Add one or multiple columns to Pandas DataFrame

Pandas

In this post, we are going to understand how to add one or multiple columns to Pandas dataframe by using the [] operator and built-in methods assign(), insert() method with the help of examples.

1. Using [] opertaor to Add column to DataFrame


In this example we are adding new ‘city’ column Using [] operator in dataframe.To Add column to DataFrame Using [] operator.we pass column name between [] operator and assign list of column values the code for this is df[‘city’] = [‘WA’, ‘CA’,’NY’]

Program Example

import pandas as pd
import numpy as np
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':[100,98,99],
    'Subject': ['Math','Math','Math']
}
 
df = pd.DataFrame(Student_dict)




df['city'] = ['WA', 'CA','NY']
print(df)


Output

   Name  Marks Subject city
0  Jack    100    Math   WA
1  Rack     98    Math   CA
2   Max     99    Math   NY

2. Add column by a dictionary in dataframe


In this example, we are adding a column city by using the dict() and zip() method. The list of city values is the zip to the existing column ‘Name’ to create a dictionary.

Program Example

import pandas as pd
import numpy as np
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':[100,98,99],
    'Subject': ['Math','Math','Math']
}
 
df = pd.DataFrame(Student_dict)

citys = ['WA', 'CA','NY']


df['city'] = dict(zip(citys, df['Name']))
print(df)


Output

   Name  Marks Subject city
0  Jack    100    Math   WA
1  Rack     98    Math   CA
2   Max     99    Math   NY

3. add column with same deafult value


To add a column with a default value in each row. Let us understand with example how to achieve this. In this example, we have added an age column with a default value of 20 in each row.

Program Example

import pandas as pd
import numpy as np
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':[100,98,99],
    'Subject': ['Math','Math','Math']
}
 
df = pd.DataFrame(Student_dict)



df['age'] = 20
print(df)

Output

   Name  Marks Subject  age
0  Jack    100    Math   20
1  Rack     98    Math   20
2   Max     99    Math   20

4. insert() method to insert column in between columns


Sometimes we want to add a new in-between another column instead of the end. By using the insert() method a new column can be added.

Program Example

import pandas as pd
import numpy as np
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':[100,98,99],
    'Subject': ['Math','Math','Math']
}
 
df = pd.DataFrame(Student_dict)

df.insert(2, "city", ['WA', 'CA','NY'], True)


print(df)

Output

   Name  Marks city Subject
0  Jack    100   WA    Math
1  Rack     98   CA    Math
2   Max     99   NY    Math

5. Add column to Dataframe using assign() method


The assign() function assigns a new column to the dataframe and returns a new object with all the existing columns in addition to the new one. if existing columns is re-assign will be overwritten.

Syntax

dataframe.assign(***kwargs)

Parameters

***kwargs: The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns.

Program Example

import pandas as pd
import numpy as np
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':[100,98,99],
    'Subject': ['Math','Math','Math']
}
 
df = pd.DataFrame(Student_dict)



result_df = df.assign(city = ['WA', 'CA','NY'])
print(result_df)

Output

   Name  Marks Subject city
0  Jack    100    Math   WA
1  Rack     98    Math   CA
2   Max     99    Math   NY

6.assign() method to Add mutiple columns


In this example, we are using the assign() method to add multiple columns city, age, grade to the dataframe.

Program Example

import pandas as pd
import numpy as np
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':[100,98,99],
    'Subject': ['Math','Math','Math']
}
 
df = pd.DataFrame(Student_dict)



result_df = df.assign(city = ['WA', 'CA','NY'],Age=[18,18,20],grade=['A','B','B'])
print(result_df)




Output

   Name  Marks Subject city  Age grade
0  Jack    100    Math   WA   18     A
1  Rack     98    Math   CA   18     B
2   Max     99    Math   NY   20     B

6. Add new column based on value of other column


In this example, we are adding the ‘grade’ column based on the ‘Marks’ column value.

We have to define a custom function add_column(df) that accepts a dataframe as an argument. In this, we are checking condition where condition marks == 100 then the grade is ‘A’ and else ‘B’

Program Example

import pandas as pd
import numpy as np


def add_column(df):
  res_df = df.assign(grade=np.where(df['Marks']== 100, 'A', 'B'))  
  return res_df
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':[100,98,99],
    'Subject': ['Math','Math','Math']
}
 
df = pd.DataFrame(Student_dict)

result_df = add_column(df)
 
print(result_df) 

Output

  Name  Marks Subject grade
0  Jack    100    Math     A
1  Rack     98    Math     B
2   Max     99    Math     B

Summary

In this post we have learned multiple ways of how to add one or multiple columns to Pandas DataFrame using [] operator, assign(), insert() method with example