data['type_two_error_adjusted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "746cd32c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count all decisions made by a user in a session and the error count out of all the decisions in a session\n",
    "UG_raw_data['total_decisions'] = UG_raw_data.groupby(['uid', 'session'])['uid'].transform('count')\n",
    "UG_raw_data['total_errors'] = UG_raw_data.groupby(['uid', 'session'])['error_count'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fef5c78c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>total_decisions</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>6</td>\n",
       "      <td>12</td>\n",
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       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
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       "      <td>6/14/23 4:25</td>\n",
       "      <td>RWzKToUNUfDJ12HI...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>active</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>101</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
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       "      <td>won</td>\n",
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       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/14/23</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>-5.0</td>\n",
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       "      <td>True</td>\n",
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       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
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       "      <td>Female</td>\n",
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       "      <td>Secondary/HighSc...</td>\n",
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       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>jmqHB14yopLLywxF...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/24/23 22:58</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>ULZxNZEaM6jLa2PJ...</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>6/25/23 8:58</td>\n",
       "      <td>not_completed</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/14/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>D60e8Qi21WGvpoU4...</td>\n",
       "      <td>6/25/23 9:14</td>\n",
       "      <td>6/25/23 9:27</td>\n",
       "      <td>won</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>goat</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uid user_created_on           session_id session_started_on  \\\n",
       "0  UGGM001    6/14/23 4:25  aRrqSip8ZsNIe4tH...       7/8/23 17:46   \n",
       "1  UGGM001    6/14/23 4:25  RWzKToUNUfDJ12HI...      7/15/23 17:47   \n",
       "2  UGGM001    6/14/23 4:25  jmqHB14yopLLywxF...      6/14/23 14:25   \n",
       "3  UGGM001    6/14/23 4:25  jmqHB14yopLLywxF...      6/14/23 14:25   \n",
       "4  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "\n",
       "  session_completed_on session_status             level_id level_started_on  \\\n",
       "0         7/15/23 7:47      abandoned  FxYg8oCcuhVMqMnK...     7/8/23 18:28   \n",
       "1                  NaN         active  OFBBm88wq61hZTjI...    7/15/23 18:28   \n",
       "2        6/24/23 22:58      abandoned  IaB7ROpU70m5djRH...    6/14/23 14:25   \n",
       "3        6/24/23 22:58      abandoned  ULZxNZEaM6jLa2PJ...    6/14/23 15:00   \n",
       "4          7/1/23 7:30      abandoned  D60e8Qi21WGvpoU4...     6/25/23 9:14   \n",
       "\n",
       "  level_completed_on   level_status  level_number  played_periods  \\\n",
       "0       7/8/23 18:48            won             3             139   \n",
       "1                NaN  not_completed             4              30   \n",
       "2      6/14/23 15:00            won             1              20   \n",
       "3       6/25/23 8:58  not_completed             2              21   \n",
       "4       6/25/23 9:27            won             2              32   \n",
       "\n",
       "   period_number  cash_at_start  cash_at_end decision  income_per_period  \\\n",
       "0             92           33.4         33.4  dontBuy                  1   \n",
       "1             30           38.0         41.0  dontBuy                  1   \n",
       "2              1           50.0         50.0  dontBuy                  1   \n",
       "3             10           15.0         22.0  dontBuy                  1   \n",
       "4             32           44.0         50.0  dontBuy                  2   \n",
       "\n",
       "   life_expectancy  price  risk_level asset_type country treatment_group  \\\n",
       "0                6     12         0.0    chicken      UG            UGGM   \n",
       "1                6     12         0.0    chicken      UG            UGGM   \n",
       "2                6     11         0.0    chicken      UG            UGGM   \n",
       "3                6     11         0.0    chicken      UG            UGGM   \n",
       "4                5     15         0.0       goat      UG            UGGM   \n",
       "\n",
       "  level_date  decision_buycash  decision_dontbuy  decision_loan  \\\n",
       "0     7/8/23                 0                 1              0   \n",
       "1    7/15/23                 0                 1              0   \n",
       "2    6/14/23                 0                 1              0   \n",
       "3    6/14/23                 0                 1              0   \n",
       "4    6/25/23                 0                 1              0   \n",
       "\n",
       "   expected_income_from_asset  actual_price_asset  roi_from_asset  \\\n",
       "0                  6.0                       12.0            -6.0   \n",
       "1                  6.0                       12.0            -6.0   \n",
       "2                  6.0                       11.0            -5.0   \n",
       "3                  6.0                       11.0            -5.0   \n",
       "4                 10.0                       15.0            -5.0   \n",
       "\n",
       "   should_buy_cash  can_afford_in_cash  can_afford_via_loan  should_borrow  \\\n",
       "0            False                True                 True          False   \n",
       "1            False                True                 True          False   \n",
       "2            False                True                 True          False   \n",
       "3            False                True                 True          False   \n",
       "4            False                True                 True          False   \n",
       "\n",
       "   branch                group       id  gender    age            education  \\\n",
       "0  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "1  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "2  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "3  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "4  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "\n",
       "   number_of_dependents income   income_source1 digital_lit1 fin_training  \\\n",
       "0                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "1                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "2                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "3                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "4                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "\n",
       "   type_one_error  type_two_error  correct_decision  session  \\\n",
       "0           False           False              True        4   \n",
       "1           False           False              True        5   \n",
       "2           False           False              True        1   \n",
       "3           False           False              True        1   \n",
       "4           False           False              True        2   \n",
       "\n",
       "   type_two_error_adjusted  error_count  total_decisions  total_errors  \n",
       "0                False            False              206            44  \n",
       "1                False            False              332           101  \n",
       "2                False            False               39             0  \n",
       "3                False            False               39             0  \n",
       "4                False            False               81             4  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the maximum number of columns to be displayed\n",
    "pd.set_option('display.max_columns', None)\n",
    "UG_raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "604d018e",
   "metadata": {},
   "source": [
    "## 4. Create sub dataframes for regression models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5b26ba95",
   "metadata": {},
   "outputs": [],
   "source": [
    "only_level_one_df = UG_raw_data[UG_raw_data['level_number'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "16f4c289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24182"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(only_level_one_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ea9dfa76",
   "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>uid</th>\n",
       "      <th>user_created_on</th>\n",
       "      <th>session_id</th>\n",
       "      <th>session_started_on</th>\n",
       "      <th>session_completed_on</th>\n",
       "      <th>session_status</th>\n",
       "      <th>level_id</th>\n",
       "      <th>level_started_on</th>\n",
       "      <th>level_completed_on</th>\n",
       "      <th>level_status</th>\n",
       "      <th>level_number</th>\n",
       "      <th>played_periods</th>\n",
       "      <th>period_number</th>\n",
       "      <th>cash_at_start</th>\n",
