pytae

Fast, ergonomic Pandas tools and zero-code CLI for tabular data manipulation, feature engineering, DuckDB SQL, and visualization.

pytae offers one and only one right way to perform every tabular operation across two complementary workflows:

Python Library & DataFrame Accessor (df.pt): Expressive, chainable verbs (filter, select, mutate, arrange, distinct, by, ungroup, pick, agg, long, wide, pivot, sql, plot) on standard Pandas DataFrames.
Unix Pipeline CLI (pytae): Inspect, filter, transform, aggregate, diff, and export files (.parquet, .csv, .tsv, .jsonl, .sas7bdat, .gz) from the terminal with zero boilerplate.

👉 New to Pytae? Check out Pytae vs. Pandas vs. dplyr (Rosetta Stone & Naming Rationale).

Installation

pip install pytae              # Core library and CLI
pip install "pytae[plot]"       # Includes matplotlib for plotting
pip install "pytae[sql]"        # Includes DuckDB for SQL queries
pip install "pytae[plot,sql]"   # All optional dependencies

Quick Start: Python Library

Use verbs as standalone functions (pt.verb(df, ...)) or chain them via .pt:

import pytae as pt

penguins = pt.sample("penguins")

# Clean, method-chainable pipeline with zero index wrestling
summary = (
    penguins
    .pt.filter(lambda d: d["body_mass_g"] >= 3500)
    .pt.by("species")
    .pt.mutate(
        mass_kg="body_mass_g / 1000",
        avg_mass="mean(body_mass_g)",
        diff="body_mass_g - avg_mass",
    )
    .pt.arrange("species, -mass_kg")
    .pt.select("species", "island", "mass_kg", "diff")
    .pt.agg(mean_mass="mass_kg:mean", n="n")
)

👉 Full Library API Hub: docs/library.md

Quick Start: CLI

Flags execute from left to right as an in-memory pipeline:

# 1. Zero-cost inspection (reads Parquet headers without loading rows)
pytae penguins.parquet -shape -head 5

# 2. Filter, engineer features, sort, and export
pytae penguins.parquet \
  -filter "species = 'Adelie', body_mass_g > 3500" \
  -mutate "mass_kg = body_mass_g / 1000" \
  -arrange "mass_kg desc" \
  -select "species,island,mass_kg" \
  -o heavy_adelie.csv

# 3. Grouped aggregation & reshaping
pytae penguins.parquet -by species -agg "avg_mass=body_mass_g:mean, count=n"

# 4. Deduplicate rows across key columns
pytae penguins.parquet -distinct "species,island"

# 5. Embedded DuckDB SQL
pytae sales.parquet -sql "select region, sum(revenue) from data group by 1"

# 6. Fast dataset comparison
pytae current.parquet -diff baseline.parquet

👉 Full CLI Reference Hub: docs/cli.md

Key Syntax & Conventions

Operation	Python (`df.pt`)	CLI Flag	Example
Filter Rows	`.pt.filter(...)`	`-filter "..."`	`df.pt.filter(lambda d: d['mass'] > 4000)`
Group Context	`.pt.by(*cols)`	`-by ...`	`df.pt.by("species", "island")`
Clear Grouping	`.pt.ungroup()`	N/A	`df.pt.ungroup()`
Drop Null Rows	`df.dropna(...)`	`-dropna [COLS]`	`pytae data.parquet -dropna "age,income"`
Sort Rows	`.pt.arrange(...)`	`-arrange "..."`	`df.pt.arrange("species, -mass_kg")`
Pick Rows	`.pt.pick(...)`	`-pick "..."`	`df.pt.pick("mass", n=3, order="max")`
Select Columns	`.pt.select(...)`	`-select "..."`	`df.pt.select("species", contains="bill", "-temp")`
Mutate Columns	`.pt.mutate(...)`	`-mutate "..."`	`df.pt.mutate(ratio="[bill length] / [bill depth]")`
Grouped Summary	`.pt.agg(...)`	`-by ... -agg "..."`	`df.pt.by("species").pt.agg(avg_mass="mass:mean", n="n")`
2D Pivot Table	`.pt.pivot(...)`	`-pivot "..."`	`df.pt.pivot(r="island", c="species", v="mass", a="mean")`
Pure Reshape	`.pt.long()`, `.pt.wide()`	`-long`, `-wide`	`df.pt.wide(c="metric", v="val", index="id")`
Distinct Rows	`.pt.distinct(*cols)`	`-distinct [COLS]`	`df.pt.distinct("species", "island")`
DuckDB SQL	`.pt.sql("...")`	`-sql "..."`	`df.pt.sql("select * from data order by 1")`
Glimpse Table	`.pt.glimpse()`	`-glimpse`	`df.pt.glimpse()`
Copy to Clipboard	`df.to_clip()`	`-o clip`	`df.head().to_clip()`

Documentation Directory

Guides & Specifications

Architecture & Rosetta Stone: Pytae vs. Pandas vs. dplyr (Rationale)
Library API Guide: docs/library.md
CLI Reference Hub: docs/cli.md
CLI Feature Guides:
- Row Filtering (-filter, -dropna, intervals, set membership)
- Row Picking (-pick, top/bottom N or proportion per group)
- Row Sorting (-arrange, -col, desc, spaced columns)
- Distinct Rows (-distinct across all or specific column subsets)
- Column Selection (-select, negative drops, slices)
- Mutating & Computing (-mutate, conditionals, window calcs)
- Reshaping (-long, -wide pure 1-to-1 unmelting)
- 2D Pivot Tables (-pivot with flat 1D headers)
- Aggregations & Grouping (-by, -agg, grand totals)
- Inspection & Metadata (-shape, -cols, -meta, -freq, -hist, -value_counts)
- Data Cleaning (-clean_columns, -replace_values, -handle_missing)
- DuckDB SQL (-sql)
- Plotting & Figures (-plot, -finalize)
- Dataset Diffing (-diff)
- File I/O & Export (-o, -out_dir, STDIN, compression)
- Multi-File Pipelines (-file, -merge, -concat)
- Other Utilities & Modifiers (-nrows, -limit, -dlim, -encoding, -pretty, -round, -pager, -progress)

Interactive Jupyter Notebook Tutorials (`docs/library/`)

Run any interactive walkthrough directly:

Filtering: filter.ipynb
Selection: select.ipynb
Feature Engineering: mutate.ipynb
Sorting: arrange.ipynb
Picking Rows: pick.ipynb
Distinct Rows: distinct.ipynb
Pure Reshaping: reshape.ipynb
2D Pivot Tables: pivot.ipynb
Aggregation: agg.ipynb
DuckDB SQL: sql.ipynb
Plotting: plotting.ipynb
Other Utilities: other_utilities.ipynb

License

MIT License. See LICENSE for details.

Metadata

Release files for pytae 3.8.1

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Source distribution for pytae 3.8.1
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