如何绘制 NxM 矩阵中列的平均值，python

Question

大家好，我有一个任务，我应该创建两个图，其中输入是一个名为 Grades 的变量，它是一个 N×M 矩阵，包含给 N 个学生的 7 步等级的成绩 在 M 个不同的任务上。

第一个图是我通过创建 for 循环成功绘制的条形图。 第二个图必须将每个作业的平均成绩显示为一条线。

这是我尝试过的，但请注意我已经给出了变量等级值来测试绘图是否有效：

import numpy as np
import matplotlib.pyplot as plt


grades=np.array([[12,10,7,4,2,-3,4,7,10,12],[2,0,-3,10,4,7,2,0,-3,10],[-3,0,2,4,7,12,12,4,7,2]])
len(grades)



fig2=plt.figure()
y=''
x=''

for i in range(len(grades)):
    y=np.array([np.mean(grades[:,i],axis=0)])
    x=np.array([i+1])

    plt.plot(x,y,'-',label=f'Average of assignment {i+1}')
   
#when taking the length of a matrix by calling the rows you get the amount of columns

plt.xlabel('Assignments')
plt.ylabel('Grades')
    
plt.title('The distribution of grades per assignment') 
    
    
#putting legends next to the graph.
plt.legend(loc='center left',prop={"size":10},bbox_to_anchor=(1, 0.5))
    
#displaying the plot.
plt.savefig('Gradesperassignment',bbox_inches='tight')

Answer 1

import numpy as np
import matplotlib.pyplot as plt

grades=np.array([[12,10,7,4,2,-3,4,7,10,12],[2,0,-3,10,4,7,2,0,-3,10],[-3,0,2,4,7,12,12,4,7,2]])
len(grades)
fig2=plt.figure()
y=''
x=''
X=[]
Y=[]
for i in range(len(grades)):
    y=np.array([np.mean(grades[:,i],axis=0)])
    x=np.array([i+1])
    print(x,y)
    X.append(x)
    Y.append(y)
plt.plot(X,Y)
#when taking the lentgh of a matrix by calling the rows you get the amount of columns
plt.xlabel('Assignments')
plt.ylabel('Grades') 
plt.title('The distribution of grades per assignment')   
#putting legends next to the graph.
plt.legend(loc='center left',prop={"size":10},bbox_to_anchor=(1, 0.5)) 
#displaying the plot.
#plt.savefig('Gradesperassignment',bbox_inches='tight')
plt.show()

在里面为你计算一对 x,y 并绘制它。但为了画一条线，它至少需要 2 个点。在 X 列表中添加所有 x，在 Y 列表中添加 y，然后绘制 X,Y

Answer 2

这里@virxen：

import numpy as np
import matplotlib.pyplot as plt

from FinalGradeFunction import computeFinalGrades
from loaddatafunction import dataLoad

data=dataLoad('data.csv')
df1= data.drop(['StudentID', 'Name'], axis=1)
             
grades=df1.to_numpy()
grades is in this case=array([[ 7,  7,  4],
       [12, 10, 10],
       [-3,  7,  2],
       [10, 12, 12],
       [ 7,  7,  5],
       [12, 10, 10],
       [-3,  7,  2],
       [10, 12, 12]], dtype=int64)

def gradesPlot(grades):
    # gradesPlot is a function which generates two plots.
    #one which is a bar plot and the other is a graph.
    # Usage: gradesPlot(grades)
    #
    # Input:the grades of the students given as an NxM matrix 
    # containing the grades on the 7-step-scale given to N students on M different assignments
    # Output grades (two plots).
    #
    # Authors: Yaren Kart, 2021.

    
    #Plot1 Find Grades
    
    #Counting the occurrences of the grades from the 7-step-scale:
    #creating a vector made up of the number of zeros equivalent to the length of the Final_Grades.
    Final_Grades=computeFinalGrades(grades)
    
    v=np.zeros(len(Final_Grades))
 
    #counting how many times the different grades occur and putting the values in the vector v. 
    for i in range(len(v)):
        v[i]=sum(Final_Grades==i+1)
        
    #Ploting the bar plot:
    #ploting the figure.
    fig1 = plt.figure()  
    
    #determining the space between the bars.
    ax = fig1.add_axes([0,0,1.5,1.5]) 
    
    #setting the 7-step-scale.
    x = ['-3', '00', '02', '4','7','10','12'] 
    
    #creating the bar plot.
    ax.bar(x,v) 
    
    #labelling the x-axis.
    plt.xlabel('The 7-step-scale grades') 
    
