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First round of fiddling

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Justin Fargione
2026-02-02 16:26:10 -05:00
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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# Virtual environments
.venv/
venv/
ENV/
# Jupyter
.ipynb_checkpoints/
# IDE
.idea/
.vscode/
*.swp
*.swo
# OS
.DS_Store
Thumbs.db
# Environment
.env
.python-version
# Data (uncomment if you want to ignore data files)
# *.csv

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2026-01-02_query-01-booking-people.sql Normal file
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-- First establish some base parameters for our users
WITH config AS (
SELECT
14 AS short_term,
90 AS medium_term,
360 AS long_term,
CAST('2023-01-01' AS timestamp) AS date_cutoff
),
-- Then establish a standard core set of user data
users_with_churn_stats AS (
SELECT id, active, billing_state,
DATE_TRUNC('month', created_at) AS month_created,
created_at, free_trial_ends_at, updated_at,
updated_at::date - created_at::date AS lifespan
FROM users, config
-- We'll only look at newish users.
WHERE created_at >= config.date_cutoff
),
-- Next use that core data to extract the records we want to analyze
-- and categorize them by date and status
users_to_examine AS (
-- First the users who exited just after the trial
SELECT 'quick-exit' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan <= config.short_term
UNION
-- Next, users who gave the tools some time but still left
SELECT 'fair-trial' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan < config.medium_term
AND lifespan > config.short_term
UNION
-- Then some longer-time users who still churned.
-- We'll cut this off after 6 months since afterwards
-- company closures, etc. could factor in.
SELECT 'short-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan >= config.medium_term
AND lifespan < config.long_term
UNION
-- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around.
SELECT 'active-user' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE BILLING_STATE != 'CHURNED'
AND lifespan >= config.long_term
),
-- Well add booking forms created by era
booking_form_counts AS (
SELECT bf.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT booking_forms.user_id, booking_forms.created_at::date - users.created_at::date AS created_age
FROM config, booking_forms JOIN users ON booking_forms.user_id = users.id
WHERE booking_forms.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) bf
GROUP BY bf.user_id
),
-- Bookings made by era
booking_counts AS (
SELECT bk.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT booking_forms.user_id, bookings.created_at::date - users.created_at::date AS created_age
FROM config, booking_forms
JOIN users ON booking_forms.user_id = users.id
JOIN bookings ON bookings.booking_form_id = booking_forms.id
WHERE booking_forms.created_at >= config.date_cutoff
AND bookings.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) bk
GROUP BY bk.user_id
),
-- Employees added by era
employee_counts AS (
SELECT empl.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT employees.user_id, employees.created_at::date - users.created_at::date AS created_age
FROM config, employees JOIN users ON employees.user_id = users.id
WHERE employees.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) empl
GROUP BY empl.user_id
),
-- Contacts created by era
contact_counts AS (
SELECT cust.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT customers.user_id, customers.created_at::date - users.created_at::date AS created_age
FROM config, customers JOIN users ON customers.user_id = users.id
WHERE customers.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) cust
GROUP BY cust.user_id
),
-- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id,
users_to_examine.category,
COALESCE(booking_form_counts.short_term, 0) AS booking_forms_short_term,
COALESCE(booking_form_counts.medium_term, 0) AS booking_forms_medium_term,
COALESCE(booking_form_counts.long_term, 0) AS booking_forms_long_term,
COALESCE(booking_form_counts.total, 0) AS booking_forms,
COALESCE(booking_counts.short_term, 0) AS bookings_short_term,
COALESCE(booking_counts.medium_term, 0) AS bookings_medium_term,
COALESCE(booking_counts.long_term, 0) AS bookings_long_term,
COALESCE(booking_counts.total, 0) AS bookings,
COALESCE(employee_counts.short_term, 0) AS employees_short_term,
COALESCE(employee_counts.medium_term, 0) AS employees_medium_term,
COALESCE(employee_counts.long_term, 0) AS employees_long_term,
COALESCE(employee_counts.total, 0) AS employees,
COALESCE(contact_counts.short_term, 0) AS contacts_short_term,
COALESCE(contact_counts.medium_term, 0) AS contacts_medium_term,
COALESCE(contact_counts.long_term, 0) AS contacts_long_term,
COALESCE(contact_counts.total, 0) AS contacts
FROM users_to_examine
LEFT OUTER JOIN booking_form_counts ON booking_form_counts.user_id = users_to_examine.id
LEFT OUTER JOIN booking_counts ON booking_counts.user_id = users_to_examine.id
LEFT OUTER JOIN employee_counts ON employee_counts.user_id = users_to_examine.id
LEFT OUTER JOIN contact_counts ON contact_counts.user_id = users_to_examine.id
;

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-- First establish some base parameters for our users
WITH config AS (
SELECT
14 AS short_term,
90 AS medium_term,
360 AS long_term,
CAST('2023-01-01' AS timestamp) AS date_cutoff
),
-- Then establish a standard core set of user data
users_with_churn_stats AS (
SELECT id, active, billing_state,
DATE_TRUNC('month', created_at) AS month_created,
created_at, free_trial_ends_at, updated_at,
updated_at::date - created_at::date AS lifespan
FROM users, config
-- We'll only look at newish users.
