How to Create a New Column Based on a Condition in Pandas

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Often you may want to create a new column in a pandas DataFrame based on some condition.

This tutorial provides several examples of how to do so using the following DataFrame:

import pandas as pd
import numpy as np

#create DataFrame
df = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],
                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],
                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],
                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})

#view DataFrame
df

	rating	points	assists	rebounds
0	90	25	5	11
1	85	20	7	8
2	82	14	7	10
3	88	16	8	6
4	94	27	5	6
5	90	20	7	9
6	76	12	6	6
7	75	15	9	10
8	87	14	9	10
9	86	19	5	7

Example 1: Create a New Column with Binary Values

The following code shows how to create a new column called ‘Good’ where the value is ‘yes’ if the points in a given row is above 20 and ‘no’ if not:

#create new column titled 'Good'
df['Good'] = np.where(df['points']>20, 'yes', 'no')

#view DataFrame 
df

        rating	points	assists	rebounds  Good
0	90	25	5	11	  yes
1	85	20	7	8	  no
2	82	14	7	10	  no
3	88	16	8	6	  no
4	94	27	5	6	  yes
5	90	20	7	9	  no
6	76	12	6	6	  no
7	75	15	9	10	  no
8	87	14	9	10	  no
9	86	19	5	7	  no

Example 2: Create a New Column with Multiple Values

The following code shows how to create a new column called ‘Good’ where the value is:

• ‘Yes’ if the points ≥ 25
• ‘Maybe’ if 15 ≤ points
• ‘No’ if points

#define function for classifying players based on points
def f(row):
    if row['points'] elif row['points'] else:
        val = 'yes'
    return val

#create new column 'Good' using the function above
df['Good'] = df.apply(f, axis=1)

#view DataFrame 
df

        rating	points	assists	rebounds Good
0	90	25	5	11	 yes
1	85	20	7	8	 maybe
2	82	14	7	10	 no
3	88	16	8	6	 maybe
4	94	27	5	6	 yes
5	90	20	7	9	 maybe
6	76	12	6	6	 no
7	75	15	9	10	 maybe
8	87	14	9	10	 no
9	86	19	5	7	 maybe

Example 3: Create a New Column Based on Comparison with Existing Column

The following code shows how to create a new column called ‘assist_more’ where the value is:

• ‘Yes’ if assists > rebounds.
• ‘No’ otherwise.

#create new column titled 'assist_more'
df['assist_more'] = np.where(df['assists']>df['rebounds'], 'yes', 'no')

#view DataFrame 
df

        rating	points	assists	rebounds assist_more
0	90	25	5	11	 no
1	85	20	7	8	 no
2	82	14	7	10	 no
3	88	16	8	6	 yes
4	94	27	5	6	 no
5	90	20	7	9	 no
6	76	12	6	6	 no
7	75	15	9	10	 no
8	87	14	9	10	 no
9	86	19	5	7	 no

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