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Python Panda Tutorial
- Python Pandas Tutorial
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- Pandas Library In Python
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Python Selenium
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Python Flask Tutorial
Python Django
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Numpy
- Numpy Introduction
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- numpy.diff()
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- Why we learn and use Numpy?
Tensorflow
- Introduction To Tensorflow
- INTRODUCTION TO DEEP LEARNING
- EXPLAIN NEURAL NETWORK?
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- INTRODUCTION TO TENSORFLOW
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Interview Questions & Answers
Why we learn and use Numpy?
Introduction
NumPy is one of the most powerful Python libraries. It is used in the industry for array computing. This article will outline the core features of the NumPy library. It will also provide an overview of the common mathematical functions in an easy-to-follow manner.
Numpy is gaining popularity and is being used in a number of production systems.NumPy is an open-source numerical Python library. It contains a multi-dimensional array and matrix data structures. It can be utilised to perform a number of mathematical operations on arrays such as trigonometric, statistical, and algebraic routines. Therefore, the library contains a large number of mathematical, algebraic, and transformation functions. NumPy is an extension of Numeric and Numarray. It also contains random number generators. It is a wrapper around a library implemented in C. Pandas objects rely heavily on NumPy objects. Essentially, Pandas extends Numpy.
NumPy is a Python package that stands for ‘Numerical Python’. It is the core library for scientific computing, which contains a powerful n-dimensional array object. Python NumPy arrays provide tools for integrating C, C++, etc. It is also useful in linear algebra, random number capability etc. NumPy array can also be used as an efficient multi-dimensional container for generic data. Now, let me tell you what exactly is a Python NumPy array.
Python NumPy Array:
Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. We can initialize NumPy arrays from nested Python lists and access it elements.
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.
Advantages
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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.
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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.