WHERE created_at >= config.date_cutoff
),
-- Next use that core data to extract the records we want to analyze
-- and categorize them by date and status
users_to_examine AS (
-- First the users who exited just after the trial
SELECT 'quick-exit' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan <= config.short_term
UNION
-- Next, users who gave the tools some time but still left
SELECT 'fair-trial' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan < config.medium_term
AND lifespan > config.short_term
UNION
-- Then some longer-time users who still churned.
-- We'll cut this off after 6 months since afterwards
-- company closures, etc. could factor in.
SELECT 'short-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan >= config.medium_term
AND lifespan < config.long_term
UNION
-- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around.
SELECT 'active-user' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE BILLING_STATE != 'CHURNED'
AND lifespan >= config.long_term
),
-- Emails sent by era
email_counts AS (
SELECT email.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT sent_emails.user_id, sent_emails.created_at::date - users.created_at::date AS created_age
FROM config, sent_emails JOIN users ON sent_emails.user_id = users.id
WHERE sent_emails.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) email
GROUP BY email.user_id
)
-- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id,
users_to_examine.category,
COALESCE(email_counts.short_term, 0) AS emails_short_term,
COALESCE(email_counts.medium_term, 0) AS emails_medium_term,
COALESCE(email_counts.long_term, 0) AS emails_long_term,
COALESCE(email_counts.total, 0) AS emails
FROM users_to_examine
LEFT OUTER JOIN email_counts ON email_counts.user_id = users_to_examine.id
;

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-- First establish some base parameters for our users
WITH config AS (
SELECT
14 AS short_term,
90 AS medium_term,
360 AS long_term,
CAST('2023-01-01' AS timestamp) AS date_cutoff
),
-- Then establish a standard core set of user data
users_with_churn_stats AS (
SELECT id, active, billing_state,
DATE_TRUNC('month', created_at) AS month_created,
created_at, free_trial_ends_at, updated_at,
updated_at::date - created_at::date AS lifespan
FROM users, config
-- We'll only look at newish users.
WHERE created_at >= config.date_cutoff
),
-- Next use that core data to extract the records we want to analyze
-- and categorize them by date and status
users_to_examine AS (
-- First the users who exited just after the trial
SELECT 'quick-exit' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan <= config.short_term
UNION
-- Next, users who gave the tools some time but still left
SELECT 'fair-trial' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan < config.medium_term
AND lifespan > config.short_term
UNION
-- Then some longer-time users who still churned.
-- We'll cut this off after 6 months since afterwards
-- company closures, etc. could factor in.
SELECT 'short-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan >= config.medium_term
AND lifespan < config.long_term
UNION
-- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around.
