pytae

Pandas helpers for everyday data-science tasks (filtering, selection, reshaping, aggregation, plotting), plus a pytae CLI that exposes the same operations for inspecting and converting tabular files (.parquet, .csv, .txt, .dat, .sas7bdat) without writing any Python.

Install

pip install pytae             # Core library and CLI
pip install "pytae[plot]"      # Adds Plotter (matplotlib, scipy)
pip install "pytae[sql]"       # Adds SQL engine (duckdb)

CLI

pytae data.parquet -head
pytae data.parquet -qry "species='Adelie'" -select "species,body_mass_g" -convert -o subset.csv
pytae data.parquet -sql "select species, avg(body_mass_g) from data group by species"
pytae -file "data1.parquet=df1; data2.parquet=df2" -merge "left=df1,right=df2,on=id"

See docs/CLI.md for the full CLI reference and flag guide, and docs/CLI_MULTI_FILE.md for -file/-merge/-concat.

Plotting

Plotter: method-chainable plots on top of pandas.plot() (pip install pytae[plot]).

from pytae.plotting import Plotter

Plotter().data(penguins).plot(
    x="bill_length_mm", y="bill_depth_mm", kind="scatter", c="species", cmap="viridis"
).finalize()

See docs/PLOTTING.md for more examples with sample data.

Library

Import pytae as pt. Same verbs work as pt.select(df, ...) or as df.pt.select(...) (mix with pandas: df.rename(...).pt.agg_df(...)). Notebooks use the accessor chain. CLI flags (-select, -qry, …) are unchanged.

import pytae as pt
penguins = pt.sample("penguins")
pt.select(penguins, "species", contains="bill")
(penguins
 .pt.select("species", "island", "bill_length_mm", "body_mass_g")
 .pt.agg_df(a=["mean", "n"])
)

Filtering — pt.qry() / df.pt.qry(): string expressions, dicts, or keyword filters (equality, lists, in/not in, comparisons, intervals)
Selection — pt.select(): columns by name, regex, dtype, or name pattern
Mutating — pt.mutate(): create/overwrite columns via formulas, if_else(), case_when(), map()
Reshaping — pt.long() / pt.wide(): melt numeric columns to rows, pivot back to columns
Aggregation — pt.agg_df(): auto-detects group columns and aggregates the rest
Utilities — df.to_clip(), pt.handle_missing(), pt.cols(), pt.group_x(), pt.clean_columns(), pt.replace_values()
SQL — pt.sql() / df.pt.sql() via duckdb (pip install pytae[sql]); the frame is table data

See docs/LIBRARY.md for examples of each.

Key Conventions & Syntax Cheat Sheet

Task	Syntax	Example
Copy to Clipboard	`df.to_clip()` in Python; `-to_clip` in CLI	`df.head().to_clip()` vs. `pytae data.parquet -head -to_clip`
Mapping vs. Assignment	`:` maps old to new; `=` assigns values	`-rename "old:new"` vs. `-mutate "col = expr"`
Spaced Columns (Filter)	String expressions handle spaces directly	`df.pt.qry("bill length mm > 40")`
Spaced Columns (Create)	Unpack dictionary with `**`	`df.pt.mutate(**{"body mass kg": "body_mass_g / 1000"})`
Spaced Columns (Expr)	Reference via brackets `[col]` or backticks `col`	`df.pt.mutate(ratio="[bill length mm] / [bill depth mm]")`
Selective Grouping	`agg_df` groups by all non-numeric cols; select first	`df.pt.select("species", "body_mass_g").pt.agg_df("mean")`

License

MIT — see LICENSE.

Release files for pytae 3.5.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pytae 3.5.2
File	Size	Uploaded
pytae-3.5.2.tar.gz	2.5 MB	Sep 25, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for pytae 3.5.2
File	Interpreter	ABI	Platform	Reset
pytae-3.5.2-py3-none-any.whl 1.7 MB Sep 25, 2026	Python 3	none	any	Details

Total release size: 4.2 MB

Release files / pytae-3.5.2.tar.gz

Download URL	pytae-3.5.2.tar.gz
Size	2.5 MB
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SHA-256 checksum How to use checksums	`ec733e7b971e0fd971ad3d1d22a5b3573e4e5a147df1742f480f168d9eb88fd0`
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Uploaded via	`twine/7.0.0 CPython/3.13.14`

Release files / pytae-3.5.2-py3-none-any.whl

Download URL	pytae-3.5.2-py3-none-any.whl
Size	1.7 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`fa7ee5772cf7d2b7f97237da6289c37c13b50c44a576429d0c797c5fef3daf30`
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Upload date	Sep 25, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.14`