Selecting rows of a dataframe based on two conditions in Pandas python

Question

I have a df, and I want to run something like:

subsetdf= df.loc[(df['Item_Desc'].str.contains('X')==True) or \
                 (df['Item_Desc'].str.contains('Y')==True ),:]

that selects all rows that have the Item Desc column a substring of "X" or "Y".

The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). 

I get the error when I run that. Any help?

Answer 1

Use | instead of or. So:

df.loc[(cond1) | (cond2), :]

The or operator wants to compare two boolean values (or two expression that evaluate to True or False). But a Series (or numpy array) does not simply evaluates to True or False, and in this case we want to compare both series element-wise. For this you can use | which is called 'bitwise or'.

Pandas follows here the numpy conventions. See here in the pandas docs for an explanation on it.