import matplotlib.pyplot as plt
%matplotlib notebook
# create a new figure
plt.figure()

# plot the point (3,2) using the circle marker
plt.plot(3, 2, ‘o‘)

# get the current axes
ax = plt.gca()

# Set axis properties [xmin, xmax, ymin, ymax]
ax.axis([0,6,0,10])

# get all the child objects the axes contains
ax.get_children()

 1 # First let‘s set the backend without using mpl.use() from the scripting layer
 2 from matplotlib.backends.backend_agg import FigureCanvasAgg
 3 from matplotlib.figure import Figure
 4 
 5 # create a new figure
 6 fig = Figure()
 7 
 8 # associate fig with the backend
 9 canvas = FigureCanvasAgg(fig)
10 
11 # add a subplot to the fig
12 ax = fig.add_subplot(111)
13 
14 # plot the point (3,2)
15 ax.plot(3, 2, ‘.‘)
16 
17 # save the figure to test.png
18 # you can see this figure in your Jupyter workspace afterwards by going to
19 # https://hub.coursera-notebooks.org/
20 canvas.print_png(‘test.png‘)

 1 import matplotlib.pyplot as plt
 2 %matplotlib notebook
 3 
 4 plt.figure()
 5 # plot a data series ‘Tall students‘ in red using the first two elements of x and y
 6 plt.scatter(x[:2], y[:2], s=100, c=‘red‘, label=‘Tall students‘)
 7 # plot a second data series ‘Short students‘ in blue using the last three elements of x and y 
 8 plt.scatter(x[2:], y[2:], s=100, c=‘blue‘, label=‘Short students‘)
 9 
10 # add a label to the x axis
11 plt.xlabel(‘The number of times the child kicked a ball‘)
12 # add a label to the y axis
13 plt.ylabel(‘The grade of the student‘)
14 # add a title
15 plt.title(‘Relationship between ball kicking and grades‘)
16 # add the legend to loc=4 (the lower right hand corner), also gets rid of the frame and adds a title
17 plt.legend(loc=4, frameon=False, title=‘Legend‘)
18 # get children from current axes (the legend is the second to last item in this list)
19 plt.gca().get_children()
20 # get the legend from the current axes
21 legend = plt.gca().get_children()[-2]
22 # you can use get_children to navigate through the child artists
23 legend.get_children()[0].get_children()[1].get_children()[0].get_children()

 1 # import the artist class from matplotlib
 2 from matplotlib.artist import Artist
 3 
 4 def rec_gc(art, depth=0):
 5     if isinstance(art, Artist):
 6         # increase the depth for pretty printing
 7         print("  " * depth + str(art))
 8         for child in art.get_children():
 9             rec_gc(child, depth+2)
10 
11 # Call this function on the legend artist to see what the legend is made up of
12 rec_gc(plt.legend())

 1 import numpy as np
 2 
 3 linear_data = http://www.mamicode.com/np.array([1,2,3,4,5,6,7,8])
 4 exponential_data = http://www.mamicode.com/linear_data**2"color: #008080"> 5 
 6 plt.figure()
 7 # plot the linear data and the exponential data
 8 plt.plot(linear_data, ‘-o‘, exponential_data, ‘-o‘)
 9 # plot another series with a dashed red line
10 plt.plot([22,44,55], ‘--r‘)
11 plt.xlabel(‘Some data‘)
12 plt.ylabel(‘Some other data‘)
13 plt.title(‘A title‘)
14 # add a legend with legend entries (because we didn‘t have labels when we plotted the data series)
15 plt.legend([‘Baseline‘, ‘Competition‘, ‘Us‘])
16 # fill the area between the linear data and exponential data
17 plt.gca().fill_between(range(len(linear_data)), 
18                        linear_data, exponential_data, 
19                        facecolor=‘blue‘, 
20                        alpha=0.25)

