What each library is for

NumPy is about arrays of numbers. Pandas is about tables with labels. Almost everything else follows from that one distinction.

A NumPy array holds one type, in a fixed shape, laid out efficiently in memory. Because of that, an operation on a million numbers happens in one step in fast compiled code rather than in a Python loop. The speed difference is not small; it is often a hundred times. This is why every data library in Python sits on top of NumPy, including pandas.

Pandas gives you two things NumPy does not: column names and an index. A DataFrame is a table where columns have names and types that can differ, and rows have labels. That is what real data looks like once it leaves a textbook.

In practice you work in pandas and drop into NumPy for the numerical parts. Reading a CSV, grouping by branch, joining two tables, handling missing values: pandas. Element wise mathematics across a whole column, random numbers, matrix work: NumPy, usually without noticing, because pandas hands it the work.

The habit to build from the first day is vectorised thinking. Whenever you find yourself writing a loop over rows, stop. There is nearly always a whole column operation that does the same thing, and it will be shorter to write, faster to run, and less likely to be wrong. Looping over a DataFrame row by row is the single clearest sign of a beginner, and it is the first thing to unlearn.