📦 What is Parquet in Python?

Overview

Parquet is a columnar storage file format. It is highly efficient for storing large datasets because it:

• Compresses data very well.
• Optimizes read speed by only loading necessary columns.
• Is language-agnostic and compatible with many data platforms.

In Python, it's commonly used with libraries like pandas and pyarrow.

Why Use Parquet?

Compared to CSV or JSON, Parquet files:

• Have better compression rates
• Are faster to read/write
• Support efficient querying on big data systems

Installing Required Libraries

pip install pandas pyarrow

You can also use fastparquet as an alternative to pyarrow.

Writing a DataFrame to Parquet

import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35]
})

df.to_parquet("people.parquet", engine="pyarrow")

Reading a Parquet File

df = pd.read_parquet("people.parquet", engine="pyarrow")
print(df)

Columnar Advantage

Suppose you only want to read the name column:

names = pd.read_parquet("people.parquet", columns=["name"], engine="pyarrow")

This is faster and uses less memory than reading the full file.

Parquet vs CSV

Feature	CSV	Parquet
Compression	Low	High
Read/Write Speed	Slow for large files	Fast, especially column-wise
Data Types	All strings by default	Preserves native types
Partial Loading	No	Yes

Conclusion

Parquet is ideal for large, complex datasets where performance and space efficiency are key. It's widely used in big data tools like Apache Spark, Dask, and Hadoop, and integrates seamlessly with pandas in Python.