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An introduction to Python programming covering installation, basic concepts, variables, data types, control flow, loops, input/output, data structures, functions, modules, error handling, object-oriented programming, file handling, datetime, random data generation, regular expressions, JSON handling, and database connections.
Study group focused on the Pandas library for data analysis in Python, with links to resources and courses for mastering data wrangling.
Seaborn provides various types of plots for data visualization, including relational plots for visualizing relationships between variables, categorical plots for showing relationships involving categorical data, and distribution, matrix, regression, timeseries, and pairwise plots. Additionally, it offers customization and styling options for enhancing visualizations.
NumPy is a powerful Python library for numerical computing that provides support for n-dimensional arrays and a variety of mathematical functions. It offers flexibility and speed, making it accessible for programmers. The tutorial covers array creation, manipulation, matrix operations, and advanced features like reshaping, indexing, and linear algebra, along with practical applications and exercises.
Matplotlib offers two main interfaces for plotting: the Pyplot interface, which is user-friendly for simple plots but has limitations in customization, and the Object-Oriented interface, which provides greater control and is better suited for complex visualizations. Key features include basic plot customization, advanced layouts, interactive features, and specialized plots like bar charts, histograms, scatter plots, and pie charts. Users can also manage axes, legends, and annotations effectively, while utilizing various styling options and colormaps for enhanced visual appeal.
Comprehensive guide covering Python fundamentals, including introduction, variables, data types, control flow, loops, functions, modules, exceptions, and object-oriented programming concepts such as classes, attributes, and methods. Key topics include Python input/output, basic data structures, and various types of function arguments.
A collection of Python project ideas and resources focused on data cleaning, preparation, and various programming concepts. Highlights include a cookbook for data cleaning, a low-code library for data preparation, and a comprehensive framework for data profiling. Project ideas span multiple modules, covering basics, data structures, error handling, OOP, testing, and more, with specific examples like a CLI unit converter, frequency analyzer, and market regime dashboard.
The best YouTube channels and resources for learning Plotly in Python include the official Plotly channel, Derek Banas's comprehensive tutorial, Onur BaltacΔ±'s fast-track course, and various playlists for beginners. Additional channels like PyLenin and Charming Data provide broader data visualization content, while MOOCs offer free self-paced courses. These resources cater to all skill levels and focus on practical applications in data visualization and dashboard development.
uv is a fast Python package and project manager that replaces multiple tools like pip and poetry. It offers built-in virtual environment and Python version management, supports lock files for reproducibility, and optimizes disk space usage. Key features include easy installation, efficient package management, and advanced commands for dependency and cache management. Best practices emphasize using virtual environments, pinning Python versions, and utilizing lock files for production setups.
The next book to read is "Cleaning Data for Effective Data Science."