Learn NUMPY with Real Code Examples

NumPy provides an n-dimensional array object, along with functions for linear algebra, statistics, and other numerical operations.

It is widely used in data analysis, scientific computing, and as a base for other libraries like Pandas, SciPy, and machine learning frameworks.

NumPy emphasizes performance through vectorized operations and C-backed implementations.

ndarray: fast multi-dimensional arrays

Universal functions (ufuncs) for element-wise operations

Broadcasting rules for array arithmetic

Array indexing, slicing, and masking

Random number generation and linear algebra modules

Array: n-dimensional container for homogeneous data

Shape: dimensions of an array

Dtype: data type of array elements

Axis: dimension along which operations are applied

Broadcasting: rules for operating on arrays of different shapes

main.py - scripts using NumPy computations

data/ - datasets in arrays or CSVs

utils/ - helper functions for array operations

notebooks/ - Jupyter notebooks for experimentation

tests/ - unit tests for numerical functions

Import NumPy and create ndarrays

Perform element-wise arithmetic and mathematical operations

Use slicing, indexing, and masking for data selection

Apply linear algebra, statistics, and aggregation functions

Integrate arrays with other Python libraries for analysis or ML

Beginner: basic array creation and arithmetic

Intermediate: matrix multiplication, slicing, indexing

Advanced: broadcasting, vectorization, and custom ufuncs

Expert: high-performance simulations and large-scale computations

Enterprise: numerical preprocessing for ML pipelines

NumPy vs PyTorch: general numerical library vs ML focus

NumPy vs TensorFlow: core array ops vs full ML platform

NumPy vs Pandas: array operations vs labeled tabular data

NumPy vs SciPy: arrays vs scientific algorithms

NumPy vs JAX: CPU/GPU computation vs automatic differentiation

2005 – NumPy created by Travis Oliphant

2006–2010 – Consolidation of Numeric and Numarray features

2011 – NumPy 1.x stable API released

2015 – Continued optimization and adoption in scientific computing

2025 – Latest version with extended performance and ecosystem integration

ndarray: multi-dimensional array

dtype: type of elements

shape: dimensions of array

axis: dimension for operations

broadcasting: operation on different-shaped arrays