Python for Data Science: From Zero to Data Analysis

Explore Python basics by examining program structure, input and output, indentation, code blocks, and the interpreter, while learning data types, numerical operations, type conversion, slicing, docstrings, f-strings, and PyCharm.

Explore Python basics in Google Colab by writing your first program, printing Hello World, and using strings, variables, and formatted strings to personalize output with user input and auto-execute.

Run Python from the interpreter on Mac, Windows, and Linux using the terminal. Save code as .py files, practice print and for loops, and use range in terminal or IDE.

Expand the print function in Python, printing to files, using multiple data types including booleans and numbers, and controlling separators, end, and newlines via backslash n.

Explore basic python data types, arithmetic, and type checking in google colab, including integers, floats, booleans, none type, and strings as python objects.

Learn variable names and conventions in Python, including case sensitivity, valid identifiers, not starting with numbers or using reserved keywords, and the snake_case versus camel_case styles.

Import the Python keyword module and print keyword.kwlist to reveal reserved words like false, None, true, and assert. Learn why you should avoid keyword-like names such as lambda.

Explore escape sequences in Python, including backslash usage for newlines, tabs, backspace, and quotes; learn choosing single vs double quotes, escaping backslashes, and using Unicode characters with raw strings.

Explore data type conversions in Python, turning strings into integers or floats and back to strings, with emphasis on types, valid input, and potential errors.

Learn to install and set up PyCharm Community Edition, configure a Python project with a virtual environment, adjust appearance and fonts, create a welcome script, and use breakpoints to debug.

Explore control flow and conditional statements in Python, focusing on the if statement, logical operators, complex conditions, and nested if statements.

Explore boolean logic and conditional statements in Python, including and, or, not, and comparison operators. Build if expressions and understand truthiness of none, strings, and numbers.

master using for loops with range in Python to control start, stop, and step, compute sums in arithmetic progressions, and avoid code duplication and off-by-one errors.

Loop over lists and tuples with for loops, compare mutable lists to immutable tuples, and validate nonnegative numbers to compute square roots using num ** 0.5.

Demonstrate prime checks by testing divisors in a form with a slider, using modulo in a for loop starting at two, break on zero remainder, and print prime status.

Learn to loop over words in a sentence by splitting strings with Python's split function, then iterate and print the resulting list for debugging.

Explore the while loop using n, an index i, and sum accumulation with sum += i, and compare it to a for loop for the first n natural numbers.

Compute factorials with a while loop, including zero factorial equals one and five factorial examples. Explore an infinite while loop and how to break out.

Showcase factorial calculation with a while loop and incremental assignment, replacing lines with factorial times equals n and n minus equals to one, then compute ten factorial.

Demonstrate nested loops by creating an n by n multiplication table using two loops, format i times j, and print rows; discuss nesting efficiency and n squared growth.

Explore turtle graphics to review variables and loops while creating a visually engaging drawing. Learn about avoiding magic numbers and generalizing special cases with parameters, culminating in a capstone project.

Explore turtle graphics in Python by drawing with forward and right movements, adjusting speed, and using loops to reduce redundancy, then experiment with range and directions in the sandbox.

Learn to avoid magic numbers in Python turtle graphics by using color lists and lengths, applying i modulo len(colors) instead of a hard-coded value.

Generalize the example by introducing n as a parameter to compute angle = 360/n. Create polygons with n sides and parameterize side length, speed, and iterations to explore turtle graphics.

Explore turtle graphics in Python by drawing circles, changing colors, and looping radii with pen control, position, and turtle visibility.

Define and call Python functions using def, parameters, and optional defaults; explore docstrings, indentation, return values, and built-in versus user-defined examples like greet and average.

Explore functions by building a custom average calculator with pairwise inputs and avoiding duplicates, then implement a prime tester using a square-root bound.

Master default parameters in Python by calculating areas: circle area with a default radius of 1 and rectangle area with default width and height of 1, using math.pi for precision.

Explore function scope, local and global variables, and call by value in Python, showing how the interpreter searches local then global names and why inputs remain unchanged.

Explore how to access a global variable from within a function in Python, showing how local shadowing affects x and y and why this practice is discouraged.

