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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "12f77d3b",
+   "metadata": {},
+   "source": [
+    "![logo_ironhack_blue 7](https://user-images.githubusercontent.com/23629340/40541063-a07a0a8a-601a-11e8-91b5-2f13e4e6b441.png)\n",
+    "\n",
+    "# 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",
+    "\n",
+    "### Metrics to Analyze\n",
+    "\n",
+    "You will calculate and analyze the following metrics for each cohort:\n",
+    "\n",
+    "1. **Frequency of Service Usage:** Understand how often users from each cohort utilize IronHack Payments' cash advance services over time.\n",
+    "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.\n",
+    "3. **Revenue Generated by the Cohort:** Calculate the total revenue generated by each cohort over months to assess the financial impact of user behavior.\n",
+    "4. **New Relevant Metric:** Propose and calculate a new relevant metric that provides additional insights into user behavior or the performance of IronHack Payments' services.\n",
+    "\n",
+    "### Data Analysis Tools\n",
+    "\n",
+    "You are expected to perform the cohort analysis using Python, primarily leveraging the Pandas library for data manipulation and analysis. However, the main analysis should be conducted using Python.\n",
+    "\n",
+    "### Exploratory Data Analysis (EDA)\n",
+    "\n",
+    "Before diving into cohort analysis, conduct an exploratory data analysis to gain a comprehensive understanding of the dataset. Explore key statistics, distributions, and visualizations to identify patterns and outliers. EDA will help you make informed decisions on data preprocessing and analysis strategies.\n",
+    "\n",
+    "### Data Quality Analysis\n",
+    "\n",
+    "Assess the quality of the dataset by identifying missing values, data inconsistencies, and potential errors. Implement data cleaning and preprocessing steps to ensure the reliability of your analysis. Document any data quality issues encountered and the steps taken to address them.\n",
+    "\n",
+    "### Deliverables\n",
+    "\n",
+    "1. **Python Code:** Provide well-documented Python code that conducts the cohort analysis, including data loading, preprocessing, cohort creation, metric calculation, and visualization.\n",
+    "2. **Tableau Dashboard**: Publish a dashboard in Tableau Public regarding your analysis.\n",
+    "3. **Exploratory Data Analysis Report:** Prepare a report summarizing the findings from your exploratory data analysis. Include visualizations and insights that help understand the dataset.\n",
+    "4. **Data Quality Analysis Report:** Document the results of your data quality analysis, highlighting any issues and the steps taken to resolve them.\n",
+    "5. **Short Presentation:** Create a concise presentation (maximum of 4 slides) summarizing your findings from the cohort analysis and key insights gained from EDA and data quality analysis. This presentation should be suitable for sharing with the IronHack Payments team.\n",
+    "   \n",
+    "### Bonus:\n",
+    "1. **Operationalize your analysis**: Make sure all the code is in a .py that can be called from the Terminal and whose execution makes sense (if in doubt, ask the Teacher for clarification on this)\n",
+    "2. **StreamLit**: Read about the StreamLit package and create a StreamLit app about this data (you can leverage on ideias from your dashboard)\n",
+    "3. **OPP vs Function**: Take your code and replicate it using an oposite strategy than you have done.\n",
+    "\n",
+    "<!-- this version uses Tableau which consorci doesn't use -->\n",
+    "\n",
+    "<!-- https://github.com/ironhack-labs/project-1-ironhack-payments-2-en -->\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 422,
+   "id": "e57f917c-b434-4374-9926-c85520b8e55c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from datetime import date\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 423,
+   "id": "70fcd56b-cb7b-4586-aa6a-36c3e401b8b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read pd\n",
+    "file_paths = [r\"./project_dataset/extract - cash request - data analyst.csv\",r\"./project_dataset/extract - fees - data analyst - .csv\"]\n",
+    "\n",
+    "cashreq_df = pd.read_csv(file_paths[0])\n",
+    "fees_df = pd.read_csv(file_paths[1])\n",
+    "\n",
+    "\n",
+    "cashreq_df_copy = cashreq_df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 424,
+   "id": "102440a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>status</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>updated_at</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>moderated_at</th>\n",
