https://github.com/patilni3/numpy-in-depth
Python's NumPy Library for Data Analysis, Machine Learning, Data Science and many more...
https://github.com/patilni3/numpy-in-depth
data-analysis data-engineering data-science machine-learning numpy pandas
Last synced: 10 months ago
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Python's NumPy Library for Data Analysis, Machine Learning, Data Science and many more...
- Host: GitHub
- URL: https://github.com/patilni3/numpy-in-depth
- Owner: PatilNi3
- Created: 2024-10-31T11:37:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-04T09:12:41.000Z (over 1 year ago)
- Last Synced: 2025-02-08T23:27:09.263Z (about 1 year ago)
- Topics: data-analysis, data-engineering, data-science, machine-learning, numpy, pandas
- Language: Jupyter Notebook
- Homepage:
- Size: 30.3 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NumPy-In-Depth
Python's NumPy Library for Data Analysis, Machine Learning, Data Science and many more...
## Introduction:
NumPy (Numerical Python) is a powerful library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. This README covers the essential features of NumPy, including array creation, attributes, indexing, slicing, operations, and more.
## Key Features:
1. [Creating Arrays](#creating-arrays)
2. [Array Attributes](#array-attributes)
3. [Indexing and Slicing](#indexing-and-slicing)
4. [Adding and Sorting](#adding-and-sorting)
5. [Reshaping Arrays](#reshaping-arrays)
6. [Stacking Operations](#stacking-operations)
7. [Aggregate Functions](#aggregate-functions)
8. [Trigonometric Functions](#trigonometric-functions)
9. [Unique Items and Counts](#unique-items-and-counts)
10. [Index Retrieval Functions](#index-retrieval-functions)
11. [Saving and Loading NumPy Objects](#saving-and-loading-numpy-objects)
## Installations:
pip is the package installer for Python. It allows you to install and manage additional libraries and dependencies that are not included in the standard Python distribution.
To install NumPy, you use the command in your command prompt/terminal: **pip install numpy**.
This command tells pip to download and install the latest version of NumPy from the Python Package Index (PyPI) along with any necessary dependencies.
## Getting Started:
To get started with NumPy, you can create a new Python script or Jupyter notebook and import the library: **import numpy as np**
## Resources:
Here are some valuable real-time resources for learning and utilizing Pandas:
1. **Official Documentation:** The official [NumPy documentation](https://numpy.org/doc/2.1/user/absolute_beginners.html) is the best place to start. It includes detailed explanations of all features, methods, and use cases.
2. **Online Courses:** You will find Online courses on platforms like [Udemy](https://www.udemy.com/courses/search/?price=price-free&q=numpy&sort=relevance&src=ukw), [Coursera](https://www.coursera.org/courses?query=numpy), YouTube and many more...
## Books:
**"Python for Data Analysis"** by Wes McKinney
Written by the creator of pandas, this book covers the use of NumPy in data analysis and includes practical examples and applications.
**"Numerical Python: A Practical Techniques Approach for Industry"** by Robert Johansson
This book provides a solid introduction to NumPy and focuses on practical applications in industry, making it ideal for engineers and scientists.
## Conclusion:
NumPy is an essential library for numerical computations in Python, providing efficient tools for handling large datasets. This README has covered key functionalities, but there's much more to explore in the numpy.ipynb file.