While working with data in Pandas, we perform a vast array of operations on the data to get the data in the desired form. One of these operations could be that we want to create new columns in the DataFrame based on the result of some operations on the existing columns in the DataFrame. Let’s discuss several ways in which we can do that.
Given a Dataframe containing data about an event, we would like to create a new column called ‘Discounted_Price’, which is calculated after applying a discount of 10% on the Ticket price.
Example 1: We can use DataFrame.apply() function to achieve this task.
Python3
# importing pandas as pdimport pandas as pd# Creating the DataFramedf = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Event':['Music', 'Poetry', 'Theatre', 'Comedy'], 'Cost':[10000, 5000, 15000, 2000]})# Print the dataframeprint(df) |
Output :
Date Event Cost
0 10/2/2011 Music 10000
1 11/2/2011 Poetry 5000
2 12/2/2011 Theatre 15000
3 13/2/2011 Comedy 2000
Now we will create a new column called ‘Discounted_Price’ after applying a 10% discount on the existing ‘Cost’ column.
Python3
# using apply function to create a new columndf['Discounted_Price'] = df.apply(lambda row: row.Cost - (row.Cost * 0.1), axis = 1)# Print the DataFrame after addition# of new columnprint(df) |
Output :
Date Event Cost Discounted_Price
0 10/2/2011 Music 10000 9000.0
1 11/2/2011 Poetry 5000 4500.0
2 12/2/2011 Theatre 15000 13500.0
3 13/2/2011 Comedy 2000 1800.0
Example 2: We can achieve the same result by directly performing the required operation on the desired column element-wise.
Python3
import pandas as pd# Creating the DataFramedf = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Event':['Music', 'Poetry', 'Theatre', 'Comedy'], 'Cost':[10000, 5000, 15000, 2000]})# Create a new column 'Discounted_Price' after applying# 10% discount on the existing 'Cost' column.# create a new columndf['Discounted_Price'] = df['Cost'] - (0.1 * df['Cost'])# Print the DataFrame after# addition of new columnprint(df) |
Output :
Date Event Cost Discounted_Price
0 10/2/2011 Music 10000 9000.0
1 11/2/2011 Poetry 5000 4500.0
2 12/2/2011 Theatre 15000 13500.0
3 13/2/2011 Comedy 2000 1800.0
Example 3: Using DataFrame.map() function to create new column from existing column using a mapping function
We will create a dataframe with some sample data:
Python3
data = { "name": ["John", "Ted", "Dev", "Brad", "Rex", "Smith", "Samuel", "David"], "salary": [10000, 20000, 50000, 45500, 19800, 95000, 5000, 50000]}# create dataframe from data dictionarydf = pd.DataFrame(data)# print the dataframedisplay(df.head()) |
Output:
name salary
0 John 10000
1 Ted 20000
2 Dev 50000
3 Brad 45500
4 Rex 19800
Now, we will create a mapping function (salary_stats) and use the DataFrame.map() function to create a new column from an existing column
Python3
def salary_stats(value): if value < 10000: return "very low" if 10000 <= value < 25000: return "low" elif 25000 <= value < 40000: return "average" elif 40000 <= value < 50000: return "better" elif value >= 50000: return "very good"df['salary_stats'] = df['salary'].map(salary_stats)display(df.head()) |
Output:
name salary salary_stats
0 John 10000 low
1 Ted 20000 low
2 Dev 50000 very good
3 Brad 45500 better
4 Rex 19800 low
Explanation: Here we have used pandas DataFrame.map() function to map each value to a string based on our defined mapping logic. The resultant series of values is assigned to a new column, “salary_stats”.
