From f2f7aa54d9511ce1e46f9e87239824e7ba0d04bd Mon Sep 17 00:00:00 2001
From: Sara Gonzales <39350413+SaraynesGS@users.noreply.github.com>
Date: Fri, 29 Nov 2024 16:30:58 +0100
Subject: [PATCH 1/4] Create Code
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From dcdcdf44568d40310b2c84df9906496a03cc3656 Mon Sep 17 00:00:00 2001
From: Sara Gonzales <39350413+SaraynesGS@users.noreply.github.com>
Date: Fri, 29 Nov 2024 16:34:23 +0100
Subject: [PATCH 2/4] Delete Code
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From d0c3495a8ef0bf3dbc761e23ad80fd7498f01352 Mon Sep 17 00:00:00 2001
From: Sara Gonzales <39350413+SaraynesGS@users.noreply.github.com>
Date: Fri, 29 Nov 2024 16:35:58 +0100
Subject: [PATCH 3/4] Week 2 - Project 1 - Business challenge
---
project_dataset/Business_Challenge.ipynb | 2324 ++++++++++++++++++++++
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "df091add-70ef-4f4d-ab91-9cd35a651519",
+ "metadata": {},
+ "source": [
+ "# Business Challenge: Cohort Analysis for Ironhack Payments (Project 1)\n",
+ "\n",
+ "## Introduction\n",
+ "\n",
+ "IronHack Payments, a forward-thinking financial services company, has been offering innovative cash advance solutions since its inception in 2020. With a commitment to providing money advancements for free and transparent pricing, IronHack Payments has garnered a substantial user base. As part of their continuous effort to enhance their services and understand user behavior, IronHack Payments has commissioned a cohort analysis project.\n",
+ "\n",
+ "## Project Overview\n",
+ "\n",
+ "In this project, you will conduct a comprehensive cohort analysis based on data provided by IronHack Payments. **The main objective is to analyze user cohorts defined by the month of creation of their first cash advance.** You will track the monthly evolution of key metrics for these cohorts, enabling IronHack Payments to gain valuable insights into user behavior and the performance of their financial services.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0b9bdb79",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib inline\n",
+ "pd.set_option('display.max_columns', None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "24e82eb1-9b43-44a9-9cbf-83a11f37d57c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_cash = pd.read_csv(\"extract - cash request - data analyst.csv\")\n",
+ "data_fees = pd.read_csv(\"extract - fees - data analyst - .csv\")\n",
+ "\n",
+ "df_cash = data_cash.copy()\n",
+ "df_fees = data_fees.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "28cb98aa-363f-404d-a33f-09b0bca24167",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['id', 'amount', 'status', 'created_at', 'updated_at', 'user_id',\n",
+ " 'moderated_at', 'deleted_account_id', 'reimbursement_date',\n",
+ " 'cash_request_received_date', 'money_back_date', 'transfer_type',\n",
+ " 'send_at', 'recovery_status', 'reco_creation', 'reco_last_update'],\n",
+ " dtype='object')\n",
+ "Index(['id', 'cash_request_id', 'type', 'status', 'category', 'total_amount',\n",
+ " 'reason', 'created_at', 'updated_at', 'paid_at', 'from_date', 'to_date',\n",
+ " 'charge_moment'],\n",
+ " dtype='object')\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df_cash.columns)\n",
+ "print(df_fees.columns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "eb3c3e1f-ac8e-4b4b-8c25-cd363fdafb81",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "df_cash shape is (23970, 16)\n",
+ "df_fees shape is (21061, 13)\n"
+ ]
+ }
+ ],
+ "source": [
+ "len(df_cash)\n",
+ "len(df_fees)\n",
+ "print(f\"df_cash shape is {df_cash.shape}\")\n",
+ "print(f\"df_fees shape is {df_fees.shape}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "0f45c48e-71f5-4963-9c6b-ae5ba15d2aa9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_cash_row = df_cash.shape[0]\n",
+ "df_cash_col = df_cash.shape[1]\n",
+ "df_fees_row = df_fees.shape[0]\n",
+ "df_fees_col = df_fees.shape[1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42507017",
+ "metadata": {},
+ "source": [
+ "### Merging and cleaning the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b5a087e6-a61c-4c1e-b277-b0df9db190d7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " id \n",
+ " cash_request_id \n",
+ " type \n",
+ " status \n",
+ " category \n",
+ " total_amount \n",
+ " reason \n",
+ " created_at \n",
+ " updated_at \n",
+ " paid_at \n",
+ " from_date \n",
+ " to_date \n",
+ " charge_moment \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 6537 \n",
+ " 14941.0 \n",
+ " instant_payment \n",
+ " rejected \n",
+ " NaN \n",
+ " 5.0 \n",
+ " Instant Payment Cash Request 14941 \n",
+ " 2020-09-07 10:47:27.42315+00 \n",
+ " 2020-10-13 14:25:09.396112+00 \n",
+ " 2020-12-17 14:50:07.47011+00 \n",
+ " NaN \n",
+ " NaN \n",
+ " after \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 6961 \n",
+ " 11714.0 \n",
+ " incident \n",
+ " accepted \n",
+ " rejected_direct_debit \n",
+ " 5.0 \n",
+ " rejected direct debit \n",
+ " 2020-09-09 20:51:17.998653+00 \n",
+ " 2020-10-13 14:25:15.537063+00 \n",
+ " 2020-12-08 17:13:10.45908+00 \n",
+ " NaN \n",
+ " NaN \n",
+ " after \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 16296 \n",
+ " 23371.0 \n",
+ " instant_payment \n",
+ " accepted \n",
+ " NaN \n",
+ " 5.0 \n",
+ " Instant Payment Cash Request 23371 \n",
+ " 2020-10-23 10:10:58.352972+00 \n",
+ " 2020-10-23 10:10:58.352994+00 \n",
+ " 2020-11-04 19:34:37.43291+00 \n",
+ " NaN \n",
+ " NaN \n",
+ " after \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id cash_request_id type status category \\\n",
+ "0 6537 14941.0 instant_payment rejected NaN \n",
+ "1 6961 11714.0 incident accepted rejected_direct_debit \n",
+ "2 16296 23371.0 instant_payment accepted NaN \n",
+ "\n",
+ " total_amount reason \\\n",
+ "0 5.0 Instant Payment Cash Request 14941 \n",
+ "1 5.0 rejected direct debit \n",
+ "2 5.0 Instant Payment Cash Request 23371 \n",
+ "\n",
+ " created_at updated_at \\\n",
+ "0 2020-09-07 10:47:27.42315+00 2020-10-13 14:25:09.396112+00 \n",
+ "1 2020-09-09 20:51:17.998653+00 2020-10-13 14:25:15.537063+00 \n",
+ "2 2020-10-23 10:10:58.352972+00 2020-10-23 10:10:58.352994+00 \n",
+ "\n",
+ " paid_at from_date to_date charge_moment \n",
+ "0 2020-12-17 14:50:07.47011+00 NaN NaN after \n",
+ "1 2020-12-08 17:13:10.45908+00 NaN NaN after \n",
+ "2 2020-11-04 19:34:37.43291+00 NaN NaN after "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_fees.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "2bf822df",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "type status \n",
+ "instant_payment accepted 10296\n",
+ "postpone cancelled 4780\n",
+ " accepted 2985\n",
+ "incident accepted 1560\n",
+ "instant_payment rejected 673\n",
+ "incident rejected 520\n",
+ " cancelled 97\n",
+ "instant_payment confirmed 69\n",
+ " cancelled 61\n",
+ "incident confirmed 19\n",
+ "postpone rejected 1\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#df_fees.type.value_counts()\n",
+ "df_fees[['type', 'status']].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "9e41ea1b-2686-4960-8e23-ad204e31cb7b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_fees_cash = df_fees.merge(df_cash, left_on= \"cash_request_id\", right_on=\"id\" , how='inner')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "35c167d0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['id_x', 'cash_request_id', 'type', 'status_x', 'category',\n",
+ " 'total_amount', 'reason', 'created_at_x', 'updated_at_x', 'paid_at',\n",
+ " 'from_date', 'to_date', 'charge_moment', 'id_y', 'amount', 'status_y',\n",
+ " 'created_at_y', 'updated_at_y', 'user_id', 'moderated_at',\n",
+ " 'deleted_account_id', 'reimbursement_date',\n",
+ " 'cash_request_received_date', 'money_back_date', 'transfer_type',\n",
+ " 'send_at', 'recovery_status', 'reco_creation', 'reco_last_update'],\n",
+ " dtype='object')\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_fees_cash_row = df_fees_cash.shape[0]\n",
+ "df_fees_cash_col = df_fees_cash.shape[1]\n",
+ "print(df_fees_cash.columns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "fa7ddcf4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# convert object to date\n",
+ "date_columns = ['created_at_x', 'updated_at_x', 'paid_at', 'from_date', 'to_date', 'created_at_y', 'updated_at_y', 'moderated_at', 'cash_request_received_date', 'money_back_date', 'reco_creation', 'reco_last_update']\n",
+ "\n",
+ "for column in date_columns:\n",
+ " if column in df_fees_cash.columns: \n",
+ " df_fees_cash[column] = pd.to_datetime(df_fees_cash[column], errors='coerce').dt.date"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "8e8d7852",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "type status_x created_at_y\n",
+ "postpone cancelled 2020-08-10 111\n",
+ " 2020-10-23 99\n",
+ " 2020-07-15 90\n",
+ " 2020-10-08 87\n",
+ " 2020-08-12 85\n",
