Charles Kelly explains how to create inline plots using matplotlib within Jupyter notebooks.
- [Voiceover] Pandas has great visualization techniques…and you can view them by navigating to this website.…In the last video in this chapter, DataFrame plots,…we'll look at some of these examples.…However there are many occasions where you'll want…more sophisticated capabilities…than the Pandas plots by themselves.…In that case you can use Matplotlib.…If you watch my course on Numpy…and viewed the plotting chapter, you'll notice…that the videos in this chapter are very much the same.…
You can use the videos in this chapter as a refresher…on your previous knowledge of Matplotlib.…The inline file from your exercises file folder…is pre-populated with a magic directive…for inline plotting, import statements for Numpy Matplotlib…and statements for creating a complex histogram,…along with tables for colors and markers.…This is the table of available colors…and this is the table of available markers.…
Begin by placing your cursor in this cell…and execute the cell by pressing shift enter.…The documentation for visualization tells us that…
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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