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Python scientific tool -- Matplotlib visualization (histogram, histogram, pie chart)

編輯：Python

One 、 Draw column ( shape ) chart

Histogram （bar chart）, It is a statistical report chart that takes the length of a rectangle as a variable , The data distribution is represented by a series of longitudinal stripes with different heights , Used to compare two or more values （ Different time or different conditions ）.
Or say , It refers to the comparison between different things or the advantages and disadvantages of the same thing at different times , It can clearly reflect the difference of data , Generally, it is used to reflect the comparison between classification items .
pyplot Plot the function in the histogram bar, Its grammatical form .
plt.bar(x, height, width=0.8, bottom=None, *, align=‘center’, **kwargs)
Parameter description ：
1）x: Express x The number of divisions of the shaft .
2）height: Express x The data value corresponding to the axis ,y value ( The height of the column ).
3）width: Width of each column .（0,1） Between float, Default 0.8.
4）bottom: Histogram y The starting point of the boundary . Default 0.
5）align: x The position where the coordinate on the axis is aligned with the cylinder .center( In the middle ）,edge( edge ）.
6）color: Columnar fill color
7）tick_label： Label name of each column
8）alpha： The transparency of the column fill color
9）edgecolor： The border color of the column
10）linewidth： Width of column border line

Two 、bar() Practice

draw 2007–2016 National employment in , Histogram of urban employees and rural employees （Employedpopulation.csv).
among , Employed persons nationwide （ ten thousand people ） Use a red column to indicate , Urban employees （ ten thousand people ） Use a green column to show , Rural employees （ ten thousand people ） Use blue columns to show .

1. Source code

import numpy as np
import matplotlib.pyplot as plt
Emp_data= np.loadtxt('Employedpopulation.csv',delimiter = ",",
usecols=(1,2,3,4,5,6,7,8,9,10),dtype=int)
# Set up matplotlib Normal display of Chinese and minus sign 
plt.rcParams['font.sans-serif']=['SimHei'] # Show Chinese in bold 
plt.rcParams['axes.unicode_minus']=False # The minus sign is displayed normally 
# Create a drawing object , And set the width and height of the object 
plt.figure(figsize=(12, 4))
# Draw a histogram of all employed persons 
plt.bar(Emp_data[0],Emp_data[1], width = 0.3,color = 'red')
# Draw a histogram of urban employees 
plt.bar(Emp_data[0],Emp_data[2],width = 0.3,color = 'green')
# Draw a histogram of rural employees 
plt.bar(Emp_data[0],Emp_data[3], width = 0.3,color = 'blue')
x = [i for i in range(2006,2017)]
plt.xlabel(' year ')
plt.ylabel(' personnel ( ten thousand people )')
plt.ylim((30000,80000))
plt.xlim(2006,2017)
plt.xticks(x)
plt.title("2007-2016 Year town 、 Histogram of rural and all employed persons ")
# Add legend 
plt.legend((' Full employment ',' Urban employment ',' Rural employment '))
plt.savefig('Employedpopulation_bar.png')
plt.show()

2. Adjust the width of the histogram , Position of column display after translation ¶

import numpy as np
import matplotlib.pyplot as plt
Emp_data= np.loadtxt('Employedpopulation.csv',delimiter = ",",
usecols=(1,2,3,4,5,6,7,8,9,10),dtype=int)
# Set up matplotlib Normal display of Chinese and minus sign 
plt.rcParams['font.sans-serif']=['SimHei'] # Show Chinese in bold 
plt.rcParams['axes.unicode_minus']=False # The minus sign is displayed normally 
# Create a drawing object , And set the width and height of the object 
plt.figure(figsize=(12, 4))
# Draw a histogram of all employed persons , Translate the column forward 0.3
plt.bar(Emp_data[0]-0.3,Emp_data[1], width = 0.3,color = 'red')
# Draw a histogram of urban employees 
plt.bar(Emp_data[0],Emp_data[2],width = 0.3,color = 'green')
# Draw a histogram of rural employees , Translate the column backwards 0.3,
plt.bar(Emp_data[0]+0.3,Emp_data[3], width = 0.3,color = 'blue')
x = [i for i in range(2006,2017)]
plt.xlabel(' year ')
plt.ylabel(' personnel ( ten thousand people )')
plt.ylim((30000,80000))
plt.xlim(2006,2017)
plt.xticks(x)
plt.title("2007-2016 Year town 、 Histogram of rural and all employed persons ")
# Add legend 
plt.legend((' Full employment ',' Urban employment ',' Rural employment '))
plt.savefig('Employedpopulation_bar.png')
plt.show()

