5 Best Ways to Convert Python CSV to CSV

πŸ’‘ Problem Formulation: Converting a CSV file to another CSV format is a common task that involves reading a CSV file, processing the data, and writing the processed data back into a CSV format. For example, you might need to filter rows, modify values, or rearrange columns, before saving the edited data into a new CSV file. This article covers five robust methods for performing such transformations using Python.

Method 1: Standard Python CSV library

In this method, we use Python’s built-in CSV module to read and write CSV files. It provides a straightforward interface for parsing and writing CSV files and offers flexibility to customize the CSV dialect. This approach is ideal for handling standard CSV operations without installing additional libraries.

Here’s an example:

import csv

with open('input.csv', 'r') as infile, open('output.csv', 'w', newline='') as outfile:
    reader = csv.reader(infile)
    writer = csv.writer(outfile)
    for row in reader:
        # Process the row if needed
        writer.writerow(row)

The output will be a new CSV file named ‘output.csv’ containing the same data as ‘input.csv’.

This snippet opens an input CSV file, reads its content line-by-line, potentially processes each row, and writes the unmodified or modified rows to an output CSV file.

Method 2: Using pandas DataFrames

Pandas is a powerful data manipulation library in Python that provides high-level data structures and functions designed for practical data analysis. By using Pandas, we can easily read a CSV, perform complex transformations, and save the data back to a CSV file with minimal code.

Here’s an example:

import pandas as pd

# Read the CSV into a DataFrame
df = pd.read_csv('input.csv')

# Perform operations on the DataFrame
# For example, let's filter for rows where the first column is greater than a threshold
df = df[df[df.columns[0]] > 10]

# Write the modified DataFrame back to a CSV
df.to_csv('output.csv', index=False)

The output is a new CSV file ‘output.csv’ containing the filtered data from ‘input.csv’.

This code snippet reads a CSV into a pandas DataFrame, filters rows based on a condition, and writes the resulting DataFrame to a new CSV. Using pandas simplifies complex operations and data handling.

Method 3: Using DictReader and DictWriter

Python’s CSV module also includes DictReader and DictWriter classes which allow you to read and write CSV data using dictionaries. This makes it easier to work with CSV files when the column headers are important to your application logic.

Here’s an example:

import csv

with open('input.csv', 'r') as infile, open('output.csv', 'w', newline='') as outfile:
    reader = csv.DictReader(infile)
    fieldnames = reader.fieldnames
    writer = csv.DictWriter(outfile, fieldnames=fieldnames)
    writer.writeheader()
    
    for row in reader:
        # Process the row if needed
        writer.writerow(row)

The output is ‘output.csv’, which contains the same row data arranged by headers as ‘input.csv’.

In the example, the code uses dictionaries to handle CSV rows, enabling easy access and modification of data based on column names instead of column positions.

Method 4: Using csvkit

csvkit is a suite of command-line tools for converting to and working with CSV. It can handle CSV files with complex formats or large sizes and provides utilities such as csvclean, csvcut, and csvsql for different operations.

Here’s an example:

# Assuming csvkit is installed
# Convert a CSV file to another CSV after selecting just the 'name' and 'age' columns
!csvcut -c name,age input.csv > output.csv

The output is a simplified ‘output.csv’ containing only the ‘name’ and ‘age’ columns from ‘input.csv’.

This one-liner uses the command-line tool csvcut from csvkit to select specific columns from a CSV file and outputs them to a new CSV file. It’s an easy and fast solution for CSV column manipulation.

Bonus One-Liner Method 5: Using UNIX-like command line tools

For simple CSV to CSV operations, UNIX-like command line tools like awk can be powerful. This is only applicable to systems with such tools available, like Linux or macOS.

Here’s an example:

# Assuming a UNIX-like environment
# Print all rows where second column value is greater than 10
awk -F',' '$2 > 10' input.csv > output.csv

The output will be ‘output.csv’, containing rows from ‘input.csv’ where the second column value is greater than 10.

This method involves using awk, a powerful UNIX text-processing tool, effectively for lightweight CSV processing tasks that don’t require the overhead of Python scripts.

Summary/Discussion

  • Method 1: Standard Python CSV library. Strength: Comes with Python, no additional libraries needed. Weakness: Less convenient for complex data manipulations.
  • Method 2: Pandas DataFrames. Strength: Simplifies complex data operations. Weakness: Large library; might be overkill for simple tasks.
  • Method 3: DictReader and DictWriter. Strength: Handles CSV data with ease using column headers. Weakness: Not as intuitive as pandas for complex data transformation.
  • Method 4: csvkit. Strength: Robust set of command-line tools, great for large or complex CSVs. Weakness: Requires installation and learning command-line utilities.
  • Bonus Method 5: UNIX-like command line tools. Strength: Fast and efficient for simple transformations. Weakness: Limited to systems with UNIX-like commands available; not as versatile as Python.