excluding rows from a pandas dataframe based on column value and not index value

Question

I looked at the unique values in a column of a dataframe - pandas that I have. And there are some names in one of the columns that I do not want to include, how do I remove those rows from the dataframe, without using index value notation, but by saying if row value = "this" then remove

like...

new = df.copy

df['some column'].drop_values('this','that','other')

behzad.nouri · Accepted Answer · 2016-03-02 23:13:50Z

45

See indexing with isin (also, boolean indexing):

mask = df['some column'].isin(['this', 'that', 'other'])
df[~mask]

edited Mar 2, 2016 at 23:13

answered Mar 13, 2014 at 23:05

behzad.nouri

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3 Comments

yoshiserry Over a year ago

Thanks @Behzad! does the [~idx] notation, mean show everything in the dataframe which are not in variable, idx? Is this also another way to do it? df[df.line_race != 0] stackoverflow.com/questions/18172851/…

behzad.nouri Over a year ago

@yoshiserry see Boolean indexing

behzad.nouri Over a year ago

@yoshiserry also see indexing with isin

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