3. Add words to the picture text, plt.text()

Use the loop to set the position of text increase .
for x,y in zip(X,Y1):
plt.text(x+a, y+b, ‘%.2f’ % y, ha=‘center’, va= ‘bottom’)
among a,b For offset .

import numpy as np
import matplotlib.pyplot as plt
Emp_data= np.loadtxt('Employedpopulation.csv',delimiter = ",",
usecols=(1,2,3,4,5,6,7,8,9,10),dtype=int)
# Set up matplotlib Normal display of Chinese and minus sign 
plt.rcParams['font.sans-serif']=['SimHei'] # Show Chinese in bold 
plt.rcParams['axes.unicode_minus']=False # The minus sign is displayed normally 
# Create a drawing object , And set the width and height of the object 
plt.figure(figsize=(12, 4))
# Draw a histogram of all employed persons , Translate the column forward 0.3
plt.bar(Emp_data[0]-0.3,Emp_data[1], width = 0.3,color = 'red')
# Draw a histogram of urban employees 
plt.bar(Emp_data[0],Emp_data[2],width = 0.3,color = 'green')
# Draw a histogram of rural employees , Translate the column backwards 0.3,
plt.bar(Emp_data[0]+0.3,Emp_data[3], width = 0.3,color = 'blue')
# Gituga text
X = Emp_data[0] # Set up X coordinate 
Y1 = Emp_data[1] # Set up y coordinate 
for x, y in zip(X, Y1):
plt.text(x - 0.3, y +1000, '%i' % y, ha='center')
Y2 = Emp_data[2]
for x, y in zip(X, Y2):
plt.text(x , y + 1000, '%i' % y, ha='center')
Y3 = Emp_data[3]
for x, y in zip(X, Y3):
plt.text(x + 0.3, y + 1000, '%i' % y, ha='center')
x = [i for i in range(2006,2017)]
plt.xlabel(' year ')
plt.ylabel(' personnel ( ten thousand people )')
plt.ylim((30000,81000))
plt.xlim(2006,2017)
plt.xticks(x)
plt.title("2007-2016 Year town 、 Histogram of rural and all employed persons ")
# Add legend 
plt.legend((' Full employment ',' Urban employment ',' Rural employment '))
plt.savefig('Employedpopulation_bar.png')
plt.show()

4. Select column chart when there are few comparative data （bar()). You can also convert a column chart to a bar chart (barh())

plt.barh(y, width, height=0.8, left=None, *, align='center', **kwargs)

barh() Function height Represents the width of the horizontal column .

import numpy as np
import matplotlib.pyplot as plt
Emp_data= np.loadtxt('Employedpopulation.csv',delimiter = ",",
usecols=(1,2,3,4,5,6,7,8,9,10),dtype=int)
# Set up matplotlib Normal display of Chinese and minus sign 
plt.rcParams['font.sans-serif']=['SimHei'] # Show Chinese in bold 
plt.rcParams['axes.unicode_minus']=False # The minus sign is displayed normally 
# Create a drawing object , And set the width and height of the object 
plt.figure(figsize=(8, 4))
# Draw a histogram of all employed persons , Translate the column forward 0.3
plt.barh(Emp_data[0]+0.3,Emp_data[1], color = 'red',height=0.3)
# Draw a histogram of urban employees 
plt.barh(Emp_data[0],Emp_data[2],color = 'green',height=0.3)
# Draw a histogram of rural employees , Translate the column backwards 0.3,
plt.barh(Emp_data[0]-0.3,Emp_data[3], color = 'blue',height=0.3)
# Gituga text
y = [i for i in range(2006,2017)]
plt.ylabel(' year ')
plt.xlabel(' personnel ( ten thousand people )')
plt.xlim((30000,81000))
plt.ylim(2006,2017)
plt.yticks(y)
plt.title("2007-2016 Year town 、 Histogram of rural and all employed persons ")
# Add legend 
plt.legend((' Full employment ',' Urban employment ',' Rural employment '))
plt.savefig('Employedpopulation_bar.png')
plt.show()

5.bar() And barh() Selection of graph

Column chart and bar chart are drawn , It can be observed from the figure that , The abscissa and ordinate exchange positions .
Advantages of bar charts ： When the text to draw different items is long and there are too many categories , Because the space of horizontal axis of column graph is limited , It is not always possible to display all the text , The bar chart can completely display the text information , Without truncation or omission .
Such as , Calculate the purchase price of a book on different websites . And draw bar chart and column chart .

