Python for Beginners
Klassenstufen: Grade 12
Bildungssystem: American
Learn Python programming from scratch.
#Python
Learn Python programming from scratch.
#Python
1. Install CPython 3.x and verify the interpreter from the command line (e.g., python --version).
2. Create and activate an isolated virtual environment with python -m venv and confirm site-packages are sandboxed.
3. Manage packages with pip (install, upgrade, uninstall) and export dependencies with pip freeze > requirements.txt.
4. Configure VS Code (or comparable IDE) to run Python scripts, open an integrated terminal, and launch the REPL.
5. Run Python code from the terminal, in the REPL, and in a Jupyter Notebook to validate the toolchain.
1. Download the official CPython 3.x installer or package via platform-appropriate channels and select options for PATH integration.
2. Execute the installation and verify python --version and pip --version return expected versions on the command line.
3. Configure multiple Python versions and print sys.version to confirm the active interpreter.
4. Upgrade pip safely using python -m pip install --upgrade pip and confirm the updated version.
5. Diagnose and resolve PATH conflicts by inspecting where python/where pip (Windows) or which python/which pip (Unix) outputs.
6. Document installation steps and capture a verification transcript with commands and outputs for reproducibility.
1. Execute python -c and python -m commands to run inline code and standard modules from the CLI.
2. Launch the interactive REPL, evaluate expressions, import modules, and exit cleanly using exit() or Ctrl-D/Z.
3. Navigate the filesystem with cd and list files with ls/dir and run scripts using python path/to/script.py via relative and absolute paths.
4. Configure shell profiles to define Python aliases or leverage the py launcher on Windows for version selection.
5. Compare python vs py (Windows) vs python3 (Unix) invocations and select the correct command based on OS conventions.
6. Troubleshoot common CLI errors (e.g., command not found, PermissionError) using actionable checks and fixes.
1. Create a virtual environment with python -m venv .venv and activate it across Windows, macOS, and Linux shells.
2. Verify interpreter isolation by comparing sys.executable and sys.path inside and outside the environment.
3. Inspect and explain site-packages location to confirm sandboxing of dependencies.
4. Deactivate and remove environments safely and recreate them deterministically when necessary.
5. Configure the IDE to use the venv interpreter for linting, testing, and execution.
6. Automate environment activation using workspace settings, terminal profiles, or direnv where appropriate.
1. Install, upgrade, and uninstall packages using pip install, pip install --upgrade, and pip uninstall with semantic version specifiers.
2. Generate and consume requirements.txt using pip freeze > requirements.txt and pip install -r requirements.txt for reproducible builds.
3. Audit installed packages with pip list and pip show to inspect versions, dependencies, and locations.
4. Resolve conflicting or broken dependencies by applying version constraints and reinstalling cleanly with --force-reinstall and --no-cache-dir.
5. Configure pip via pip config and trusted-index-url to work behind proxies or with private indexes securely.
6. Document a minimal, pinned dependency set that supports the project’s runtime and testing needs.
1. Install and configure the VS Code Python extension and select the correct interpreter per workspace.
2. Run and debug Python scripts using launch.json configurations, breakpoints, and the integrated terminal.
3. Create tasks.json to automate common commands such as pytest, black, and flake8 for rapid feedback.
4. Open and manage the integrated terminal, activate the venv, and verify environment variables within VS Code.
5. Enable linting and formatting on save while avoiding conflicts between tools through settings.json.
6. Configure workspace settings to switch between script, REPL, and notebook workflows efficiently.
1. Execute scripts from the terminal and handle arguments, exit codes, and redirected I/O when appropriate.
2. Drive exploratory coding in the REPL, import local modules, and inspect help() and dir() for discovery.
3. Create and run a Jupyter Notebook, select the virtual environment kernel, and execute cells reliably.
4. Convert notebooks to scripts or HTML using jupyter nbconvert or VS Code export features for sharing.
5. Validate the end-to-end toolchain by running the same function in terminal, REPL, and Notebook contexts and comparing outputs.
