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I have a dataframe from which I need to replace all strings in column sense where the string is exactly pertain. I can apply the following code to do the same

pd[pd['sense'] == 'pertain']['sense'].str.replace('pertain','pertaining')

But this does not take effect on the original dataframe. How can I make sure that the effect takes palce on the original dataset and all the pertain in the column will change to pertaining

 | affix    | word           |   sense       | meaning                         |
0   ical    neuroanatomical     pertaining   of or pertaining to [[neuroanatomy]]
1   ical    neuroanatomical     pertain      of or pertaining to [[neuroanatomy]]
2   ical    biotechnological    pertaining   of or pertaining to [[biotechnology]]
3   ical    biotechnological    pertain      of or pertaining to [[biotechnology]]
4   ical    educological        relating     relating to [[educology]].

So Ideally, after the operation my output should look like the following

 | affix    | word           |   sense       | meaning                         |
0   ical    neuroanatomical     pertaining   of or pertaining to [[neuroanatomy]]
1   ical    neuroanatomical     pertaining   of or pertaining to [[neuroanatomy]]
2   ical    biotechnological    pertaining   of or pertaining to [[biotechnology]]
3   ical    biotechnological    pertaining   of or pertaining to [[biotechnology]]
4   ical    educological        relating     relating to [[educology]].
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1 Answer 1

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2

You can use loc with assignment, where you pass a logical vector to indicate rows where the column sense's value should be replaced (assuming your data frame is named df):

df.loc[df.sense == "pertain", "sense"] = "pertaining"

df
#  affix                word         sense   meaning
#0  ical     neuroanatomical    pertaining  of or pertaining to [[neuroanatomy]]
#1  ical     neuroanatomical    pertaining  of or pertaining to [[neuroanatomy]]
#2  ical    biotechnological    pertaining  of or pertaining to [[biotechnology]]
#3  ical    biotechnological    pertaining  of or pertaining to [[biotechnology]]
#4  ical        educological    relating    relating to [[educology]].

Another method is to use replace only on sense column and then assign it back to the original data frame, which replaces the sense column with modified version:

df['sense'] = df.sense.replace("pertain", "pertaining")

With all being said, your method also works, you just need to assign it back to your original data frame as the second option above.

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