import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Different website book price data 
price = [39.5, 39.9, 45.4, 38.9, 33.34,37.89,37.5]
# Create a canvas 
plt.figure(figsize=((8,6)))
plt.subplot(211)
# Draw the first picture 
name=[' dangdang ', ' China Book Network ', ' Jingdong Mall books ',
' Tmall ',' Suning e-buy ',' TaoBao ',' Amazon - E-book ']
plt.barh(range(7), price, height=0.7, color='steelblue', alpha=0.8)
# Draw from the bottom up 
plt.yticks(range(7),name )
plt.xlim(30,47)
plt.xlabel(" Price ")
plt.ylabel(' Different platforms ')
plt.title(" Different platforms 《XX》 The price list of the book -- Bar chart ")
for x, y in enumerate(price):
plt.text(y + 0.2, x - 0.1, '%s' % y)
plt.subplot(212)
plt.ylabel(' Price ')
plt.xlabel(' Different platforms ')
plt.xticks(range(7),name)
plt.title(" Different platforms 《XX》 The price list of the book -- Bar charts ")
plt.tight_layout(3,1,1)
plt.ylim(10,50)
for x, y in enumerate(price):
plt.text(x-0.2, y + 0.5, '%s' % y)
plt.bar(name,price,0.7,color='pink')
plt.show()
# Compare the effect of bar chart and bar chart ！

3、 ... and 、 Draw histogram

Histogram （Histogram） Also known as mass distribution diagram , It's a two-dimensional statistical chart . It is represented by a series of longitudinal stripes or line segments with different heights , Generally, the horizontal axis is used to represent the category of data , Number or proportion in vertical axis .
plt.hist(x, bins=10, range=None,
weights=None, cumulative=False, bottom=None,
histtype=‘bar’, align=‘mid’, orientation=‘vertical’,
rwidth=None, log=False, color=None,
label=None, stacked=False)
Parameter description ：
1）x： Specify the data to draw the histogram .
2）bins： Specify the number of histogram bars . receive int, Sequence or auto.
3）range： Specify the upper and lower bounds of histogram data , Ignore lower or higher outliers . The default contains the maximum and minimum values of plot data .
4）density=True Indicates the frequency distribution ;density=False It represents the frequency distribution . Default False.
5）weights： This parameter can set the weight for each data point . And x Weight array with the same shape . take x Each element in is multiplied by the corresponding weight value and then counted . If density The value is True, Then the weight will be normalized . This parameter can be used to draw the histogram of the merged data .
6）cumulative： Whether it is necessary to calculate the cumulative frequency or frequency . Boolean value , If True, Then calculate the cumulative frequency . If density The value is True, Then calculate the cumulative frequency .
7）bottom： You can add a baseline to each bar of the histogram , The default is 0. The bottom of each column is relative to y=0 The location of . If it's a scalar value , Then each column is relative to y=0 Up / The downward offset is the same . If it's an array , Move the corresponding column according to the value of the array element .
8）histtype： Specifies the type of histogram , The default is ’bar’, besides , also ’barstacked’,‘step’, ‘stepfilled’.
9）align： Set the alignment of bar boundary values , The default is mid, And then there is left and right.
10）orientation： Set the placement direction of histogram , The default is vertical direction .
11）rwidth： Set the width of the histogram bar .
12）color： Set the fill color of the histogram .
13）edgecolor： Set the histogram border color .
14）label： Set the label of histogram , It can be done by legend Show its legend .
15）stacked： When there are multiple data , Whether it is necessary to stack the histograms , Default horizontal placement .

Four 、hist() Practice

1. Salary distribution
Draw the employee salary of a company （salary.csv) Distribution map .
By segment 1000-3000,3000-5000,5000-7000,7000-9000,9000~12000 Statistics of employees' wages .

import numpy as np
import matplotlib.pyplot as plt
salary= np.loadtxt('salary.csv',delimiter = ",",
usecols=(3,),skiprows=1,dtype=int)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=((14,6)))
group = [i for i in range(1000,13000,2000)]
plt.xticks(group)
plt.hist(salary, group,rwidth=0.8,histtype='bar')
plt.show()

2.hist() The return value of

import numpy as np
import matplotlib.pyplot as plt
salary= np.loadtxt('salary.csv',delimiter = ",",
usecols=(3,),skiprows=1,dtype=int)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=((14,6)))
group = [i for i in range(1000,13000,2000)]
plt.xticks(group)
return_V=plt.hist(salary, group,rwidth=0.8,histtype='bar')
print(return_V) # Returns a tuple 
plt.show()

