Question 1

Feather vs Parquet: which one should I use?

Accepted Answer

Feather is optimized for read/write speed and interoperability between Python, R, and Julia, making it great for intermediate data exchange. Parquet offers better compression and is more suited for long-term storage and big data processing. Choose Feather for fast workflows and Parquet for archival needs.

Question 2

How to read a Feather file in Python with the latest setup?

Accepted Answer

Install the feather-format package via pip, but note it's a wrapper around pyarrow.feather. Use `import feather; df = feather.read_dataframe('file.feather')` or directly use `pyarrow.feather.read_feather` for better performance, as recommended in the README.

Question 3

Is Feather faster than CSV for storing data frames?

Accepted Answer

Yes, Feather is significantly faster than CSV for reading and writing data frames because it uses a binary columnar format based on Apache Arrow. This reduces parsing overhead and maintains data type fidelity, especially with null values and variable-length strings.

Question 4

Can I use Feather to share data between pandas and R data.frames?

Accepted Answer

Absolutely, Feather is designed for this exact use case. It provides bindings for both Python (via pandas-compatible functions) and R, allowing seamless data frame sharing without losing types or null values, as emphasized in the README.

Question 5

What are the main limitations of the Feather format?

Accepted Answer

Feather has limited language support beyond Python, R, and Julia, no built-in compression, and a simplified schema that may not handle complex nested data well. Also, since development is now in Apache Arrow, using standalone packages might be less optimal.

Question 6

How do I install feather for R and ensure I have the fast version?

Accepted Answer

Install the feather package via `install.packages('feather')`, but for better performance, use `arrow::read_feather` from the arrow package, as the README notes it's a much faster implementation. Install arrow via `install.packages('arrow')`.

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Overview

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Pros & Cons

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Frequently Asked Questions