Transforming Your Data with Python: CSV to Parquet Conversion and NaN Handling

Efficient data storage and processing are crucial for businesses and organizations dealing with large datasets. Apache Parquet is a popular columnar storage format offering fast query performance and data compression, while CSV is a row-based format that may not be suitable for large-scale processing. This blog post covers how to convert CSV files to Parquet files in Python, including dropping NaN values to prepare the data for analysis. It concludes by highlighting the advantages of using Parquet files.

Step 1: Import required libraries

We begin by importing the required libraries to read the CSV file, convert it to a Parquet file, and work with data in Pandas DataFrame format.

import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq

Here, we use pandas to read the CSV file, pyarrow to convert the Pandas DataFrame to PyArrow Table format, and pyarrow.parquet to write the PyArrow Table to a Parquet file.

Step 2: Define a function to convert CSV to Parquet

We define a function convert_csv_to_parquet that takes the following arguments:

• input_file_path: Path to the CSV file to be converted
• output_file_path: Path to the output Parquet file
• drop_option: Option to drop rows or columns with NaN values (if any)