       "      <th>cash_at_end</th>\n",
       "      <th>decision</th>\n",
       "      <th>income_per_period</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>price</th>\n",
       "      <th>risk_level</th>\n",
       "      <th>asset_type</th>\n",
       "      <th>country</th>\n",
       "      <th>treatment_group</th>\n",
       "      <th>level_date</th>\n",
       "      <th>decision_buycash</th>\n",
       "      <th>decision_dontbuy</th>\n",
       "      <th>decision_loan</th>\n",
       "      <th>expected_income_from_asset</th>\n",
       "      <th>actual_price_asset</th>\n",
       "      <th>roi_from_asset</th>\n",
       "      <th>should_buy_cash</th>\n",
       "      <th>can_afford_in_cash</th>\n",
       "      <th>can_afford_via_loan</th>\n",
       "      <th>should_borrow</th>\n",
       "      <th>branch</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>number_of_dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>income_source1</th>\n",
       "      <th>digital_lit1</th>\n",
       "      <th>fin_training</th>\n",
       "      <th>type_one_error</th>\n",
       "      <th>type_two_error</th>\n",
       "      <th>correct_decision</th>\n",
       "      <th>session</th>\n",
       "      <th>type_two_error_adjusted</th>\n",
       "      <th>error_count</th>\n",
       "      <th>total_decisions</th>\n",
       "      <th>total_errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>jmqHB14yopLLywxF...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/24/23 22:58</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>IaB7ROpU70m5djRH...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>won</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/14/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>RWzKToUNUfDJ12HI...</td>\n",
       "      <td>7/15/23 17:47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>active</td>\n",
       "      <td>LWdyOxCGjjHSRUWr...</td>\n",
       "      <td>7/15/23 17:47</td>\n",
       "      <td>7/15/23 17:53</td>\n",
       "      <td>won</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>59.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>7/15/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>332</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>njhSSwTHGGsmloAV...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>6/25/23 9:14</td>\n",
       "      <td>won</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>goat</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>njhSSwTHGGsmloAV...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>6/25/23 9:14</td>\n",
       "      <td>won</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>45.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>aRrqSip8ZsNIe4tH...</td>\n",
       "      <td>7/8/23 17:46</td>\n",
       "      <td>7/15/23 7:47</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>iFiznPqS1MQ1kcIe...</td>\n",
       "      <td>7/8/23 17:46</td>\n",
       "      <td>7/8/23 18:22</td>\n",
       "      <td>won</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>buyCash</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>7/8/23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>206</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        uid user_created_on           session_id session_started_on  \\\n",
       "2   UGGM001    6/14/23 4:25  jmqHB14yopLLywxF...      6/14/23 14:25   \n",
       "22  UGGM001    6/14/23 4:25  RWzKToUNUfDJ12HI...      7/15/23 17:47   \n",
       "31  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "32  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "57  UGGM001    6/14/23 4:25  aRrqSip8ZsNIe4tH...       7/8/23 17:46   \n",
       "\n",
       "   session_completed_on session_status             level_id level_started_on  \\\n",
       "2         6/24/23 22:58      abandoned  IaB7ROpU70m5djRH...    6/14/23 14:25   \n",
       "22                  NaN         active  LWdyOxCGjjHSRUWr...    7/15/23 17:47   \n",
       "31          7/1/23 7:30      abandoned  njhSSwTHGGsmloAV...     6/25/23 9:04   \n",
       "32          7/1/23 7:30      abandoned  njhSSwTHGGsmloAV...     6/25/23 9:04   \n",
       "57         7/15/23 7:47      abandoned  iFiznPqS1MQ1kcIe...     7/8/23 17:46   \n",
       "\n",
       "   level_completed_on level_status  level_number  played_periods  \\\n",
       "2       6/14/23 15:00          won             1              20   \n",
       "22      7/15/23 17:53          won             1              23   \n",
       "31       6/25/23 9:14          won             1              18   \n",
       "32       6/25/23 9:14          won             1              18   \n",
       "57       7/8/23 18:22          won             1              25   \n",
       "\n",
       "    period_number  cash_at_start  cash_at_end decision  income_per_period  \\\n",
       "2               1           50.0         50.0  dontBuy                  1   \n",
       "22             14           59.0         64.0  dontBuy                  1   \n",
       "31              2           43.0         45.0  dontBuy                  2   \n",
       "32              3           45.0         47.0  dontBuy                  1   \n",
       "57              9           65.0         56.0  buyCash                  1   \n",
       "\n",
       "    life_expectancy  price  risk_level asset_type country treatment_group  \\\n",
       "2                 6     11         0.0    chicken      UG            UGGM   \n",
       "22                6     11         0.0    chicken      UG            UGGM   \n",
       "31                5     14         0.0       goat      UG            UGGM   \n",
       "32                6     10         0.0    chicken      UG            UGGM   \n",
       "57                6     10         0.0    chicken      UG            UGGM   \n",
       "\n",
       "   level_date  decision_buycash  decision_dontbuy  decision_loan  \\\n",
       "2     6/14/23                 0                 1              0   \n",
       "22    7/15/23                 0                 1              0   \n",
       "31    6/25/23                 0                 1              0   \n",
       "32    6/25/23                 0                 1              0   \n",
       "57     7/8/23                 1                 0              0   \n",
       "\n",
       "    expected_income_from_asset  actual_price_asset  roi_from_asset  \\\n",
       "2                   6.0                       11.0            -5.0   \n",
       "22                  6.0                       11.0            -5.0   \n",
       "31                 10.0                       14.0            -4.0   \n",
       "32                  6.0                       10.0            -4.0   \n",
       "57                  6.0                       10.0            -4.0   \n",
       "\n",
       "    should_buy_cash  can_afford_in_cash  can_afford_via_loan  should_borrow  \\\n",
       "2             False                True                 True          False   \n",
       "22            False                True                 True          False   \n",
       "31            False                True                 True          False   \n",
       "32            False                True                 True          False   \n",
       "57            False                True                 True          False   \n",
       "\n",
       "    branch                group       id  gender    age            education  \\\n",
       "2   Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "22  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "31  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "32  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "57  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "\n",
       "    number_of_dependents income   income_source1 digital_lit1 fin_training  \\\n",
       "2                     1     Yes  Self-employment  Rarely used          Yes   \n",
       "22                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "31                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "32                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "57                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "\n",
       "    type_one_error  type_two_error  correct_decision  session  \\\n",
       "2            False           False              True        1   \n",
       "22           False           False              True        5   \n",
       "31           False           False              True        2   \n",
       "32           False           False              True        2   \n",
       "57            True           False             False        4   \n",
       "\n",
       "    type_two_error_adjusted  error_count  total_decisions  total_errors  \n",
       "2                 False            False               39             0  \n",
       "22                False            False              332           101  \n",
       "31                False            False               81             4  \n",
       "32                False            False               81             4  \n",
       "57                False             True              206            44  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_level_one_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cb438dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_first_decisions = UG_raw_data[UG_raw_data['period_number'] <= 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "68609df6",
   "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>uid</th>\n",
       "      <th>user_created_on</th>\n",
       "      <th>session_id</th>\n",
       "      <th>session_started_on</th>\n",
       "      <th>session_completed_on</th>\n",
