pandas-smartcols 0.1.0

Pandas column manipulation toolkit

smartcols

Utilities for reordering and grouping pandas DataFrame columns without the usual index gymnastics. smartcols keeps column manipulations readable and testable while exposing a single API that works with pure or in-place workflows.

Features

• Swap, move before/after, push to front/end, or reorder multiple columns at once
• sort_columns with several strategies (default, variance, std_dev, nan_ratio, correlation, mean, custom)
• Column grouping helpers (dtype, NaN ratio buckets, regex, metadata maps, custom keys) and flatten helpers
• Optional inplace=True on every mutator to avoid extra copies without breaking method chains via DataFrame.pipe
• Typed API with validation and descriptive error messages for faster debugging

Installation

pip install smartcols

Quick start

import pandas as pd
from smartcols import (
    swap_columns,
    move_after,
    move_before,
    move_to_front,
    move_to_end,
    sort_columns,
    group_columns,
)

# Base DataFrame
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9], "D": [10, 11, 12]})

# Swap columns
swap_df = swap_columns(df, "A", "D")

# Move multiple columns after a target
reordered = move_after(df, ["B", "C"], "A")

# Update in place
move_to_front(df, "D", inplace=True)

# Combine with pipe for readable pipelines
pipe_df = (
    df.copy()
      .pipe(move_to_front, ["C", "A"])
      .pipe(move_after, "B", "C")
)

# Sort by variance
variance_sorted = sort_columns(df, by="variance")

# Group by NaN ratio buckets
nan_groups = group_columns(
    pd.DataFrame({
        "good": [1, 2, 3],
        "ok": [1, None, 3],
        "bad": [None, None, 1],
        "terrible": [None, None, None],
    }),
    by="nan_ratio",
)

API overview

Function	Description
swap_columns(df, col1, col2, *, inplace=False)	Swap two columns by name
move_after(df, cols, target, *, inplace=False)	Move one or more columns immediately after target
move_before(df, cols, target, *, inplace=False)	Move columns immediately before target
move_to_front(df, cols, *, inplace=False)	Push column(s) to the front
move_to_end(df, cols, *, inplace=False)	Push column(s) to the end
sort_columns(df, by='default', order='ascending', target='', key=None)	Reorder columns by alphabetical order, NaN ratio, variance, std dev, correlation, mean, or custom key
group_columns(df, by, ...)	Split columns into groups based on dtype, NaN ratio buckets, regex patterns, metadata map, or custom key
group_columns_flattened(...)	Same as group_columns but returns a single DataFrame with grouped segments stacked
save_column_order(df) / apply_column_order(df, order)	Capture column order snapshots and reapply them

Every mutating helper returns the reordered DataFrame unless inplace=True, in which case it returns None and the supplied DataFrame is updated.

Testing

pip install -e .[tests]
pytest

(If pytest complains about temp directories in constrained environments, set TMPDIR to a writable location first.)

Versioning

smartcols follows semantic versioning. Current release is 0.1.0.

License

MIT License. See LICENSE for details.