在Python中解析CSV文件
我有一个CSV文件，其中有以下几列：在Python中解析CSV文件,python,csv,Python,Csv,我有一个CSV文件，其中有以下几列： id,index,value,lenght 1,2 9 5,2 9 5,10 2,3 5 8,3 5 8,10 3,1,7,1 from __future__ import division from sympy import * import numpy as np import csv readVektor = csv.DictReader(open("data.csv")) for column in readVektor:
id,index,value,lenght
1,2 9 5,2 9 5,10
2,3 5 8,3 5 8,10
3,1,7,1
from __future__ import division
from sympy import *
import numpy as np
import csv
readVektor = csv.DictReader(open("data.csv"))
for column in readVektor:
print (column)
for column in readVektor:
id = int(column["id"])
index = int(column["index"])
value = int(column["value"])
length = int(column["lenght"])
#with open('data.csv', newline='') as readVektor: # Einlesen der Daten für Vektor 1
# data = csv.reader(readVektor, delimiter=';', quotechar='|')
# for row in data:
# print('; '.join(row))
#pprint(data)
class sparse(object): # Klasse, deren Objekte sparse Vektoren sind
def __init__(self, index, value, lenght, skalar): # Konstruktor
self.Index = index
self.Value = value
self.Laenge = lenght
self.Skalar = skalar
def maxNorm(self, value): # Berechnung der Maxmimums-Norm
maxNorm = max(value)
max_idx = value.index(maxNorm)
#return max_idx, maxNorm
print ("maxNorm: ", maxNorm)
def sMult(self, skalar, value): # Skalarmultiplikation
sMult = skalar * value
#return sMult
print ("sMult: ", sMult)
def Summe(self, index1, index2, value1, value2): # Berechnung der Summe
#for i, j in range(len(index1, index2)):
# if index1(i) < index2(i):
# self.summe(i) = value1(i)
# if index1(i) > index2(i):
# self.summe(i) = value2(i)
# if index1(i) == index2(i):
# self.summe(i) = value1(i) + value2(i)
# return summe
summe = value1 + value2
#return summe
print ("summe: ", summe)
def Differenz(self, index1, index2, value1, value2):
differenz = value1 - value2
#return differenz
print ("differenz: ", differenz)
def iProd(self, value1, value2):
iProd = np.dot (value1, value2)
#return iProd
print ("iProd: ", iProd)
#def Differenz(self, index1, index2, value1, value2): # Berechnung der Differenz
# for i in range(len(index1, index2))
# if index1(i) < index2(i)
# self.differenz(i) = value1(i)
# if index1(i) > index2(i)
# self.differenz(i) = value2(i)
# if index1(i) == index2(i)
# self.differenz(i) = value1(i) - value1(i)
#self.differenz = value1 - value2
#def Skalarprodukt(self, value1, value2): #Skalarprodukt
# for i in range(len(index1, index2))
# if index1(i) < index2(i)
# self.skalarprodukt(i) = 0
# if index1(i) > index2(i)
# self.skalarprodukt(i) = 0
# if index1(i) == index2(i)
# self.skalarprodukt(i) = value1(i) * value1(i)
#self.skalarprodukt = np.dot(value1, value2)
#print ("maxNorm: ", maxNorm)
#print ("summe: ", summe)
#print ("differenz: ", differenz)
#print ("sMult: ", sMult)
#print ("iProd: ", iProd)
sparsevector1 = sparse([2,9,5],[2,9,5],10,7)
sparsevector2 = sparse([3,5,8],[3,5,8],10,7)
sparsevector1.maxNorm([2,9,5])
sparsevector2.maxNorm([3,5,8])
sparsevector1.sMult(7,[2,9,5])
