pytae
Fast, ergonomic Pandas-based Python tools and high-performance Polars-based CLI for tabular data manipulation, feature engineering, SQL queries, and visualization.
pytae offers one and only one right way to perform every tabular operation across two complementary, specialized workflows:
- Python Library & DataFrame Accessor (
df.pt) — 100% Pandas-Based: Zero external engine overhead. Operates directly on standard Pandas DataFrames with expressive, chainable verbs (filter,select,mutate,arrange,distinct,by,ungroup,pick,agg,long,wide,pivot,plot). Importingpytaein Python is pure Pandas and never loads Polars into memory. - Unix Pipeline CLI (
pytae) — 100% Polars-Based: Blazing-fast multithreaded SIMD vector engine. Ingests tabular files (.parquet,.csv,.tsv,.jsonl,.sas7bdat,.gz), filters, transforms, aggregates, reshapes, and executes analytical ANSI SQL queries (-sql) natively in Polars vector memory with zero boilerplate.
👉 New to Pytae? Check out Pytae vs. Pandas vs. dplyr (Rosetta Stone & Naming Rationale).
Installation
pip install pytae # Core Pandas library and Polars-powered CLI
pip install "pytae[plot]" # Includes matplotlib for plotting
Quick Start: Python Library (Pure Pandas)
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 (Polars-Powered)
Flags execute from left to right as an in-memory pipeline powered natively by Polars:
# 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. Polars SQL Query
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]") |
| Multi-Col Mutate | .pt.mutate("(...) = ...") |
-mutate "(...) = ..." |
df.pt.mutate("(rev, cost)_usd = _ * 1.08") |
| 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") |
| SQL Query (CLI) | N/A (CLI only) | -sql "..." |
pytae data.parquet -sql "select * from data order by 1" |
| Glimpse Table | .pt.glimpse() |
-glimpse |
df.pt.glimpse() |
| ASCII Freq & Hist | .pt.freq(), .pt.hist() |
-freq, -hist |
df.pt.freq("species"), df.pt.hist("mass:10") |
| 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 (Complete reference for the pure Pandas Python API:
df.ptandpt.*) - CLI Reference Hub: docs/cli.md (Complete reference for the Polars-powered command-line pipeline)
Library Feature Guides (docs/library/)
Explore step-by-step Python API guides with inline outputs:
- Row Filtering (
pt.filter(), callables, intervals, set membership) - Column Selection (
pt.select(), negative drops-col, slices, regex, dtypes) - Feature Engineering (
pt.mutate(), formulas,(col1, col2) = _ * 2, helpers) - Row Sorting (
pt.arrange(),-col,desc, bracketed spaced columns) - Row Picking (
pt.pick(), top/bottom N or proportion per group viapt.by()) - Distinct Rows (
pt.distinct(), deduplication across column subsets) - Pure Reshaping (
pt.long(),pt.wide(), pure 1-to-1 unmelting) - 2D Pivot Tables (
pt.pivot(), Excel-styler, c, v, amatrices, flat 1D headers) - Aggregations & Grouping (
pt.by(),pt.agg(), group summaries & grand totals) - Plotting & Figures (
pt.Plotter,df.pt.plot(), interactive notebook walkthrough) - Utilities & Cleaning (
glimpse,freq,hist,clean_columns,replace_values,handle_missing,to_clip)
CLI Feature Guides (docs/cli/)
Explore step-by-step CLI pipeline guides with terminal outputs:
- Row Filtering (
-filter,-dropna, intervals, set membership) - Column Selection (
-select, negative drops-col, slices, regex, dtypes) - Feature Engineering (
-mutate, formulas,(col1, col2) = _ * 2, helpers) - Row Sorting (
-arrange,-col,desc, bracketed spaced columns) - Row Picking (
-pick, top/bottom N or proportion per group with-by) - Distinct Rows (
-distinct, deduplication across column subsets) - Pure Reshaping (
-long,-wide, pure 1-to-1 unmelting) - 2D Pivot Tables (
-pivot, Excel-styler, c, v, amatrices, flat 1D headers) - Aggregations & Grouping (
-by,-agg, grand totals) - Plotting & Figures (
-plot,-finalize) - Inspection & Metadata (
-shape,-cols,-meta,-freq,-hist,-value_counts) - Data Cleaning (
-clean_columns,-replace_values,-handle_missing) - SQL Engine (
-sql, Polars SQLContext queries, CTEs, joins) - Dataset Diffing (
-diff, schema drift, shape deltas, cell mismatches) - 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)
License
MIT License. See LICENSE for details.
1 maintainer
Metadata
1 maintainer
Release files for pytae 3.9.0
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