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Function argmin

numpy/_core/fromnumeric.py:1360–1452  ·  view source on GitHub ↗

Returns the indices of the minimum values along an axis. Parameters ---------- a : array_like Input array. axis : int, optional By default, the index is into the flattened array, otherwise along the specified axis. out : array, optional If pr

(a, axis=None, out=None, *, keepdims=np._NoValue)

Source from the content-addressed store, hash-verified

1358
1359@array_function_dispatch(_argmin_dispatcher)
1360def argmin(a, axis=None, out=None, *, keepdims=np._NoValue):
1361 """
1362 Returns the indices of the minimum values along an axis.
1363
1364 Parameters
1365 ----------
1366 a : array_like
1367 Input array.
1368 axis : int, optional
1369 By default, the index is into the flattened array, otherwise
1370 along the specified axis.
1371 out : array, optional
1372 If provided, the result will be inserted into this array. It should
1373 be of the appropriate shape and dtype.
1374 keepdims : bool, optional
1375 If this is set to True, the axes which are reduced are left
1376 in the result as dimensions with size one. With this option,
1377 the result will broadcast correctly against the array.
1378
1379 .. versionadded:: 1.22.0
1380
1381 Returns
1382 -------
1383 index_array : ndarray of ints
1384 Array of indices into the array. It has the same shape as `a.shape`
1385 with the dimension along `axis` removed. If `keepdims` is set to True,
1386 then the size of `axis` will be 1 with the resulting array having same
1387 shape as `a.shape`.
1388
1389 See Also
1390 --------
1391 ndarray.argmin, argmax
1392 amin : The minimum value along a given axis.
1393 unravel_index : Convert a flat index into an index tuple.
1394 take_along_axis : Apply ``np.expand_dims(index_array, axis)``
1395 from argmin to an array as if by calling min.
1396
1397 Notes
1398 -----
1399 In case of multiple occurrences of the minimum values, the indices
1400 corresponding to the first occurrence are returned.
1401
1402 Examples
1403 --------
1404 >>> import numpy as np
1405 >>> a = np.arange(6).reshape(2,3) + 10
1406 >>> a
1407 array([[10, 11, 12],
1408 [13, 14, 15]])
1409 >>> np.argmin(a)
1410 0
1411 >>> np.argmin(a, axis=0)
1412 array([0, 0, 0])
1413 >>> np.argmin(a, axis=1)
1414 array([0, 0])
1415
1416 Indices of the minimum elements of an N-dimensional array:
1417

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Calls 1

_wrapfuncFunction · 0.85

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