为什么 Numpy 在Python中更快？

NumPy 是一个Python基础包，用于对高级数学函数、多维数组、线性代数、傅里叶变换、随机数功能等进行高效操作和操作。它提供了在Python中集成 C、C++ 和 Fortran 代码的工具. NumPy 主要在Python中用于科学计算。

让我们看看下面的程序，它在执行时间方面比较了Python中的 NumPy 数组和列表。

Python3

# importing required packages
import numpy
import time
 
# size of arrays and lists
size = 1000000  
 
# declaring lists
list1 = range(size)
list2 = range(size)
 
# declaring arrays
array1 = numpy.arange(size) 
array2 = numpy.arange(size)
 
# list
initialTime = time.time()
resultantList = [(a * b) for a, b in zip(list1, list2)]
 
# calculating execution time
print("Time taken by Lists :",
      (time.time() - initialTime),
      "seconds")
 
# NumPy array
initialTime = time.time()
resultantArray = array1 * array2
 
# calculating execution time
print("Time taken by NumPy Arrays :",
      (time.time() - initialTime),
      "seconds")

输出：

Time taken by Lists : 1.1984527111053467 seconds
Time taken by NumPy Arrays : 0.13434123992919922 seconds

从上述程序的输出中，我们看到 NumPy 数组的执行速度比Python中的列表快得多。数组和列表的执行时间有很大的不同。

由于以下原因，NumPy 数组比Python列表更快：

• 数组是存储在连续内存位置的同类数据类型的集合。另一方面， Python中的列表是存储在非连续内存位置的异构数据类型的集合。
• NumPy 包将一个任务分解为多个片段，然后并行处理所有片段。
• NumPy 包在Python中集成了 C、C++ 和 Fortran 代码。与Python相比，这些编程语言的执行时间非常短。

下面是一个比较 NumPy 数组和Python列表上不同操作的执行时间的程序：

Python3

# importing required packages
import numpy
import time
  
 
# size of arrays and lists
size = 1000000 
  
# declaring lists
list1 = [i for i in range(size)]
list2 = [i for i in range(size)]
 
# declaring arrays
array1 = numpy.arange(size)
array2 = numpy.arange(size)
 
# Concatenation
print("\nConcatenation:")
 
# list
initialTime = time.time()
list1 = list1 + list2
 
# calculating execution time
print("Time taken by Lists :",
      (time.time() - initialTime),
      "seconds")
  
# NumPy array
initialTime = time.time()
array = numpy.concatenate((array1, array2),
                          axis = 0)
 
# calculating execution time
print("Time taken by NumPy Arrays :",
      (time.time() - initialTime),
      "seconds")
 
 
# Dot Product
dot = 0
print("\nDot Product:")
 
# list
initialTime = time.time()
for a, b in zip(list1, list2):
        dot = dot + (a * b)
         
# calculating execution time
print("Time taken by Lists :",
      (time.time() - initialTime),
      "seconds")
  
# NumPy array
initialTime = time.time()
array = numpy.dot(array1, array2)
 
# calculating execution time
print("Time taken by NumPy Arrays :",
      (time.time() - initialTime),
      "seconds")
 
 
# Scalar Addition
print("\nScalar Addition:")
 
# list
initialTime = time.time()
list1 =[i + 2 for i in range(size)]
 
# calculating execution time
print("Time taken by Lists :",
      (time.time() - initialTime),
      "seconds")
  
# NumPy array
initialTime = time.time()
array1 = array1 + 2
 
# calculating execution time
print("Time taken by NumPy Arrays :",
      (time.time() - initialTime),
      "seconds")
 
 
# Deletion
print("\nDeletion: ")
 
# list
initialTime = time.time()
del(list1)
 
# calculating execution time
print("Time taken by Lists :",
      (time.time() - initialTime),
      "seconds")
  
# NumPy array
initialTime = time.time()
del(array1)
 
# calculating execution time
print("Time taken by NumPy Arrays :",
      (time.time() - initialTime),
      "seconds")

输出：

Concatenation:
Time taken by Lists : 0.02946329116821289 seconds
Time taken by NumPy Arrays : 0.011709213256835938 seconds

Dot Product:
Time taken by Lists : 0.179551362991333 seconds
Time taken by NumPy Arrays : 0.004144191741943359 seconds

Scalar Addition:
Time taken by Lists : 0.09385180473327637 seconds
Time taken by NumPy Arrays : 0.005884408950805664 seconds

Deletion: 
Time taken by Lists : 0.01268625259399414 seconds
Time taken by NumPy Arrays : 3.814697265625e-06 seconds

从上面的程序中，我们得出结论，对 NumPy 数组的操作比Python列表执行得更快。此外，与程序中的其他操作相比，删除操作在数组和列表之间的执行时间差异最大。