sparsevector2.sMult(7,[3,5,8])
sparsevector1.Summe([2,9,5],[3,5,8],[2,9,5],[3,5,8])
sparsevector2.Summe([2,9,5],[3,5,8],[2,9,5],[3,5,8])
#sparsevector1.Differenz([2,9,5],[3,5,8],[2,9,5],[3,5,8])
#sparsevector2.Differenz([2,9,5],[3,5,8],[2,9,5],[3,5,8])
sparsevector1.iProd([2,9,5],[3,5,8])
sparsevector2.iProd([2,9,5],[3,5,8])
for column in readVektor:
id = int(column["id"])
length = int(column["lenght"])
index = [int(x) for x in column["index"].split(' ')]
value = [int(x) for x in column["value"].split(' ')]
id,index,value,lenght
1,2 9 5,2 9 5,10
2,3 5 8,3 5 8,10
3,1,7,1
from __future__ import division
from sympy import *
import numpy as np
import csv
readVektor = csv.DictReader(open("data.csv"))
for column in readVektor:
print (column)
for column in readVektor:
id = int(column["id"])
index = int(column["index"])
value = int(column["value"])
length = int(column["lenght"])
#with open('data.csv', newline='') as readVektor: # Einlesen der Daten für Vektor 1
# data = csv.reader(readVektor, delimiter=';', quotechar='|')
# for row in data:
# print('; '.join(row))
#pprint(data)
class sparse(object): # Klasse, deren Objekte sparse Vektoren sind
def __init__(self, index, value, lenght, skalar): # Konstruktor
self.Index = index
self.Value = value
self.Laenge = lenght
self.Skalar = skalar
def maxNorm(self, value): # Berechnung der Maxmimums-Norm
maxNorm = max(value)
max_idx = value.index(maxNorm)
#return max_idx, maxNorm
print ("maxNorm: ", maxNorm)
def sMult(self, skalar, value): # Skalarmultiplikation
sMult = skalar * value
#return sMult
print ("sMult: ", sMult)
def Summe(self, index1, index2, value1, value2): # Berechnung der Summe
#for i, j in range(len(index1, index2)):
# if index1(i) < index2(i):
# self.summe(i) = value1(i)
# if index1(i) > index2(i):
# self.summe(i) = value2(i)
# if index1(i) == index2(i):
# self.summe(i) = value1(i) + value2(i)
# return summe
summe = value1 + value2
#return summe
print ("summe: ", summe)
def Differenz(self, index1, index2, value1, value2):
differenz = value1 - value2
#return differenz
print ("differenz: ", differenz)
def iProd(self, value1, value2):
iProd = np.dot (value1, value2)
#return iProd
print ("iProd: ", iProd)
#def Differenz(self, index1, index2, value1, value2): # Berechnung der Differenz
# for i in range(len(index1, index2))
# if index1(i) < index2(i)
# self.differenz(i) = value1(i)
# if index1(i) > index2(i)
# self.differenz(i) = value2(i)
# if index1(i) == index2(i)
# self.differenz(i) = value1(i) - value1(i)
#self.differenz = value1 - value2
#def Skalarprodukt(self, value1, value2): #Skalarprodukt
# for i in range(len(index1, index2))
# if index1(i) < index2(i)
# self.skalarprodukt(i) = 0
# if index1(i) > index2(i)
# self.skalarprodukt(i) = 0
# if index1(i) == index2(i)
# self.skalarprodukt(i) = value1(i) * value1(i)
#self.skalarprodukt = np.dot(value1, value2)
#print ("maxNorm: ", maxNorm)
#print ("summe: ", summe)
#print ("differenz: ", differenz)
#print ("sMult: ", sMult)
#print ("iProd: ", iProd)
