4 Ways To Read a Text File With Python

Reading text files with Python is a common task and can be done in several ways. In this article, we will cover the most popular methods for reading text files in Python:

Read a Text File Using with open()

The modern and recommended way to read a text file in Python is to use the with open statement:

• The with statement automatically closes the file after the indented block of code is executed, ensuring the file is always closed properly.
• The open() function takes the file path as its only argument and returns a file object that can be used to read the file’s contents all at once with the read() method.

Here is an example of how to open and read a text file in Python this way:

with open('names.txt') as f:
    # Read the contents of the file into a variable
    names = f.read()
    
    # Print the names
    print(names)

Read our more extensive articles about using files in Python to learn more.

Using open() and close() manually

The more basic method for reading a text file in Python is to use the open() and close() functions manually. I recommend you use the first method for most cases since you don’t have to worry about closing the file, which can be easily forgotten. Here is an example anyway:

f = open('names.txt')
# Read the contents of the file into a variable
names = f.read()
print(names)

# Don't forget to close the file again
f.close()

Read our more extensive articles about using files in Python to learn more.

Read a Text File Using Pandas

If you are working with large text files and need to perform advanced operations on the data, you may want to use the pandas library. This library provides a powerful and easy-to-use DataFrame object that can read and manipulate text files.

To read text files into a DataFrame, the file needs to have a clearly defined format. Since this usually is CSV, let’s focus on that. Here is an example of reading a CSV file into a DataFrame:

import pandas as pd

# Read a CSV file into a DataFrame
df = pd.read_csv('example.csv')
print(df)

In addition to reading CSV files, pandas also provides functions for reading other types of text files, such read_json() for reading JSON files, and read_html() for reading HTML tables.

Here’s an example of reading a JSON file into a DataFrame:

import pandas as pd

# Read a JSON file into a DataFrame
df = pd.read_json('example.json')
print(df)

Read a Text File Using NumPy

NumPy is a library for working with arrays and matrices of numerical data. It provides several functions for reading text files, including loadtxt() and genfromtxt().

numpy.loadtxt() allows parameters that specify how the data is delimited, which rows and columns to skip, and how to handle missing data. For example, to specify that the data is delimited by commas (typical for a CSV file), you can pass the delimiter parameter:

import numpy as np

# Read a CSV file into a NumPy array
data = np.loadtxt('example.csv', delimiter=',')
print(data)