![Matplotlib
● Two main goals with Matplotlib:
○ Be able to plot out a functional
relationship:
■ y = 2x
○ Be able to plot out a relationship
between raw data points:
■ x = [1,2,3,4]
■ y = [2,4,6,8]](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-10-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-22-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
FIGURE](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-23-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
FIGURE
(0,0)
(x,y)
Lower Left Corner of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-24-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 1, 1, 1])
FIGURE
(0,1)
(x,y)
Lower Left Corner of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-25-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([ 0.5, 0.5, 1, 1])
FIGURE
(0.5,0.5)
(x,y)
Lower Left Corner of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-26-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
FIGURE
(width,height)
of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-27-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
(width,height)
of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-28-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 0.5, 0.5])
FIGURE
(width,height)
of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-29-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 0.5, 0.5])
(width,height)
of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-30-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 0.5, 1])
FIGURE
(width,height)
of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-31-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 0.5, 1])
(width,height)
of Axes](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-32-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
axes.plot(x, y)](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-33-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
axes.plot(x, y)](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-34-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
axes.plot(x, y)](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-35-320.jpg)
![Matplotlib
● fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
axes.plot(x, y)](https://image.slidesharecdn.com/04-matplotlib-240627221822-fcc21411/85/Matplotlib-pptx-for-data-analysis-and-visualization-36-320.jpg)
Matplotlib.pptx for data analysis and visualization
- 1. Matplotlib
- 2. Matplotlib ● Visualizing data is crucial to quickly understanding trends and relationships in your dataset. ● Matplotlib is one of the most popular libraries for plotting with Python.
- 3. Matplotlib ● Matplotlib is known as the “Grandfather” of plotting and visualization libraries for Python. ● Many other visualization libraries are built directly off of Matplotlib (e.g. seaborn and pandas built-in visualization).
- 4. Matplotlib ● Matplotlib is heavily inspired by the plotting functions of the MatLab programming language. ● It allows for the creation of almost any plot type and heavy customization.
- 5. Matplotlib ● This ability to heavily customize a plot comes at a trade-off for beginners, since it can be confusing to learn the Matplolib syntax at first. ● This is mainly due to the fact that there are actually two separate approaches to creating plots, functional based methods and OOP based methods.
- 6. Matplotlib ● This Matplotlib section seeks to clear up any confusion by clearly separating out these two approaches. ○ Matplotlib Basics ■ Functional Method ○ Matplotlib Figures and Subplots ■ OOP Method
- 7. Matplotlib ● Topics Covered ○ Matplotlib Basics and Functions ○ Matplotlib Figures ○ Matplotlib Subplots ○ Matplotlib Styling ○ Exercise Questions and Solutions
- 8. Matplotlib ● Specialized plot types such as histograms won’t be covered with matplotlib, since we will later learn how to use seaborn to easily create statistical plots. ● It is important to learn matplotlib first however, since seaborn builds directly off of Matplotlib.
- 9. Matplotlib ● Throughout this section we will be referencing the excellent Matplotlib online documentation: ○ https://matplotlib.org/ ● As well as the gallery of example plots and codes (very useful!) ○ https://matplotlib.org/gallery.html
- 10. Matplotlib ● Two main goals with Matplotlib: ○ Be able to plot out a functional relationship: ■ y = 2x ○ Be able to plot out a relationship between raw data points: ■ x = [1,2,3,4] ■ y = [2,4,6,8]
- 13. Matplotlib ● The most basic way to use Matplotlib is through the function plot calls: ○ plt.plot(x,y) ● These function calls are simple to use, but don’t allow for very high degrees of control.
- 14. Matplotlib ● We recommend using these simple plt.plot() calls for quickly visualizing relationships and data. ● Later on we will explore the more robust OOP Matplotlib Figure API.
- 15. Matplotlib ● Note! ○ There are slight differences in displaying plots within a notebook versus running a python script. ○ If you are running .py scripts instead of .ipynb notebooks, you will need to add the plt.show() command discussed in this video.
- 16. Matplotlib Figure Object PART ONE : UNDERSTANDING THE FIGURE
- 17. Matplotlib ● The more comprehensive Matplotlib OOP API makes use of a Figure object. ● We then add axes to this Figure object and then plot on those axes. ● This allows for very robust controls over the entire plot.
