How to see complete rows in Google Colab

I am using Google Colab python 3.x and I have a Dataframe as below. I would like to see all cells on each row and column. How can I do this? I tried pd.set_option('display.max_columns', 3000) but it didn’t work.

# importing pandas as pd 
import pandas as pd 
   
# dictionary of lists 
dict = {'name':["a1", "b2", "c2", "d3"], 
        'degree': ["We explained to customer how correct fees (100) were charged. Account balance was too low", "customer was late in paying fees and we have to charge fine", "customer's credit score was too low and we have to charge higher interest rate", "customer complained a lot and didnt listen to our explanation. I had to escalate the call"], 
        'score':[90, 40, 80, 98]} 
  
# creating a dataframe from a dictionary  
df = pd.DataFrame(dict) 
print (df)


  name                                             degree  score
0   a1  We explained to customer how correct fees (100...     90
1   b2  customer was late in paying fees and we have t...     40
2   c2  customer's credit score was too low and we hav...     80
3   d3  customer complained a lot and didnt listen to ...     98

Answer

use pd.set_option('max_colwidth', <width>) for column width & pd.set_option('max_rows', <rows>) for number of rows.
see https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html

[] pd.set_option('max_rows', 99999)
[] pd.set_option('max_colwidth', 400)
[] pd.describe_option('max_colwidth')

display.max_colwidth : int
    The maximum width in characters of a column in the repr of
    a pandas data structure. When the column overflows, a "..."
    placeholder is embedded in the output.
    [default: 50] [currently: 400]

[] df = pd.DataFrame(d)
[] df

  name                                                                                     degree   score
0   a1  We explained to customer how correct fees (100) were charged. Account balance was too low   90
1   b2  customer was late in paying fees and we have to charge fine                                 40
2   c2  customer's credit score was too low and we have to charge higher interest rate              80
3   d3  customer complained a lot and didnt listen to our explanation. I had to escalate the call   98