使用相关条件过滤DataFrame中的行

Question

Apologies if this has already been asked:

I want to remove all rows with values between 15-25 in one column AND have a specific string in another column.

For example:

options = ['pizza', 'pasta'] 

df2 = df[(~df['columnA'].between(15, 25)) & df.loc[~df['columnB'].isin(options)]]

So if a row has a value of 15-25 in columnA but does not have 'pizza' or 'pasta' in columnB, then I want that row to be retained...

Solution:

df[~((df['columnA'].between(15, 25)) & (df['columnB'].isin(options)))]

Answer 1

The easiest to understand is negating the entire condition, like ~((...) & (...)) :

df[~((df['columnA'].between(15, 25)) & (df['columnB'].isin(options)))]

Or you can use De Morgan's laws [wiki] , and specify this as (~ ...) | (~ ...) (~ ...) | (~ ...)

df[(~df['columnA'].between(15, 25)) | (~df['columnB'].isin(options))]

So the negation of x ∧ y is (¬x)∨(¬y) .