Python Training Courses

In this Numerical Programming chapter

0. Numerical Programming with Python
1. Introduction to NumPy
2. Creating Numpy Arrays
3. Numpy Data Objects, dtype
4. Numerical Operations on Numpy Arrays
5. Numpy Arrays: Concatenating, Flattening and Adding Dimensions
6. Python, Random Numbers and Probability
7. Weighted Probabilities
8. Synthetical Test Data With Python
9. Numpy: Boolean Indexing
10. Matrix Arithmetics under NumPy and Python
11. Reading and Writing Data Files: ndarrays
12. Overview of Matplotlib
13. Formatting a plot in Matplotlib
14. Matplotlib Object Hierarchy
15. Spines and Ticks in Matplotlib
16. Adding Legends and Annotations in Matplotlib
17. Creating Subplots in Matplotlib
18. Gridspec in Matplotlib
19. Histograms with Matplotlib
20. Contour Plots with Matplotlib
21. Image Processing in Python with Matplotlib
22. Image Processing Techniques with Python and Matplotlib
23. Creating Videos from One or More Images
24. Moving Watermarks Video with Python
25. Introduction to Pandas
26. Pandas DataFrame
27. Accessing and Changing values of DataFrames
28. Pandas Styling
29. Pandas Pivot
30. Pandas: groupby
31. Pandas Groupby Example
32. Reading and Writing Data in Pandas
33. Dealing with NaN
34. Binning in Python and Pandas
35. Multi-level Indexing in Pandas
36. Data Visualization with Pandas
37. Python Date and Time
38. Time Series in Pandas and Python
39. Expenses and income example with Pandas and Python
40. Net Income Method Example with Numpy, Matplotlib and Scipy
41. Estimation of Corona cases with Python and Pandas
42. Linear Combinations in Python

13. Formatting a plot in Matplotlib

By Bernd Klein. Last modified: 24 Mar 2022.

Format Parameter

import matplotlib.pyplot as plt

plt.plot([-1, -4.5, 16, 23, 78, 22, 3])
plt.show()

What we see is a continuous graph, even though we provided discrete data for the Y values. By adding a format string to the function call of plot, we can create a graph with discrete values, in our case blue circle markers. The format string defines the way how the discrete points have to be rendered.

import matplotlib.pyplot as plt

plt.plot([-1, -4.5, 16, 23, 78, 22, 3], "ob")
plt.show()

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The format parameter of pyplot.plot

We have used "ob" in our previous example as the format parameter. It consists of two characters. The first one defines the line style or the dicrete value style, i.e. the markers, while the second one chooses a colour for the graph. The order of the two characters could have been reversed, i.e. we could have written it as "bo" as well. If the format parameter is not given, as in the first example, the default value is "b-", i.e. a solid blue line.

The following format string characters are accepted to control the line style or marker:

=============================================
character       description
=============================================
'-'             solid line style
'--'            dashed line style
'-.'            dash-dot line style
':'             dotted line style
'.'             point marker
','             pixel marker
'o'             circle marker
'v'             triangle_down marker
'^'             triangle_up marker
'<'             triangle_left marker
'>'             triangle_right marker
'1'             tri_down marker
'2'             tri_up marker
'3'             tri_left marker
'4'             tri_right marker
's'             square marker
'p'             pentagon marker
'*'             star marker
'h'             hexagon1 marker
'H'             hexagon2 marker
'+'             plus marker
'x'             x marker
'D'             diamond marker
'd'             thin_diamond marker
'|'             vline marker
'_'             hline marker
===============================================

The following color abbreviations are supported:

==================
character   color
==================
'b'         blue
'g'         green
'r'         red
'c'         cyan
'm'         magenta
'y'         yellow
'k'         black
'w'         white
==================

As you may have guessed already, you can add X values to the plot function. We will use the multiples of 3 starting at 3 below 22 as the X values of the plot in the following example:

import matplotlib.pyplot as plt

# our X values:
days = list(range(0, 22, 3))
print("Values of days:", days)
# our Y values:
celsius_values = [25.6, 24.1, 26.7, 28.3, 27.5, 30.5, 32.8, 33.1]

plt.plot(days, celsius_values)
plt.show()

Values of days: [0, 3, 6, 9, 12, 15, 18, 21]

... and once more with discrete values:

plt.plot(days, celsius_values, 'bo')
plt.show()

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