{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager\n",
    "import warnings, os, fnmatch\n",
    "from datetime import datetime\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "#pd.set_option('display.float_format', '{:.0f}'.format)\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "output_folder = './script/merchant_risk/output'\n",
    "for file in os.listdir(output_folder):\n",
    "    if fnmatch.fnmatch(file, 'merchant_risk_after_adj*'):\n",
    "        file_path = os.path.join(output_folder, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(file_path,parse_dates=['analysis_date','disbursement_date'], \n",
    "                        dayfirst=True)\n",
    "df = df[df['merchant_name'] != 'Fundly']\n",
    "df['disbursement_date'] = pd.to_datetime(df['disbursement_date']) #.dt.date\n",
    "df['bureau_score'] = df['bureau_score'].astype('float64')\n",
    "df['monthly_income'] = df['monthly_income'].astype('float64').fillna(0)\n",
    "df['lender_type'] = df['lender_name'].apply(lambda x: 'On-book' if x == 'K. M. Global Credit Private Limited' else 'Off-book')\n",
    "analysis_date = datetime.strftime(df['analysis_date'][0],'%b-%Y')\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Category wise AUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>merchant_category</th>\n",
       "      <th>principal_outstanding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cosmetics</td>\n",
       "      <td>2,563,611.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dental Care</td>\n",
       "      <td>9,073,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Education</td>\n",
       "      <td>6,292.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Electronic Battery</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Electronic Products</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     merchant_category principal_outstanding\n",
       "0            Cosmetics          2,563,611.00\n",
       "1          Dental Care          9,073,250.00\n",
       "2            Education              6,292.00\n",
       "3   Electronic Battery                  0.00\n",
       "4  Electronic Products                  0.00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# excl. closed, cancelled and write off cases and calculate category wise POS\n",
    "# df_excl_closed_cancelled_WO = df[(~df['application_status'].isin(['closed','cancelled','settled'])) & (df['write_off_flag'] == 0)].reset_index(drop=True)\n",
    "df_excl_closed_cancelled_WO = df[(~df['application_status'].isin(['closed','cancelled','settled'])) & (df['current_month_revised_dpd'] < 365)].reset_index(drop=True)\n",
    "merchant_category_wise_POS = df_excl_closed_cancelled_WO.groupby(['merchant_category']).agg({'principal_outstanding':'sum'}).reset_index()\n",
    "\n",
    "fintech_pos_df = pd.read_excel('./script/merchant_risk/input/fintech_sheet.xlsx',sheet_name=0)\n",
    "fintech_pos_df.rename(columns={'principal_outstanding_amount':'principal_outstanding'},inplace=True)\n",
    "fintech_pos_df.drop(columns='outstanding_count',axis=1,inplace=True)\n",
    "\n",
    "# Merge both df\n",
    "category_wise_POS = pd.concat([merchant_category_wise_POS, fintech_pos_df[['merchant_category','principal_outstanding']]], ignore_index=True)\n",
    "main_category_wise_POS = category_wise_POS\n",
    "\n",
    "total_pos = category_wise_POS.sum(numeric_only=True)\n",
    "total_row = category_wise_POS.sum(numeric_only=True)\n",
    "total_row['merchant_category'] = 'Total'  # You can specify a label for the total row\n",
    "category_wise_POS = pd.concat([category_wise_POS, total_row.to_frame().T], ignore_index=True)\n",
    "category_wise_POS.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>main_category</th>\n",
       "      <th>principal_outstanding</th>\n",
       "      <th>% of Portfolio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EV</td>\n",
       "      <td>366,685.00</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Earned Wage</td>\n",
       "      <td>10,238,030.00</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Education</td>\n",
       "      <td>445,288,885.00</td>\n",
       "      <td>21.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Healthcare</td>\n",
       "      <td>63,326,857.00</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Home_Decor</td>\n",
       "      <td>220,514,288.18</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Other</td>\n",
       "      <td>706,293.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>807,768.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RBF_SME</td>\n",
       "      <td>1,171,525.00</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Solar</td>\n",
       "      <td>1,327,983,080.05</td>\n",
       "      <td>64.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   main_category  principal_outstanding  % of Portfolio\n",
       "0             EV             366,685.00            0.02\n",
       "1    Earned Wage          10,238,030.00            0.49\n",
       "2      Education         445,288,885.00           21.51\n",
       "3     Healthcare          63,326,857.00            3.06\n",
       "4     Home_Decor         220,514,288.18           10.65\n",
       "5          Other             706,293.00            0.03\n",
       "6  Personal Loan             807,768.00            0.04\n",
       "7        RBF_SME           1,171,525.00            0.06\n",
       "8          Solar       1,327,983,080.05           64.14"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_category_dict = {\n",
    "    'Solar' : ['Solar and Rooftop Solutions','Solar Channel Financing'],\n",
    "    'Healthcare' : ['Cosmetics','Dental Care','Equipment Financing','IVF and Fertility Treatments', 'Insurance Premium Financing','Multispeciality Treatments','Stemcell banking and treatment','Healthcare Channel Financing'],\n",
    "    'Education' : ['Education','Entrance prep','K12 Coaching', 'K12 Edtech', 'K12 Education','LSP - Green Energy & Education','Upskilling Edtech'],\n",
    "    'Home_Decor' : ['Home Downpayment','Loose Furniture','Modular Kitchen, wardrobes and Interior','Painting Services','Water Purifier'],\n",
    "    'EV' : ['Electronic Products','Electronic Vehicles'],\n",
    "    'Other' : ['Electronic Battery','Jewelry','Other'],\n",
    "    'RBF_SME' : ['Revenue Based Financing','Fundly','Fundly offbook']\n",
    "}\n",
    "\n",
    "def get_main_category(category):\n",
    "    for main_category, category_list in main_category_dict.items():\n",
    "        if category in category_list:\n",
    "            return main_category\n",
    "    return category  # In case the category does not match any list\n",
    "\n",
    "# Apply the function to create the main_category column\n",
    "main_category_wise_POS['main_category'] = main_category_wise_POS['merchant_category'].apply(get_main_category)\n",
    "main_category_wise_POS = main_category_wise_POS.groupby('main_category').agg({'principal_outstanding':'sum'}).reset_index()\n",
    "main_category_wise_POS['% of Portfolio'] = round((main_category_wise_POS['principal_outstanding'] / float(total_pos)) * 100, 2)\n",
    "main_category_wise_POS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "custom_colors = ['#33FF33', '#FF33FF', '#3333FF', '#58d68d', '#f5cba7', '#873600', '#D2691E', '#33fff9', '#DFFF00']\n",
    "plt.pie(main_category_wise_POS['% of Portfolio'], autopct='%1.2f%%', colors=custom_colors,wedgeprops={'width': 0.6},textprops={'fontsize': 14, 'fontweight': 'bold'})\n",
    "font_properties = font_manager.FontProperties(weight='bold', size=12)\n",
    "\n",
    "# plt.pie(main_category_wise_POS['% of Portfolio'], labels=main_category_wise_POS['main_category'], autopct='%1.2f%%', startangle=140, \n",
    "#         colors=plt.cm.Paired.colors, wedgeprops={'width': 0.6})\n",
    "\n",
    "# Add title and make it look like a donut\n",
    "plt.title('Portfolio Distribution by Main Category',fontweight='bold')\n",
    "plt.legend(main_category_wise_POS['main_category'],fontsize=12,ncol=4, loc='upper center')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.tight_layout()\n",
    "picture1 = plt.gcf()\n",
    "plt.show()\n",
    "\n",
    "output_folder = './script/merchant_risk/output/'\n",
    "if not os.path.exists(output_folder):\n",
    "    os.makedirs(output_folder)\n",
    "picture1.savefig(f'{output_folder}portfolio_mix_chart_{analysis_date}.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Merchant wise AUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>merchant_name</th>\n",
