Reading Space-Delimited Files with Pandas

Pandas, a powerful Python library for data analysis and manipulation, offers straightforward methods to handle space-delimited files efficiently. Here’s how:

Using pandas.read_csv() with delimiter parameter

pandas.read_csv() is one of the function that can read the csv files and that can handle various delimited forms you many think that it can only only handle comma separated values as the name suggests but it can also also handle other delimited forms such as space, tab, newline etc,.

By setting sep=’ ‘, we explicitly specify that space is the delimiter.

Output:

      Name        Age  
0    Syam          25    
1    Hari          22     
2   Hemanth        30

Using pd.read_table()

The pd.read_table() function is versatile and can read various delimited files.

Similar to pd.read_csv(), specify sep=’ ‘ to handle space-delimited files.

Output :

      Name        Age  
0    Syam          25    
1    Hari          22     
2   Hemanth        30

Handling Multiple spaces

Some files may contain irregularity of spaces that means sometimes it may contains 2 or 3 spaces which is inconsistent . We can overcome this problem by using a regex operator, ‘\s+’ .

• sep=’\s+’ , this argument controls how the function separates values within the file. It’s crucial here because the file doesn’t use standard commas as delimiters.
• =’\s+’ assigns a regular expression pattern as the separator.
• \s+ matches any single whitespace character (space, tab, newline, etc.).
• + quantifier means “one or more,” so \s+ matches one or more consecutive whitespace characters.

Output :

      Name        Age  
0    Syam          25    
1    Hari          22     
2   Hemanth        30

In this article, We’ll learn to efficiently read and process space-delimited files with variable spaces using Pandas in Python.