Add Column in a Pandas Data Frame

How to add a column in an existing data frame using Pandas?

In this tutorial, we will show you different ways of adding columns in the Pandas data frame.

For the examples, we have the following data frame created:

Code:

import pandas as pd


inflation_data = {

"Year": [2015, 2016, 2016, 2017],

"Inflation": ['1.75%', '2%', '3%', '3.5%']

}


#Creating a Data frame

df = pd.DataFrame(inflation_data)


#Display Data Frame

print(df)

Output:

   Year Inflation

0  2015     3.25%

1  2016     2.15%

2  2016        3%

3  2017      1.5%

Using the insert method of the data frame

A duplicate column name can also be added if allow_duplicate is set to True.

See the example below where we will add a new column (“Difference”) in the above-created data frame.

Code:

import pandas as pd

inflation_data = {

"Year": [2015, 2016, 2016, 2017],

"Inflation": ['1.75%', '2%', '3%', '3.5%']

}

#Creating a Data frame

df = pd.DataFrame(inflation_data)

df.insert(1, "Difference", [0.75, 0.25,1,0.5])


#Display DF after adding a column

print(df)

Output:

Second Way – want to add a column at the end of the existing Data Frame?

This is the easiest way of adding a new column along with data in the existing data frame. However, the drawback is that it can add a column at the end.

Have a look at the example below where we will add the “Difference” column in the above-created DF.

Code:

import pandas as pd

inflation_data = {

"Year": [2015, 2016, 2016, 2017],

"Inflation": ['1.75%', '2%', '3%', '3.5%']

}


#Creating a Data frame

df = pd.DataFrame(inflation_data)


#Adding a new column with values

df["Difference"] = [0.75, 0.25,1,0.5]


#Display DF after adding a column

print(df)

Result:

You can see the newly added column is placed at the end of DF.

Third way – Using a dictionary/map for adding a column

You may also use a Python dictionary and map combination for adding a new column in the existing data frame.

Code:

import pandas as pd

#Creating a dictionary


tel_dir = {'Name': ['Mike','Haynes','Mina'],

'Phone No':[123445689,45678910, 635363636]}


#Create data frame based on dict

df_dir = pd.DataFrame.from_dict(tel_dir)

email = {'Mike':'mike@abc.com','Haynes':'haynes@abc.com','Mina':'mina@abc.com'}

#Adding a column to DF by passing dict to map

df_dir['Email'] = df_dir['Name'].map(email)


#Display data frame

print(df_dir)

Output:

How did it work?

• In that case, we passed the dictionary to the map
• It will perform a lookup and return the associated value for that key.
• Alternatively, we used a column from the existing DF and passed a new column’s value to it.