Numpy "where" with multiple conditions

Try this: Using the setup from @Maxu

col         = 'consumption_energy'
conditions  = [ df2[col] >= 400, (df2[col] < 400) & (df2[col]> 200), df2[col] <= 200 ]
choices     = [ "high", 'medium', 'low' ]
    
df2["energy_class"] = np.select(conditions, choices, default=np.nan)


  consumption_energy energy_class
0                 459         high
1                 416         high
2                 186          low
3                 250       medium
4                 411         high
5                 210       medium
6                 343       medium
7                 328       medium
8                 208       medium
9                 223       medium

You can use a ternary:

np.where(consumption_energy > 400, 'high', 
         (np.where(consumption_energy < 200, 'low', 'medium')))

I like to keep the code clean. That's why I prefer np.vectorize for such tasks.

def conditions(x):
    if   x > 400:   return "High"
    elif x > 200:   return "Medium"
    else:           return "Low"

func         = np.vectorize(conditions)
energy_class = func(df_energy["consumption_energy"])

Then just add numpy array as a column in your dataframe using:

df_energy["energy_class"] = energy_class

The advantage in this approach is that if you wish to add more complicated constraints to a column, it can be done easily. Hope it helps.

I would use the cut() method here, which will generate very efficient and memory-saving category dtype:

In [124]: df
Out[124]:
   consumption_energy
0                 459
1                 416
2                 186
3                 250
4                 411
5                 210
6                 343
7                 328
8                 208
9                 223

In [125]: pd.cut(df.consumption_energy,
                 [0, 200, 400, np.inf],
                 labels=['low','medium','high']
          )
Out[125]:
0      high
1      high
2       low
3    medium
4      high
5    medium
6    medium
7    medium
8    medium
9    medium
Name: consumption_energy, dtype: category
Categories (3, object): [low < medium < high]