Array Operations Example - Numpy Typing CST Test
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Array Operations Example — Numpy Code
A minimal NumPy example demonstrating array creation, arithmetic, and basic statistics.
import numpy as np
# Create arrays
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([5, 6, 7, 8])
# Array arithmetic
sum_arr = arr1 + arr2
print('Sum:', sum_arr)
# Statistical operations
print('Mean of arr1:', np.mean(arr1))
print('Standard deviation of arr2:', np.std(arr2))
# Multi-dimensional arrays
matrix = np.array([[1,2],[3,4]])
print('Matrix:
', matrix)
print('Transpose:
', matrix.T)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.