Update muliple column values based on condition in python

Question

I have a dataframe like this,

ID    00:00  01:00  02:00  ...   23:00   avg_value
22      4.7     5.3     6   ...    8         5.5
37       0      9.2    4.5  ...    11.2      9.2
4469     2      9.8    11   ...    2         6.4

Can I use np.where to apply conditions on multiple columns at once? I want to update the values from 00:00 to 23:00 to 0 and 1. If the value at the time of day is greater than avg_value then I change it to 1, else to 0.

I know how to apply this method to one single column.

np.where(df['00:00']>df['avg_value'],1,0)

Can I change it to multiple columns?

Output will be like,

ID    00:00  01:00  02:00  ...   23:00   avg_value
22      0     1       1    ...      1       5.5
37      0     0       0    ...      1       9.2
4469    0     1       1    ...      0       6.4

jezrael · Accepted Answer · 2020-02-17 08:34:22Z

Select all columns without last by DataFrame.iloc, compare by DataFrame.gt and casting to integers and last add avg_value column by DataFrame.join:

df = df.iloc[:, :-1].gt(df['avg_value'], axis=0).astype(int).join(df['avg_value'])
print (df)
      00:00  01:00  02:00  23:00  avg_value
ID                                         
22        0      0      1      1        5.5
37        0      0      0      1        9.2
4469      0      1      1      0        6.4

Or use DataFrame.pop for extract column:

s = df.pop('avg_value')
df = df.gt(s, axis=0).astype(int).join(s)
print (df)
      00:00  01:00  02:00  23:00  avg_value
ID                                         
22        0      0      1      1        5.5
37        0      0      0      1        9.2
4469      0      1      1      0        6.4

Because if assign to same columns integers are converted to floats (it is bug):

df.iloc[:, :-1] = df.iloc[:, :-1].gt(df['avg_value'], axis=0).astype(int)
print (df)
      00:00  01:00  02:00  23:00  avg_value
ID                                         
22      0.0    0.0    1.0    1.0        5.5
37      0.0    0.0    0.0    1.0        9.2
4469    0.0    1.0    1.0    0.0        6.4

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