+       "      <th>deleted_account_id</th>\n",
+       "      <th>reimbursement_date</th>\n",
+       "      <th>cash_request_received_date</th>\n",
+       "      <th>money_back_date</th>\n",
+       "      <th>transfer_type</th>\n",
+       "      <th>send_at</th>\n",
+       "      <th>recovery_status</th>\n",
+       "      <th>reco_creation</th>\n",
+       "      <th>reco_last_update</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>rejected</td>\n",
+       "      <td>2019-12-10 19:05:21.596873+00</td>\n",
+       "      <td>2019-12-11 16:47:42.40783+00</td>\n",
+       "      <td>804.0</td>\n",
+       "      <td>2019-12-11 16:47:42.405646+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-01-09 19:05:21.596363+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>regular</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>70</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>rejected</td>\n",
+       "      <td>2019-12-10 19:50:12.34778+00</td>\n",
+       "      <td>2019-12-11 14:24:22.900054+00</td>\n",
+       "      <td>231.0</td>\n",
+       "      <td>2019-12-11 14:24:22.897988+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-01-09 19:50:12.34778+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>regular</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>rejected</td>\n",
+       "      <td>2019-12-10 19:13:35.82546+00</td>\n",
+       "      <td>2019-12-11 09:46:59.779773+00</td>\n",
+       "      <td>191.0</td>\n",
+       "      <td>2019-12-11 09:46:59.777728+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-01-09 19:13:35.825041+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>regular</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>rejected</td>\n",
+       "      <td>2019-12-10 19:16:10.880172+00</td>\n",
+       "      <td>2019-12-18 14:26:18.136163+00</td>\n",
+       "      <td>761.0</td>\n",
+       "      <td>2019-12-18 14:26:18.128407+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-01-09 19:16:10.879606+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>regular</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1594</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>rejected</td>\n",
+       "      <td>2020-05-06 09:59:38.877376+00</td>\n",
+       "      <td>2020-05-07 09:21:55.34008+00</td>\n",
+       "      <td>7686.0</td>\n",
+       "      <td>2020-05-07 09:21:55.320193+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-06-05 22:00:00+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>regular</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     id  amount    status                     created_at  \\\n",
+       "0     5   100.0  rejected  2019-12-10 19:05:21.596873+00   \n",
+       "1    70   100.0  rejected   2019-12-10 19:50:12.34778+00   \n",
+       "2     7   100.0  rejected   2019-12-10 19:13:35.82546+00   \n",
+       "3    10    99.0  rejected  2019-12-10 19:16:10.880172+00   \n",
+       "4  1594   100.0  rejected  2020-05-06 09:59:38.877376+00   \n",
+       "\n",
+       "                      updated_at  user_id                   moderated_at  \\\n",
+       "0   2019-12-11 16:47:42.40783+00    804.0  2019-12-11 16:47:42.405646+00   \n",
+       "1  2019-12-11 14:24:22.900054+00    231.0  2019-12-11 14:24:22.897988+00   \n",
+       "2  2019-12-11 09:46:59.779773+00    191.0  2019-12-11 09:46:59.777728+00   \n",
+       "3  2019-12-18 14:26:18.136163+00    761.0  2019-12-18 14:26:18.128407+00   \n",
+       "4   2020-05-07 09:21:55.34008+00   7686.0  2020-05-07 09:21:55.320193+00   \n",
+       "\n",
+       "   deleted_account_id             reimbursement_date  \\\n",
+       "0                 NaN  2020-01-09 19:05:21.596363+00   \n",
+       "1                 NaN   2020-01-09 19:50:12.34778+00   \n",
+       "2                 NaN  2020-01-09 19:13:35.825041+00   \n",
+       "3                 NaN  2020-01-09 19:16:10.879606+00   \n",
+       "4                 NaN         2020-06-05 22:00:00+00   \n",
+       "\n",
+       "  cash_request_received_date money_back_date transfer_type send_at  \\\n",
+       "0                        NaN             NaN       regular     NaN   \n",
+       "1                        NaN             NaN       regular     NaN   \n",
+       "2                        NaN             NaN       regular     NaN   \n",
+       "3                        NaN             NaN       regular     NaN   \n",
+       "4                        NaN             NaN       regular     NaN   \n",
+       "\n",
+       "  recovery_status reco_creation reco_last_update  \n",