       "      <th>session_status</th>\n",
       "      <th>level_id</th>\n",
       "      <th>level_started_on</th>\n",
       "      <th>level_completed_on</th>\n",
       "      <th>level_status</th>\n",
       "      <th>level_number</th>\n",
       "      <th>played_periods</th>\n",
       "      <th>period_number</th>\n",
       "      <th>cash_at_start</th>\n",
       "      <th>cash_at_end</th>\n",
       "      <th>decision</th>\n",
       "      <th>income_per_period</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>price</th>\n",
       "      <th>risk_level</th>\n",
       "      <th>asset_type</th>\n",
       "      <th>country</th>\n",
       "      <th>treatment_group</th>\n",
       "      <th>level_date</th>\n",
       "      <th>decision_buycash</th>\n",
       "      <th>decision_dontbuy</th>\n",
       "      <th>decision_loan</th>\n",
       "      <th>expected_income_from_asset</th>\n",
       "      <th>actual_price_asset</th>\n",
       "      <th>roi_from_asset</th>\n",
       "      <th>should_buy_cash</th>\n",
       "      <th>can_afford_in_cash</th>\n",
       "      <th>can_afford_via_loan</th>\n",
       "      <th>should_borrow</th>\n",
       "      <th>branch</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>number_of_dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>income_source1</th>\n",
       "      <th>digital_lit1</th>\n",
       "      <th>fin_training</th>\n",
       "      <th>type_one_error</th>\n",
       "      <th>type_two_error</th>\n",
       "      <th>correct_decision</th>\n",
       "      <th>session</th>\n",
       "      <th>type_two_error_adjusted</th>\n",
       "      <th>error_count</th>\n",
       "      <th>total_decisions</th>\n",
       "      <th>total_errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>jmqHB14yopLLywxF...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/24/23 22:58</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>IaB7ROpU70m5djRH...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>won</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/14/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>jmqHB14yopLLywxF...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/24/23 22:58</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>ULZxNZEaM6jLa2PJ...</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>6/25/23 8:58</td>\n",
       "      <td>not_completed</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/14/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>KUUrNMbWGp2e2nzh...</td>\n",
       "      <td>6/25/23 9:27</td>\n",
       "      <td>6/25/23 9:40</td>\n",
       "      <td>won</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>KUUrNMbWGp2e2nzh...</td>\n",
       "      <td>6/25/23 9:27</td>\n",
       "      <td>6/25/23 9:40</td>\n",
       "      <td>won</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.2</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>goat</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>qdCgsaCpxENPZG7O...</td>\n",
       "      <td>7/1/23 17:30</td>\n",
       "      <td>7/8/23 7:46</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>Blk9pOlWZhMz0lb3...</td>\n",
       "      <td>7/1/23 17:58</td>\n",
       "      <td>7/1/23 18:09</td>\n",
       "      <td>won</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>7/1/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>141</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uid user_created_on           session_id session_started_on  \\\n",
       "2  UGGM001    6/14/23 4:25  jmqHB14yopLLywxF...      6/14/23 14:25   \n",
       "3  UGGM001    6/14/23 4:25  jmqHB14yopLLywxF...      6/14/23 14:25   \n",
       "5  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "6  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "7  UGGM001    6/14/23 4:25  qdCgsaCpxENPZG7O...       7/1/23 17:30   \n",
       "\n",
       "  session_completed_on session_status             level_id level_started_on  \\\n",
       "2        6/24/23 22:58      abandoned  IaB7ROpU70m5djRH...    6/14/23 14:25   \n",
       "3        6/24/23 22:58      abandoned  ULZxNZEaM6jLa2PJ...    6/14/23 15:00   \n",
       "5          7/1/23 7:30      abandoned  KUUrNMbWGp2e2nzh...     6/25/23 9:27   \n",
       "6          7/1/23 7:30      abandoned  KUUrNMbWGp2e2nzh...     6/25/23 9:27   \n",
       "7          7/8/23 7:46      abandoned  Blk9pOlWZhMz0lb3...     7/1/23 17:58   \n",
       "\n",
       "  level_completed_on   level_status  level_number  played_periods  \\\n",
       "2      6/14/23 15:00            won             1              20   \n",
       "3       6/25/23 8:58  not_completed             2              21   \n",
       "5       6/25/23 9:40            won             3              31   \n",
       "6       6/25/23 9:40            won             3              31   \n",
       "7       7/1/23 18:09            won             3              43   \n",
       "\n",
       "   period_number  cash_at_start  cash_at_end decision  income_per_period  \\\n",
       "2              1           50.0         50.0  dontBuy                  1   \n",
       "3             10           15.0         22.0  dontBuy                  1   \n",
       "5              3           12.0          9.0  dontBuy                  1   \n",
       "6              8           16.2         19.2  dontBuy                  2   \n",
       "7              1           15.0         15.0  dontBuy                  1   \n",
       "\n",
       "   life_expectancy  price  risk_level asset_type country treatment_group  \\\n",
       "2                6     11         0.0    chicken      UG            UGGM   \n",
       "3                6     11         0.0    chicken      UG            UGGM   \n",
       "5                6     11         0.0    chicken      UG            UGGM   \n",
       "6                5     15         0.0       goat      UG            UGGM   \n",
       "7                6     11         0.0    chicken      UG            UGGM   \n",
       "\n",
       "  level_date  decision_buycash  decision_dontbuy  decision_loan  \\\n",
       "2    6/14/23                 0                 1              0   \n",
       "3    6/14/23                 0                 1              0   \n",
       "5    6/25/23                 0                 1              0   \n",
       "6    6/25/23                 0                 1              0   \n",
       "7     7/1/23                 0                 1              0   \n",
       "\n",
       "   expected_income_from_asset  actual_price_asset  roi_from_asset  \\\n",
       "2                  6.0                       11.0            -5.0   \n",
       "3                  6.0                       11.0            -5.0   \n",
       "5                  6.0                       11.0            -5.0   \n",
       "6                 10.0                       15.0            -5.0   \n",
       "7                  6.0                       11.0            -5.0   \n",
       "\n",
       "   should_buy_cash  can_afford_in_cash  can_afford_via_loan  should_borrow  \\\n",
       "2            False                True                 True          False   \n",
       "3            False                True                 True          False   \n",
       "5            False                True                 True          False   \n",
       "6            False                True                 True          False   \n",
       "7            False                True                 True          False   \n",
       "\n",
       "   branch                group       id  gender    age            education  \\\n",
       "2  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "3  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "5  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "6  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "7  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "\n",
       "   number_of_dependents income   income_source1 digital_lit1 fin_training  \\\n",
       "2                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "3                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "5                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "6                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "7                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "\n",
       "   type_one_error  type_two_error  correct_decision  session  \\\n",
       "2           False           False              True        1   \n",
       "3           False           False              True        1   \n",
       "5           False           False              True        2   \n",
       "6           False           False              True        2   \n",
       "7           False           False              True        3   \n",
       "\n",
       "   type_two_error_adjusted  error_count  total_decisions  total_errors  \n",
       "2                False            False               39             0  \n",
       "3                False            False               39             0  \n",
       "5                False            False               81             4  \n",
       "6                False            False               81             4  \n",
       "7                False            False              141             8  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first_decisions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "94096987",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   session  error_count_percentage\n",
      "0        1            16.310976   \n",
      "1        2            19.400500   \n",
      "2        3            18.675624   \n",
      "3        4            19.155135   \n",
      "4        5            17.879690   \n"
