CODEWITHSUNDEEP
pythonpandasmaskcategorical
noelle
noelle

Reputation: 23

Add categorical variable based on conditional selections / dataframe masks

I made three conditional selections on my dataframe. So lets say:

final_df[(final_df['acceptance_advice'] == 'standard') & (final_df['acceptance'] == 'ok')]
final_df[(final_df['acceptance_advice'] == 'not accepted') & (final_df['acceptance'] == 'ok')]
final_df[(final_df['acceptance_advice'] == 'postponed') & (final_df['acceptance'] == 'declined')]

Now I want to add a categorical variable (the class I am going to use for prediction) from each of these selections. So let's say: the first selection should be class 1 and the second should class 2 and the third selection should be class 3.

I have tried:

cat_1 = final_df[(final_df['acceptance_advice'] == 'standard') & (final_df['acceptance'] == 'ok')]
cat_2 = final_df[(final_df['acceptance_advice'] == 'not accepted') & (final_df['acceptance'] == 'ok')]
cat_3 = final_df[(final_df['acceptance_advice'] == 'postponed') & (final_df['acceptance'] == 'declined')]

final_df['class'] = (cat_1 | cat_2 | cat_3).astype(int)

But it only worked on two categories (e.g. 0 and 1) but not on three.

final_df looks something like this:

id feature1 feature2 acceptance_advice acceptance
some value some value some value some value some value
some value some value some value some value some value
some value some value some value some value some value
some value some value some value some value some value

I want it to look like this:

id feature1 feature2 acceptance_advice acceptance class
some value some value some value some value some value 1
some value some value some value some value some value 2
some value some value some value some value some value 1
some value some value some value some value some value 3

I want to add a column class, which should be the class to be predicted.

Upvotes: 2

Views: 812

Answers (3)

Nikita Shabankin
Nikita Shabankin

Reputation: 634

You may nest np.where() several times. If these three categories cover up all your values, you may nest just two:

import numpy as np

final_df['class'] = np.where(
    # condition for class 1
    (final_df['acceptance_advice'] == 'standard') & 
    (final_df['acceptance'] == 'ok'),  
    # set value for first condition, here class 1 is set
    1, 
    # nested function in place of the third argument 
    np.where(
        # condition for class 2
        (final_df['acceptance_advice'] == 'not accepted') & 
        (final_df['acceptance'] == 'ok'),
        # set value for second condition, here class 2 is set
        2,
        # the rest of values go as class 3
        3))

Upvotes: 1

user22
user22

Reputation: 153

You can test the following to add a class column -

def set_class(df):
    
    if (df['acceptance_advice'] == 'standard') & (df['acceptance'] == 'ok'):
        return "1"
    elif (df['acceptance_advice'] == 'not accepted') & (df['acceptance'] == 'ok'):
        return "2"
    elif (df['acceptance_advice'] == 'postponed') & (df['acceptance'] == 'declined'):
        return "3"

df['class'] = df.apply(set_class, axis = 1)

Upvotes: 0

Adi OS
Adi OS

Reputation: 162

Maybe like this?

import pandas as pd

# Data thing -  we can skip it
id = [0,1,2,3,4,5]
acceptance_advice = ['standard','not accepted','postponed','standard','not accepted','postponed']
acceptance = ['ok','ok','declined','ok','ok','declined']

data = [id, acceptance_advice, acceptance]
df = pd.DataFrame(columns= ['id','acceptance_advice', 'acceptance','class'])
df['id'] = id
df['acceptance_advice'] = acceptance_advice
df['acceptance'] = acceptance

# ============================== Process ==================================

df = df.reset_index()  # make sure indexes pair with number of rows (use one time)
label = []

for index, row in df.iterrows():
    # print(row['acceptance_advice'], row['acceptance'])
    if (row['acceptance_advice'] == 'standard') & (row['acceptance'] == 'ok'):
        label.append(1)
    elif (row['acceptance_advice'] == 'not accepted') & (row['acceptance'] == 'ok'):
        label.append(2)
    elif (row['acceptance_advice'] == 'postponed') & (row['acceptance'] == 'declined'):
        label.append(3)


df['class'] = label

enter image description here

Because you didn't provide us real dataset, i can only set those dataset into the best case from your question. Actually, it have to be at least 6 possibility class (3 from accep_advice, and 2 from acceptance -> 3x2 = 6), That's the reason i use elif in the last condition (to make it strict).

Upvotes: 0

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