Python fundamentals and basics of numpy

Learn how NumPy arrays replace lists as multidimensional data types, created from Python lists with the array function, and inspect their shape as two by five.

Explore numpy dtype arrays and how homogeneous elements determine the array type, such as int 32 or float 64, by creating arrays from lists and using dtype to cast elements.

Explore how the ndim function in numpy reveals an array’s dimension, illustrated with x11 as a one dimensional array and x12 as a two dimensional array, with console prints.

Master array concatenation in NumPy, joining 2d arrays vertically and horizontally with concatenate and axis settings. Compare with vstack for equivalent results.

Learn to access numpy array elements using scalar selection for one-dimensional and two-dimensional arrays. Practice printing and assigning values with index-based operations, including numerical and logical indexing.

Explore array slicing in numpy to access elements in 1d and 2d arrays using start, end, and step; retrieve rows, columns, and subarrays efficiently.

Learn value assignment through slicing in numpy arrays, including creating a 4x3 zero array, updating the first row, and assigning a 2x2 subarray with every second row and column.

Learn how to import and use modules in Python, including importing specific functions, importing entire modules, and creating aliases like pylab as pl and numpy as np.

Learn how to create special arrays in numpy using ones, zeros, and the identity matrix, with m and n set to five to produce 5x5 arrays x28, x29, and x30.

Practice creating and updating numpy arrays of various shapes, including unit and ones arrays, with specific values and data types, and computing their product.

Learn to create and print numpy arrays, including 2d, 3d, and 4d shapes, work with dtypes such as float64 and int32, and perform elementwise multiplication to solve classwork two.

Master elementary operators for arrays, including plus, minus, product, division, and power, through simple variable examples and executions in the variable explorer.

Explore numpy broadcasting for element-wise operations between arrays and scalars, where dimensions align across axis and range and reshape examples illustrate broadcastable shapes.

Explore numpy array operations by performing element-wise addition, subtraction, multiplication, division, and power on 3x5 arrays reshaped from arange, using broadcasting.

Learn Python operator priority and precedence, including brackets for ambiguities, with a ranked table from brackets to assignment, covering power, unary, arithmetic, bitwise, comparison, and equality operators.

Explore numpy array generation using linspace to create evenly spaced points, arange and range for sequential values, and our underscore and c underscore for one and two dimensional arrays.

Explore how numpy ix_ creates an index array from one-dimensional lists to select elements not in order in a 5 by 5 array, enabling row and column extraction.

Explore rounding in numpy using around and round, including two-decimal precision, and learn floor and ceil for smallest or largest integers while generating random numbers with numpy.random.

Explore numpy mathematical functions on arrays, including sum and sum along rows or columns, and elementwise operations like prod, exp, log, sqrt, square, and absolute, with nan handling.

Explore the numpy unique function to extract unique values from an array, demonstrated by an example showing repeated elements and printing the result.

Learn how numpy's sort function arranges array elements, with axis-based and column-by-column sorting. Explore max, min, and sum, including handling none values.

Learn relational operators in numpy, including greater/less and equal/not equal, with element-wise comparisons on scalars and arrays using x44, x45, and x46 to create boolean arrays.

Explore logical operators including and, or, not, and xor, and how they yield true or false results using Python, numpy arrays and elementwise comparisons.

Explore numpy basics through class work no. 3, building 2d arrays, performing elementwise and matrix operations, using numpy functions, and practicing sorting, rounding, and array transformations.

Create and reshape numpy arrays, perform elementwise operations, transpose and print results, then use arange and random arrays to explore floor, round, sum, and sort.

Explore conditional operations in Python, using if, elif, and else with indentation rules and colon syntax. See practical examples updating x1 and x2 and printing results.

Explore for loops over ranges, numpy arrays, and lists, accumulate sums, and use nested loops with if conditions to count negative numbers.