       "      <th>principal_outstanding</th>\n",
       "      <th>% of Portfolio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SOLARSQUARE ENERGY PRIVATE LIMITED</td>\n",
       "      <td>407,890,774.00</td>\n",
       "      <td>19.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scaler Academy</td>\n",
       "      <td>182,051,840.00</td>\n",
       "      <td>8.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UPGRAD EDUCATION PRIVATE LIMITED</td>\n",
       "      <td>149,432,835.00</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Livspace</td>\n",
       "      <td>123,170,470.59</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FREYR ENERGY SERVICES PRIVATE LIMITED</td>\n",
       "      <td>82,210,889.64</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LOOM SOLAR PRIVATE LIMITED</td>\n",
       "      <td>53,812,581.00</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SURYANSH AKSHAY URJA SHOP</td>\n",
       "      <td>41,753,243.00</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SUNRISE SOLAR ENERGY</td>\n",
       "      <td>32,048,377.00</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Redington India</td>\n",
       "      <td>28,822,782.00</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Greenifit</td>\n",
       "      <td>28,705,806.28</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           merchant_name  principal_outstanding  \\\n",
       "0     SOLARSQUARE ENERGY PRIVATE LIMITED         407,890,774.00   \n",
       "1                         Scaler Academy         182,051,840.00   \n",
       "2       UPGRAD EDUCATION PRIVATE LIMITED         149,432,835.00   \n",
       "3                               Livspace         123,170,470.59   \n",
       "4  FREYR ENERGY SERVICES PRIVATE LIMITED          82,210,889.64   \n",
       "5             LOOM SOLAR PRIVATE LIMITED          53,812,581.00   \n",
       "6              SURYANSH AKSHAY URJA SHOP          41,753,243.00   \n",
       "7                   SUNRISE SOLAR ENERGY          32,048,377.00   \n",
       "8                        Redington India          28,822,782.00   \n",
       "9                              Greenifit          28,705,806.28   \n",
       "\n",
       "   % of Portfolio  \n",
       "0           19.70  \n",
       "1            8.79  \n",
       "2            7.22  \n",
       "3            5.95  \n",
       "4            3.97  \n",
       "5            2.60  \n",
       "6            2.02  \n",
       "7            1.55  \n",
       "8            1.39  \n",
       "9            1.39  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_excl_closed_cancelled_WO = df[(~df['application_status'].isin(['closed','cancelled','settled'])) & (df['write_off_flag'] == 0)].reset_index(drop=True)\n",
    "df_excl_closed_cancelled_WO = df[(~df['application_status'].isin(['closed','cancelled','settled'])) & (df['current_month_revised_dpd'] < 365)].reset_index(drop=True)\n",
    "merchant_wise_POS = df_excl_closed_cancelled_WO.groupby(['merchant_name']).agg({'principal_outstanding':'sum'}).sort_values(by='principal_outstanding',ascending=False).reset_index()\n",
    "merchant_wise_POS['% of Portfolio'] = round((merchant_wise_POS['principal_outstanding'] / float(total_pos)) * 100, 2)\n",
    "\n",
    "merchant_wise_POS.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Sector wise Gross Disbursements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Add disbursment year month\n",
    "gross_disb_df = df[['disbursement_date','merchant_category','approved_amount']]\n",
    "gross_disb_df['disbursement_year_month'] = df['disbursement_date'].dt.to_period('M')\n",
    "\n",
    "# pivot table \n",
    "sector_wise_gross_disb_df = pd.pivot_table(data = gross_disb_df,index='disbursement_year_month',columns='merchant_category',values='approved_amount',aggfunc=\"sum\").sort_values(by='disbursement_year_month',ascending=False).fillna(0)\n",
    "sector_wise_gross_disb_df.rename_axis(None, axis=1,inplace=True)\n",
    "sector_wise_gross_disb_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "fintech_disb_df = pd.read_excel('./script/merchant_risk/input/fintech_sheet.xlsx',sheet_name=1)\n",
    "fintech_disb_df['disbursement_year_month'] = pd.to_datetime(fintech_disb_df['disbursement_date'],format='%Y-%m-%d').dt.to_period('M')\n",
    "fintech_disb_df = fintech_disb_df[['disbursement_year_month','Earned Wage','Fundly','Solar Channel Financing','Healthcare Channel Financing']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disbursement_year_month</th>\n",
       "      <th>Cosmetics</th>\n",
       "      <th>Dental Care</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electronic Battery</th>\n",
       "      <th>Electronic Products</th>\n",
       "      <th>Electronic Vehicles</th>\n",
       "      <th>Entrance prep</th>\n",
       "      <th>Equipment Financing</th>\n",
       "      <th>Home Downpayment</th>\n",
       "      <th>IVF and Fertility Treatments</th>\n",
       "      <th>Insurance Premium Financing</th>\n",
       "      <th>Jewelry</th>\n",
       "      <th>K12 Coaching</th>\n",
       "      <th>K12 Edtech</th>\n",
       "      <th>K12 Education</th>\n",
       "      <th>LSP - Green Energy &amp; Education</th>\n",
       "      <th>Loose Furniture</th>\n",
       "      <th>Modular Kitchen, wardrobes and Interior</th>\n",
       "      <th>Multispeciality Treatments</th>\n",
       "      <th>Painting Services</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Revenue Based Financing</th>\n",
       "      <th>Solar and Rooftop Solutions</th>\n",
       "      <th>Stemcell banking and treatment</th>\n",
       "      <th>Upskilling Edtech</th>\n",
       "      <th>Water Purifier</th>\n",
       "      <th>Earned Wage</th>\n",
       "      <th>Fundly</th>\n",
       "      <th>Solar Channel Financing</th>\n",
       "      <th>Healthcare Channel Financing</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>200,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>108,804,853.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14,894,837.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>123,899,690.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,776,532.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>122,407,943.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18,113,616.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>142,298,091.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-03</td>\n",
       "      <td>108,675.00</td>\n",
       "      <td>45,500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59,998.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5,719,833.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8,928,851.00</td>\n",
       "      <td>750,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>187,276,564.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3,179,274.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,436,987.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23,068,660.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>230,574,342.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-02</td>\n",
       "      <td>345,600.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>270,800.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10,318,832.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10,285,907.00</td>\n",
       "      <td>460,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>124,226,709.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6,885,819.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,508,861.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19,158,625.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>173,461,153.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01</td>\n",
       "      <td>1,333,350.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>656,739.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9,457,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12,868,770.00</td>\n",
       "      <td>541,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>150,382,501.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10,938,622.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,725,377.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20,379,835.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>208,283,194.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  disbursement_year_month    Cosmetics  Dental Care  Education  \\\n",
       "0                 2025-05         0.00         0.00       0.00   \n",
       "1                 2025-04         0.00         0.00       0.00   \n",
