5 Best Ways to Convert Python CSV Files to Binary

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💡 Problem Formulation: This article addresses the conversion of CSV files into binary format using Python. This process is commonly required for performance improvement, ensuring data privacy, or to meet specific application requirements. For instance, converting tabular data from a CSV file into a binary format can significantly speed up parsing and reduce file size. Our input is a CSV file containing strings and numerals, and our desired output is a compact binary file.

Method 1: Using Built-in CSV and Binary Write Functions

This method involves reading the CSV file using Python’s csv module and writing the data to a binary file using standard file operations. The open() function in binary write mode is utilized to create the binary file.

Here’s an example:

import csv

with open('data.csv', 'r') as csvfile, open('data.bin', 'wb') as binfile:
    reader = csv.reader(csvfile)
    for row in reader:
        binfile.write(bytes(','.join(row), 'utf-8'))

Output: A binary file named ‘data.bin’ containing the CSV data.

This code snippet opens ‘data.csv’ for reading and ‘data.bin’ for binary writing. As it reads rows from the CSV file, it joins each row into a string, converts this string into bytes, and then writes those bytes to the binary file.

Method 2: Using the Pandas Library with to_pickle()

With the powerful Pandas library, one can read a CSV file into a DataFrame, and then efficiently serialize the DataFrame to a binary file using the to_pickle() method, which stores the data in Python’s pickle format.

Here’s an example:

import pandas as pd

df = pd.read_csv('data.csv')
df.to_pickle('data.pkl')

Output: A binary file named ‘data.pkl’ that contains the serialized DataFrame.

After importing Pandas and reading the CSV file into a DataFrame, the code uses the to_pickle() method to serialize the DataFrame and save it in a binary format. It preserves the DataFrame’s structure and type information.

Method 3: Using the struct Module for Serialization

The struct module in Python can be used to pack data into binary format. This method offers precise control over the data types and structure of the binary file, which is useful for interfacing with C/C++ programs.

Here’s an example:

import csv
import struct

with open('data.csv', 'r') as csvfile, open('data.bin', 'wb') as binfile:
    reader = csv.reader(csvfile)
    for row in reader:
        bin_row = struct.pack('i' * len(row), *map(int, row))
        binfile.write(bin_row)

Output: A binary file named ‘data.bin’ with packed integers from the CSV data.

This code reads rows from the CSV file, and for each row, it packs the data using struct.pack() by specifying a format string and the row data to serialize. It assumes all CSV column values are integers.

Method 4: Using NumPy for Array Serialization

NumPy can be harnessed to convert data into a binary file by reading the CSV into a NumPy array and then utilizing the tofile() function to save it as binary. This is suitable for numerical data and enables easy data manipulation.

Here’s an example:

import numpy as np

data = np.genfromtxt('data.csv', delimiter=',')
data.tofile('data.bin')

Output: A binary file named ‘data.bin’ that contains the NumPy array in a flat binary form.

The code uses NumPy to read the CSV data into an array and then immediately writes this array to a binary file using the tofile() function. This offers a straightforward approach for handling numerical CSV data.

Bonus One-Liner Method 5: Using List Comprehension and File Writing

A one-liner approach to convert CSV files to binary, this method leverages list comprehension along with file writing techniques. This suits simple CSV files and requires a basic understanding of file handling in Python.

Here’s an example:

open('data.bin', 'wb').write(b'\n'.join([bytes(','.join(row), 'utf-8') for row in csv.reader(open('data.csv'))]))

Output: A binary file named ‘data.bin’ containing the CSV data.

Using Python’s file handling and list comprehension, this one-liner opens the CSV file, reads each row, converts it to a string, then to bytes, and writes directly to the binary file in a compact form.

Summary/Discussion

  • Method 1: Built-in Functions. Easy to use. May not handle complex data efficiently.
  • Method 2: Pandas with to_pickle(). Preserves DataFrame structure. Larger file size due to overhead.
  • Method 3: struct Module. Great control over types. Can be complex for non-primitive data types.
  • Method 4: NumPy’s tofile(). Efficient for numerical data. Does not preserve shape or type information without metadata.
  • Method 5: One-Liner. Quick and easy. Not versatile for different or complex CSV structures.