+ " ... \n",
+ " accepted 2020-05-22 1\n",
+ " 2020-05-21 1\n",
+ " 2020-05-16 1\n",
+ " 2020-05-09 1\n",
+ " rejected 2020-06-10 1\n",
+ "Name: count, Length: 346, dtype: int64"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_fees_cash[df_fees_cash['type'] == \"postpone\"][['type', 'status_x', 'created_at_y']].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "75080583",
+ "metadata": {},
+ "source": [
+ "#### Null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "4f3610f9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "deleted_account_id 20151\n",
+ "category 18861\n",
+ "money_back_date 16114\n",
+ "to_date 14545\n",
+ "from_date 14308\n",
+ "reco_last_update 14163\n",
+ "reco_creation 14163\n",
+ "recovery_status 14163\n",
+ "moderated_at 9942\n",
+ "paid_at 5619\n",
+ "send_at 3487\n",
+ "cash_request_received_date 1294\n",
+ "user_id 906\n",
+ "updated_at_y 0\n",
+ "transfer_type 0\n",
+ "reimbursement_date 0\n",
+ "id_x 0\n",
+ "created_at_y 0\n",
+ "status_y 0\n",
+ "cash_request_id 0\n",
+ "id_y 0\n",
+ "charge_moment 0\n",
+ "updated_at_x 0\n",
+ "created_at_x 0\n",
+ "reason 0\n",
+ "total_amount 0\n",
+ "status_x 0\n",
+ "type 0\n",
+ "amount 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_fees_cash.isnull().sum().sort_values(ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "68e0145e",
+ "metadata": {},
+ "source": [
+ "### Metrics to Analyze\n",
+ "\n",
+ "1. **Frequency of Service Usage:** Understand how often users from each cohort utilize IronHack *Payments' cash advance* services over time.\n",
+ "\n",
+ "Keeping data from type == postpone & status_x == accepted \n",
+ "\n",
+ "- **postpone** : fees created when a user want to postpone the reimbursment of a CR\n",
+ "\n",
+ "- **status_x** : Status of the fees (= does the fees was successfully charged)\n",
+ " Possibles values are : \n",
+ " - confirmed : the user made an action who created a fee. It will normally get charged at the moment of the CR's reimbursement. In some rare cases, postpones are confirmed without being charges due to a commercial offer.\n",
+ " - rejected : the last attempt to charge the fee failed.\n",
+ " - cancelled : fee was created and cancelled for some reasons. It's used to fix issues with fees but it mainly concern postpone fees who failed. We are charging the fees at the moment of the postpone request. If it failed, the postpone is not accepted and the reimbursement date still the same.\n",
+ " - **accepted** : fees were successfully charged\n",
+ "\n",
+ "- **status_y** : Status of the CR.\n",
+ " Possibles values are : \n",
+ " - approved : CR is a 'regular' one (= without fees) and was approved either automatically or manually. Funds will be sent aprox. 7 days after the creation\n",
+ " - money_sent : We transferred the fund to the customer account. Will change to active once we detect that the user received the funds (using user's bank history)\n",
+ " - rejected : The CR needed a manual review and was rejected\n",
+ " - pending : The CR is pending a manual review from an analyst\n",
+ " - transaction_declined : We failed to send the funds to the customer\n",
+ " - waiting_user_confirmation : The user needs to confirm in-app that he want the CR (for legal reasons)\n",
+ " - direct_debit_rejected : Our last attempt of SEPA direct debit to charge the customer was rejected \n",
+ " - canceled : The user didn't confirm the cash request in-app, we automatically canceled it\n",
+ " - direct_debit_sent : We sent/scheduled a SEPA direct debit to charge the customer account. The result of this debit is not yet confirmed\n",
+ " - waiting_reimbursement : We were not able to estimate a date of reimbursement, the user needs to choose one in the app.\n",
+ " - active : Funds were received on the customer account.\n",
+ " - money_back : The CR was successfully reimbursed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "251ae39f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 23970 entries, 0 to 23969\n",
+ "Data columns (total 16 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 id 23970 non-null int64 \n",
+ " 1 amount 23970 non-null float64\n",
+ " 2 status 23970 non-null object \n",
+ " 3 created_at 23970 non-null object \n",
+ " 4 updated_at 23970 non-null object \n",
+ " 5 user_id 21867 non-null float64\n",
+ " 6 moderated_at 16035 non-null object \n",
+ " 7 deleted_account_id 2104 non-null float64\n",
+ " 8 reimbursement_date 23970 non-null object \n",
+ " 9 cash_request_received_date 16289 non-null object \n",
+ " 10 money_back_date 16543 non-null object \n",
+ " 11 transfer_type 23970 non-null object \n",
+ " 12 send_at 16641 non-null object \n",
+ " 13 recovery_status 3330 non-null object \n",
+ " 14 reco_creation 3330 non-null object \n",
+ " 15 reco_last_update 3330 non-null object \n",
+ "dtypes: float64(3), int64(1), object(12)\n",
+ "memory usage: 2.9+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_cash.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "acde0855",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Checking duplicates in id\n",
+ "df_cash.duplicated(subset=['id']).sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "9bd9c2be",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " mean \n",
+ " std \n",
+ " min \n",
+ " 25% \n",
+ " 50% \n",
+ " 75% \n",
+ " max \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " id \n",
+ " 23970.0 \n",
+ " 13910.966124 \n",
+ " 7788.117214 \n",
+ " 3.0 \n",
+ " 7427.25 \n",
+ " 14270.5 \n",
+ " 20607.75 \n",
+ " 27010.0 \n",
+ " \n",
+ " \n",
+ " amount \n",
+ " 23970.0 \n",
+ " 82.720818 \n",
+ " 26.528065 \n",
+ " 1.0 \n",
+ " 50.00 \n",
+ " 100.0 \n",
+ " 100.00 \n",
+ " 200.0 \n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " 21867.0 \n",
+ " 32581.250789 \n",
+ " 27618.565773 \n",
+ " 34.0 \n",
+ " 10804.00 \n",
+ " 23773.0 \n",
+ " 46965.00 \n",
+ " 103719.0 \n",
+ " \n",
+ " \n",
+ " deleted_account_id \n",
+ " 2104.0 \n",
+ " 9658.755228 \n",
+ " 7972.743249 \n",
+ " 91.0 \n",
+ " 3767.00 \n",
+ " 6121.5 \n",
+ " 16345.00 \n",
+ " 30445.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count mean std min 25% \\\n",
+ "id 23970.0 13910.966124 7788.117214 3.0 7427.25 \n",
+ "amount 23970.0 82.720818 26.528065 1.0 50.00 \n",
+ "user_id 21867.0 32581.250789 27618.565773 34.0 10804.00 \n",
+ "deleted_account_id 2104.0 9658.755228 7972.743249 91.0 3767.00 \n",
+ "\n",
+ " 50% 75% max \n",
+ "id 14270.5 20607.75 27010.0 \n",
+ "amount 100.0 100.00 200.0 \n",
+ "user_id 23773.0 46965.00 103719.0 \n",
+ "deleted_account_id 6121.5 16345.00 30445.0 "
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_cash.describe().T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "a21a0f3b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month\n",
+ "2019-11 1\n",
+ "2019-12 289\n",
+ "2020-01 223\n",
+ "2020-02 184\n",
+ "2020-03 244\n",
+ "2020-04 473\n",
+ "2020-05 837\n",
+ "2020-06 2615\n",
+ "2020-07 3601\n",
+ "2020-08 3417\n",
+ "2020-09 4221\n",
+ "2020-10 7725\n",
+ "2020-11 140\n",
+ "Freq: M, dtype: int64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/1595145398.py:6: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
+ " df_cash['month'] = df_cash['created_at'].dt.to_period('M')\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_cash['created_at'] = pd.to_datetime(df_cash['created_at'], errors='coerce')\n",
+ "\n",
+ "if df_cash['created_at'].isnull().any():\n",
+ " print(\"Some dates couldn't be converted. Please check the data for invalid entries.\")\n",
+ "\n",
+ "df_cash['month'] = df_cash['created_at'].dt.to_period('M') \n",
+ "\n",
+ "monthly_counts_c = df_cash.groupby('month').size()\n",
+ "\n",
+ "print(monthly_counts_c)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "150a32a3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_cash[\"duplicated\"] = df_cash[\"user_id\"].duplicated()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "dff8c517",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "recovery_status duplicated\n",
+ "completed True 1326\n",
+ " False 1142\n",
+ "pending False 558\n",
+ " True 287\n",
+ "pending_direct_debit False 9\n",
+ " True 7\n",
+ "cancelled True 1\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_cash[['recovery_status', 'duplicated']].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "433c9e0f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_cash.drop_duplicates(subset=['user_id'], inplace=True)\n",
+ "df_cash.reset_index(drop=True, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "307ccd36",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(10799, 18)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_cash.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "ae6a9e24",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "month\n",
+ "2019-12 119\n",
+ "2020-01 70\n",
+ "2020-02 56\n",
+ "2020-03 60\n",
