Pyplot introductory tutorials and examples herein reference document Pyplot tutorial
Analysis examples
1. Basic drawing polyline
Drawing (0,0), (1,1), (2,1), (3,3) connected to the four points of the polyline
import matplotlib.pyplot as plt
x=[0,1,2,3]
y=[0,1,1,3]
plt.plot(x,y)
plt.show()
2. Modify the shape of a line chart color / line
plt.plot(x,y,'r') # 修改颜色,rgb=红绿蓝,默认为蓝
plt.plot(x,y,'--') # 修改线的形状为虚线,默认为折线'-',另外'o'为点,'^'为三角
plt.plot(x,y,'g--') # 一起修改为绿色虚线
plt.axis([1,6,0,5]) # 修改坐标轴x刻度显示
3. Enter the case where only one-dimensional data
plt.plot (x, y) to accept the set of points (X, y), only when the input of one-dimensional data processing as the y-axis, x-axis generated by default [0,1,2, ...]
for example, drawing four independent the point (0,1), (1,1), (2,1), (3,1)
y=[1,1,1,1]
plt.plot(y,'ro')
plt.show()
4.list与Arrays
Original tutorial
If matplotlib were limited to working with lists, it would be fairly useless for numeric processing. Generally, you will use numpy arrays. In fact, all sequences are converted to numpy arrays internally.
For performance issues, means that all sequences (including list, etc.) are converted to numpy.array inside
t1=[1,5,1,5] #list会被转换为t2的类型
t2=np.array([5,1,5,1]) #numpy.array
plt.plot(t1)
plt.plot(t2)
plt.show()
The display a plurality of graphs in FIG.
In the fourth, we are used twice plt.plot () Loading t1 and T2, may be a single statement
plt.plot(t1,'b--',t2,'r--')For both groups (x, y) coordinates, as follows
x1=[1,2,3,4]
y1=[1,2,3,4]
x2=[2,4,6,8]
y2=[4,8,12,16]
plt.plot(x1,y1,'r-',x2,y2,'g--')
plt.show()
6. Draw the standard curve function: sin () and cos ()
Draw f (x) = sin (x) and g (x) = cos (x) image x∈ [0,20] in
x = np.arange(0, 20, 0.01)
plt.plot(x, np.sin(x), 'r-', x, np.cos(x), 'b--')
plt.axis([0,20,-3,3])
plt.show()
The display grid lines
x = np.arange(0, 20, 0.01)
plt.plot(x, x**2)
plt.grid(True) # 设置网格线
plt.show()
8. marked increase
7 as a function of the image as an example. Note that the block distortion appears Chinese input
(1) increase in x, y-axis text
plt.xlabel("Money Earned")
plt.ylabel("Consume Level")
(2) increase in headline
plt.title("Figure.1")
(3). FIG text within
the specified x-axis appears the first word, y-axis, the text itself
plt.text(2.5,100,"TEXT1")
(4) an arrow indicating
the specified text, the arrow points to the coordinates, the coordinates of the text display, the properties of the arrow
plt.annotate('max value', xy=(20, 400), xytext=(12.5, 400),
arrowprops=dict(facecolor='black', shrink=0.05),
)
(5) Integrated Results Figure
9. Set the Text property
Of text (), xlabel (), ylabel (), title (), annotate () like text attributes parameter settings may be used
e.g.
plt.xlabel("Money Earned",color="r",fontsize=20)
Optional parameters are as follows, listed here only common properties, more properties please refer to
https://matplotlib.org/api/text_api.html#matplotlib.text.Text
10. Set Curve Properties
plt.plot () Returns matplotlib.lines.Line2D , variable and can be obtained by modifying curves Line2D properties
x=np.arange(0,10,0.01)
line1,line2=plt.plot(x,np.sin(x),'-',x,np.cos(x),'--') #line1得到2D Lines
plt.setp(line1,color='r',linewidth='11.0') #设置曲线的宽度
plt.show()
11. solve the Chinese garbled
9 using the modified font Chinese font can easily solve the garbage
(1) designated as the font files in a directory
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc")
plt.xlabel("中文文本", fontproperties=font)
(2) designated as the system font
plt.xlabel("中文文本",fontname='SimHei',size=20)
#或者
plt.xlabel("中文文本",fontproperties='SimHei',fontsize=20)#size要放在后面,否则会被覆盖
(3) Global Settings
font = {'family' : 'SimHei',
'weight' : '50',
'size' : '30'}
plt.rc('font', **font)
plt.xlabel("中文文本")
12. The modified axis scale
(1) specified scale range
plt.axis([0,6,1,5]) #设定x轴刻度在(0,6) y轴刻度在(1,5)
plt.axis('off') #关闭刻度
Scale modification (2) of the subgraph
axes[0,0].set_xticks([0,250,750,1000]) #设置x轴
axes[0,0].set_xticklabels(['one','two','three'],rotation=30) #将数值改为标签,并旋转30度显示
(3) using the scale function
of a tutorial example Example
Four different scale
plt.yscale('linear')
plt.yscale('log')
plt.yscale('symlog')
plt.yscale('logit')
The four following example code of FIG.
