In this article, we will discuss different ways to add a new column in DataFrame with incremental values or numbers.
Table Of Contents
Preparing DataSet
First we will create a DataFrame from list of tuples i.e.
import pandas as pd # List of Tuples employees= [('Mark', 'US', 'Tech', 5), ('Riti', 'India', 'Tech' , 7), ('Shanky', 'India', 'PMO' , 2), ('Shreya', 'India', 'Design' , 2), ('Aadi', 'US', 'Tech', 11), ('Sim', 'US', 'Tech', 4)] # Create a DataFrame object from list of tuples df = pd.DataFrame(employees, columns=['Name', 'Location', 'Team', 'Experience']) print(df)
Output:
Name Location Team Experience 0 Mark US Tech 5 1 Riti India Tech 7 2 Shanky India PMO 2 3 Shreya India Design 2 4 Aadi US Tech 11 5 Sim US Tech 4
Now, suppose we want to add a new column in this DataFrame ‘Age’, and this column should contain incremental values like 31, 32, 33, 34, 35 etc. Let’s see how to do that.
Frequently Asked:
Add new column with incremental values in Pandas DataFrame
We can call the range() function of Python, to give a range of numbers from start
till end
. Like, start will be 30 in our case, and end will be 30 + N. Where, N is the number of rows in the DataFrame. So, it will return a sequence of numbers from 31 till 31 + N. Then we can add this squence as a new column in the DataFrame. Let’s see an example,
start = 30 # Add column with incremental values from 30 onwards df['Age'] = range(start, start + df.shape[0]) print(df)
Output:
Name Location Team Experience Age 0 Mark US Tech 5 30 1 Riti India Tech 7 31 2 Shanky India PMO 2 32 3 Shreya India Design 2 33 4 Aadi US Tech 11 34 5 Sim US Tech 4 35
Here, we added a new column ‘Age’ in the DataFrame with incremental values.
Add new DataFrame column with incremental values of equal interval
Suppose we want to a add a new column containing incremental values. But the adjacent values should separated by a given step size. We can do that using the range() function. Let’s see the example,
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# Add column with incremental values from 30 onwards # with step size 5 df['Age'] = range(start, start + (5 * df.shape[0]), 5) print(df)
Output:
Name Location Team Experience Age 0 Mark US Tech 5 30 1 Riti India Tech 7 35 2 Shanky India PMO 2 40 3 Shreya India Design 2 45 4 Aadi US Tech 11 50 5 Sim US Tech 4 55
Here, we added a new column ‘Age’ in the DataFrame with incremental values, but each value in this column is greater than previous value by 5.
Summary
Today, we saw how to add a new column in DataFrame with incremental values. Thanks.
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