How to Add Multiple Columns to Pandas DataFrame

---

You can use the following methods to add multiple columns to a pandas DataFrame:

Method 1: Add Multiple Columns that Each Contain One Value

df[['new1', 'new2', 'new3']] = pd.DataFrame([[4, 'hey', np.nan]], index=df.index)

Method 2: Add Multiple Columns that Each Contain Multiple Values

df['new1'] = [1, 5, 5, 4, 3, 6]
df['new2'] = ['hi', 'hey', 'hey', 'hey', 'hello', 'yo']
df['new3'] = [12, 4, 4, 3, 6, 7]

The following examples show how to use each method with the following pandas DataFrame:

import pandas as pd
import numpy as np

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F'],
                   'points': [18, 22, 19, 14, 14, 11],
                   'assists': [5, 7, 7, 9, 12, 9]})

#view DataFrame
df

        team	points	assists
0	A	18	5
1	B	22	7
2	C	19	7
3	D	14	9
4	E	14	12
5	F	11	9

Method 1: Add Multiple Columns that Each Contain One Value

The following code shows how to add three new columns to the pandas DataFrame in which each new column only contains one value:

#add three new columns to DataFrame
df[['new1', 'new2', 'new3']] = pd.DataFrame([[4, 'hey', np.nan]], index=df.index)

#view updated DataFrame
df

        team	points	assists	new1	new2	new3
0	A	18	5	4	hey	NaN
1	B	22	7	4	hey	NaN
2	C	19	7	4	hey	NaN
3	D	14	9	4	hey	NaN
4	E	14	12	4	hey	NaN
5	F	11	9	4	hey	NaN

Notice that three new columns – new1, new2, and new3 – have been added to the DataFrame.

Also notice that each new column contains only one specific value.

Method 2: Add Multiple Columns that Each Contain Multiple Values

The following code shows how to add three new columns to the pandas DataFrame in which each new column contains multiple values:

#add three new columns to DataFrame
df['new1'] = [1, 5, 5, 4, 3, 6]
df['new2'] = ['hi', 'hey', 'hey', 'hey', 'hello', 'yo']
df['new3'] = [12, 4, 4, 3, 6, 7]

#view updated DataFrame
df

	team	points	assists	new1	new2	new3
0	A	18	5	1	hi	12
1	B	22	7	5	hey	4
2	C	19	7	5	hey	4
3	D	14	9	4	hey	3
4	E	14	12	3	hello	6
5	F	11	9	6	yo	7

Notice that three new columns – new1, new2, and new3 – have been added to the DataFrame.

Also notice that each new column contains multiple values.

Additional Resources

The following tutorials explain how to perform other common operations in pandas:

How to Sort by Multiple Columns in Pandas
How to Check if Column Exists in Pandas
How to Rename Columns in Pandas