python - What are the rules for pandas' elementwise binary boolean operands with same length elements holding differing indexes? -


i have been applying binary boolean operators code base , came across bug surprised me. i've reconstructed minimal working example demonstrate behavior below...

import pandas s = pandas.series( [true]*4 ) d = pandas.dataframe( { 'a':[true, false, true, false] , 'b':[true]*4 } )  print(d)            b 0   true  true 1  false  true 2   true  true 3  false  true  print( s[0:2] ) 0    true 1    true dtype: bool  print( d.loc[ d['a'] , 'b' ] ) 0    true 2    true dtype: bool  print( s[0:2] & d.loc[ d['a'] , 'b' ] ) 0     true 1    false 2    false

this last statement's value catches me entirely surprise in yielding of 3 elements. realizing influence of indices here manually reset index yield result expected.

s[0:2].reset_index(drop=true) & d.loc[ d['a'] , 'b' ].reset_index( drop=true ) 0    true 1    true

needless i'll need revisit documentation , grip understand how indexing rules apply here. can 1 explain step step how operator behaves mixed indexes?

=============================================

just add comparison coming similar r background, r's data.frame equivalent operation yields i'd expect...

> = c(true,false,true,false) > b = c(true,true,true,true) >  > d = data.frame( a, b ) > d          b 1  true true 2 false true 3  true true 4 false true > s = c( true,true,true,true) > s [1] true true true true > > d[ d$a , 'b'] [1] true true > > s[0:2] [1] true true > s[0:2] & d[ d$a , 'b'] [1] true true

you comparing 2 series different indices

s[0:2]  0    true 1    true dtype: bool

and

d.loc[ d['a'] , 'b']  0    true 2    true dtype: bool

pandas needs align indices compares.

s[0:2] & d.loc[ d['a'] , 'b']  0     true  # true both indices therefore true 1    false  # true s[0:2] , missing other therefore false 2    false  # true d , missing other therefore false dtype: bool

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