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

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]

Let's start by creating a dataframe with 1000000 random numbers between 0 and 1000 to be used as test

df_energy = pd.DataFrame({'consumption_energy': np.random.randint(0, 1000, 1000000)})

[Out]:

   consumption_energy
0                 683
1                 893
2                 545
3                  13
4                 768
5                 385
6                 644
7                 551
8                 572
9                 822

A bit of a description of the dataframe

print(df.energy.describe())

[Out]:
       consumption_energy
count      1000000.000000
mean           499.648532
std            288.600140
min              0.000000
25%            250.000000
50%            499.000000
75%            750.000000
max            999.000000

There are various ways to achieve that, such as:

1. Using numpy.where

   df_energy['energy_class'] = np.where(df_energy['consumption_energy'] > 400, 'high', np.where(df_energy['consumption_energy'] > 200, 'medium', 'low'))
   

2. Using numpy.select

   df_energy['energy_class'] = np.select([df_energy['consumption_energy'] > 400, df_energy['consumption_energy'] > 200], ['high', 'medium'], default='low')
   

3. Using numpy.vectorize

   df_energy['energy_class'] = np.vectorize(lambda x: 'high' if x > 400 else ('medium' if x > 200 else 'low'))(df_energy['consumption_energy'])
   

4. Using pandas.cut

   df_energy['energy_class'] = pd.cut(df_energy['consumption_energy'], bins=[0, 200, 400, 1000], labels=['low', 'medium', 'high'])
   

5. Using Python's built in modules

   def energy_class(x):
     if x > 400:
         return 'high'
     elif x > 200:
         return 'medium'
     else:
         return 'low'
   
   df_energy['energy_class'] = df_energy['consumption_energy'].apply(energy_class)
   

6. Using a lambda function

   df_energy['energy_class'] = df_energy['consumption_energy'].apply(lambda x: 'high' if x > 400 else ('medium' if x > 200 else 'low'))
   

Time Comparison

From all the tests that I've done, by measuring time with time.perf_counter() (for other ways to measure time of execution see this), pandas.cut was the fastest approach.

                        method      time
0                   np.where()  0.124139
1                  np.select()  0.155879
2            numpy.vectorize()  0.452789
3                 pandas.cut()  0.046143
4  Python's built-in functions  0.138021
5              lambda function   0.19081

Notes:

• For the difference between pandas.cut and pandas.qcut see this: What is the difference between pandas.qcut and pandas.cut?

WARNING: Be careful with NaNs

Always be careful that if your data has missing values np.where may be tricky to use and may give you the wrong result inadvertently.

Consider this situation:

df['cons_ener_cat'] = np.where(df.consumption_energy > 400, 'high', 
         (np.where(df.consumption_energy < 200, 'low', 'medium')))

# if we do not use this second line, then
#  if consumption energy is missing it would be shown medium, which is WRONG.
df.loc[df.consumption_energy.isnull(), 'cons_ener_cat'] = np.nan

Alternatively, you can use one-more nested np.where for medium versus nan which would be ugly.

IMHO best way to go is pd.cut. It deals with NaNs and easy to use.

Examples:

import numpy as np
import pandas as pd
import seaborn as sns

df = sns.load_dataset('titanic')

# pd.cut
df['age_cat'] = pd.cut(df.age, [0, 20, 60, np.inf], labels=['child','medium','old'])


# manually add another line for nans
df['age_cat2'] = np.where(df.age > 60, 'old', (np.where(df.age <20, 'child', 'medium')))
df.loc[df.age.isnull(), 'age_cat'] = np.nan

# multiple nested where
df['age_cat3'] = np.where(df.age > 60, 'old',
                         (np.where(df.age <20, 'child',
                                   np.where(df.age.isnull(), np.nan, 'medium'))))

# outptus
print(df[['age','age_cat','age_cat2','age_cat3']].head(7))
    age age_cat age_cat2 age_cat3
0  22.0  medium   medium   medium
1  38.0  medium   medium   medium
2  26.0  medium   medium   medium
3  35.0  medium   medium   medium
4  35.0  medium   medium   medium
5   NaN     NaN   medium      nan
6  54.0  medium   medium   medium

Try this : Even if consumption_energy contains nulls don't worry about it.

def egy_class(x):
    '''
    This function assigns classes as per the energy consumed.
    ''' 
    return ('high' if x>400 else
             'low' if x<200 else 'medium')
chk = df_energy.consumption_energy.notnull()
df_energy['energy_class'] = df_energy.consumption_energy[chk].apply(egy_class)

I second using np.vectorize. It is much faster than np.where and also cleaner code wise. You can definitely tell the speed up with larger data sets. You can use a dictionary format for your conditionals as well as the output of those conditions.

# Vectorizing with numpy 
row_dic = {'Condition1':'high',
          'Condition2':'medium',
          'Condition3':'low',
          'Condition4':'lowest'}

def Conditions(dfSeries_element,dictionary):
    '''
    dfSeries_element is an element from df_series 
    dictionary: is the dictionary of your conditions with their outcome
    '''
    if dfSeries_element in dictionary.keys():
        return dictionary[dfSeries]

def VectorizeConditions():
    func = np.vectorize(Conditions)
    result_vector = func(df['Series'],row_dic)
    df['new_Series'] = result_vector

    # running the below function will apply multi conditional formatting to your df
VectorizeConditions()

myassign["assign3"]=np.where(myassign["points"]>90,"genius",(np.where((myassign["points"]>50) & (myassign["points"]<90),"good","bad"))

when you wanna use only "where" method but with multiple condition. we can add more condition by adding more (np.where) by the same method like we did above. and again the last two will be one you want.