Converting CSV to Parquet with Python and PyArrow or Fastparquet: A Simplified Guide

If you need to convert CSV files to Parquet format without relying on Spark, you can utilize Python's powerful libraries, pandas and pyarrow or fastparquet, to accomplish this task efficiently. This guide provides a step-by-step approach along with example code to help you perform this conversion.

Prerequisites

Before you begin, ensure that you have the necessary libraries installed. You can install them using pip:

pip install pandas pyarrow
or
pip install pandas fastparquet

Example Code

Below is a simple example of how to convert a CSV file to Parquet format using Python and pyarrow or fastparquet:

# Import Libraries
import pandas as pd
# Read the CSV file
input_file  'path/to/example.csv'
df  _csv(input_file)
# Convert to Parquet format
output_file  ''
_parquet(output_file, engine'pyarrow')  # or use engine'fastparquet'

Explanation of the Code

Import Libraries: Import the pandas library as pd. Read the CSV File: Use _csv to read the CSV file into a DataFrame. Convert to Parquet: Use the to_parquet method of the DataFrame to write it to a Parquet file. You can specify the engine to use, either pyarrow or fastparquet.

Additionally, you can specify further parameters in _csv and _parquet to customize how the CSV is read and how the Parquet file is written, including handling missing values, compression options, and more.

Example with Compression

If you want to compress the Parquet file, you can do so by specifying the compression parameter:

_parquet(output_file, engine'pyarrow', compression'snappy')

By default, pandas uses the snappy compression algorithm, but you can choose other compression algorithms if necessary.

Conclusion

This method is efficient for converting CSV files to Parquet format using Python without the need for Spark. Parquet files are highly efficient for storage and querying, making them an excellent choice for large datasets.

Essentially, it can be done in just a few lines of code:

import pandas as pd
df  _csv('path/to/example.csv')
_parquet('', engine'pyarrow')

With the right libraries installed, this method provides a straightforward and efficient way to handle your data conversion needs.