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Python Pandas Dataframe How to Multiply Entire Column with a Scalar

In this blog, explore efficient data manipulation with Pandas, a key Python library for data scientists and software engineers dealing with large datasets. Focusing on Pandas' Dataframe, learn how to effortlessly multiply an entire column by a scalar, streamlining tabular data operations.
By Saturn Cloud | | Miscellaneous | Updated:

As a data scientist or software engineer, working with large datasets is a common occurrence. One of the most widely used libraries for data manipulation in Python is Pandas. Pandas provides a powerful and easy-to-use data structure called Dataframe that allows us to work with tabular data efficiently. In this article, we will discuss how to multiply an entire column of a Pandas Dataframe with a scalar.

What is a Pandas Dataframe?

A Pandas Dataframe is a 2-dimensional labeled data structure with columns of potentially different types. It is similar to a spreadsheet or a SQL table, but with more powerful features. A Dataframe can be created from various data sources such as CSV, Excel, SQL database, or even from a Python dictionary.

Multiplying an Entire Column with a Scalar

Sometimes, it is necessary to multiply an entire column of a Pandas Dataframe with a scalar. For example, suppose we have a dataset that contains the sales revenue of a company for the last 5 years, and we want to increase the revenue by 10% for all the years. In this case, we can use the Pandas Dataframe’s multiply() method to achieve this.

The multiply() method multiplies the values of two Dataframes or a Dataframe and a scalar. To multiply an entire column of a Dataframe with a scalar, we can easily use * or we can the column name and the scalar to the multiply() method. Let’s see an example.

import pandas as pd

# create a sample dataframe
df = pd.DataFrame({
    'Year': [2016, 2017, 2018, 2019, 2020],
    'Revenue': [10000, 12000, 15000, 18000, 22000]
})

# multiply the 'Revenue' column with 1.1 (10% increase)
df['Revenue_with_*'] = df['Revenue'] * 1.1
df['Revenue_with_mul'] = df['Revenue'].multiply(1.1)

# print the updated dataframe
print(df)

Output:

   Year  Revenue  Revenue_with_*  Revenue_with_mul
0  2016    10000         11000.0           11000.0
1  2017    12000         13200.0           13200.0
2  2018    15000         16500.0           16500.0
3  2019    18000         19800.0           19800.0
4  2020    22000         24200.0           24200.0

In the above example, we first created a sample Dataframe that contains the sales revenue of a company for the last 5 years. We then multiplied the Revenue column with 1.1 using 2 different ways, which increased the revenue by 10%. Finally, we printed the updated Dataframe.

Conclusion

In this article, we discussed how to multiply an entire column of a Pandas Dataframe with a scalar. We simply used * and the Pandas Dataframe’s multiply() method to achieve this. Multiplying an entire column of a Dataframe with a scalar is a common operation in data manipulation and can be useful in various scenarios. Pandas provides easy and efficient ways to perform this operation.

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