NumPy (short for “Numerical Python“) is a powerful library in Python used for numerical computations.
- The NumPy library was developed by Travis Oliphant in 2005 by combining features from two earlier Python libraries, Numeric (developed by Jim Hugunin) and Numarray.
- It provides support for multi-dimensional arrays, matrices, and high-level mathematical operations that are efficient and easy to use.
- It serves as the foundation for many data science and machine learning libraries in Python, such as Pandas, SciPy, and TensorFlow.
Why Do We Use NumPy?
1.) Efficient Computation:
- NumPy is implemented in C, making operations much faster than Python’s built-in lists for numerical computations.
- It supports vectorized operations, eliminating the need for explicit loops in many cases.
2.) Multi-Dimensional Arrays:
- Provides the ndarray object for handling n-dimensional arrays, enabling the storage and manipulation of large datasets.
3.) Mathematical Functions:
- Offers a wide range of mathematical functions such as linear algebra, Fourier transforms, and statistical operations.
4.) Memory Efficiency:
- NumPy arrays consume less memory than Python lists for storing large datasets.
5.) Data Compatibility:
- NumPy arrays are compatible with a variety of other Python libraries, making it easier to process and visualize data.
6.) Ease of Use:
- Simplifies numerical operations with user-friendly syntax, enabling quick prototyping and testing.
Applications of NumPy:
- Scientific Computing
- Data Analysis
- Machine Learning
- Image and Signal Processing
- Simulation and Modeling