tidypolars-extra 0.2.0

An extension to tidypolars4sci with additional functionalities for scientific data analysis.

tidypolars-extra

tidypolars-extra is an extension of tidypolars4sci, which provides Tidyverse-like functions for data manipulation and analysis in Python using Polars as the backend.

This project builds upon the original tidypolars4sci by adding extra functionalities and improvements while maintaining the same familiar API.

Features

• Tidyverse-style API for Polars DataFrames
• Scientific research utilities including LaTeX table generation
• Fast data manipulation powered by Polars
• Familiar R-like syntax for Python users
• Joins: inner_join, left_join, full_join, semi_join, anti_join, cross_join
• Data reshaping: pivot_longer, pivot_wider, separate, unite, complete, nest, unnest
• String manipulation (stringr-style): str_detect, str_extract, str_replace, str_count, str_split, str_pad, str_squish, str_to_title, and more
• Date/time utilities (lubridate-style): year, month, floor_date, ceiling_date, difftime, today, now, duration constructors
• Statistics: mean, sd, cor, rank, scale, cumsum, ntile, weighted_mean, iqr, mad, and more
• Factor manipulation (forcats-style): fct_infreq, fct_lump, fct_recode, fct_collapse, fct_rev
• Data quality: describe, glimpse, get_dupes, assert_no_nulls, assert_unique, clean_names
• Multi-format I/O: CSV, Excel, Stata, SPSS, RDS/RData, Parquet, JSON, Google Sheets

Installation

You can install tidypolars-extra with pip:

pip install tidypolars-extra

Basic usage

tidypolars-extra methods are designed to work like tidyverse functions:

import tidypolars_extra as tp

# create tibble data frame
df = tp.tibble(x = range(3),
               y = range(3, 6),
               z = ['a', 'a', 'b'])

(
    df
    .select('x', 'y', 'z')
    .filter(tp.col('x') < 4, tp.col('y') > 1)
    .arrange(tp.desc('z'), 'x')
    .mutate(double_x = tp.col('x') * 2,
            x_plus_y = tp.col('x') + tp.col('y')
            )
)
┌─────┬─────┬─────┬──────────┬──────────┐
│ x   ┆ y   ┆ z   ┆ double_x ┆ x_plus_y │
│ --- ┆ --- ┆ --- ┆ ---      ┆ ---      │
│ i64 ┆ i64 ┆ str ┆ i64      ┆ i64      │
╞═════╪═════╪═════╪══════════╪══════════╡
│ 2   ┆ 5   ┆ b   ┆ 4        ┆ 7        │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 0   ┆ 3   ┆ a   ┆ 0        ┆ 3        │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 1   ┆ 4   ┆ a   ┆ 2        ┆ 5        │
└─────┴─────┴─────┴──────────┴──────────┘

Converting to/from pandas data frames

If you need to use a package that requires pandas or polars data frames, you can convert from a tidypolars_extra tibble to either of those DataFrame formats.

# convert to pandas...
df = df.to_pandas()
# ... or convert to polars
df = df.to_polars()

To convert from a pandas or polars DataFrame to a tidypolars tibble:

# convert from pandas...
df = tp.from_pandas(df)
# or covert from polars
df = tp.from_polars(df)

Roadmap

The following features are planned for future releases:

Missing Tidyverse Functions

• dplyr: slice_min/slice_max, rows_insert/rows_update/rows_upsert, consecutive_id, rename_with, expand/nesting, rowwise operations (c_across, row_sums, row_means)
• tidyr: expand, nesting
• stringr: word, str_to_sentence
• forcats: fct_reorder (reorder levels by summary statistic of another variable)
• purrr-style: map/map2/pmap equivalents for list columns

Statistical & Scientific Computing

• ceiling (complement to existing floor), exp, log2
• se (standard error of the mean)
• pmin/pmax (parallel min/max across columns)
• winsorize (cap extreme values at percentiles)

Data Quality & Exploration

• skim() (richer type-aware summary inspired by R's skimr)
• assert_type / assert_range (additional data validation assertions)
• Type hints across all public functions for IDE support and static analysis

Interoperability

• to_arrow() (explicit PyArrow Table export)
• DuckDB integration (to_duckdb/from_duckdb)
• write_rds in save_data (complete R round-trip)

Code Quality

• Add comprehensive type hints throughout codebase
• Configure mypy/pyright for static type checking in CI
• Add test coverage measurement and thresholds
• Expand edge case testing (empty DataFrames, NaN, Inf)

Acknowledgments

This project is an extension of:

• tidypolars4sci by Diogo Ferrari
• tidypolars — the original starting point