Python-NumPy

Explore numpy dimensions and operations by building 1d, 2d, and 3d arrays, inspecting shapes, and applying comparison operators to generate boolean results.

Explore numpy arrays by examining element size, data type, and length. The lecture demonstrates using itemsize to confirm four-byte elements and compares array length with the size.

Learn how to use NumPy range arguments to generate sequences with start, stop, and step, including the default step of one and bracket notations for printing results.

Learn how to work with NumPy shapes and reshaping, create one-dimensional and two-dimensional arrays, inspect shapes, and use reshape and flatten to organize data into four-by-three structures.

Explore numpy slicing by using start, end, and step in zero-based indexing to select and print elements, from single indices to full arrays.

learn how to use numpy.linspace to generate evenly spaced numbers, such as five values from 1 to 3 or from 2 to 10, and compute the max, min, and sum.

Generate random numbers with numpy by printing integers from 0 to 50, floats from 0 to 1, and shaping arrays, then use random.choice to select from a list.

Learn numpy axes by building a table of rows and columns, using 1s for rules and 0s for columns, then sum across axes.

Explore numpy mathematical functions by calculating sums, square roots, standard deviation of arrays, stacking with vstack, and practicing elementwise operations such as addition, subtraction, multiplication, and division.