Updated March 28, 2023
Introduction to NumPy
NumPy is a short form for Numerical Python, which is applied for scientific programming in Python, especially for numbers. It comprises multidimensional objects in arrays and a package of integrating tools for Python implementation. It is basically a mix of C and Python used as an alternative for traditionally used MATLAB programming, where data in the form of numerals are treated as arrays for multidimensional functions and rearrangement operations.
Understanding Numpy
One of the top most used libraries in Python is Numpy. Data Science techniques need the work to be done on large-size arrays and matrices, and heavy numerical computation has to be done to extract useful information from it, which is made easy by the collection of various mathematical functions under the NumPy.It is the basic yet important library for most of the scientific computing in Python; some other libraries are also dependent on NumPy arrays as their basic inputs and outputs. It also provides functions that allow developers to perform basic as well as advanced mathematical and statistical functions on multi-dimensional arrays and matrices with very few lines of code. ‘ndarray’ or n-dimensional array data structure is the main functionality of Numpy. These arrays are homogeneous, and all the elements of the array must be of the same type.
NumPy arrays are faster compared to Python lists. But python lists are more flexible than NumPy arrays as you can only store the same data type in each column.
Features of Numpy
- It is a combination of C and python.
- Multidimensional homogeneous arrays. Ndarray which are a ndimensional array
- Various functions for arrays.
- Reshaping of arrays Python can be used as an alternative to MATLAB.
How does numpy make working so easy?
You can easily create homogeneous arrays and perform various operations on it like,
- Importing it by using the following command, import numPy as numpy.
NumPy n-dimentional array
One of the most important features of Numpy is an n-dimensional array that is nd-array. The number of dimensions of an array is nothing but array rank. Here are a few examples.
arrA=numpy.array([10,20,30])
Creating a numpy array
The following line creates an array,
arrA=numpy.arange(3)
This is just like the range in python. This will create an array of size 3.
Some basic functions that can be used with a numpy array
Let’s take a look at what functions can we use with array and their purpose
import numpy as numpy
arrC=numpy.array([[10,20,30],[40,50,60]])
arrC.reshape(3,2)
Output:
arrayC([[10,20],
[30,40], [50,60]])Reshape function changes the number of columns and rows, so after reshaping the array, we will get a new view with the different number of columns and rows.
Some mathematical functions in Numpy
There are mathematical functions that can be used with Numpy arrays. Below are some examples,
import numpy as numpy
arrA=numpy.array([[1,2,3],[4,5,6]])
arrB=numpy.array([[7,8,9],[10,11,12]])
numpy.add(arrA,arrB)
This function adds array arrA and arrB
Output:
arrayC([[ 8, 10, 12], [14, 16, 18]])
Why should we Use?
We use python NumPy array instead of a list because of the below three reasons:
- Less Memory usage
- Fast performance
- Convenient to Work
The first reason to prefer python NumPy arrays is that it takes less memory than the python list. Then, it is fast in terms of execution, and at the same time, it is convenient and easy to work with it.
What can we do with Numpy?
Built-in support for Arrays is not available in python, but we can use python lists as arrays.
arrayA = ['Hello', 'world']
print(arrayA)
But it’s still a python list, not an array.
So here comes Numpy, which we can use to create 2D,3D that is multidimensional arrays. Also, we can do computations on arrays.
import numpy as num
arr = num.array([1,2,3,4,5,6])
print(arr)
Creates array arr.
Then, for 2D and 3D arrays,
import numpy as num
arr = num.array([(1,2,3,4,5),(6,7,8,9,10,11)])
print(arr)
–If you want to know the dimensions of your array, you can simply use the following function.
print(arr.ndim)
–If you want to find out the size of an array, you can simply use the following function,
print(arr.size)
–To find out the shape of an array, you can use the shape function.
print(arr.shape)
It will tell you the number of (col, rows)
You can also use slicing, reshaping, and many more methods with numpy arrays.
Why do we need it?
To make a logical and mathematical computation on array and matrices, it is needed. It performs these operations way too efficiently and faster than python lists.
Advantages
1. Numpy arrays take less space.
NumPy’s arrays are smaller in size than Python lists. A python list could take upto 20MB size while an array could take 4MB. Arrays are also easy to access for reading and writing.
2. The speed performance is also great. It performs faster computations than python lists.
As it is open-source, it doesn’t cost anything, and it uses a very popular programming language, Python, which has high-quality libraries for almost every task. Also, it is easy to connect the existing C code to the Python interpreter.
Career Growth
Among programming languages, Python is a trending technology in IT. Career opportunities in Python are increasing rapidly in number across the world. Python looks after faster code readability and conciseness with lesser lines of code as python is a high-level programming language. Python is one of the best tools for creating dynamic scripts to large and small extents.
Python is broadly used in Web development, writing of scripts, testing, development of apps and their updates. So if anyone wants to be an expert in Python, they have many career options, like one can be a python developer, python tester or even a data scientist.
Conclusion
As we can see, It is really strong in terms of the high-quality library functions it has. Anyone can perform large calculations or computations with just a few lines of code. This is what makes it a great tool for various numerical computations. If anyone wishes to become a data scientist, then they can try mastering. But first, you need to learn and know python before becoming an expert in Numpy.
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