scitex-pd 0.1.6

Pandas helpers (force_df, melt_cols, find_pval, mv, slice, sort, round, …) — standalone module from the SciTeX ecosystem

scitex-pd

Pandas helpers — coerce, reshape, find p-values, merge/melt columns, sort/slice/round.

Full Documentation · uv pip install scitex-pd[all]

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Problem and Solution

#	Problem	Solution
1	Pandas reshape boilerplate — coercing dicts/Series/lists into DataFrames, pivoting long↔wide, and locating p-value columns is repeated noise across analysis scripts	force_df, from_xyz/to_xy, find_pval — small composable helpers with sensible defaults
2	Column ops drift — every project re-implements rename / reorder / round / merge for stats tables	merge_columns, mv, round, replace, sort, slice — uniform DataFrame-in / DataFrame-out helpers

Installation

pip install scitex-pd

Architecture

src/scitex_pd/
├── __init__.py              # public API surface
├── _force_df.py             # dict / Series / list → DataFrame
├── _find_pval.py            # locate p-value columns
├── _find_indi.py            # boolean-mask helpers
├── _get_unique.py           # unique-values per column
├── _merge_columns.py        # combine columns into one
├── _melt_cols.py            # long ↔ wide reshapes
├── _mv.py                   # reorder columns
├── _replace.py              # value remapping
├── _round.py                # rounding with NaN safety
├── _slice.py                # row / column subsetting
├── _sort.py                 # multi-key sort wrappers
├── _ignore_SettingWithCopyWarning.py
└── _convert/                # long ↔ wide pivots (from_xyz / to_xy / to_xyz)

scitex-pd is a thin layer on top of pandas + numpy; the only non-stdlib dep beyond those is scitex-types (for is_listed_X).

1 Interfaces

Python API

import scitex_pd as pd_

# Coerce / convert
pd_.force_df(data)
pd_.from_xyz(df, x, y, z)
pd_.to_xy(df)
pd_.to_xyz(df)
pd_.to_numeric(df)

# Find / inspect
pd_.find_pval(df)
pd_.find_indi(df, mask)
pd_.get_unique(df, "col")

# Reshape / restructure
pd_.merge_columns(df, [...], "out")
pd_.melt_cols(df, [...])
pd_.mv(df, col, position=-1)

# Transform
pd_.replace(df, mapping)
pd_.round(df, ndigits=2)
pd_.slice(df, ...)
pd_.sort(df, ...)

# Warnings
pd_.ignore_setting_with_copy_warning()

Demo

flowchart LR
    raw["dict / Series / list / scalar"] --> force["force_df"]
    force --> df[(DataFrame)]
    df --> reshape["from_xyz / to_xy / melt_cols"]
    df --> inspect["find_pval / get_unique"]
    df --> transform["round / replace / sort / slice / mv"]
    reshape --> out[(reshaped DataFrame)]
    inspect --> out2[("p-value columns / uniques")]
    transform --> out3[(transformed DataFrame)]

Quick Start

import scitex_pd as pd_

pd_.force_df(data)              # Coerce dict / Series / list / scalar → DataFrame
pd_.from_xyz(df, x, y, z)       # Long → wide pivot
pd_.to_xy(df)                   # Wide → long
pd_.find_pval(df)               # Locate p-value columns

Status

Standalone fork of scitex.pd. Deps: numpy, pandas, scitex-types (for is_listed_X). The umbrella package's scitex.pd import path is preserved via a sys.modules-alias bridge.

Part of SciTeX

scitex-pd is part of SciTeX. Install via the umbrella with pip install scitex[pd] to use as scitex.pd (Python) or scitex pd ... (CLI).

Four Freedoms for Research

0. The freedom to run your research anywhere — your machine, your terms.
1. The freedom to study how every step works — from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

License

AGPL-3.0-only (see LICENSE).

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