3.hist() Default frequency of ,density=True Frequency distribution

import numpy as np
import matplotlib.pyplot as plt
salary= np.loadtxt('salary.csv',delimiter = ",",
usecols=(3,),skiprows=1,dtype=int)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=((14,6)))
group = [i for i in range(1000,13000,2000)]
plt.xticks(group)
plt.hist(salary, group,rwidth=0.8,histtype='bar',density=True)
plt.show()

4. Not in groups , Set up bins Parameter plot distribution

import numpy as np
import matplotlib.pyplot as plt
salary= np.loadtxt('salary.csv',delimiter = ",",
usecols=(3,),skiprows=1,dtype=int)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=((14,6)))
x=[i for i in range(1000,14000,1000)]
plt.xticks(x)
plt.xlim(2000,12000)
plt.hist(salary,bins=20,rwidth=0.8,histtype='bar')
plt.show()

5.histtype Parameters .
Specifies the type of histogram （‘bar’（ Default ）,‘barstacked’,‘step’,‘stepfilled’）
1).'bar’ Is a traditional bar histogram ;
2).'barstacked’ Is a stacked bar histogram ;
3).'step’ Is an unfilled bar histogram , Only outer border ;
4).‘stepfilled’ Is a filled histogram .
When histtype The value is ’step’ or ’stepfilled’,rwidth The setting is invalid , That is, the spacing between columns cannot be specified , Connected together by default

import numpy as np
import matplotlib.pyplot as plt
salary= np.loadtxt('salary.csv',delimiter = ",",
usecols=(3,),skiprows=1,dtype=int)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=((14,6)))
x=[i for i in range(1000,14000,1000)]
plt.xticks(x)
plt.xlim(2000,12000)
plt.hist(salary,bins=10,rwidth=0.8,histtype='barstacked',color='r')
plt.hist(salary,bins=20,rwidth=0.3,histtype='step',color='b')
plt.show()

5、 ... and 、 The pie chart

The pie chart （Pie Graph） Is the ratio of the size of items in a data series to the sum of items .
The data points in the pie chart are displayed as a percentage of the whole pie chart . The pie chart can clearly reflect the parts and the parts 、 The proportional relationship between the part and the whole . It is easy to display the size of each group of data relative to the total number , And the display mode is intuitive .
For example, the profit proportion of different categories 、 Proportion of sales of different types of customers 、 The proportion of each component in the total .
pyplot The function for drawing pie chart in is pie, Its grammatical form ：
pie(x, explode=None, labels=None, colors=None, autopct=None,
pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None,
radius=None, counterclock=True, wedgeprops=None, textprops=None,
center=(0, 0), frame=False, rotatelabels=False, hold=None, data=None)
Parameter description ：
1）x: Drawing pie chart data ;
2）labels: Description of each area （ The outside of the pie chart shows ）;
3）explode : The distance from the center of each area ;
4）startangle : Starting angle , The default graph is from x The axis is drawn counterclockwise , If set =90 From y Draw in the positive direction of the axis ;
5）shadow : Draw a shadow under the pie chart . The default value is ：False, No shadows ;
6）labeldistance :label The drawing position of the mark , The ratio to the radius , The default value is 1.1, Such as 1 It's on the inside of the pie ;
7）autopct : Control the percentage setting in the pie chart , have access to format String or format function
'%1.1f’ Refers to the number of digits before and after the decimal point ( Didn't fill in the blanks );
8）pctdistance : Be similar to labeldistance, Appoint autopct The position scale of , The default value is 0.6;
9）radius : Control the pie radius , The default value is 1;counterclock ： Specify the direction of the pointer ; Boolean value , Optional parameters , The default is ：True, That is, counter clockwise . Change the value to False It can be changed to clockwise .wedgeprops ： Dictionary type , Optional parameters , The default value is ：None. The parameter dictionary is passed to wedge Object is used to draw a pie chart . for example ：wedgeprops={‘linewidth’:3} Set up wedge The line width is 3.
10）textprops ： Set the label （labels） And the format of proportional text ; Dictionary type , Optional parameters , The default value is ：None. Pass to text The dictionary parameter of the object .
11）colors: The color of the pie chart .
12）center ： A list of floating point types , Optional parameters , The default value is ：(0,0). The center of the icon .
13）frame ： Boolean type , Optional parameters , The default value is ：False. If it is true, Drawing axis frames with tables .

rotatelabels ： Boolean type , Optional parameters , The default is ：False. If True, Rotate each label To the specified angle .

