CODEWITHSUNDEEP
pythonpandas
Cactus Philosopher
Cactus Philosopher

Reputation: 864

Filtering rows in DataFrame with dependent conditions

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)))]

Upvotes: 1

Views: 151

Answers (1)

willeM_ Van Onsem
willeM_ Van Onsem

Reputation: 476534

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).

Upvotes: 2

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