Adding a new column in an already created dataframe is quite easy. Adding a new column is actually required to process the data of dataframe created earlier. For that purpose, we can process the existing data and make a separate column to store the data. The simplest way to add a new column along with data is by creating a new column and assigning new values to it. For example:
Python3
import pandas as pd# Creating new dataframeinitial_data = {'First_name': ['Ram', 'Mohan', 'Tina', 'Jeetu', 'Meera'], 'Last_name': ['Kumar', 'Sharma', 'Ali', 'Gandhi', 'Kumari'], 'Marks': [12, 52, 36, 85, 23] }df = pd.DataFrame(initial_data, columns = ['First_name', 'Last_name', 'Marks'])df['Results']=['Fail','Pass','Pass','Pass','Fail']df |
Output:
First_name Last_name Marks Results 0 Ram Kumar 12 Fail 1 Mohan Sharma 52 Pass 2 Tina Ali 36 Pass 3 Jeetu Gandhi 85 Pass 4 Meera Kumari 23 Fail
But when the column requires some computation or we need to add new values based on values in some column, then we can use for loop. Let’s see how to create a column in pandas dataframe using for loop. In the given example a new column Result is created on the basis of marks in Marks column of the existing dataframe df. If the value in Marks column is greater than and equal to 33, then the value in the new column Result will be ‘Pass’ and if the value in Marks column is less than 0 and greater than 100 then value inserted in ‘Result’ column will be ‘Invalid ‘ otherwise it should add value as ‘Fail’.
Example#2
Python3
# importing pandasimport pandas as pd# Creating new dataframeinitial_data = {'First_name': ['Ram', 'Mohan', 'Tina', 'Jeetu', 'Meera'], 'Last_name': ['Kumar', 'Sharma', 'Ali', 'Gandhi', 'Kumari'], 'Marks': [12, 52, 36, 85, 23] }df = pd.DataFrame(initial_data, columns = ['First_name', 'Last_name', 'Marks'])# Generate result using pandasresult = []for value in df["Marks"]: if value >= 33: result.append("Pass") elif value < 0 and value > 100: result.append("Invalid") else: result.append("Fail") df["Result"] = result print(df) |
First_name Last_name Marks Result 0 Ram Kumar 12 Fail 1 Mohan Sharma 52 Pass 2 Tina Ali 36 Pass 3 Jeetu Gandhi 85 Pass 4 Meera Kumari 23 Fail
Example#3
We can also use List comprehension to create a new column.
Python3
df['Results'] = ['Pass' if m>=33 else 'Fail' for m in df['Marks']]df |
Output:
First_name Last_name Marks Results 0 Ram Kumar 12 Fail 1 Mohan Sharma 52 Pass 2 Tina Ali 36 Pass 3 Jeetu Gandhi 85 Pass 4 Meera Kumari 23 Fail
