What is the best way to populate a column of a dataframe with conditional values based on corresponding rows in another column?

Question

I have a dataframe, df, in which I am attempting to fill in values within the empty "Set" column, depending on a condition. The condition is as follows: the value of the 'Set' columns need to be "IN" whenever the 'valence_median_split' column's value is 'Low_Valence' within the corresponding row, and "OUT' in all other cases.

Please see below for an example of my attempt to solve this:

df.head()

Out[65]: 
              ID Category  Num Vert_Horizon Description  Fem_Valence_Mean  \
0  Animals_001_h  Animals    1            h  Dead Stork              2.40   
1  Animals_002_v  Animals    2            v        Lion              6.31   
2  Animals_003_h  Animals    3            h       Snake              5.14   
3  Animals_004_v  Animals    4            v        Wolf              4.55   
4  Animals_005_h  Animals    5            h         Bat              5.29   

   Fem_Valence_SD  Fem_Av/Ap_Mean  Fem_Av/Ap_SD  Arousal_Mean ...   Contrast  \
0            1.30            3.03          1.47          6.72 ...      68.45   
1            2.19            5.96          2.24          6.69 ...      32.34   
2            1.19            5.14          1.75          5.34 ...      59.92   
3            1.87            4.82          2.27          6.84 ...      75.10   
4            1.56            4.61          1.81          5.50 ...      59.77   

   JPEG_size80   LABL   LABA   LABB  Entropy  Classification  \
0       263028  51.75  -0.39  16.93     7.86                   
1       250208  52.39  10.63  30.30     6.71                   
2       190887  55.45   0.25   4.41     7.83                   
3       282350  49.84   3.82   1.36     7.69                   
4       329325  54.26  -0.34  -0.95     7.82                   

   valence_median_split  temp_selection  set  
0           Low_Valence   Animals_001_h       
1          High_Valence             NaN       
2           Low_Valence   Animals_003_h       
3           Low_Valence   Animals_004_v       
4           Low_Valence   Animals_005_h       

[5 rows x 36 columns]

df['set'] = np.where(df.loc[df['valence_median_split'] == 'Low_Valence'], 'IN', 'OUT') 

ValueError: Length of values does not match length of index

I can accomplish this by using loc to separate the df into two different df's, but wondering if there is a more elegant solution using the "np.where" or a similar approach.

Answer 1

Change to

df['set'] = np.where(df['valence_median_split'] == 'Low_Valence', 'IN', 'OUT') 

If need .loc

df.loc[df['valence_median_split'] == 'Low_Valence','set']='IN'
df.loc[df['valence_median_split'] != 'Low_Valence','set']='OUT'