6. Troubleshoot kernel, path, or import errors by adjusting kernels, working directories, or sys.path safely.
1. Declare and use variables of built-in types (int, float, str, bool) and perform explicit type conversions.
2. Evaluate expressions using arithmetic, comparison, logical, and membership operators to compute results.
3. Implement decision logic with if/elif/else using clear boolean conditions and operator precedence.
4. Construct loops (for with range and while) using iteration patterns, break/continue, and loop else clauses where appropriate.
5. Produce console I/O with input and print, formatting output with f-strings and format specifiers.
1. Declare and assign variables of type int, float, str, and bool with descriptive, PEP 8–compliant names.
2. Apply explicit type conversions using int(), float(), str(), and bool() and explain truthiness and falsiness rules.
3. Inspect and compare types with type() and isinstance() to justify design decisions for given problems.
4. Demonstrate mutability vs immutability effects when reassigning or passing values to functions.
5. Adopt naming and constant conventions (snake_case, UPPER_CASE) to improve readability and maintainability.
6. Diagnose and correct common type and ValueError scenarios arising from invalid casts or inputs.
1. Compute with arithmetic operators including exponentiation (**), floor division (//), and modulo (%) in compound expressions.
2. Evaluate comparison, logical, and membership operators while respecting operator precedence and short-circuit behavior.
3. Enforce a specific order of evaluation using parentheses to improve correctness and readability.
4. Compose f-strings with alignment, width, and precision to format numbers and text professionally.
1. Create, access, and mutate lists and dictionaries using indexing, slicing, and methods (append, extend, pop, update).
2. Select appropriate data structures based on mutability, ordering, and lookup characteristics for a given problem.
3. Construct list, set, and dict comprehensions to filter, map, and transform collections concisely.
4. Iterate over collections using enumerate, zip, and dict items/keys/values to process compound data.
5. Sort sequences using sorted or list.sort with key functions and lambda expressions to achieve custom orderings.
1. Create lists via literals and list() and explain shallow vs deep copies when duplicating structures.
2. Access and slice lists with positive and negative indices to extract, step, and reverse sublists.
3. Mutate lists using append(), extend(), insert(), remove(), pop(), and del while reasoning about side effects.
4. Apply in-place transformations using list methods and slicing assignment for bulk updates.
5. Distinguish mutating vs non-mutating operations and choose appropriately based on use case.
6. Identify and fix pitfalls such as modifying a list while iterating by iterating over a copy or indices.
1. Construct tuples and unpack multiple values, including star-unpacking for variable-length assignments.
2. Evaluate immutability benefits and trade-offs compared to lists for safety and performance.
3. Slice and index tuples and use them for fixed-size records and multi-value returns.
4. Apply multiple assignment and swapping via tuple packing and unpacking to simplify code.
5. Benchmark list vs tuple operations with timeit to inform data structure choices.
1. Define functions using positional, keyword, default, and variable-length parameters and return computed results.
2. Document functions with clear docstrings describing purpose, parameters, return values, and examples.
3. Encapsulate logic in modules; import and reuse functions across files using absolute or relative imports.
4. Apply Python’s scope rules (local, nonlocal, global) to control variable visibility and avoid unintended side effects.
