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# Introduction # Introduction
## What is Numpy? ## What is NumPy?
NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. It is open-source software. NumPy is a powerful array-processing library in Python, essential for scientific computing. It provides efficient data structures and tools for working with multidimensional arrays.
## Features of NumPy ## Key Features
NumPy has various features including these important ones: 1. **Efficient Arrays:** NumPy offers high-performance N-dimensional array objects for swift data manipulation.
2. **Broadcasting:** Advanced broadcasting enables seamless element-wise operations on arrays of varying shapes.
3. **Interoperability:** NumPy seamlessly integrates with C, C++, and Fortran, enhancing performance and versatility.
4. **Mathematical Tools:** Comprehensive support for linear algebra, Fourier transforms, and random number generation.
1. A powerful N-dimensional array object ## Installation
2. Sophisticated (broadcasting) functions
3. Tools for integrating C/C++ and Fortran code
4. Useful linear algebra, Fourier transform, and random number capabilities
## Install Numpy Ensure Python is installed in your system. If not you can install it from here([official Python website](https://www.python.org/)),then install NumPy via:
Before installing Numpy, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/).
Numpy can be installed via the following pip command:
```bash ```bash
pip install numpy pip install numpy
``` ```
# How to import NumPy # Importing NumPy
To access NumPy and its functions import it in your Python code like this: To access NumPy functions, import it with the alias `np`:
```bash ```python
import numpy as np import numpy as np
``` ```
We shorten the imported name to np for better readability of code using NumPy. This is a widely adopted convention that makes your code more readable for everyone working on it. We recommend to always use import numpy as np. Using `np` as an alias enhances code readability and is a widely adopted convention.