     ]
    }
   ],
   "source": [
    "# Group DataFrame by 'session' and calculate the mean of 'error_count' for each session\n",
    "error_count_percentage = df_first_decisions.groupby('session')['error_count'].mean() * 100\n",
    "\n",
    "# Reset the index to make 'session' a regular column\n",
    "error_count_percentage = error_count_percentage.reset_index()\n",
    "\n",
    "# Rename the 'error_count' column to 'error_count_percentage'\n",
    "error_count_percentage.rename(columns={'error_count': 'error_count_percentage'}, inplace=True)\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "print(error_count_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1abfab16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   session  error_count_percentage\n",
      "0        1            22.883355   \n",
      "1        2            26.126374   \n",
      "2        3            27.233560   \n",
      "3        4            30.366848   \n",
      "4        5            29.055841   \n"
     ]
    }
   ],
   "source": [
    "# Group DataFrame by 'session' and calculate the mean of 'error_count' for each session\n",
    "error_count_percentage = only_level_one_df.groupby('session')['error_count'].mean() * 100\n",
    "\n",
    "# Reset the index to make 'session' a regular column\n",
    "error_count_percentage = error_count_percentage.reset_index()\n",
    "\n",
    "# Rename the 'error_count' column to 'error_count_percentage'\n",
    "error_count_percentage.rename(columns={'error_count': 'error_count_percentage'}, inplace=True)\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "print(error_count_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8ecc8874",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   session  error_count_percentage\n",
      "0        1            20.074746   \n",
      "1        2            23.046792   \n",
      "2        3            22.265535   \n",
      "3        4            24.877738   \n",
      "4        5            22.719954   \n"
     ]
    }
   ],
   "source": [
    "# Group DataFrame by 'session' and calculate the mean of 'error_count' for each session\n",
    "error_count_percentage = UG_raw_data.groupby('session')['error_count'].mean() * 100\n",
    "\n",
    "# Reset the index to make 'session' a regular column\n",
    "error_count_percentage = error_count_percentage.reset_index()\n",
    "\n",
    "# Rename the 'error_count' column to 'error_count_percentage'\n",
    "error_count_percentage.rename(columns={'error_count': 'error_count_percentage'}, inplace=True)\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "print(error_count_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "01d2dd63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 1: Type 1 Error Percentage: 17.79%\n",
      "Session 2: Type 1 Error Percentage: 23.11%\n",
      "Session 3: Type 1 Error Percentage: 26.08%\n",
      "Session 4: Type 1 Error Percentage: 24.95%\n",
      "Session 5: Type 1 Error Percentage: 25.77%\n"
     ]
    }
   ],
   "source": [
    "# Group the DataFrame by 'session'\n",
    "grouped_sessions = df_first_decisions.groupby('session')\n",
    "\n",
    "# Initialize a dictionary to store the type 1 error percentage per session\n",
    "type1_error_percentage_per_session = {}\n",
    "\n",
    "# Iterate over each session group\n",
    "for session, group in grouped_sessions:\n",
    "    # Filter the group where roi_from_asset < 0 and decision is either 'buyCash' or 'loan'\n",
    "    filtered_group = group[group['decision'].isin(['buyCash', 'loan'])]\n",
    "    \n",
    "    # Calculate the type 1 error percentage for the filtered group\n",
    "    if len(filtered_group) > 0:\n",
    "        type1_error_percentage = (filtered_group['type_one_error'].sum() / len(filtered_group)) * 100\n",
    "    else:\n",
    "        type1_error_percentage = 0  # Set to 0 if no rows meet the condition\n",
    "    \n",
    "    # Store the type 1 error percentage for the session\n",
    "    type1_error_percentage_per_session[session] = type1_error_percentage\n",
    "\n",
    "# Display the type 1 error percentage per session\n",
    "for session, percentage in type1_error_percentage_per_session.items():\n",
    "    print(f\"Session {session}: Type 1 Error Percentage: {percentage:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "31bb673f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 1: Type 2 Error Percentage: 15.39%\n",
      "Session 2: Type 2 Error Percentage: 17.45%\n",
      "Session 3: Type 2 Error Percentage: 15.59%\n",
      "Session 4: Type 2 Error Percentage: 16.95%\n",
      "Session 5: Type 2 Error Percentage: 15.22%\n"
     ]
    }
   ],
   "source": [
    "# Initialize a dictionary to store the type 2 error percentage per session\n",
    "type2_error_percentage_per_session = {}\n",
    "\n",
    "# Iterate over each session group\n",
    "for session, group in grouped_sessions:\n",
    "    # Filter the group where roi_from_asset >= 3\n",
    "    filtered_group = group[group['decision'].isin(['dontBuy'])]\n",
    "    \n",
    "    # Calculate the type 2 error percentage for the filtered group\n",
    "    if len(filtered_group) > 0:\n",
    "        type2_error_percentage = (filtered_group['type_two_error_adjusted'].sum() / len(filtered_group)) * 100\n",
    "    else:\n",
    "        type2_error_percentage = 0  # Set to 0 if no rows meet the condition\n",
    "    \n",
    "    # Store the type 2 error percentage for the session\n",
    "    type2_error_percentage_per_session[session] = type2_error_percentage\n",
    "\n",
    "# Display the type 2 error percentage per session\n",
    "for session, percentage in type2_error_percentage_per_session.items():\n",
    "    print(f\"Session {session}: Type 2 Error Percentage: {percentage:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e081bf0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 1: Buying Decision Percentage: 38.34%\n",
      "Session 2: Buying Decision Percentage: 34.47%\n",
      "Session 3: Buying Decision Percentage: 29.44%\n",
      "Session 4: Buying Decision Percentage: 27.62%\n",
      "Session 5: Buying Decision Percentage: 25.24%\n"
     ]
    }
   ],
   "source": [
    "# Group the DataFrame by 'session'\n",
    "grouped_sessions = df_first_decisions.groupby('session')\n",
    "\n",
    "# Initialize a dictionary to store the buying decision percentage per session\n",
    "buying_decision_percentage_per_session = {}\n",
    "\n",
    "# Iterate over each session group\n",
    "for session, group in grouped_sessions:\n",
    "    # Count the occurrences of 'buyCash' and 'loan' decisions\n",
    "    total_decisions = len(group)\n",
    "    buy_cash_count = (group['decision'] == 'buyCash').sum()\n",
    "    loan_count = (group['decision'] == 'loan').sum()\n",
    "    \n",
    "    # Calculate the percentage of buying decisions\n",
    "    buying_decision_percentage = ((buy_cash_count + loan_count) / total_decisions) * 100\n",
    "    \n",
    "    # Store the buying decision percentage for the session\n",
    "    buying_decision_percentage_per_session[session] = buying_decision_percentage\n",
    "\n",
    "# Display the buying decision percentage per session\n",
    "for session, percentage in buying_decision_percentage_per_session.items():\n",
    "    print(f\"Session {session}: Buying Decision Percentage: {percentage:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "12622ca0",
   "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>uid</th>\n",
       "      <th>session</th>\n",
       "      <th>error_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.049383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>3</td>\n",
       "      <td>0.056738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>4</td>\n",
       "      <td>0.213592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>5</td>\n",
       "      <td>0.304217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uid  session  error_rate\n",
       "0  UGGM001        1    0.000000\n",
       "1  UGGM001        2    0.049383\n",
       "2  UGGM001        3    0.056738\n",
       "3  UGGM001        4    0.213592\n",
       "4  UGGM001        5    0.304217"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'uid' and 'session' and calculate the error rate\n",
    "error_rate_df_all = UG_raw_data.groupby(['uid', 'session']).agg(\n",
    "    error_rate=('error_count', lambda x: x.sum() / len(x))\n",
    ")\n",
    "\n",
    "# Reset index to move 'uid' and 'session' back to columns\n",
    "error_rate_df_all.reset_index(inplace=True)\n",
    "\n",
    "# Print or use error_rate_df\n",
    "error_rate_df_all.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ba8363c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export DataFrame to a CSV file\n",
    "error_rate_df_all.to_csv('error_rate_df_all.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79f17c5",
   "metadata": {},
   "source": [
    "## 5. Session Length - Calculated Field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "592206fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97758"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the DataFrame for won levels\n",
    "filtered_df = UG_raw_data[(UG_raw_data['level_status'] != 'not_completed')]\n",
    "\n",
    "# Displaying the filtered DataFrame\n",
    "len(filtered_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8565ecd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>uid</th>\n",
       "      <th>user_created_on</th>\n",
       "      <th>session_id</th>\n",
       "      <th>session_started_on</th>\n",
       "      <th>session_completed_on</th>\n",
       "      <th>session_status</th>\n",
       "      <th>level_id</th>\n",
       "      <th>level_started_on</th>\n",