from matplotlib.ticker import NullFormatter # useful for `logit` scale
# Fixing random state for reproducibility
np.random.seed(19680801)
# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))
# plot with various axes scales
plt.figure(1)
# linear
plt.subplot(221)
plt.plot(x, y)
plt.yscale('linear')
plt.title('linear')
plt.grid(True)
# log
plt.subplot(222)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)
# symmetric log
plt.subplot(223)
plt.plot(x, y - y.mean())
plt.yscale('symlog', linthreshy=0.01)
plt.title('symlog')
plt.grid(True)
# logit
plt.subplot(224)
plt.plot(x, y)
plt.yscale('logit')
plt.title('logit')
plt.grid(True)
# Format the minor tick labels of the y-axis into empty strings with
# `NullFormatter`, to avoid cumbering the axis with too many labels.
plt.gca().yaxis.set_minor_formatter(NullFormatter())
# Adjust the subplot layout, because the logit one may take more space
# than usual, due to y-tick labels like "1 - 10^{-3}"
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
wspace=0.35)
plt.show()
13. graphing: circular / rectangular / oval
import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig = plt.figure()
ax1 = fig.add_subplot(111,aspect='equal') #1x1一张图中的第1张,equal为等宽显示
rec=patches.Rectangle((0, 0), 8, 4) #顶点坐标(0,0) 宽w=8 高h=4
cir=patches.Circle((8,8),2) #圆心坐标(8,8) 半径r=1
ell=patches.Ellipse((2,8),6,3) #椭圆左顶点坐标(2,8) 长轴c1=6 短轴c2=3
ax1.add_patch(rec) #插入patch图像
ax1.add_patch(cir)
ax1.add_patch(ell)
plt.plot() #显示多个
plt.show() 
patches There are many kinds of graphics such as various arrows, arrows, and other polygons.
14. histogrammed - standard normal distribution
plt.hist () is used to draw a histogram
[Mu], also known as standard normal distribution, the mean is μ = 0, the normal distribution standard deviation σ = 1, denoted by N (0,1)
code is as follows
mu, sigma = 0,1
x = np.random.normal(mu,sigma,10000)
n, bins, patches = plt.hist(x,bins=100,facecolor='g', alpha=0.75)
plt.text(-3, 250, r'$\mu=0,\ \sigma=1$')
plt.grid(True)
plt.show()
15. Draw a scatter plot
plt.scatter () is used to draw scattergram
Example: positions 1000 to draw normal random distribution points
x = np.random.normal(0, 1, 1000) # 1000个点的x坐标
y = np.random.normal(0, 1, 1000) # 1000个点的y坐标
c = np.random.rand(1000) #1000个颜色
s = np.random.rand(100)*100 #100种大小
plt.scatter(x, y, c=c, s=s,alpha=0.5)
plt.grid(True)
plt.show() 
16. A plurality of graph display
names = ['Anime', 'Comic', 'Game']
values = [30, 10, 20]
plt.subplot(221) #构建2x2张图中的第1张子图
plt.bar(names, values) #统计图
plt.subplot(222)
plt.scatter(names, values) #散点图
plt.subplot(223)
plt.plot(names, values) #折线图
plt.suptitle('三种图示',fontname='SimHei')
plt.show()
The above is a configuration of each sub-picture, and then draw in the submap It is also possible to construct all of the sub-picture, and then specify the drawing sheets in which sub-picture by the subscript
fig,axes=plt.subplots(2,2) #构造2x2的子图
axes[0,1].plot(names, values) #通过下标访问
axes[1,0].scatter(names, values)
axes[1,1].bar(names, values)
plt.show()
17. FIG sub-interval adjustment
fig,axes=plt.subplots(2,2,sharex=True,sharey=True) #构造2x2的子图,子图共享x,y轴
for i in range(2):
for j in range(2):
axes[i,j].hist(np.random.rand(500),bins=100,alpha=0.7,color='k')
plt.subplots_adjust(hspace=0,wspace=0) #修改内部的宽,高间距为0
plt.show()
(And can not be obtained in the same book python3.7 result, i.e. there is no data to achieve an internal spacer sub-picture visualization)
18. Draw a bar graph
Vertical histogram plt.bar (name, values)
level histogram plt.barh (name, values)
x=np.random.randint(1,10,8)
label=list('abcdefgh')
plt.subplot(211)
plt.bar(label,x)
plt.subplot(212)
plt.barh(label,x)
plt.show()
A vertical bar graph comparing the two sets of data
no longer uses plt.bar () , but the use of pd.DataFrame.plot.bar ()
x=np.random.randint(1,10,8)
y=np.random.randint(1,10,8)
data=pd.DataFrame([x,y],index=['X','Y'],columns=list('abcdefgh'))
>>> data
data
a b c d e f g h
X 6 2 9 5 5 2 7 7
Y 6 6 9 1 1 5 2 4
data.plot.bar()
plt.show()
To obtain a classification index, histogram data columns
two data cross bar graph comparing vertical, and simply exchange columns to index
data.transpose().plot.bar() #data.transpose()转置
plt.show()
