How To Load Data From Text File into Pandas

Loading data stored in text files into pandas DataFrames with Python

Oct 5, 2022

3 min read

Pandas has been the de-facto Python package the lets users perform data transformation and analysis in-memory. In many cases, this data initially resides into external sources, such as text files. Through its powerful API, pandas allows users to load data from such sources with various methods.

In today’s article we will demonstrate how to use some of these methods in order to load data from text files into pandas DataFrames. Additionally, we will discuss about how to deal with delimiters and column names (aka headers).

First, let’s create an example text file called employees.txt that we will be using in today’s short tutorial in order to demonstrate a few concepts. Note that the fields are separated by a single space character and the first line corresponds to the headers.

name surname dob department
George Brown 12/02/1993 Engineering
Andrew Black 15/04/1975 HR
Maria Green 12/02/1989 Engineering
Helen Fox 21/10/2000 Marketing
Joe Xiu 10/11/1998 Engineering
Ben Simsons 01/12/1987 Engineering
Jess Middleton 12/12/1997 Marketing

Using read_csv()

A comma separated file (csv) is on fact a text file that uses commas as delimiters in order to separate the record values for each field. Therefore, it then makes sense to use [pandas.read_csv()](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) method in order to load data from a text file, even if the file itself does not have a .csv extension.

In order to read our text file and load it into a pandas DataFrame all we need to provide to the read_csv() method is the filename, the separator/delimiter (which in our case is a whitespace) and the row containing the columns names which seems to be the first row.

import pandas as pd

df = pd.read_csv('employees.txt', sep=' ', header=0)

print(df)
     name    surname         dob   department
0  George      Brown  12/02/1993  Engineering
1  Andrew      Black  15/04/1975           HR
2   Maria      Green  12/02/1989  Engineering
3   Helen        Fox  21/10/2000    Marketing
4     Joe        Xiu  10/11/1998  Engineering
5     Ben    Simsons  01/12/1987  Engineering
6    Jess  Middleton  12/12/1997    Marketing

Note that if the file you are looking into loading as a pandas DataFrame has a different separator, such as a comma ,, colon : or tab t, all you need to do is specify that character in sep or delimiter argument when calling read_csv().

Using read_table()

Alternatively, you can take advantage of [pandas.read_table()](https://pandas.pydata.org/docs/reference/api/pandas.read_table.html) method that is used to read general delimited file into a pandas DataFrame.

import pandas as pd

df = pd.read_table('employees.txt', sep=' ', header=0)

print(df)
     name    surname         dob   department
0  George      Brown  12/02/1993  Engineering
1  Andrew      Black  15/04/1975           HR
2   Maria      Green  12/02/1989  Engineering
3   Helen        Fox  21/10/2000    Marketing
4     Joe        Xiu  10/11/1998  Engineering
5     Ben    Simsons  01/12/1987  Engineering
6    Jess  Middleton  12/12/1997    Marketing

Final Thoughts

One of the most common ways to create a pandas DataFrame is by loading data stored in external sources, such as text or csv files. In today’s short tutorial we went through a step by step process that can eventually help you construct a pandas DataFrame by loading data from a text file where every field is separated by a specific character (tab, space or whatever).

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