Python language module is currently in progress.
Comprehensive guide to Python programming covering installation, variables, control flow, data structures, functions, error handling, object-oriented programming, file handling, datetime manipulation, random data generation, regular expressions, JSON, and database connectivity.
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.
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.
Overview of Python functions, including types, creation, calling, and variable scope. Discussion on class attributes, detailing instance vs. class variables, their creation, access, and modification methods.
Overview of Python resources for data cleaning and project ideas, including a cookbook for data cleaning, low-code libraries, and a comprehensive framework for profiling. Project ideas span various topics like file I/O, error handling, OOP, and web requests, offering practical applications such as a CLI unit converter, data quality dashboards, and visualization tools.
The best resources for learning Plotly for Python in 2025 include the official Plotly YouTube channel, Derek Banas's comprehensive tutorial, Onur Baltacı's fast-track course, and various playlists that cover fundamentals and advanced features. Additional channels like PyLenin and Charming Data provide broader data visualization content, while MOOCs offer free self-paced courses for various skill levels.