       "2                 2025-03   108,675.00    45,500.00       0.00   \n",
       "3                 2025-02   345,600.00         0.00       0.00   \n",
       "4                 2025-01 1,333,350.00         0.00       0.00   \n",
       "\n",
       "   Electronic Battery  Electronic Products  Electronic Vehicles  \\\n",
       "0                0.00                 0.00                 0.00   \n",
       "1                0.00                 0.00                 0.00   \n",
       "2                0.00                 0.00                 0.00   \n",
       "3                0.00                 0.00                 0.00   \n",
       "4                0.00                 0.00                 0.00   \n",
       "\n",
       "   Entrance prep  Equipment Financing  Home Downpayment  \\\n",
       "0           0.00                 0.00              0.00   \n",
       "1           0.00                 0.00              0.00   \n",
       "2      59,998.00                 0.00              0.00   \n",
       "3     270,800.00                 0.00              0.00   \n",
       "4     656,739.00                 0.00              0.00   \n",
       "\n",
       "   IVF and Fertility Treatments  Insurance Premium Financing  Jewelry  \\\n",
       "0                          0.00                         0.00     0.00   \n",
       "1                          0.00                         0.00     0.00   \n",
       "2                  5,719,833.00                         0.00     0.00   \n",
       "3                 10,318,832.00                         0.00     0.00   \n",
       "4                  9,457,000.00                         0.00     0.00   \n",
       "\n",
       "   K12 Coaching  K12 Edtech  K12 Education  LSP - Green Energy & Education  \\\n",
       "0          0.00        0.00           0.00                            0.00   \n",
       "1          0.00        0.00           0.00                            0.00   \n",
       "2          0.00        0.00           0.00                            0.00   \n",
       "3          0.00        0.00           0.00                            0.00   \n",
       "4          0.00        0.00           0.00                            0.00   \n",
       "\n",
       "   Loose Furniture  Modular Kitchen, wardrobes and Interior  \\\n",
       "0             0.00                                     0.00   \n",
       "1             0.00                             1,776,532.00   \n",
       "2             0.00                             8,928,851.00   \n",
       "3             0.00                            10,285,907.00   \n",
       "4             0.00                            12,868,770.00   \n",
       "\n",
       "   Multispeciality Treatments  Painting Services  Personal Loan  \\\n",
       "0                        0.00               0.00     200,000.00   \n",
       "1                        0.00               0.00           0.00   \n",
       "2                  750,000.00               0.00           0.00   \n",
       "3                  460,000.00               0.00           0.00   \n",
       "4                  541,000.00               0.00           0.00   \n",
       "\n",
       "   Revenue Based Financing  Solar and Rooftop Solutions  \\\n",
       "0                     0.00               108,804,853.00   \n",
       "1                     0.00               122,407,943.00   \n",
       "2                     0.00               187,276,564.00   \n",
       "3                     0.00               124,226,709.00   \n",
       "4                     0.00               150,382,501.00   \n",
       "\n",
       "   Stemcell banking and treatment  Upskilling Edtech  Water Purifier  \\\n",
       "0                            0.00               0.00            0.00   \n",
       "1                            0.00               0.00            0.00   \n",
       "2                            0.00       3,179,274.00            0.00   \n",
       "3                            0.00       6,885,819.00            0.00   \n",
       "4                            0.00      10,938,622.00            0.00   \n",
       "\n",
       "   Earned Wage  Fundly  Solar Channel Financing  Healthcare Channel Financing  \\\n",
       "0         0.00    0.00            14,894,837.00                          0.00   \n",
       "1         0.00    0.00            18,113,616.00                          0.00   \n",
       "2 1,436,987.00    0.00            23,068,660.00                          0.00   \n",
       "3 1,508,861.00    0.00            19,158,625.00                          0.00   \n",
       "4 1,725,377.00    0.00            20,379,835.00                          0.00   \n",
       "\n",
       "           Total  \n",
       "0 123,899,690.00  \n",
       "1 142,298,091.00  \n",
       "2 230,574,342.00  \n",
       "3 173,461,153.00  \n",
       "4 208,283,194.00  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sector_wise_gross_disb_df = pd.merge(sector_wise_gross_disb_df,fintech_disb_df,on='disbursement_year_month',how='left')\n",
    "final_sector_wise_gross_disb_df['Total'] = final_sector_wise_gross_disb_df.select_dtypes(include=['number']).sum(axis=1)\n",
    "\n",
    "final_sector_wise_gross_disb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Sector wise Disbursement Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Add disbursment year month\n",
    "disb_count = df[['disbursement_date','merchant_category','application_id']]\n",
    "disb_count['disbursement_year_month'] = disb_count['disbursement_date'].dt.to_period('M')\n",
    "\n",
    "# pivot table \n",
    "sector_wise_disb_count_df = pd.pivot_table(data = disb_count,index='disbursement_year_month',columns='merchant_category',values='application_id',aggfunc=\"count\").sort_values(by='disbursement_year_month',ascending=False).fillna(0)\n",
    "sector_wise_disb_count_df.rename_axis(None, axis=1,inplace=True)\n",
    "sector_wise_disb_count_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "fintech_disb_df_count = pd.read_excel('./script/merchant_risk/input/fintech_sheet.xlsx',sheet_name=2)\n",
    "fintech_disb_df_count['disbursement_year_month'] = pd.to_datetime(fintech_disb_df_count['disbursement_date'],format='%Y-%m-%d').dt.to_period('M')\n",
    "fintech_disb_df_count = fintech_disb_df_count[['disbursement_year_month','Earned Wage','Fundly','Solar Channel Financing','Healthcare Channel Financing']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disbursement_year_month</th>\n",
       "      <th>Cosmetics</th>\n",
       "      <th>Dental Care</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electronic Battery</th>\n",
       "      <th>Electronic Products</th>\n",
       "      <th>Electronic Vehicles</th>\n",
       "      <th>Entrance prep</th>\n",
       "      <th>Equipment Financing</th>\n",
       "      <th>Home Downpayment</th>\n",
       "      <th>IVF and Fertility Treatments</th>\n",
       "      <th>Insurance Premium Financing</th>\n",
       "      <th>Jewelry</th>\n",
       "      <th>K12 Coaching</th>\n",
       "      <th>K12 Edtech</th>\n",
       "      <th>K12 Education</th>\n",
       "      <th>LSP - Green Energy &amp; Education</th>\n",
       "      <th>Loose Furniture</th>\n",
       "      <th>Modular Kitchen, wardrobes and Interior</th>\n",
       "      <th>Multispeciality Treatments</th>\n",
       "      <th>Painting Services</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Revenue Based Financing</th>\n",
       "      <th>Solar and Rooftop Solutions</th>\n",
       "      <th>Stemcell banking and treatment</th>\n",
       "      <th>Upskilling Edtech</th>\n",
       "      <th>Water Purifier</th>\n",
       "      <th>Earned Wage</th>\n",
       "      <th>Fundly</th>\n",
       "      <th>Solar Channel Financing</th>\n",
       "      <th>Healthcare Channel Financing</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>375.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>385.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>419.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>433.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-03</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>612.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>744.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-02</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>432.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>514.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>94.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>792.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  disbursement_year_month  Cosmetics  Dental Care  Education  \\\n",
