深入淺析Python科學(xué)計(jì)算庫(kù)Scipy及安裝步驟

  io.savemat(file_name,mdict)
  io.savemat('a.mat',{''array:a})

  io.loadmat(file_name)
  data = io.loadmat('a.mat')

from scipy import io    #導(dǎo)入io
import numpy as np    #導(dǎo)入numpy并命名為np 
arr = np.array([1,2,3,4,5,6])
io.savemat('test.mat',{'arr1':arr})
loadArr=io.loadmat('test.mat')

from matplotlib import pyplot as plt
from scipy import io
import numpy as np
matrix1 = np.arange(1,10).reshape(3,3)   #創(chuàng)建矩陣
io.savemat("matrix1.mat", {"array": matrix1}) #保存矩陣文件
data=io.loadmat('matrix1.mat')     #讀取矩陣文件
print (data["array"])       #輸出矩陣

p1 = np.random.normal(size = 10000) #新建隨機(jī)數(shù)
plt.hist(p1)       #繪制柱形圖
plt.show()       #顯示

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

arr = stats.norm.rvs(size=900)
(mean,std) = stats.norm.fit(arr)
print('平均值',mean)  #mean平均值
print('std標(biāo)準(zhǔn)差',std)  #std標(biāo)準(zhǔn)差
(skewness,pvalue1) = stats.skewtest(arr)
print('偏度值') 
print(skewness)
print('符合正態(tài)分布數(shù)據(jù)的概率為')
print(pvalue1)
(Kurtosistest,pvalue2) = stats.kurtosistest(arr)
print('Kurtosistest',Kurtosistest) #峰度
print('pvalue2',pvalue2)
(Normltest,pvalue3) = stats.normaltest(arr)
print('Normltest',Normltest)   #服從正太分布度
print('pvalue3',pvalue3)
num = stats.scoreatpercentile(arr,95) #某一百分比處的數(shù)值
print('在95%處的數(shù)值：')    #某一百分比處的數(shù)值
print num
indexPercent = stats.percentileofscore(arr,1) #某一數(shù)值處的百分比
print ('在數(shù)值1處的百分比：')     #某一數(shù)值處的百分比
print indexPercent
plt.hist(arr) #設(shè)置直方圖
plt.show()  #顯示圖

 num = stats.scoreatpercentile(arr,95) 
    print num

arrEasy=np.array([[0,2],[2.5,4],[5,6],[7.5,9],[10,13],[12.5,16],[15,19],[17.5,23],
    [20,27],[22.5,31],[25,35],[27.5,40],[30,53],[32.5,68],[35,90],
    [37.5,110],[40,130],[42.5,148],[45,165],[47.5,182],[50,195],
    [52.5,208],[55,217],[57.5,226],[60,334],[62.5,342],[65,349],
    [67.5,500],[70,511],[72.5,300],[75,200],[77.5,80],[80,20]])
arrDiff=np.array([[0,20],[2.5,30],[5,45],[7.5,70],[10,100],[12.5,135],[15,170],
    [17.5,205],[20,226],[22.5,241],[25,251],[27.5,255],[30,256],
    [32.5,253],[35,249],[37.5,242],[40,234],[42.5,226],[45,217],
    [47.5,208],[50,195],[52.5,182],[55,165],[57.5,148],[60,130],
    [62.5,110],[65,40],[67.5,30],[70,20],[72.5,5],[75,5],[77.5,0],[80,0]])

def createScore(arr):
 score = []   #所有學(xué)員分?jǐn)?shù)
 row = arr.shape[0]
 for i in np.arange(0,row):
  for j in np.arange(0,int(arr[i][1])):
  score.append(arr[i][1]))
 score = np.array(score)
 return score

score_Easy, num_Easy = arrEasy[:,0],arrEasy[:,1] #所有行第一列；所有行第二列
score_Diff, num_Diff = arrDiff[:,0],arrDiff[:,1] #同上
print (score_Easy,num_Easy )
print (score_Diff,num_Diff )

