Pandas: Develop Brandnew Column The usage of A couple of If Else Situations

You’ll be able to virtue refer to syntax to manufacture a brandnew column in a pandas DataFrame the usage of more than one if else statuses:

#outline statuses
statuses = [
    (df['column1'] == 'A') & (df['column2'] < 20),
    (df['column1'] == 'A') & (df['column2'] >= 20),
    (df['column1'] == 'B') & (df['column2'] < 20),
    (df['column1'] == 'B') & (df['column2'] >= 20)
]

#outline effects
effects = ['result1', 'result2', 'result3', 'result4']

#manufacture brandnew column in line with statuses in column1 and column2
df['new_column'] = np.make a choice(statuses, effects)

This actual instance creates a column known as new_column whose values are in line with the values in column1 and column2 within the DataFrame.

Please see instance displays find out how to virtue this syntax in apply.

Instance: Develop Brandnew Column The usage of A couple of If Else Situations in Pandas

Think we now have refer to pandas DataFrame that accommodates details about diverse basketball gamers:

import pandas as pd

#manufacture DataFrame
df = pd.DataFrame({'workforce': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'issues': [15, 18, 22, 24, 12, 17, 20, 28]})

#view DataFrame
print(df)

  workforce  issues
0    A      15
1    A      18
2    A      22
3    A      24
4    B      12
5    B      17
6    B      20
7    B      28

Now think we wish to manufacture a brandnew column known as elegance that classifies each and every participant into one among refer to 4 teams:

• Bad_A if workforce is A and issues < 20
• Good_A if workforce is A and issues ≥ 20
• Bad_B if workforce is B and issues < 20
• Good_B if workforce is B and issues ≥ 20

We will be able to virtue refer to syntax to take action:

import numpy as np

#outline statuses
statuses = [
    (df['team'] == 'A') & (df['points'] < 20),
    (df['team'] == 'A') & (df['points'] >= 20),
    (df['team'] == 'B') & (df['points'] < 20),
    (df['team'] == 'B') & (df['points'] >= 20)
]

#outline effects
effects = ['Bad_A', 'Good_A', 'Bad_B', 'Good_B']

#manufacture brandnew column in line with statuses in column1 and column2
df['class'] = np.make a choice(statuses, effects)

#view up to date DataFrame
print(df)

  workforce  issues   elegance
0    A      15   Bad_A
1    A      18   Bad_A
2    A      22  Good_A
3    A      24  Good_A
4    B      12   Bad_B
5    B      17   Bad_B
6    B      20  Good_B
7    B      28  Good_B

The brandnew column known as elegance shows the classification of each and every participant in line with the values within the workforce and issues columns.

Observe: You’ll be able to in finding your entire documentation for the NumPy make a choice() serve as right here.

Supplementary Assets

Please see tutorials give an explanation for find out how to carry out alternative usual duties in pandas:

Pandas: The right way to Develop Boolean Column In accordance with Status
Pandas: The right way to Rely Values in Column with Status
Pandas: The right way to Utility Groupby and Rely with Status