Charles Kelly explains how to use vectorized operations for element-by-element calculations within Pandas series.
- [Narrator] The Vectorized Operations file…from your exercise's files folder…is pre-populated with import statements…for pandas, numpy and a series created…from a Python dictionary.…Place your cursor in this cell,…then execute this cell by pressing shift + enter.…In the following cell, we create a dictionary…with the keys, a, b and c…and the values 45, minus 19.5…and 4,444.…Then we take the dictionary and we pass it as an argument…to the series function.…
Then we display my_series.…We can add the elements in this series,…those elements that came from the dictionary,…without writing a loop.…We copy my_series and paste it, plus itself,…as we execute the cell, we see that each element…is exactly twice the value of the elements…in the original series.…Also note that the value…of the original series doesn't change.…We can note this by creating a new cell, esc + b…and typing the name my_series.…
Press shift + enter to display it.…So, we added the series together without writing a loop.…We didn't change the value of the series itself.…
Watch this course to gain an overview of Pandas. Charles Kelly helps you get started with time series, data frames, panels, plotting, and visualization. All you need is a copy of the free and interactive Jupyter Notebook app to practice and follow along.
- Using the Markdown language and Jupyter Notebook
- Creating objects
- Selecting objects
- Using operations
- Merging data
- Creating series
- Creating data frames
- Creating panels
- Annotating plots and data frame plots
Skill Level Intermediate
Introduction to Data Analysis with Pythonwith Michele Vallisneri2h 16m Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
SQL for Data Reporting and Analysiswith Emma Saunders2h 16m Intermediate
1. Introduction to Notebooks
2. Pandas Overview
4. Data Frames and Panels
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