    # labeling the y-axis.
    plt.ylabel('The number of students')
    
    #giving the bar plot a title.
    plt.title('The distribution of final grades') 
    
    plt.savefig('Finalgrades',bbox_inches='tight')
        

    
#-----------------------------------------------------------------------------------------------------------------------------------------------    
    #Plot2 "grades per assignment"
   
    fig2=plt.figure()
    
    #will move the x- and y-values so that the dots wont overlap
    jitter_min = -0.1
    jitter_max = 0.1


    #creating a for loop
    #iterate over the grades array, one row at a time. 
    #enumerate returns an index - a, and the row.
    for a,row in enumerate(grades):
        
        x = (a*np.ones(len(row)) + (jitter_max-jitter_min)*np.random.random(size=len(row))+jitter_min)
        plt.plot(x, row, 'o', label='Assignment {:1d}'.format(a), clip_on=False)

    plt.xlabel('Assignments')
    plt.ylabel('Grades')
        
    plt.title('The distribution of grades per assignment) 
    
    
    #putting legends next to the graph.
    plt.legend(loc='center left',prop={"size":10},bbox_to_anchor=(1, 0.5))
   
    y=''
    x=''
    X=[]
    Y=[]

    #Creating a for loop
    for i in range(len(grades)):
        y=np.array([np.mean(grades[:,i],axis=0)])
        x=np.array([i+1])
        
        #Adding all the x values into X and all the y-values into Y.
        X.append(x)
        Y.append(y)
    
    plt.plot(X,Y,label='The average of the assignments.')
    
    #assigning labels and a title to the graph.
    plt.xlabel('Assignments')
    plt.ylabel('Grades') 
    plt.title('The distribution of grades per assignment')   
   
    #putting legends next to the graph.
    plt.legend(loc='center left',prop={"size":10},bbox_to_anchor=(1, 0.5)) 
   
    #plt.savefig('Gradesperassignment',bbox_inches='tight')
 
    plt.savefig('Gradesperassignment',bbox_inches='tight')    
    

print(gradesPlot(grades))
   
   
   
   
   
   
   
   
   
   
   
   
   


    
   
   
   
   
   
   
   
   
   
   
   
   
   

Answer 3

import numpy as np
import matplotlib.pyplot as plt

grades=np.array([[ 7,  7,  4],
       [12, 10, 10],
       [-3,  7,  2],
       [10, 12, 12],
       [ 7,  7,  5],
       [12, 10, 10],
       [-3,  7,  2],
       [10, 12, 12]], dtype='int64')

def gradesPlot(grades):  
    #Plot1 Find Grades
    Final_Grades=np.array([7,10,7,12,7,2,12])
    v=np.zeros(len(Final_Grades))
    fig1 = plt.figure() 
    ax = fig1.add_axes([0,0,1.5,1.5]) 
    x = ['-3', '00', '02', '4','7','10','12']
    for i in range(len(v)):
        v[i]=sum(Final_Grades==i+1)
    ax.bar(x,v) 
    plt.xlabel('The 7-step-scale grades') 
    plt.ylabel('The number of students')
    plt.title('The distribution of final grades') 
    plt.savefig('Finalgrades',bbox_inches='tight')
#-----------------------------------------------------------------------------------------------------------------------------------------------    
    #Plot2 "grades per assignment"
    fig2=plt.figure()
    jitter_min = -0.1
    jitter_max = 0.1
    XX=[]
    YY=[]
    for a,row in enumerate(grades):
        x = (a*np.ones(len(row)) + (jitter_max-jitter_min)*np.random.random(size=len(row))+jitter_min)
        XX.append(x)
        YY.append(row)
    plt.plot(XX, YY, 'o', label='Assignment {:1d}'.format(a), clip_on=False)#
    plt.xlabel('Assignments')
    plt.ylabel('Grades')
    plt.title('The distribution of grades per assignment')
    plt.legend(loc='center left',prop={"size":10},bbox_to_anchor=(1, 0.5))
    y=''
    x=''
    X=[]
    Y=[]
    for i in range(len(grades[0])):
        y=np.array([np.mean(grades[:,i],axis=0)])
        x=np.array([i+1])
        X.append(x)
        Y.append(y)
    plt.plot(X,Y,label='The average of the assignments.')#
    plt.xlabel('Assignments')
    plt.ylabel('Grades') 
    plt.title('The distribution of grades per assignment')   
    plt.legend(loc='center left',prop={"size":10},bbox_to_anchor=(1, 0.5)) 
    plt.savefig('Gradesperassignment',bbox_inches='tight')    
    plt.show()
print(gradesPlot(grades))

这对你有用吗？