How to Read Text Files in Pandas: 4 Approaches

Here are four approaches to reading text files in Pandas. One thing I want to share with you is there is no direct method for reading text. However, you can achieve it using the read_csv and fwf methods.

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Approaches to Read Text Files in Pandas

Approaches to Read Text Files in Pandas

When you work with Pandas, reading text files is a common task and there are different methods to accomplish this.
In Pandas, you can use read_csv, read_table, read_fwf, read_excel, and read_html to read different types of text files such as delimited data, fixed-width formatted files, Excel spreadsheets, or HTML tables.
Each method serves a specific purpose and can be used for specific types of text files. Pandas provides a variety of tools to efficiently handle diverse text file formats. Let’s explore these methods and understand how to read text files using Pandas.

read_csv()

This method is commonly used to read text files with delimited data, such as CSV files. For example:

mport pandas as pd

df = pd.read_csv('file.txt', delimiter='\t')

read_table()

This method is similar to read_csv() but assumes the delimiter is a tab character (‘\t’) by default. For example:

import pandas as pd

df = pd.read_table('file.txt')

read_fwf()

This method reads fixed-width formatted files, where the data is arranged in columns of specified widths. For example:

import pandas as pd

df = pd.read_fwf('file.txt', widths=[10, 8, 12])

read_excel()

This method can read text files saved in Excel format (.xls or .xlsx). For example:

import pandas as pd

df = pd.read_excel('file.xlsx', sheet_name='Sheet1')

read_html()

This method is used to read tables from HTML files or web pages. It returns a list of dataframes, one for each table found in the HTML. For example:

import pandas as pd

dfs = pd.read_html('file.html')
df = dfs[0] # Assuming the first table is of interest

These are some of the commonly used methods, but there are other specialized methods available in Pandas for specific file formats.

Srini

Data Engineer with deep AI and Generative AI expertise, crafting high-performance data pipelines in PySpark, Databricks, and SQL. Skilled in Python, AWS, and Linux—building scalable, cloud-native solutions for smart applications.