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I am new to Pandas and I am trying to drop all rows that contain some respective countries (Albania, Uzbekistan, Brazil) from the Column country. However, the way I figured it out to do is one by one, as below:

indexCountry = df[df['country'] == 'Albania'].index
df.drop(indexCountry, inplace = True)

indexCountry = df[df['country'] == 'Uzbekistan'].index
df.drop(indexCountry, inplace = True)

indexCountry = df[df['country'] == 'Brazil'].index
df.drop(indexCountry, inplace = True)

Is there a way to do this in one single code line, instead of having to do one for every country?

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3 Answers 3

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You can do a filter like this:

df = df[~df["country"].isin(["Alabania", "Uzbekistan", "Brazil"])]

~ is the negation of what it is followed by.

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Perfect! Thank you very much @Yilun Zhang
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Try:

list_of_countries = ['Albania',  'Uzbekistan', 'Brazil']
indexCountry = df[df['country'].isin(list_of_countries)].index 
df.drop(indexCountry, inplace = True)

or just:
list_of_countries = ['Albania',  'Uzbekistan', 'Brazil']
df[~df["country"].isin(list_of_countries)]
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Comments

0

You can also use this:

df = df[~df.country.str.contains('|'.join(["Albania","Uzbekistan","Brazil"]))]
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