Numpy "where" with multiple conditions

I try to add a new column "energy_class" to a dataframe "df_energy" which it contains the string "high" if the "consumption_energy" value > 400, "medium" if the "consumption_energy" value is between 200 and 400, and "low" if the "consumption_energy" value is under 200. I try to use np.where from numpy, but I see that numpy.where(condition[, x, y]) treat only two condition not 3 like in my case.

Any idea to help me please?

Thank you in advance

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]

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

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.