+ "2020-04 177\n",
+ "2020-05 490\n",
+ "2020-06 1075\n",
+ "2020-07 1192\n",
+ "2020-08 761\n",
+ "2020-09 1720\n",
+ "2020-10 4991\n",
+ "2020-11 88\n",
+ "Freq: M, dtype: int64"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_cash.groupby('month').size()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "7a93bd53",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Example data (presume monthly_counts_c is your grouped data)\n",
+ "monthly_counts_c = pd.Series([1, 289, 223, 184, 244, 473, 837, 2615, 3601, 3417, 4221, 7725, 140], \n",
+ " index=pd.to_datetime(['2019-11', '2019-12', '2020-01', '2020-02', '2020-03',\n",
+ " '2020-04', '2020-05', '2020-06', '2020-07', '2020-08',\n",
+ " '2020-09', '2020-10', '2020-11']))\n",
+ "\n",
+ "# Dictionary to map month numbers to specified short names\n",
+ "month_abbr = {'11': 'NOV', '12': 'DEC', '01': 'ENE', '02': 'FEB', '03': 'MAR', '04': 'APR',\n",
+ " '05': 'MAY', '06': 'JUN', '07': 'JUL', '08': 'AUG', '09': 'SEP', '10': 'OCT'}\n",
+ "\n",
+ "# Plotting the frequency of entries by month\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "plt.bar(monthly_counts_c.index.strftime('%m-%Y'), monthly_counts_c.values, color='blue', edgecolor='black', alpha=0.7)\n",
+ "\n",
+ "# Retrieve month abbreviations using the mapping\n",
+ "month_labels = [month_abbr[month] for month in monthly_counts_c.index.strftime('%m')]\n",
+ "\n",
+ "# Customize x-ticks for these abbreviations\n",
+ "plt.xticks(ticks=range(len(monthly_counts_c)), labels=month_labels, rotation=0)\n",
+ "\n",
+ "# Add secondary ticks for years\n",
+ "ax = plt.gca()\n",
+ "ax.set_xticks([-1, 13], minor=True) # Positions for \"2019\" and \"2020\"\n",
+ "ax.set_xticklabels(['2019', '2020'], minor=True, fontsize=10)\n",
+ "\n",
+ "# Add vertical line between 'DEC' and 'ENE'\n",
+ "ax.axvline(x=1.5, color='black', linestyle='--', linewidth=1)\n",
+ "\n",
+ "# Set parameters to avoid overlaps and adjust for visibility\n",
+ "ax.tick_params(axis='x', which='minor', length=0, pad=15) # Padding for better readability\n",
+ "\n",
+ "plt.xlabel('Creation of first cash advance (month)')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Frequency of service usage: Bar plot of students cohorts')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "54f8d6a6",
+ "metadata": {},
+ "source": [
+ "**Mistake** Understanding only postpone"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "4ded2610-b2e1-4303-9be4-38efb6ac87c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_fees_postpone = df_fees_cash[df_fees_cash[\"type\"] == \"postpone\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "d7619ba4-f0ff-4db2-9f47-c92642abdfbd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(7766, 29)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df_fees_postpone.shape)\n",
+ "df_fees_postpone_row = df_fees_postpone.shape[0]\n",
+ "df_fees_postpone_col = df_fees_postpone.shape[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "edfd82dd-0b77-4ef2-8667-aa60ae06183e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['id_x', 'cash_request_id', 'type', 'status_x', 'category',\n",
+ " 'total_amount', 'reason', 'created_at_x', 'updated_at_x', 'paid_at',\n",
+ " 'from_date', 'to_date', 'charge_moment', 'id_y', 'amount', 'status_y',\n",
+ " 'created_at_y', 'updated_at_y', 'user_id', 'moderated_at',\n",
+ " 'deleted_account_id', 'reimbursement_date',\n",
+ " 'cash_request_received_date', 'money_back_date', 'transfer_type',\n",
+ " 'send_at', 'recovery_status', 'reco_creation', 'reco_last_update'],\n",
+ " dtype='object')\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df_fees_postpone.columns)\n",
+ "cols_1 = ['id_x', 'cash_request_id', 'id_y', 'created_at_x', 'created_at_y', 'type', 'status_x', 'total_amount', 'amount', 'status_y']\n",
+ "df_fees_postpone2 = df_fees_postpone[cols_1]\n",
+ "\n",
+ "#df_fees_postpone2 = df_fees_postpone2.drop(columns=[\"id_y\", 'category', 'reason', 'created_at_x', 'updated_at_x', 'paid_at', 'from_date', 'to_date', 'charge_moment', 'updated_at_y', 'moderated_at', 'deleted_account_id', 'reimbursement_date', 'cash_request_received_date', 'money_back_date', 'transfer_type', 'send_at', 'recovery_status', 'reco_creation', 'reco_last_update'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "ca4b2e2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " id_x \n",
+ " cash_request_id \n",
+ " id_y \n",
+ " created_at_x \n",
+ " created_at_y \n",
+ " type \n",
+ " status_x \n",
+ " total_amount \n",
+ " amount \n",
+ " status_y \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 15431 \n",
+ " 22106.0 \n",
+ " 22106 \n",
+ " 2020-10-21 \n",
+ " 2020-10-19 \n",
+ " postpone \n",
+ " cancelled \n",
+ " 5.0 \n",
+ " 100.0 \n",
+ " money_back \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " 15434 \n",
+ " 21897.0 \n",
+ " 21897 \n",
+ " 2020-10-21 \n",
+ " 2020-10-18 \n",
+ " postpone \n",
+ " accepted \n",
+ " 5.0 \n",
+ " 100.0 \n",
+ " money_back \n",
+ " \n",
+ " \n",
+ " 12 \n",
+ " 20199 \n",
+ " 25490.0 \n",
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+ " \n",
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+ "\n",
+ " status_x total_amount amount status_y \n",
+ "9 cancelled 5.0 100.0 money_back \n",
+ "11 accepted 5.0 100.0 money_back \n",
+ "12 cancelled 5.0 100.0 money_back "
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_fees_postpone2.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "8d968a68",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/4043831793.py:2: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_fees_postpone2.sort_values(by=['created_at_y'], inplace=True)\n"
+ ]
+ }
+ ],
+ "source": [
+ "#df_fees_postpone2['created_at_y'] = pd.to_datetime(df_fees_postpone2['created_at_y']).dt.date\n",
+ "df_fees_postpone2.sort_values(by=['created_at_y'], inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "c3f59f9c-0eb9-4a6a-a2e0-c5b0952600d9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Index: 7766 entries, 16227 to 37\n",
+ "Data columns (total 10 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 id_x 7766 non-null int64 \n",
+ " 1 cash_request_id 7766 non-null float64\n",
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+ " 3 created_at_x 7766 non-null object \n",
+ " 4 created_at_y 7766 non-null object \n",
+ " 5 type 7766 non-null object \n",
+ " 6 status_x 7766 non-null object \n",
+ " 7 total_amount 7766 non-null float64\n",
+ " 8 amount 7766 non-null float64\n",
+ " 9 status_y 7766 non-null object \n",
+ "dtypes: float64(3), int64(2), object(5)\n",
+ "memory usage: 667.4+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_fees_postpone2.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "266ee681-9341-4c5d-89a5-e89803b7389f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ " count mean std min 25% 50% \\\n",
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+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#df_fees_postpone2 = df_fees_postpone2.drop(columns=[\"user_id\"])\n",
+ "print(df_fees_postpone2.isna().sum().sort_values(ascending=False))\n",
+ "df_fees_postpone2.describe().T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "3ceff064-0f08-4f52-976a-3345d2544f74",
+ "metadata": {},
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+ " 100.0 \n",
+ " money_back \n",
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " id_x cash_request_id id_y created_at_x created_at_y type \\\n",
+ "16227 44 1456.0 1456 2020-06-23 2020-04-29 postpone \n",
+ "1221 13 1518.0 1518 2020-06-23 2020-05-01 postpone \n",
+ "19647 17 1518.0 1518 2020-06-23 2020-05-01 postpone \n",
+ "15829 7 1518.0 1518 2020-06-23 2020-05-01 postpone \n",
+ "2001 115 1518.0 1518 2020-06-23 2020-05-01 postpone \n",
+ "\n",
+ " status_x total_amount amount status_y \n",
+ "16227 cancelled 5.0 100.0 direct_debit_rejected \n",
+ "1221 accepted 5.0 100.0 money_back \n",
+ "19647 cancelled 5.0 100.0 money_back \n",
+ "15829 cancelled 5.0 100.0 money_back \n",
+ "2001 cancelled 5.0 100.0 money_back "
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_fees_postpone2.head(5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "ff6a054e-a7b7-45ef-8214-622dcbf6069c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month\n",
+ "2020-04 1\n",
+ "2020-05 213\n",
+ "2020-06 1185\n",
+ "2020-07 1293\n",
+ "2020-08 1500\n",
+ "2020-09 1681\n",
+ "2020-10 1893\n",
+ "Freq: M, dtype: int64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/1433017807.py:2: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_fees_postpone2['created_at_y'] = pd.to_datetime(df_fees_postpone2['created_at_y'], errors='coerce')\n",
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/1433017807.py:9: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_fees_postpone2['month'] = df_fees_postpone2['created_at_y'].dt.to_period('M')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Ensure 'created_at_y' is converted to datetime\n",
