Matplotlib is a 2D plotting library for Python that can generate publication-quality figures in both hardcopy and interactive formats. The document provides examples of using Matplotlib to plot lines, histograms, pie charts, scatter plots, subplots, and mathematical functions. Additional resources are also listed for learning more about Matplotlib and an example dataset on apple production by variety.

2.  python 2D plotting library which produces
publication quality figures in
› a variety of hardcopy formats
› interactive environments

3. import matplotlib.pyplot as plt
# x data, y data, style
plt.plot([1,2,3,4], [5,6,7,8], 'r-')
# [x min, x max, y min, y max]
plt.axis([0, 5, 0, 9])
plt.ylabel('some numbers')
plt.show()

5.  Draw the graph(y =
x^2).

6. x = range(100)
y = [i * i for i in x]
plt.plot(x, y, 'r-')
plt.ylabel('y = x^2')
plt.show()

7. import matplotlib.pyplot as plt
data = [2, 7, 6, 4, 1, 10, 3, 2, 4, 5, 3, 1]
plt.hist(data, bins=8, facecolor='blue')
plt.show()

9. import matplotlib.pyplot as plt
# The slices will be ordered and plotted counter-clockwise.
labels = 'Fuji', 'Tsugaru', 'Orin', 'Jonagold', 'Other'
sizes = [235500, 50600, 47100, 45700, 89100]
colors = ['orangered', 'red', 'greenyellow', 'orangered', 'gold']
explode = (0, 0, 0.1, 0, 0) # only "explode" the 3rd slice (i.e. 'Orin')
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
autopct='%1.1f%%', shadow=True, startangle=90, counterclock=False)
# Set aspect ratio to be equal so that pie is drawn as a circle.
plt.axis('equal')
plt.show()

11. import numpy as np # use numpy
import matplotlib.pyplot as plt
x = np.array(range(10))
# red line
plt.plot(x, x, ls='-', c='red')
# blue circles
plt.plot(x, x**1.25, ls='', c='blue', marker='o')
# green stars
plt.plot(x, x**1.5, ls='', c='green', marker='*')
plt.show()

13.  For more properties, do
› lines=plt.plot([1, 2, 3])
› plt.setp(lines)

14. import numpy as np
import matplotlib.pyplot as plt
x = np.array(range(10))
# first figure
plt.figure(1)
# num of row, num of column, num of axis
plt.subplot(2, 3, 1)
plt.plot(x, x, 'r')
plt.subplot(2, 3, 2)
plt.plot(x, x, 'bo')
plt.show()

16. import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 1, 0.001)
y = x**1.5
plt.plot(x, y, 'r')
plt.xlabel(r'$x$')
plt.ylabel(r'$y=x^{1.5}$')
plt.title(r'Graph of $y=x^{1.5}$')
plt.text(0.3, 0.25, r'$y=x^{1.5}$')
plt.grid(True)
plt.show()

18.  Draw the graph of
binary entropy
function
𝐻 𝑝 = −𝑝log2 𝑝 − 1 − 𝑝 log2 1 − 𝑝
› Hint: use np.log2 to
calculate log2

19. import numpy as np
import matplotlib.pyplot as plt
p = np.arange(0, 1, 0.001)
H = -p*np.log2(p) - (1-p)*np.log2(1-p)
plt.plot(p, H, 'r')
plt.xlabel(r'$p$')
plt.ylabel(r'$H(p)=-p ¥log_{2}(p)-(1-p)¥log_{2}(1-p)$')
plt.title('Binary Entropy Function')
plt.grid(True)
plt.show()

20.  Matplotlib tutorial
http://matplotlib.org/users/pyplot_tutorial.html
 りんご大学 - 品種別生産量の推移
http://www.ringodaigaku.com/study/statistics/
production_kind.html