What is NumPy?
NumPy is an open source package of python. It can be used for scientific and numerical computing. Mostly it is used for more efficient computation on arrays. It is based and written in C and Python.
Definition:
It is a python package and the word Numpy means Numerical Python. It is mainly used for processing of homogeneous multidimensional array. It is a core library for scientific computations. Hence it has powerful multidimensional array objects and integrating tools which are useful while working with these arrays.
It is important in almost every scientific programming in python, which includes machine learning, statistics, bioinformatics, etc. It provides some really good functionality that is very well written and runs efficiently. It’s mostly focused on performing mathematical operations on contiguous arrays much like the arrays that you have in lower level languages such as C.
In other words, it is used in the manipulation of numerical data. Because of it, python can be used as an alternative to MATLAB.
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 have 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, also some other libraries are 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 multidimensional arrays and matrices with very less number of lines of code. ‘ndarray’ or ndimensional 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.
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Features –
 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 ndimentional array
One of the most important features of Numpy is an ndimensional array that is ndarray. 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
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 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 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 very first reason to prefer python numpy arrays is that it takes less memory as compared to 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?
Builtin 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 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?
To make a logical and mathematical computation on array and matrices numpy is needed. It performs these operations way too efficient 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 opensource, it doesn’t cost anything and it uses a very popular programming language Python which has highquality 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. As python is a highlevel programming language, Python looks after faster code readability and conciseness, with lesser lines of code. Python is one of the best tools for creating dynamic scripts on 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 expert in Python they have many career options, like one can be python developer, python tester or even a data scientist.
Conclusion:
Now as we can see, It is really strong in terms of the highquality library functions that 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 in Numpy. But first, you need to learn and know python before becoming an expert in Numpy.
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