Vectorized Operations with Series
Vectorized Operations with Series Data Science Project
Intro to Pandas for Data Analysis

Vectorized Operations with Series

Vectorized Operations is one of the fundamental concepts in pandas (and numpy and other libraries). It's what makes Pandas extremely fast for numeric processing. In this project, you'll learn how it works by creating your own vectorized expressions.

Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

codevalidated

Subtract $50B from all companies in `revenue_in_billions`

The recession just hit! Let's say you need to subtract $50B from all the companies in revenue_in_billions. Store the new series in the variable revenue_recession

codevalidated

Create a new series expressing revenue in dollars (units)

The accounting team needs more detail when calculating EBITDA. They need revenue expressed in dollar units (instead of millions or billions). Use either series revenue_in_millions or revenue_in_billions to create a new series revenue_in_dollars.

codevalidated

Calculate revenue per employee, in dollars

Using the series number_of_employees (given in the notebook), your job is to calculate revenue per employee, expressed in dollars (units). Store it in the variable revenue_per_employee.

Vectorized Operations with SeriesVectorized Operations with Series
Author

Santiago Basulto

This project is part of

Intro to Pandas for Data Analysis

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