       "      <th>level_completed_on</th>\n",
       "      <th>level_status</th>\n",
       "      <th>level_number</th>\n",
       "      <th>played_periods</th>\n",
       "      <th>period_number</th>\n",
       "      <th>cash_at_start</th>\n",
       "      <th>cash_at_end</th>\n",
       "      <th>decision</th>\n",
       "      <th>income_per_period</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>price</th>\n",
       "      <th>risk_level</th>\n",
       "      <th>asset_type</th>\n",
       "      <th>country</th>\n",
       "      <th>treatment_group</th>\n",
       "      <th>level_date</th>\n",
       "      <th>decision_buycash</th>\n",
       "      <th>decision_dontbuy</th>\n",
       "      <th>decision_loan</th>\n",
       "      <th>expected_income_from_asset</th>\n",
       "      <th>actual_price_asset</th>\n",
       "      <th>roi_from_asset</th>\n",
       "      <th>should_buy_cash</th>\n",
       "      <th>can_afford_in_cash</th>\n",
       "      <th>can_afford_via_loan</th>\n",
       "      <th>should_borrow</th>\n",
       "      <th>branch</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>number_of_dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>income_source1</th>\n",
       "      <th>digital_lit1</th>\n",
       "      <th>fin_training</th>\n",
       "      <th>type_one_error</th>\n",
       "      <th>type_two_error</th>\n",
       "      <th>correct_decision</th>\n",
       "      <th>session</th>\n",
       "      <th>type_two_error_adjusted</th>\n",
       "      <th>error_count</th>\n",
       "      <th>total_decisions</th>\n",
       "      <th>total_errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>aRrqSip8ZsNIe4tH...</td>\n",
       "      <td>7/8/23 17:46</td>\n",
       "      <td>7/15/23 7:47</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>FxYg8oCcuhVMqMnK...</td>\n",
       "      <td>7/8/23 18:28</td>\n",
       "      <td>7/8/23 18:48</td>\n",
       "      <td>won</td>\n",
       "      <td>3</td>\n",
       "      <td>139</td>\n",
       "      <td>92</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>7/8/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>206</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>jmqHB14yopLLywxF...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/24/23 22:58</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>IaB7ROpU70m5djRH...</td>\n",
       "      <td>6/14/23 14:25</td>\n",
       "      <td>6/14/23 15:00</td>\n",
       "      <td>won</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/14/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>D60e8Qi21WGvpoU4...</td>\n",
       "      <td>6/25/23 9:14</td>\n",
       "      <td>6/25/23 9:27</td>\n",
       "      <td>won</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>goat</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>KUUrNMbWGp2e2nzh...</td>\n",
       "      <td>6/25/23 9:27</td>\n",
       "      <td>6/25/23 9:40</td>\n",
       "      <td>won</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chicken</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>6/14/23 4:25</td>\n",
       "      <td>z9bHHq7WCreqEOcj...</td>\n",
       "      <td>6/25/23 9:04</td>\n",
       "      <td>7/1/23 7:30</td>\n",
       "      <td>abandoned</td>\n",
       "      <td>KUUrNMbWGp2e2nzh...</td>\n",
       "      <td>6/25/23 9:27</td>\n",
       "      <td>6/25/23 9:40</td>\n",
       "      <td>won</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.2</td>\n",
       "      <td>dontBuy</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>goat</td>\n",
       "      <td>UG</td>\n",
       "      <td>UGGM</td>\n",
       "      <td>6/25/23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Kisakyamukama Bu...</td>\n",
       "      <td>UGGM001</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employment</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Yes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uid user_created_on           session_id session_started_on  \\\n",
       "0  UGGM001    6/14/23 4:25  aRrqSip8ZsNIe4tH...       7/8/23 17:46   \n",
       "2  UGGM001    6/14/23 4:25  jmqHB14yopLLywxF...      6/14/23 14:25   \n",
       "4  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "5  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "6  UGGM001    6/14/23 4:25  z9bHHq7WCreqEOcj...       6/25/23 9:04   \n",
       "\n",
       "  session_completed_on session_status             level_id level_started_on  \\\n",
       "0         7/15/23 7:47      abandoned  FxYg8oCcuhVMqMnK...     7/8/23 18:28   \n",
       "2        6/24/23 22:58      abandoned  IaB7ROpU70m5djRH...    6/14/23 14:25   \n",
       "4          7/1/23 7:30      abandoned  D60e8Qi21WGvpoU4...     6/25/23 9:14   \n",
       "5          7/1/23 7:30      abandoned  KUUrNMbWGp2e2nzh...     6/25/23 9:27   \n",
       "6          7/1/23 7:30      abandoned  KUUrNMbWGp2e2nzh...     6/25/23 9:27   \n",
       "\n",
       "  level_completed_on level_status  level_number  played_periods  \\\n",
       "0       7/8/23 18:48          won             3             139   \n",
       "2      6/14/23 15:00          won             1              20   \n",
       "4       6/25/23 9:27          won             2              32   \n",
       "5       6/25/23 9:40          won             3              31   \n",
       "6       6/25/23 9:40          won             3              31   \n",
       "\n",
       "   period_number  cash_at_start  cash_at_end decision  income_per_period  \\\n",
       "0             92           33.4         33.4  dontBuy                  1   \n",
       "2              1           50.0         50.0  dontBuy                  1   \n",
       "4             32           44.0         50.0  dontBuy                  2   \n",
       "5              3           12.0          9.0  dontBuy                  1   \n",
       "6              8           16.2         19.2  dontBuy                  2   \n",
       "\n",
       "   life_expectancy  price  risk_level asset_type country treatment_group  \\\n",
       "0                6     12         0.0    chicken      UG            UGGM   \n",
       "2                6     11         0.0    chicken      UG            UGGM   \n",
       "4                5     15         0.0       goat      UG            UGGM   \n",
       "5                6     11         0.0    chicken      UG            UGGM   \n",
       "6                5     15         0.0       goat      UG            UGGM   \n",
       "\n",
       "  level_date  decision_buycash  decision_dontbuy  decision_loan  \\\n",
       "0     7/8/23                 0                 1              0   \n",
       "2    6/14/23                 0                 1              0   \n",
       "4    6/25/23                 0                 1              0   \n",
       "5    6/25/23                 0                 1              0   \n",
       "6    6/25/23                 0                 1              0   \n",
       "\n",
       "   expected_income_from_asset  actual_price_asset  roi_from_asset  \\\n",
       "0                  6.0                       12.0            -6.0   \n",
       "2                  6.0                       11.0            -5.0   \n",
       "4                 10.0                       15.0            -5.0   \n",
       "5                  6.0                       11.0            -5.0   \n",
       "6                 10.0                       15.0            -5.0   \n",
       "\n",
       "   should_buy_cash  can_afford_in_cash  can_afford_via_loan  should_borrow  \\\n",
       "0            False                True                 True          False   \n",
       "2            False                True                 True          False   \n",
       "4            False                True                 True          False   \n",
       "5            False                True                 True          False   \n",
       "6            False                True                 True          False   \n",
       "\n",
       "   branch                group       id  gender    age            education  \\\n",
       "0  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "2  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "4  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "5  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "6  Kamuli  Kisakyamukama Bu...  UGGM001  Female  58-67  Secondary/HighSc...   \n",
       "\n",
       "   number_of_dependents income   income_source1 digital_lit1 fin_training  \\\n",
       "0                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "2                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "4                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "5                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "6                    1     Yes  Self-employment  Rarely used          Yes   \n",
       "\n",
       "   type_one_error  type_two_error  correct_decision  session  \\\n",
       "0           False           False              True        4   \n",
       "2           False           False              True        1   \n",
       "4           False           False              True        2   \n",
       "5           False           False              True        2   \n",
       "6           False           False              True        2   \n",
       "\n",
       "   type_two_error_adjusted  error_count  total_decisions  total_errors  \n",
       "0                False            False              206            44  \n",
       "2                False            False               39             0  \n",
       "4                False            False               81             4  \n",
       "5                False            False               81             4  \n",