6、 ... and 、pie() Practice

draw 2016 Urban and rural employees in (Employedpopulation.csv) The pie chart of .

1. Source code

import numpy as np
import matplotlib.pyplot as plt
# Import 2016 Annual employment data 
Emp_data= np.loadtxt('Employedpopulation.csv',delimiter = ",",
usecols=(1),dtype=int)
# Set up matplotlib Normal display of Chinese and minus sign 
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
# extract 2016 The annual urban employment data and rural employment data are assigned to X
X = [Emp_data[2],Emp_data[3]]
# Create a drawing object , Set the canvas to square , The pie chart drawn is a positive circle 
plt.figure(figsize=(7, 7))
label = [' Urban employment ',' Rural employment '] # Define the label of the pie chart , Tags are lists 
explode = [0.01,0.02] # Set the distance from the center of the circle n radius 
# Draw the pie chart （ data , radius , The label corresponding to the data , Keep two decimal places for the percentage ）
plt.pie(X,explode = explode, labels=label,autopct='%.3f%%')
# Add the title 
plt.title("2016 Pie chart of urban and rural employment in ")
# Add legend 
plt.legend({
' Urban employment ',' Rural employment '})
plt.savefig('Employedpopulation_pie.png')
plt.show()

2. Set the color of the pie chart

1. Set up color The parameter is pie chart color
2. stay matplotlib in , There are many ways to set colors .
（1） Commonly used red, Shorthand for r;green, Shorthand for g;blue, Shorthand for b;yellow, Shorthand for y;cyan, Shorthand for c ;magenta, Shorthand for m;black, Shorthand for k;white, Shorthand for w.
（2）X11 Series of colors correspond to specific color codes by name , Later, CSS Color code is also based on its development , stay matplotlib in ,X11/CSS4 The relevant color names and hexadecimal codes are stored in a dictionary , You can view it in the following ways .
import matplotlib._color_data as mcd
for key in mcd.CSS4_COLORS:
print(‘{}: {}’.format(key, mcd.CSS4_COLORS[key]))
（3） RGB/RGBA Tuples
All the colors have RGB Three primary colors make up , stay matplotlib in , Can be represented by a tuple red, green, blue The proportion of the three primary colors , And an optional alpha Value for transparency , The range of values is 0 To 1, Usage is as follows
plt.pie(x=[1,2,3,4], colors=[(0.1, 0.2, 0.5),(0.1, 0.3, 0.5),(0.1, 0.4, 0.5),(0.1, 0.5, 0.5)])

import matplotlib._color_data as mcd
for key in mcd.CSS4_COLORS:
print('{}: {}'.format(key, mcd.CSS4_COLORS[key]))
# Create a drawing object , Set the canvas to square , The pie chart drawn is a positive circle 
plt.figure(figsize=(7, 7))
# Set the color of the pie chart 
color=['royalblue','#FFF5EE']
plt.pie(X,explode = explode, labels=label,autopct='%.3f%%',colors=color)
# Add the title 
plt.title("2016 Pie chart of urban and rural employment in ")
# Add legend 
plt.legend({
' Urban employment ',' Rural employment '})
plt.show()

3. Set text labels

textprops={‘fontsize’:18,‘color’:‘k’} Set the font size to 18, The color black ¶

# Create a drawing object , Set the canvas to square , The pie chart drawn is a positive circle 
plt.figure(figsize=(7, 7))
label = [' Urban employment ',' Rural employment '] # Define the label of the pie chart , Tags are lists 
explode = [0.01,0.02] # Set the distance from the center of the circle n radius 
# Set text labels 
textprops={
'fontsize':16,'color':'k'}
color=['royalblue','#FFF5EE']
plt.pie(X,explode = explode, labels=label,autopct='%.3f%%',
colors=color,textprops=textprops)
# Add the title 
plt.title("2016 Pie chart of urban and rural employment in ")
# Add legend 
plt.legend({
' Urban employment ',' Rural employment '})
plt.show()