+       "0             NaN           NaN              NaN  \n",
+       "1             NaN           NaN              NaN  \n",
+       "2             NaN           NaN              NaN  \n",
+       "3             NaN           NaN              NaN  \n",
+       "4             NaN           NaN              NaN  "
+      ]
+     },
+     "execution_count": 424,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cashreq_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 425,
+   "id": "c28c2813-57cc-4673-b330-2527e170823a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\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": [
+    "cashreq_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 426,
+   "id": "1370c2c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_27976\\727240225.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  cashreq_df[col] = pd.to_datetime(cashreq_df[col], errors='coerce').dt.strftime('%m/%d/%Y')\n",
+      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_27976\\727240225.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  cashreq_df[col] = pd.to_datetime(cashreq_df[col], errors='coerce').dt.strftime('%m/%d/%Y')\n",
+      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_27976\\727240225.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  cashreq_df[col] = pd.to_datetime(cashreq_df[col], errors='coerce').dt.strftime('%m/%d/%Y')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Identify potential date columns based on column names and data types\n",
+    "date_columns = [col for col in cashreq_df.columns if 'date' in col.lower() or 'time' in col.lower() or pd.api.types.is_object_dtype(cashreq_df[col])]\n",
+    "\n",
+    "for col in date_columns:\n",
+    "    try:\n",
+    "        cashreq_df[col] = pd.to_datetime(cashreq_df[col], errors='coerce').dt.strftime('%m/%d/%Y')\n",
+    "    except Exception as e:\n",
+    "        print(f\"Could not process column {col}: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 427,
+   "id": "52b1a1da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert specified columns to string type\n",
+    "cashreq_df[['id', 'user_id', 'deleted_account_id']] = cashreq_df[['id', 'user_id', 'deleted_account_id']].astype('string')\n",
+    "\n",
+    "# Extract date from the 'created_at' column and assign it to the variable 'created_date'\n",
+    "def extract_created_date(df, column_name):\n",
+    "    \"\"\"\n",
+    "    Extracts the date from the specified column and assigns it to the 'created_date' variable.\n",
+    "\n",
+    "    Parameters:\n",
+    "    df (DataFrame): The input DataFrame.\n",
+    "    column_name (str): The name of the column to extract the date from.\n",
+    "\n",
+    "    Returns:\n",
+    "    DataFrame: Updated DataFrame with a new column 'created_date'.\n",
+    "    \"\"\"\n",
+    "    df['created_date'] = pd.to_datetime(df[column_name], errors='coerce').dt.strftime('%m-%d-%Y')\n",
+    "    return df\n",
+    "\n",
+    "# Apply the function to extract the date\n",
+    "cashreq_df = extract_created_date(cashreq_df, 'created_at')\n",
+    "\n",
+    "# Sort the data by user_id and created_date in ascending order\n",
+    "cashreq_df['created_date'] = pd.to_datetime(cashreq_df['created_date'], errors='coerce')\n",
+    "cashreq_df = cashreq_df.sort_values(by=['user_id', 'created_date'], ascending=True)\n",
+    "\n",
+    "# Assign the cohort based on the earliest transaction's month name for each user_id\n",
+    "cashreq_df['cohort'] = cashreq_df.groupby('user_id')['created_date'].transform(\n",
+    "    lambda x: x.min().strftime('%B') if pd.notnull(x.min()) else None\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff44ec6a",
+   "metadata": {},
+   "source": [
+    "## Cohort Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 428,
+   "id": "99466e35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cohort_order = [\n",
+    "    \"January\", \"February\", \"March\", \"April\", \"May\", \"June\",\n",
+    "    \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"\n",
+    "]\n",
+    "cohort_counts = cashreq_df['cohort'].value_counts()\n",
+    "cohort_counts = cohort_counts.reindex(cohort_order, fill_value=0)\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "cohort_counts.plot(kind='bar', color='skyblue', edgecolor='black')\n",
+    "plt.title('Cohort Distribution', fontsize=14)\n",
+    "plt.xlabel('Cohort (Month)', fontsize=12)\n",
+    "plt.ylabel('Number of Users', fontsize=12)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "673c0ce2",
+   "metadata": {},
