prepx 0.1.0

One-line DataFrame cleaning and EDA for pandas.

prepx

One-line cleaning and exploratory data analysis for pandas DataFrames.

from prepx import clean, eda

cleaned_df, clean_report = clean(df)
eda_report = eda(cleaned_df, target="price")

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Installation

pip install .

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clean(df, **kwargs) → (DataFrame, dict)

Cleans the DataFrame and returns the cleaned version plus a full action report.

Parameter	Default	What it does
drop_duplicates	True	Remove exact duplicate rows
handle_missing	"auto"	"auto"/"fill" → median/mode fill; "drop" → drop NaN rows; "none" → skip
missing_threshold	0.6	Drop columns with > this fraction missing
fix_dtypes	True	Coerce object columns to numeric or datetime when ≥80% parse
strip_whitespace	True	Strip leading/trailing whitespace from strings
standardize_columns	True	Rename columns to snake_case
remove_outliers	False	Cap outliers via IQR fences or Z-score
outlier_method	"iqr"	"iqr" or "zscore"
outlier_threshold	3.0	Z-score cut-off (only used for "zscore")
drop_constant_cols	True	Drop columns with only one unique value
verbose	True	Print a human-readable report

Example output

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  prepx  ·  Cleaning Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Initial shape                   1000 rows × 12 cols
  Final shape                      961 rows ×  9 cols
  Rows removed                      39
  Columns removed                    3
──────────────────────────────────────────────────────────────

  ✦ Column names → snake_case  (4 renamed)
  ✦ Whitespace stripped  (5 string columns)
  ✦ Type coercion  (2 columns updated)
      Numeric  : age, income
  ✦ Duplicate rows removed  : 12
  ✦ Constant columns dropped : id_copy
  ✦ High-missing columns dropped  (>60% NaN)
      notes
  ✦ Missing values filled  (3 columns)
      age       4 NaN → median (32.0)
      city      2 NaN → mode ('London')

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eda(df, **kwargs) → dict

Full exploratory analysis. Returns a rich dict and prints a report.

Parameter	Default	What it does
target	None	Target column — adds class balance + per-feature correlations
top_n_categories	10	How many top categories to show per object column
correlation_method	"pearson"	"pearson", "spearman", or "kendall"
verbose	True	Print a human-readable report

Report dict keys

Key	Contents
overview	Shape, duplicates, memory
dtypes	Column lists by type + per-column dtype
missing	Total missing, per-column counts and percentages
numeric_stats	Mean, std, min/max, percentiles, skewness, kurtosis, outliers
categorical_stats	Unique count, top-N values, mode, entropy
correlations	Full matrix + high-correlation pairs (
target_analysis	Class balance + feature correlations (if target set)
warnings	Auto-generated issues (duplicates, skew, multicollinearity, etc.)

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Quick start

import pandas as pd
from prepx import clean, eda

df = pd.read_csv("data.csv")

# 1 — clean
cleaned, clean_report = clean(df, remove_outliers=True)

# 2 — explore
eda_report = eda(cleaned, target="churn")

# 3 — use the report programmatically
print(eda_report["warnings"])
print(eda_report["numeric_stats"]["age"])