Data analysis techniques in Python

Data analysis in Python involves collecting, cleaning, organizing, and interpreting data to discover useful information and support decision-making. Python provides powerful libraries such as NumPy, Pandas, Matplotlib, and Seaborn for data manipulation, statistical analysis, and visualization. Common techniques include data cleaning, descriptive statistics, data visualization, and trend analysis. These techniques help users understand patterns, relationships, and insights from large datasets efficiently.