Pandas - Compare Positive/negative Values

I have a dataframe 'df': x y 0 1 -1 1 -2 -3 2 3 4 3 4 5 4 9 6 I am trying to determine what percentage of x and y values are in agreement in terms of being p

Solution 1:

If we compare a product of x*y >= 0 - this should give us "good" rows:

In [19]: df['x'].mul(df['y']).ge(0)
Out[19]:
0    False
1     True
2     True
3     True
4     True
dtype: bool


In [20]: df.loc[df['x'].mul(df['y']).ge(0)]
Out[20]:
   x  y
1 -2 -3234345496

In [21]: len(df.loc[df['x'].mul(df['y']).ge(0)])/len(df)
Out[21]: 0.8

or as proposed by @NickilMaveli a faster and more "Pandaic" version:

In [23]: df['x'].mul(df['y']).ge(0).mean()
Out[23]: 0.80000000000000004

the same idea, but this time using df.eval() method:

In [27]: df.eval('x * y >= 0').mean()
Out[27]: 0.80000000000000004