       "6                False            False               81             4  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5f29b6ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r6/dvxf46c55m58pjzd5rcnbzq00000gn/T/ipykernel_43113/919022496.py:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  filtered_df['level_started_on'] = pd.to_datetime(filtered_df['level_started_on'])\n",
      "/var/folders/r6/dvxf46c55m58pjzd5rcnbzq00000gn/T/ipykernel_43113/919022496.py:2: 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",
      "  filtered_df['level_started_on'] = pd.to_datetime(filtered_df['level_started_on'])\n",
      "/var/folders/r6/dvxf46c55m58pjzd5rcnbzq00000gn/T/ipykernel_43113/919022496.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  filtered_df['level_completed_on'] = pd.to_datetime(filtered_df['level_completed_on'])\n",
      "/var/folders/r6/dvxf46c55m58pjzd5rcnbzq00000gn/T/ipykernel_43113/919022496.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",
      "  filtered_df['level_completed_on'] = pd.to_datetime(filtered_df['level_completed_on'])\n"
     ]
    }
   ],
   "source": [
    "# Convert 'level_started_on' and 'level_completed_on' columns to datetime if they are not already\n",
    "filtered_df['level_started_on'] = pd.to_datetime(filtered_df['level_started_on'])\n",
    "filtered_df['level_completed_on'] = pd.to_datetime(filtered_df['level_completed_on'])\n",
    "\n",
    "# Group by 'uid' and 'session' and calculate the earliest 'level_started_on' and latest 'level_completed_on'\n",
    "session_start_end = filtered_df.groupby(['uid', 'session']).agg(\n",
    "    session_start=('level_started_on', 'min'),\n",
    "    session_end=('level_completed_on', 'max')\n",
    ")\n",
    "\n",
    "# Calculate the session length as the difference between session_end and session_start\n",
    "session_start_end['session_length'] = (session_start_end['session_end'] - session_start_end['session_start']).dt.total_seconds() / 60  # Convert to minutes\n",
    "\n",
    "# Merge the session length back to the original DataFrame\n",
    "filtered_df = filtered_df.merge(session_start_end['session_length'], on=['uid', 'session'], how='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "56f63c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df['level_completed_on'] = pd.to_datetime(filtered_df['level_completed_on'])\n",
    "filtered_df['level_started_on'] = pd.to_datetime(filtered_df['level_started_on'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9f87c44a",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df['level_duration_minutes'] = (filtered_df['level_completed_on'] - filtered_df['level_started_on']).dt.total_seconds() / 60\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2aecf493",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r6/dvxf46c55m58pjzd5rcnbzq00000gn/T/ipykernel_43113/4067480719.py:6: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x='session', y='session_length', data=filtered_df, palette='Set2')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create boxplot\n",
    "plt.figure(figsize=(10, 6))  # Adjust the figure size as needed\n",
    "sns.boxplot(x='session', y='session_length', data=filtered_df, palette='Set2')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Session', fontsize=14)\n",
    "plt.ylabel('Session Length (minutes)', fontsize=14)\n",
    "plt.title('Session Length Distribution per User', fontsize=14)\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6932c706",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics of Session Length in Minutes per Session:\n",
      "           count  mean   std  min   25%   50%   75%    max\n",
      "session                                                   \n",
      "1        4552.00 23.20  8.85 3.00 18.00 23.00 28.00  50.00\n",
      "2       10576.00 38.56 10.58 9.00 31.00 38.00 46.00  73.00\n",
      "3       17580.00 51.95 20.76 5.00 34.00 48.00 66.00  93.00\n",
      "4       28524.00 61.75 17.11 7.00 51.00 61.00 74.00 112.00\n",
      "5       36526.00 61.61 23.00 2.00 43.00 61.00 78.00 148.00\n"
     ]
    }
   ],
   "source": [
    "# Calculate summary statistics per session\n",
    "summary_stats_per_session = filtered_df.groupby('session')['session_length'].describe()\n",
    "\n",
    "# Set the display format for float numbers\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "\n",
    "# Print title\n",
    "print(\"Summary Statistics of Session Length in Minutes per Session:\")\n",
    "\n",
    "# Print summary statistics\n",
    "print(summary_stats_per_session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e45a5c84",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df['total_decisions'] = filtered_df.groupby(['uid', 'session'])['uid'].transform('count')\n",
    "filtered_df['total_errors'] = filtered_df.groupby(['uid', 'session'])['error_count'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7eefa953",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df = filtered_df.groupby(['uid', 'session', 'level_number']).first().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4c3d7d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the DataFrame by 'uid', 'session', and 'level' columns to ensure proper order\n",
    "grouped_df = grouped_df.sort_values(by=['uid', 'session', 'level_number'])\n",
    "\n",
    "# Calculate the accumulated duration spent by each player in each session up to a certain level\n",
    "grouped_df['accumulated_duration'] = grouped_df.groupby(['uid', 'session'])['level_duration_minutes'].cumsum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d87c3bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting specific columns\n",
    "selected_columns = ['uid', 'level_number','session', 'branch','digital_lit1','gender','age','education','session_length','total_decisions', 'total_errors', 'level_duration_minutes','accumulated_duration']\n",
    "\n",
    "# Filter grouped_df to only include selected columns\n",
    "filtered_grouped_df = grouped_df[selected_columns]\n",
    "\n",
    "# Alternatively, you can use loc\n",
    "filtered_grouped_df = grouped_df.loc[:, selected_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b5d1ec98",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_grouped_df['decisions_per_minute'] = filtered_grouped_df['total_decisions'] / grouped_df['session_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8b228074",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_grouped_df['error_rate'] = filtered_grouped_df['total_errors'] / grouped_df['total_decisions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "a46d6a53",
   "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>uid</th>\n",
       "      <th>level_number</th>\n",
       "      <th>session</th>\n",
       "      <th>branch</th>\n",
       "      <th>digital_lit1</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>session_length</th>\n",
       "      <th>total_decisions</th>\n",
       "      <th>total_errors</th>\n",
       "      <th>level_duration_minutes</th>\n",
       "      <th>accumulated_duration</th>\n",
       "      <th>decisions_per_minute</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>shortest_session_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSchool</td>\n",
       "      <td>35.00</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSchool</td>\n",
       "      <td>36.00</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSchool</td>\n",
       "      <td>36.00</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>13.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSchool</td>\n",
       "      <td>36.00</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>13.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Kamuli</td>\n",
       "      <td>Rarely used</td>\n",
       "      <td>Female</td>\n",
       "      <td>58-67</td>\n",
       "      <td>Secondary/HighSchool</td>\n",
       "      <td>38.00</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "      <td>27.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.06</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uid  level_number  session  branch digital_lit1  gender    age  \\\n",
       "0  UGGM001             1        1  Kamuli  Rarely used  Female  58-67   \n",
       "1  UGGM001             1        2  Kamuli  Rarely used  Female  58-67   \n",
       "2  UGGM001             2        2  Kamuli  Rarely used  Female  58-67   \n",
       "3  UGGM001             3        2  Kamuli  Rarely used  Female  58-67   \n",
       "4  UGGM001             2        3  Kamuli  Rarely used  Female  58-67   \n",
       "\n",
       "              education  session_length  total_decisions  total_errors  \\\n",
       "0  Secondary/HighSchool           35.00               18             0   \n",
       "1  Secondary/HighSchool           36.00               81             4   \n",
       "2  Secondary/HighSchool           36.00               81             4   \n",
       "3  Secondary/HighSchool           36.00               81             4   \n",
       "4  Secondary/HighSchool           38.00              115             7   \n",
       "\n",
       "   level_duration_minutes  accumulated_duration  decisions_per_minute  \\\n",
       "0                   35.00                 35.00                  0.51   \n",
       "1                   10.00                 10.00                  2.25   \n",
       "2                   13.00                 23.00                  2.25   \n",
       "3                   13.00                 36.00                  2.25   \n",
       "4                   27.00                 27.00                  3.03   \n",
       "\n",
       "   error_rate  shortest_session_length  \n",
       "0        0.00                    35.00  \n",