4. Set the separation and shadow of pie chart

Pie chart separation explode Value settings , shadow ：shadow=True

# Create a drawing object , Set the canvas to square , The pie chart drawn is a positive circle 
plt.figure(figsize=(7, 7))
label = [' Urban employment ',' Rural employment '] # Define the label of the pie chart , Tags are lists 
# Set the radius of each item from the center of the circle 
explode = [0.0,0.06]
# Set text labels 
textprops={
'fontsize':18,'color':'k'}
color=['royalblue','#FFF5EE']
plt.pie(X,explode = explode, labels=label,autopct='%.3f%%',
colors=color,textprops=textprops,shadow=True)
# Add the title 
plt.title("2016 Pie chart of urban and rural employment in ")
# Add legend 
plt.legend({
' Urban employment ',' Rural employment '})
plt.show()

5. Set start angle startangle

plt.figure(figsize=(4,6))
plt.pie(X,explode = explode, labels=label,autopct='%.3f%%',
colors=color,textprops=textprops,shadow=True,startangle=90)
# Add the title 
plt.title("2016 Pie chart of urban and rural employment in ")
# Add legend 
plt.legend({
' Urban employment ',' Rural employment '})
plt.show()

6. Project practice

Know the advanced mathematics of a class 、 Program design course examination results （student.xlsx）, It is required to draw a pie chart to show the results of each course （85 More than ）、 pass （60-84 branch ）、 fail, （60 Below ） The proportion of .

import numpy as np
import matplotlib.pyplot as plt
# Set up matplotlib Normal display of Chinese and minus sign 
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
# Import student grades Advanced mathematics ma, Programming pr
data= np.loadtxt('student.csv',delimiter=',',
usecols=(1,2),dtype=np.int,skiprows=1)
# Count the number of people in each score segment 
ma_count=[]
for num in data.T:
n=m=k=0
for i in num:
if i>=85:
n=n+1
elif i>=60:
m=m+1
else:
k=k+1
ma_count.append([n,m,k])
# Define the label and distance of the pie chart 
label = [' good ',' pass ',' fail, ']
explode =[0.0,0.0,0.08]
# Color the pie chart 
color=['#FF69B4','#6495ED','#9ACD32']
plt.figure(figsize=(10,10))
plt.subplot(121)
plt.title(" Distribution of advanced mathematics ")
plt.pie(ma_count[0], labels=label,autopct='%.2f%%',explode=explode,
colors=color,shadow=True)
plt.legend(loc=4)
plt.subplot(122)
plt.title(" Program design score distribution ")
plt.pie(ma_count[1], labels=label,autopct='%.2f%%',explode=explode,
colors=color,shadow=True)
# Add legend 
plt.legend(loc=4)
plt.show()

If set to five levels ： good , good , secondary , pass , fail, .

import numpy as np
import matplotlib.pyplot as plt
# Set up matplotlib Normal display of Chinese and minus sign 
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
# Import student grades Advanced mathematics ma, Programming pr
data= np.loadtxt('student.csv',delimiter=',',
usecols=(1,2),dtype=np.int,skiprows=1)
# Count the number of people in each score segment 
ma_count=[]
# Create an empty list 
for num in data.T:
n=m=k=j=r=0
# Set initial value 
for i in num:
if i>=95:
n=n+1
# The number of outstanding people 
elif i>=85:
m=m+1
# Get good numbers 
elif i>=70:
k=k+1
# Get a medium number 
elif i>=60:
j=j+1
# The number of people who have passed 
else:
r=r+1
# The number of people who failed 
ma_count.append([n,m,k,j,r])
# Define the label and distance of the pie chart 
label = [' good ',' good ',' secondary ',' pass ',' fail, ']
# It refers to the distance between this block and the center point 
explode =[0.05,0.05,0.05,0.05,0.05]
# Color the pie chart 
color=['#FF69B4','#6495ED','#9ACD32','#FFE4E1','#808080']
# Five colors are set 
plt.figure(figsize=(10,10))
plt.subplot(121)
plt.title(" Distribution of advanced mathematics ")
plt.pie(ma_count[0], labels=label,autopct='%.2f%%',explode=explode,
colors=color,shadow=True)
# Add legend 
plt.legend(loc=4)
plt.subplot(122)
plt.title(" Program design score distribution ")
plt.pie(ma_count[1], labels=label,autopct='%.2f%%',explode=explode,
colors=color,shadow=True)
# Add legend 
plt.legend(loc=4)
#loc=4 Position it in the lower right corner , Otherwise, it will be placed where other systems think it is most appropriate 
print(data)
plt.savefig('StudentsGrades_pie.png')
plt.show()

above ,matplotlib Library about histograms , Histogram , Bar chart , That's all for the pie chart , Have you learned ？
If you need the above data table to simulate , Please contact the editor QQ：2122961493 receive .