In this article, we will learn about Numpy. what is Numpy, uses of Numpy, application of Numpy, How to install Nupmy, and perform some examples for more understanding.
What is Numpy?
NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
Why use Numpy?
- NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use. NumPy uses much less memory to store data and it provides a mechanism of specifying the data types.
- NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.
- Arrays are very frequently used in data science, where speed and resources are very important.
Applications of Numpy
There are a number of applications of Numpy applications as well as the areas where the Numpy library is used in python. Some of them are listed below:
1) Shape Manipulations
If the output produces the same number of elements then the user can change array dimensions at the runtime. np. reshape(…) function is applied on the array to reshape it.
2) Array Generation
We generate the array data set for implementing various functions. We can also generate a predefined set of numbers for the array elements using the np.arrange(…)function. Reshape function is useful to generate a different set of dimensions.
3) NumPy with SciPy
Scipy is an open-source library in Python. It is the most important scientific library in python. It has been built upon the functionalities of NumPy.There are advanced functionalities in SciPy for scientific computations.
4) NumPy with Tkinter
Tkinter is a standard library for GUI. We use Tkinter for the GUI representation of the NumPy data. Its combination with NumPy can implement fast and easy GUIs.
Before we move on with the code for understanding the NumPy Arrays, let’s get Numpy installed in your system using the following command.
pip install numpy
Numpy Array Uses:
1) Creating array using NumPy
NumPy is used to work with arrays. The array object in NumPy is called ndarray.
import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr) print(type(arr))
2) NumPy Array Indexing
Array indexing is the same as accessing an array element. You can access an array element by referring to its index number.
import numpy as np arr = np.array([1, 2, 3, 4]) print(arr + arr)
3) Array of zeros
NumPy lets you create an array of all zeros using the np.zeros() method. All you have to do is pass the shape of the desired array:
import numpy as np np.zeros(5) //1-D array #output array([0., 0., 0., 0., 0.]) np.zeros((2,3)) //2-D array #output array([[0., 0., 0.], [0., 0., 0.]])
4) Array of ones
You could also create an array of all 1s using the np.ones() method:
np.ones(5,dtype=np.int32) #output array([1, 1, 1, 1, 1])
5) Reshaping a NumPy array
Reshaping a ndarray can be done using the np.reshape() method. It changes the shape of the ndarray without changing the data within the ndarray:
import numpy as np arr = np.array([3,6,9,12]) np.reshape(arr,(2,2)) #output array([[ 3, 6], [ 9, 12]])
To learn more about Numpy, check the official documentation.
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