In this article, we will show you why to use NumPy arrays instead of nested Python lists, and the similarities and differences between them. Python NumPy Library NumPy is a Python library designed to work efficiently with arrays in Python. It is fast, sim...
In this article, we will show you why to use NumPy arrays instead of nested Python lists, and the similarities and differences between them.
NumPy is a Python library designed to work efficiently with arrays in Python. It is fast, simple to learn, and efficient in storage. It also improves the way data is handled for the process. In NumPy, we may generate an n-dimensional array. To use NumPy, we simply import it into our program, and then we can easily use NumPy's functionality in our code.
A Python list is a mutable and ordered collection of elements. In Python, lists are denoted by square brackets.
It is possible for the nested lists to be homogenous or heterogeneous.
On the list, element-wise operations are not possible.
Python lists are one-dimensional by default. However, we can make an N-Dimensional list. But it will also be a 1D list storing another 1D list.
A list's elements do not h**e to be contiguous in memory.
Python lists are useful general-purpose containers that can be used for inserting, adding, deleting, and concatenating data.
Lists h**e two drawbacks: they don't enable "vectorized" operations like element-wise addition and multiplication, and because they can contain objects of different types, Python must store type information for each element and execute type dispatching code when working on each element.
NumPy is more efficient and convenient since it offers a huge number of vector and matrix operations for free, reducing effort and code complexity. Numpy is also more efficient when compared to nested loops.
NumPy arrays are faster and include more built-in functions for doing FFTs, convolutions, rapid searching, linear algebra, basic statistics, histograms, and other tasks.
Numpy arrays allow for advanced mathematical and other operations on huge amounts of data.
The array is Homogeneous by default, which implies that all data in the array must be of the same Datatype. (Note that a structured array can similarly be created in Python.)
Element-by-element operation is feasible.
Numpy array includes a variety of functions, methods, and variables to help us with our matrix computation work.
An array's elements are kept together in memory. A two-dimensional array, for example, must h**e the same number of columns in all of its rows. A three-dimensional array, on the other hand, must h**e the same number of rows and columns on each card.
On executing, the above program will generate the following output −
[[2, 7, 8], [1, 5, 4]]
Following are the Algorithm/steps to be followed to perform the desired task −
Use the import keyword, to import the numpy module with an alias name(np).
Use array() function(returns an ndarray. The ndarray is an array object that satisfies the given requirements) of the numPy module to create a 1-Dimensional numpy array.
Print the 1-Dimensional array
On executing, the above program will generate the following output −
[12 4 8 6]
On executing, the above program will generate the following output −
[[2 7 8] [1 5 4]]
Uses less memory.
Faster than the Python List.
Simple to use.
They both make use of square brackets ([]).
Both arrays and lists are mutable(elements of both can be modified).
Both the NumPy arrays and lists can be indexed and can be used for slicing.
| Numpy Array | Python List |
|---|---|
| Arrays can directly handle mathematical operations | A list cannot do mathematical operations directly. |
| Consumes less memory than a list | Consumes more memory |
| Array is faster than a list | Lists is relatively slower as compared to array |
| Bit complex to modify | Easier to modify |
| Array cannot include different data sizes | A list can include several nested data sizes. |
| Arrays cannot store different data types | list can store different data types |
On executing, the above program will generate the following output −
The size of each item of list(bytes) = 88 The size of the entire list(bytes) = 176 Each element of Numpy array(bytes) size = 8 The entire Numpy array(bytes) size = 48
We can observe that the nested list consumes significantly more memory than the numpy arrays.
In this article, we learned why NumPy arrays are more useful in Python than nested lists. We used an example to demonstrate the memory efficiency of NumPy arrays over nested lists.