In this article, I’ll explain 2 efficient methods to convert CSV files to Parquet format in Python.

1. The first method utilizes the pandas library, a popular data manipulation tool in Python. With pandas, we’ll read the CSV file into a DataFrame and then save it as a Parquet file.
2. The second method employs the pyarrow library, which is specifically designed for efficient data interchange between Python and other data storage formats. Using pyarrow, you can convert the CSV file into a PyArrow Table and then write it to a Parquet file.

Both methods offer flexibility and scalability, catering to different use cases and preferences in data processing and storage.

Method 1 – Using pandas

You can use the pandas library in Python to convert a CSV file to Parquet format.

Make sure you have the pandas library installed.

You can install it via pip if you haven’t already:

pip install pandas

This code assumes that your CSV file has a header row with column names. If your CSV file doesn’t have a header row, you can specify column names using the names parameter in read_csv().

Additionally, you may want to specify other parameters depending on the specifics of your CSV file, such as delimiter or encoding.

import pandas as pd

# Read CSV file into a pandas DataFrame
df = pd.read_csv('your_input.csv')

# Write DataFrame to Parquet file
df.to_parquet('your_output.parquet')

Method 2 Using pyarrow library

The second method to convert a CSV file to Parquet format in Python is by using the pyarrow library.

As a first step, make sure you have the pyarrow library installed.

You can install it via pip.

pip install pyarrow

This code reads the CSV file using PyArrow’s read_csv function, which returns a PyArrow Table.

Then, it writes this Table to a Parquet file using PyArrow’s write_table function.

import pyarrow.csv as pv
import pyarrow.parquet as pq

# Define CSV and Parquet file paths
csv_file = 'your_input.csv'
parquet_file = 'your_output.parquet'

# Read CSV file into a PyArrow Table
table = pv.read_csv(csv_file)

# Write PyArrow Table to Parquet file
pq.write_table(table, parquet_file)

Pandas vs PyArrow Compared

Determining which method is better depends on various factors such as the size of the data, performance requirements, ease of use, and compatibility with existing workflows.

Let’s compare both of them.

1. Using pandas

Pros

• Simple and intuitive API, making it easy for beginners to use.
• Good performance for small to medium-sized datasets.
• Integration with other pandas functionalities for data manipulation and analysis.

Cons

• Limited scalability for very large datasets due to memory constraints.
• May not be the most efficient method for large-scale data processing.

2. Using pyarrow

Pros

• Optimized for high-performance data processing, suitable for large-scale datasets.
• Provides advanced features for efficient data manipulation and conversion.
• Integration with other tools in the Apache Arrow ecosystem for seamless data interchange.

Cons

• May have a steeper learning curve compared to pandas for beginners.
• Requires additional installation of the `pyarrow` library, which might be an overhead if not already in use.

If you’re working with small to medium-sized datasets and prioritize ease of use and integration with other pandas functionalities, using pandas might be a better choice.

On the other hand, if you’re dealing with large-scale datasets and require high-performance data processing capabilities, especially in a production environment, using pyarrow would be more suitable.

Yes, the best method depends on your specific requirements and constraints.