我已经创建了python代码来读取csv文件，将其写入临时文件[＆＃34; dump2.csv＆＃34;]并在其上运行简单的统计信息。程序的这一部分工作正常，但是，当我编写3D绘图时 - 我得到了一个很好看的情节但是，它似乎不包括所有的列数据（例如109个案例 - 我将展示我的3D附加的jpg / pic中的图形输出）。任何答案？ 这是我的代码：
import numpy as np
import csv as csv
import time
# import pandas as pd
# import pandas as pd
# import statistics
# ======================================================
# MY ATTEMPT AT ACCOUNTING FOR MISSING DATA via adding a "." to blank entries
with open('totaleval4.csv') as fin, open('dump2.csv','wt', newline='') as fout:
csvin = csv.reader(fin)
csvout = csv.writer(fout)
for row in csvin:
# this "FOR/ELSE" statement - takes the missing ('.') data and codes
#....it for "999999" and if there is no missing data - it simply writes
# it. These operations are reading-in "totaltest2.csv" and writing
# it's findings ...to the "dump1.csv" file
#==========This IF statement looks for blank cells (" ") and it a cell
# is NOT blank - it writes the row - if it is it deletes it from
# the output
if "." not in row:
csvout.writerow(row)
# fout.close()
# MISSING DATA IS ACCOUNTED FOR AT THIS POINT - AND PRINTED DATA SET WITH NEW
#....missing data code - inserted in new file (e.g., here "dump.csv")
# ======================================
# Statistical Analysis for new 'dump.csv' file accounting for missing data...
# Statistical Analysis for new 'dump.csv' file accounting for missing data...
readdata = csv.reader(open('dump2.csv', 'r'))
data = []
for row in readdata:
data.append(row)
Header = data[0]
data.pop(0)
year = []
enrol = []
q2 = []
q3 = []
q4 = []
q5 = []
q6 = []
q8 = []
q9 = []
q10 = []
q11 = []
q12 = []
q13 = []
# =============================
# =============================
for i in range(len(data)):
year.append(int(data[i][1]))
enrol.append(int(data[i][3]))
q2.append(int(data[i][4]))
q3.append(int(data[i][5]))
q4.append(int(data[i][6]))
q5.append(int(data[i][7]))
q6.append(int(data[i][8]))
q8.append(int(data[i][9]))
q9.append(int(data[i][10]))
q10.append(int(data[i][11]))
q11.append(int(data[i][12]))
q12.append(int(data[i][13]))
q13.append(int(data[i] [14]))
# ==============================
# ==============================
# Statistical work-ups/section
# ==============================
print ('Mean Number of Students: ', (np.mean(enrol)))
print ('Mean 0-5 Question-2: ', (np.mean(q2)))
print ('Mean 0-5 Question-3: ', (np.mean(q3)))
print ('Mean 0-5 Question-4: ', (np.mean(q4)))
print ('Mean 0-5 Question-5: ', (np.mean(q5)))
print ('Mean 0-5 Question-6: ', (np.mean(q6)))
print ('Mean 0-5 Question-8: ', (np.mean(q8)))
print ('Mean 0-5 Question-9: ', (np.mean(q9)))
print ('Mean 0-5 Question-10: ', (np.mean(q10)))
print ('Mean 0-5 Question-11: ', (np.mean(q11)))
print ('Mean 0-5 Question-12: ', (np.mean(q12)))
print ('Mean 0-5 Question-13: ', (np.mean(q13)))
print ('Std of Number of Students ', (np.std(enrol)))
print ('Std of 0-5 Question-2 ', (np.std(q2)))
# ======================================================
time.sleep(2)
# ======================================================
# ======= 3D Graph Code ========
# =======3D Scatter Plot========
# ==============================
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = [year]
y = [q2]
z = [q3]
ax.scatter(x, y, z, c='r', marker='o')
ax.set_xlim(2013, 2018)
ax.set_ylim(0, 6)
ax.set_zlim(0, 6)
ax.set_xlabel('yr')
ax.set_ylabel('q2')
ax.set_zlabel('q3')
plt.show()
time.sleep(2)
