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mixpanel_data

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SUMMARY

Complete programmable interface to Mixpanel analytics. Python library and CLI for discovery, querying, and data extraction. Designed with coding agent use in mind.

README.md

mixpanel_data

Python
License

⚠️ Pre-release Software: This package is under active development and not yet published to PyPI. APIs may change between versions.

A complete programmable interface to Mixpanel analytics—Python library and CLI for discovery, querying, and data extraction.

Why mixpanel_data?

Mixpanel's web UI is powerful for interactive exploration, but programmatic access requires navigating multiple REST endpoints with different conventions. mixpanel_data provides a unified interface: discover your schema, run analytics queries, and extract data—all through consistent Python methods or CLI commands.

Core analytics—segmentation, funnels, retention, saved reports—plus entity management (dashboards, reports, cohorts), raw JQL execution, and local SQL analysis via DuckDB.

Installation

Install directly from GitHub (package not yet published to PyPI):

pip install git+https://github.com/jaredmcfarland/mixpanel_data.git

Requires Python 3.10+. Verify installation:

mp --version

Quick Start

1. Authenticate

Option A: OAuth Login (interactive, recommended)

mp auth login --region us --project-id 12345  # Opens browser
mp auth status  # Verify connection

Option B: Service Account (scripts, CI/CD)

# Interactive prompt (secure)
mp auth add production --username sa_xxx --project 12345 --region us
# You'll be prompted for the service account secret with hidden input

mp auth test  # Verify connection

Alternative methods for CI/CD:

# Via inline environment variable (secret is only exposed to this command)
MP_SECRET=xxx mp auth add production --username sa_xxx --project 12345

# Via stdin (useful when secret is already in a variable)
echo "$SECRET" | mp auth add production --username sa_xxx --project 12345 --secret-stdin

Or set all credentials as environment variables: MP_USERNAME, MP_SECRET, MP_PROJECT_ID, MP_REGION

2. Explore Your Data

mp inspect events                      # List all events
mp inspect properties --event Purchase # Properties for an event
mp inspect funnels                     # Saved funnels

3. Fetch Events to Local Storage

# Sequential fetch for small date ranges
mp fetch events jan --from 2025-01-01 --to 2025-01-31

# Parallel fetch for large date ranges (up to 10x faster)
mp fetch events q1 --from 2025-01-01 --to 2025-03-31 --parallel

4. Query with SQL

mp query sql "SELECT event_name, COUNT(*) FROM jan GROUP BY 1 ORDER BY 2 DESC" --format table

5. Run Live Analytics

mp query segmentation --event Purchase --from 2025-01-01 --to 2025-01-31 --on country

6. Or Stream Directly (No Storage)

# Stream events as JSONL for piping to other tools
mp fetch events --from 2025-01-01 --to 2025-01-31 --stdout | jq '.event_name'

Python API

import mixpanel_data as mp

ws = mp.Workspace()

# Discover what's in your project
events = ws.list_events()
props = ws.list_properties("Purchase")
funnels = ws.list_funnels()
cohorts = ws.list_cohorts()

# Run live analytics queries
result = ws.segmentation(
    event=events[0].name,
    from_date="2025-01-01",
    to_date="2025-01-31",
    on="country"
)
print(result.df)  # pandas DataFrame

# Query a saved funnel
funnel = ws.funnel(
    funnel_id=funnels[0].id,
    from_date="2025-01-01",
    to_date="2025-01-31"
)

# Manage entities
dashboards = ws.list_dashboards()
cohort = ws.create_cohort(mp.CreateCohortParams(name="Power Users"))

# Fetch events into local DuckDB for SQL analysis
ws.fetch_events("jan", from_date="2025-01-01", to_date="2025-01-31")

# Use parallel=True for large date ranges (up to 10x faster)
ws.fetch_events("q1", from_date="2025-01-01", to_date="2025-03-31", parallel=True)

# Fetch profiles (use parallel=True for large datasets, up to 5x faster)
ws.fetch_profiles("users", parallel=True)

df = ws.sql("""
    SELECT
        DATE_TRUNC('day', event_time) as day,
        event_name,
        COUNT(*) as count
    FROM jan
    GROUP BY 1, 2
    ORDER BY 1, 3 DESC
""")

Temporary Workspaces

For one-off analysis without persisting data:

# Ephemeral: temp file with compression (best for large datasets)
with mp.Workspace.ephemeral() as ws:
    ws.fetch_events("events", from_date="2025-01-01", to_date="2025-01-31")
    total = ws.sql_scalar("SELECT COUNT(*) FROM events")
# Database automatically deleted

# In-memory: zero disk footprint (best for small datasets, testing)
with mp.Workspace.memory() as ws:
    ws.fetch_events("events", from_date="2025-01-01", to_date="2025-01-07")
    total = ws.sql_scalar("SELECT COUNT(*) FROM events")
# No files ever created

