How to create a new column in Pandas – Python

Let us learn to create a new column in pandas!

Pandas is an open-source data manipulation module. It is built on top of the Numpy Library. Pandas has a lot of benefits- Analysis becomes easy because of the flexibility of Python. It also is efficient when working with big data so it is a great Python module for data science. There are two important data types we use in pandas- Series and DataFrames. I would recommend you to read the documentation before you get started. Here is the link.

But first, let’s create a DataFrame using a dictionary. You can also add a new column to any of your already existing DataFrame.

import pandas as pd
Timetable={ 'Day':['Monday','Tuesday','Wednesday'], 
           'Class':['Statistics','Economics','Finance'], 
           'Faculty':['Rahul','Rohit','Ram'] 
           } 
df=pd.DataFrame(Timetable) 
print(df)

Day

Class

Faculty

0

Monday

Statistics

Rahul

1

Tuesday

Economics

Rohit

2

Wednesday

Finance

Ram

There are various methods to add a new column in Pandas. One approach is by accessing the DataFrame and then calling the new column.

class_hour=['9am','8am','2pm']
df['class_hour']=class_hour
print(df)

Day

Class

Faculty

class_hour

0

Monday

Statistics

Rahul

9am

1

Tuesday

Economics

Rohit

8am

2

Wednesday

Finance

Ram

2pm

Another method is using the insert() function:
Syntax for the insert function is -> DataFrame.insert(loc, column, value, allow_duplicates=False)
choose the location where you would want your new column to be added. Give a column name of your choice followed by the values of the new column to be added.

df.insert(loc=3,column='no_of_hours',value=[2,1,3]) 
print(df)

Day

Class

Faculty

no_of_hours

class_hour

0

Monday

Statistics

Rahul

2

9am

1

Tuesday

Economics

Rohit

1

8am

2

Wednesday

Finance

Ram

3

2pm

There is another function called assign where we can assign values to our new column

df=df.assign(no_of_students=[35,23,50])
print(df)

These are some of the ways you can create a new column in pandas to the already existing DataFrame.