5. Handle runtime errors using try/except/else/finally and raise appropriate exceptions to signal invalid states.
6. Import and apply standard-library modules (e.g., math, statistics, random) to implement common computations.
1. Define functions with positional and keyword parameters that return computed values.
2. Specify default parameter values safely and avoid mutable defaults to prevent shared state bugs.
3. Accept variable-length arguments via *args and **kwargs and forward parameters to wrapped calls.
4. Structure small, pure functions that minimize side effects and maximize testability and reuse.
5. Annotate functions with type hints to communicate expected inputs and outputs to readers and tools.
6. Verify function behavior with doctests or simple pytest cases to confirm correctness.
1. Illustrate Python’s argument passing model and demonstrate effects of mutating arguments inside functions.
2. Construct flexible signatures using keyword-only and positional-only parameters to constrain call sites.
3. Implement overload-like behavior using parameter defaults, sentinel values, and None handling.
4. Employ partial application with functools.partial to preconfigure functions for reuse in callbacks.
1. Read and write text files using with statements and appropriate encodings to ensure correct resource management.
2. Parse and generate CSV files using csv.reader/csv.DictReader and csv.writer for tabular data.
3. Serialize and deserialize JSON using json.load/json.dump with correct Python data structures.
4. Manage file paths and directories with pathlib to create, inspect, and traverse cross-platform file systems.
5. Validate and transform raw text data (strip, split, join, cast) prior to storage or further processing.
1. Open files with with statements to guarantee resource cleanup on success and failure.
2. Read and write text using read(), readline(), readlines(), and iteration over file objects as needed.
3. Specify encodings when opening files and handle Unicode errors gracefully with error handling strategies.
4. Process large files efficiently by streaming and chunking to control memory usage.
5. Handle file-not-found and permission errors with targeted exception handling and user-friendly messages.
6. Validate file outputs by asserting content and line counts in tests to confirm correctness.
1. Construct Path objects and navigate directories using joinpath(), parent, and glob() patterns.
2. Create, rename, and remove files and directories cross-platform with pathlib methods safely.
3. Read and write files via Path.open() for consistent, object-oriented path handling.
4. Resolve absolute paths and handle relative working directories robustly across scripts and tests.
5. Serialize paths to strings for use with libraries that do not accept Path objects.
6. Script cross-platform workflows without hard-coded separators or OS-specific assumptions.
1. Write unit tests with pytest that assert expected outputs and edge cases for individual functions.
2. Execute and interpret pytest results using test discovery and basic fixtures to isolate test setup.
3. Debug programs by setting breakpoints and stepping through execution in an IDE or with pdb to inspect state.
4. Configure logging with logging.basicConfig and emit messages at appropriate levels instead of using print.
5. Format code automatically with Black and resolve linting issues reported by Flake8 to meet PEP 8 style.
6. Initialize a Git repository, create meaningful commits, and push to a remote to maintain version history.
1. Write pytest test functions that assert expected outputs, exceptions, and edge cases for individual functions.
2. Structure test modules and names to leverage pytest discovery conventions effectively.
3. Use assert statements with informative messages and approximate comparisons for floats where needed.
4. Parametrize tests to cover multiple inputs without duplication using @pytest.mark.parametrize.
5. Mark slow or flaky tests and isolate external dependencies with fakes or stubs.
6. Generate minimal coverage reports and prioritize uncovered lines or branches for additional tests.
1. Execute tests via the pytest CLI using selection flags -k, -m, and -q for focused and quick runs.
2. Configure conftest.py and basic fixtures to share setup and teardown logic across tests.
3. Capture logs and stdout during tests and assert on outputs when appropriate using caplog and capsys.
4. Integrate tests into VS Code tasks and enable continuous runs on save for rapid feedback.
5. Validate expressions using interactive assertions and small REPL snippets to confirm expected results.
6. Refactor complex expressions into readable intermediate variables without changing semantics.
1. Implement conditional branches using if/elif/else with clear, testable boolean conditions.
2. Apply Python truthiness and guard clauses to reduce nesting and improve clarity.
3. Combine conditions with and, or, and not while honoring operator precedence.
4. Use is vs == appropriately for None and booleans to avoid subtle bugs.
5. Encapsulate branching logic in functions to increase testability and reuse.
6. Test decision paths with representative inputs and edge cases using simple assertions.
1. Select between chained conditionals, dictionary dispatch, and match-case (3.10+) when appropriate for the problem.
2. Design informative error messages and user prompts when validation fails to guide corrective action.
3. Prevent and handle invalid input using defensive checks prior to computation.
4. Simplify complex conditions by extracting well-named boolean variables and helper functions.
