Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs. Parameters ---------- a : array_like Input data. axis : int,
(a, axis=None, out=None, *, keepdims=np._NoValue)
| 500 | |
| 501 | @array_function_dispatch(_nanargmin_dispatcher) |
| 502 | def nanargmin(a, axis=None, out=None, *, keepdims=np._NoValue): |
| 503 | """ |
| 504 | Return the indices of the minimum values in the specified axis ignoring |
| 505 | NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results |
| 506 | cannot be trusted if a slice contains only NaNs and Infs. |
| 507 | |
| 508 | Parameters |
| 509 | ---------- |
| 510 | a : array_like |
| 511 | Input data. |
| 512 | axis : int, optional |
| 513 | Axis along which to operate. By default flattened input is used. |
| 514 | out : array, optional |
| 515 | If provided, the result will be inserted into this array. It should |
| 516 | be of the appropriate shape and dtype. |
| 517 | |
| 518 | .. versionadded:: 1.22.0 |
| 519 | keepdims : bool, optional |
| 520 | If this is set to True, the axes which are reduced are left |
| 521 | in the result as dimensions with size one. With this option, |
| 522 | the result will broadcast correctly against the array. |
| 523 | |
| 524 | .. versionadded:: 1.22.0 |
| 525 | |
| 526 | Returns |
| 527 | ------- |
| 528 | index_array : ndarray |
| 529 | An array of indices or a single index value. |
| 530 | |
| 531 | See Also |
| 532 | -------- |
| 533 | argmin, nanargmax |
| 534 | |
| 535 | Examples |
| 536 | -------- |
| 537 | >>> a = np.array([[np.nan, 4], [2, 3]]) |
| 538 | >>> np.argmin(a) |
| 539 | 0 |
| 540 | >>> np.nanargmin(a) |
| 541 | 2 |
| 542 | >>> np.nanargmin(a, axis=0) |
| 543 | array([1, 1]) |
| 544 | >>> np.nanargmin(a, axis=1) |
| 545 | array([1, 0]) |
| 546 | |
| 547 | """ |
| 548 | a, mask = _replace_nan(a, np.inf) |
| 549 | if mask is not None: |
| 550 | mask = np.all(mask, axis=axis) |
| 551 | if np.any(mask): |
| 552 | raise ValueError("All-NaN slice encountered") |
| 553 | res = np.argmin(a, axis=axis, out=out, keepdims=keepdims) |
| 554 | return res |
| 555 | |
| 556 | |
| 557 | def _nanargmax_dispatcher(a, axis=None, out=None, *, keepdims=None): |
nothing calls this directly
no test coverage detected