sparsevector1 = sparse([2,9,5],[2,9,5],10,7)
sparsevector2 = sparse([3,5,8],[3,5,8],10,7)
sparsevector1.maxNorm([2,9,5])
sparsevector2.maxNorm([3,5,8])
sparsevector1.sMult(7,[2,9,5])
sparsevector2.sMult(7,[3,5,8])
sparsevector1.Summe([2,9,5],[3,5,8],[2,9,5],[3,5,8])
sparsevector2.Summe([2,9,5],[3,5,8],[2,9,5],[3,5,8])
#sparsevector1.Differenz([2,9,5],[3,5,8],[2,9,5],[3,5,8])
#sparsevector2.Differenz([2,9,5],[3,5,8],[2,9,5],[3,5,8])
sparsevector1.iProd([2,9,5],[3,5,8])
sparsevector2.iProd([2,9,5],[3,5,8])
for column in readVektor:
id = int(column["id"])
length = int(column["lenght"])
index = [int(x) for x in column["index"].split(' ')]
value = [int(x) for x in column["value"].split(' ')]
从sympy进口*
将numpy作为np导入
导入csv
readVektor=csv.DictReader（打开（“data.csv”））
对于readVektor中的列：
打印（列）
对于readVektor中的列：
id=int（列[“id”]）
索引=int（列[“索引”]）
value=int（列[“value”]）
长度=整数（列[“长度”]）
#使用open（'data.csv'，换行符=''）作为readVektor:#Einlesen der Daten für Vektor 1
#data=csv.reader（readVektor，分隔符=“；”，引号=“|”）
#对于数据中的行：
#打印（“；”。连接（行））
#pprint（数据）
类稀疏（对象）：#Klasse，deren Objekte稀疏Vektoren sind
定义初始值（自身、索引、值、长度、斯卡拉）：#Konstruktor
self.Index=索引
自我价值=价值
self.Laenge=长度
self.Skalar=Skalar
定义最大范数（自身，值）：#最大范数
maxNorm=max（值）
max_idx=值索引（maxNorm）
#返回max_idx，maxNorm
打印（“maxNorm:，maxNorm”）
def sMult（self、skalar、value）：#Skalarmultiplikation
sMult=skalar*值
#返回污迹
打印（“弄脏：”，弄脏）
def总和（自身、index1、index2、值1、值2）：#用于总和
#对于范围内的i，j（len（index1，index2））：
#如果index1（i）index2（i）：
#自我总和（i）=值2（i）
#如果index1（i）=index2（i）：
#自我总和（i）=值1（i）+值2（i）
#返回总和
总和=值1+值2
#返回总和
打印（“总和：，总和”）
def差异Z（自身、index1、index2、值1、值2）：
差分Z=值1-值2
#返回差
打印（“differenz:，differenz”）
def iProd（自身、值1、值2）：
iProd=np.dot（值1，值2）
#回油管
打印（“iProd:，iProd”）
#定义差异（自、索引1、索引2、值1、值2）：#在差异下进行设置
#对于范围内的i（len（index1，index2））
#如果index1（i）index2（i）
#自差z（i）=值2（i）
#如果index1（i）=index2（i）
#自差（i）=值1（i）-值1（i）
#self.differencez=值1-值2
#def Skalarprodukt（自身、值1、值2）：#Skalarprodukt
#对于范围内的i（len（index1，index2））
#如果index1（i）index2（i）
#self.skalarprodukt（i）=0
#如果index1（i）=index2（i）
#self.skalarprodukt（i）=值1（i）*值1（i）
#self.skalarprodukt=np.dot（值1，值2）
#打印（“maxNorm:，maxNorm”）
#打印（“总和：，总和”）
#打印（“differenz:，differenz”）
#打印（“弄脏：”，弄脏）
#打印（“iProd:，iProd”）
sparsevector1=稀疏（[2,9,5]，[2,9,5]，10,7）
sparsevector2=稀疏（[3,5,8]，[3,5,8]，10,7）
SparSevector 1.最大范数（[2,9,5]）
SparSevector 2.最大范数（[3,5,8]）
斯帕塞维克特1.斯马特（7，[2,9,5]）
斯帕塞维克特2.斯马特（7，[3,5,8]）
sparsevector1.Summe（[2,9,5]，[3,5,8]，[2,9,5]，[3,5,8]）
sparsevector2.Summe（[2,9,5]，[3,5,8]，[2,9,5]，[3,5,8]）
#sparsevector1.微分（[2,9,5]，[3,5,8]，[2,9,5]，[3,5,8]）
#sparsevector2.微分（[2,9,5]，[3,5,8]，[2,9,5]，[3,5,8]）
sparsevector1.iProd（[2,9,5]，[3,5,8]）
sparsevector2.iProd（[2,9,5]，[3,5,8]）
我如何解析必要的信息并将其传递给函数？看起来您的稀疏类将数组作为输入…也许可以尝试以下方法：
id,index,value,lenght
1,2 9 5,2 9 5,10
2,3 5 8,3 5 8,10
3,1,7,1
from __future__ import division
from sympy import *
import numpy as np
import csv
readVektor = csv.DictReader(open("data.csv"))
for column in readVektor:
print (column)
for column in readVektor:
id = int(column["id"])
index = int(column["index"])
value = int(column["value"])
