Array Errors

Most early NumPy errors are shape errors, dtype surprises, or index errors.

The fix is rarely to guess. Print the shape and dtype first.

Shape mismatch

Elementwise operations need compatible shapes.

This fails because the arrays do not line up.

You will learn broadcasting later. For now, use the simple rule: when elementwise operations fail, inspect both shapes.

Dtype surprise

If one value is text, NumPy may create a string array:

That can make later numerical operations confusing. Keep input data clean and convert near the boundary. NumPy does not silently turn that text back into a number merely because it contains the character 3.

Index error

Array indexing follows the same boundary rule as lists:

There are three values, but the last valid positive index is 2.

The debugging checklist

When an array operation fails:

1. print each relevant shape;
2. print each relevant dtype;
3. make the array smaller;
4. try the operation again;
5. explain what each axis means.