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Matplotlib: - Exercises, Practice, Solution

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Matplotlib is a Python plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

The best way we learn anything is by practice and exercise questions. Here you have the opportunity to practice the NumPy concepts by solving the exercises starting from basic to more complex exercises. A sample solution is provided for each exercise. It is recommended to do these exercises by yourself first before checking the solution.

Hope, these exercises help you to improve your Matplotlib coding skills. Currently, following sections are available, we are working hard to add more exercises .... Happy Coding!

List of Matplotlib Exercises:

Matplotlib Basics

Matplotlib: matplotlib-logo

Creating Plots

Figure

Operator Description
fig = plt.figures() a container that contains all plot elements

Axes

Operator Description
fig.add_axes()
a = fig.add_subplot(222)
Initializes subplot
A subplot is an axes on a grid system row-col-num.
fig, b = plt.subplots(nrows=3, nclos=2) Adds subplot
ax = plt.subplots(2, 2) Creates subplot

Plotting

1D Data

Operator Description
lines = plt.plot(x,y) Plot data connected by lines
plt.scatter(x,y) Creates a scatterplot, unconnected data points
plt.bar(xvalue, data , width, color...) simple vertical bar chart
plt.barh(yvalue, data, width, color...) simple horizontal bar
plt.hist(x, y) Plots a histogram
plt.boxplot(x,y) Box and Whisker plot
plt.violinplot(x, y) Creates violin plot
ax.fill(x, y, color='lightblue')
ax.fill_between(x,y,color='yellow')
Fill area under/between plots

2D Data

Operator Description
fig, ax = plt.subplots()
im = ax.imshow(img, cmap, vmin...)
Colormapped or RGB arrays

Saving plots

Operator Description
plt.savefig('pic.png') Saves plot/figure to image
plt.savefig('transparentback.png') Saves transparent plot/figure to image

Customization

Color

Operator Description
plt.plot(x, y, color='lightblue')
plt.plot(x, y, alpha = 0.4)
colors plot to color blue
plt.colorbar(mappable,
orientation='horizontal')
mappable: the Image, Contourset etc to which colorbar applies

Markers

Operator Description
plt.plot(x, y, marker='*') adds * for every data point
plt.scatter(x, y, marker='.') adds . for every data point

Lines

Operator Description
plt.plot(x, y, linewidth=2) Sets line width
plt.plot(x, y, ls='solid') Sets linestyle, ls can be ommitted, see 2 below
plt.plot(x, y, ls='--') Sets linestyle, ls can be ommitted, see below
plt.plot(x,y,'--', x**2, y**2, '-.') Lines are '--' and '_.'
plt.setp(lines,color='red',linewidth=2) Sets properties of plot lines

Text

Operator Description
plt.text(1, 1,'Example
Text',style='italic')
Places text at coordinates 1/1
ax.annotate('some annotation', xy=(10, 10)) Annotate the point with coordinatesxy with text s
plt.title(r'$delta_i=20$', fontsize=10) Mathtext

Limits

Operators Description
plt.xlim(0, 7) Sets x-axis to display 0 - 7
other = array.copy() Creates deep copy of array
plt.ylim(-0.5, 9) Sets y-axis to display -0.5 - 9
ax.set(xlim=[0, 7], ylim=[-0.5, 9])
ax.set_xlim(0, 7)
Sets limits
plt.margins(x=1.0, y=1.0) Set margins: add padding to a plot, values 0 - 1
plt.axis('equal') Set the aspect ratio of the plot to 1

Legends/Labels

Operator Description
plt.title('just a title') Sets title of plot
plt.xlabel('x-axis') Sets label next to x-axis
plt.ylabel('y-axis') Sets label next to y-axis
ax.set(title='axis', ylabel='Y-Axis', xlabel='X-Axis') Set title and axis labels
ax.legend(loc='best') No overlapping plot elements

Ticks

Operator Description
plt.xticks(x, labels, rotation='vertical') Set ticks
ax.xaxis.set(ticks=range(1,5), ticklabels=[3,100,-12,"foo"]) Set x-ticks
ax.tick_params(axis='y', direction='inout', length=10) Make y-ticks longer and go in and out

Popularity of Programming Language
Worldwide, April 2020 compared to a year ago:

Rank Change Language Share Trend
1 Python 30.61 % +3.9 %
2 Java 18.45 % -1.9 %
3 Javascript 7.91 % -0.4 %
4 C# 7.27 % -0.0 %
5 PHP 6.07 % -1.1 %
6 C/C++ 5.76 % -0.2 %
7 R 3.8 % -0.2 %
8 Objective-C 2.4 % -0.4 %
9 Swift 2.23 % -0.2 %
10 up arrow TypeScript 1.85 % +0.2 %
11 down arrow Matlab 1.77 % -0.2 %
12 up arrow Kotlin 1.63 % +0.4 %
13 VBA 1.33 % +0.0 %
14 up arrow Go 1.26 % +0.2 %
15 down arrow Ruby 1.23 % -0.1 %
16 Scala 0.99 % -0.1 %
17 down arrow Visual Basic 0.92 % -0.2 %
18 up arrow Rust 0.67 % +0.2 %
19 Abap 0.51 % -0.1 %
20 down arrow Perl 0.5 % -0.1 %
21 up arrow Dart 0.46 % +0.2 %
22 down arrow Groovy 0.41 % -0.1 %
23   Ada 0.39 % +0.0 %
24 down arrow Lua 0.36 % -0.0 %
25 down arrow Cobol 0.34 % +0.0 %
26 down arrow Haskell 0.32 % +0.0 %
27 up arrow Julia 0.3 % +0.1 %
28 down arrow Delphi 0.26 % +0.0 %

Source : http://pypl.github.io/PYPL.html

TIOBE Index for April 2020

Apr 2020 Apr 2019 Change Programming Language Ratings Change
1 1 Java 16.73% +1.69%
2 2 C 16.72% +2.64%
3 4 up arrow Python 9.31% +1.15%
4 3 down arrow C++ 6.78% -2.06%
5 6 up arrow C# 4.74% +1.23%
6 5 down arrow Visual Basic 4.72% -1.07%
7 7 JavaScript 2.38% -0.12%
8 9 up arrow PHP 2.37% +0.13%
9 8 down arrow SQL 2.17% -0.10%
10 16 up arrow R 1.54% +0.35%
11 19 up arrow Swift 1.52% +0.54%
12 18 up arrow Go 1.36% +0.35%
13 13 Ruby 1.25% -0.02%
14 10 down arrow Assembly language 1.16% -0.55%
15 22 up arrow PL/SQL 1.05% +0.26%
16 14 down arrow Perl 0.97% -0.30%
17 11 down arrow Objective-C 0.94% -0.57%
18 12 down arrow MATLAB 0.93% -0.36%
19 17 down arrow Classic Visual Basic 0.83% -0.23%
20 27 up arrow Scratch 0.77% +0.28%

Source : https://www.tiobe.com/tiobe-index/

List of Exercises with Solutions :