การทำดัชนีและการตัดแบ่งอาร์เรย์

Real analysis means pulling out parts of your data. NumPy's indexing lets you grab single values, rows, columns, or ranges with ease. ✂️

Just like a list, you reach one element by its index. Counting starts at zero, so arr[0] is the very first value.

Negative indices count backward. arr[-1] hands you the last element without needing to know the array's length.

A slice uses start:stop to pull a range. The start is included, the stop is not, just like Python lists.

Add a third number for a step. Using ::2 keeps every other element, an easy way to thin out data.

For a 2D array, give row and column in one bracket: arr[row, col]. This is cleaner than chaining two sets of brackets.

A lone colon means take everything along that axis. So m[0, :] returns the entire first row.

Flip it to pull a column. m[:, 0] sweeps every row and keeps just the first column's values.

Careful: a slice is a view into the original. Change the slice and the source array changes too, which can surprise you.

When you need independence, call copy. It hands back a separate array so edits never touch the original.

Indexing works on the left side as well. Assign to a slice and you update a whole range in one move.

Let's test selecting a column from a 2D array.

You can now carve up arrays: index single values, slice ranges with steps, target rows and columns, and copy when you need to stay safe. 🎉