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In this article, I am going to talk about Numpy, Python’s one of the most important libraries.

### What is Numpy

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NumPy is the fundamental package for scientific computing in Python. Numpy stands for Numerical Python.Â
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If you want to work with machine learning or data science, Numpy is a Python library you will mostly use.
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It is a Python library that provides a multidimensional array object for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, and much more.Â
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It can be imported in a simple way as follows :
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``````import numpy as np

# np label can be used to access the numpy library. ``````

### Why Use Numpy

So why do we use Numpy when we have the list data structure in Python?Â  Here are the reasons :

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• Numpy is faster than list :Â

Numpy stores its data in a continuous location in memory, unlike Python list data structures. Therefore Numpy can access its data much more easily and efficiently.

• Â NumPy uses much less memory to store data :Â

Numpy arrays take up less memory space than Python list data structures.Â

• Â Convenient to useÂ Â
• Â Lots of Built-in FunctionsÂ

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If you are interested in machine learning or data science, Numpy is a Python library you will use frequently.

So how do you learn Numpy? As with everything else in life, the best way to learn something is to learn by doing it. Below I have listed the best courses and books for you to learn Numpy. These courses and books will enable you to learn Numpy as efficiently as possible.

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### Numpy Courses

#### Codeacademy - Learn Statistics with Numpy

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Codeacademy is one of the best platforms among e-learning platforms. The course I recommend here is Codeacademy – Learn Statistics with Numpy.

Course Description

Why Learn NumPy?

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NumPy is a popular Python library that will help you calculate large quantities and common descriptive statistics without writing these functions from scratch. These courses also teach the fundamentals of statistical distributions that can be used to describe datasets.

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Take-Away Skills:

In this set of courses, you will learn the basics of NumPy, a Python module for performing numerical operations on large quantities of data.
Youâ€™ll learn how to:
Create arrays, the basic data type in NumPy, and how to perform calculations like addition, subtraction, and selection.
Calculate descriptive statistics, such as means, medians, and ranges.
Explore and calculate common statistical distributions, such as the normal and binomial distributions.
Explore and create histograms, a great way of visualizing large quantities of numerical data.

### DataCamp - Introduction to Python

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Datacamp is an e-learning platform that offers high-quality courses in the field of data science. The course I recommend here is the Datacamp Introduction to Python course. This course starts by teaching the programming language Python and then continues with Numpy.

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Course DescriptionÂ
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Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, youâ€™ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses.

### Udemy - Python Numpy for Absolute Beginners

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In this course, you are going to learn :Â
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• Learn How To Use Python Interactively And By Using a Script
• Use Python for Data Science and Machine Learning
• Create Your First Numpy array and Acquaint Yourself With Python Numpy
• Learn to work with powerful tools in the NumPy array, and get started with data exploration.
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Course DescriptionÂ
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With not just tutorials but also example walkthroughs, this boot camp offers a seamless collection of videos taking you through Python Numpy with programs to work with as examples.Â This course offers in-depth yet simplified explanations for the fundamental concepts of Python Numpy.
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If you want to learn the fundamentals of Python Numpy for Data Science and want to use it for creating awesome programs that can help you with real-world situations, this course is just for you!

### Best Numpy Books

#### Python Data Science Handbook: Essential Tools for Working with Data

Official DescriptionÂ

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allâ€”IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
With this handbook, youâ€™ll learn how to use:
• IPython and Jupyter:Â provide computational environments for data scientists using Python
• NumPy:Â includes theÂ ndarrayÂ for efficient storage and manipulation of dense data arrays in Python
• Pandas:Â features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
• Matplotlib:Â includes capabilities for a flexible range of data visualizations in Python
• Scikit-Learn:Â for efficient and clean Python implementations of the most important and established machine learning algorithms

#### Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Official DescriptionÂ
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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. Youâ€™ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. Itâ€™s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
• Use the IPython shell and Jupyter notebook for exploratory computing
• Learn basic and advanced features in NumPy (Numerical Python)
• Get started with data analysis tools in the pandas library
• Use flexible tools to load, clean, transform, merge, and reshape data
• Create informative visualizations with matplotlib
• Apply the pandas groupby facility to slice, dice, and summarize datasets
• Analyze and manipulate regular and irregular time series data
• Learn how to solve real-world data analysis problems with thorough, detailed examples.

#### Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Official DescriptionÂ
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Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.Â
Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis.Â
After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.
What You’ll Learn
• Work with vectors and matrices using NumPy
• Plot and visualize data with Matplotlib
• Perform data analysis tasks with Pandas and SciPy
• Review statistical modeling and machine learning with statsmodels and scikit-learn
• Optimize Python code using Numba and Cython
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Who This Book Is For
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Developers who want to understand how to use Python and its related ecosystem for numerical computing.
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