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NumPy can collapse a whole array into a single number. Methods like sum and mean turn many values into one quick summary.

The mean divides the total by the count. For the array above it returns 4.0, the everyday average you already know.

With no extra argument, these methods reduce everything to one scalar. Every element joins the calculation, ignoring rows and columns.

The real power is axis. It tells NumPy which direction to collapse, so you can summarize per row or per column instead.

Set axis=0 to collapse the rows. NumPy walks down each column and returns one value per column.

Set axis=1 to collapse the columns. NumPy sweeps across each row and returns one value per row.

Think of axis as the dimension that disappears. Collapsing axis 0 removes the row dimension, leaving a result shaped like one row.

The axis rule is identical for mean, min, max, and more. Learn it once and it applies across every reduction NumPy offers.

Pass keepdims=True to keep the collapsed axis as size 1. This makes the result broadcast cleanly back against the original array.

Reductions pair with shape to answer real questions: total per category, average per day, or a count of items down each column.

Choosing the wrong axis is a classic bug. Always ask which dimension you want to keep, then collapse the other one.

You have a 2D array of rows and columns.

You summarized whole arrays, then steered axis to summarize per row or per column. Remember: the axis you name is the one that vanishes. 👍