       "0                 2025-05       0.00         0.00       0.00   \n",
       "1                 2025-04       0.00         0.00       0.00   \n",
       "2                 2025-03       2.00         1.00       0.00   \n",
       "3                 2025-02       4.00         0.00       0.00   \n",
       "4                 2025-01      21.00         0.00       0.00   \n",
       "\n",
       "   Electronic Battery  Electronic Products  Electronic Vehicles  \\\n",
       "0                0.00                 0.00                 0.00   \n",
       "1                0.00                 0.00                 0.00   \n",
       "2                0.00                 0.00                 0.00   \n",
       "3                0.00                 0.00                 0.00   \n",
       "4                0.00                 0.00                 0.00   \n",
       "\n",
       "   Entrance prep  Equipment Financing  Home Downpayment  \\\n",
       "0           0.00                 0.00              0.00   \n",
       "1           0.00                 0.00              0.00   \n",
       "2           2.00                 0.00              0.00   \n",
       "3           7.00                 0.00              0.00   \n",
       "4          13.00                 0.00              0.00   \n",
       "\n",
       "   IVF and Fertility Treatments  Insurance Premium Financing  Jewelry  \\\n",
       "0                          0.00                         0.00     0.00   \n",
       "1                          0.00                         0.00     0.00   \n",
       "2                         27.00                         0.00     0.00   \n",
       "3                         51.00                         0.00     0.00   \n",
       "4                         47.00                         0.00     0.00   \n",
       "\n",
       "   K12 Coaching  K12 Edtech  K12 Education  LSP - Green Energy & Education  \\\n",
       "0          0.00        0.00           0.00                            0.00   \n",
       "1          0.00        0.00           0.00                            0.00   \n",
       "2          0.00        0.00           0.00                            0.00   \n",
       "3          0.00        0.00           0.00                            0.00   \n",
       "4          0.00        0.00           0.00                            0.00   \n",
       "\n",
       "   Loose Furniture  Modular Kitchen, wardrobes and Interior  \\\n",
       "0             0.00                                     0.00   \n",
       "1             0.00                                     3.00   \n",
       "2             0.00                                    20.00   \n",
       "3             0.00                                    18.00   \n",
       "4             0.00                                    24.00   \n",
       "\n",
       "   Multispeciality Treatments  Painting Services  Personal Loan  \\\n",
       "0                        0.00               0.00           1.00   \n",
       "1                        0.00               0.00           0.00   \n",
       "2                        3.00               0.00           0.00   \n",
       "3                        3.00               0.00           0.00   \n",
       "4                        5.00               0.00           0.00   \n",
       "\n",
       "   Revenue Based Financing  Solar and Rooftop Solutions  \\\n",
       "0                     0.00                       375.00   \n",
       "1                     0.00                       419.00   \n",
       "2                     0.00                       612.00   \n",
       "3                     0.00                       432.00   \n",
       "4                     0.00                       514.00   \n",
       "\n",
       "   Stemcell banking and treatment  Upskilling Edtech  Water Purifier  \\\n",
       "0                            0.00               0.00            0.00   \n",
       "1                            0.00               0.00            0.00   \n",
       "2                            0.00              25.00            0.00   \n",
       "3                            0.00              49.00            0.00   \n",
       "4                            0.00              94.00            0.00   \n",
       "\n",
       "   Earned Wage  Fundly  Solar Channel Financing  Healthcare Channel Financing  \\\n",
       "0         0.00    0.00                     9.00                          0.00   \n",
       "1         0.00    0.00                    11.00                          0.00   \n",
       "2        42.00    0.00                    10.00                          0.00   \n",
       "3        55.00    0.00                    16.00                          0.00   \n",
       "4        63.00    0.00                    11.00                          0.00   \n",
       "\n",
       "   Total  \n",
       "0 385.00  \n",
       "1 433.00  \n",
       "2 744.00  \n",
       "3 635.00  \n",
       "4 792.00  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector_wise_disb_count_df = pd.merge(sector_wise_disb_count_df,fintech_disb_df_count,on='disbursement_year_month',how='left')\n",
    "sector_wise_disb_count_df['Total'] = sector_wise_disb_count_df.select_dtypes(include=['number']).sum(axis=1)\n",
    "\n",
    "sector_wise_disb_count_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Main Category wise Disbursement Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>disbursement_year_month</th>\n",
       "      <th>Solar and Green Energy</th>\n",
       "      <th>Health, Home and Others.</th>\n",
       "      <th>Education - Upskilling</th>\n",
       "      <th>RBF/SME (Fintech Partners)</th>\n",
       "      <th>Earned Wage (Jify)</th>\n",
       "      <th>Grand Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-05</td>\n",
       "      <td>123,699,690.00</td>\n",
       "      <td>200,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>123,899,690.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-04</td>\n",
       "      <td>140,521,559.00</td>\n",
       "      <td>1,776,532.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>142,298,091.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-03</td>\n",
       "      <td>210,345,224.00</td>\n",
       "      <td>15,552,859.00</td>\n",
       "      <td>3,239,272.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,436,987.00</td>\n",
       "      <td>230,574,342.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-02</td>\n",
       "      <td>143,385,334.00</td>\n",
       "      <td>21,410,339.00</td>\n",
       "      <td>7,156,619.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,508,861.00</td>\n",
       "      <td>173,461,153.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01</td>\n",
       "      <td>170,762,336.00</td>\n",
       "      <td>24,200,120.00</td>\n",
       "      <td>11,595,361.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,725,377.00</td>\n",
       "      <td>208,283,194.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  disbursement_year_month  Solar and Green Energy  Health, Home and Others.  \\\n",
       "0                 2025-05          123,699,690.00                200,000.00   \n",
       "1                 2025-04          140,521,559.00              1,776,532.00   \n",
       "2                 2025-03          210,345,224.00             15,552,859.00   \n",
       "3                 2025-02          143,385,334.00             21,410,339.00   \n",
       "4                 2025-01          170,762,336.00             24,200,120.00   \n",
       "\n",
       "   Education - Upskilling  RBF/SME (Fintech Partners)  Earned Wage (Jify)  \\\n",
       "0                    0.00                        0.00                0.00   \n",
       "1                    0.00                        0.00                0.00   \n",
       "2            3,239,272.00                        0.00        1,436,987.00   \n",
       "3            7,156,619.00                        0.00        1,508,861.00   \n",
       "4           11,595,361.00                        0.00        1,725,377.00   \n",
       "\n",
       "     Grand Total  \n",
       "0 123,899,690.00  \n",
       "1 142,298,091.00  \n",
       "2 230,574,342.00  \n",
       "3 173,461,153.00  \n",
       "4 208,283,194.00  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_cat_disb_df = final_sector_wise_gross_disb_df.copy()\n",
