Array Indexing and Slicing - Numpy Typing CST Test
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Array Indexing and Slicing — Numpy Code
Demonstrates indexing and slicing of NumPy arrays.
import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print('Original array:', arr)
# Indexing
print('Element at index 2:', arr[2])
# Slicing
print('Elements from index 1 to 3:', arr[1:4])
print('Every second element:', arr[::2])Numpy Language Guide
NumPy (Numerical Python) is an open-source Python library that provides high-performance, multi-dimensional arrays and a wide range of mathematical functions to operate on these arrays, forming the foundation of scientific computing in Python.
Primary Use Cases
- ▸Numerical computations with large datasets
- ▸Matrix operations, linear algebra, and array manipulations
- ▸Data preprocessing for machine learning and AI
- ▸Scientific simulations and mathematical modeling
- ▸Integration with other Python libraries for analytics and visualization
Notable Features
- ▸N-dimensional array (ndarray) data structure
- ▸Vectorized operations for high performance
- ▸Broadcasting to handle operations between different shapes
- ▸Extensive mathematical, statistical, and linear algebra functions
- ▸Interoperability with C/C++ and other Python libraries
Origin & Creator
NumPy was created by Travis Oliphant in 2005 as an extension of the older Numeric and Numarray libraries to unify array computing in Python.
Industrial Note
NumPy is essential in virtually all scientific and engineering computing in Python and underpins libraries like SciPy, Pandas, Matplotlib, PyTorch, and TensorFlow.