Pandas Read Text File

Pandas offers several methods to read plain text (.txt) files and convert them to Pandas DataFrame.

We can read text files in Pandas in the following ways:

• Using the read_fwf() function
• Using the read_table() function
• Using the read_csv() function

Using the above methods, let's read a sample text file named data.txt with the following content.

John  25  170
Alice 28  165
Bob   30  180

Read Text Using read_fwf()

The acronym fwf in the read_fwf() function in Pandas stands for fixed-width lines, and it is used to load DataFrames from files such as text files.

The text file should be separated into columns of fixed-width for it to be read using read_fwf().

Syntax

The syntax of read_fwf() in Pandas is:

pandas.read_fwf(
    filepath_or_buffer,
    colspecs = [],
    widths=None,
    infer_nrows=100,
    **kwds
)

• filepath_or_buffer: specifies the file path or a file-like object from which the data will be read
• colspecs: defines the column positions or ranges in the file
• widths (optional): an alternative to colspecs and can be used to define the width of each column in the file
• infer_nrows (optional): specifies the number of rows to be used for inferring the column widths if widths is not explicitly provided
• **kwds (optional): allows additional keyword arguments to be passed for further customization

To learn more, please refer to the official documentation on read_fwf().

Example: read_fwf()

The content of the data.txt file is the same as mentioned in the introduction section.

import pandas as pd

# read the fixed-width file
df = pd.read_fwf('data.txt', colspecs=[(0, 5), (6, 10), (11, 15)], names = ['Name', 'Age', 'Height'])

print(df)

Output

    Name  Age  Height
0   John   25      70
1  Alice   28      65
2    Bob   30      80

In the above example, we read a text file 'data.txt' using read_fwf().

Here,

• colspecs = [(0,5), (6,10), (11,15)]: specifies the position of each column in the text file
• names = ['Name', 'Age', 'Height']: specifies the names to be assigned to each column

The names argument is an example of a keyword argument in **kwds.

Read Text Using read_table()

The read_table() function in pandas is used to read tabular data from a file or a URL. It is a convenient way to read data from delimited text files.

Syntax

The syntax of read_table() in Pandas is:

df = pd.read_table(
    filepath_or_buffer,
    sep='\t',
    header='infer',
    names=None
)

Here,

• filepath_or_buffer: specifies the path to the file to be read or a URL pointing to the file
• sep: specifies the separator or delimiter used in the file to separate columns
• header: specifies the row number (0-indexed) to be used as the column names
• names: a list of column names for the DataFrame

To learn more, please refer to the official documentation on read_table().

Example: read_table()

The content of the data.txt file is the same as mentioned in the introduction section.

import pandas as pd

# read the file using read_table()
df = pd.read_table("data.txt", sep="\s+", names=['Name', 'Age', 'Height'])

print(df)

Output

    Name  Age  Height
0   John   25     170
1  Alice   28     165
2    Bob   30     180

Here, the sep="\s+" parameter is used to specify that the data is separated by one or more whitespace characters.

The separator is selected based on the format of the text file. For example, if the text file contained comma separated values, we would have used sep = ','.

Read Text Using read_csv()

The read_csv() function in Pandas is used to read csv files.

It can also be used to read text files as read_csv() allows use of other separators like white spaces, tabs, semicolons etc in addition to commas.

Syntax

The syntax of read_csv() in Pandas is given below:

df = pd.read_csv(
    filepath_or_buffer,
    sep=',',
    header=0,
    names=['col1', 'col2', 'col3'],
    index_col='col1'
)

Here,

• filepath_or_buffer: represents the path or buffer object containing the CSV data to be read
• sep(optional): specifies the delimiter used in the CSV file
• header(optional): indicates the row number to be used as the header or column names
• names(optional): a list of column names to assign to the DataFrame
• index_col(optional): specifies the column to be used as the index of the DataFrame

These are some commonly used arguments of the read_csv() function. All of them are optional except filepath_or_buffer. There are many other optional arguments that can be used with read_csv().

To learn more, please visit our Pandas CSV article.

Example: read_csv()

The content of the data.txt file is the same as mentioned in the introduction section. The values in the file are thus separated by whitespaces.

import pandas as pd

# read the file using read_table()
df = pd.read_csv("data.txt", sep="\s+", header = None, names=['Name', 'Age', 'Height'])

print(df)

Output

    Name  Age  Height
0   John   25     170
1  Alice   28     165
2    Bob   30     180

Here, header = None indicates that none of the rows in the text file is a header row.

Also, notice the use of sep="\s+" which indicates that the values in the files are separated by one or more spaces.