Python: Add column to dataframe in Pandas ( based on other column or list or default value)

In this tutorial, we are going to discuss different ways to add columns to the dataframe in pandas. Moreover, you can have an idea about the Pandas Add Column, Adding a new column to the existing DataFrame in Pandas and many more from the below explained various methods.

Pandas Add Column

Pandas is one such data analytics library created explicitly for Python to implement data manipulation and data analysis. The Pandas library made of specific data structures and operations to deal with numerical tables, analyzing data, and work with time series.

Basically, there are three ways to add columns to pandas i.e., Using [] operator, using assign() function & using insert().

We will discuss it all one by one.

First, let’s create a dataframe object,

import pandas as pd
# List of Tuples
students = [('Rakesh', 34, 'Agra', 'India'),
            ('Rekha', 30, 'Pune', 'India'),
            ('Suhail', 31, 'Mumbai', 'India'),
            ('Neelam', 32, 'Bangalore', 'India'),
            ('Jay', 16, 'Bengal', 'India'),
            ('Mahak', 17, 'Varanasi', 'India')]
# Create a DataFrame object
df_obj = pd.DataFrame(students,
                      columns=['Name', 'Age', 'City', 'Country'],
                      index=['a', 'b', 'c', 'd', 'e', 'f'])
print(df_obj )

Output:

    Name    Age  City        Country
a  Rakesh  34     Agra        India
b  Rekha   30     Pune        India
c  Suhail    31    Mumbai   India
d  Neelam 32   Bangalore India
e  Jay         16   Bengal      India
f  Mahak    17  Varanasi     India

Do Check:

Add column to dataframe in pandas using [] operator

import pandas as pd
# List of Tuples
students = [('Rakesh', 34, 'Agra', 'India'),
            ('Rekha', 30, 'Pune', 'India'),
            ('Suhail', 31, 'Mumbai', 'India'),
            ('Neelam', 32, 'Bangalore', 'India'),
            ('Jay', 16, 'Bengal', 'India'),
            ('Mahak', 17, 'Varanasi', 'India')]
# Create a DataFrame object
df_obj = pd.DataFrame(students,
                      columns=['Name', 'Age', 'City', 'Country'],
                      index=['a', 'b', 'c', 'd', 'e', 'f'])

# Add column with Name Score
df_obj['Score'] = [10, 20, 45, 33, 22, 11]
print(df_obj )

Output:

      Name     Age   City        Country   Score
a    Rakesh    34    Agra          India      10
b    Rekha     30    Pune          India      20
c    Suhail     31     Mumbai    India      45
d    Neelam  32    Bangalore  India      33
e    Jay         16     Bengal       India      22
f    Mahak    17    Varanasi     India       11

So in the above example, you have seen we have added one extra column ‘score’ in our dataframe. So in this, we add a new column to Dataframe with Values in the list. In the above dataframe, there is no column name ‘score’ that’s why it added if there is any column with the same name that already exists then it will replace all its values.

Add new column to DataFrame with same default value

import pandas as pd
# List of Tuples
students = [('Rakesh', 34, 'Agra', 'India'),
            ('Rekha', 30, 'Pune', 'India'),
            ('Suhail', 31, 'Mumbai', 'India'),
            ('Neelam', 32, 'Bangalore', 'India'),
            ('Jay', 16, 'Bengal', 'India'),
            ('Mahak', 17, 'Varanasi', 'India')]
# Create a DataFrame object
df_obj = pd.DataFrame(students,
                      columns=['Name', 'Age', 'City', 'Country'],
                      index=['a', 'b', 'c', 'd', 'e', 'f'])

df_obj['Total'] = 100
print(df_obj)

Output:

         Name    Age    City     Country      Total
a       Rakesh   34    Agra       India          100
b       Rekha    30    Pune       India          100
c       Suhail     31   Mumbai  India          100
d       Neelam  32  Bangalore India        100
e       Jay          16  Bengal       India        100
f       Mahak     17 Varanasi     India        100

So in the above example, we have added a new column ‘Total’ with the same value of 100 in each index.

