dfcleanerpro 0.2.2

Simple DataFrame cleaning toolkit

dfcleanerpro

Professional DataFrame cleaning toolkit for Data Engineers and Data Scientists.

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Install

pip install dfcleanerpro

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Features

• One-line DataFrame cleaning
• Fluent pipeline API with method chaining
• Audit log — track every transformation
• Schema validator — enforce column types
• Numeric-safe missing value filling
• Dataset quality analyzer

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Quick Start

Simple Clean

from dfcleanerpro import clean_dataframe

cleaned = clean_dataframe(
    df,
    drop_duplicates=True,
    snake_case=True,
    fill_missing="mean",
    remove_empty_cols=True
)

Pipeline API

from dfcleanerpro import DFPipeline

result, audit = (
    DFPipeline(df)
    .snake_case()
    .remove_duplicates()
    .drop_empty_cols()
    .fill_missing("mean")
    .run(audit=True)
)

print(audit)
# {
#   'steps_applied': ['snake_case', 'remove_duplicates', 'drop_empty_cols', 'fill_missing_mean'],
#   'duplicates_removed': 3,
#   'cols_dropped': ['empty_col'],
#   'cols_filled': ['age', 'salary'],
#   'rows_before': 100,
#   'rows_after': 97
# }

Schema Validator

result = (
    DFPipeline(df)
    .validate_schema({"age": "int", "salary": "float"})
    .clean()
    .run()
)

Dataset Analyzer

from dfcleanerpro import analyze_dataframe

report = analyze_dataframe(df)
# {
#   'rows': 500,
#   'columns': 8,
#   'duplicate_rows': 12,
#   'missing_values': {'age': 3, 'salary': 7},
#   'dtypes': {'age': 'int64', 'name': 'object'}
# }

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API Reference

clean_dataframe(df, ...)

Parameter	Type	Default	Description
drop_duplicates	bool	True	Remove duplicate rows
snake_case	bool	True	Convert column names to snake_case
fill_missing	str/None	None	'zero', 'mean', 'median', or None
remove_empty_cols	bool	True	Drop all-null columns

DFPipeline(df)

Method	Description
.snake_case()	Convert column names to snake_case
.remove_duplicates()	Drop duplicate rows
.drop_empty_cols()	Drop all-null columns
.fill_missing(method)	Fill numeric NaNs — zero/mean/median
.validate_schema(dict)	Enforce column types
.run(audit=False)	Execute pipeline, optionally with audit

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Roadmap

• auto_dtype_conversion()
• trim_string_columns()
• detect_outliers()
• data_quality_report() — HTML export
• CLI support: dfcleanerpro clean file.csv

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License

MIT