SELECT 'active-user' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE BILLING_STATE != 'CHURNED'
AND lifespan >= config.long_term
),
-- Messages sent by era
sms_counts AS (
SELECT texts.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT sent_texts.user_id, sent_texts.created_at::date - users.created_at::date AS created_age
FROM config, sent_texts JOIN users ON sent_texts.user_id = users.id
WHERE sent_texts.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) texts
GROUP BY texts.user_id
),
-- Appointments by era
appointment_counts AS (
SELECT appts.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT appointments.user_id, appointments.created_at::date - users.created_at::date AS created_age
FROM config, appointments JOIN users ON appointments.user_id = users.id
WHERE appointments.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) appts
GROUP BY appts.user_id
),
-- Checklists created by era
checklist_counts AS (
SELECT checks.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT checklist_templates.user_id, checklists.created_at::date - users.created_at::date AS created_age
FROM config, checklist_templates
JOIN users ON checklist_templates.user_id = users.id
JOIN checklists ON checklists.checklist_template_id = checklist_templates.id
WHERE checklist_templates.created_at >= config.date_cutoff
AND checklists.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) checks
GROUP BY checks.user_id
),
-- Checklist events created by era
checklist_filled_counts AS (
SELECT events.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT employees.user_id, checklist_item_events.created_at::date - users.created_at::date AS created_age
FROM config, employees
JOIN users ON employees.user_id = users.id
JOIN checklist_item_events ON checklist_item_events.employee_id = employees.id
WHERE checklist_item_events.created_at >= config.date_cutoff
AND checklist_item_events.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) events
GROUP BY events.user_id
)
-- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id,
users_to_examine.category,
users_to_examine.month_created,
users_to_examine.lifespan,
COALESCE(sms_counts.short_term, 0) AS sms_short_term,
COALESCE(sms_counts.medium_term, 0) AS sms_medium_term,
COALESCE(sms_counts.long_term, 0) AS sms_long_term,
COALESCE(sms_counts.total, 0) AS sms,
COALESCE(appointment_counts.short_term, 0) AS appointments_short_term,
COALESCE(appointment_counts.medium_term, 0) AS appointments_medium_term,
COALESCE(appointment_counts.long_term, 0) AS appointments_long_term,
COALESCE(appointment_counts.total, 0) AS appointments,
COALESCE(checklist_counts.short_term, 0) AS checklists_short_term,
COALESCE(checklist_counts.medium_term, 0) AS checklists_medium_term,
COALESCE(checklist_counts.long_term, 0) AS checklists_long_term,
COALESCE(checklist_counts.total, 0) AS checklists,
COALESCE(checklist_filled_counts.short_term, 0) AS checklists_filled_short_term,
COALESCE(checklist_filled_counts.medium_term, 0) AS checklists_filled_medium_term,
COALESCE(checklist_filled_counts.long_term, 0) AS checklists_filled_long_term,
COALESCE(checklist_filled_counts.total, 0) AS checklists_filled
FROM users_to_examine
LEFT OUTER JOIN sms_counts ON sms_counts.user_id = users_to_examine.id
LEFT OUTER JOIN appointment_counts ON appointment_counts.user_id = users_to_examine.id
LEFT OUTER JOIN checklist_counts ON checklist_counts.user_id = users_to_examine.id
LEFT OUTER JOIN checklist_filled_counts ON checklist_filled_counts.user_id = users_to_examine.id
;

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# Jupyter Analysis
## Setup
```bash
poetry install
```
## Run
```bash
poetry run jupyter notebook
# or
poetry run jupyter lab
```

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"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\n",
"df = pd.read_csv('churn.csv')\n",
"\n",
"category_labels = {\n",
" 'quick-exit': 'Free trial only',\n",
" 'fair-trial': '74 day churn',\n",
" 'short-termer': '6 month churn',\n",
" 'active-user': 'No churn'\n",