       "1        0.05                    35.00  \n",
       "2        0.05                    35.00  \n",
       "3        0.05                    35.00  \n",
       "4        0.06                    35.00  "
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ff10e190",
   "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>uid</th>\n",
       "      <th>session</th>\n",
       "      <th>error_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>3</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>4</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>5</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uid  session  error_rate\n",
       "0  UGGM001        1        0.00\n",
       "1  UGGM001        2        0.03\n",
       "2  UGGM001        3        0.10\n",
       "3  UGGM001        4        0.12\n",
       "4  UGGM001        5        0.20"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'uid' and 'session' and calculate the error rate\n",
    "error_rate_df_first_ten = df_first_decisions.groupby(['uid', 'session']).agg(\n",
    "    error_rate=('error_count', lambda x: x.sum() / len(x))\n",
    ")\n",
    "\n",
    "# Reset index to move 'uid' and 'session' back to columns\n",
    "error_rate_df_first_ten.reset_index(inplace=True)\n",
    "\n",
    "# Print or use error_rate_df\n",
    "error_rate_df_first_ten.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b66268b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export DataFrame to a CSV file\n",
    "error_rate_df_first_ten.to_csv('error_rate_df_first_ten.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "96e5084b",
   "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>uid</th>\n",
       "      <th>session</th>\n",
       "      <th>error_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>4</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UGGM001</td>\n",
       "      <td>5</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UGGM005</td>\n",
       "      <td>1</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uid  session  error_rate\n",
       "0  UGGM001        1        0.00\n",
       "1  UGGM001        2        0.00\n",
       "2  UGGM001        4        0.44\n",
       "3  UGGM001        5        0.22\n",
       "4  UGGM005        1        0.14"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'uid' and 'session' and calculate the error rate\n",
    "error_rate_df_level_one = only_level_one_df.groupby(['uid', 'session']).agg(\n",
    "    error_rate=('error_count', lambda x: x.sum() / len(x))\n",
    ")\n",
    "\n",
    "# Reset index to move 'uid' and 'session' back to columns\n",
    "error_rate_df_level_one.reset_index(inplace=True)\n",
    "\n",
    "# Print or use error_rate_df\n",
    "error_rate_df_level_one.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "542b8dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export DataFrame to a CSV file\n",
    "error_rate_df_level_one.to_csv('error_rate_df_level_one.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa696ba1",
   "metadata": {},
   "source": [
    "## 6. Distribution of Decisions Made per User"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c8b9cae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution\n",
    "plt.figure(figsize=(10, 6))  # Adjust the figure size as needed\n",
    "plt.hist(row_counts_per_uid, bins=20, color='skyblue', edgecolor='black')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Number of Investment Decisions per User')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Total Decisions per User')\n",
    "\n",
    "# Setting x ticks\n",
    "plt.xticks(range(0, max(row_counts_per_uid)+1, 250))\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7c7eb8",
   "metadata": {},
   "source": [
    "## 7. Histogram Survey Question Baseline: Digital Learning Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7e44f06f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r6/dvxf46c55m58pjzd5rcnbzq00000gn/T/ipykernel_43113/1368437634.py:21: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(x=category_order, y=category_percentages, palette=\"Set2\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group DataFrame by 'uid' and keep only the first row of each group\n",
    "grouped_df = UG_raw_data.groupby('uid').first().reset_index()\n",
    "\n",
    "# Count the frequency of each category\n",
    "category_counts = grouped_df['digital_lit1'].value_counts()\n",
    "\n",
    "# Sort the categories by frequency in descending order\n",
    "category_order = category_counts.index.tolist()\n",
    "\n",
    "# Calculate the percentage of each category\n",
    "total_responses = len(grouped_df)\n",
    "category_percentages = (category_counts / total_responses) * 100\n",
    "\n",
    "# Set the style to a modern one\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Plot the countplot with ordered categories\n",
    "ax = sns.barplot(x=category_order, y=category_percentages, palette=\"Set2\")\n",
    "\n",
    "# Annotate each bar with the percentage value, adding distance between bar and text\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{:.2f}%'.format(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center', fontsize=12, color='black', xytext=(0, 10),  # Increase y-offset to add more distance\n",
    "                textcoords='offset points')\n",
    "\n",
    "plt.ylabel('Percentage', fontsize=14)  # Updated y-axis label\n",
    "plt.xticks(rotation=45, ha='right', fontsize=14)  # Increase font size of x-axis labels\n",
    "plt.title('Baseline Survey: Familiarity with Digital Leaning Tools', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ee56af09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group DataFrame by 'uid' and keep only the first row of each group\n",
    "grouped_df = UG_raw_data.groupby('uid').first().reset_index()\n",
    "\n",
    "# Count the frequency of each category\n",
    "category_counts = grouped_df['digital_lit1'].value_counts()\n",
    "\n",
    "# Sort the categories by frequency in descending order\n",
    "category_order = category_counts.index.tolist()\n",
    "\n",
    "# Calculate the percentage of each category within each gender group\n",
    "category_counts = grouped_df.groupby(['digital_lit1', 'gender']).size().reset_index(name='count')\n",
    "total_per_gender = category_counts.groupby('gender')['count'].transform('sum')\n",
    "category_counts['percentage'] = (category_counts['count'] / total_per_gender) * 100\n",
    "\n",
    "# Set the style to a modern one\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "# Plot the barplot with ordered categories and hue by gender\n",
    "sns.barplot(x='digital_lit1', y='percentage', data=category_counts, order=category_order, hue='gender', palette=\"husl\")\n",
    "plt.ylabel('Percentage', fontsize=14)\n",
    "plt.xlabel('')\n",
    "#plt.title('Percentage of Digital Literacy Level by Gender')\n",
    "plt.xticks(rotation=45, ha='right', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a939d54",
   "metadata": {},
   "source": [
    "## 8. Error Distribution Type 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "638aec94",
   "metadata": {},
   "outputs": [],
   "source": [
    "UG_raw_data['error'] = np.where(UG_raw_data['type_one_error'], 'type1', \n",
    "                                                np.where(UG_raw_data['type_two_error_adjusted'], 'type2', None))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5c1b53ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the unique session numbers in the DataFrame\n",
    "unique_sessions = UG_raw_data['session'].unique()\n",
    "unique_sessions = np.sort(unique_sessions)\n",
    "\n",
    "# Initialize lists to store error percentages for each session\n",
    "type1_error_percentages = []\n",
    "type2_error_percentages = []\n",
    "\n",
    "# Iterate over each session\n",
    "for session in unique_sessions:\n",
    "    # Filter data for the current session\n",
    "    session_data = UG_raw_data[UG_raw_data['session'] == session]\n",
    "    \n",
    "    # Extract relevant data for the current session\n",
    "    total_decisions = len(session_data[session_data['decision'].isin(['buyCash', 'loan', 'dontBuy'])]) # Total decisions\n",
    "    buy_decisions = len(session_data[session_data['decision'].isin(['buyCash', 'loan'])])\n",
    "    dontbuy_decisions = len(session_data[session_data['decision'].isin(['dontBuy'])])\n",
    "    \n",
    "    # Count type 1 errors for buy decisions (buyCash or loan)\n",
    "    type1_error_count_buy = len(session_data[(session_data['error'] == 'type1') & session_data['decision'].isin(['buyCash', 'loan'])])\n",
    "    \n",
    "    # Count type 2 errors for don't buy decisions (dontBuy)\n",
    "    type2_error_count_dont_buy = len(session_data[(session_data['error'] == 'type2') & (session_data['decision'] == 'dontBuy')])\n",
    "    \n",
    "    # Calculate error percentages\n",
    "    type1_error_percentage = (type1_error_count_buy / buy_decisions) * 100 if buy_decisions > 0 else 0\n",
    "    type2_error_percentage = (type2_error_count_dont_buy / dontbuy_decisions) * 100 if dontbuy_decisions > 0 else 0\n",
    "    \n",
    "    # Append error percentages to the lists\n",
    "    type1_error_percentages.append(type1_error_percentage)\n",
    "    type2_error_percentages.append(type2_error_percentage)\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Set the x positions for the bars\n",
    "x = np.arange(len(unique_sessions))\n",
    "\n",
    "# Plot the combined bar chart with a larger size\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.bar(x - bar_width/2, type1_error_percentages, bar_width, color='coral', label='Type 1 Error (Buy)')\n",