length = int(column["lenght"])
#with open('data.csv', newline='') as readVektor: # Einlesen der Daten für Vektor 1
# data = csv.reader(readVektor, delimiter=';', quotechar='|')
# for row in data:
# print('; '.join(row))
#pprint(data)
class sparse(object): # Klasse, deren Objekte sparse Vektoren sind
def __init__(self, index, value, lenght, skalar): # Konstruktor
self.Index = index
self.Value = value
self.Laenge = lenght
self.Skalar = skalar
def maxNorm(self, value): # Berechnung der Maxmimums-Norm
maxNorm = max(value)
max_idx = value.index(maxNorm)
#return max_idx, maxNorm
print ("maxNorm: ", maxNorm)
def sMult(self, skalar, value): # Skalarmultiplikation
sMult = skalar * value
#return sMult
print ("sMult: ", sMult)
def Summe(self, index1, index2, value1, value2): # Berechnung der Summe
#for i, j in range(len(index1, index2)):
# if index1(i) < index2(i):
# self.summe(i) = value1(i)
# if index1(i) > index2(i):
# self.summe(i) = value2(i)
# if index1(i) == index2(i):
# self.summe(i) = value1(i) + value2(i)
# return summe
summe = value1 + value2
#return summe
print ("summe: ", summe)
def Differenz(self, index1, index2, value1, value2):
differenz = value1 - value2
#return differenz
print ("differenz: ", differenz)
def iProd(self, value1, value2):
iProd = np.dot (value1, value2)
#return iProd
print ("iProd: ", iProd)
#def Differenz(self, index1, index2, value1, value2): # Berechnung der Differenz
# for i in range(len(index1, index2))
# if index1(i) < index2(i)
# self.differenz(i) = value1(i)
# if index1(i) > index2(i)
# self.differenz(i) = value2(i)
# if index1(i) == index2(i)
# self.differenz(i) = value1(i) - value1(i)
#self.differenz = value1 - value2
#def Skalarprodukt(self, value1, value2): #Skalarprodukt
# for i in range(len(index1, index2))
# if index1(i) < index2(i)
# self.skalarprodukt(i) = 0
# if index1(i) > index2(i)
# self.skalarprodukt(i) = 0
# if index1(i) == index2(i)
# self.skalarprodukt(i) = value1(i) * value1(i)
#self.skalarprodukt = np.dot(value1, value2)
#print ("maxNorm: ", maxNorm)
#print ("summe: ", summe)
#print ("differenz: ", differenz)
#print ("sMult: ", sMult)
#print ("iProd: ", iProd)
sparsevector1 = sparse([2,9,5],[2,9,5],10,7)
sparsevector2 = sparse([3,5,8],[3,5,8],10,7)
sparsevector1.maxNorm([2,9,5])
sparsevector2.maxNorm([3,5,8])
sparsevector1.sMult(7,[2,9,5])
sparsevector2.sMult(7,[3,5,8])
sparsevector1.Summe([2,9,5],[3,5,8],[2,9,5],[3,5,8])
sparsevector2.Summe([2,9,5],[3,5,8],[2,9,5],[3,5,8])
#sparsevector1.Differenz([2,9,5],[3,5,8],[2,9,5],[3,5,8])
#sparsevector2.Differenz([2,9,5],[3,5,8],[2,9,5],[3,5,8])
sparsevector1.iProd([2,9,5],[3,5,8])
sparsevector2.iProd([2,9,5],[3,5,8])
for column in readVektor:
id = int(column["id"])
length = int(column["lenght"])
index = [int(x) for x in column["index"].split(' ')]
value = [int(x) for x in column["value"].split(' ')]
然后，索引和值都将是整数列表。您的标题与解析CSV相关，但快速扫描代码表明CSV解析已经起作用。你的代码也很长-请阅读另一个问题：我必须使用魔法方法加减两个向量。我使用以下两个函数：def uuu add uuuu（self，other）：我还需要做什么才能使这些函数工作？定义（自身、其他）：