Learn about the matplotlib plotting library. See how it’s the most used package for visualization and learn the power of it. You can also see the gallery which is a good starting point for drawing your own charts.
- [Instructor] Displaying charts is…an important part of the data science process…both in the exploratory state, and when presenting reports.…So far, we've used Panda's plotting abilities…to display charts.…Under the hood, Pandas uses a library…called matplotlib.…There are several other plotting libraries…in Python, but matplotlib is by far the most used.…Matplotlib is very powerful.…It includes many chart types…such as line plots, bar plots, arrow plots, and more.…Using matplotlib, you have a lot of control…on the look and feel of your charts.…
However, it can be low level at times.…For example, when working with time based indices.…This is why we prefer to use Pandas whenever we can.…Pandas abstracts a lot of the low level details for us,…but at times we'll need to work directly…with matplotlib to get optimal results.…The way I usually work is head over to the gallery,…find the shot that looks like what I want to do,…click on it, and start with the code below,…tweaking it to my needs.…
- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
1. Scientific Python Overview
2. The Jupyter Notebook
3. NumPy Basics
Manage environments5m 11s
6. Folium and Geo
7. NY Taxi Data
10. Other Packages
11. Development Process
Next steps1m 33s
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