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

For multiple conditions ie. (df['employrate'] <=55) & (df['employrate'] > 50)

use this:

df['employrate'] = np.where(
   (df['employrate'] <=55) & (df['employrate'] > 50) , 11, df['employrate']
   )

or you can do it this way as well,

gm.loc[(gm['employrate'] <55) & (gm['employrate'] > 50),'employrate']=11

here informal syntax can be:

<dataset>.loc[<filter1> & (<filter2>),'<variable>']='<value>'

---

out[108]:
       country  employrate alcconsumption
0  Afghanistan   55.700001            .03
1      Albania   11.000000           7.29
2      Algeria   11.000000            .69
3      Andorra         nan          10.17
4       Angola   75.699997           5.57

therefore syntax we used here is:

 df['<column_name>'] = np.where((<filter 1> ) & (<filter 2>) , <new value>, df['column_name'])

---

for single condition, ie. ( 'employrate'] > 70 )

       country        employrate alcconsumption
0  Afghanistan  55.7000007629394            .03
1      Albania  51.4000015258789           7.29
2      Algeria              50.5            .69
3      Andorra                            10.17
4       Angola  75.6999969482422           5.57

use this:

df.loc[df['employrate'] > 70, 'employrate'] = 7

---

       country  employrate alcconsumption
0  Afghanistan   55.700001            .03
1      Albania   51.400002           7.29
2      Algeria   50.500000            .69
3      Andorra         nan          10.17
4       Angola    7.000000           5.57

therefore syntax here is:

df.loc[<mask>(here mask is generating the labels to index) , <optional column(s)> ]

---

Answer 2

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