5 Best Ways to Count Rows in a Python CSV File

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πŸ’‘ Problem Formulation: When working with CSV files in Python, it’s often essential to know the total number of rows, especially when performing data analysis or preprocessing tasks. For example, an input CSV file may have an unknown number of rows, and the desired output is the exact row count, excluding the header. This article explores various methods to achieve this goal using Python.

Method 1: Using the CSV Module

This method involves the native Python CSV module, which provides functionality for reading and writing CSV files. For counting rows, we can use the csv.reader() object and sum up the rows iteratively, excluding the header with an initial call to next().

Here’s an example:

import csv

with open('example.csv', 'r') as file:
    csv_reader = csv.reader(file)
    next(csv_reader)  # Skip the header
    row_count = sum(1 for row in csv_reader)

print(row_count)

Output:

42

This code snippet opens the ‘example.csv’ file, creates a csv reader, skips the header, and then iterates over each row, using a generator expression to count the total number of rows present.

Method 2: Looping Without the CSV Module

For a quick row count, we can simply loop over the file lines directly. Though not using the CSV module explicitly, this method assumes the CSV does not contain any newline characters within quoted fields.

Here’s an example:

row_count = -1  # Start at -1 to exclude the header
with open('example.csv', 'r') as file:
    for row in file:
        row_count += 1

print(row_count)

Output:

42

This code opens the CSV file, iterates over each line, and increments a count. The initial value is set to -1 to ensure that the header is not counted. Note, this method could produce incorrect results if the CSV file contains multiline fields.

Method 3: Using the Pandas Library

The Pandas library is a powerful and popular data analysis tool. It simplifies reading and analyzing CSV files with a single function. We can load the data into a DataFrame and get the number of rows using the shape attribute.

Here’s an example:

import pandas as pd

df = pd.read_csv('example.csv')
row_count = df.shape[0]

print(row_count)

Output:

42

By reading the CSV file into a DataFrame, we automatically skip the header and can access the number of rows using the shape attribute, where shape[0] denotes the number of rows.

Method 4: Using the Python Standard Library

A straightforward approach using the standard library is to count the lines using open() and readlines() to create a list of lines and then get the length of the list, subtracting one for the header.

Here’s an example:

with open('example.csv', 'r') as file:
    row_count = len(file.readlines()) - 1

print(row_count)

Output:

42

This simple yet slightly less efficient method reads the entire file into memory as a list of lines. The total count of rows is then obtained by using the len() function after reducing it by one to exclude the header.

Bonus One-Liner Method 5: Using wc and subprocess

By combining the Unix wc command with Python’s subprocess module, we can count the rows in a file with a one-liner, excluding the header by subtracting one.

Here’s an example:

import subprocess

result = subprocess.run(['wc', '-l', 'example.csv'], stdout=subprocess.PIPE)
row_count = int(result.stdout) - 1

print(row_count)

Output:

42

This Python snippet runs the wc command-line utility via the subprocess module. The -l option counts the newlines in the file, and Python captures this output to calculate the total number of rows excluding the header.

Summary/Discussion

  • Method 1: CSV Module. Well-suited for CSV-specific operations. Handles different CSV formats well. Requires iterating over each row which can be slower for large files.
  • Method 2: Direct Looping. Simple and quick. Can be inaccurate if the CSV contains multiline entries. Doesn’t depend on external libraries.
  • Method 3: Pandas. Very convenient and handles complex data well. Requires an external library which may not be ideal for some minimalist applications.
  • Method 4: Standard Library. Utilizes built-in functions. Can be memory-intensive as it reads the whole file into memory at once. Simple and easy to understand.
  • Method 5: wc with subprocess. Fast, one-liner method suitable for Unix systems. Requires understanding of subprocess and shell commands. Not cross-platform as wc is not available on Windows.