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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minutenumpy.argmax() returns the index of a maximum value, not the value itself. For example, np.argmax(np.array([10, 30, 20])) returns 1, because the largest value, 30, is at zero-based index 1. Add an axis to find a maximum position in each row, column, or other slice.
What does numpy.argmax() return?
argmax() returns an integer index for a one-dimensional result, or an array of indices when it searches along an axis. It does not return the maximum values. Use np.max() (also available as np.amax()) to get those values, or index the original array with the position returned by argmax(). NumPy documents the function and its behavior in the argmax reference and the value reduction in the amax reference.
import numpy as np
a = np.array([4, 9, 2])
index = np.argmax(a)
value = a[index]
# index: 1
# value: 9The documented signature is numpy.argmax(a, axis=None, out=None, *, keepdims=<no value>). The stable manual, checked August 18, 2026, is labeled NumPy v2.5; that label describes the manual, not necessarily the version installed in your environment.
a: array-like input.axis: the axis along which to find maximum positions; the default,None, searches a flattened array.out: optional array into which the index result is written.keepdims: whether to retain the reduced axis as a dimension of length one.
Find a maximum index in a one-dimensional array
For a one-dimensional array, the result is the position of the largest element, using zero-based indexing.
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a = np.array([7, 2, 9, 4])
np.argmax(a)
# 2
# a[2] is 9If the maximum occurs more than once, argmax() returns the first occurrence:
a = np.array([7, 9, 3, 9])
np.argmax(a)
# 1This gives one winner, not every tied position. NumPy specifies the first-occurrence behavior in its argmax documentation.
How does axis change the result?
For a two-dimensional array with shape (rows, columns), axis=0 searches down the rows, producing one row index per column. axis=1 searches across the columns, producing one column index per row. By default, the searched axis disappears from the output shape.
a = np.array([
[10, 20, 30],
[40, 50, 60]
])
np.argmax(a, axis=0) # array([1, 1, 1]): winning row in each column
np.argmax(a, axis=1) # array([2, 2]): winning column in each row
np.argmax(a, axis=0).shape # (3,)
np.argmax(a, axis=1).shape # (2,)In the first result, each element is a row index: the largest value in every column is in row 1. In the second, each element is a column index: the largest value in each row is in column 2. The NumPy examples demonstrate these axis reductions.
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Negative axes
Negative axis numbers count backward from the final dimension. For an array of shape (2, 3, 4), axis=-1 is the last axis (equivalent to axis=2), axis=-2 is equivalent to axis=1, and axis=-3 is equivalent to axis=0. This is useful when code should operate on the final dimension regardless of how many leading dimensions an array has.
Higher-dimensional arrays
In three or more dimensions, each result still identifies a position along the selected axis; it is not a full coordinate in the original array. For example, the following array has shape (2, 2, 3):
x = np.array([
[[0, 1, 2], [3, 4, 5]],
[[6, 0, 1], [2, 3, 4]]
])
np.argmax(x, axis=0).shape # (2, 3)
np.argmax(x, axis=1).shape # (2, 3)
np.argmax(x, axis=2).shape # (2, 2)The output shape is the input shape with the selected axis removed. For additional multidimensional indexing patterns, see NumPy’s indexing guide.
Get maximum values using the returned indices
For row-wise maxima, use the row numbers together with the column indices returned by argmax(). Alternatively, calculate the values directly with np.max() when you do not need the positions.
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scores = np.array([
[72, 91, 84],
[88, 79, 95],
[90, 93, 89]
])
best_column = np.argmax(scores, axis=1)
best_scores = scores[np.arange(scores.shape[0]), best_column]
# best_column: array([1, 2, 1])
# best_scores: array([91, 95, 93])
# np.max(scores, axis=1): array([91, 95, 93])For column-wise results, np.argmax(scores, axis=0) returns array([2, 1, 1]), the winning row in each column; np.max(scores, axis=0) returns the values array([90, 93, 95]).
Use take_along_axis() for general arrays
For N-dimensional arrays, np.take_along_axis() is a general way to select values using the index slices produced by argmax(). Keep the selected axis as a length-one dimension so the index array matches the data along that axis:
indices = np.argmax(scores, axis=1, keepdims=True)
values = np.take_along_axis(scores, indices, axis=1)
# indices: array([[1], [2], [1]])
# values: array([[91], [95], [93]])See NumPy’s take_along_axis reference for how it applies indices along an axis.
When should you use keepdims=True?