+ "df_fees_postpone2['created_at_y'] = pd.to_datetime(df_fees_postpone2['created_at_y'], errors='coerce')\n",
+ "\n",
+ "# Check for non-convertible entries if there are any\n",
+ "if df_fees_postpone2['created_at_y'].isnull().any():\n",
+ " print(\"Some dates couldn't be converted. Please check the data for invalid entries.\")\n",
+ "\n",
+ "# Extract month from the 'created_at_y' series\n",
+ "df_fees_postpone2['month'] = df_fees_postpone2['created_at_y'].dt.to_period('M') \n",
+ "\n",
+ "# Tabulate the count by month\n",
+ "monthly_counts = df_fees_postpone2.groupby('month').size()\n",
+ "\n",
+ "# Display the result\n",
+ "print(monthly_counts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "5766bf2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Index: 7766 entries, 16227 to 37\n",
+ "Data columns (total 11 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 id_x 7766 non-null int64 \n",
+ " 1 cash_request_id 7766 non-null float64 \n",
+ " 2 id_y 7766 non-null int64 \n",
+ " 3 created_at_x 7766 non-null object \n",
+ " 4 created_at_y 7766 non-null datetime64[ns]\n",
+ " 5 type 7766 non-null object \n",
+ " 6 status_x 7766 non-null object \n",
+ " 7 total_amount 7766 non-null float64 \n",
+ " 8 amount 7766 non-null float64 \n",
+ " 9 status_y 7766 non-null object \n",
+ " 10 month 7766 non-null period[M] \n",
+ "dtypes: datetime64[ns](1), float64(3), int64(2), object(4), period[M](1)\n",
+ "memory usage: 728.1+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_fees_postpone2.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "61237cca",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/570264075.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_fees_postpone2['created_at_y'] = pd.to_datetime(df_fees_postpone2['created_at_y'])\n",
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/570264075.py:14: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
+ " months = pd.date_range(df_fees_postpone2['created_at_y'].min(), df_fees_postpone2['created_at_y'].max(), freq='M')\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\" \\nStatus of the fees (= does the fees was successfully charged)\\n\\nPossibles values are : \\n- confirmed : the user made an action who created a fee. It will normally get charged at the moment of the CR's reimbursement. In some rare cases, postpones are confirmed without being charges due to a commercial offer.\\n- rejected : the last attempt to charge the fee failed.\\n- cancelled : fee was created and cancelled for some reasons. It's used to fix issues with fees but it mainly concern postpone fees who failed. We are charging the fees at the moment of the postpone request. If it failed, the postpone is not accepted and the reimbursement date still the same.\\n- accepted : fees were successfully charged\\n\""
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_fees_postpone2['created_at_y'] = pd.to_datetime(df_fees_postpone2['created_at_y'])\n",
+ "\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "\n",
+ "# Plot for 'cancelled'\n",
+ "plt.hist(df_fees_postpone2[df_fees_postpone2['status_x'] == 'cancelled']['created_at_y'],\n",
+ " bins=20, alpha=0.3, label='Cancelled', color='blue')\n",
+ "\n",
+ "# Plot for 'accepted'\n",
+ "plt.hist(df_fees_postpone2[df_fees_postpone2['status_x'] == 'accepted']['created_at_y'],\n",
+ " bins=20, alpha=0.3, label='Accepted', color='green')\n",
+ "\n",
+ "# Add lines at the end of each month\n",
+ "months = pd.date_range(df_fees_postpone2['created_at_y'].min(), df_fees_postpone2['created_at_y'].max(), freq='M')\n",
+ "for month in months:\n",
+ " plt.axvline(month, color='black', linestyle='--')\n",
+ "\n",
+ "plt.xlabel('Creation of first cash advance (date)')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Frequency of service usage: Histogram of students cohorts by status of the fees')\n",
+ "plt.legend(loc='upper left')\n",
+ "plt.show()\n",
+ "\n",
+ "\"\"\" \n",
+ "Status of the fees (= does the fees was successfully charged)\n",
+ "\n",
+ "Possibles values are : \n",
+ "- confirmed : the user made an action who created a fee. It will normally get charged at the moment of the CR's reimbursement. In some rare cases, postpones are confirmed without being charges due to a commercial offer.\n",
+ "- rejected : the last attempt to charge the fee failed.\n",
+ "- cancelled : fee was created and cancelled for some reasons. It's used to fix issues with fees but it mainly concern postpone fees who failed. We are charging the fees at the moment of the postpone request. If it failed, the postpone is not accepted and the reimbursement date still the same.\n",
+ "- accepted : fees were successfully charged\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6216859b",
+ "metadata": {},
+ "source": [
+ "2. **Incident Rate:** Determine the incident rate, specifically focusing on payment incidents, for each cohort. Identify if there are variations in incident rates among different cohorts."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "65c6e5bf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " absolute_frequency \n",
+ " relative_frequency \n",
+ " \n",
+ " \n",
+ " type \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " instant_payment \n",
+ " 11095 \n",
+ " 0.53 \n",
+ " \n",
+ " \n",
+ " postpone \n",
+ " 7766 \n",
+ " 0.37 \n",
+ " \n",
+ " \n",
+ " incident \n",
+ " 2196 \n",
+ " 0.10 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " absolute_frequency relative_frequency\n",
+ "type \n",
+ "instant_payment 11095 0.53\n",
+ "postpone 7766 0.37\n",
+ "incident 2196 0.10"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\"\"\"\n",
+ "Frequency table in 3 steps: \n",
+ " (1) merging absoluty frequency with relative frequenceies\n",
+ " (2) calculate the last row: totals\n",
+ " (3) concat frequencies and total\n",
+ " (*) Alternatively: use pd.crosstab()\n",
+ "\"\"\"\n",
+ "t_frequency = df_fees_cash[['type']].value_counts()\n",
+ "t_proportion = df_fees_cash[['type']].value_counts(normalize=True).round(2)\n",
+ "\n",
+ "# merging absoluty frequency with relative frequenceies\n",
+ "t_df = pd.concat([t_frequency, t_proportion], axis=1)\n",
+ "t_df.columns = ['absolute_frequency', 'relative_frequency']\n",
+ "t_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "6c3e17a7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " absolute_frequency \n",
+ " relative_frequency \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Total \n",
+ " 21057 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " absolute_frequency relative_frequency\n",
+ "Total 21057 1.0"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tt_absolute = t_frequency.sum()\n",
+ "tt_relative = t_proportion.sum()\n",
+ "\n",
+ "tt_row = pd.DataFrame({\n",
+ " 'absolute_frequency': [tt_absolute],\n",
+ " 'relative_frequency': [tt_relative]\n",
+ "}, index=['Total'])\n",
+ "tt_row"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "53bd6b07",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " absolute_frequency \n",
+ " relative_frequency \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " (instant_payment,) \n",
+ " 11095 \n",
+ " 0.53 \n",
+ " \n",
+ " \n",
+ " (postpone,) \n",
+ " 7766 \n",
+ " 0.37 \n",
+ " \n",
+ " \n",
+ " (incident,) \n",
+ " 2196 \n",
+ " 0.10 \n",
+ " \n",
+ " \n",
+ " Total \n",
+ " 21057 \n",
+ " 1.00 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " absolute_frequency relative_frequency\n",
+ "(instant_payment,) 11095 0.53\n",
+ "(postpone,) 7766 0.37\n",
+ "(incident,) 2196 0.10\n",
+ "Total 21057 1.00"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t_df2 = pd.concat([t_df,tt_row])\n",
+ "t_df2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "2d58ad86",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " col_0 \n",
+ " count \n",
+ " \n",
+ " \n",
+ " type \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " incident \n",
+ " 2196 \n",
+ " \n",
+ " \n",
+ " instant_payment \n",
+ " 11095 \n",
+ " \n",
+ " \n",
+ " postpone \n",
+ " 7766 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "col_0 count\n",
+ "type \n",
+ "incident 2196\n",
+ "instant_payment 11095\n",
+ "postpone 7766"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.crosstab(index=df_fees_cash.type,\n",
+ " columns='count')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "b6fcd07a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " month 0\n",
+ "0 2020-05 44\n",
+ "1 2020-06 560\n",
+ "2 2020-07 659\n",
+ "3 2020-08 498\n",
+ "4 2020-09 385\n",
+ "5 2020-10 50\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_fees_cash['created_at_y'] = pd.to_datetime(df_fees_cash['created_at_y'], errors='coerce')\n",
+ "\n",
+ "if df_fees_cash['created_at_y'].isnull().any():\n",
+ " print(\"Some dates couldn't be converted. Please check the data for invalid entries.\")\n",
