Pretty Printing a Pandas Dataframe

Pretty-print an entire Pandas Series / DataFrame

You can also use the option_context, with one or more options:

with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also
    print(df)

This will automatically return the options to their previous values.

If you are working on jupyter-notebook, using display(df) instead of print(df) will use jupyter rich display logic (like so).

Pretty print a pandas dataframe in VS Code

As of the January 2021 release of the python extension, you can now view pandas dataframes with the built-in data viewer when debugging native python programs. When the program is halted at a breakpoint, right-click the dataframe variable in the variables list and select "View Value in Data Viewer"

Instead of printing to console create a dataframe for output

I am making several assumptions here to include:

1. Compare.txt is a text file consisting of a list of single words 1 word per line.

2. Source.txt is a free flowing text file, which includes multiple words per line and each word is separated by a space.

3. When comparing to determine if a compare word is in source, is is found if and only if, no punctuation marks (i.e. " ' , . ?, etc) are appended to the word in source .

4. The output dataframe will only contain the words found in compare.txt.

5. The final output is a printed version of the pandas dataframe.

With these assumptions:

import pandas as pd
from collections import defaultdict
compare = 'compare.txt'
source = 'source.txt'

rslt = defaultdict(list)

def getCompareTxt(fid: str) -> list:
    clist = []
    with open(fid, 'r') as cmpFile:
        for line in cmpFile.readlines():
            clist.append(line.lower().strip('\n'))
    return clist    
    
cmpList = getCompareTxt(compare)
if cmpList:
    with open(source, 'r') as fsrc:
        items = []
        for item in (line.strip().split(' ') for line in fsrc):
            items.extend(item)
        
    print(items)
    for cmpItm in cmpList:
        rslt['Name'].append(cmpItm)
        if cmpItm in items:
            rslt['State'].append('Present')
        else:
            rslt['State'].append('Not Present')
                
    df = pd.DataFrame(rslt, index=range(len(cmpList))) 
    print(df)        
else:
    print('No compare data present')

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