Formatted display of pandas DataFrame

Sample data: Rakuten recipe category

from tabulate import tabulate
import pandas as pd
from pprint import pprint

df = pd.read_csv('small_category.csv', encoding='utf-8-sig')
br = '\n'

print(br + 'print'.center(20, '=') + br)
print(df.head())

print(br + 'pprint'.center(20, '=') + br)
pprint(df.head())

print(br + 'tabulate psql'.center(20, '=') + br)
print(tabulate(df.head(), headers='keys',
               tablefmt='psql',
               numalign='right',
               stralign='left', showindex=False))

print(br + 'tabulate simple'.center(20, '=') + br)
print(tabulate(df.head(), headers='keys',
               tablefmt='simple',
               numalign='right',
               stralign='left', showindex=False))

----------output----------

=======print========

     Id   Type  mediumId  largeId         Name
0 50 small 66 10 Sausage wiener
1 1491 small 67 10 Prosciutto
2 1492 small 67 10 chicken ham
3 321 small 67 10 Other hams
4 49 small 68 10 bacon

=======pprint=======

     Id   Type  mediumId  largeId         Name
0 50 small 66 10 Sausage wiener
1 1491 small 67 10 Prosciutto
2 1492 small 67 10 chicken ham
3 321 small 67 10 Other hams
4 49 small 68 10 bacon

===tabulate psql====

+------+--------+------------+-----------+-------------+
|   Id | Type   |   mediumId |   largeId | Name        |
|------+--------+------------+-----------+-------------|
|   50 | small  |         66 |        10 |Sausage wiener|
| 1491 | small  |         67 |        10 |Raw ham|
| 1492 | small  |         67 |        10 |chicken ham|
|  321 | small  |         67 |        10 |Other ham|
|   49 | small  |         68 |        10 |bacon|
+------+--------+------------+-----------+-------------+

==tabulate simple===

  Id  Type      mediumId    largeId  Name
----  ------  ----------  ---------  -----------
50 small 66 10 sausage wiener
1491 small 67 10 Prosciutto
1492 small 67 10 chicken ham
321 small 67 10 Other ham
49 small 68 10 bacon