Read Plain Text Files with Pandas: 3 Essential Methods

Unlocking the Power of Pandas: Reading Plain Text Files

When working with data, it’s essential to be able to read and manipulate various file formats. Pandas, a popular Python library, offers several methods to read plain text (.txt) files and convert them into a DataFrame, a two-dimensional table of data.

Method 1: read_fwf() – Fixed-Width Lines

The read_fwf() function is ideal for loading DataFrames from files with fixed-width columns. This method requires the text file to be separated into columns of fixed-width.

* Syntax Breakdown *

• 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

Example: read_fwf() in Action

Let’s read a sample text file named data.txt using read_fwf(). The content of the file is:


John 25 170
Alice 30 160
Bob 35 180


By specifying colspecs = [(0,5), (6,10), (11,15)] and names = ['Name', 'Age', 'Height'], we can easily read the file into a DataFrame.

Method 2: read_table() – Tabular Data

The read_table() function is a convenient way to read tabular data from a file or a URL. It’s perfect for delimited text files.

* Syntax Breakdown *

• 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

Example: read_table() in Action

Let’s read the same data.txt file using read_table(). By specifying sep="\s+", we can indicate that the data is separated by one or more whitespace characters.

Method 3: read_csv() – Comma Separated Values

The read_csv() function is commonly used to read csv files, but it can also be used to read text files by specifying alternative separators.

* Syntax Breakdown *

• 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

Example: read_csv() in Action

Let’s read the same data.txt file using read_csv(). By specifying header = None and sep="\s+", we can easily read the file into a DataFrame.

By mastering these three methods, you’ll be able to unlock the full potential of Pandas and efficiently read plain text files into DataFrames.