Comprehensive Guide to NumPy Array Creation, Operations, and Manipulation

This guide introduces NumPy as one of the most useful tools in Python for handling large datasets and demonstrates the most important functions for data science.

Array Creation

Functions covered include numpy.array for creating one‑dimensional or multi‑dimensional arrays, numpy.linspace for generating evenly spaced floating‑point numbers, numpy.arange for integer ranges, numpy.random.uniform , numpy.random.randint , numpy.random.random , and numpy.logspace . Examples show how to import NumPy and create arrays with specific values.

Array Operations

Common operations such as np.min , np.max , np.unique , np.mean , np.median , np.digitize , np.reshape , np.expand_dims , np.squeeze , np.count_nonzero , np.argwhere , np.argmax , np.argmin , np.sort , np.abs , np.round , and np.clip are explained with code snippets and output.

Array Comparison

Functions np.allclose and np.equal are used to compare arrays element‑wise or within a tolerance.

Set Operations

Set‑like functions np.intersect1d , np.setdiff1d , np.setxor1d , and np.union1d return common, different, exclusive, or merged elements of two arrays.

Array Splitting and Stacking

Horizontal and vertical splitting with np.hsplit and np.vsplit , and stacking with np.hstack and np.vstack are demonstrated.

Advanced Operations

The Einstein summation convention is illustrated using np.einsum . Statistical functions such as np.histogram , np.percentile , np.std , and np.var compute histograms, percentiles, standard deviation, and variance.

Printing and Persistence

Printing options are customized with np.set_printoptions . Saving and loading arrays to/from text files are shown using np.savetxt and np.loadtxt .

Throughout the article, each code block is presented inside pre tags to preserve formatting.