5 Best Ways to Python CSV Get Last Row

💡 Problem Formulation: You need to extract the last row from a CSV file using Python. Let’s say you have a CSV file containing sales data with columns: Date, Product, and Revenue. The goal is to retrieve the latest sales data recorded—the last row in the CSV file—which in Python could be used to track trends or trigger events.

Method 1: Using the CSV module and a for-loop

This method involves using Python’s built-in csv module to read the CSV file and a for-loop to iterate through the rows to reach the last one. It is the most straightforward method and does not require any external libraries.

Here’s an example:

import csv

def get_last_row(filename):
    with open(filename, 'r') as file:
        reader = csv.reader(file)
        last_row = None
        for row in reader:
            last_row = row
        return last_row

print(get_last_row('sales_data.csv'))

Assuming ‘sales_data.csv’ ends with “2023-04-01,Widget,2000”, the output will be:

['2023-04-01', 'Widget', '2000']

This code opens the CSV file and iterates over each row. The variable last_row is updated on each iteration and finally contains the last row after the loop completes.

Method 2: Using the CSV module and next()

This method utilizes the csv module in combination with the next() function and reversing the row iterator to directly access the last row without looping through the entire file.

Here’s an example:

import csv

def get_last_row(filename):
    with open(filename, 'r') as file:
        reader = reversed(list(csv.reader(file)))
        last_row = next(reader)
        return last_row

print(get_last_row('sales_data.csv'))

Assuming ‘sales_data.csv’ ends with “2023-04-01,Widget,2000”, the output will be:

['2023-04-01', 'Widget', '2000']

By reversing the reader iterator and using next(), this method reads the last row directly. It’s more efficient than method 1 because it avoids iterating through all rows in large files.

Method 3: Using Pandas

Pandas is a powerful data manipulation library that makes it very easy to work with tabular data. Using the tail() function, one can immediately get the last row of the CSV file into a DataFrame.

Here’s an example:

import pandas as pd

def get_last_row(filename):
    df = pd.read_csv(filename)
    last_row = df.tail(1)
    return last_row

print(get_last_row('sales_data.csv'))

Assuming ‘sales_data.csv’ ends with “2023-04-01,Widget,2000”, the output will be:

Date Product Revenue 99 2023-04-01 Widget 2000

The code snippet reads the entire CSV file into a DataFrame and then uses the tail() function to get the last row. The returned object is a DataFrame containing only the last row of the original CSV.

Method 4: Using file.seek() and file.readlines()

This method uses low-level file operations to seek to the end of the file, reads lines in reverse, and fetches the last non-empty line. It is useful when the file is too large to be loaded into memory.

Here’s an example:

def get_last_row(filename):
    with open(filename, 'rb') as file:
        file.seek(-2, 2)  # Go to the second to last byte.
        while file.read(1) != b'\n':  # Keep stepping back until a newline is found.
            file.seek(-2, 1)
        last_row = file.readline().decode()
        return last_row

print(get_last_row('sales_data.csv'))

Assuming ‘sales_data.csv’ ends with “2023-04-01,Widget,2000”, the output will be:

'2023-04-01,Widget,2000'

This code opens the CSV file in binary mode and moves towards the file’s end to find the start of the last line. Once found, it reads and decodes the last line to return it.

Bonus One-Liner Method 5: Using a List Comprehension

Using a list comprehension, you can read the file and extract the last line with Python’s concise syntax. This is a one-liner version of Method 1 but loads all rows into memory first.

Here’s an example:

print(open('sales_data.csv', 'r').readlines()[-1])

Assuming ‘sales_data.csv’ ends with “2023-04-01,Widget,2000”, the output will be:

'2023-04-01,Widget,2000\n'

This concise code snippet opens the file, reads all lines into a list, and prints the last line (denoted by [-1]) from that list.

Summary/Discussion

• Method 1: CSV Module with For-Loop. Simple and requires no external libraries. Inefficient for large files due to its need to iterate through all rows.
• Method 2: CSV Module with next(). Avoids unnecessary iteration by reversing the row iterator and is more efficient as a result. However, it loads the entire file into memory which might be problematic for very large files.
• Method 3: Pandas Library. Most straightforward for those already using Pandas for data analysis, but adds a heavy dependency if not already included in the project.
• Method 4: File seek and readlines. Works well for very large files as it doesn’t load the entire file into memory. More complex and not as readable as other methods.
• Method 5: List Comprehension. Extremely concise, but not memory-efficient because it loads all lines into memory before selecting the last one.