Streaming

For ETL pipelines or one-time processing without storage:

# Stream events directly to external system
for event in ws.stream_events(from_date="2025-01-01", to_date="2025-01-31"):
    send_to_warehouse(event)

CLI Reference

mp auth — Authentication: login, logout, status, token (OAuth); list, add, remove, switch, show, test (service accounts)

mp fetch — Extract data: events, profiles (add --parallel for up to 10x faster event exports or 5x faster profile exports, --stdout to stream as JSONL)

mp query — Run analytics: sql, segmentation, funnel, retention, jql, saved-report, flows, and 7 more

mp dashboards — Dashboard management: list, create, get, update, delete, favorite, pin, blueprints, and more

mp reports — Report management: list, create, get, update, delete, bulk operations, history

mp cohorts — Cohort management: list, create, get, update, delete, bulk operations

mp inspect — Discover schema: events, properties, funnels, cohorts, bookmarks; local DB: tables, schema, drop, and 5 more

All commands support --format (json, jsonl, table, csv, plain) and --help.

Filtering with --jq

Commands that output JSON support --jq for client-side filtering:

# Get first 5 events
mp inspect events --format json --jq '.[:5]'

# Extract total from segmentation
mp query segmentation --event Purchase --from 2025-01-01 --to 2025-01-31 \
    --format json --jq '.total'

# Filter SQL results
mp query sql "SELECT * FROM events LIMIT 100" --format json \
    --jq '.[] | select(.event_name == "Purchase")'

See CLI Reference for complete documentation.

DuckDB JSON Queries

Mixpanel properties are stored as JSON columns:

-- Extract property
SELECT properties->>'$.country' as country FROM events

-- Filter on property
SELECT * FROM events WHERE properties->>'$.plan' = 'premium'

-- Cast numeric
SELECT SUM(CAST(properties->>'$.amount' AS DECIMAL)) FROM events

Documentation

Full documentation: jaredmcfarland.github.io/mixpanel_data

For Humans and Agents

The entire surface area is self-documenting. Every CLI command supports --help with complete argument descriptions. The Python API uses typed dataclasses for all return values—IDEs show you what fields are available. Exceptions include error codes and context for programmatic handling. This means both human developers and AI coding agents can explore capabilities without external documentation.

Key design features:

  • Entity CRUD: Full lifecycle management of dashboards, reports, and cohorts via Mixpanel App API
  • Discoverable schema: list_events(), list_properties(), list_funnels(), list_cohorts(), list_bookmarks() reveal what's in your project before you query
  • Consistent interfaces: Same operations available as Python methods and CLI commands
  • Structured output: All CLI commands support --format json for machine-readable responses, plus --jq for inline filtering
  • Parallel fetching: Up to 10x faster event exports for large date ranges, 5x faster profile exports via --parallel or parallel=True
  • Local SQL iteration: Fetch once, query repeatedly—no re-fetching needed
  • Dual authentication: Service accounts (Basic Auth) for automation, OAuth 2.0 PKCE for interactive use
  • Typed exceptions: Error codes and context for programmatic handling

Claude Code Plugin

This project also includes a Claude Code plugin that brings analytics workflows directly into conversational AI interactions.

Ask questions about your Mixpanel data in natural language and get guided, interactive analytics workflows—all within Claude Code.

Installation:

/plugin marketplace add jaredmcfarland/mixpanel_data
/plugin install mixpanel-data

Then restart Claude Code.

What you get:

  • Auto-discovery skill: mixpanel-data skill activates when you mention Mixpanel, analytics, funnels, or retention—loads comprehensive reference docs and guides your workflow
  • 7 interactive commands:
    • /mp-auth - Secure credential management with account switching
    • /mp-inspect - 12-operation schema explorer (events, properties, funnels, cohorts, tables)
    • /mp-fetch - Guided data ingestion with validation
    • /mp-query - Universal query builder (SQL, JQL, live analytics)
    • /mp-funnel - Conversion analysis with visualizations
    • /mp-retention - Retention curves and cohort analysis
    • /mp-report - Comprehensive reporting with automated insights
  • 4 specialist agents: Auto-invoked based on your questions
    • mixpanel-analyst - General analytics, SQL/JQL query building
    • funnel-optimizer - Conversion analysis and drop-off diagnostics
    • retention-specialist - Cohort behavior and retention curves
    • jql-expert - Advanced JavaScript queries and transformations
  • Multiple query paths: SQL (DuckDB local analysis), JQL (complex transforms), or Mixpanel API (live analytics)
  • Secure by design: Credentials managed outside conversation context

Learn more: Plugin Documentation

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT

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