mixpanel-data-marketplace

mixpanel_data

⚠️ 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, streaming, and entity management.

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, stream data, and manage entities—all through consistent Python methods or CLI commands.

Core analytics—typed Insights engine queries (DAU/WAU/MAU, formulas, filters, breakdowns), segmentation, funnels, retention, saved reports—plus entity management (dashboards, reports, cohorts, feature flags, experiments), raw JQL execution, and streaming data extraction.

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. Run Analytics Queries

import mixpanel_data as mp

ws = mp.Workspace()

# Typed insights query (recommended)
result = ws.query("Purchase", math="unique", group_by="country", last=30)
print(result.df)

# Or use the CLI for legacy query methods
mp query segmentation --event Purchase --from 2025-01-01 --to 2025-01-31 --on country

4. Stream Data (Python API)

import mixpanel_data as mp

ws = mp.Workspace()
for event in ws.stream_events(from_date="2025-01-01", to_date="2025-01-31"):
    print(event["event"])

Python API

import mixpanel_data as mp
from mixpanel_data import Metric, Filter, Formula, GroupBy

ws = mp.Workspace()

# Discover what's in your project
events = ws.events()
props = ws.properties("Purchase")
funnels = ws.funnels()
cohorts = ws.cohorts()

# Insights queries — typed, composable analytics
result = ws.query("Login")                            # simple event count
result = ws.query("Login", math="dau", last=90)       # DAU trend
result = ws.query("Purchase", math="total",             # revenue by country
    math_property="amount", group_by="country")
result = ws.query(                                     # conversion rate formula
    [Metric("Signup", math="unique"), Metric("Purchase", math="unique")],
    formula="(B / A) * 100",
    formula_label="Conversion Rate",
    unit="week",
)
result = ws.query("Purchase",                          # filtered with breakdown
    where=Filter.equals("country", "US"),
    group_by=GroupBy("amount", property_type="number", bucket_size=50),
)
print(result.df)  # pandas DataFrame

# Legacy live analytics queries
result = ws.segmentation(
    event=events[0],
    from_date="2025-01-01",
    to_date="2025-01-31",
    on="country"
)

# 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"))

# Feature flags and experiments
flags = ws.list_feature_flags()
flag = ws.create_feature_flag(mp.CreateFeatureFlagParams(name="Dark Mode", key="dark_mode"))
experiments = ws.list_experiments()
exp = ws.create_experiment(mp.CreateExperimentParams(name="Checkout Flow Test"))

# Operational tooling
alerts = ws.list_alerts()
annotations = ws.list_annotations(from_date="2025-01-01")
webhooks = ws.list_webhooks()