Data Analysis with Python Tutorial

Learn data analysis fundamentals using Python and JupyterLab in this comprehensive tutorial designed for learners with basic Python knowledge including variables, data types, loops, conditionals, lists, dictionaries, and functions. Master the essential data analysis workflow by setting up and navigating JupyterLab, a web-based environment that runs through a Python kernel on a local server and provides productivity features for managing analysis projects. Discover how to use IPython "magic commands" with single-% for line magics and double-%% for cell magics to enhance your workspace navigation and code execution. Explore pandas library fundamentals by loading data into DataFrames from dictionaries and CSV files, parsing dates, and defining ordered categories such as device health status. Practice data validation techniques using .info(), descriptive statistics with .describe(), and data type selection to sanity-check your datasets. Learn to identify missing values and outliers while using pandas aggregation functions for data summarization. Master data manipulation through slicing and filtering techniques using labels, positions, and boolean masks with loc and iloc methods to quickly answer analytical questions like finding critical devices with high CPU usage. Apply boolean indexing and categorical data handling to create sophisticated data masks for targeted analysis, transforming large, messy datasets into clear insights for better decision-making.