Frictionless Data

5.18.1 · active · verified Sun Apr 12

Frictionless is a data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data (DEVT Framework). It supports a great deal of data sources and formats, as well as provides popular platform integrations. The framework is powered by the lightweight yet comprehensive Frictionless Standards. The current version is 5.18.1. The project has an active development and release cadence, with version 2.0 of the underlying Data Package standard recently released in June 2024, and significant updates to the Python library.

Warnings

breaking Frictionless Framework v5, released in December 2022, introduced several low-level breaking changes compared to v4. Users migrating from v4 or earlier should consult the official v5 announcement and migration guide for a smooth transition.
Fix: Review the official Frictionless v5 migration guide and update your code to reflect the new API where necessary.
gotcha Support for certain data formats or schemes (e.g., SQL databases, Pandas DataFrames, HTML, Parquet) requires installing additional plugins (e.g., `pip install frictionless[sql]`). Attempting to use these features without the corresponding plugin will result in an error message with installation instructions.
Fix: Install the necessary plugins using `pip install frictionless[plugin_name]` as indicated by the error message or documentation.
gotcha Argument naming conventions differ across Frictionless interfaces: `snake_case` for Python arguments, `camelCase` for dictionary/JSON objects, and `dashes-case` for command-line interface arguments. Be mindful of these differences when moving between interfaces.
Fix: Always refer to the specific interface's documentation for correct argument naming. For Python, use `snake_case`. For JSON/dictionaries, use `camelCase`. For CLI, use `--dashed-arguments`.
gotcha The underlying Frictionless Data Package standard was updated to version 2.0 in June 2024. While `frictionless-py` aims for backward compatibility, new features or stricter adherence to the v2 spec might subtly change how data packages are processed or validated compared to older versions.
Fix: Familiarize yourself with the Data Package v2 specification, especially for defining custom data packages. Ensure your data package descriptors conform to the latest standard for optimal compatibility.

Install

pip install frictionless Core library
pip install frictionless[sql] For SQL database support
pip install frictionless[pandas] For Pandas DataFrame support

Imports

describe
```
from frictionless import describe
```
extract
```
from frictionless import extract
```
validate
```
from frictionless import validate
```
Package
```
from frictionless import Package
```
Top-level functions and classes are directly exposed under the `frictionless` namespace for ease of use.

Quickstart

This quickstart demonstrates how to use `frictionless` to infer metadata (schema) from a CSV file and then extract its data as Python rows. It first creates a temporary CSV file, then uses `describe` to automatically generate a schema, and `extract` to read the data.

import os
from frictionless import describe, extract

# Create a dummy CSV file for demonstration
csv_content = """id,name,value
1,apple,100
2,banana,200
3,orange,150
"""
file_path = "data.csv"
with open(file_path, "w") as f:
    f.write(csv_content)

# Describe the data to infer metadata (Table Schema)
print("--- Inferred Schema ---")
report = describe(file_path)
print(report.to_json(indent=2))

# Extract data as rows from the file
print("\n--- Extracted Data ---")
rows = extract(file_path)
for row in rows:
    print(row)

# Clean up the dummy file
os.remove(file_path)

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