Introduction to Parquet Files, Read/Write using Python

Introduction to Parquet Files
Reading and Writing Parquet Files with Python
Big Data Storage Optimization: Understanding Parquet
Essential Parquet Handling Tips for Python Data Analysis
Columnar Storage Insights: Parquet Files and Performance
Working with Nested Parquet Data using Python and PyArrow
From CSV to Parquet: A Python Data Format Conversion Guide

What is a Parquet File?

Parquet is a columnar storage file format optimized for large-scale data processing. It is commonly used in big data frameworks like Apache Spark, Hadoop, and Pandas for efficient storage and fast retrieval of tabular data.

Parquet can be simply thought of as a transpose of CSV. However, CSV is text-based, while Parquet is binary. From the storage perspective, columnar storage can be understood as swapping rows and columns, but it should be noted that Parquet is binary, supports compression and nested types, and is more than just a ‘transpose’.

Why Use Parquet?

• Columnar Storage: Stores data by columns, which improves query performance for analytics tasks.
• Compression: Supports efficient compression techniques to reduce storage size.
• Interoperability: Works with multiple data processing frameworks.
• Schema Evolution: Supports adding/removing columns without breaking existing data.

Installing Required Libraries

To work with Parquet in Python, you need pandas and pyarrow (or fastparquet):

pip install pandas pyarrow

Reading Parquet Files in Python

Here is an example of reading a Parquet file using pandas and pyarrow:

import pandas as pd
import pyarrow.parquet as pq

# Path to your Parquet file
file_path = "example.parquet"

# Read Parquet file into DataFrame
df = pd.read_parquet(file_path)

# Display first 5 rows
print(df.head())

Writing Data to Parquet

You can also write a DataFrame to a Parquet file easily:

import pandas as pd

# Create a sample DataFrame
data = {
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["London", "Paris", "New York"]
}
df = pd.DataFrame(data)

# Save as Parquet
df.to_parquet("output.parquet", engine="pyarrow", index=False)

Working with Nested Data

Parquet supports nested data like lists or structs. You can read them using pyarrow directly:

import pyarrow.parquet as pq
from io import BytesIO

# Read Parquet file directly
table = pq.read_table("example.parquet")
df = table.to_pandas()
print(df.head())

Conclusion

Parquet files are highly efficient for storing and processing large-scale tabular data. Using Python’s pandas and pyarrow, you can easily read, write, and manipulate Parquet files for data analysis, ETL pipelines, and big data applications.

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• Introduction to Parquet Files: Read & Write using Python
• Python Match vs Traditional If-Elif: A Modern Take on Switch Statements

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