5 Best Ways to Convert CSV to Binary in Python

by

💡 Problem Formulation: Converting CSV files to a binary format in Python is a common necessity for data processing tasks. This article discusses five methods to achieve this transformation. For illustration purposes, suppose you have a CSV file with rows of integer values and you want to represent these values in a binary file, which can be more compact and faster to read for certain applications.

Method 1: Using Built-in Modules

This method relies on the built-in csv and struct modules in Python. The csv module is used for reading and writing data in CSV format, while struct is used for converting Python values to C structs represented as Python bytes objects. This method is cross-platform and doesn’t require any third-party libraries.

Here’s an example:

import csv
import struct

with open('data.csv', 'r') as csv_file:
    csv_reader = csv.reader(csv_file)
    with open('data.bin', 'wb') as bin_file:
        for row in csv_reader:
            bin_file.write(struct.pack('i' * len(row), *[int(value) for value in row]))

The output is a binary file containing the binary representation of the integer values.

This code snippet opens a CSV file for reading, iterates over each row, and writes the binary representation of the row’s integers to a new binary file. Each value is converted into a C-style integer and then packed into a bytes object using the struct.pack() function.

Method 2: Using NumPy’s fromfile and tofile

NumPy is a powerful library for numerical computations in Python. It provides fromfile() and tofile() methods that can be used for reading and writing binary data with ease. This method is well-suited for numerical data and leverages NumPy’s optimized operations for efficient processing.

Here’s an example:

import csv
import numpy as np

# Read CSV data into a NumPy array
with open('data.csv', 'r') as csv_file:
    csv_reader = csv.reader(csv_file)
    data = np.array([row for row in csv_reader], dtype=np.int32)

# Write the NumPy array to a binary file
data.tofile('data.bin')

The output is a binary file containing the numerical data in a binary format that NumPy can read efficiently.

In this snippet, a CSV file is read into a NumPy array, which is then simply written to a binary file using the tofile() method. This technique is particularly efficient for large datasets due to NumPy’s optimized data handling.

Method 3: Using Python File I/O with Bytearrays

This approach directly utilizes Python’s file input/output capabilities along with the bytearray type. Bytearrays are mutable sequences of bytes. This method is suitable for lower-level manipulation of binary data and can be a good option when you need more control over data processing.

Here’s an example:

import csv

with open('data.csv', 'r') as csv_file:
    csv_reader = csv.reader(csv_file)
    with open('data.bin', 'wb') as bin_file:
        for row in csv_reader:
            bytearray_data = bytearray(int(value).to_bytes(4, byteorder='little') for value in row)
            bin_file.write(bytearray_data)

The output is a binary file that contains the binary representation of each number in the CSV, written in little-endian order.

In this code snippet, each integer from the CSV file is converted to 4 bytes in little-endian format and appended to a bytearray, which is then written to a binary file. It provides a low-level manipulation without using any external libraries.

Method 4: Using the Pickle Module

Pickle is Python’s built-in module for serializing and de-serializing Python object structures. Although pickle is typically used for more complex data types, it can be utilized to convert a list of integers from a CSV file to a binary format and vice versa.

Here’s an example:

import csv
import pickle

with open('data.csv', 'r') as csv_file:
    csv_reader = csv.reader(csv_file)
    data = [int(value) for row in csv_reader for value in row]

with open('data.bin', 'wb') as bin_file:
    pickle.dump(data, bin_file)

The output will be a binary file containing the serialized list of integers.

The code snippet here takes a flat list of integers from a CSV file and serializes it using the pickle module’s dump() function. It’s a quick method when you work with Python-specific applications.

Bonus One-Liner Method 5: Comprehensions and Context Managers

For those who love Python for its expressiveness, the following one-liner combines file context managers and list comprehensions to achieve our goal succinctly. Great for quick scripts and code golf, but not recommended for production due to its decreased readability.

Here’s an example:

with open('data.csv', 'r') as f, open('data.bin', 'wb') as b: b.write(bytearray(int(x) for line in f for x in line.split(',')))

The output is analogous, a compact binary file representation of the CSV numerical values.

This one-liner opens the CSV file and a binary file in a single context manager, processes each line in the CSV to integers, then writes their byte representations directly to the binary file.

Summary/Discussion

  • Method 1: Built-in Modules. Platform-independent and doesn’t require external libraries. However, may not be as efficient for large data sets.
  • Method 2: NumPy’s fromfile and tofile. High performance and simplicity for numerical data. The primary drawback is the need for the NumPy dependency.
  • Method 3: Python bytearray. Offers fine-grained control and does not rely on external libraries. More verbose and slightly more complex to implement.
  • Method 4: Pickle Module. Fast serialization of Python objects, but pickle files are not easily readable by non-Python programs and may be less secure.
  • Bonus Method 5: Comprehensions and Context Managers. Extremely concise, but can be difficult to read and debug. Useful for one-off scripts or small data sets.