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Creating a dataframe using Excel files
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Creating a dataframe using CSV files

Last Updated : 08 Apr, 2025
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CSV (Comma-Separated Values) files are widely used in data science for storing tabular data, similar to Excel sheets. In Python the Pandas library is essential for handling and analyzing large datasets stored in CSV format. Below are three methods to create a Pandas DataFrame from a CSV file:

Method 1: Using read_csv()

read_csv() function is the most commonly used and efficient way to read CSV files into a DataFrame. We can download the dataset from here.

Python 
import pandas as pd  # Load CSV file into a DataFrame df = pd.read_csv("CardioGoodFitness.csv") print(df.head())

Output:

csv-to-df-pandas

Displays the first few rows of the dataset with columns and their respective values.

Method 2: Using read_table()

read_table() function allows us to specify a custom delimiter, such as a comma to read CSV files.

Python 
import pandas as pd  # Load CSV file using read_table with a comma delimiter df = pd.read_table("CardioGoodFitness.csv", delimiter=",") print(df.head())

Output:

csv-to-df-pandas

Method 3: Using the csv Module (Manual Way)

We can also use Python’s built-in csv module to read the file and then manually convert it to a Pandas DataFrame.

Python 
import pandas as pd import csv  with open("CardioGoodFitness.csv") as csv_file:     csv_reader = csv.reader(csv_file)     # Create DataFrame from the CSV reader object     df = pd.DataFrame(list(csv_reader)) df.head()

Output: 

Screenshot-2025-04-08-160634

Using the csv Module

Key Takeaways:

  • Use read_csv() for simplicity and efficiency when working with standard CSV files.
  • Use read_table() if your data uses a custom delimiter or requires specific parsing options.
  • Use the csv module for advanced parsing needs or when integrating with other Python libraries.


Next Article
Creating a dataframe using Excel files

A
AmiyaRanjanRout
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Article Tags :
  • Python
  • Python pandas-dataFrame
  • Python-pandas
Practice Tags :
  • python

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