5 Best Ways to Delete Only One Row in CSV with Python

When working with CSV files in Python, you may encounter situations where you need to delete a specific row. The challenge lies in preserving the structure of the CSV while removing only the target row. For instance, given a CSV file with multiple entries, you might want to delete a row with a user_id of ‘1234’ but keep the rest of the data intact. The methods below describe how to achieve this.

Method 1: Using the CSV Module and a Temporary List

The CSV module in Python provides functionality for reading and writing to CSV files. This method involves reading the CSV file into memory as a list of rows, omitting the row to be deleted, and writing the rest back to the file.

Here’s an example:

import csv

def delete_row(csv_filename, row_index):
    rows = []
    with open(csv_filename, 'r') as file:
        reader = csv.reader(file)
        rows = [row for idx, row in enumerate(reader) if idx != row_index]
    with open(csv_filename, 'w') as file:
        writer = csv.writer(file)
        writer.writerows(rows)

delete_row('data.csv', 2)

Output: The specified row at index 2 has been removed from ‘data.csv’.

This code snippet reads all rows into a list except the one at index 2, which is the target for deletion. The remaining rows are then written back to ‘data.csv’, effectively deleting the specific row.

Method 2: Using Pandas DataFrames

Pandas is a powerful data manipulation library in Python. In this method, we load the CSV file into a DataFrame, drop the row in question, and save the DataFrame back to CSV.

Here’s an example:

import pandas as pd

def delete_row_pandas(csv_filename, row_index):
    df = pd.read_csv(csv_filename)
    df = df.drop(row_index).reset_index(drop=True)
    df.to_csv(csv_filename, index=False)

delete_row_pandas('data.csv', 2)

Output: The row at index 2 has been deleted from ‘data.csv’.

This example creates a DataFrame from the csv file, drops the specified row, and writes the DataFrame back to csv without the row. This method is very concise, but requires the additional Pandas library.

Method 3: Using File Input/Output Directly

This method bypasses libraries to directly manipulate the file’s contents with built-in file input/output operations.

Here’s an example:

def delete_row_directly(csv_filename, row_index):
    with open(csv_filename, 'r') as file:
        lines = file.readlines()
    with open(csv_filename, 'w') as file:
        for idx, line in enumerate(lines):
            if idx != row_index:
                file.write(line)

delete_row_directly('data.csv', 2)

Output: The row at index 2 has been removed from ‘data.csv’.

This code reads all lines from the file, then writes back only those lines whose index does not match the row_index to be deleted. This method is straightforward but less efficient for large files.

Method 4: Stream Processing for Large Files

For large files, stream processing can be used to handle one line at a time, writing to a new file and then replacing the old one.

Here’s an example:

import os

def delete_row_stream(csv_filename, row_index):
    temp_filename = 'temp.csv'
    with open(csv_filename, 'r') as read_file, open(temp_filename, 'w') as write_file:
        for idx, line in enumerate(read_file):
            if idx != row_index:
                write_file.write(line)
    os.replace(temp_filename, csv_filename)

delete_row_stream('data.csv', 2)

Output: ‘data.csv’ has been updated, with the row at index 2 removed.

This technique reads from the original file and writes to a temporary file, skipping the row to be deleted. After processing, the temporary file replaces the original. It is memory-efficient but requires additional disk space and operations.

Bonus One-Liner Method 5: Shell Command via Python

Utilize the command line directly with Python to execute a shell command that handles the row deletion.

Here’s an example:

import subprocess

def delete_row_shell(csv_filename, row_index):
    command = f"sed -i '{row_index+1}d' {csv_filename}"
    subprocess.run(command, shell=True)

delete_row_shell('data.csv', 2)

Output: Command executed without error, and the row is removed from ‘data.csv’.

This succinct method uses the sed command to delete a row from the file. It is a powerful one-liner, especially on UNIX-like systems, but less portable to environments without shell access or sed installed.

Summary/Discussion

• Method 1: CSV Module and Temporary List. Strengths: Utilizes built-in Python libraries; does not require external dependencies. Weaknesses: Memory-intensive for large CSV files.
• Method 2: Pandas DataFrames. Strengths: Clean and concise; powerful data manipulation capabilities. Weaknesses: Requires installation of the Pandas library; not as memory-efficient.
• Method 3: Direct File I/O. Strengths: Does not rely on external libraries; straightforward implementation. Weaknesses: Inefficient for large files; may be slower.
• Method 4: Stream Processing. Strengths: Suitable for large files; memory-efficient. Weaknesses: Requires additional temporary storage; more complex to implement.
• Method 5: Shell Command via Python. Strengths: Extremely concise; very fast execution. Weaknesses: Platform-dependent; requires shell and sed.