- 18. Matplotlib ● Let’s quickly visually build an understanding of the Figure object before coding it with Python… ● Note: ○ The Figure object we’re about to show is technically not visible until you add axes to it.
- 19. Matplotlib ● plt.figure() <Figure size 432x288 with 0 Axes>
- 20. Matplotlib ● plt.figure(figsize=(10,10)) <Figure size 720x720 with 0 Axes>
- 21. Matplotlib ● fig = plt.figure() ● Blank canvas, waiting for a set of axes for plotting. <Figure size 432x288 with 0 Axes>
- 22. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1])
- 23. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1]) FIGURE
- 24. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1]) FIGURE (0,0) (x,y) Lower Left Corner of Axes
- 25. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 1, 1, 1]) FIGURE (0,1) (x,y) Lower Left Corner of Axes
- 26. Matplotlib ● fig = plt.figure() axes = fig.add_axes([ 0.5, 0.5, 1, 1]) FIGURE (0.5,0.5) (x,y) Lower Left Corner of Axes
- 27. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1]) FIGURE (width,height) of Axes
- 28. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1]) (width,height) of Axes
- 29. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 0.5, 0.5]) FIGURE (width,height) of Axes
- 30. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 0.5, 0.5]) (width,height) of Axes
- 31. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 0.5, 1]) FIGURE (width,height) of Axes
- 32. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 0.5, 1]) (width,height) of Axes
- 33. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1]) axes.plot(x, y)
- 34. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1]) axes.plot(x, y)
- 35. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1]) axes.plot(x, y)
- 36. Matplotlib ● fig = plt.figure() axes = fig.add_axes([0, 0, 1, 1]) axes.plot(x, y)
- 37. Matplotlib ● This methodology allows us to add in multiple axes as well as move and resize the axes.
- 38. Matplotlib ● In theory we could set axes side by side using plt.figure() calls, but typically it is easier to use plt.subplots() function calls for this. ● We’ll explore multiple side by side plots in a future lecture, for now let’s explore the Figure object methodology for Matplotlib!
- 39. Matplotlib Figure Object PART TWO: IMPLEMENTING FIGURES AND AXES
- 40. Matplotlib Figure Object PART THREE: FIGURE PARAMETERS
- 42. Matplotlib ● In theory we could create a Figure object and then manually add and arrange sets of axes to line up multiple plots side by side. ● However, Matplotlib comes with a pre- configured function call plt.subplots() that automatically does this for us!
- 43. Matplotlib ● The plt.subplots() call allows us to easily create Figure and Axes objects in side by side formations. ● The plt.subplots() command returns a tuple containing the Figure canvas and then a numpy array holding the axes objects.
- 44. Matplotlib ● fig, axes = plt.subplots(nrows=1, ncols=2)
- 45. Matplotlib ● fig, axes = plt.subplots(nrows=2, ncols=2)
- 46. Matplotlib ● plt.subplots() returns a tuple which by common convention we label (fig,axes): ○ fig ■ This is the entire Figure canvas. ○ axes ■ This is a numpy array holding each of the axes according to position in the overall canvas.
- 47. Matplotlib ● Let’s explore how to use plt.subplots() to easily create and align multiple plots!
- 48. Matplotlib Styling PART ONE: LEGENDS
- 49. Matplotlib ● Matplotlib offers very robust styling functions that allow us to edit colors, legends, line widths, markers, and much more! ● Note: Due to the wide amount of possible variations, we will be copying and pasting from the lecture notebook to save typing time in the video.
- 50. Matplotlib ● Main Styling Discussed: ○ Legends ○ Visual Styling ■ Colors ■ Editing Lines ● Colors, Widths, Styles ■ Editing Markers ● Colors, Size, Styles, Edges
- 51. Matplotlib ● Let’s begin with adding legends!
- 52. Matplotlib Styling PART TWO: VISUAL STYLING
- 54. Matplotlib ● Matplotlib is a huge library! ● We’ve added a notebook with some additional concepts you may want to explore on your own. ● We don’t use these concepts in the course however, so feel free to skip this notebook for now.
- 55. Matplotlib ● An important note is that almost any Matplotlib question you can think of already has an answer in StackOverflow or an example in the Matplotlib gallery. ● Leverage these many examples to your advantage and do not waste energy and time into memorizing esoteric commands!