Try:

(df['A'] + df['B']).where((df['A'] < 0) | (df['B'] > 0), df['A'] / df['B'])

The difference between the numpy where and DataFrame where is that the default values are supplied by the DataFrame that the where method is being called on (docs).

I.e.

np.where(m, A, B)

is roughly equivalent to

A.where(m, B)

If you wanted a similar call signature using pandas, you could take advantage of the way method calls work in Python:

pd.DataFrame.where(cond=(df['A'] < 0) | (df['B'] > 0), self=df['A'] + df['B'], other=df['A'] / df['B'])

or without kwargs (Note: that the positional order of arguments is different from the numpy where argument order):

pd.DataFrame.where(df['A'] + df['B'], (df['A'] < 0) | (df['B'] > 0), df['A'] / df['B'])

Think of loc as a filter - give me only the parts of the df that conform to a condition.

where originally comes from numpy. It runs over an array and checks if each element fits a condition. So it gives you back the entire array, with a result or NaN. A nice feature of where is that you can also get back something different, e.g. df2 = df.where(df['Goals']>10, other='0'), to replace values that don't meet the condition with 0.

ID  Run Distance Goals Gender
0   1   234      12     m
1   2   35       23     m
2   3   77       56     m
3   0   0        0      0
4   0   0        0      0
5   0   0        0      0
6   0   0        0      0
7   0   0        0      0
8   0   0        0      0
9   10  123      34     m

Also, while where is only for conditional filtering, loc is the standard way of selecting in Pandas, along with iloc. loc uses row and column names, while iloc uses their index number. So with loc you could choose to return, say, df.loc[0:1, ['Gender', 'Goals']]:

    Gender  Goals
0   m   12
1   m   23