+ "\n",
+ "df_fees_cash['month'] = df_fees_cash['created_at_y'].dt.to_period('M') \n",
+ "monthly_counts_c2 = df_fees_cash[df_fees_cash['type'] == \"incident\"].groupby('month').size()\n",
+ "monthly_counts_c2 = monthly_counts_c2.reset_index() \n",
+ "\n",
+ "print(monthly_counts_c2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "1ae55f98",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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ZM2dKkvbu3atPP/1UMTExSk9P1wcffGC5XgDXhxEpAIVS27ZtdfHiRct/w6d58+bauXOn/Yf5LPPnz3d437hxYxUrVky7du2yj6xc+XJ3d7dc95W/oc4vm80md3d3hx+0kpKSss3ad7V9dujQQTt27FDFihVzPL6C/oE3a+awUqVK2Y8hq75/mjFjRoHtc968eQ7v169fr8OHD191tsSsSUTmzp3r0P7555/r3LlzDpOMWLmeVrabHzabTXfddZcmTZqkYsWKaevWrdesM2vikG3btjm0L1261OF91vm68nx++umn2WZ4vBH3VV5GqTw8PNS0aVO9/fbbkv6ezTA3WY8ZTp8+Pdc+DzzwgKTs1ys+Pl67d+/O1/Vq0aKFdu3a5XBtJGn27Nmy2Wxq3rz5NbeR1xG7ypUr67XXXlPNmjWz7Q/AzcGIFIBC6YknnlBsbKz69++vPXv2qHnz5srMzNRPP/2katWq6fHHH89xvcGDB+uTTz5R+/btNWbMGPusfb/++qtDP19fX02ZMkW9evXSX3/9pUcffVSlSpXSyZMn9csvv+jkyZNX/SEsNzVr1tSaNWu0dOlSlS5dWn5+fqpSpYrl7XTo0EGLFy/WgAED9Oijj+ro0aN68803Vbp0ae3bty9P+xw9erSWL1+uRo0aadCgQapSpYouXryoQ4cO6ZtvvtEHH3xg/018dHS0Zs2apf379+fpc1J//PGH/RG9ixcvKiEhQWPGjFGxYsXUp08fSVLVqlVVsWJF/etf/5IxRoGBgVq6dKmWL19u+XzkZvPmzXrqqaf02GOP6ejRoxoxYoTKlCmjAQMG5LpOVFSUWrdurVdeeUWpqalq3LixfXa92rVrq0ePHva+WaNBixYtUoUKFeTp6amaNWte93bz6uuvv9a0adPUqVMnVahQQcYYLV68WKdOnVJUVJRDnTndA+3atVNgYKB95jpXV1fFxcXp6NGjDvupVq2annzySU2ePFlubm5q2bKlduzYYZ9N7p+s3Fd5lXVO3377bbVt21YuLi6qVauWxowZo2PHjqlFixYqW7asTp06pXfffVdubm5q2rRprtu7//771aNHD40ZM0Z//PGHOnToIA8PD/3888/y9vbWwIEDVaVKFT3zzDOaMmWK7rjjDrVt29Y+a19YWJhefPFFS8cgSS+++KJmz56t9u3ba/To0SpXrpz+7//+T9OmTdOzzz6rypUrX3MbFStWlJeXl+bNm6dq1arJ19dXoaGh+vPPP/X888/rscceU0REhNzd3bVq1Spt27btmiOTAG4QJ050AeA2l9usfTnNVpbT7GIXLlwwb7zxhomIiDDu7u4mKCjIPPDAA2b9+vX2PlfO2meMMbt27TJRUVHG09PTBAYGmujoaPPll186zNqXZe3ataZ9+/YmMDDQuLm5mTJlypj27dubzz77LFttJ0+ezPH4smY+M8aYhIQE07hxY+Pt7W0kXXXGt6yZu955550cl7/11lumfPnyxsPDw1SrVs189NFHOZ6nq+3z5MmTZtCgQSY8PNy4ubmZwMBAU6dOHTNixAiHGfayZlP857HkRlfM1ufm5mYqVKhg+vTpY3777TeHvlnXws/PzxQvXtw89thj5siRI0aSGTlypL1fbuc4N1nnftmyZaZHjx6mWLFixsvLy7Rr187s27fPoe+Vs/YZ8/e99corr5hy5coZNzc3U7p0afPss8+alJQUh36HDh0yrVq1Mn5+fkZStu1cKa/bzeusfb/++qt54oknTMWKFY2Xl5cJCAgw9erVM3FxcQ79rnYPbNq0yTRq1Mj4+PiYMmXKmJEjR5qPP/442/VOS0szQ4YMMaVKlTKenp6mQYMGZsOGDTl+jeXlvrra/X3l9U9LSzNPPfWUKVmypLHZbPbavv76a9O2bVtTpkwZ4+7ubkqVKmXatWtnfvzxx2ueu8uXL5tJkyaZyMhI4+7ubgICAkzDhg3N0qVLHfq8/fbbpnLlysbNzc2UKFHCPPnkk+bo0aMO22ratKmpUaNGtn3kdG8dPnzYdOvWzQQFBRk3NzdTpUoV884775jLly/b+1zra3/BggWmatWqxs3NzX6u/vjjD9O7d29TtWpV4+PjY3x9fU2tWrXMpEmTHGZFBHDz2Iwx5qalNgAACkBcXJz69Omj+Ph41a1b19nlAACKID4jBQAAAAAWEaQAAAAAwCIe7QMAAAAAixiRAgAAAACLCFIAAAAAYBFBCgAAAAAs4g/ySsrMzNTx48fl5+cnm83m7HIAAAAAOIkxRmfOnFFoaKjuuCP3cSeClKTjx48rLCzM2WUAAAAAKCSOHj2qsmXL5rqcICXJz89P0t8ny9/f38nVAAAAAHCW1NRUhYWF2TNCbghSkv1xPn9/f4IUAAAAgGt+5IfJJgAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWOTq7AIA4HZ18uRJpaamOruMIs3f318lS5Z0dhkAgNsQQQoAboCTJ0+qW7dnlZyc5uxSirSgIA/Nnz+dMAUAKHAEKQC4AVJTU5WcnCYPjyHy8gpzdjlF0oULR5WcPEGpqakEKQBAgSNIAcAN5OUVJh+fis4uo8hKY0AQAHCDMNkEAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKnB6nff/9dTz75pIKCguTt7a27775bW7ZssS83xigmJkahoaHy8vJSs2bNtHPnTodtpKWlaeDAgSpRooR8fHzUsWNHHTt27GYfCgAAAIAiwqlBKiUlRY0bN5abm5u+/fZb7dq1SxMmTFCxYsXsfcaPH6+JEydq6tSpio+PV0hIiKKionTmzBl7n8GDB2vJkiVauHCh1q1bp7Nnz6pDhw66fPmyE44KAAAAwO3O1Zk7f/vttxUWFqbY2Fh7W/ny5e3/b4zR5MmTNWLECHXu3FmSNGvWLAUHB2v+/Pnq16+fTp8+rZkzZ2rOnDlq2bKlJGnu3LkKCwvTihUr1Lp165t6TAAAAABuf04dkfrqq69Ut25dPfbYYypVqpRq166tjz76yL784MGDSkpKUqtWrextHh4eatq0qdavXy9J2rJliy5duuTQJzQ0VJGRkfY+V0pLS1NqaqrDCwAAAADyyqlB6sCBA5o+fboiIiL0/fffq3///ho0aJBmz54tSUpKSpIkBQcHO6wXHBxsX5aUlCR3d3cVL1481z5XGjdunAICAuyvsLCwgj40AAAAALcxpwapzMxM3XPPPRo7dqxq166tfv366emnn9b06dMd+tlsNof3xphsbVe6Wp/hw4fr9OnT9tfRo0ev70AAAAAAFClODVKlS5dW9erVHdqqVaumI0eOSJJCQkIkKdvI0okTJ+yjVCEhIUpPT1dKSkqufa7k4eEhf39/hxcAAAAA5JVTg1Tjxo21Z88eh7a9e/eqXLlykqTw8HCFhIRo+fLl9uXp6elau3atGjVqJEmqU6eO3NzcHPokJiZqx44d9j4AAAAAUJCcOmvfiy++qEaNGmns2LHq0qWLNm3apA8//FAffvihpL8f6Rs8eLDGjh2riIgIRUREaOzYsfL29la3bt0kSQEBAYqOjtaQIUMUFBSkwMBADR06VDVr1rTP4gcAAAAABcmpQeree+/VkiVLNHz4cI0ePVrh4eGaPHmyunfvbu8zbNgwXbhwQQMGDFBKSorq16+vZcuWyc/Pz95n0qRJcnV1VZcuXXThwgW1aNFCcXFxcnFxccZhAQAAALjN2YwxxtlFOFtqaqoCAgJ0+vRpPi8FoEDs379fjz02WMWKTZaPT0Vnl1MknTu3X6dODdZnn01WxYpcAwBA3uQ1Gzj1M1IAAAAAcCsiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsMjV2QUARdXJkyeVmprq7DKKNH9/f5UsWdLZZQAAgFsQQQpwgpMnT6pbt2eVnJzm7FKKtKAgD82fP50wBQAALCNIAU6Qmpqq5OQ0eXgMkZdXmLPLKZIuXDiq5OQJSk1NJUgBAADLCFKAE3l5hcnHp6Kzyyiy0hgQBAAA+cRkEwAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYJFTg1RMTIxsNpvDKyQkxL7cGKOYmBiFhobKy8tLzZo1086dOx22kZaWpoEDB6pEiRLy8fFRx44ddezYsZt9KAAAAACKEKePSNWoUUOJiYn21/bt2+3Lxo8fr4kTJ2rq1KmKj49XSEiIoqKidObMGXufwYMHa8mSJVq4cKHWrVuns2fPqkOHDrp8+bIzDgcAAABAEeDq9AJcXR1GobIYYzR58mSNGDFCnTt3liTNmjVLwcHBmj9/vvr166fTp09r5syZmjNnjlq2bClJmjt3rsLCwrRixQq1bt36ph4LAAAAgKLB6SNS+/btU2hoqMLDw/X444/rwIEDkqSDBw8qKSlJrVq1svf18PBQ06ZNtX79eknSli1bdOnSJYc+oaGhioyMtPfJSVpamlJTUx1eAAAAAJBXTh2Rql+/vmbPnq3KlSvrjz/+0JgxY9SoUSPt3LlTSUlJkqTg4GCHdYKDg3X48GFJUlJSktzd3VW8ePFsfbLWz8m4ceM0atSoAj4aAEBRdPLkSX4h52T+/v4qWbKks8sAUMQ4NUi1bdvW/v81a9ZUw4YNVbFiRc2aNUsNGjSQJNlsNod1jDHZ2q50rT7Dhw/XSy+9ZH+fmpqqsLCw/BwCAKAIO3nypLp1e1bJyWnOLqVICwry0Pz50wlTAG4qp39G6p98fHxUs2ZN7du3T506dZL096hT6dKl7X1OnDhhH6UKCQlRenq6UlJSHEalTpw4oUaNGuW6Hw8PD3l4eNyYgwAAFBmpqalKTk6Th8cQeXnxCzlnuHDhqJKTJyg1NZUgBeCmKlRBKi0tTbt379b999+v8PBwhYSEaPny5apdu7YkKT09XWvXrtXbb78tSapTp47c3Ny0fPlydenSRZKUmJioHTt2aPz48U47DgBA0eLlFSYfn4rOLqPISmNAEIATODVIDR06VA8++KDuvPNOnThxQmPGjFFqaqp69eolm82mwYMHa+zYsYqIiFBERITGjh0rb29vdevWTZIUEBCg6OhoDRkyREFBQQoMDNTQoUNVs2ZN+yx+AAAAAFDQnBqkjh07pieeeEJ//vmnSpYsqQYNGmjjxo0qV66cJGnYsGG6cOGCBgwYoJSUFNWvX1/Lli2Tn5+ffRuTJk2Sq6urunTpogsXLqhFixaKi4uTi4uLsw4LAAAAwG3OqUFq4cKFV11us9kUExOjmJiYXPt4enpqypQpmjJlSgFXBwAAAAA5c/rfkQIAAACAWw1BCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwpNkBo3bpxsNpsGDx5sbzPGKCYmRqGhofLy8lKzZs20c+dOh/XS0tI0cOBAlShRQj4+PurYsaOOHTt2k6sHAAAAUJQUiiAVHx+vDz/8ULVq1XJoHz9+vCZOnKipU6cqPj5eISEhioqK0pkzZ+x9Bg8erCVLlmjhwoVat26dzp49qw4dOujy5cs3+zAAAAAAFBFOD1Jnz55V9+7d9dFHH6l48eL2dmOMJk+erBEjRqhz586KjIzUrFmzdP78ec2fP1+SdPr0ac2cOVMTJkxQy5YtVbt2bc2dO1fbt2/XihUrnHVIAAAAAG5zTg9Szz33nNq3b6+WLVs6tB88eFBJSUlq1aqVvc3Dw0NNmzbV+vXrJUlbtmzRpUuXHPqEhoYqMjLS3icnaWlpSk1NdXgBAAAAQF65OnPnCxcu1NatWxUfH59tWVJSkiQpODjYoT04OFiHDx+293F3d3cYycrqk7V+TsaNG6dRo0Zdb/kAAAAAiiinjUgdPXpUL7zwgubOnStPT89c+9lsNof3xphsbVe6Vp/hw4fr9OnT9tfRo0etFQ8AAACgSHNakNqyZYtOnDihOnXqyNXVVa6urlq7dq3ee+89ubq62keirhxZOnHihH1ZSEiI0tPTlZKSkmufnHh4eMjf39/hBQAAAAB5la8gdfDgwevecYsWLbR9+3YlJCTYX3Xr1lX37t2VkJCgChUqKCQkRMuXL7evk56errVr16pRo0aSpDp16sjNzc2hT2Jionbs2GHvAwAAAAAFLV+fkapUqZKaNGmi6OhoPfroo1d9NC83fn5+ioyMdGjz8fFRUFCQvX3w4MEaO3asIiIiFBERobFjx8rb21vdunWTJAUEBCg6OlpDhgxRUFCQAgMDNXToUNWsWTPb5BUAAAAAUFDyNSL1yy+/qHbt2hoyZIhCQkLUr18/bdq0qaBr07BhwzR48GANGDBAdevW1e+//65ly5bJz8/P3mfSpEnq1KmTunTposaNG8vb21tLly6Vi4tLgdcDAAAAAFI+g1RkZKQmTpyo33//XbGxsUpKStJ9992nGjVqaOLEiTp58mS+ilmzZo0mT55sf2+z2RQTE6PExERdvHhRa9euzTaK5enpqSlTpig5OVnnz5/X0qVLFRYWlq/9AwAAAEBeXNdkE66urnr44Yf16aef6u2339b+/fs1dOhQlS1bVj179lRiYmJB1QkAAAAAhcZ1BanNmzdrwIABKl26tCZOnKihQ4dq//79WrVqlX7//Xc99NBDBVUnAAAAABQa+ZpsYuLEiYqNjdWePXvUrl07zZ49W+3atdMdd/ydy8LDwzVjxgxVrVq1QIsFAAC42U6ePKnU1FRnl1Gk+fv7q2TJks4uA3CQryA1ffp09e3bV3369FFISEiOfe68807NnDnzuooDAABwppMnT6pbt2eVnJzm7FKKtKAgD82fP50whUIlX0Fq37591+zj7u6uXr165WfzAAAAhUJqaqqSk9Pk4TFEXl5MZuUMFy4cVXLyBKWmphKkUKjkK0jFxsbK19dXjz32mEP7Z599pvPnzxOgAADAbcXLK0w+PhWdXUaRlcaAIAqhfE028dZbb6lEiRLZ2kuVKqWxY8ded1EAAAAAUJjlK0gdPnxY4eHh2drLlSunI0eOXHdRAAAAAFCY5StIlSpVStu2bcvW/ssvvygoKOi6iwIAAACAwixfQerxxx/XoEGDtHr1al2+fFmXL1/WqlWr9MILL+jxxx8v6BoBAAAAoFDJ12QTY8aM0eHDh9WiRQu5uv69iczMTPXs2ZPPSAEAAAC47eUrSLm7u2vRokV688039csvv8jLy0s1a9ZUuXLlCro+AAAAACh08hWkslSuXFmVK1cuqFoAAAAA4JaQryB1+fJlxcXFaeXKlTpx4oQyMzMdlq9atapAigMAAACAwihfQeqFF15QXFyc2rdvr8jISNlstoKuCwAAAAAKrXwFqYULF+rTTz9Vu3btCroeAAAAACj08jX9ubu7uypVqlTQtQAAAADALSFfQWrIkCF69913ZYwp6HoAAAAAoNDL16N969at0+rVq/Xtt9+qRo0acnNzc1i+ePHiAikOAAAAAAqjfAWpYsWK6eGHHy7oWgAAAADglpCvIBUbG1vQdQAAAADALSNfn5GSpIyMDK1YsUIzZszQmTNnJEnHjx/X2bNnC6w4AAAAACiM8jUidfjwYbVp00ZHjhxRWlqaoqKi5Ofnp/Hjx+vixYv64IMPCrpOAAAAACg08jUi9cILL6hu3bpKSUmRl5eXvf3hhx/WypUrC6w4AAAAACiM8j1r3//+9z+5u7s7tJcrV06///57gRQGAAAAAIVVvkakMjMzdfny5Wztx44dk5+f33UXBQAAAACFWb6CVFRUlCZPnmx/b7PZdPbsWY0cOVLt2rUrqNoAAAAAoFDK16N9kyZNUvPmzVW9enVdvHhR3bp10759+1SiRAktWLCgoGsEAAAAgEIlX0EqNDRUCQkJWrBggbZu3arMzExFR0ere/fuDpNPAAAAAMDtKF9BSpK8vLzUt29f9e3btyDrAQAAAIBCL19Bavbs2Vdd3rNnz3wVAwAAAAC3gnwFqRdeeMHh/aVLl3T+/Hm5u7vL29ubIAUAAADgtpavWftSUlIcXmfPntWePXt03333MdkEAAAAgNtevoJUTiIiIvTWW29lG60CAAAAgNtNgQUpSXJxcdHx48cLcpMAAAAAUOjk6zNSX331lcN7Y4wSExM1depUNW7cuEAKAwAAAIDCKl9BqlOnTg7vbTabSpYsqQceeEATJkwoiLoAAAAAoNDKV5DKzMws6DoAAAAA4JZRoJ+RAgAAAICiIF8jUi+99FKe+06cODE/uwAAAACAQitfQernn3/W1q1blZGRoSpVqkiS9u7dKxcXF91zzz32fjabrWCqBAAAAIBCJF9B6sEHH5Sfn59mzZql4sWLS/r7