Python - Read CSV Columns Into List

CSV (Comma-Separated Values) files are widely used to store tabular data. Each line in a CSV file corresponds to a data record, and each record consists of one or more fields separated by commas. In this article, you’ll learn how to extract specific columns from a CSV file and convert them into Python lists.

In this article we’ll explore two common methods:

1. Using the Pandas library
2. Using Python’s built-in csv module

To use the file used in this article, click here.

Method 1: Using Pandas

Pandas is a powerful library for data manipulation. The read_csv() function reads the CSV file into a DataFrame, and then tolist() converts column data to Python lists.

Approach:

• Import pandas.
• Use read_csv() to load the file.
• Access specific columns.
• Convert each column to a list using .tolist().
• Print the lists.

Example:

import pandas as pd

# Reading the CSV file
data = pd.read_csv("company_sales_data.csv")

# Converting specific columns into lists
month = data['month_number'].tolist()
fc = data['facecream'].tolist()
fw = data['facewash'].tolist()
tp = data['toothpaste'].tolist()
sh = data['shampoo'].tolist()

# Printing the lists
print("Facecream:", fc)
print("Facewash:", fw)
print("Toothpaste:", tp)
print("Shampoo:", sh)

Output:

Method 2: Using csv.DictReader

If you prefer using built-in libraries or want more control over parsing, the csv module is a good alternative. DictReader reads the file as a dictionary where each row is mapped using column headers as keys.

Approach:

• Import the csv module.
• Open the file in read mode.
• Use csv.DictReader() to parse rows.
• Create empty lists for desired columns.
• Append values to lists inside a loop.

import csv

# Open the CSV file
with open('company_sales_data.csv', mode='r') as file:
    reader = csv.DictReader(file)

    # Empty lists to store column values
    month = []
    moisturizer = []
    total_units = []

    # Iterating over each row
    for row in reader:
        month.append(row['month_number'])
        moisturizer.append(row['moisturizer'])
        total_units.append(row['total_units'])

# Printing the results
print("Month:", month)
print("Moisturizer:", moisturizer)
print("Total Units:", total_units)

Output:

Related Articles:

• Pandas library
• csv files