python - Replacing values in a pandas dataframe based on multiple conditions

Question

I have a fairly simple question based on this sample code:

x1 = 10*np.random.randn(10,3)
df1 = pd.DataFrame(x1)

I am looking for a single DataFrame derived from df1 where positive values are replaced with "up", negative values are replaced with "down", and 0 values, if any, are replaced with "zero". I have tried using the .where() and .mask() methods but could not obtain the desired result.

I have seen other posts which filter according to multiple conditions at once, but they do not show how to replace values according to different conditions.

Answer 1

df1.apply(np.sign).replace({-1: 'down', 1: 'up', 0: 'zero'})

Output:

      0     1     2
0  down    up    up
1    up  down  down
2    up  down  down
3  down  down    up
4  down  down    up
5  down    up    up
6  down    up  down
7    up  down  down
8    up    up  down
9  down    up    up

P.S. Getting exactly zero with randn is pretty unlikely, of course