CODEWITHSUNDEEP
pythonpandas
richie
richie

Reputation: 18638

pandas apply filter for boolean type

These are the types for my DataFrame;

count     int64
word     object
cat1       bool
cat2     object
cat3       bool
dtype: object

How do I do a filter for boolean values from 'cat1' and 'cat2'?

Something like...

data[(data['cat1'].bool() == FALSE) & (data['cat3'].bool() == FALSE)]

Upvotes: 2

Views: 6613

Answers (2)

Pradeep
Pradeep

Reputation: 376

You can simply do this,

data[(data['cat1']==False) & (data['cat3']==False)]

Example:

test = pd.DataFrame({'word':'','cat1':bool(),'cat2':'','cat3':bool()},index=[])
test.loc[0]=['test1',True,'try',False]
test.loc[1]=['test1',False,'try',False]
test[(test['cat1']==False) & (test['cat3']==False)]

Upvotes: 0

EdChum
EdChum

Reputation: 393863

Just do:

data[(data['cat1'] == FALSE) & (data['cat2'] == FALSE)]

cat2 is an object dtype so it is probably a string, I think you want:

data[(data['cat1'] == FALSE) & (data['cat3'] == FALSE)]

otherwise if it really is string values then you can do

data[(data['cat1'] == FALSE) & (data['cat2'] == 'FALSE')]

or

data[(data['cat1'] == FALSE) & (data['cat2'].str.contains('FALSE'))]

Upvotes: 1

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