By default, reducing an axis removes it from the output shape. With keepdims=True, NumPy retains that axis at length one, which can make the result broadcast-compatible with the original array and is useful with take_along_axis().
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a = np.arange(24).reshape(2, 3, 4)
np.argmax(a, axis=1).shape # (2, 4)
np.argmax(a, axis=1, keepdims=True).shape # (2, 1, 4)
indices = np.argmax(a, axis=-1, keepdims=True)
max_values = np.take_along_axis(a, indices, axis=-1)NumPy documents keepdims as added to argmax() in version 1.22.0, so check compatibility if your code must run on an older NumPy release.
Find the global maximum’s coordinates
With the default axis=None, NumPy treats a multidimensional array as flattened and returns a single flat index—not a row and column. For example, a 2-by-3 array laid out in row-major order has flat positions 0 through 5; its bottom-right element has flat index 5.
a = np.array([
[10, 20, 30],
[40, 50, 60]
])
flat_index = np.argmax(a) # 5
coordinates = np.unravel_index(flat_index, a.shape) # (1, 2)
value = a[coordinates] # 60np.unravel_index() converts a flat index into coordinates for the supplied shape; its default order is 'C' (row-major). The unravel_index reference documents the conversion. The same pattern works for arrays of any dimension: pass the array’s shape, then index with the returned coordinate tuple.
Find all tied maximum positions
If every location tied for the maximum matters, first get the maximum value and then collect every matching position. For a one-dimensional array, use np.flatnonzero(); for an array of any dimension, np.argwhere() returns coordinates of matches.
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a = np.array([5, 9, 2, 9, 1])
max_value = np.max(a)
all_indices = np.flatnonzero(a == max_value)
# array([1, 3])For a multidimensional array, the corresponding pattern is np.argwhere(a == np.max(a)). Use argmax() instead when one first-occurring winner is the result you want.
Handle NaN values deliberately
If NaN should be ignored while finding a maximum position, use np.nanargmax(). It raises ValueError for a slice that is entirely NaN; NumPy also warns that results cannot be trusted when a slice contains only NaN values and negative infinity. Consult the nanargmax reference for these conditions.
a = np.array([
[np.nan, 4],
[2, 3]
])
np.nanargmax(a) # 1Choose between argmax() and nanargmax() based on what a NaN means in your data. The latter ignores missing values; it is not a universal replacement for the former.
When is the out parameter useful?
out writes the index result into a preallocated array. Its shape must match the result and its dtype must be suitable for integer indices. This is useful when code needs to control allocations or reuse an existing buffer; ordinary code can omit it for simplicity.
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a = np.array([
[10, 20, 30],
[40, 50, 60]
])
out = np.empty(3, dtype=np.intp)
np.argmax(a, axis=0, out=out)
# out: array([1, 1, 1])The parameter requirements are described in NumPy’s argmax reference.
Choose argmax() or a related function
| Need | Function or approach |
|---|---|
| One maximum index for an array or each slice along an axis | np.argmax() |
| Maximum values, without their positions | np.max() or np.amax() |
| Maximum index while ignoring NaNs | np.nanargmax(), after accounting for all-NaN and negative-infinity caveats |
| Positions of all elements in sorted order | np.argsort() |
| Partial top-k selection without fully sorting the selected values | np.argpartition(); the selected portion is not necessarily ordered |
| Every position tied at the maximum | Compare against the maximum, then use np.flatnonzero() or np.argwhere() |
| Convert a flattened index to multidimensional coordinates | np.unravel_index() |
argsort() and argpartition() are separate sorting and selection tools, not alternatives that return the same single-winner result as argmax(). NumPy lists them in its sorting, searching, and counting reference.
Quick Recap
Check these points when a result looks wrong
- If you expected a number from the array, use
np.max()or index the array with theargmax()result. - For a matrix, confirm whether you want a result per column (
axis=0) or per row (axis=1). - If
axis=Nonewas used, convert the flat index withnp.unravel_index()before treating it as coordinates. - If ties matter, collect all matches rather than relying on the single first index returned.
- If missing values are present, decide whether they should be ignored and whether any slice is all
NaN. - If later operations require matching dimensions, use
keepdims=Trueand check the resulting shape.
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