5. Evaluate readability and performance trade-offs among decision strategies with small benchmarks.
6. Document decision rules with inline comments and example-driven tests for future maintainers.
1. Construct for loops with range() and iterate over sequences, files, and other iterables idiomatically.
2. Implement while loops with clear termination conditions and maintain loop invariants to avoid infinite loops.
3. Apply break, continue, and loop else clauses to control flow deliberately in search and validation tasks.
4. Use enumerate() and zip() within loops to process indexed and parallel data clearly.
5. Refactor loop bodies into small functions to reduce cognitive complexity and improve testability.
6. Validate loop behavior on edge cases including empty sequences and off-by-one boundaries via tests.
1. Read user input with input() and cast safely to required types while handling invalid entries gracefully.
2. Produce formatted output using f-strings with alignment, width, precision, and numeric grouping.
3. Integrate decisions, loops, and I/O to build a small menu-driven script that solves a basic task.
4. Sanitize and validate user-provided strings prior to computation to prevent errors downstream.
5. Separate pure computation from I/O to keep logic testable and reusable.
6. Exercise simple tests of I/O flows by injecting input and asserting printed output where feasible.
6. Select a sequence type based on ordering, mutability, memory, and usage patterns for a given problem.
1. Create and access dictionaries with literals and dict(), retrieving values via indexing and get() with defaults.
2. Update and merge dictionaries using assignment, update(), and the | operator (Python 3.9+).
3. Iterate over items(), keys(), and values() to process records and aggregates clearly.
4. Remove entries with pop(), popitem(), and del while handling KeyError safely.
5. Model nested structures with dicts and access nested fields defensively using get and defaults.
6. Choose dicts for membership tests and indexed retrieval when constant-time lookups are desired.
1. Construct sets and frozensets and explain duplicate elimination and lack of ordering.
2. Apply union, intersection, difference, and symmetric difference for dataset algebra.
3. Test membership efficiently and compare performance against list containment checks.
4. Mutate sets using add(), update(), remove(), discard(), and clear() appropriately based on semantics.
5. Compute derived sets (unique values, overlaps) to support filtering and analytics tasks.
6. Benchmark set-based solutions vs alternatives on realistic input sizes to justify selections.
1. Construct list, set, and dict comprehensions to map, filter, and transform collections concisely.
2. Integrate conditional clauses in comprehensions for selective inclusion and filtering.
3. Replace simple nested loops with nested comprehensions judiciously while maintaining readability.
4. Contrast comprehension readability and performance against equivalent loops using timeit.
5. Avoid side effects and external state mutations within comprehensions to keep expressions pure.
6. Refactor verbose data pipeline code into clear compositions of comprehensions for maintainability.
1. Iterate efficiently with enumerate() and zip() to process aligned sequences and indexed data.
2. Traverse dictionaries using items(), keys(), and values() to express intent clearly.
3. Sort sequences with sorted() and list.sort(), applying key functions and reverse ordering when needed.
4. Write and apply lambda expressions and operator.itemgetter for custom sort keys.
5. Stabilize sort operations and chain multi-key sorts via tuple keys and the key parameter.
6. Verify sorting behavior and stability using representative datasets and edge cases in tests.
5. Profile performance-sensitive functions with timeit and optimize hotspots with simple refactors.
6. Refactor long parameter lists into cohesive data structures to improve readability and maintainability.
1. Write clear docstrings that describe purpose, parameters, return values, raised exceptions, and examples.
2. Adopt a consistent docstring style (reST or Google) and render documentation in IDE tooltips for discoverability.
3. Embed usage examples that double as tests via doctest when appropriate for simple functions.
4. Apply naming conventions and module-level documentation to clarify public APIs and internal helpers.
5. Generate reference documentation automatically using pydoc or a minimal Sphinx configuration.
6. Review and iterate on docstrings through peer feedback to improve clarity and completeness.
1. Apply Python’s LEGB scope rules to predict name resolution accurately in nested functions and modules.
2. Use local, nonlocal, and global statements to control variable visibility intentionally when refactoring.
3. Handle exceptions with try/except/else/finally and ensure resources are released reliably.
4. Raise appropriate built-in exceptions with informative messages to signal invalid states and inputs.
5. Create custom exception classes when domain semantics require specificity and granularity.
6. Implement robust input validation and error propagation strategies to maintain program correctness.
1. Encapsulate logic into modules and packages with clear directory layouts and __init__.py where needed.
2. Import and reuse functions across files using absolute and relative imports correctly.
3. Configure the import path for local development without polluting global site-packages or relying on sys.path hacks.
4. Separate application code from reusable libraries to enable testing and cross-project reuse.