Build a sum function with variable numeric arguments and type checking for int or float. Print invalid arguments and return the sum, then review indentation and testing examples.

Explore how string methods transform text with dot notation, from upper and title case to strip, split, join, and replace, while distinguishing methods from functions like len.

Learn how to work with lists in Python, including creation, access, methods, slicing, and nested lists, then master list comprehension with filtering and appending with loops.

Create and explore Python lists by building lists of integers, strings, and a mixed list, and access elements with indexing and slicing, including negative indexing for the last item.

Learn Python list methods for data science, including append, remove, sort, reverse, insert, pop, index, count, extend, and clear, and distinguish list references from copies.

Create and manipulate nested lists as a matrix of rows, access elements with nested indices, and learn pitfalls like index out of range, with a nod to future numpy arrays.

Explore list slicing in Python by converting ranges to lists, inspecting types and lengths, and applying start, end, and step parameters, including negative indices and reversing.

Explore list comprehensions and filtering in Python by building even-number filters with range and modulo, and by extracting words with at least five letters using concise, powerful one-line expressions.

Solve exercises on your own to learn problem-solving, then review the provided solutions to compare approaches and improve programming skills.

Explore core loop concepts in Python for data science: write loops to compute averages, sum even digits, find min and max in lists, count vowels, and generate a multiplication table.

Define a compute_average function in Python with type annotations to calculate the average of a list of numbers. Practice implementations using sum and len, for loop, and while loop.

Learn to compute the sum of even digits of an integer using a loop, modulo ten, and integer division, while handling negatives with absolute value and comparing a string-based alternative.

Define a function find_min_max(nums) that returns a (min, max) tuple or none for empty lists, handling integers and floats with a simple iterative comparison.

Learn two approaches to counting vowels in a string, using an explicit loop with type annotations and a sum-based method, and test with empty strings and hello world examples.

Explore list comprehensions in Python by creating a list of squares for even numbers from 1 to 20 inclusive and learn how to express this in code.

Demonstrates computing squares of even numbers using range and x times x, validates the range, and prepares for the next exercise in list comprehensions.

Explore filtering with Python list comprehensions on a word list. Apply if conditions, length checks, and first-letter tests to produce targeted results.

Practice exercise three and list comprehensions to generate a list of tuples (i, j, i*j) for i and j from one to five, comparing list comprehensions with nested loops.

Learn to flatten a nested list in Python using a list comprehension, turning a list of lists into a single sequence. Explore multiple for clauses with optional if conditions.

Flatten a nested list with a list comprehension to gather all numbers into a single list, turning the multi-level structure into 1 through 9.

Explore identity versus equality and aliasing in Python lists. See how aliasing lets changes to one list affect another, and learn simple ways to copy lists to avoid this.

Discover how shallow copies behave with lists of lists in Python, see how changes propagate, and learn when to apply deep copy to avoid side effects.

Learn to deep copy a list of lists with the copy module, verify the original stays unchanged, and compare loop and list comprehension methods before recommending the copy function.

Explore object comparison by contrasting type and isinstance, demonstrating how to test integers and numbers using type and isinstance, and highlighting when isinstance offers flexibility for type checks.

Explore how Python compares strings and numbers using less than and related operators, covering lexicographic order, case sensitivity, ord/chr, and emoji handling, with lowercasing options.

Discover reverse sorting in python with fruit_list.sort(reverse=True) and compare it to sorting then reversing. Learn that reverse flips order, not sorts.

Sort with a Python key function, ordering by length or last letter, using lower and ord for case-insensitive comparisons. Handle mixed lists and consider crashes if type checks are removed.

Learn to identify syntax errors like missing colons, indentation, and unclosed parentheses. Use try/except to handle runtime errors like division by zero, index out of range, and type errors.

Explore handling type errors in a Python average function by using try-except versus an if statement. The lecture highlights type hints and practical implications when adding ints and strings.

Learn how to catch multiple exceptions in Python, testing value errors and division by zero with try, except, and finally. Practice robust input handling and always-executed code blocks.

Explore safe division by handling zero division errors with try/except, returning a result or none, and using finally to print execution completed, testing ten by two and ten by zero.