Data manipulation and analysis with Pandas
Data analysis involves inspecting, cleaning, transforming, and modeling data to discover useful information and support decision-making. Pandas is the primary library for data analysis in Python, providing DataFrame and Series structures that make working with structured data intuitive. Key operations include: loading data from various sources (CSV, Excel, SQL, etc.), handling missing data, filtering and selecting data, grouping and aggregation, merging and joining datasets, and time series analysis. Effective data analysis requires understanding both the technical aspects of data manipulation and the domain context to ask meaningful questions of the data. Pandas' expressive API allows complex data transformations to be expressed concisely, making it possible to explore data quickly and iteratively. Mastering data analysis with Pandas is fundamental to any data science workflow.