https://github.com/devwithzain/numpy-learning

https://github.com/devwithzain/numpy-learning

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• Host: GitHub
• URL: https://github.com/devwithzain/numpy-learning
• Owner: devwithzain
• Created: 2024-02-23T05:21:13.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-03-22T18:19:40.000Z (8 months ago)
• Last Synced: 2024-03-22T19:35:46.176Z (8 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 79.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
NumPy


**Introduction:**

NumPy is a powerful Python library for numerical computing. It provides support for arrays, matrices, and a wide range of mathematical functions to operate on these data structures efficiently. NumPy is widely used in scientific computing, data analysis, machine learning, and many other fields where numerical operations are fundamental.

**Key Features:**

1. **Multidimensional Arrays:** NumPy provides the `ndarray` object, which represents multidimensional arrays of homogeneous data types. These arrays are much more efficient than Python lists for numerical operations.

   

2. **Efficient Operations:** NumPy's array operations are implemented in C, making them much faster than equivalent operations using Python lists. This efficiency is crucial for handling large datasets and complex computations.

   

3. **Broadcasting:** NumPy allows for arithmetic operations between arrays of different shapes and sizes through broadcasting. This feature simplifies code and makes it more readable.

   

4. **Linear Algebra Operations:** NumPy offers a comprehensive suite of linear algebra functions, including matrix multiplication, decomposition, and solving linear equations.

   

5. **Random Number Generation:** NumPy provides functions for generating random numbers from various probability distributions. This feature is essential for tasks such as simulating experiments and generating random data.

   

6. **Integration with Other Libraries:** NumPy seamlessly integrates with other Python libraries, such as SciPy, Pandas, and Matplotlib, forming a powerful ecosystem for scientific computing and data analysis.

**Installation:**

You can install NumPy using pip, the Python package manager, by running the following command:

```bash

pip install numpy

```

**Getting Started:**

Once installed, you can import NumPy in your Python scripts or interactive sessions using:

```python

import numpy as np

```

Now you can start using NumPy's features, such as creating arrays, performing mathematical operations, and utilizing its various functions.

**Example:**

```python

import numpy as np

# Create a 1D array

arr1 = np.array([1, 2, 3, 4, 5])

# Create a 2D array

arr2 = np.array([[1, 2, 3], [4, 5, 6]])

# Perform arithmetic operations

result = arr1 + arr2

print(result)

```

This example demonstrates the creation of arrays and performing element-wise addition using NumPy.

**Conclusion:**

NumPy is a fundamental library for numerical computing in Python. Its efficient array operations, extensive mathematical functions, and integration with other libraries make it a versatile tool for a wide range of applications. Whether you're working on scientific research, data analysis, or machine learning projects, NumPy provides the building blocks for efficient and scalable computations.