Registry / ai-ml / tensorflow-data-validation

tensorflow-data-validation

library1.21.0pypypiunverified

TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It computes descriptive statistics, infers a schema, detects anomalies, and supports data drift/skew detection. Current version is 1.17.0 (requires Python 3.9+), with releases following TensorFlow's cadence.

ai-ml data testing

pip install tensorflow-data-validation

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · TENSORFLOW-DATA-VA

tensorflow-data-validation

ai-mlpythonv1.21.0

Install

—

Import

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Disk

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Pass rate

0/ 10

Env Coverage0 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v? · pip install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

tensorflow_data_validation

✓ import tensorflow_data_validation as tfdv

✗ import tfdv

tfdv is not a top-level module; must import tensorflow_data_validation

StatsOptions

✓ from tensorflow_data_validation.utils.stats_options import StatsOptions

StatsOptions is used to customize statistics computation

Compute statistics from a DataFrame, infer schema, and validate new data.

import tensorflow_data_validation as tfdv import pandas as pd data = pd.DataFrame({ 'feature1': [1, 2, 3, 4, 5], 'feature2': ['a', 'b', 'c', 'd', 'e'] }) # Generate statistics stats = tfdv.generate_statistics_from_dataframe(data) # Infer schema schema = tfdv.infer_schema(stats) print(schema) # Validate new data test_data = pd.DataFrame({ 'feature1': [1, 2, 6], 'feature2': ['x', 'y', 'z'] }) anomalies = tfdv.validate_statistics( tfdv.generate_statistics_from_dataframe(test_data), schema ) print(anomalies)

Debug

Known issues

breakingTFDV 1.0+ changed the API for `generate_statistics_from_csv` and `generate_statistics_from_dataframe`. The old `tfdv.generate_statistics` is deprecated.

fix

Use `generate_statistics_from_csv` or `generate_statistics_from_dataframe` instead of `generate_statistics`.

affects: <1.0

deprecated`tfdv.visualize_statistics` is deprecated in favor of using `tfdv.utils.display_util.display_stats` for Jupyter visualization.

fix

Use `from tensorflow_data_validation.utils.display_util import display_stats`.

affects: >=1.10

gotchaTFDV statistics generation may be slow on large datasets; use Apache Beam for distributed processing.

fix

Install apache-beam (`pip install apache-beam`) and use `generate_statistics_from_csv` with `beam_pipeline_args`.

affects: all

gotchaSchema inference from small samples may produce overly strict constraints; use `tfdv.update_schema_with_stats` or manual tuning.

fix

Consider using `tfdv.update_schema_with_stats` to relax schema constraints based on full statistics.

affects: all

Upgrade

Version history

1.21.0latest on PyPI · released Jun 11, 2026

Audit

Dependencies

tensorflowrequiredTFDV relies on TF for internal ops and data handling

apache-beamoptionalRequired for distributed processing (optional in some installs)

Agent activity

18 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/tensorflow/data-validation ↗

packagehttps://pypi.org/project/tensorflow-data-validation/ ↗

homepagehttps://www.tensorflow.org/tfx/data_validation/get_started ↗