Install & Compatibility
Where this runs
tested against v0.0.12 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
py 3.10
✕ build_error
✓ 16.15s
py 3.11
✕ build_error
✓ 15.35s
py 3.12
✕ build_error
✓ 14.6s
py 3.13
✕ build_error
✕ build_error
py 3.9
✕ build_error
✓ 19.28s
285MB installed
● package 285MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
DataHubClassifier
✓ from datahub_classify import DataHubClassifier
✗ from datahub_classify import DataHubClassifier
This quickstart demonstrates how to initialize the DataHubClassifier and apply it to a pandas DataFrame to infer information types for columns. The output will show the original and classified DataFrames, including the detected info types for each cell.
import pandas as pd
from datahub_classify.classifier.classifier import DataHubClassifier
# Create a sample DataFrame
data = {
'email_address': ['test1@example.com', 'test2@example.com'],
'first_name': ['John', 'Jane'],
'social_security_number': ['XXX-XX-1234', 'XXX-XX-5678'],
'city': ['New York', 'Los Angeles']
}
df = pd.DataFrame(data)
# Initialize the classifier
classifier = DataHubClassifier()
# Classify the DataFrame
classified_df = classifier.classify_dataframe(df)
# Print results
print("Original DataFrame:")
print(df)
print("\nClassified DataFrame with info types:")
print(classified_df)
# Accessing inferred info types for a specific column
# For example, 'email_address'
print("\nInferred info types for 'email_address' column:")
if 'email_address' in classified_df.columns:
print(classified_df['email_address'].iloc[0].metadata.dataType.type.infoType)
Debug
Known issues
breakingThis library (`acryl-datahub-classify`) is explicitly deprecated as of version 0.0.12 and will no longer receive updates or support from Acryl Data. It is recommended to migrate to alternative data classification solutions.fixDiscontinue use of `acryl-datahub-classify`. Explore DataHub's native classification features if they meet your needs, or integrate with other third-party classification tools.
affects: 0.0.12 and later
gotchaDue to its deprecated status, the pinned or range-based dependencies (e.g., `pandas<2.0.0`, `spacy>=3.0.0`) may quickly become outdated, leading to dependency conflicts with newer libraries in your environment or compatibility issues with newer Python versions.fixIf continued use is unavoidable, carefully manage its dependencies in an isolated virtual environment to prevent conflicts. Be prepared for potential issues with newer Python interpreters or other libraries.
affects: All versions
Upgrade
Version history
0.0.12latest on PyPI · released Feb 19, 2026
Audit
Dependencies
pydanticrequiredRuntime dependency for data modeling.
spacyrequiredCore dependency for natural language processing and entity recognition.
pandasrequiredUsed for DataFrame manipulation and data processing.
langdetectrequiredUsed for language detection of text data.
scikit-learnrequiredMachine learning utilities and models.
scipyrequiredScientific computing library, often a dependency of scikit-learn.
joblibrequiredUsed for parallel processing and caching.
regexrequiredAdvanced regular expression operations.
daciterequiredCreates data classes from dictionaries.