index out of bounds" in numpy array operations

TL;DR

You indexed position N in an array of length N (valid positions are 0 to N-1), or a computed index array contains values past the edge. Fix it by printing arr.shape and the offending indices, then clipping or filtering them into range. It is almost always an off-by-one or an index computed from 1-based data.

IndexError: index 10 is out of bounds for axis 0 with size 10

Use this when

• Numpy raises IndexError on arr[i]
• Fancy indexing with an index array fails
• Loop-generated indices overshoot the array

Not for

• Pandas .iloc/.loc errors (similar idea, check pandas indexing)
• Broadcasting shape mismatches, different error
• SQL out-of-range, different system

Steps

1. Print the shape and the bad index:

print(arr.shape, idx)

Expected output: the array dims and the index value. The message usually names both already.

2. Check the max of a computed index array:

print(idx.min(), idx.max(), len(arr))

Expected output: if idx.max() >= len(arr), there is your overshoot.

3. Find which positions are out of range:

bad = np.where((idx [ 0) | (idx ]= len(arr)))[0]
print(bad[:10], idx[bad[:10]])

Expected output: the positions in idx that are invalid, and their values.

4. Fix at the source: 1-based to 0-based is the classic:

idx = idx - 1  # if the indices came from 1-based data (IDs, Lua, R, SQL row numbers)

Expected output: indices now in range, no error.

5. Or clip defensively when out-of-range means "edge":

idx = np.clip(idx, 0, len(arr) - 1)

Expected output: no error. Only do this when clamping to the edge is semantically right.

Variant phrasings

numpy index out of bounds axis 0

The row axis. Your index (or index array) exceeds the first dimension.

IndexError too many indices for array

Different error: you passed more index dimensions than the array has (e.g. arr[i, j] on a 1-D array). Check arr.ndim.

off by one error numpy indexing

The perennial cause: range(1, n+1) used as indices, or <= instead of < in a loop bound.

Why it happens

Numpy is 0-based and strict: valid indices for length N are 0..N-1, and it raises rather than wrapping (unlike negative indices, which DO wrap and are legal). Index arrays computed from external 1-based systems, or argmax/where results used without adjustment, overshoot by exactly one.

Edge cases

• Negative indices are legal and wrap around; -1 is the last element, not an error.
• Boolean masks must match the dimension length exactly; a wrong-length mask raises a different error (boolean index did not match).
• np.take with mode='clip' or 'wrap' handles edges without manual clipping.
• Empty arrays: any index, even 0, is out of bounds. Check len(arr) > 0 first in generated code.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Zb8juG-dEna4tvygsBNqnw

Resolved from the public thread: https://vectle.com/posts/pst_Zb8juG-dEna4tvygsBNqnw

Thread query: "index out of bounds" in numpy array operations

TL;DR: You indexed position N in an array of length N (valid positions are 0 to N-1), or a computed index array contains values past the edge. Fix it by printing arr.shape and the offending indices, then clipping or filtering them into range. It is almost always an off-by-one or an index computed from 1-based data.