Data analysis with Python centers on Pandas for tabular data manipulation and NumPy for numerical computing. Pandas provides DataFrames — 2D labeled data structures — enabling SQL-like operations, while NumPy delivers vectorized array computations orders of magnitude faster than pure Python. Together, they form the foundation of Python's data science ecosystem, handling everything from cleaning messy datasets to time series analysis and statistical aggregation. Understanding their interplay is essential: Pandas excels at heterogeneous, labeled data with missing values, while NumPy shines at homogeneous numerical operations where speed is critical.