 1 import pandas as pd
 2 import numpy as np
 3 
 4 plt.figure()
 5 observation_dates = np.arange(‘2017-01-01‘, ‘2017-01-09‘, dtype=‘datetime64[D]‘)
 6 observation_dates = list(map(pd.to_datetime, observation_dates)) # convert the map to a list to get rid of the error
 7 plt.plot(observation_dates, linear_data, ‘-o‘,  observation_dates, exponential_data, ‘-o‘)
 8 x = plt.gca().xaxis
 9 
10 # rotate the tick labels for the x axis
11 for item in x.get_ticklabels():
12     item.set_rotation(45)
13     # adjust the subplot so the text doesn‘t run off the image
14 plt.subplots_adjust(bottom=0.25)
15 ax = plt.gca()
16 ax.set_xlabel(‘Date‘)
17 ax.set_ylabel(‘Units‘)
18 # you can add mathematical expressions in any text element
19 ax.set_title("Exponential ($x^2$) vs. Linear ($x$) performance")

 1 import numpy as np
 2 import pandas as pd
 3 
 4 linear_data = http://www.mamicode.com/np.array([1,2,3,4,5,6,7,8])
 5 exponential_data = http://www.mamicode.com/linear_data**2"color: #008080"> 6 
 7 plt.figure()
 8 xvals = range(len(linear_data))
 9 plt.bar(xvals, linear_data, width = 0.3)
10 
11 new_xvals = []
12 # plot another set of bars, adjusting the new xvals to make up for the first set of bars plotted
13 for item in xvals:
14     new_xvals.append(item+0.3)
15 
16 plt.bar(new_xvals, exponential_data, width = 0.3 ,color=‘red‘)
17 
18 # This will plot a new set of bars with errorbars using the list of random error values
19 #plt.bar(xvals, linear_data, width = 0.3, yerr=linear_err)
20 # stacked bar charts are also possible
21 plt.figure()
22 xvals = range(len(linear_data))
23 plt.bar(xvals, linear_data, width = 0.3, color=‘b‘)
24 plt.bar(xvals, exponential_data, width = 0.3, bottom=linear_data, color=‘r‘)
25 
26 # or use barh for horizontal bar charts
27 plt.figure()
28 xvals = range(len(linear_data))
29 plt.barh(xvals, linear_data, height = 0.3, color=‘b‘)
30 plt.barh(xvals, exponential_data, height = 0.3, left=linear_data, color=‘r‘)

 1 # import matplotlib.pyplot as plt
 2 # import numpy as np
 3 
 4 # plt.figure()
 5 
 6 # languages = [‘Python‘, ‘SQL‘, ‘Java‘, ‘C++‘, ‘JavaScipt‘]
 7 # pos = np.arange(len(languages))
 8 # popularity = [56, 39, 34, 34, 29]
 9 
10 # plt.bar(pos, popularity, align=‘center‘)
11 # plt.xticks(pos, languages)
12 # plt.ylabel(‘% Popularity‘)
13 # plt.title(‘Top 5 language for Math & Data \nby % popularity on Stack Overflow‘, alpha=0.8)
14 
15 # plt.show()
16 import matplotlib.pyplot as plt
17 import numpy as np
18 
19 plt.figure()
20 
21 languages = [‘Python‘, ‘SQL‘, ‘Java‘, ‘C++‘, ‘JavaScipt‘]
22 pos = np.arange(len(languages))
23 popularity = [56, 39, 34, 34, 29]
24 
25 # change the bar color to be less bright blue
26 bars = plt.bar(pos, popularity, align=‘center‘, linewidth=0, color=‘lightslategrey‘)
27 # change one bar, the python bar, to a contrasting color
28 bars[0].set_color(‘#1F77B4‘)
29 
30 
31 # soften all labels by turning grey
32 plt.xticks(pos, languages, alpha=0.8)
33 #plt.ylabel(‘% Popularity‘,alpha=0.8)
34 plt.title(‘Top 5 language for Math & Data \nby % popularity on Stack Overflow‘, alpha=0.8)
35 
36 # Remove all the ticks and tick labels on the Y axis
37 plt.tick_params(top=‘off‘, bottom=‘off‘, left=‘off‘, right=‘off‘, labelleft=‘off‘, labelbottom=‘on‘)
38 
39 # Remove the frame of the chart
40 for spine in plt.gca().spines.values():
41     spine.set_visible(False)
42 
43 # Direct label each bar with Y axis values
44 for bar in bars:
45     plt.gca().text(bar.get_x() + bar.get_width()/2, bar.get_height() -5, str(int(bar.get_height())) + ‘%‘, ha=‘center‘
46                    ,color=‘w‘,fontsize=11)
47 
48 plt.show()