    "main_cat_disb_df['Solar and Green Energy'] = main_cat_disb_df[['Solar and Rooftop Solutions','Solar Channel Financing','Electronic Vehicles','Electronic Battery']].sum(axis=1)\n",
    "main_cat_disb_df['Health, Home and Others.'] = main_cat_disb_df[['Cosmetics','Dental Care','Equipment Financing','IVF and Fertility Treatments', 'Insurance Premium Financing','Multispeciality Treatments','Stemcell banking and treatment',\n",
    "                                  'Healthcare Channel Financing','Home Downpayment','Loose Furniture','Modular Kitchen, wardrobes and Interior','Painting Services','Water Purifier','Electronic Products','Jewelry','Personal Loan']].sum(axis=1)\n",
    "main_cat_disb_df['Education - Upskilling'] = main_cat_disb_df[['Education','Entrance prep','K12 Coaching', 'K12 Edtech', 'K12 Education','LSP - Green Energy & Education','Upskilling Edtech']].sum(axis=1)\n",
    "main_cat_disb_df['RBF/SME (Fintech Partners)'] = main_cat_disb_df[['Revenue Based Financing','Fundly']].sum(axis=1)\n",
    "main_cat_disb_df['Earned Wage (Jify)'] = main_cat_disb_df[['Earned Wage']]\n",
    "main_cat_disb_df = main_cat_disb_df[['disbursement_year_month','Solar and Green Energy','Health, Home and Others.','Education - Upskilling','RBF/SME (Fintech Partners)','Earned Wage (Jify)']]\n",
    "main_cat_disb_df['Grand Total'] = main_cat_disb_df.select_dtypes(include=['number']).sum(axis=1)\n",
    "main_cat_disb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Main Category wise Disbursement Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>disbursement_year_month</th>\n",
       "      <th>Solar and Green Energy</th>\n",
       "      <th>Health, Home and Others.</th>\n",
       "      <th>Education - Upskilling</th>\n",
       "      <th>RBF/SME (Fintech Partners)</th>\n",
       "      <th>Earned Wage (Jify)</th>\n",
       "      <th>Grand Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-05</td>\n",
       "      <td>384.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>385.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-04</td>\n",
       "      <td>430.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>433.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-03</td>\n",
       "      <td>622.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>744.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-02</td>\n",
       "      <td>448.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01</td>\n",
       "      <td>525.00</td>\n",
       "      <td>97.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>792.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  disbursement_year_month  Solar and Green Energy  Health, Home and Others.  \\\n",
       "0                 2025-05                  384.00                      1.00   \n",
       "1                 2025-04                  430.00                      3.00   \n",
       "2                 2025-03                  622.00                     53.00   \n",
       "3                 2025-02                  448.00                     76.00   \n",
       "4                 2025-01                  525.00                     97.00   \n",
       "\n",
       "   Education - Upskilling  RBF/SME (Fintech Partners)  Earned Wage (Jify)  \\\n",
       "0                    0.00                        0.00                0.00   \n",
       "1                    0.00                        0.00                0.00   \n",
       "2                   27.00                        0.00               42.00   \n",
       "3                   56.00                        0.00               55.00   \n",
       "4                  107.00                        0.00               63.00   \n",
       "\n",
       "   Grand Total  \n",
       "0       385.00  \n",
       "1       433.00  \n",
       "2       744.00  \n",
       "3       635.00  \n",
       "4       792.00  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_cat_disb_count_df = sector_wise_disb_count_df.copy()\n",
    "main_cat_disb_count_df['Solar and Green Energy'] = main_cat_disb_count_df[['Solar and Rooftop Solutions','Solar Channel Financing','Electronic Vehicles','Electronic Battery']].sum(axis=1)\n",
    "main_cat_disb_count_df['Health, Home and Others.'] = main_cat_disb_count_df[['Cosmetics','Dental Care','Equipment Financing','IVF and Fertility Treatments', 'Insurance Premium Financing','Multispeciality Treatments','Stemcell banking and treatment',\n",
    "                                  'Healthcare Channel Financing','Home Downpayment','Loose Furniture','Modular Kitchen, wardrobes and Interior','Painting Services','Water Purifier','Electronic Products','Jewelry','Personal Loan']].sum(axis=1)\n",
    "main_cat_disb_count_df['Education - Upskilling'] = main_cat_disb_count_df[['Education','Entrance prep','K12 Coaching', 'K12 Edtech', 'K12 Education','LSP - Green Energy & Education','Upskilling Edtech']].sum(axis=1)\n",
    "main_cat_disb_count_df['RBF/SME (Fintech Partners)'] = main_cat_disb_count_df[['Revenue Based Financing','Fundly']].sum(axis=1)\n",
    "main_cat_disb_count_df['Earned Wage (Jify)'] = main_cat_disb_count_df[['Earned Wage']]\n",
    "main_cat_disb_count_df = main_cat_disb_count_df[['disbursement_year_month','Solar and Green Energy','Health, Home and Others.','Education - Upskilling','RBF/SME (Fintech Partners)','Earned Wage (Jify)']]\n",
    "main_cat_disb_count_df['Grand Total'] = main_cat_disb_count_df.select_dtypes(include=['number']).sum(axis=1)\n",
    "main_cat_disb_count_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Sector wise Net disbursements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Net disbursment need to be added in metabase query, will add later\n",
    "net_disb_df = df[['disbursement_date','merchant_category','net_disbursement_amount']]\n",
    "net_disb_df['disbursement_year_month'] = df['disbursement_date'].dt.to_period('M')\n",
    "\n",
    "# pivot table \n",
    "sector_wise_net_disb_df = pd.pivot_table(data = net_disb_df,index='disbursement_year_month',columns='merchant_category',values='net_disbursement_amount',aggfunc=\"sum\").sort_values(by='disbursement_year_month',ascending=False).fillna(0)\n",
    "sector_wise_net_disb_df.rename_axis(None, axis=1,inplace=True)\n",
    "sector_wise_net_disb_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disbursement_year_month</th>\n",
       "      <th>Cosmetics</th>\n",
       "      <th>Dental Care</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electronic Battery</th>\n",
       "      <th>Electronic Products</th>\n",
       "      <th>Electronic Vehicles</th>\n",
       "      <th>Entrance prep</th>\n",
       "      <th>Equipment Financing</th>\n",
       "      <th>Home Downpayment</th>\n",
       "      <th>IVF and Fertility Treatments</th>\n",
       "      <th>Insurance Premium Financing</th>\n",
       "      <th>Jewelry</th>\n",
       "      <th>K12 Coaching</th>\n",
       "      <th>K12 Edtech</th>\n",
       "      <th>K12 Education</th>\n",
       "      <th>LSP - Green Energy &amp; Education</th>\n",
       "      <th>Loose Furniture</th>\n",
       "      <th>Modular Kitchen, wardrobes and Interior</th>\n",
       "      <th>Multispeciality Treatments</th>\n",
       "      <th>Painting Services</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Revenue Based Financing</th>\n",
       "      <th>Solar and Rooftop Solutions</th>\n",
       "      <th>Stemcell banking and treatment</th>\n",
       "      <th>Upskilling Edtech</th>\n",
       "      <th>Water Purifier</th>\n",
       "      <th>Earned Wage</th>\n",
       "      <th>Fundly</th>\n",
       "      <th>Solar Channel Financing</th>\n",
       "      <th>Healthcare Channel Financing</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>200,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>91,672,334.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14,894,837.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106,767,171.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,380,507.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104,494,491.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18,113,616.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>123,988,614.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-03</td>\n",
       "      <td>81,407.00</td>\n",
       "      <td>37,770.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47,188.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3,559,866.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7,458,529.00</td>\n",