+   "source": [
+    "## Average Transaction Amount"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 429,
+   "id": "52f38055",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate and display average transaction amount per cohort\n",
+    "average_amount_per_cohort = cashreq_df.groupby('cohort')['amount'].mean().reindex(cohort_order, fill_value=0)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "bars = average_amount_per_cohort.plot(kind='bar', color='skyblue', edgecolor='black')\n",
+    "plt.title('Average Transaction Amount per Cohort', fontsize=14)\n",
+    "plt.xlabel('Cohort (Month)', fontsize=12)\n",
+    "plt.ylabel('Average Transaction Amount', fontsize=12)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Annotate bars with their values\n",
+    "for bar in bars.containers[0]:\n",
+    "    plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), f'{bar.get_height():.2f}', \n",
+    "             ha='center', va='bottom', fontsize=10)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e253bd7d",
+   "metadata": {},
+   "source": [
+    "## Transfer Type by Status (Percentage)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 433,
+   "id": "f702ceb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a cross-tabulation for Transfer Type by Status\n",
+    "transfer_status_crosstab = pd.crosstab(\n",
+    "    cashreq_df_copy['transfer_type'],\n",
+    "    cashreq_df_copy['status'],\n",
+    "    rownames=['Transfer Type'],\n",
+    "    colnames=['Status']\n",
+    ")\n",
+    "\n",
+    "# Check if the crosstab has data\n",
+    "if not transfer_status_crosstab.empty:\n",
+    "    # Convert crosstab to total percentages\n",
+    "    transfer_status_total_percentage = (transfer_status_crosstab / transfer_status_crosstab.values.sum()) * 100\n",
+    "\n",
+    "    # Plot the crosstab as a heatmap with total percentages\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    sns.heatmap(transfer_status_total_percentage, annot=True, fmt='.2f', cmap='Blues', cbar_kws={'label': 'Percentage of Total'})\n",
+    "    plt.title('Transfer Type by Status (Percentage of Total)', fontsize=14)\n",
+    "    plt.xlabel('Status', fontsize=12)\n",
+    "    plt.ylabel('Transfer Type', fontsize=12)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"No data available for Transfer Type by Status heatmap.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c854571",
+   "metadata": {},
+   "source": [
+    "## Transfer Type by Status (Percentage of Total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 434,
+   "id": "8c65a9ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a cross-tabulation for Transfer Type by Status\n",
+    "# Create a crosstab for transfer_type and status\n",
+    "transfer_status_crosstab = pd.crosstab(\n",
+    "    cashreq_df_copy['transfer_type'],\n",
+    "    cashreq_df_copy['status'],\n",
+    "    rownames=['Transfer Type'],\n",
+    "    colnames=['Status']\n",
+    ")\n",
+    "\n",
+    "# Convert crosstab to total percentages\n",
+    "transfer_status_total_percentage = (transfer_status_crosstab / transfer_status_crosstab.values.sum()) * 100\n",
+    "\n",
+    "# Plot the crosstab as a heatmap with total percentages\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(transfer_status_total_percentage, annot=True, fmt='.2f', cmap='Blues', cbar_kws={'label': 'Percentage of Total'})\n",
+    "plt.title('Transfer Type by Status (Percentage of Total)', fontsize=14)\n",
+    "plt.xlabel('Status', fontsize=12)\n",
+    "plt.ylabel('Transfer Type', fontsize=12)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 448,
+   "id": "0bb8286e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_27976\\3366418207.py:25: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  .apply(calculate_serv_freq)  # Apply the frequency calculation\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Ensure the 'created_date' column is in datetime format\n",
+    "cashreq_df['created_date'] = pd.to_datetime(cashreq_df['created_date'], errors='coerce')\n",
+    "\n",
+    "# Define the function to calculate service usage frequency for each group\n",
+    "def calculate_serv_freq(group):\n",
+    "    \"\"\"\n",
+    "    Calculate the frequency of service usage for each cohort.\n",
+    "\n",
+    "    Parameters:\n",
+    "    group (DataFrame): A grouped DataFrame for a specific cohort.\n",
+    "\n",
+    "    Returns:\n",
+    "    float: Transactions per day for each cohort.\n",
+    "    \"\"\"\n",
+    "    num_transactions = len(group)  # Total number of transactions\n",
