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FastF1 can be installed from PyPI using pip or from conda-forge using conda.

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Requirements

Before installing FastF1, ensure you have:
  • Python 3.10 or higher
  • pip or conda package manager

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Installation Methods

The recommended method is to install FastF1 using pip:
pip install fastf1
This will install FastF1 and all required dependencies.

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Dependencies

FastF1 automatically installs the following core dependencies:
  • pandas (≥2.1.1): DataFrame functionality
  • numpy (≥1.26.0): Numerical operations
  • matplotlib (≥3.8.0): Plotting and visualization
  • requests (≥2.30.0): HTTP requests
  • requests-cache (≥1.0.0): HTTP response caching
  • scipy (≥1.11.0): Scientific computing
Additional dependencies include: cryptography, platformdirs, pydantic, pyjwt, python-dateutil, signalrcore, rapidfuzz, timple, and websockets.
For the complete and current list of dependency version requirements, refer to the dependencies section in pyproject.toml.

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Configuring the Cache

Enabling the cache is critical for performance and to avoid exceeding API rate limits. Always configure the cache before loading any session data.
FastF1 uses a two-stage caching system to store API responses and parsed data. Configure the cache immediately after importing FastF1: 
import fastf1

# Enable cache with a custom directory
fastf1.Cache.enable_cache('path/to/cache/folder')

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Cache Directory Options

The cache directory is determined by the following precedence:
  1. Explicit configuration via Cache.enable_cache()
  2. Environment variable FASTF1_CACHE
  3. OS-specific default location:
    • Windows: %LOCALAPPDATA%\Temp\fastf1
    • macOS: ~/Library/Caches/fastf1
    • Linux: ~/.cache/fastf1 (or ~/.fastf1 if ~/.cache doesn’t exist)
Create a dedicated directory for the cache. The cache can grow to several gigabytes depending on usage, but you can delete cached data at any time to reclaim disk space.

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Cache Configuration Example

import fastf1
import os

# Create cache directory if it doesn't exist
cache_dir = os.path.expanduser('~/fastf1_cache')
if not os.path.exists(cache_dir):
    os.makedirs(cache_dir)

# Enable the cache
fastf1.Cache.enable_cache(cache_dir)

# Now you can load sessions
session = fastf1.get_session(2023, 'Monaco', 'Q')
session.load()

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Advanced Cache Options

The enable_cache() method supports additional parameters: 
fastf1.Cache.enable_cache(
    cache_dir='path/to/cache',
    ignore_version=False,      # Ignore cache version mismatches (not recommended)
    force_renew=False,          # Force re-download of all data
    use_requests_cache=True     # Enable Stage 1 HTTP response caching
)

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Verifying Installation

Verify that FastF1 is installed correctly: 
import fastf1

print(fastf1.__version__)
You can also check the cache status: 
import fastf1

fastf1.Cache.enable_cache('~/fastf1_cache')
print(fastf1.Cache)  # Shows cache location and size

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Special Environments

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Pyodide and WASM

FastF1 has limited compatibility with Pyodide, JupyterLite, and other WASM-based environments. Installation requires additional steps:

JupyterLite FastF1 Guide

External repository with installation instructions and examples for WASM environments
See GitHub Issue #667 for community discussion and workarounds.

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Troubleshooting

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Cache Directory Not Found

If you see NotADirectoryError: Cache directory does not exist!, create the directory first: 
import os

cache_dir = 'path/to/cache'
os.makedirs(cache_dir, exist_ok=True)
fastf1.Cache.enable_cache(cache_dir)

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Module Import Errors

Ensure all dependencies are installed correctly: 
pip install --upgrade fastf1

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Next Steps

Quickstart Guide

Learn how to load session data and access telemetry with a complete working example

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