       "      <td>425,001.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>157,393,531.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,842,558.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,436,987.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23,068,660.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>196,351,497.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-02</td>\n",
       "      <td>238,760.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>208,043.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6,270,146.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8,676,818.00</td>\n",
       "      <td>274,817.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104,490,107.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6,260,738.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,508,861.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19,158,625.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>147,086,915.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01</td>\n",
       "      <td>972,859.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>509,309.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5,797,069.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10,877,184.00</td>\n",
       "      <td>316,487.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>124,764,920.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9,742,161.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,725,377.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20,379,835.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175,085,201.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  disbursement_year_month  Cosmetics  Dental Care  Education  \\\n",
       "0                 2025-05       0.00         0.00       0.00   \n",
       "1                 2025-04       0.00         0.00       0.00   \n",
       "2                 2025-03  81,407.00    37,770.00       0.00   \n",
       "3                 2025-02 238,760.00         0.00       0.00   \n",
       "4                 2025-01 972,859.00         0.00       0.00   \n",
       "\n",
       "   Electronic Battery  Electronic Products  Electronic Vehicles  \\\n",
       "0                0.00                 0.00                 0.00   \n",
       "1                0.00                 0.00                 0.00   \n",
       "2                0.00                 0.00                 0.00   \n",
       "3                0.00                 0.00                 0.00   \n",
       "4                0.00                 0.00                 0.00   \n",
       "\n",
       "   Entrance prep  Equipment Financing  Home Downpayment  \\\n",
       "0           0.00                 0.00              0.00   \n",
       "1           0.00                 0.00              0.00   \n",
       "2      47,188.00                 0.00              0.00   \n",
       "3     208,043.00                 0.00              0.00   \n",
       "4     509,309.00                 0.00              0.00   \n",
       "\n",
       "   IVF and Fertility Treatments  Insurance Premium Financing  Jewelry  \\\n",
       "0                          0.00                         0.00     0.00   \n",
       "1                          0.00                         0.00     0.00   \n",
       "2                  3,559,866.00                         0.00     0.00   \n",
       "3                  6,270,146.00                         0.00     0.00   \n",
       "4                  5,797,069.00                         0.00     0.00   \n",
       "\n",
       "   K12 Coaching  K12 Edtech  K12 Education  LSP - Green Energy & Education  \\\n",
       "0          0.00        0.00           0.00                            0.00   \n",
       "1          0.00        0.00           0.00                            0.00   \n",
       "2          0.00        0.00           0.00                            0.00   \n",
       "3          0.00        0.00           0.00                            0.00   \n",
       "4          0.00        0.00           0.00                            0.00   \n",
       "\n",
       "   Loose Furniture  Modular Kitchen, wardrobes and Interior  \\\n",
       "0             0.00                                     0.00   \n",
       "1             0.00                             1,380,507.00   \n",
       "2             0.00                             7,458,529.00   \n",
       "3             0.00                             8,676,818.00   \n",
       "4             0.00                            10,877,184.00   \n",
       "\n",
       "   Multispeciality Treatments  Painting Services  Personal Loan  \\\n",
       "0                        0.00               0.00     200,000.00   \n",
       "1                        0.00               0.00           0.00   \n",
       "2                  425,001.00               0.00           0.00   \n",
       "3                  274,817.00               0.00           0.00   \n",
       "4                  316,487.00               0.00           0.00   \n",
       "\n",
       "   Revenue Based Financing  Solar and Rooftop Solutions  \\\n",
       "0                     0.00                91,672,334.00   \n",
       "1                     0.00               104,494,491.00   \n",
       "2                     0.00               157,393,531.00   \n",
       "3                     0.00               104,490,107.00   \n",
       "4                     0.00               124,764,920.00   \n",
       "\n",
       "   Stemcell banking and treatment  Upskilling Edtech  Water Purifier  \\\n",
       "0                            0.00               0.00            0.00   \n",
       "1                            0.00               0.00            0.00   \n",
       "2                            0.00       2,842,558.00            0.00   \n",
       "3                            0.00       6,260,738.00            0.00   \n",
       "4                            0.00       9,742,161.00            0.00   \n",
       "\n",
       "   Earned Wage  Fundly  Solar Channel Financing  Healthcare Channel Financing  \\\n",
       "0         0.00    0.00            14,894,837.00                          0.00   \n",
       "1         0.00    0.00            18,113,616.00                          0.00   \n",
       "2 1,436,987.00    0.00            23,068,660.00                          0.00   \n",
       "3 1,508,861.00    0.00            19,158,625.00                          0.00   \n",
       "4 1,725,377.00    0.00            20,379,835.00                          0.00   \n",
       "\n",
       "           Total  \n",
       "0 106,767,171.00  \n",
       "1 123,988,614.00  \n",
       "2 196,351,497.00  \n",
       "3 147,086,915.00  \n",
       "4 175,085,201.00  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sector_wise_net_disb_df = pd.merge(sector_wise_net_disb_df,fintech_disb_df,on='disbursement_year_month',how='left')\n",
    "final_sector_wise_net_disb_df['Total'] = final_sector_wise_net_disb_df.select_dtypes(include=['number']).sum(axis=1)\n",
    "\n",
    "final_sector_wise_net_disb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Main Category wise Net Disbursement value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disbursement_year_month</th>\n",
       "      <th>Solar and Green Energy</th>\n",
       "      <th>Health, Home and Others.</th>\n",
       "      <th>Education - Upskilling</th>\n",
       "      <th>RBF/SME (Fintech Partners)</th>\n",
       "      <th>Earned Wage (Jify)</th>\n",
       "      <th>Grand Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-05</td>\n",
       "      <td>106,567,171.00</td>\n",
       "      <td>200,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106,767,171.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-04</td>\n",
       "      <td>122,608,107.00</td>\n",
       "      <td>1,380,507.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>123,988,614.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-03</td>\n",
       "      <td>180,462,191.00</td>\n",
       "      <td>11,562,573.00</td>\n",
       "      <td>2,889,746.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,436,987.00</td>\n",
       "      <td>196,351,497.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-02</td>\n",
       "      <td>123,648,732.00</td>\n",
       "      <td>15,460,541.00</td>\n",
       "      <td>6,468,781.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,508,861.00</td>\n",
       "      <td>147,086,915.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01</td>\n",
       "      <td>145,144,755.00</td>\n",
       "      <td>17,963,599.00</td>\n",