+    "    # Total active period in days, adding 1 to avoid division by zero for single-day cohorts\n",
+    "    time_period = (group[\"created_date\"].max() - group[\"created_date\"].min()).days + 1\n",
+    "    return num_transactions / time_period  # Transactions per day\n",
+    "\n",
+    "# Group the data by 'cohort' and calculate frequency service usage\n",
+    "frequency = (\n",
+    "    cashreq_df.groupby(\"cohort\")\n",
+    "    .apply(calculate_serv_freq)  # Apply the frequency calculation\n",
+    "    .reset_index(name=\"frequency\")  # Reset index and name the column 'frequency'\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48a787f3",
+   "metadata": {},
+   "source": [
+    "## Frequency of Service Usage by Cohort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 449,
+   "id": "b3413f2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the frequency of service usage\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "bars = plt.bar(frequency['cohort'], frequency['frequency'], color='skyblue', edgecolor='black')\n",
+    "\n",
+    "# Add numbers on top of the bars (rounded to 1 decimal place)\n",
+    "for bar in bars:\n",
+    "    height = bar.get_height()\n",
+    "    plt.text(bar.get_x() + bar.get_width() / 2, height, f'{height:.1f}', \n",
+    "             ha='center', va='bottom', fontsize=10, color='black')\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.title('Frequency of Service Usage by Cohort', fontsize=14)\n",
+    "plt.xlabel('Cohort', fontsize=12)\n",
+    "plt.ylabel('Transactions per Day', fontsize=12)\n",
+    "plt.xticks(rotation=45, fontsize=10)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a783f2e",
+   "metadata": {},
+   "source": [
+    "## Total Revenue by Cohort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 462,
+   "id": "85dc8b17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Ensure 'created_date' is in datetime format\n",
+    "cashreq_df['created_date'] = pd.to_datetime(cashreq_df['created_at'], errors='coerce')\n",
+    "\n",
+    "# Assign cohorts based on 'created_date'\n",
+    "cashreq_df['cohort'] = cashreq_df['created_date'].dt.to_period('M')\n",
+    "\n",
+    "# Calculate revenue by cohort\n",
+    "revenue_by_cohort = (\n",
+    "    cashreq_df\n",
+    "    .groupby('cohort')\n",
+    "    .agg(\n",
+    "        total_revenue=('amount', 'sum'),  # Summing up the 'amount' column for revenue\n",
+    "        total_transactions=('amount', 'count')  # Count of transactions per cohort\n",
+    "    )\n",
+    "    .reset_index()\n",
+    ")\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.bar(revenue_by_cohort['cohort'].astype(str), revenue_by_cohort['total_revenue'], color='blue', edgecolor='black')\n",
+    "plt.title('Total Revenue by Cohort', fontsize=14)\n",
+    "plt.xlabel('Cohort', fontsize=12)\n",
+    "plt.ylabel('Total Revenue', fontsize=12)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4427450",
+   "metadata": {},
+   "source": [
+    "## Average Revenue Per User (ARPU) by Cohort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 465,
+   "id": "381b5703",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'ARPU')"
+      ]
+     },
+     "execution_count": 465,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ensure 'created_date' is in datetime format\n",
+    "cashreq_df['created_date'] = pd.to_datetime(cashreq_df['created_at'], errors='coerce')\n",
+    "\n",
+    "# Assign cohorts based on 'created_date'\n",
+    "cashreq_df['cohort'] = cashreq_df['created_date'].dt.to_period('M')\n",
+    "\n",
+    "# Calculate total revenue and total users by cohort\n",
+    "cohort_summary = (\n",
+    "    cashreq_df\n",
+    "    .groupby('cohort')\n",
+    "    .agg(\n",
+    "        total_revenue=('amount', 'sum'),  # Sum of revenue for each cohort\n",
+    "        total_users=('user_id', 'nunique')  # Unique user count for each cohort\n",
+    "    )\n",
+    "    .reset_index()\n",
+    ")\n",
+    "\n",
+    "# Calculate Average Revenue Per User (ARPU)\n",
+    "cohort_summary['avg_revenue_per_user'] = cohort_summary['total_revenue'] / cohort_summary['total_users']\n",
+    "\n",
+    "# Visualize ARPU by cohort\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.bar(cohort_summary['cohort'].astype(str), cohort_summary['avg_revenue_per_user'], color='blue', edgecolor='black')\n",
+    "plt.title('Average Revenue Per User (ARPU) by Cohort', fontsize=14)\n",
+    "plt.xlabel('Cohort', fontsize=12)\n",
+    "plt.ylabel('ARPU', fontsize = 12)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74661c2d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}