jackzhenguo/python-small-examples

Features

• Python Code Examples - Offers a comprehensive library of Python code examples demonstrating various programming tasks and design patterns.
• Python Programming Guides - Learning fundamental Python syntax through practical examples of data types, loops, and basic logic.
• Python Visualization - Implements practical examples of charts, heatmaps, and animated plots using Python visualization libraries.
• Date and Time Libraries - Managing calendar calculations, timestamp formatting, and time series generation for temporal data.
• Missing Data Removal - Provides a tool to filter out rows or columns containing missing data based on axis nullity.
• Null Value Counting - Implements a utility to calculate total missing entries for every column in a dataset.
• Null Value Filling - Implements a tool to replace missing data with specified values or statistics like mean and median.
• Tabular Data Analysis - Provides capabilities for cleaning, resampling, and feature engineering on tabular datasets using Pandas.
• Concurrent Task Execution - Implements the execution of multiple operations in separate threads to share CPU time and process tasks in parallel.
• Concurrency Control Examples - Provides educational examples of multithreading, race conditions, and synchronization mechanisms in Python.
• Programming Design Patterns - Demonstrates object-oriented design, functional programming patterns, and advanced Python language features.
• Python Data Analysis Tutorials - Provides code snippets for cleaning, manipulating, and analyzing tabular data using scientific libraries.
• Common Algorithmic Tasks - Provides a suite of practical Python operations for flattening lists, calculating modes, and sampling datasets.
• Concurrent Thread Execution - Manages concurrent tasks using shared-memory threads and locking mechanisms to handle synchronization.
• Functional Transformations - Applies functional programming patterns using anonymous lambda functions for mapping, filtering, and data encoding.
• Race Condition Prevention - Implements locking mechanisms to restrict a block of code to a single thread to prevent data races.
• Data Ranking Utilities - Provides algorithms for assigning numerical ranks to data samples based on value priority.
• Data Visualizations - Creating charts, heatmaps, and animated plots to visually represent complex data and relationships.
• Scientific Computing - Integrates NumPy and Pandas to perform scientific computing, including matrix operations and data cleaning.
• File System Automation - Provides programmatic tools for bulk file renaming, extension modification, and directory traversal.
• Asynchronous Concurrency Managers - Implementing multithreading, locking mechanisms, and parallel task execution to handle shared resources and race conditions.
• Lazy Sequence Processing - Uses lazy evaluation and generators to process large datasets and nested lists without allocating full intermediate collections.
• Function Decorators - Implements function decorators to inject cross-cutting concerns like execution timing and logging.
• Object-Oriented Design Patterns - Illustrates object-oriented design patterns including class methods, property decorators, and metaclasses.
• Regular Expressions - Implements text searching, group capturing, and data extraction using regular expressions.
• Regex Pattern Matching - Employs regular expressions to validate string formats and extract structured information from text.
• Funnel Charts - Renders sequential processes or data reduction using funnel-shaped diagrams.
• Time Series Generation - Implements a tool to generate data frames with time-based indices for testing temporal patterns.
• Task Automation Scripts - Includes various automation scripts for common file system and directory management tasks.
• Coordinate-Based Plotting - Transforms numerical arrays into coordinate-based graphical representations using specialized plotting libraries.
• Image Filters - Transforms visual data using predefined filters to create effects such as contouring.
• Image Orientation Manipulators - Provides utilities for rotating and resizing images to adjust orientation while maintaining aspect ratios.
• Archive Compression - A Python utility that walks a directory tree and archives all files into a single compressed zip file.
• Metaprogramming Patterns - Utilizes Python meta-programming and inheritance patterns to define custom class behaviors and property decorators.
• Functional Programming Patterns - Implements functional programming patterns using lambda functions, filters, and mapping.
• Sequence Generation Algorithms - Implements mathematical sequence generation, specifically the Fibonacci sequence, using language-specific patterns.
• Custom Iterators - Provides practical implementations of custom iterator objects for traversing sequences.
• Generative Iterators - Provides examples of generator expressions that create lazy iterators with filtering conditions.

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