Here, we will understand the difference between Python List and Python Numpy array.
What is a Numpy array?
NumPy is the fundamental package for scientific computing in Python. Numpy arrays facilitate advanced mathematical and other types of operations on large numbers of data. Typically, such operations are executed more efficiently and with less code than is possible using Python’s built-in sequences. Numpy is not another programming language but a Python extension module. It provides fast and efficient operations on arrays of homogeneous data.
Some important points about Numpy arrays:
- We can create an N-dimensional array in Python using Numpy.array().
- The array is by default Homogeneous, which means data inside an array must be of the same Datatype. (Note You can also create a structured array in Python).
- Element-wise operation is possible.
- Numpy array has various functions, methods, and variables, to ease our task of matrix computation.
- Elements of an array are stored contiguously in memory. For example, all rows of a two-dimensioned array must have the same number of columns. A three-dimensional array must have the same number of rows and columns on each card.
Representation of Numpy array
- Single Dimensional Numpy Array
Python3
import numpy as npa = np.array([1, 2, 3])print(a) |
O
[1 2 3]
- Multi-dimensional Numpy Array
Python3
import numpy as npa = np.array([(1, 2, 3), (4, 5, 6)])print(a) |
Output:
[[1 2 3]
[4 5 6]]
What is Python List?
A Python list is a collection that is ordered and changeable. In Python, lists are written with square brackets.
Some important points about Python Lists:
- The list can be homogeneous or heterogeneous.
- Element-wise operation is not possible on the list.
- Python list is by default 1-dimensional. But we can create an N-Dimensional list. But then too it will be 1 D list storing another 1D list
- Elements of a list need not be contiguous in memory.
Below are some examples which clearly demonstrate how Numpy arrays are better than Python lists by analyzing the memory consumption, execution time comparison, and operations supported by both of them.
Representation of Python List
Here we are creating Python List using []
Python3
Var = ["Geeks", "for", "Geeks"]print(Var) |
Output:
["Geeks", "for", "Geeks"]
Comparison between Numpy array and Python List
Python Lists
- Element Overhead: Lists in Python store additional information about each element, such as its type and reference count. This overhead can be significant when dealing with a large number of elements.
- Datatype: Lists can hold different data types, but this can decrease memory efficiency and slow numerical operations.
- Memory Fragmentation: Lists may not store elements in contiguous memory locations, causing memory fragmentation and inefficiency.
- Performance: Lists are not optimized for numerical computations and may have slower mathematical operations due to Python’s interpretation overhead. They are generally used as general-purpose data structures.
- Functionality: Lists can store any data type, but lack specialized NumPy functions for numerical operations.
Numpy Arrays
- Homogeneous Data: NumPy arrays store elements of the same data type, making them more compact and memory-efficient than lists.
- Fixed Data Type: NumPy arrays have a fixed data type, reducing memory overhead by eliminating the need to store type information for each element.
- Contiguous Memory: NumPy arrays store elements in adjacent memory locations, reducing fragmentation and allowing for efficient access.
- Array Metadata: NumPy arrays have extra metadata like shape, strides, and data type. However, this overhead is usually smaller than the per-element overhead in lists.
- Performance: NumPy arrays are optimized for numerical computations, with efficient element-wise operations and mathematical functions. These operations are implemented in C, resulting in faster performance than equivalent operations on lists.
Memory consumption between Numpy array and lists
In Python, a list is a built-in data structure that can hold elements of varying data types. However, the flexibility of lists comes at the cost of memory efficiency.
Python’s NumPy library supports optimized numerical array and matrix operations.
Memory Allocation
In this example, a Python list and a Numpy array of size 1000 will be created. The size of each element and then the whole size of both containers will be calculated and a comparison will be done in terms of memory consumption.
Python3
# importing numpy packageimport numpy as np# importing system moduleimport sys# declaring a list of 1000 elementsS= range(1000)# printing size of each element of the listprint("Size of each element of list in bytes: ",sys.getsizeof(S))# printing size of the whole listprint("Size of the whole list in bytes: ",sys.getsizeof(S)*len(S))# declaring a Numpy array of 1000 elementsD= np.arange(1000)# printing size of each element of the Numpy arrayprint("Size of each element of the Numpy array in bytes: ",D.itemsize)# printing size of the whole Numpy arrayprint("Size of the whole Numpy array in bytes: ",D.size*D.itemsize) |
Output:
Size of each element of list in bytes: 48
Size of the whole list in bytes: 48000
Size of each element of the Numpy array in bytes: 8
Size of the whole Numpy array in bytes: 8000
Time comparison between Numpy array and Python lists
In this example, here two Python lists and two Numpy arrays will be created and each container has 1000000 elements. Multiplication of elements in both the lists and Numpy arrays respectively will be carried out and the difference in time needed for the execution for both the containers will be analyzed to determine which one takes less time to perform the operation.
Python3
# importing required packagesimport numpyimport time# size of arrays and listssize = 1000000# declaring listslist1 = range(size)list2 = range(size)# declaring arraysarray1 = numpy.arange(size)array2 = numpy.arange(size)# capturing time before the multiplication of Python listsinitialTime = time.time()# multiplying elements of both the lists and stored in another listresultantList = [(a * b) for a, b in zip(list1, list2)]# calculating execution timeprint("Time taken by Lists to perform multiplication:", (time.time() - initialTime), "seconds")# capturing time before the multiplication of Numpy arraysinitialTime = time.time()# multiplying elements of both the Numpy arrays and stored in another Numpy arrayresultantArray = array1 * array2# calculating execution timeprint("Time taken by NumPy Arrays to perform multiplication:", (time.time() - initialTime), "seconds") |
Output:
Time taken by Lists to perform multiplication: 0.07256507873535156 seconds
Time taken by NumPy Arrays to perform multiplication: 0.006612300872802734 seconds
Effect of operations on Numpy array and Python Lists
In this example, the incapability of the Python list to carry out a basic operation is demonstrated. A Python list and a Numpy array having the same elements will be declared and an integer will be added to increment each element of the container by that integer value without looping statements. The effect of this operation on the Numpy array and Python list will be analyzed.
Python3
# importing Numpy packageimport numpy as np# declaring a listls =[1, 2, 3]# converting the list into a Numpy arrayarr = np.array(ls)try: # adding 4 to each element of list ls = ls + 4 except(TypeError): print("Lists don't support list + int")# now on arraytry: # adding 4 to each element of Numpy array arr = arr + 4 # printing the Numpy array print("Modified Numpy array: ",arr) except(TypeError): print("Numpy arrays don't support list + int") |
Output:
Lists don't support list + int
Modified Numpy array: [5 6 7]
Conclusion
Advantages of using Numpy Arrays Over Python Lists:
- Consumes less memory.
- Fast as compared to the python List.
- Convenient to use.
