Get df subsection based on many conditions

Question

I am trying to extract lines from df based on several conditions, ALL conditions must be met before any lines are selected differently.

My df

columns = ['is_net', 'is_pct', 'is_mean', 'is_wgted', 'is_sum'] index = ['a','b','c','d'] data = [['True','True','False','False', 'False'], ['True','True','True','False', 'False'], ['True','True','False','False', 'True'], ['True','True','False','True', 'False']] df = pd.DataFrame(columns=columns, index=index, data=data) df is_net is_pct is_mean is_wgted is_sum a True True False False False b True True True False False c True True False False True d True True False True False 

My conditions, which should be in this format:

 conditions = {'is_net': 'True', 'is_pct': 'True', 'is_mean': 'False', 'is_wgted': 'True', 'is_sum': 'False'} 

Expected Result:

  is_net is_pct is_mean is_wgted is_sum d True True False True False 

Answer 1

I think the trick is to make conditions in a series:

 >>> pd.Series(conditions) is_mean False is_net True is_pct True is_sum False is_wgted True dtype: object >>> (df == pd.Series(conditions)) is_mean is_net is_pct is_sum is_wgted a True True True True False b False True True True False c True True True False False d True True True True True >>> (df == pd.Series(conditions)).all(axis=1) a False b False c False d True dtype: bool >>> df[(df == pd.Series(conditions)).all(axis=1)] is_net is_pct is_mean is_wgted is_sum d True True False True False