"}\n",
"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",
"def fix_dataset_label(k):\n",
" parts = k.split('_')\n",
" if \"term\" in parts:\n",
" timeframe = timeframe_labels[parts[-2:].join(\" \")]\n",
" name = parts[0:-2].join(\" \").title()\n",
" else:\n",
" timeframe = 'Lifetime'\n",
" name = parts.join(\" \").title()\n",
" return f\"{name}: {timeframe}\"\n",
"\n",
"def x_labels(categories):\n",
" return [category_labels[cat] for cat in categories]\n",
"\n",
"def dataset_labels(columns):\n",
" return [fix_dataset_label(col) for col in columns]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>booking_forms_short_term</th>\n",
" <th>booking_forms_medium_term</th>\n",
" <th>booking_forms_long_term</th>\n",
" </tr>\n",
" <tr>\n",
" <th>category</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>active-user</th>\n",
" <td>0.046701</td>\n",
" <td>0.041571</td>\n",
" <td>0.040686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fair-trial</th>\n",
" <td>0.268002</td>\n",
" <td>0.020303</td>\n",
" <td>0.000271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>quick-exit</th>\n",
" <td>0.232673</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>short-termer</th>\n",
" <td>0.290954</td>\n",
" <td>0.082213</td>\n",
" <td>0.029034</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" booking_forms_short_term booking_forms_medium_term \\\n",
"category \n",
"active-user 0.046701 0.041571 \n",
"fair-trial 0.268002 0.020303 \n",
"quick-exit 0.232673 0.000000 \n",
"short-termer 0.290954 0.082213 \n",
"\n",
" booking_forms_long_term \n",
"category \n",
"active-user 0.040686 \n",
"fair-trial 0.000271 \n",
"quick-exit 0.000000 \n",
"short-termer 0.029034 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"columns = ['booking_forms_short_term', 'booking_forms_medium_term', 'booking_forms_long_term']\n",
"categories = category_labels.keys() # All\n",
"\n",
"# Group by category and make sure they're in the correct order\n",
"df.groupby('category')[columns].mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "# Order categories as defined in category_labels\ncategory_order = [category_labels[k] for k in category_labels.keys()]"
},
{
"cell_type": "code",
"source": "# Generate charts for all metrics\nmetrics = [\n 'booking_forms',\n 'bookings',\n 'employees', \n 'contacts',\n 'emails',\n 'sms',\n 'appointments',\n 'checklists',\n 'checklists_filled'\n]\n\n# Get total users per category for percentage calculations\ncategory_totals = df.groupby('category').size()\n\ndef create_charts_for_metric(metric_name):\n cols = [f'{metric_name}_short_term', f'{metric_name}_medium_term', f'{metric_name}_long_term']\n title_name = metric_name.replace('_', ' ').title()\n \n # Chart 1: Average usage\n grouped = df.groupby('category')[cols].mean().reset_index()\n melted = grouped.melt(id_vars='category', var_name='timeframe', value_name='value')\n melted['category_label'] = melted['category'].map(category_labels)\n \n fig1 = px.bar(\n melted,\n x='category_label',\n y='value',\n color='timeframe',\n barmode='group',\n title=f'{title_name} Usage by Churn Category and Timeframe',\n category_orders={'category_label': category_order}\n )\n fig1.update_layout(\n xaxis_title='Category',\n yaxis_title='Average Usage',\n legend_title='Timeframe'\n )\n fig1.show()\n \n # Chart 2: Percentage of users with at least one\n counts = df.groupby('category')[cols].apply(lambda x: (x > 0).sum()).reset_index()\n counts_melted = counts.melt(id_vars='category', var_name='timeframe', value_name='count')\n counts_melted['total'] = counts_melted['category'].map(category_totals)\n counts_melted['percentage'] = (counts_melted['count'] / counts_melted['total']) * 100\n counts_melted['category_label'] = counts_melted['category'].map(category_labels)\n \n fig2 = px.bar(\n counts_melted,\n x='category_label',\n y='percentage',\n color='timeframe',\n barmode='group',\n title=f'% of Users with At Least One {title_name} by Churn Category and Timeframe',\n category_orders={'category_label': category_order}\n )\n fig2.update_layout(\n xaxis_title='Category',\n yaxis_title='% of Users',\n legend_title='Timeframe'\n )\n fig2.show()\n\n# Generate all charts\nfor metric in metrics:\n create_charts_for_metric(metric)",