Explore advanced for loops in Python using arange and len to count negatives in a 43-element random array, and generate a six by six array of zeros with diagonal logic.

Explore for loops in numpy with nested iterations and conditional logic on a 5x5 zero array, including index updates and enumerate over linspace to show index and value.

Explore using the break statement to terminate a for loop while generating 1000 random numbers with numpy, printing values until one exceeds 1.2 and noting the last value of I.

Explore how the continue statement works in for loops with a numpy range example, skipping an iteration and restarting at the loop head, and contrast it with break.

See how while loops run until tests are true, updating i and x, and using break; compare with for loops and observe a numpy example producing 28.

Practice handling errors with try and except in Python by converting values to float and catching value errors, as shown with division by zero, missing files, and offline network cases.

Learn list comprehension in Python with numpy to create and transform lists from ranges, including squaring values and filtering even numbers. Compare one-line lists with traditional loops.

Explore dictionary and tuple comprehension in Python by mapping each i from a range to i^2 for a dict and to i^3 for a tuple, using numpy range examples.

practice python fundamentals with class work no 4 by defining a fonts function using if statements, using for loops and list comprehension to modify arrays and derive z1 and z2.

Master Python fundamentals and NumPy basics by implementing a conditional function, looping over arrays, counting values with conditions in NumPy, and using list comprehension for index-based transformations.

Learn to define and call Python functions using def and return, handle zero to many inputs, and return multiple values as tuples, including numpy array operations.

Learn how to use keyword arguments in Python, compare them with positional arguments, and compute sum of absolute differences raised to a power using a numpy example.

Discover how default values work in Python function declarations with a fonts file example. When p is omitted, the default applies; when p is provided, it overrides the default.

Learn to handle a variable number of inputs with star args and kwargs in Python, returning a list of sums of absolute d raised to different powers using numpy.

Explore variable inputs with *args and **kwargs in Python, including keyword arguments that form a dictionary, and compute the sum of absolute differences raised to a power using NumPy arrays.

Explore how docstrings describe function inputs, parameters, and return values, and use help to view them in Python and numpy examples.

Learn how to create anonymous functions in Python with lambda, pass inputs, and compute expressions like x + y and x + y * z. Examples yield 20 and 7.

Learn how modules group functions into a single file and how to access them with import. The example defines square and cube in core_module.py and calls them from example.py.

Explore the special __main__ concept in Python, showing how a module can run directly or be imported, and use a square function with if __name__ == '__main__' to execute.

Learn how Python packages organize large codebases using directory structure and __init__.py. See sound with its effects and filters, and NumPy’s core and random modules for import patterns.

Practice building python functions from scratch: find the minimum of a list without built-ins, use defaults and varargs, implement a mean function, and create tests for arithmetic and kwargs-based tasks.

Learn to implement from scratch a function that finds the minimum of a list, with optional and variadic parameters, plus tests in Python using NumPy.

Explore object oriented programming by using objects with data and methods such as analyze, manipulate, and visualize in Python, delivering cleaner code than procedural approaches.

Learn Python class declaration using the class keyword, show inheritance from object, and implement an __init__ constructor with self and attributes like pos.

Explore how attributes store object data in Python, initialize with __init__, and add attributes dynamically using a dedicated method, illustrated with a data viz class and numpy arrays.

Learn how properties control attribute access with getter and setter via the property decorator, and apply size checks in a numpy data viz example.

Explore methods as functions that operate on an object and access its attributes; the lecture shows a Dataviz class with init parameters and a not implemented method raising an exception.

Learn how self accesses attributes within an object via self dot var, print self dot v1, and distinguish methods requiring self as the first argument from static methods.

Define rectangle, dog, and bank customer classes with length, width, name, and balance; provide perimeter, area, is square, and add trick functionality, plus withdraw and deposit methods.

build a rectangle class with length and width and methods for perimeter, area, and square check; explore a dog class with tricks and a customer class with deposit and withdraw.