5. Organize a simple src-based project structure to align with tooling and packaging best practices.
6. Demonstrate module reuse across scripts and notebooks to reduce duplication.
1. Import and apply math, statistics, and random to implement common computations and analyses.
2. Configure random seeding to produce reproducible pseudo-random sequences for tests and simulations.
3. Use decimal and fractions for precise numeric calculations and explain trade-offs versus float.
4. Select appropriate standard-library modules for specific tasks and justify choices in short design notes.
5. Compose a small utilities module that wraps standard library calls behind a clean API for reuse.
6. Test and document the utilities module to ensure reliable behavior and maintainability.
1. Read CSV files using csv.reader and csv.DictReader to extract rows and typed fields.
2. Configure dialects, delimiters, quoting, and newline handling to accommodate real-world datasets.
3. Normalize headers and handle missing or malformed values defensively with defaults or skips.
4. Convert string fields to appropriate types using casting and custom parsing functions.
5. Validate row counts and required columns before processing to avoid downstream errors.
6. Write unit tests that cover sample CSV inputs and edge cases to verify robustness.
1. Write CSV files using csv.writer and csv.DictWriter with explicit fieldnames and quoting rules.
2. Stream results to avoid holding entire datasets in memory for large outputs.
3. Compose headers and data transformations cleanly prior to writing for clarity and maintainability.
4. Append to existing files safely while avoiding duplicate headers using file existence checks.
5. Roundtrip data by reading a CSV, transforming records, and writing a new file for validation.
6. Package CSV operations into reusable functions with clear error handling and return contracts.
1. Load JSON from files and strings using json.load() and json.loads() with proper encoding.
2. Dump JSON to files and strings using json.dump() and json.dumps() with indent and sort_keys options.
3. Map JSON structures to Python lists and dicts and back without loss of fidelity.
4. Handle non-serializable objects by providing default encoders or pre-conversion strategies.
5. Validate JSON input against required keys and types prior to processing and raise informative errors.
6. Roundtrip JSON data and verify with deep equality assertions in tests.
1. Clean raw text using strip(), split(), join(), replace(), and case normalization pipelines.
2. Tokenize and parse delimited text lines into domain-specific fields with robust splitting rules.
3. Detect and correct whitespace, BOM, and newline inconsistencies across platforms.
4. Implement idempotent text cleaning functions that can run repeatedly without unintended side effects.
5. Log data quality issues and collect metrics on rejected or corrected records for observability.
6. Prepare sanitized data for storage or further analysis, preserving required formats and encodings.
5. Debug failing tests by reproducing locally, inspecting tracebacks, and isolating minimal repro cases.
6. Document a simple testing strategy for the course project including structure, data, and coverage goals.
1. Set breakpoints, step into, step over, and step out, and inspect variables using the VS Code debugger.
2. Invoke the command-line debugger with python -m pdb and navigate stack frames and variables.
3. Evaluate expressions on the fly and modify state to test hypotheses safely during a debug session.
4. Reproduce and fix off-by-one, type, and state bugs using systematic test-driven techniques.
5. Persist and share minimal reproduction cases that demonstrate defects for collaboration.
6. Verify bug fixes with targeted tests to prevent regressions and ensure stability.
1. Configure logging with logging.basicConfig and choose appropriate log levels for different audiences.
2. Replace print statements with logging calls that include context, variables, and structured information.
3. Format log messages with timestamps and module names to aid diagnosis in multi-module programs.
4. Direct logs to files and rotate them using handlers for longer-running scripts and tools.
5. Apply logger hierarchy and per-module loggers to control verbosity across components.
6. Review logs to confirm expected control flow and verify error handling under edge conditions.
1. Install and run Black to reformat code consistently and configure line length and path exclusions.
2. Run Flake8 to identify style and correctness issues and interpret common error codes and warnings.
3. Resolve linter findings by refactoring code, adding noqa pragmatically only when justified.
4. Configure pre-commit hooks to enforce Black and Flake8 before each commit for consistent quality gates.
5. Tailor VS Code to format on save and display real-time linting diagnostics for rapid feedback.
6. Document code style guidelines aligned with PEP 8 to guide contributors and future maintenance.
1. Initialize a repository, create a .gitignore, and make atomic commits with meaningful messages.
2. Branch feature work, merge changes, and resolve simple conflicts using standard workflows.
3. Inspect history with git log, diff, and blame to understand code evolution and ownership.
4. Connect to a remote, push branches, and open pull requests for review and collaboration.
5. Tag releases and create minimal CHANGELOG entries to track milestones and versions.
6. Recover from mistakes using restore, reset, and revert safely to maintain a clean history.