Moving forward in python numpy tutorial, let’s focus on some of its operations. Therefore, the above examples proves the point as to why you should go for python numpy array rather than a list! That’s why working with numpy is much easier and convenient when compared to the lists. Now, if you noticed we had run a ‘for’ loop for a list which returns the concatenation of both the lists whereas for numpy arrays, we have just added the two array by simply printing A1+A2. List took 380ms whereas the numpy array took almost 49ms. If you see the output of the above program, there is a significant change in the two values. Then, we have compared the time taken in order to find the sum of lists and sum of numpy arrays both. In the above code, we have defined two lists and two numpy arrays. Next, let’s talk how python NumPy array is faster and more convenient when compared to list. From this, you can conclude that there is a major difference between the two and this makes Python NumPy array as the preferred choice over list. The above output shows that the memory allocated by list (denoted by S) is 14000 whereas the memory allocated by the NumPy array is just 4000. Don’t worry, I am going to prove the above points one by one practically in P圜harm. So these are the major advantages that Python NumPy array has over list. Then, it is pretty fast in terms of execution and at the same time, it is very convenient to work with NumPy. The very first reason to choose python NumPy array is that it occupies less memory as compared to list. We use python NumPy array instead of a list because of the below three reasons: Python NumPy Array v/s List Why NumPy is used in Python? Many of you must be wondering that why do we use python NumPy if we already have Python list? So, let us understand with some examples in this python NumPy tutorial. Let us see how it is implemented in P圜harm: Single-dimensional Numpy Array: In the above image, we have 3 columns and 4 rows available. It is said to be two dimensional because it has rows as well as columns. Here, I have different elements that are stored in their respective memory locations. Moving ahead in python numpy tutorial, let us understand what exactly is a multi-dimensional numPy array. Once the installation is completed, go to your IDE (For example: P圜harm) and simply import it by typing: “import numpy as np” To install Python NumPy, go to your command prompt and type “pip install numpy”. In order to perform these NumPy operations, the next question which will come in your mind is: How do I install NumPy? We can initialize NumPy arrays from nested Python lists and access it elements. Python NumPy Array: Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. Now, let me tell you what exactly is a Python NumPy array. NumPy array can also be used as an efficient multi-dimensional container for generic data. It is also useful in linear algebra, random number capability etc. Python NumPy arrays provide tools for integrating C, C++, etc. It is the core library for scientific computing, which contains a powerful n-dimensional array object. NumPy is a Python package that stands for ‘Numerical Python’. So, let’s get started! :-) What are NumPy Arrays?
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