    "plt.bar(x + bar_width/2, type2_error_percentages, bar_width, color='teal', label='Type 2 Error (Don\\'t Buy)')\n",
    "\n",
    "plt.xlabel('Session Number', fontsize=14)\n",
    "plt.ylabel('Percentage of Decisions', fontsize=14)\n",
    "plt.title('Error Percentage Distribution by Session', fontsize=14)\n",
    "plt.xticks(x, unique_sessions, fontsize=12)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40a47d6e",
   "metadata": {},
   "source": [
    "## 9. Decisions Speed Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "15b9e7a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         count  mean   std  min   25%   50%   75%    max\n",
      "session                                                 \n",
      "1       169.00 22.39  9.26 3.00 16.00 22.00 28.00  50.00\n",
      "2       257.00 36.64 11.55 9.00 29.00 36.00 44.00  73.00\n",
      "3       341.00 48.81 20.32 5.00 33.00 42.00 64.00  93.00\n",
      "4       429.00 58.72 17.95 7.00 47.00 56.00 73.00 112.00\n",
      "5       569.00 57.31 23.62 2.00 40.00 57.00 75.00 148.00\n"
     ]
    }
   ],
   "source": [
    "# Calculate summary statistics of session length per session\n",
    "session_summary_stats = filtered_grouped_df.groupby('session')['session_length'].describe()\n",
    "\n",
    "# Assuming session_summary_stats is your DataFrame containing summary statistics\n",
    "print(session_summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5d1a0dd0",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>session</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169.00</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.61</td>\n",
       "      <td>3.42</td>\n",
       "      <td>9.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>257.00</td>\n",
       "      <td>3.11</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.69</td>\n",
       "      <td>3.68</td>\n",
       "      <td>14.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>341.00</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.29</td>\n",
       "      <td>3.22</td>\n",
       "      <td>5.03</td>\n",
       "      <td>17.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>429.00</td>\n",
       "      <td>4.81</td>\n",
       "      <td>2.67</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2.88</td>\n",
       "      <td>4.32</td>\n",
       "      <td>6.29</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>569.00</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.01</td>\n",
       "      <td>0.55</td>\n",
       "      <td>3.54</td>\n",
       "      <td>5.25</td>\n",
       "      <td>7.60</td>\n",
       "      <td>15.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count  mean  std  min  25%  50%  75%   max\n",
       "session                                            \n",
       "1       169.00  2.82 1.65 0.43 1.64 2.61 3.42  9.29\n",
       "2       257.00  3.11 1.98 0.43 1.85 2.69 3.68 14.35\n",
       "3       341.00  3.91 2.61 0.02 2.29 3.22 5.03 17.16\n",
       "4       429.00  4.81 2.67 0.57 2.88 4.32 6.29 12.93\n",
       "5       569.00  5.75 3.01 0.55 3.54 5.25 7.60 15.43"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate summary statistics per session\n",
    "session_summary_stats = filtered_grouped_df.groupby('session')['decisions_per_minute'].describe()\n",
    "\n",
    "# Round summary statistics to two decimals\n",
    "session_summary_stats_rounded = session_summary_stats.round(2)\n",
    "\n",
    "# Display rounded summary statistics for each session in a single table\n",
    "session_summary_stats_rounded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "72b2b07d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r6/dvxf46c55m58pjzd5rcnbzq00000gn/T/ipykernel_43113/3143961290.py:6: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.violinplot(x='session', y='decisions_per_minute', data=filtered_grouped_df, inner=None, linewidth=1.3, palette=\"Set2\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the style of seaborn\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create the violin plot\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.violinplot(x='session', y='decisions_per_minute', data=filtered_grouped_df, inner=None, linewidth=1.3, palette=\"Set2\")\n",
    "#sns.swarmplot(x='session', y='decisions_per_minute', data=filtered_grouped_df, color=\"k\", alpha=0.5)\n",
    "\n",
    "plt.xlabel('Session', fontsize=18)\n",
    "plt.ylabel('Decisions per Minute', fontsize=18)\n",
    "plt.title('Distribution of Decisions per Minute for Each Session', fontsize=18)\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66c18a3",
   "metadata": {},
   "source": [
    "## 10. Overview Decisions based on ROI and decision made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a73f16c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_without_cant_afford = UG_raw_data[UG_raw_data['can_afford_in_cash'] == True]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5541954c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming df_without_cant_afford is your DataFrame and 'roi_from_asset' and 'decision' are the column names\n",
    "roi_values = df_without_cant_afford['roi_from_asset']\n",
    "buy_decision = df_without_cant_afford['decision']\n",
    "\n",
    "# Get unique buy decisions for labeling\n",
    "unique_decisions = buy_decision.unique()\n",
    "\n",
    "# Calculate the histogram data for each buy decision\n",
    "hist_data = []\n",
    "for decision in unique_decisions:\n",
    "    hist_data.append(roi_values[buy_decision == decision])\n",
    "\n",
    "# Plotting the distribution of ROI in a stacked bar chart\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.hist(hist_data, bins=20, stacked=True, alpha=0.5, label=unique_decisions, edgecolor='black')\n",
    "\n",
    "plt.xlabel('ROI')\n",
    "plt.ylabel('Amount of Decisions in Game')\n",
    "plt.title('Distribution of ROI by Investment Decision')\n",
    "plt.legend()\n",
    "plt.xticks(range(-10, 12, 2))  # Setting ticks from -10 to 10 with intervals of 2\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a74bed7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Count  Percentage\n",
      "decision                   \n",
      "dontBuy   74142       77.62\n",
      "buyCash   16123       16.88\n",
      "loan       5255        5.50\n"
     ]
    }
   ],
   "source": [
    "# Group the DataFrame by \"uid\" and count the number of rows for each unique value in \"treatment_group\"\n",
    "grouped_counts = df_without_cant_afford['decision'].value_counts()\n",
    "\n",
    "# Calculate percentages\n",
    "total_rows = grouped_counts.sum()\n",
    "percentages = round((grouped_counts / total_rows) * 100, 2)\n",
    "\n",
    "# Create a DataFrame to display counts and percentages\n",
    "summary_table = pd.concat([grouped_counts, percentages], axis=1)\n",
    "summary_table.columns = ['Count', 'Percentage']\n",
    "\n",
    "print(summary_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d8ed578c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assuming df_without_cant_afford is your DataFrame and 'roi_from_asset', 'decision', and 'session' are the column names\n",
    "\n",
    "# Get the unique session numbers in the DataFrame\n",
    "unique_sessions = df_without_cant_afford['session'].unique()\n",
    "\n",
    "# Define fixed colors for decisions\n",
    "decision_colors = {'loan': 'blue', 'buyCash': 'green'}\n",
    "\n",
    "# Determine the number of rows and columns for subplots based on the number of unique sessions\n",
    "num_rows = (len(unique_sessions) + 2) // 3  # Divide by 3 and round up\n",
    "num_cols = min(len(unique_sessions), 3)\n",
    "\n",
    "# Create subplots for each session\n",
    "fig, axs = plt.subplots(num_rows, num_cols, figsize=(15, 8))\n",
    "\n",
    "for i, session in enumerate(unique_sessions):\n",
    "    # Filter data for the current session and only include 'buyCash' and 'loan' decisions\n",
    "    session_data = df_without_cant_afford[(df_without_cant_afford['session'] == session) & df_without_cant_afford['decision'].isin(['buyCash', 'loan'])]\n",
    "    \n",
    "    # Extract ROI values and buy decisions for the current session\n",
    "    roi_values = session_data['roi_from_asset']\n",
    "    buy_decision = session_data['decision']\n",
    "    \n",
    "    # Get unique buy decisions for labeling\n",
    "    unique_decisions = buy_decision.unique()\n",
    "    \n",
    "    # Calculate the histogram data for each buy decision\n",
    "    hist_data = []\n",
    "    for decision in unique_decisions:\n",
    "        hist_data.append(roi_values[buy_decision == decision])\n",
    "    \n",
    "    # Determine the subplot position\n",
    "    row = i // 3\n",
    "    col = i % 3\n",
    "    \n",
    "    # Plotting the distribution of ROI in a stacked bar chart for the current session\n",
    "    bins = np.linspace(-10, 10, 21)  # Ensure the same bin size for each session\n",
    "    axs[row, col].hist(hist_data, bins=bins, stacked=True, alpha=0.7, label=unique_decisions, color=[decision_colors[decision] for decision in unique_decisions], edgecolor='black', density=True)\n",
    "    \n",
    "    # Convert y-axis ticks to percentages\n",
    "    axs[row, col].yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x*100:.0f}%'))\n",
    "    \n",
    "    axs[row, col].set_xlabel('ROI')\n",
    "    axs[row, col].set_ylabel('% of Decisions in Game')\n",
    "    axs[row, col].set_title(f'Distribution of ROI by Buy Decision (Session {session})')\n",
    "    axs[row, col].legend()\n",
    "    axs[row, col].set_xticks(range(-10, 12, 2))  # Setting ticks from -10 to 10 with intervals of 2\n",
    "    axs[row, col].grid(True)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9508cedd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}