Add column based on another column

Let’s add a new column ‘Percentage‘ where entrance at each index will be added by the values in other columns at that index i.e.,

df_obj['Percentage'] = (df_obj['Marks'] / df_obj['Total']) * 100
df_obj

Output:

    Name  Age       City    Country  Marks  Total  Percentage
a   jack   34     Sydeny  Australia     10     50        20.0
b   Riti   30      Delhi      India     20     50        40.0
c  Vikas   31     Mumbai      India     45     50        90.0
d  Neelu   32  Bangalore      India     33     50        66.0
e   John   16   New York         US     22     50        44.0
f   Mike   17  las vegas         US     11     50        22.0

Append column to dataFrame using assign() function

So for this, we are going to use the same dataframe which we have created in starting.

Syntax:

DataFrame.assign(**kwargs)

Let’s add columns in DataFrame using assign().

import pandas as pd
# List of Tuples
students = [('Rakesh', 34, 'Agra', 'India'),
            ('Rekha', 30, 'Pune', 'India'),
            ('Suhail', 31, 'Mumbai', 'India'),
            ('Neelam', 32, 'Bangalore', 'India'),
            ('Jay', 16, 'Bengal', 'India'),
            ('Mahak', 17, 'Varanasi', 'India')]
# Create a DataFrame object
df_obj = pd.DataFrame(students,
                      columns=['Name', 'Age', 'City', 'Country'],
                      index=['a', 'b', 'c', 'd', 'e', 'f'])
mod_fd = df_obj.assign(Marks=[10, 20, 45, 33, 22, 11])
print(mod_fd)

Output:

Add-a-column-using-assign

It will return a new dataframe with a new column ‘Marks’ in that Dataframe. Values provided in the list will be used as column values.

Add column in DataFrame based on other column using lambda function

In this method using two existing columns i.e, score and total value we are going to create a new column i.e..’ percentage’.

import pandas as pd
# List of Tuples
students = [('Rakesh', 34, 'Agra', 'India'),
            ('Rekha', 30, 'Pune', 'India'),
            ('Suhail', 31, 'Mumbai', 'India'),
            ('Neelam', 32, 'Bangalore', 'India'),
            ('Jay', 16, 'Bengal', 'India'),
            ('Mahak', 17, 'Varanasi', 'India')]
# Create a DataFrame object
df_obj = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Country'],
                      index=['a', 'b', 'c', 'd', 'e', 'f'])
df_obj['Score'] = [10, 20, 45, 33, 22, 11]
df_obj['Total'] = 100
df_obj = df_obj.assign(Percentage=lambda x: (x['Score'] / x['Total']) * 100)
print(df_obj)

Output:

Add-column-based-on-another-column

Add new column to Dataframe using insert()

import pandas as pd
# List of Tuples
students = [('Rakesh', 34, 'Agra', 'India'),
            ('Rekha', 30, 'Pune', 'India'),
            ('Suhail', 31, 'Mumbai', 'India'),
            ('Neelam', 32, 'Bangalore', 'India'),
            ('Jay', 16, 'Bengal', 'India'),
            ('Mahak', 17, 'Varanasi', 'India')]
# Create a DataFrame object
df_obj = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Country'],
                      index=['a', 'b', 'c', 'd', 'e', 'f'])
# Insert column at the 2nd position of Dataframe
df_obj.insert(2, "Marks", [10, 20, 45, 33, 22, 11], True)
print(df_obj)

Output:

add-a-column-using-insert

 

In other examples, we have added a new column at the end of the dataframe, but in the above example, we insert a new column in between the other columns of the dataframe, then we can use the insert() function.

Add a column to Dataframe by dictionary

import pandas as pd
# List of Tuples
students = [('Rakesh', 34, 'Agra', 'India'),
            ('Rekha', 30, 'Pune', 'India'),
            ('Suhail', 31, 'Mumbai', 'India'),
            ('Neelam', 32, 'Bangalore', 'India'),
            ('Jay', 16, 'Bengal', 'India'),
            ('Mahak', 17, 'Varanasi', 'India')]
# Create a DataFrame object
df_obj = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Country'],
                      index=['a', 'b', 'c', 'd', 'e', 'f'])
ids = [11, 12, 13, 14, 15, 16]
# Provide 'ID' as the column name and for values provide dictionary
df_obj['ID'] = dict(zip(ids, df_obj['Name']))
print(df_obj)

Output:

Add-a-column-using-dictionary

Want to expert in the python programming language? Exploring Python Data Analysis using Pandas tutorial changes your knowledge from basic to advance level in python concepts.

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