pytae

Fast, ergonomic tabular data manipulation, feature engineering, and visualization powered natively by Polars for multithreaded computation and backed by Pandas & Matplotlib for plotting across both command-line and Python library.

pytae delivers one and only one obvious way to work with tabular data:

Unix Pipeline CLI (pytae): Blazing-fast multithreaded SIMD vector engine sharing the exact same Polars core. Inspect tabular files (.parquet, .csv, .tsv, .jsonl, .sas7bdat, .gz), filter, transform, aggregate, reshape, plot directly in the terminal, and execute ANSI SQL queries with zero boilerplate.
Python Library (pt.DataFrame / df.pt): High-performance tabular data frame backed directly by Polars vector execution. Enter via pt(pd_df), pt(pl_df), pt.read_parquet("file.parquet"), or calling .pt off any Pandas or Polars DataFrame (.by(), .mutate(), .filter(), .arrange(), .agg(), .pivot(), .plot()).

👉 Quick Links: CLI Cheat Sheet & Cookbook | vs. Pandas | vs. dplyr | vs. SAS.

Installation

pip install pytae              # Polars-native CLI and Python library
pip install "pytae[plot]"       # Includes matplotlib for plotting

Quick Start: Command-Line (CLI)

Flags execute from left to right as an in-memory pipeline powered natively by Polars SIMD:

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

# 2. Filter, engineer features, sort, and export to snappy Parquet
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.parquet

# 3. In-terminal frequency table & ASCII bar visualization
pytae penguins.parquet -freq species -bar

# 4. Instant format conversion (SAS7BDAT to Parquet or CSV)
pytae legacy.sas7bdat -o clean.parquet

# 5. Dataset diff & numeric drift detection between pipelines
pytae yesterday.parquet -diff today.parquet -tol 1e-4

# 6. Stream piping from STDIN
cat data.csv | pytae - -fmt csv -filter "amount > 100" -shape

# 7. Grouped aggregation & ANSI SQL query
pytae penguins.parquet -by species -agg "avg_mass=body_mass_g:mean, total=count"
pytae sales.parquet -sql "select region, sum(revenue) from data group by 1"

👉 CLI Quick Reference: Cheat Sheet & Cookbook (Markdown) or run pytae -cheatsheet in your terminal! | Full CLI Command Guide: docs/detailed_docs/cli/

Quick Start: Python & Jupyter SDK

Need the same expressive verbs in Python scripts, Airflow, or Jupyter? import pytae as pt gives you the exact same superpowers:

import pytae as pt

# Read directly with Polars-powered fast auto-detection
df = pt.read_parquet("sales_report.parquet")

# Or chain directly off pandas or polars - only one .pt call needed!
summary = (
    df.filter("amount > 0")
    .by("region")
    .mutate(pct_total="amount / sum(amount)", prev_sale="shift(amount, 1)")
    .arrange("amount desc")
    .to_pandas()  # or .to_polars(), .to_clip()
)

👉 Full Master Documentation Hub: docs/pytae.md

Visualizations & Dashboards (Powered by Pandas & Matplotlib)

Pytae follows a clean, pragmatic architecture: Compute in Polars, Visualize in Pandas.

While Polars provides multithreaded SIMD speed for filtering, feature engineering, and aggregation, Pandas and Matplotlib remain the undisputed industry standard for Python data visualization. Calling .plot() automatically converts the prepared data into a Pandas DataFrame and delegates directly to pandas.DataFrame.plot()—giving you 100% compatibility with all standard plot kinds, styling, and mosaic dashboarding without any manual conversion boilerplate:

import pytae as pt

penguins = pt.sample("penguins")

# 1. Method chaining: compute in Polars, visualize in Pandas
(
    penguins
    .filter("body_mass_g.notna()")
    .by("species")
    .agg(avg_mass="body_mass_g:mean")
    .plot(kind="bar", x="species", y="avg_mass", title="Average Penguin Mass (g)")
    .finalize(save="mass_by_species.png")
)

# 2. Multi-panel dashboard layouts (pt.Plotter with ASCII mosaics)
plotter = pt.Plotter("""
    AAAB
    AAAC
""", figsize=(12, 6))

plotter.add("A", penguins, kind="scatter", x="bill_length_mm", y="bill_depth_mm", color="species")
plotter.add("B", penguins.by("species").count(), kind="bar", x="species", y="n")
plotter.add("C", penguins, kind="box", y="body_mass_g", by="species")
plotter.finalize(title="Penguin Morphology Dashboard", save="dashboard.png")

On the command line, generate distribution charts directly in your terminal or save figures headlessly:

# In-terminal distribution histogram
pytae penguins.parquet -hist body_mass_g

# Headless plot generation
pytae penguins.parquet -by species -agg "avg_mass=body_mass_g:mean" \
  -plot "kind=bar, x=species, y=avg_mass" -finalize "save=mass.png"

👉 Interactive Plotting Notebook & Guide: docs/detailed_docs/plotting/plotting.ipynb

Documentation Directory

Guides & Specifications

Master Reference & Architecture Hub: docs/pytae.md
Ecosystem Comparisons & Migration Guides:

Detailed Topic Guides (`docs/detailed_docs/`)

Comprehensive documentation split into two dedicated folders:

docs/detailed_docs/library/: Interactive, fully executed Jupyter Notebooks (.ipynb)
docs/detailed_docs/cli/: In-depth Command Line Markdown manuals (.md)
Data Ingestion & Sinks: Notebook | CLI Guide (pt.read_*, pt.scan_*, write_*, -o, -od, -fmt, -progress)
Feature Engineering: Notebook | CLI Guide (mutate, formulas, sequential assignments, if_else, map, shift, @var)
Row Filtering: Notebook | CLI Guide (filter, intervals, set membership, or/and, callables, pushdown)
Column Selection: Notebook | CLI Guide (select, negative drops !col, regex, pattern matches, rename)
Row Sorting: Notebook | CLI Guide (arrange, -col, desc, bracketed spaced columns)
Row Slicing: Notebook | CLI Guide (arrange + head/tail, top/bottom N or proportion per group via by, ties)
Distinct Rows: Notebook | CLI Guide (distinct, deduplication across column subsets, keep)
Table Reshaping: Notebook | CLI Guide (long, wide, pure 1-to-1 unmelting, unified rcva grammar)
Relational Joins & Stacking: Notebook | CLI Guide (df.join(), pt.join(), pt.concat(), -join, -concat, colon keys, validate)
2D Pivot Tables: Notebook | CLI Guide (pivot, r, c, v, a matrices, flat 1D headers)
Aggregations & Grouping: Notebook | CLI Guide (by, agg, dropna=False zero data loss retention)
Visualizations & Plotting: Notebook | CLI Guide (df.plot(), -plot, -finalize, mosaic dashboards)
Utilities & Inspection: Notebook | CLI Guide (fillna, dropna, rename, diff, glimpse, clean)
In-Pipeline SQL: Notebook | CLI Guide (df.sql(), pt.sql(), -sql DuckDB/Polars SQL engine)
Multi-File Pipelines: Notebook | CLI Guide (-file, multi-table pipelines, batch globbing, UNIX pipes)

License

MIT License. See LICENSE for details.

Metadata

Release files for pytae 3.10.3

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Source distribution for pytae 3.10.3
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