Big Data 7 min read

Why Feather Beats CSV for Large-Scale Data: Speed, Size, and Simplicity

This article explains the limitations of CSV for big datasets, introduces the Feather binary format, shows how to install and use it with Python and pandas, and compares its saving/loading speed and storage size against CSV, highlighting Feather's advantages for efficient data handling.

Python Crawling & Data Mining
Python Crawling & Data Mining
Python Crawling & Data Mining
Why Feather Beats CSV for Large-Scale Data: Speed, Size, and Simplicity

Why Say Goodbye to CSV?

When processing large data with Python, CSV is often used for saving and loading, but it has a row limit in Excel, larger file size, and slower read/write speed. Although CSV has no row limit, handling millions of rows can still be time‑consuming.

What Is Feather?

Feather is a binary columnar storage format originally designed for fast data exchange between Python and R. It is lightweight, supports short‑term storage, and is now available for most major programming languages.

How to Use Feather in Python

Install the feather-format package:

# pip install feather-format
pip install feather-format

# Anaconda
conda install -c conda-forge feather-format

Create a large DataFrame (5 columns, 10 million rows) for demonstration:

import feather
import numpy as np
import pandas as pd

np.random.seed = 42
df_size = 10000000

df = pd.DataFrame({
    'a': np.random.rand(df_size),
    'b': np.random.rand(df_size),
    'c': np.random.rand(df_size),
    'd': np.random.rand(df_size),
    'e': np.random.rand(df_size)
})
df.head()

The usage is as simple as CSV:

Saving

Two ways to save:

df.to_feather('1M.feather')
feather.write_dataframe(df, '1M.feather')

Loading

Two ways to load:

df = pd.read_feather('1M.feather')
df = feather.read_dataframe('1M.feather')

Comparison with CSV

Performance tests show Feather is roughly 150× faster than CSV for saving large DataFrames and consumes less than half the disk space.

Reading speed also favors Feather, with CSV being significantly slower.

File size comparison shows CSV files are more than twice the size of Feather files.

If you need even higher compression, Parquet is another efficient alternative to CSV.

Conclusion

Feather provides dramatically faster read/write performance and smaller storage footprints compared to CSV, making it ideal for large‑scale data processing where speed and space matter. For everyday small tasks, CSV remains convenient, but for big data workloads, switching to Feather (or Parquet) yields substantial benefits.

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Python Crawling & Data Mining
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