j/T26dNH999/v4YMGVKgRQIAAABAYZKvz0hNmDBB48aNs4coSSpevLjGjBnDrH0AAAAAbnv5ClKpqan6448/srWfOHFCZ86cue6iAAAAAKAwy1eQevjhh9WnTx/997//1bFjx3Ts2DH997//VXR0tDp37lzQNQIAAABAoZKvz0h98MEHGjp0qJ588kldunTp7w25uio6OlrvvPNOgRYIAAAAAIVNvoKUt7e3pk2bpnfeeUf79++XMUaVKlWSj49PQdcHAAAAAIXOdf1B3sTERCUmJqpy5cry8fGRMaag6gIAAACAQitfQSo5OVktWrRQ5cqV1a5dOyUmJkqSnnrqKaY+BwAAAHDby1eQevHFF+Xm5qYjR47I29vb3t61a1d99913BVYcAAAAABRG+fqM1LJly/T999+rbNmyDu0RERE6fPhwgRQGAAAAAIVVvkakzp075zASleXPP/+Uh4fHdRcFAAAAAIVZvoJUkyZNNHv2bPt7m82mzMxMvfPOO2revHmBFQcAAAAAhVG+Hu1755131KxZM23evFnp6ekaNmyYdu7cqb/++kv/+9//CrpGAAAAAChU8jUiVb16dW3btk316tVTVFSUzp07p86dO+vnn39WxYoVC7pGAAAAAChULI9IXbp0Sa1atdKMGTM0atSoG1ETAAAAABRqlkek3NzctGPHDtlstuve+fTp01WrVi35+/vL399fDRs21LfffmtfboxRTEyMQkND5eXlpWbNmmnnzp0O20hLS9PAgQNVokQJ+fj4qGPHjjp27Nh11wYAAAAAucnXo309e/bUzJkzr3vnZcuW1VtvvaXNmzdr8+bNeuCBB/TQQw/Zw9L48eM1ceJETZ06VfHx8QoJCVFUVJTOnDlj38bgwYO1ZMkSLVy4UOvWrdPZs2fVoUMHXb58+brrAwAAAICc5GuyifT0dH388cdavny56tatKx8fH4flEydOzNN2HnzwQYf3//73vzV9+nRt3LhR1atX1+TJkzVixAh17txZkjRr1iwFBwdr/vz56tevn06fPq2ZM2dqzpw5atmypSRp7ty5CgsL04oVK9S6desc95uWlqa0tDT7+9TU1DwfOwAAAABYGpE6cOCAMjMztWPHDt1zzz3y9/fX3r179fPPP9tfCQkJ+Srk8uXLWrhwoc6dO6eGDRvq4MGDSkpKUqtWrex9PDw81LRpU61fv16StGXLFvtntrKEhoYqMjLS3icn48aNU0BAgP0VFhaWr5oBAAAAFE2WRqQiIiKUmJio1atXS5K6du2q9957T8HBwfkuYPv27WrYsKEuXrwoX19fLVmyRNWrV7cHoSu3HRwcrMOHD0uSkpKS5O7uruLFi2frk5SUlOs+hw8frpdeesn+PjU1lTAFAAAAIM8sBSljjMP7b7/9VufOnbuuAqpUqaKEhASdOnVKn3/+uXr16qW1a9fal185qYUx5poTXVyrj4eHhzw8PK6rbgAAAABFV74mm8hyZbDKD3d3d1WqVEl169bVuHHjdNddd+ndd99VSEiIJGUbWTpx4oR9lCokJETp6elKSUnJtQ8AAAAAFDRLQcpms2Ub6SmIadD/yRijtLQ0hYeHKyQkRMuXL7cvS09P19q1a9WoUSNJUp06deTm5ubQJzExUTt27LD3AQAAAICCZvnRvt69e9sfi7t48aL69++fbda+xYsX52l7r776qtq2bauwsDCdOXNGCxcu1Jo1a/Tdd9/JZrNp8ODBGjt2rCIiIhQREaGxY8fK29tb3bp1kyQFBAQoOjpaQ4YMUVBQkAIDAzV06FDVrFnTPosfAAAAABQ0S0GqV69eDu+ffPLJ69r5H3/8oR49eigxMVEBAQGqVauWvvvuO0VFRUmShg0bpgsXLmjAgAFKSUlR/fr1tWzZMvn5+dm3MWnSJLm6uqpLly66cOGCWrRoobi4OLm4uFxXbQAAAACQG0tBKjY2tkB3fq0/6muz2RQTE6OYmJhc+3h6emrKlCmaMmVKgdYGAAAAALm5rskmAAAAAKAoIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAi5wapMaNG6d7771Xfn5+KlWqlDp16qQ9e/Y49DHGKCYmRqGhofLy8lKzZs20c+dOhz5paWkaOHCgSpQoIR8fH3Xs2FHHjh27mYcCAAAAoAhxapBau3atnnvuOW3cuFHLly9XRkaGWrVqpXPnztn7jB8/XhMnTtTUqVMVHx+vkJAQRUVF6cyZM/Y+gwcP1pIlS7Rw4UKtW7dOZ8+eVYcOHXT58mVnHBYAAACA25yrM3f+3XffObyPjY1VqVKltGXLFjVp0kTGGE2ePFkjRoxQ586dJUmzZs1ScHCw5s+fr379+un06dOaOXOm5syZo5YtW0qS5s6dq7CwMK1YsUKtW7e+6ccFAAAA4PZWqD4jdfr0aUlSYGCgJOngwYNKSkpSq1at7H08PDzUtGlTrV+/XpK0ZcsWXbp0yaFPaGioIiMj7X2ulJaWptTUVIcXAAAAAORVoQlSxhi99NJLuu+++xQZGSlJSkpKkiQFBwc79A0ODrYvS0pKkru7u4oXL55rnyuNGzdOAQEB9ldYWFhBHw4AAACA21ihCVLPP/+8tm3bpgULFmRbZrPZHN4bY7K1XelqfYYPH67Tp0/bX0ePHs1/4QAAAACKnEIRpAYOHKivvvpKq1evVtmyZe3tISEhkpRtZOnEiRP2UaqQkBClp6crJSUl1z5X8vDwkL+/v8MLAAAAAPLKqUHKGKPnn39eixcv1qpVqxQeHu6wPDw8XCEhIVq+fLm9LT09XWvXrlWjRo0kSXXq1JGbm5tDn8TERO3YscPeBwAAAAAKklNn7Xvuuec0f/58ffnll/Lz87OPPAUEBMjLy0s2m02DBw/W2LFjFRERoYiICI0dO1be3t7q1q2bvW90dLSGDBmioKAgBQYGaujQoapZs6Z9Fj8AAAAAKEhODVLTp0+XJDVr1syhPTY2Vr1795YkDRs2TBcuXNCAAQOUkpKi+vXra9myZfLz87P3nzRpklxdXdWlSxdduHBBLVq0UFxcnFxcXG7WoQAAAAAoQpwapIwx1+xjs9kUExOjmJiYXPt4enpqypQpmjJlSgFWBwAAAAA5KxSTTQAAAADArYQgBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARU4NUj/88IMefPBBhYaGymaz6YsvvnBYboxRTEyMQkND5eXlpWbNmmnnzp0OfdLS0jRw4ECVKFFCPj4+6tixo44dO3YTjwIAAABAUePUIHXu3Dndddddmjp1ao7Lx48fr4kTJ2rq1KmKj49XSEiIoqKidObMGXufwYMHa8mSJVq4cKHWrVuns2fPqkOHDrp8+fLNOgwAAAAARYyrM3fetm1btW3bNsdlxhhNnjxZI0aMUOfOnSVJs2bNUnBwsObPn69+/frp9OnTmjlzpubMmaOWLVtKkubOnauwsDCtWLFCrVu3vmnHAgAAAKDoKLSfkTp48KCSkpLUqlUre5uHh4eaNm2q9evXS5K2bNmiS5cuOfQJDQ1VZGSkvU9O0tLSlJqa6vACAAAAgLwqtEEqKSlJkhQcHOzQHhwcbF+WlJQkd3d3FS9ePNc+ORk3bpwCAgLsr7CwsAKuHgAAAMDtrNAGqSw2m83hvTEmW9uVrtVn+PDhOn36tP119OjRAqkVAAAAQNFQaINUSEiIJGUbWTpx4oR9lCokJETp6elKSUnJtU9OPDw85O/v7/ACAAAAgLwqtEEqPDxcISEhWr58ub0tPT1da9euVaNGjSRJderUkZubm0OfxMRE7dixw94HAAAAAAqaU2ftO3v2rH777Tf7+4MHDyohIUGBgYG68847NXjwYI0dO1YRERGKiIjQ2LFj5e3trW7dukmSAgICFB0drSFDhigoKEiBgYEaOnSoatasaZ/FDwAAAAAKmlOD1ObNm9W8eXP7+5deekmS1KtXL8XFxWnYsGG6cOGCBgwYoJSUFNWvX1/Lli2Tn5+ffZ1JkybJ1dVVXbp00YULF9SiRQvFxcXJxcXlph8PAAAAgKLBqUGqWbNmMsbkutxmsykmJkYxMTG59vH09NSUKVM0ZcqUG1AhAAAAAGRXaD8jBQAAAACFFUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgkauzCwAAAACc6eTJk0pNTXV2GUWev7+/SpYs6ewy8owgBQAAgCLr5MmT6tbtWSUnpzm7lCIvKMhD8+dPv2XCFEEKAAAARVZqaqqSk9Pk4TFEXl5hzi6nyLpw4aiSkycoNTWVIAUAAADcKry8wuTjU9HZZRRpabfYoCBBqhDiOV3nu9We0QUAAMDNRZAqZHhOt3C41Z7RBQAAwM1FkCpkeE7X+W7FZ3QBAABwcxGkCime03WuW+0ZXQAAANxc/EFeAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACy6bYLUtGnTFB4eLk9PT9WpU0c//vijs0sCAAAAcJu6LYLUokWLNHjwYI0YMUI///yz7r//frVt21ZHjhxxdmkAAAAAbkO3RZCaOHGioqOj9dRTT6latWqaPHmywsLCNH36dGeXBgAAAOA25OrsAq5Xenq6tmzZon/9618O7a1atdL69etzXCctLU1paWn296dPn5Ykpaam3rhC8+jMmTO6fPmSzpz5VRkZZ5xdTpF04cLv/+8anLlh9wTX2flu9HXmGjsfX8tFA1/Ltz+ucdFwM75n51XW/o0xV+1nM9fqUcgdP35cZcqU0f/+9z81atTI3j527FjNmjVLe/bsybZOTEyMRo0adTPLBAAAAHALOXr0qMqWLZvr8lt+RCqLzWZzeG+MydaWZfjw4XrppZfs7zMzM/XXX38pKCgo13WQd6mpqQoLC9PRo0fl7+/v7HJwA3CNiwau8+2Pa1w0cJ1vf1zjgmWM0ZkzZxQaGnrVfrd8kCpRooRcXFyUlJTk0H7ixAkFBwfnuI6Hh4c8PDwc2ooVK3ajSiyy/P39+WK+zXGNiwau8+2Pa1w0cJ1vf1zjghMQEHDNPrf8ZBPu7u6qU6eOli9f7tC+fPlyh0f9AAAAAKCg3PIjUpL00ksvqUePHqpbt64aNmyoDz/8UEeOHFH//v2dXRoAAACA29BtEaS6du2q5ORkjR49WomJiYqMjNQ333yjcuXKObu0IsnDw0MjR47M9vgkbh9c46KB63z74xoXDVzn2x/X2Dlu+Vn7AAAAAOBmu+U/IwUAAAAANxtBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCCFa+rdu7dsNluO08kPGDBANptNvXv3dmhfv369XFxc1KZNG3vbnDlz5OPjo99++82h7/Hjx1W8eHG9++67N6R+5F3v3r3VqVMnSVKzZs00ePDgbH2++OIL2Ww2+/u4uDjZbDaHay1Jp06dks1m05o1a25gxciv/F5r/nh54ZfT919JWrNmjWw2m06dOpVtnbvvvlsxMTEObT///LO6du2q0qVLy8PDQ+XKlVOHDh20dOlSMU+V8504cUL9+vXTnXfeKQ8PD4WEhKh169basGGDJKl8+fKy2WzZXm+99ZYk6dChQw7txYsXV5MmTbR27VpnHhb+4ejRo4qOjlZoaKjc3d1Vrlw5vfDCC0pOTnbo99tvv6lPnz4qW7asPDw8FB4erieeeEKbN2+2/xt9tRf/TucfQQp5EhYWpoULF+rChQv2tosXL2rBggW68847s/X/5JNPNHDgQK1bt05HjhyRJPXo0UOtW7dWr169lJmZae/7zDPPqHbt2ho0aNCNPxDcEK6urlq5cqVWr17t7FKAIi+n779Wffnll2rQoIHOnj2rWbNmadeuXfrss8/UqVMnvfbaazp9+nQBVw2rHnnkEf3yyy+aNWuW9u7dq6+++krNmjXTX3/9Ze+T9Wdh/vkaOHCgw3ZWrFihxMRErV27Vv7+/mrXrp0OHjx4sw8HVzhw4IDq1q2rvXv3asGCBfrtt9/0wQcfaOXKlWrYsKH9Om/evFl16tTR3r17NWPGDO3atUtLlixR1apVNWTIEHXt2tXh+jds2FBPP/20Q1ujRo2cfLS3rtvi70jhxrvnnnt04MABLV68WN27d5ckLV68WGFhYapQoYJD33PnzunTTz9VfHy8kpKSFBcXpzfeeEOSNGPGDEVGRmrixIkaOnSo4uLi9OOPP2rbtm0Ov/nGrcXHx0ddunTRv/71L/3000/OLgcosq72/dfKNqKjo9W+fXstXrzY3l6xYkXVq1dPTz31FCNSTnbq1CmtW7dOa9asUdOmTSVJ5cqVU7169Rz6+fn5KSQk5KrbCgoKUkhIiEJCQjRjxgyVLVtWy5YtU79+/W5Y/bi25557Tu7u7lq2bJm8vLwkSXfeeadq166tihUrasSIEZo2bZp69+6tiIgI/fjjj7rjjv9/fOTuu+/WCy+8IC8vL/v6kuTu7i5vb+9r3hfIG0akkGd9+vRRbGys/f0nn3yivn37Zuu3aNEiValSRVWqVNGTTz6p2NhY+z+6JUuW1IwZM/T6669r+fLlevHFF/Xuu+/yx5NvAzExMdq+fbv++9//OrsUoMi62vffvFq2bJmSk5M1bNiwXPvwiy/n8vX1la+vr7744gulpaUV2Ha9vb0lSZcuXSqwbcK6v/76S99//70GDBjgEIIkKSQkRN27d9eiRYuUkJCgnTt3asiQIQ4hKguPYt94BCnkWY8ePbRu3TodOnRIhw8f1v/+9z89+eST2frNnDnT3t6mTRudPXtWK1eutC/v1KmTunTpojZt2qhJkybZPl+FW1NoaKheeOEFjRgxQhkZGc4uByiSrvX9Ny/27t0rSapSpYq9LT4+3v7Du6+vr77++uuCKxqWubq6Ki4uTrNmzVKxYsXUuHFjvfrqq9q2bZtDv1deecXhuvn6+ub6eZhz585p+PDhcnFxsY9ywTn27dsnY4yqVauW4/Jq1aopJSVF+/btkyRVrVr1ZpaHfyBIIc9KlCih9u3ba9asWYqNjVX79u1VokQJhz579uzRpk2b9Pjjj0v6+5t9165d9cknnzj0e/3115WZmanXX3/9ptWPG++VV17RyZMns11vADdeXr//5ketWrWUkJCghIQEnTt3jl+WFAKPPPKIjh8/rq+++kqtW7fWmjVrdM899yguLs7e5+WXX7Zft6xX/fr1HbbTqFEj+fr6ys/PT0uXLlVcXJxq1qx5k48GVmSNMmf9lxFi5+EzUrCkb9++ev755yVJ77//frblM2fOVEZGhsqUKWNvM8bIzc1NKSkpKl68uKS//4H/539R+Pj7++f4gfJTp07J398/x3WKFSum4cOHa9SoUerQocONLhEFJD/XGoXPtb7/Zl3L06dPZ3vk59SpUwoICJAkRURESPo7mDVo0ECS5OHhoUqVKt2Eo4AVnp6eioqKUlRUlN544w099dRTGjlypP1JjxIlSlzzui1atEjVq1dXsWLFFBQUdBOqxrVUqlRJNptNu3btss+u+k+//vqrihcvrsqVK0uSdu/erbvvvvvmFglJjEjBojZt2ig9PV3p6elq3bq1w7KMjAzNnj1bEyZMcPjt1y+//KJy5cpp3rx5Tqoa+VG1alVt3rw5W3t8fLzDIz9XGjhwoO644w6ms7+F5Pdao/DIy/ffiIgI3XHHHYqPj3dYNzExUb///rv9Wrdq1UqBgYF6++23nXEouA7Vq1fXuXPnLK0TFhamihUrEqIKkaCgIEVFRWnatGkOsyVLUlJSkubNm6euXbvq7rvvVvXq1TVhwgSH2ZCz5PSnDlCwGA6AJS4uLtq9e7f9///p66+/VkpKiqKjo+2/2czy6KOPaubMmfbRLBR+AwYM0NSpU/Xcc8/pmWeekZeXl5YvX66ZM2dqzpw5ua7n6empUaNG6bnnnruJ1eJ6WLnWly9fVkJCgkObu7u7qlevfhMrxpXy+v23X79+GjJkiFxdXXXXXXfp+PHjGjFihKpVq6ZWrVpJ+nsig48//lhdu3ZV+/btNWjQIEVEROjs2bP67rvvJGX//o+bKzk5WY899pj69u2rWrVqyc/PT5s3b9b48eP10EMP2fudOXNGSUlJDut6e3sz0nwLmDp1qho1aqTWrVtrzJgxCg8P186dO/Xyyy+rTJky+ve//y2bzabY2Fi1bNlSTZo00auvvqqqVavq7NmzWrp0qZYtW8bfBbvBGJGCZf7+/jl+E545c6ZatmyZ7R9x6e9nuRMSErR169abUSLyKTMz0/64Zfny5fXjjz9q//79atWqle69917FxcUpLi5Ojz322FW306tXr2zT4qNwye+1Pnv2rGrXru3wateunTMOAf+Q1++/kyZN0lNPPaVXX31VNWrUUPfu3RUeHq5ly5Y5PGr98MMPa/369fL29lbPnj1VpUoVPfDAA1q1apUWLlzIo7tO5uvrq/r162vSpElq0qSJIiMj9frrr+vpp5/W1KlT7f3eeOMNlS5d2uF1tdkYUXhERERo8+bNqlixorp27aqKFSvqmWeeUfPmzbVhwwYFBgZKkurVq2fv9/TTT6tatWrq2LGjdu7cqcmTJzv3IIoAm+GPQQD4f9q0aaNKlSo5/EOM2xPXGgCA68OIFAClpKTo//7v/7RmzRq1bNnS2eXgBuJaAwBQMPiMFAD17dtX8fHxGjJkiMPz9bj9cK0BACgYPNoHAAAAABbxaB8AAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAG4b48aN07333is/Pz+VKlVKnTp10p49exz6GGMUExOj0NBQeXl5qVmzZtq5c6d9+V9//aWBAweqSpUq8vb21p133qlBgwbp9OnTDttJSUlRjx49FBAQoICAAPXo0UOnTp26GYcJACgECFIAgNvG2rVr9dxzz2njxo1avny5MjIy1KpVK507d87eZ/z48Zo4caKmTp2q+Ph4hYSEKCoqSmfOnJEkHT9+XMePH9d//vMfbd++XXFxcfruu+8UHR3tsK9u3bopISFB3333nb777jslJCSoR48eN/V4AQDOw/TnAIDb1smTJ1WqVCmtXbtWTZo0kTFGoaGhGjx4sF555RVJUlpamoKDg/X222+rX79+OW7ns88+05NPPqlz587J1dVVu3fvVvXq1bVx40bVr19fkrRx40Y1bNhQv/76q6pUqXLTjhEA4ByMSAEAbltZj+MFBgZKkg4ePKikpCS1atXK3sfDw0NNmzbV+vXrr7odf39/ubr+/XfsN2zYoICAAHuIkqQGDRooICDgqtsBANw+CFIAgNuSMUYvvfSS7rvvPkVGRkqSkpKSJEnBwcEOfYODg+3LrpScnKw333zTYbQqKSlJpUqVyta3VKlSuW4HAHB7cXV2AQAA3AjPP/+8tm3bpnXr1mVbZrPZHN4bY7K1SVJqaqrat2+v6tWra+TIkVfdxtW2AwC4/TAiBQC47QwcOFBfffWVVq9erbJly9rbQ0JCJCnbqNGJEyeyjVKdOXNGbdq0ka+vr5YsWSI3NzeH7fzxxx/Z9nvy5Mls2wEA3J4IUgCA24YxRs8//7wWL16sVatWKTw83GF5eHi4QkJCtHz5cntbenq61q5dq0aNGtnbUlNT1apVK7m7u+urr76Sp6enw3YaNmyo06dPa9OmTfa2n376SadPn3bYDgDg9sWsfQCA28aAAQM0f/58ffnllw4z5wUEBMjLy0uS9Pbbb2vcuHGKjY1VRESExo4dqzVr1mjPnj3y8/PTmTNnFBUVpfPnz2vJkiXy8fGxb6dkyZJycXGRJLVt21bHjx/XjBkzJEnPPPOMypUrp6VLl97EIwYAOAtBCgBw28jt80mxsbHq3bu3pL9HrUaNGqUZM2YoJSVF9evX1/vvv2+fkGLNmjVq3rx5jts5ePCgypcvL+nvP9w7aNAgffXVV5Kkjh07aurUqSpWrFiBHhMAoHAiSAEAAACARXxGCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoACpny5ctr8uTJzi5D58+f1yOPPCJ/f3/ZbDadOnUqx34ffvihwsLCdMcdd2jy5MmKiYnR3XfffVNrvRFu5nE0a9ZMgwcPvin7utGSk5NVqlQpHTp0yNml5Mpms+mLL77Idfn27dtVtmxZnTt37uYVBeCWQ5ACUKQlJSVp4MCBqlChgjw8PBQWFqYHH3xQK1euvOH7jouLU7FixbK1x8fH65lnnrnh+7+WWbNm6ccff9T69euVmJiogICAbH1SU1P1/PPP65VXXtHvv/+uZ555RkOHDr3u83c7BYuiZty4cXrwwQdVvnx5Z5eS7zBcs2ZN1atXT5MmTSr4ogDcNlydXQAAOMuhQ4fUuHFjFStWTOPHj1etWrV06dIlff/993ruuef066+/5rjepUuX5ObmdsPqKlmy5A3bthX79+9XtWrVFBkZmWufI0eO6NKlS2rfvr1Kly5tb/f19c11nfT0dLm7uxdorSgcLly4oJkzZ+qbb75xdinXrU+fPurfv7+GDx8uFxcXZ5cDoBBiRApAkTVgwADZbDZt2rRJjz76qCpXrqwaNWropZde0saNG+39bDabPvjgAz300EPy8fHRmDFjJElLly5VnTp15OnpqQoVKmjUqFHKyMiwrzdx4kTVrFlTPj4+CgsL04ABA3T27FlJ0po1a9SnTx+dPn1aNptNNptNMTExkrI/2nfkyBE99NBD8vX1lb+/v7p06aI//vjDvjzrt+5z5sxR+fLlFRAQoMcff1xnzpy56vF//vnnqlGjhjw8PFS+fHlNmDDBvqxZs2aaMGGCfvjhB9lsNjVr1izb+nFxcapZs6YkqUKFCrLZbDp06FC2UYDevXurU6dOGjdunEJDQ1W5cmVJ0rRp0xQRESFPT08FBwfr0Ucftfdfu3at3n33Xfu5ye0xsbS0NA0bNkxhYWHy8PBQRESEZs6cKUm6fPmyoqOjFR4eLi8vL1WpUkXvvvuuw/pr1qxRvXr15OPjo2LFiqlx48Y6fPiwQx8r5zU5OVlPPPGEypYtK29vb9WsWVMLFixw6HPu3Dn17NlTvr6+Kl26tMN5l6Thw4erQYMG2bZdq1YtjRw5UtLfo5ZRUVEqUaKEAgIC1LRpU23dutWhv81m08cff6yHH35Y3t7eioiI0FdffeXQZ+fOnWrfvr38/f3l5+en+++/X/v377cvj42NVbVq1eTp6amqVatq2rRpuR67JH377bdydXVVw4YN7W1r1qyRzWbT999/r9q1a8vLy0sPPPCATpw4oW+//VbVqlWTv7+/nnjiCZ0/f96+XlpamgYNGqRSpUrJ09NT9913n+Lj47Ntd+XKlapbt668vb3VqFEj7dmzR9Lf9+eoUaP0yy+/2O+juLg4+/p//vnnVc9N69atlZycrLVr1171mAEUYQYAiqDk5GRjs9nM2LFjr9lXkilVqpSZOXOm2b9/vzl06JD57rvvjL+/v4mLizP79+83y5YtM+XLlzcxMTH29SZNmmRWrVplDhw4YFauXGmqVKlinn32WWOMMWlpaWby5MnG39/fJCYmmsTERHPmzBljjDHlypUzkyZNMsYYk5mZaWrXrm3uu+8+s3nzZrNx40Zzzz33mKZNm9r3M3LkSOPr62s6d+5stm/fbn744QcTEhJiXn311VyPafPmzeaOO+4wo0ePNnv27DGxsbHGy8vLxMbG2s/P008/bRo2bGgSExNNcnJytm2cP3/erFixwkgymzZtMomJiSYjI8OMHDnS3HXXXfZ+vXr1Mr6+vqZHjx5mx44dZvv27SY+Pt64uLiY+fPnm0OHDpmtW7ead9991xhjzKlTp0zDhg3N008/bT83GRkZOR5Hly5dTFhYmFm8eLHZv3+/WbFihVm4cKExxpj09HTzxhtvmE2bNpkDBw6YuXPnGm9vb7No0SJjjDGXLl0yAQEBZujQoea3334zu3btMnFxcebw4cP5Pq/Hjh0z77zzjvn555/N/v37zXvvvWdcXFzMxo0b7X2effZZU7ZsWbNs2TKzbds206FDB+Pr62teeOEFY4wx27dvN5LMb7/9Zl9nx44dRpLZs2ePMcaYlStXmjlz5phdu3aZXbt2mejoaBMcHGxSU1Pt60gyZcuWNfPnzzf79u0zgwYNMr6+vvZreezYMRMYGGg6d+5s4uPjzZ49e8wnn3xifv31V2OMMR9++KEpXbq0+fzzz82BAwfM559/bgIDA01cXFyux//CCy+YNm3aOLStXr3aSDINGjQw69atM1u3bjWVKlUyTZs2Na1atTJbt241P/zwgwkKCjJvvfWWfb1BgwaZ0NBQ880335idO3eaXr16meLFi9vrz9pu/fr1zZo1a8zOnTvN/fffbxo1amSM+fv+HDJkiKlRo4b9Pjp//nyezk2WevXqOXxNA8A/EaQAFEk//fSTkWQWL158zb6SzODBgx3a7r///mwhbM6cOaZ06dK5bufTTz81QUFB9vexsbEmICAgW79/Bqlly5YZFxcXc+TIEfvynTt32sOLMX//wO/t7e3wQ/TLL79s6tevn2st3bp1M1FRUQ5tL7/8sqlevbr9/QsvvOAQ2HLy888/G0nm4MGD9racglRwcLBJS0uzt33++efG39/foeZ/atq0qT1Y5GbPnj1Gklm+fPlV+/3TgAEDzCOPPGKM+TssSjJr1qzJsW9+zmtO2rVrZ4YMGWKMMebMmTPG3d3dHvay6vDy8nI43lq1apnRo0fb3w8fPtzce++9ue4jIyPD+Pn5maVLl9rbJJnXXnvN/v7s2bPGZrOZb7/91r7N8PBwk56enuM2w8LCzPz58x3a3nzzTdOwYcNc63jooYdM3759HdqyAs+KFSvsbePGjTOSzP79++1t/fr1M61bt7bX6ubmZubNm2dfnp6ebkJDQ8348eNz3e7//d//GUnmwoULxpjs92Jez02Whx9+2PTu3TvX4wVQtPFoH4AiyRgj6e/Hn/Kibt26Du+3bNmi0aNHy9fX1/56+umnlZiYaH88afXq1YqKilKZMmXk5+ennj17Kjk52dJMYLt371ZYWJjCwsLsbdWrV1exYsW0e/due1v58uXl5+dnf1+6dGmdOHHiqttt3LixQ1vjxo21b98+Xb58Oc/15VXNmjUdPhcVFRWlcuXKqUKFCurRo4fmzZvn8FhXXiQkJMjFxUVNmzbNtc8HH3ygunXrqmTJkvL19dVHH32kI0eOSJICAwPVu3dvtW7dWg8++KDeffddJSYmOqxv9bxevnxZ//73v1WrVi0FBQXJ19dXy5Yts+9z//79Sk9Pd3j0LTAwUFWqVHHYTvfu3TVv3jxJf9+rCxYsUPfu3e3LT5w4of79+6ty5coKCAhQQECAzp49a99Pllq1atn/38fHR35+fvb6ExISdP/99+f4eb+TJ0/q6NGjio6OdrjHx4wZ4/Do35UuXLggT0/PHJf9s5bg4GB5e3urQoUKDm1Zte3fv1+XLl1yuEfd3NxUr149h/v+yu1mfU7vatcop/WuPDdZvLy8LN+XAIoOghSAIikiIkI2my3bD2W58fHxcXifmZmpUaNGKSEhwf7avn279u3bJ09PTx0+fFjt2rVTZGSkPv/8c23ZskXvv/++pL8nq8grY0yOYe/K9it/GLbZbMrMzLS03axweSNcef78/Py0detWLViwQKVLl9Ybb7yhu+66K9cp1nPi5eV11eWffvqpXnzxRfXt21fLli1TQkKC+vTpo/T0dHuf2NhYbdiwQY0aNdKiRYtUuXJlh8/HWT2vEyZM0KRJkzRs2DCtWrVKCQkJat26tX2feT3H3bp10969e7V161atX79eR48e1eOPP25f3rt3b23ZskWTJ0/W+vXrlZCQoKCgIIdju1b9Vzt/WX0++ugjh3t8x44dDufnSiVKlFBKSkqOy/5Zi81mu2ptuf2iI6f79srt/rP+q8nLtf3rr78KzeQvAAofghSAIikwMFCtW7fW+++/n+MI0bV+oL/nnnu0Z88eVapUKdvrjjvu0ObNm5WRkaEJEyaoQYMGqly5so4fP+6wDXd392uO/lSvXl1HjhzR0aNH7W27du3S6dOnVa1atbwfcA7bXbdunUPb+vXrVbly5Zs2Q5mrq6tatmyp8ePHa9u2bTp06JBWrVolKW/npmbNmsrMzMx1MoAff/xRjRo10oABA1S7dm1VqlQpx9GU2rVra/jw4Vq/fr0iIyM1f/78fB/Tjz/+qIceekhPPvmk7rrrLlWoUEH79u2zL69UqZLc3NwcwkhKSor27t3rsJ2yZcuqSZMmmjdvnubNm6eWLVsqODjYYT+DBg1Su3bt7BOG/Pnnn5ZqrVWrln788cccg31wcLDKlCmjAwcOZLu/w8PDc91m7dq1tWvXLkt15KRSpUpyd3d3uEcvXbqkzZs3W7rv83IfXc2OHTtUu3btfK8P4PZGkAJQZE2bNk2XL19WvXr19Pnnn2vfvn3avXu33nvvPYdHr3LyxhtvaPbs2YqJidHOnTu1e/duLVq0SK+99pokqWLFisrIyNCUKVN04MABzZkzRx988IHDNsqXL6+zZ89q5cqV+vPPP3N8hKhly5aqVauWunfvrq1bt2rTpk3q2bOnmjZtmu1xQyuGDBmilStX6s0339TevXs1a9YsTZ06VUOHDs33Nq34+uuv9d577ykhIUGHDx/W7NmzlZmZaX/ErXz58vrpp5906NAh/fnnnzmOMJQvX169evVS37599cUXX+jgwYNas2aNPv30U0l//zC+efNmff/999q7d69ef/11h1nfDh48qOHDh2vDhg06fPiwli1bpr17915XQK1UqZKWL1+u9evXa/fu3erXr5+SkpLsy319fRUdHa2XX35ZK1eu1I4dO9S7d2/dcUf2f467d++uhQsX6rPPPtOTTz6ZbT9z5szR7t279dNPP6l79+7XHKG70vPPP6/U1FQ9/vjj2rx5s/bt26c5c+bYZ72LiYnRuHHj9O6772rv3r3avn27YmNjNXHixFy32bp1a+3cuTPXUam88vHx0bPPPquXX35Z3333nXbt2qWnn35a58+fV3R0dJ63U758eR08eFAJCQn6888/lZaWlud1Dx06pN9//10tW7bMzyEAKAIIUgCKrPDwcG3dulXNmzfXkCFDFBkZqaioKK1cuVLTp0+/6rqtW7fW119/reXLl+vee+9VgwYNNHHiRJUrV06SdPfdd2vixIl6++23FRkZqXnz5mncuHEO22jUqJH69++vrl27qmTJkho/fny2/dhsNn3xxRcqXry4mjRpopYtW6pChQpatGjRdR37Pffco08//VQLFy5UZGSk3njjDY0ePVq9e/e+ru3mVbFixbR48WI98MADqlatmj744AMtWLBANWrUkCQNHTpULi4uql69ukqWLJntsz9Zpk+frkcffVQDBgxQ1apV9fTTT9tHGPv376/OnTura9euql+/vpKTkzVgwAD7ut7e3vr111/1yCOPqHLlynrmmWf0/PPPq1+/fvk+rtdff1333HOPWrdurWbNmikkJESdOnVy6PPOO++oSZMm6tixo1q2bKn77rtPderUybatxx57TMnJyTp//ny2bXzyySdKSUlR7dq11aNHD/s04VYEBQVp1apVOnv2rJo2bao6deroo48+sj/y9tRTT+njjz+2T3PftGlTxcXFXXVEqmbNmqpbt649zF6Pt956S4888oh69Oihe+65R7/99pu+//57FS9ePM/beOSRR9SmTRs1b95cJUuWzDYV/dUsWLBArVq1sn9NA8CVbOZGPhQPAACKlG+++UZDhw7Vjh07chxpuxWkpaUpIiJCCxYsyDYpCwBkcXV2AQAA4PbRrl077du3T7///rvDbJO3ksOHD2vEiBGEKABXxYgUAAAAAFh0a465AwAAAIATEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFv1/HLBA/jBD+TwAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data = {'month': ['2020-05', '2020-06', '2020-07', '2020-08', '2020-09', '2020-10'],\n",
+ " 'frequency': [44, 560, 659, 498, 385, 50]}\n",
+ "monthly_counts_c2 = pd.DataFrame(data)\n",
+ "monthly_counts_c2['month'] = pd.to_datetime(monthly_counts_c2['month'])\n",
+ "\n",
+ "# Define month and year labels\n",
+ "month_labels = ['MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT']\n",
+ "year_labels = ['2020']\n",
+ "\n",
+ "# Create the bar plot\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.bar(monthly_counts_c2['month'].dt.strftime('%Y-%m'), monthly_counts_c2['frequency'], \n",
+ " color='blue', edgecolor='black', alpha=0.7)\n",
+ "\n",
+ "# Set the month x-ticks\n",
+ "plt.xticks(ticks=range(len(month_labels)), labels=month_labels, rotation=0)\n",
+ "\n",
+ "# Access the current Axes\n",
+ "ax = plt.gca()\n",
+ "\n",
+ "# Add secondary tick for the year\n",
+ "ax.set_xticks([2.5], minor=True) # Positioning the year '2020' under the month ticks\n",
+ "ax.set_xticklabels(year_labels, minor=True, fontsize=10)\n",
+ "\n",
+ "# Modify the tick parameters for better readability\n",
+ "ax.tick_params(axis='x', which='minor', length=0, pad=20) # Increased pad for separation\n",
+ "\n",
+ "plt.xlabel('Creation of first cash advance (month)')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Incident rate: Bar plot of students cohorts')\n",
+ "\n",
+ "# Show plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39a0f484",
+ "metadata": {},
+ "source": [
+ "**Maybe overcomplicated ... :S**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "41ece5a7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "type status_x created_at_y\n",
