acidgenomics-pipette

pipette

Unified reading and writing of data in Python.

Installation

uv method

This is a Python package hosted on PyPI as acidgenomics-pipette. The import name is unchanged: pipette. We recommend using uv to install.

uv add 'acidgenomics-pipette[extra]'

Or with pip:

pip install 'acidgenomics-pipette[extra]'

Conda method

Configure Conda to use the Bioconda channels.

# Don't install recipe into base environment.
name='pipette'
conda create --name="$name" "$name"
conda activate "$name"
python -c 'import pipette'

Conda has no equivalent of Python extras. For the optional format support that pipette[extra] provides, add the dependencies to the environment: conda create --name="$name" "$name" openpyxl pyarrow pyyaml scipy.

Quick Start

Read

import pipette

# Read a CSV file.
df = pipette.read("data.csv")

# Read a TSV file.
df = pipette.read("data.tsv")

# Read an Excel file (requires openpyxl).
df = pipette.read("data.xlsx")

# Read JSON.
data = pipette.read("data.json")

# Read from a URL.
df = pipette.read("https://example.com/data.csv")

Write

import pandas as pd
import pipette

df = pd.DataFrame(
    {"sample1": [1, 2, 3], "sample2": [4, 5, 6]},
    index=["gene1", "gene2", "gene3"],
)

# Write to CSV.
pipette.write(df, "output.csv")

# Write to TSV.
pipette.write(df, "output.tsv")

# Write compressed.
pipette.write(df, "output.csv.gz")

Data Transformation

# Sanitize NA values in string columns.
df = pipette.sanitize_na(df)

# Convert columns with duplicates to categorical.
df = pipette.categorize(df)

# Convert categorical columns back to their underlying type.
df = pipette.uncategorize(df)

# Drop columns holding nested (non-scalar) values.
df = pipette.drop_nested_columns(df)

Supported Formats

Read

Write

Compressed output is supported via .gz, .bz2, .xz, and .zip suffixes.

Optional Dependencies

• openpyxl: Excel file support.
• pyarrow: Parquet and Feather file support.
• pyyaml: YAML file support.
• scipy: MTX (Matrix Market) sparse matrix support.

License

Apache-2.0 — Copyright 2026 Acid Genomics LLC — see LICENSE.