NumPy Tutorial

NumPy Tutorial

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This NumPy tutorial provides detailed information with working examples on various topics, such as creating and manipulating arrays, indexing and slicing arrays, and more. This tutorial is helpful for both beginners and advanced learners.

What is NumPy?

NumPy, short for Numerical Python, is an open-source Python library. It supports multi-dimensional arrays (matrices) and provides a wide range of mathematical functions for array operations. It is used in scientific computing, and in areas like data analysis, machine learning, etc.

Why to Use NumPy?

The following are some of the key reasons to use NumPy:

  • NumPy provides various math functions for calculations like addition, algebra, and data analysis.
  • NumPy provides various objects representing arrays and multi-dimensional arrays which can be used to handle large data such as images, sounds, etc.
  • NumPy also works with other libraries like SciPy (for scientific computing), Pandas (for data analysis), and scikit-learn (for machine learning).
  • NumPy is fast and reliable, which makes it a great choice for numerical computing in Python.

NumPy Applications

The following are some common application areas where NumPy is extensively used:

  • Data Analysis: In Data analysis, while handling data, we can create data (in the form of array objects), filter the data, and perform various operations such as mean, finding the standard deviations, etc.
  • Machine Learning & AI: Popular machine learning tools like TensorFlow and PyTorch use NumPy to manage input data, handle model parameters, and process the output values.
  • Array Manipulation: NumPy allows you to create, resize, slice, index, stack, split, and combine arrays.
  • Finance & Economics: NumPy is used for financial analysis, including portfolio optimization, risk assessment, time series analysis, and statistical modelling.
  • Image & Signal Processing: NumPy helps process and analyze images and signals for various applications.
  • Data Visualization: NumPy independently does not create visualizations, but it works with libraries like Matplotlib and Seaborn to generate charts and graphs from numerical data.

NumPy Example

The following is an example of Python NumPy:

# Importing NumPy Array
import numpy as np

# Creating an array using np.array() method
arr = np.array([10, 20, 30, 40, 50])

# Printing
print(arr) # Prints [10 20 30 40 50]

NumPy Compiler

To practice the NumPy example, we provided an online compiler. Practice your NumPy programs here:

NumPy Practice

You can quickly revise and practice the concepts of NumPy using the following:

Who can Learn NumPy?

This NumPy tutorial has been prepared for those who want to learn about the basics and functions of NumPy. It is specifically useful in data science, engineering, agriculture science, management, statistics, research, and other related domains where numerical computation is required. After completing this tutorial, you will find yourself at a moderate level of expertise from where you can take yourself to higher levels of expertise.

Prerequisites to Learn NumPy

You should have a basic understanding of computer programming terminologies. A basic understanding of Python and any of the programming languages is a plus.

NumPy Codebase

NumPy's source code can be found at this github repository: https://github.com/numpy/numpy

NumPy Documentation

NumPy's documentation, reference manuals, and user guide can be found at these links:

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