+ "incident accepted 2020-07-07 45\n",
+ " 2020-06-18 38\n",
+ " 2020-07-15 32\n",
+ " 2020-06-30 29\n",
+ " 2020-08-10 28\n",
+ " ..\n",
+ " cancelled 2020-07-12 1\n",
+ " 2020-07-13 1\n",
+ " 2020-07-14 1\n",
+ " 2020-07-17 1\n",
+ " rejected 2020-10-15 1\n",
+ "Name: count, Length: 320, dtype: int64"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_fees_cash[df_fees_cash['type'] == \"incident\"][['type', 'status_x', 'created_at_y']].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "e385432e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_fees_incident = df_fees_cash[df_fees_cash[\"type\"] == \"incident\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "389149a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['id_x', 'cash_request_id', 'type', 'status_x', 'category',\n",
+ " 'total_amount', 'reason', 'created_at_x', 'updated_at_x', 'paid_at',\n",
+ " 'from_date', 'to_date', 'charge_moment', 'id_y', 'amount', 'status_y',\n",
+ " 'created_at_y', 'updated_at_y', 'user_id', 'moderated_at',\n",
+ " 'deleted_account_id', 'reimbursement_date',\n",
+ " 'cash_request_received_date', 'money_back_date', 'transfer_type',\n",
+ " 'send_at', 'recovery_status', 'reco_creation', 'reco_last_update',\n",
+ " 'month'],\n",
+ " dtype='object')\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df_fees_incident.columns)\n",
+ "cols_1 = ['id_x', 'cash_request_id', 'id_y', 'created_at_x', 'created_at_y', 'type', 'status_x', 'total_amount', 'amount', 'status_y']\n",
+ "df_fees_incident2 = df_fees_incident[cols_1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "8c58f95a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month\n",
+ "2020-05 44\n",
+ "2020-06 560\n",
+ "2020-07 659\n",
+ "2020-08 498\n",
+ "2020-09 385\n",
+ "2020-10 50\n",
+ "Freq: M, dtype: int64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/1484176937.py:2: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_fees_incident2['created_at_y'] = pd.to_datetime(df_fees_incident2['created_at_y'], errors='coerce')\n",
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/1484176937.py:9: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_fees_incident2['month'] = df_fees_incident2['created_at_y'].dt.to_period('M')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Ensure 'created_at_y' is converted to datetime\n",
+ "df_fees_incident2['created_at_y'] = pd.to_datetime(df_fees_incident2['created_at_y'], errors='coerce')\n",
+ "\n",
+ "# Check for non-convertible entries if there are any\n",
+ "if df_fees_incident2['created_at_y'].isnull().any():\n",
+ " print(\"Some dates couldn't be converted. Please check the data for invalid entries.\")\n",
+ "\n",
+ "# Extract month from the 'created_at_y' series\n",
+ "df_fees_incident2['month'] = df_fees_incident2['created_at_y'].dt.to_period('M') \n",
+ "\n",
+ "# Tabulate the count by month\n",
+ "monthly_counts_i = df_fees_incident2.groupby('month').size()\n",
+ "\n",
+ "# Display the result\n",
+ "print(monthly_counts_i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "64aa6422",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/f1/x77kh9w17j56ckntc3dq_ztm0000gn/T/ipykernel_12051/1499821579.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
+ " end_days = pd.date_range(start=df_fees_incident2['created_at_y'].min(),\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "end_days = pd.date_range(start=df_fees_incident2['created_at_y'].min(),\n",
+ " end=df_fees_incident2['created_at_y'].max(),\n",
+ " freq='M') # 'M' stands for Month End\n",
+ "\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "\n",
+ "# Define the order of plotting\n",
+ "statuses = ['accepted', 'rejected', 'cancelled']\n",
+ "\n",
+ "# Plot histogram for each status\n",
+ "for status in statuses:\n",
+ " plt.hist(df_fees_incident2[df_fees_incident2['status_x'] == status]['created_at_y'], \n",
+ " bins=20, alpha=0.5, label=status.capitalize())\n",
+ "\n",
+ "for date in end_days:\n",
+ " plt.axvline(x=date, color='black', linestyle='--', linewidth=0.7)\n",
+ "\n",
+ "plt.xlabel('Creation of first cash advance (date)')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Incident rate: Histogram of students cohorts by status of the fees')\n",
+ "\n",
+ "plt.legend(loc='upper right')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "72dd5154",
+ "metadata": {},
+ "source": [
+ "3. **Revenue Generated by the Cohort:** Calculate the total revenue generated by each cohort over months to assess the financial impact of user behavior."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "af554f0a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "id_x 0\n",
+ "transfer_type 0\n",
+ "reimbursement_date 0\n",
+ "updated_at_y 0\n",
+ "created_at_y 0\n",
+ "status_y 0\n",
+ "id_y 0\n",
+ "charge_moment 0\n",
+ "amount 0\n",
+ "month 0\n",
+ "cash_request_id 0\n",
+ "created_at_x 0\n",
+ "reason 0\n",
+ "total_amount 0\n",
+ "type 0\n",
+ "status_x 0\n",
+ "updated_at_x 0\n",
+ "user_id 906\n",
+ "cash_request_received_date 1294\n",
+ "send_at 3487\n",
+ "paid_at 5619\n",
+ "moderated_at 9942\n",
+ "reco_last_update 14163\n",
+ "recovery_status 14163\n",
+ "reco_creation 14163\n",
+ "from_date 14308\n",
+ "to_date 14545\n",
+ "money_back_date 16114\n",
+ "category 18861\n",
+ "deleted_account_id 20151\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_fees_cash.isnull().sum().sort_values(ascending=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "9419b333",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 21057.00\n",
+ "mean 81.83\n",
+ "std 26.95\n",
+ "min 1.00\n",
+ "25% 50.00\n",
+ "50% 100.00\n",
+ "75% 100.00\n",
+ "max 200.00\n",
+ "Name: amount, dtype: float64"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "round(df_fees_cash['amount'].describe(),2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "f8e92a9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.boxplot(data=df_fees_cash.amount, \n",
+ " color='blue')\n",
+ "\n",
+ "plt.ylabel(\"Amount of fees\")\n",
+ "plt.title(\"Box plot of the amount of fees\")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "54256376",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.histplot(df_fees_cash.amount, bins=\"auto\", \n",
+ " color='blue', edgecolor='black', alpha=0.7)\n",
+ "\n",
+ "plt.xlabel(\"Amount of fees\")\n",
+ "plt.ylabel(\"Count\")\n",
+ "plt.title(\"Histogram of the amount of fees\")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "8c156ea3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " month amount\n",
+ "0 2020-04 100.0\n",
+ "1 2020-05 24510.0\n",
+ "2 2020-06 162993.0\n",
+ "3 2020-07 186900.0\n",
+ "4 2020-08 307038.0\n",
+ "5 2020-09 384598.0\n",
+ "6 2020-10 648295.0\n",
+ "7 2020-11 8735.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_fees_cash['month'] = df_fees_cash['created_at_y'].dt.to_period('M') # generate month date\n",
+ "monthly_sum = df_fees_cash.groupby('month')['amount'].sum() # sum amount by month\n",
+ "monthly_sum_df = monthly_sum.reset_index() # Reset the index to convert the result to a DataFrame\n",
+ "\n",
+ "print(monthly_sum_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "a4ac06a5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Define the abbreviated month names\n",
+ "month_labels = ['APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV']\n",
+ "\n",
+ "# Plotting the bar chart\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.bar(monthly_sum_df['month'].astype(str), monthly_sum_df['amount'], color='blue', edgecolor='black', alpha=0.7)\n",
+ "\n",
+ "# Set primary x-ticks for month labels\n",
+ "plt.xticks(ticks=range(len(month_labels)), labels=month_labels, rotation=0)\n",
+ "\n",
+ "# Access current Axes\n",
+ "ax = plt.gca()\n",
+ "\n",
+ "# Add secondary x-tick for the year\n",
+ "ax.set_xticks([3.5], minor=True) # Center \"2020\" below the sequence of months\n",
+ "ax.set_xticklabels(['2020'], minor=True)\n",
+ "\n",
+ "# Adjust tick parameters\n",
+ "ax.tick_params(axis='x', which='minor', length=0, pad=20)\n",
+ "\n",
+ "plt.xlabel('Creation of first cash advance (month)')\n",
+ "plt.ylabel('Sum of the amount of fees')\n",
+ "plt.title('Bar plot of the revenue generated by cohort')\n",
+ "\n",
+ "# Show plot\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
From 16d2ec0dbcf763bf2b7d9b381d6e79a6d10ce321 Mon Sep 17 00:00:00 2001
From: Sara Gonzales <39350413+SaraynesGS@users.noreply.github.com>
Date: Fri, 29 Nov 2024 16:40:14 +0100
Subject: [PATCH 4/4] Business challenge - Presentation
---
project_dataset/Business_Challenge.pptx | Bin 0 -> 718675 bytes
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 project_dataset/Business_Challenge.pptx
diff --git a/project_dataset/Business_Challenge.pptx b/project_dataset/Business_Challenge.pptx
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