Adding new column to existing DataFrame in Pandas
Pandas Data Frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. It can be created using python dict, list and series etc. In this article we will see how to add a new column to an existing data frame.
So first let's create a data frame using pandas series. In the below example we are converting a pandas series to a Data Frame of one column, giving it a column name Month_no.
Example
Live Demo
import pandas as pd
s = pd.Series([6,8,3,1,12])
df = pd.DataFrame(s,columns=['Month_No'])
print (df)
Output
Running the above code gives us the following result:
Month_No
0 6
1 8
2 3
3 1
4 12
using insert() function
We can use the insert() function of pandas which will insert the column at the position specified by its index. Below we add No of Days in a month as a column to the existing pandas DataFrame at index position 1.
Example
Live Demo
import pandas as pd
s = pd.Series([6,8,3,1,12])
df = pd.DataFrame(s,columns=['Month_No'])
# Insert the new column at position 1.
df.insert(1,"No_of_days",[30,31,31,31,31],True)
print (df)
Output
Running the above code gives us the following result −
Month_No No_of_days
0 6 30
1 8 31
2 3 31
3 1 31
4 12 31
Using assign() function
The assign() function
Example
Live Demo
import pandas as pd
s = pd.Series([6,8,3,1,12])
df = pd.DataFrame(s,columns=['Month_No'])
# Insert a column at the end
df = df.assign(No_of_days = [30,31,31,31,31])
print (df)
Output
Running the above code gives us the following result −
Month_No No_of_days
0 6 30
1 8 31
2 3 31
3 1 31
4 12 31
Published on 23-Aug-2019 08:59:21
