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Work with fast, memory-efficient multi-dimensional arrays for numerical data.
Access a wide range of vectorized math operations like sin, log, exp, and dot.
Solve systems, compute eigenvalues, and perform matrix decompositions.
Apply operations across arrays without explicit loops for performance gains.
Use `pip install numpy` and `import numpy as np` to get started.
Use `np.array()`, `np.zeros()`, or `np.random.rand()` to initialize data.
Use vectorized math functions, slicing, and broadcasting for fast computation.
Use `np.linalg` for matrix multiplication, inversion, and decomposition.
Use NumPy arrays as inputs for Pandas, Scikit-learn, TensorFlow, and more.
# NumPy Matrix Multiplication Example
import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
result = np.dot(A, B)
print(result)Normalize, scale, and transform features before model training.
Run physics, chemistry, or engineering simulations with matrix math.
Compute means, variances, correlations, and distributions efficiently.
Manipulate pixel arrays or time-series data for analysis and modeling.
Explore NumPy’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about NumPy’s capabilities, usage, and ecosystem.