"metadata": {},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": "# Generate cumulative charts for all metrics\ndef create_cumulative_charts_for_metric(metric_name):\n cols = [f'{metric_name}_short_term', f'{metric_name}_medium_term', f'{metric_name}_long_term']\n title_name = metric_name.replace('_', ' ').title()\n \n # Chart 1: Cumulative average usage\n grouped = df.groupby('category')[cols].mean().reset_index()\n # Make cumulative\n grouped['cumulative_short'] = grouped[cols[0]]\n grouped['cumulative_medium'] = grouped[cols[0]] + grouped[cols[1]]\n grouped['cumulative_long'] = grouped[cols[0]] + grouped[cols[1]] + grouped[cols[2]]\n \n cumulative_cols = ['cumulative_short', 'cumulative_medium', 'cumulative_long']\n melted = grouped.melt(id_vars='category', value_vars=cumulative_cols, var_name='timeframe', value_name='value')\n melted['category_label'] = melted['category'].map(category_labels)\n \n timeframe_labels_cumulative = {\n 'cumulative_short': 'Through trial',\n 'cumulative_medium': 'Through 90 days',\n 'cumulative_long': 'Through 6 months'\n }\n melted['timeframe_label'] = melted['timeframe'].map(timeframe_labels_cumulative)\n \n fig1 = px.bar(\n melted,\n x='category_label',\n y='value',\n color='timeframe_label',\n barmode='group',\n title=f'{title_name} Cumulative Usage by Churn Category',\n category_orders={\n 'category_label': category_order,\n 'timeframe_label': ['Through trial', 'Through 90 days', 'Through 6 months']\n }\n )\n fig1.update_layout(\n xaxis_title='Category',\n yaxis_title='Cumulative Average Usage',\n legend_title='Timeframe'\n )\n fig1.show()\n \n # Chart 2: Cumulative percentage of users with at least one\n # Check if user used feature in ANY period up to that point (not sum of counts)\n df['_cumul_short'] = df[cols[0]] > 0\n df['_cumul_medium'] = (df[cols[0]] > 0) | (df[cols[1]] > 0)\n df['_cumul_long'] = (df[cols[0]] > 0) | (df[cols[1]] > 0) | (df[cols[2]] > 0)\n \n counts = df.groupby('category')[['_cumul_short', '_cumul_medium', '_cumul_long']].sum().reset_index()\n counts.columns = ['category', 'cumulative_short', 'cumulative_medium', 'cumulative_long']\n \n counts_melted = counts.melt(id_vars='category', value_vars=cumulative_cols, var_name='timeframe', value_name='count')\n counts_melted['total'] = counts_melted['category'].map(category_totals)\n counts_melted['percentage'] = (counts_melted['count'] / counts_melted['total']) * 100\n counts_melted['category_label'] = counts_melted['category'].map(category_labels)\n counts_melted['timeframe_label'] = counts_melted['timeframe'].map(timeframe_labels_cumulative)\n \n fig2 = px.bar(\n counts_melted,\n x='category_label',\n y='percentage',\n color='timeframe_label',\n barmode='group',\n title=f'Cumulative % of Users with At Least One {title_name} by Churn Category',\n category_orders={\n 'category_label': category_order,\n 'timeframe_label': ['Through trial', 'Through 90 days', 'Through 6 months']\n }\n )\n fig2.update_layout(\n xaxis_title='Category',\n yaxis_title='Cumulative % of Users',\n legend_title='Timeframe'\n )\n fig2.show()\n\n# Generate all cumulative charts\nfor metric in metrics:\n create_cumulative_charts_for_metric(metric)",
"metadata": {},
"execution_count": null,
"outputs": []
}
],
"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": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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[tool.poetry]
name = "jupyter-analysis"
version = "0.1.0"
description = "Jupyter notebook project for data analysis and charting"
authors = ["Justin"]
package-mode = false
[tool.poetry.dependencies]
python = "^3.13"
jupyter = "^1.1.1"
pandas = "^3.0.0"
plotly = "^6.0.0"
[build-system]
requires = ["poetry-core>=2.0.0,<3.0.0"]
build-backend = "poetry.core.masonry.api"