def calStatValue(score):
 #集中趨勢(shì)度量
 print('均值')
 print(np.mean(score))
 print('中位數(shù)')
 print(np.median(score))
 print('眾數(shù)')
 print(stats.mode(score))
 #離散趨勢(shì)度量
 print('極差')
 print(np.ptp(score))
 print('方差')
 print(np.var(score))
 print('標(biāo)準(zhǔn)差')
 print(np.std(score))
 print('變異系數(shù)')
 print(np.mean(score)/np.std(score))
 #偏度與峰度的度量
 print('偏度')
 print(stats.skewness(score))
 print('峰度')
 print(stats.Kurtosis(score))

def drawGraghic(score)
 plt.boxplot([score],labels['score']) #箱線圖
 plt.title('箱線圖')
 plt.show()
 plt.hist(score,100)
 plt.show()

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def createScore(arr):
 score = []     #所有學(xué)員分?jǐn)?shù)
 row = arr.shape[0]   #獲取多少組元素
 for i in np.arange(0,row): #遍歷所有元素組
  for j in np.arange(0,int(arr[i][1])):#從0開始填充次數(shù)，第i行第1列
   score.append(arr[i][0]))
 score = np.array(score)
 return score
_________________________________
使用切片獲取分?jǐn)?shù)
score_Easy, num_Easy = arrEasy[:,0],arrEasy[:,1] #所有行第一列；所有行第二列
score_Diff, num_Diff = arrDiff[:,0],arrDiff[:,1] #同上
print (score_Easy,num_Easy)   #查看分?jǐn)?shù)，人數(shù)
print (score_Diff,num_Diff)   #同上
All_score_Easy = np.repeat(list(score_Easy),list(num_Easy)) #所有分?jǐn)?shù)
All_score_Diff = np.repeat(list(score_Diff),list(num_Diff)) #所有分?jǐn)?shù)
________________________________
def createOneScore():
 arrEasy=np.array([[0,2],[2.5,4],[5,6],[7.5,9],[10,13],[12.5,16],[15,19],[17.5,23],
    [20,27],[22.5,31],[25,35],[27.5,40],[30,53],[32.5,68],[35,90],
    [37.5,110],[40,130],[42.5,148],[45,165],[47.5,182],[50,195],
    [52.5,208],[55,217],[57.5,226],[60,334],[62.5,342],[65,349],
    [67.5,500],[70,511],[72.5,300],[75,200],[77.5,80],[80,20]])
 return createScore(arrOne)
def createTwoScore():  
 arrDiff=np.array([[0,20],[2.5,30],[5,45],[7.5,70],[10,100],[12.5,135],[15,170],
    [17.5,205],[20,226],[22.5,241],[25,251],[27.5,255],[30,256],
    [32.5,253],[35,249],[37.5,242],[40,234],[42.5,226],[45,217],
    [47.5,208],[50,195],[52.5,182],[55,165],[57.5,148],[60,130],
    [62.5,110],[65,40],[67.5,30],[70,20],[72.5,5],[75,5],[77.5,0],[80,0]])
 return createScore(arrTwo)
def calStatValue(score):
 #集中趨勢(shì)度量
 print('均值')
 print(np.mean(score))
 print('中位數(shù)')
 print(np.median(score))
 print('眾數(shù)')
 print(stats.mode(score))
 #離散趨勢(shì)度量
 print('極差')
 print(np.ptp(score))
 print('方差')
 print(np.var(score))
 print('標(biāo)準(zhǔn)差')
 print(np.std(score))
 print('變異系數(shù)')
 print(np.mean(score)/np.std(score))
 #偏度與峰度的度量
 (skewness,pvalue1) = stats.skewtest(score) 
 print('偏度')
 print(stats.skewness(score))
 (Kurtosistest,pvalue2) = stats.kurtosistest(arr)
 print('峰度')
 print(stats.Kurtosis(score)) 
 return
#畫圖
def drawGraghic(score)
 plt.boxplot([score],labels['score']) #箱線圖
 plt.title('箱線圖')
 plt.show()
 plt.hist(score,100)
 plt.show()
 return