       "      <td>10,251,470.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,725,377.00</td>\n",
       "      <td>175,085,201.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  disbursement_year_month  Solar and Green Energy  Health, Home and Others.  \\\n",
       "0                 2025-05          106,567,171.00                200,000.00   \n",
       "1                 2025-04          122,608,107.00              1,380,507.00   \n",
       "2                 2025-03          180,462,191.00             11,562,573.00   \n",
       "3                 2025-02          123,648,732.00             15,460,541.00   \n",
       "4                 2025-01          145,144,755.00             17,963,599.00   \n",
       "\n",
       "   Education - Upskilling  RBF/SME (Fintech Partners)  Earned Wage (Jify)  \\\n",
       "0                    0.00                        0.00                0.00   \n",
       "1                    0.00                        0.00                0.00   \n",
       "2            2,889,746.00                        0.00        1,436,987.00   \n",
       "3            6,468,781.00                        0.00        1,508,861.00   \n",
       "4           10,251,470.00                        0.00        1,725,377.00   \n",
       "\n",
       "     Grand Total  \n",
       "0 106,767,171.00  \n",
       "1 123,988,614.00  \n",
       "2 196,351,497.00  \n",
       "3 147,086,915.00  \n",
       "4 175,085,201.00  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_cat_net_disb_df = final_sector_wise_net_disb_df.copy()\n",
    "main_cat_net_disb_df['Solar and Green Energy'] = main_cat_net_disb_df[['Solar and Rooftop Solutions','Solar Channel Financing','Electronic Vehicles','Electronic Battery']].sum(axis=1)\n",
    "main_cat_net_disb_df['Health, Home and Others.'] = main_cat_net_disb_df[['Cosmetics','Dental Care','Equipment Financing','IVF and Fertility Treatments', 'Insurance Premium Financing','Multispeciality Treatments','Stemcell banking and treatment',\n",
    "                                  'Healthcare Channel Financing','Home Downpayment','Loose Furniture','Modular Kitchen, wardrobes and Interior','Painting Services','Water Purifier','Electronic Products','Jewelry','Personal Loan']].sum(axis=1)\n",
    "main_cat_net_disb_df['Education - Upskilling'] = main_cat_net_disb_df[['Education','Entrance prep','K12 Coaching', 'K12 Edtech', 'K12 Education','LSP - Green Energy & Education','Upskilling Edtech']].sum(axis=1)\n",
    "main_cat_net_disb_df['RBF/SME (Fintech Partners)'] = main_cat_net_disb_df[['Revenue Based Financing','Fundly']].sum(axis=1)\n",
    "main_cat_net_disb_df['Earned Wage (Jify)'] = main_cat_net_disb_df[['Earned Wage']]\n",
    "main_cat_net_disb_df = main_cat_net_disb_df[['disbursement_year_month','Solar and Green Energy','Health, Home and Others.','Education - Upskilling','RBF/SME (Fintech Partners)','Earned Wage (Jify)']]\n",
    "main_cat_net_disb_df['Grand Total'] = main_cat_net_disb_df.select_dtypes(include=['number']).sum(axis=1)\n",
    "main_cat_net_disb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Onbook-Offbook wise disbursement%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>disbursement_year_month</th>\n",
       "      <th>Off-book</th>\n",
       "      <th>On-book</th>\n",
       "      <th>Total</th>\n",
       "      <th>% Off-book</th>\n",
       "      <th>% On-book</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-05</td>\n",
       "      <td>17,357,970.00</td>\n",
       "      <td>74,514,364.00</td>\n",
       "      <td>106,767,171.00</td>\n",
       "      <td>16.26</td>\n",
       "      <td>83.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-04</td>\n",
       "      <td>14,063,909.00</td>\n",
       "      <td>91,811,089.00</td>\n",
       "      <td>123,988,614.00</td>\n",
       "      <td>11.34</td>\n",
       "      <td>88.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-03</td>\n",
       "      <td>24,607,702.00</td>\n",
       "      <td>147,238,148.00</td>\n",
       "      <td>196,351,497.00</td>\n",
       "      <td>12.53</td>\n",
       "      <td>87.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-02</td>\n",
       "      <td>9,669,094.00</td>\n",
       "      <td>116,750,335.00</td>\n",
       "      <td>147,086,915.00</td>\n",
       "      <td>6.57</td>\n",
       "      <td>93.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01</td>\n",
       "      <td>10,334,113.00</td>\n",
       "      <td>142,645,876.00</td>\n",
       "      <td>175,085,201.00</td>\n",
       "      <td>5.90</td>\n",
       "      <td>94.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  disbursement_year_month      Off-book        On-book          Total  \\\n",
       "0                 2025-05 17,357,970.00  74,514,364.00 106,767,171.00   \n",
       "1                 2025-04 14,063,909.00  91,811,089.00 123,988,614.00   \n",
       "2                 2025-03 24,607,702.00 147,238,148.00 196,351,497.00   \n",
       "3                 2025-02  9,669,094.00 116,750,335.00 147,086,915.00   \n",
       "4                 2025-01 10,334,113.00 142,645,876.00 175,085,201.00   \n",
       "\n",
       "   % Off-book  % On-book  \n",
       "0       16.26      83.74  \n",
       "1       11.34      88.66  \n",
       "2       12.53      87.47  \n",
       "3        6.57      93.43  \n",
       "4        5.90      94.10  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lender_disb_df = df[['disbursement_date','lender_type','net_disbursement_amount']]\n",
    "lender_disb_df['disbursement_year_month'] = df['disbursement_date'].dt.to_period('M')\n",
    "\n",
    "lender_type_wise_lender_disb_df = pd.pivot_table(data = lender_disb_df,index='disbursement_year_month',columns='lender_type',values='net_disbursement_amount',aggfunc=\"sum\").sort_values(by='disbursement_year_month',ascending=False).fillna(0)\n",
    "lender_type_wise_lender_disb_df.rename_axis(None, axis=1,inplace=True)\n",
    "lender_type_wise_lender_disb_df.reset_index(inplace=True)\n",
    "lender_type_wise_lender_disb_df = pd.merge(lender_type_wise_lender_disb_df,final_sector_wise_net_disb_df[['disbursement_year_month','Total']],on='disbursement_year_month',how='left')\n",
    "lender_type_wise_lender_disb_df['% Off-book'] = (lender_type_wise_lender_disb_df['Off-book']/lender_type_wise_lender_disb_df['Total'])*100\n",
    "lender_type_wise_lender_disb_df['% On-book'] = (1 - (lender_type_wise_lender_disb_df['Off-book']/lender_type_wise_lender_disb_df['Total']))*100\n",
    "\n",
    "lender_type_wise_lender_disb_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_off_book_df = lender_type_wise_lender_disb_df.head(12)\n",
    "plot_off_book_df['disbursement_year_month'] = plot_off_book_df['disbursement_year_month'].dt.to_timestamp() + pd.offsets.MonthEnd(0) # convert month to timestamp and start of month to end of month\n",
    "\n",
    "plt.figure(figsize=(11, 5))\n",
    "plt.plot(plot_off_book_df['disbursement_year_month'],plot_off_book_df['% Off-book'],label='% Off-book', color='blue')\n",
    "plt.plot(plot_off_book_df['disbursement_year_month'],plot_off_book_df['% On-book'],label='% On-book',color='red')\n",
    "\n",
    "for i, row in plot_off_book_df.iterrows():\n",
    "    plt.text(row['disbursement_year_month'], row['% Off-book'], f'{row[\"% Off-book\"]:.2f}%', va='top', fontsize=7)\n",
    "    plt.text(row['disbursement_year_month'], row['% On-book'], f'{row[\"% On-book\"]:.2f}%', va='top', fontsize=7)\n",
    "\n",
    "plt.title('% of Disbursement in Off-book & On-book over Time')\n",
    "plt.xlabel('Disbursement Year-Month')\n",
    "plt.legend()\n",
    "plt.xticks(plot_off_book_df['disbursement_year_month'], rotation=45)\n",
    "plt.tight_layout()\n",
    "picture1 = plt.gcf()\n",
    "plt.show()\n",
    "\n",
    "picture1.savefig(f'{output_folder}on_book_off_book_disbursement_chart_{analysis_date}.png')\n",
    "# picture1.savefig('off_book_disbursement_chart.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Lender wise AUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>lender_name</th>\n",
       "      <th>principal_outstanding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aditya Birla Finance Limited</td>\n",
       "      <td>48,275,603.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Avanse Financial Services Ltd.</td>\n",
       "      <td>81,665,650.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAPRI GLOBAL CAPITAL LIMITED</td>\n",
       "      <td>6,591,575.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Capri Global-DA</td>\n",
