Let’s see how to create a column in pandas dataframe using for loop. Such operation is needed sometimes when we need to process the data of dataframe created earlier for that purpose, we need this type of computation so we can process the existing data and make a separate column to store the data.
It can be easily done by for-loop. The data of column can be taken from the existing Dataframe or any of the array.
# importing libraries import pandas as pd import numpy as np raw_Data = {'Voter_name': ['Geek1', 'Geek2', 'Geek3', 'Geek4', 'Geek5', 'Geek6', 'Geek7', 'Geek8'], 'Voter_age': [15, 23, 25, 9, 67, 54, 42, np.NaN]} df = pd.DataFrame(raw_Data, columns = ['Voter_name', 'Voter_age']) # //DataFrame will look like # # Voter_name Voter_age # Geek1 15 # Geek2 23 # Geek3 25 # Geek4 09 # Geek5 67 # Geek6 54 # Geek7 42 # Geek8 not a number eligible = [] # For each row in the column for age in df['Voter_age']: if age >= 18: # if Voter eligible eligible.append('Yes') elif age < 18: # if voter is not eligible eligible.append("No") else: eligible.append("Not Sure") # Create a column from the list df['Voter'] = eligible print(df) |
Output:
Voter_name Voter_age Voter
0 Geek1 15 No
1 Geek2 23 Yes
2 Geek3 25 Yes
3 Geek4 9 No
4 Geek5 67 Yes
5 Geek6 54 Yes
6 Geek7 42 Yes
7 Geek8 NaN Not Sure
