NumPy for Data Science Beginners: 2021

Explore numpy arrays by examining dimension, shape, and size; learn to reshape and reconfigure dimensions, inspect data types and item size, and understand array basics.

Learn indexing and slicing in NumPy to manipulate and extract data across one to three dimensional arrays, using start, stop, and step, including negative indexing and element assignment.

Explore how to iterate over arrays in numpy, from simple one-dimensional lists to two- and three-dimensional arrays, using for loops, indexing, and shortcuts to access elements efficiently.

Learn numpy array types with arange, linspace, and logspace, exploring start, stop, and step, integer and float outputs, and ranges like 10 to 20 for plotting with Matplotlib.

Learn how to create numpy arrays with zeros and ones, specify shape and data type, explore identity and empty arrays, and use all and any for boolean checks.

Explore numpy string operations, including concatenation, multiplication, centering, and padding with characters. Learn to transform strings with capitalize, title, lower, join, split, and replace to manipulate text efficiently.

Discover how NumPy random functions generate shaped arrays and sample values. Use random to draw numbers between 0 and 100, random.rand for 0–1 values, and random.choice, shuffle, or permutation.

Explore numpy's matrix creation with empty, zeros, ones, identity, and random, practicing shapes like 2x2 and 3x3. Compare matrix versus array and learn simple type conversions.

Learn to use numpy's sort to organize numbers, strings, and booleans, showing alphabetical ordering for strings and false-before-true sorting for booleans, and apply sorting to multidimensional arrays.

Explore NumPy's search and searchsorted functions to locate element indices in arrays, retrieve positions for single or multiple values, and control indexing from left or right.

Learn how to split NumPy arrays into multiple parts using split, hsplit, and vsplit, control division with axis, and apply to 1d and 2d arrays.

Learn how to join arrays in numpy using concatenate and stack to combine data along different axes, including horizontal and vertical stacking with axis parameters.

Learn how to save and load data with numpy using file-based workflows, including binary numpy files and text files, with examples of naming, extensions, and reading back data.

Learn to visualize data with NumPy and Matplotlib, creating simple plots, labeling axes and titles, and plotting sine waves and other trigonometric functions for data science insights.