       "      <td>20,834,952.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HLF-PTC</td>\n",
       "      <td>43,323,519.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JM Financial Products Ltd</td>\n",
       "      <td>10,714,893.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>K. M. GLOBAL P2P FINANCE PRIVATE LIMITED</td>\n",
       "      <td>8,027,640.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>K. M. Global Credit Private Limited</td>\n",
       "      <td>1,628,384,707.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lendbox LSP</td>\n",
       "      <td>5,475,926.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lendbox P2P</td>\n",
       "      <td>20,431,348.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mufin DA June 24</td>\n",
       "      <td>11,666,130.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pride Financial Services Private Limited</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Singularity Creditworld Private Limited</td>\n",
       "      <td>141,029,825.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Vivriti Capital Limited</td>\n",
       "      <td>443,153.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Earned Wage</td>\n",
       "      <td>10,238,030.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Fundly</td>\n",
       "      <td>1,171,525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Solar Channel Financing</td>\n",
       "      <td>32,128,934.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Healthcare Channel Financing</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Fundly offbook</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 lender_name  principal_outstanding\n",
       "0               Aditya Birla Finance Limited          48,275,603.00\n",
       "1             Avanse Financial Services Ltd.          81,665,650.00\n",
       "2               CAPRI GLOBAL CAPITAL LIMITED           6,591,575.03\n",
       "3                            Capri Global-DA          20,834,952.00\n",
       "4                                    HLF-PTC          43,323,519.00\n",
       "5                  JM Financial Products Ltd          10,714,893.00\n",
       "6   K. M. GLOBAL P2P FINANCE PRIVATE LIMITED           8,027,640.00\n",
       "7        K. M. Global Credit Private Limited       1,628,384,707.00\n",
       "8                                Lendbox LSP           5,475,926.00\n",
       "9                                Lendbox P2P          20,431,348.00\n",
       "10                          Mufin DA June 24          11,666,130.00\n",
       "11  Pride Financial Services Private Limited                   0.00\n",
       "12   Singularity Creditworld Private Limited         141,029,825.63\n",
       "13                   Vivriti Capital Limited             443,153.57\n",
       "14                               Earned Wage          10,238,030.00\n",
       "15                                    Fundly           1,171,525.00\n",
       "16                   Solar Channel Financing          32,128,934.00\n",
       "17              Healthcare Channel Financing                   0.00\n",
       "18                            Fundly offbook                   0.00"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lender_wise_aum = df_excl_closed_cancelled_WO.groupby('lender_name').agg({'principal_outstanding':'sum'}).reset_index()\n",
    "lender_wise_aum = pd.concat([lender_wise_aum, fintech_pos_df.rename(columns={'merchant_category':'lender_name'})], ignore_index=True)\n",
    "\n",
    "lender_wise_aum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>lender_type</th>\n",
       "      <th>principal_outstanding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Off-book</td>\n",
       "      <td>398,480,215.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>On-book</td>\n",
       "      <td>1,671,923,196.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lender_type  principal_outstanding\n",
       "0    Off-book         398,480,215.23\n",
       "1     On-book       1,671,923,196.00"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lender_wise_aum['lender_type'] = lender_wise_aum['lender_name'].apply(lambda x: 'On-book' if x in ['K. M. Global Credit Private Limited','Earned Wage','Fundly','Solar Channel Financing','Healthcare Channel Financing'] else 'Off-book')\n",
    "lender_type_wise_aum = lender_wise_aum.groupby('lender_type').agg({'principal_outstanding':'sum'}).reset_index()\n",
    "\n",
    "lender_type_wise_aum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "category_wise_POS = category_wise_POS.apply(lambda x: x / 10**7 if x.dtype in ['float64', 'int64'] else x)\n",
    "category_wise_POS.rename(columns={'principal_outstanding':'Principal Outstanding (in Cr.)'},inplace=True)\n",
    "\n",
    "main_category_wise_POS = main_category_wise_POS.apply(lambda x: x / 10**7 if x.name in ['principal_outstanding'] else x)\n",
    "main_category_wise_POS.rename(columns={'principal_outstanding':'Principal Outstanding (in Cr.)'},inplace=True)\n",
    "\n",
    "merchant_wise_POS = merchant_wise_POS.apply(lambda x: x / 10**7 if x.name in ['principal_outstanding'] else x)\n",
    "merchant_wise_POS.rename(columns={'principal_outstanding':'Principal Outstanding (in Cr.)'},inplace=True)\n",
    "\n",
    "final_sector_wise_gross_disb_df = final_sector_wise_gross_disb_df.apply(lambda x: x / 10**7 if x.dtype in ['float64', 'int64'] else x)\n",
    "\n",
    "final_sector_wise_net_disb_df = final_sector_wise_net_disb_df.apply(lambda x: x / 10**7 if x.dtype in ['float64', 'int64'] else x)\n",
    "\n",
    "main_cat_disb_df = main_cat_disb_df.apply(lambda x : x / 10 ** 7 if x.dtype in ['float64', 'int64'] else x)\n",
    "\n",
    "main_cat_net_disb_df = main_cat_net_disb_df.apply(lambda x : x / 10 ** 7 if x.dtype in ['float64', 'int64'] else x)\n",
    "\n",
    "lender_type_wise_lender_disb_df = lender_type_wise_lender_disb_df.apply(lambda x: x / 10**7 if x.name in ['Off-book','On-book','Total'] else x)\n",
    "\n",
    "lender_wise_aum = lender_wise_aum.apply(lambda x: x / 10**7 if x.name in ['principal_outstanding'] else x)\n",
    "lender_wise_aum.rename(columns={'principal_outstanding':'Principal Outstanding (in Cr.)'},inplace=True)\n",
    "\n",
    "lender_type_wise_aum.rename(columns={'principal_outstanding':'Principal Outstanding (in Cr.)'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "file_name = f'merchant_risk_consolidated_reports_{analysis_date}.xlsx'\n",
    "output_folder = './script/merchant_risk/output/'\n",
    "\n",
    "\n",
    "with pd.ExcelWriter(f'{output_folder}{file_name}', engine='xlsxwriter') as writer:\n",
    "    \n",
    "    # Write DataFrames to the Excel file in their respective sheets\n",
    "    category_wise_POS.to_excel(writer, sheet_name='Category_wise_AUM', index=False)\n",
    "    main_category_wise_POS.to_excel(writer, sheet_name='Main_Category_wise_AUM', index=False)\n",
    "    merchant_wise_POS.to_excel(writer, sheet_name='Merchant_wise_AUM', index=False)\n",
    "    final_sector_wise_gross_disb_df.to_excel(writer, sheet_name='Sector_wise_Gross_Disb_Value', index=False)\n",
    "    final_sector_wise_net_disb_df.to_excel(writer, sheet_name='Sector_wise_Net_Disb_Value', index=False)\n",
    "    sector_wise_disb_count_df.to_excel(writer, sheet_name='Sector_wise_Disb_Count', index=False)\n",
    "    main_cat_disb_df.to_excel(writer, sheet_name='Main_Category_Gross_Disb_Value', index=False)\n",
    "    main_cat_disb_count_df.to_excel(writer, sheet_name='Main_Category_Disb_Count', index=False)\n",
    "    main_cat_net_disb_df.to_excel(writer, sheet_name='Main_Category_Net_Disb_Value', index=False)\n",
    "    lender_type_wise_lender_disb_df.to_excel(writer, sheet_name='Lender_type_wise_Disburements', index=False)\n",
    "    lender_wise_aum.to_excel(writer, sheet_name='Lender_wise_AUM', index=False)\n",
    "\n",
    "    workbook = writer.book\n",
    "    image_sheet1 = workbook.add_worksheet('Portfolio Mix')  # Create a new sheet for the chart\n",
    "\n",
    "    # Insert the image into the new sheet\n",
    "    image_path = f'{output_folder}portfolio_mix_chart_{analysis_date}.png'\n",
    "    image_sheet1.insert_image('E5', image_path)  # Insert the image at cell E5\n",
    "\n",
    "    image_sheet2 = workbook.add_worksheet('Onbook_Offbook_Disb%') \n",
    "\n",
    "    image_path = f'{output_folder}on_book_off_book_disbursement_chart_{analysis_date}.png'\n",
    "    image_sheet2.insert_image('E5', image_path)"
   ]
  }
 ],
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