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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup and initialization\n",
    "import json\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "\n",
    "# Load CSV file into a raw dataframe\n",
    "df = pd.read_csv('churn.csv')\n",
    "\n",
    "# Pull some quick stats\n",
    "category_totals = df.groupby('category').size()\n",
    "\n",
    "user_category_labels = {\n",
    "    'quick-exit': 'Free trial only',\n",
    "    'fair-trial': '74 day churn',\n",
    "    'short-termer': '6 month churn',\n",
    "    'long-termer': '> 6 months active'\n",
    "}\n",
    "metric_timeframe_labels = {\n",
    "    'short term': 'During free trial',\n",
    "    'medium term': 'After trial, before 90 days',\n",
    "    'long term': 'After 90 days, first 6 months'\n",
    "}\n",
    "\n",
    "category_order = {\n",
    "    'category': list(user_category_labels.keys())\n",
    "}\n",
    "\n",
    "def metric_label(metric_key):\n",
    "    parts = metric_key.split('_')\n",
    "    if \"term\" in parts:\n",
    "        timeframe = metric_timeframe_labels[\" \".join(parts[-2:])]\n",
    "        name = \" \".join(parts[0:-2]).title()\n",
    "    else:\n",
    "        timeframe = 'Lifetime'\n",
    "        name = parts.join(\" \").title()\n",
    "    return f\"{name}: {timeframe}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weekly data processing\n",
    "max_weeks = 30\n",
    "\n",
    "def weekly_averages(metric):\n",
    "    records = []\n",
    "    metric_col = f\"{metric}_weekly_counts\"\n",
    "    for category in df['category'].unique():\n",
    "        cat_df = df[df['category'] == category]\n",
    "        num_users = len(cat_df)\n",
    "        \n",
    "        weekly_totals = [0] * max_weeks\n",
    "        \n",
    "        for value in cat_df[metric_col]:\n",
    "            if pd.notna(value) and value != '{}':\n",
    "                try:\n",
    "                    counts_dict = json.loads(value)\n",
    "                    for key, count in counts_dict.items():\n",
    "                        week_num = int(key.replace('week_', ''))\n",
    "                        if week_num < max_weeks:\n",
    "                            weekly_totals[week_num] += count\n",
    "                except (json.JSONDecodeError, ValueError):\n",
    "                    pass\n",
    "        \n",
    "        # Calculate cumulative averages (cumulative total / number of users)\n",
    "        cumulative = np.cumsum(weekly_totals)\n",
    "        cumulative_avg = cumulative / num_users\n",
    "        \n",
    "        for week in range(max_weeks):\n",
    "            records.append({\n",
    "                'category': user_category_labels[category],\n",
    "                'week': week,\n",
    "                'cumulative_avg': cumulative_avg[week]\n",
    "            })\n",
    "    return records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Averages chart renderer\n",
    "def averages_chart(metric_name, title):\n",
    "    user_categories_to_include = ['quick-exit', 'fair-trial', 'short-termer', 'active-user']\n",
    "    metrics_to_include = [f'{metric_name}_short_term', f'{metric_name}_medium_term', f'{metric_name}_long_term']\n",
    "    metric_labels = [metric_label(key) for key in metrics_to_include]\n",
    "    # Get our averages\n",
    "    grouped = df.groupby('category')[metrics_to_include].mean().reset_index()\n",
    "    # Pivot the data to a form Plotly will understand\n",
    "    melted = grouped.melt(id_vars='category', var_name='timeframe', value_name='value')\n",
    "    # Set up the basics of the chart\n",
    "    bar_chart = px.bar(\n",
    "        melted,\n",
    "        x='category',\n",
    "        y='value',\n",
    "        color='timeframe',\n",
    "        barmode='group',\n",
    "        title=title,\n",
    "        category_orders=category_order,\n",
    "        labels=metric_labels\n",
    "    )\n",
    "    # Make some UX tweaks\n",
    "    bar_chart.update_layout(\n",
    "        xaxis_title='User Group',\n",
    "        yaxis_title='Average Created',\n",
    "        legend_title='Creation Timeframe'\n",
    "    )\n",
    "    bar_chart.update_xaxes(\n",
    "        ticktext=list(user_category_labels.values()),\n",
    "        tickvals=list(user_category_labels.keys())\n",
    "    )\n",
    "    for idx, name in enumerate(metric_labels):\n",
    "        bar_chart.data[idx].name = name\n",
    "    # Go!!!\n",
    "    \n",
    "    bar_chart.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cumulative averages chart renderer\n",
    "def cumulative_averages_chart(metric_name, title):\n",
    "    user_categories_to_include = ['quick-exit', 'fair-trial', 'short-termer', 'active-user']\n",
    "    metrics_to_include = [f'{metric_name}_short_term', f'{metric_name}_medium_term', f'{metric_name}_long_term']\n",
    "    metric_labels = [metric_label(key) for key in metrics_to_include]\n",
    "    # Get our averages\n",
    "    grouped = df.groupby('category')[metrics_to_include].mean().reset_index()\n",
    "    # Make each column represent itself plus the total of its predecessors to represet a \"snapshot\" at that point.\n",
    "    for i in range(1, len(metrics_to_include)):\n",
    "        grouped[metrics_to_include[i]] += grouped[metrics_to_include[i - 1]]\n",
    "    # Pivot the data to a form Plotly will understand\n",
    "    melted = grouped.melt(id_vars='category', var_name='timeframe', value_name='value')\n",
    "    # Set up the basics of the chart\n",
    "    bar_chart = px.bar(\n",
    "        melted,\n",
    "        x='category',\n",
    "        y='value',\n",
    "        color='timeframe',\n",
    "        barmode='group',\n",
    "        title=title,\n",
    "        category_orders=category_order,\n",
    "        labels=metric_labels\n",
    "    )\n",
    "    # Make some UX tweaks\n",
    "    bar_chart.update_layout(\n",
    "        xaxis_title='User Group',\n",
    "        yaxis_title='Average Created',\n",
    "        legend_title='Creation Timeframe'\n",
    "    )\n",
    "    bar_chart.update_xaxes(\n",
    "        ticktext=list(user_category_labels.values()),\n",
    "        tickvals=list(user_category_labels.keys())\n",
    "    )\n",
    "    for idx, name in enumerate(metric_labels):\n",
    "        bar_chart.data[idx].name = name\n",
    "    # Go!!!\n",
    "    \n",
    "    bar_chart.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weekly averages line chart renderer\n",
    "def weekly_averages_chart(metric_name, title):\n",
    "    chart_data = pd.DataFrame(weekly_averages(metric_name))\n",
    "    \n",
    "    # Plot\n",
    "    line_chart = px.line(\n",
    "        chart_data,\n",
    "        x='week',\n",
    "        y='cumulative_avg',\n",
    "        color='category',\n",
    "        title=title,\n",
    "        category_orders=category_order,\n",
    "        labels={'week': 'Week', 'cumulative_avg': 'Average Created', 'category': 'User Group'},\n",
    "        markers=True\n",
    "    )\n",
    "    line_chart.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
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