Looping through values in a specific column and changing values Python and Pandas

user_17727722 0
Viewed 47

I have a dataframe as follows (example is simplified):

id       prediction1          prediction2
1234     Cocker_spaniel       german_Shepard
5678     rhodesian_ridgeback  australian_shepard

I need to remove the underscores and make sure the string is in lower case so I can search it easier later.

I am not quite sure how to loop through this. My initial student thought is something like what follows:

for row in image_predictions['p1']:
    image_predictions['p1'] = image_predictions['p1'].replace('_', ' ')

The above code is for replacing the underscore with a space and I believe the code would be similar for lowercase using the .lower() method.

Any advice to point me in the right direction?

pandas python loops replace
3 Answers

For in place modification you can use:

df.update(df[['prediction1', 'prediction2']]
          .apply(lambda c: c.str.lower()
                            .str.replace('_', ' ', regex=False))
          )

Output:

     id          prediction1         prediction2
0  1234       cocker spaniel      german shepard
1  5678  rhodesian ridgeback  australian shepard
user_163434640

You can use image_predictions['p1'].apply() to apply a function to each cell of the p1 column:

def myFunction(x):
    return x.replace('_', ' ')
image_predictions['p1'] = image_predictions['p1'].apply(myFunction)
user_197453300

Wanted to see if it was possible to not have to specify the columns for replacement. This approach creates a dict to replace A -> a, B -> b, etc, and _ -> space. Then uses replace with regex=True

import string

replace_dict = dict(zip(string.ascii_uppercase,string.ascii_lowercase))
replace_dict['_'] = ' '

df.replace(replace_dict, regex=True, inplace=True)

print(df)
user_65805430
Related