Learn how to create inline plots within Jupyter notebooks using matplotlib and NumPy in this video tutorial by Charles Kelly. These are explained in the context of computer science and data science to technologists and students in preparation for machine
- [Instructor] The file Inline Plotting…from the Exercises Folders is pre-populated…with a magic directive for Inline Plotting,…import statements for NumPy and Matplotlib,…statements for creating a complex histogram,…along with tables for colors and markers.…You can see the documentation from Matplotlib…by navigating to this url.…The documentation tells us that Matplotlib…is a python 2D plotting library that produces…publication quality figures in hard copy formats…and interactive environments across platforms.…
The important phrase is publication quality.…You can create plots inline within a Jupyter Notebook…or you can create them in a separate window…that provides additional tools for enhancing…the publication quality of your plots.…I'll demonstrate an Inline Plot in this video.…If you want to create Inline Plots…first execute a magic directive in Cell 1.…You should execute this before you import Matplotlib.…
The code in Cell 4 generates a histogram…for the relative distribution of intelligence quotients.…A pyplot interface is a wrapper…
- Using Jupyter Notebook
- Creating NumPy arrays from Python structures
- Slicing arrays
- Using Boolean masking and broadcasting techniques
- Plotting in Jupyter notebooks
- Joining and splitting arrays
- Rearranging array elements
- Creating universal functions
- Finding patterns
- Building magic squares and magic cubes with NumPy and Python
Skill Level Intermediate
2. Create NumPy Arrays
3. Index, Slice, and Iterate
4. Plots: Matplotlib and Pyplot
5. Manipulate Arrays
6. Short Examples
7. Extended Examples
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