For a Python list, use max() with enumerate() to get the maximum value and its zero-based index in one pass. For a NumPy array, use np.argmax() for the index and read the value at that position. Both approaches return the first occurrence when the maximum is tied.
Find the maximum value and index in a Python list
In Python, “array” often means a built-in list. Pair each value with its index using enumerate(), then tell max() to compare the pairs by their values:
values = [4, 12, 7, 12, 3]
index, value = max(enumerate(values), key=lambda pair: pair[1])
print(value) # 12
print(index) # 1enumerate() yields an index-value pair for each item, starting at zero unless you provide another starting value. The key function makes max() compare each pair’s value, while returning the whole pair for unpacking.
Python’s built-in documentation says that if multiple items are maximal, max() returns the first one encountered. That is why this example returns index 1, not 3. See the Python 3.13 built-in functions reference.
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Other ways to get the maximum and its index
Use max() and list.index()
If a second pass is acceptable, this can be especially readable:
value = max(values)
index = values.index(value)list.index() returns the first matching position, so this also selects the first maximum in a tie. It scans the list again after max() has found the value.
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Use a loop when you need explicit control
A loop makes custom rules or input checks visible. Initialize from the first item rather than from zero; starting at zero would give the wrong result for a list containing only negative values.
if not values:
index = value = None # Choose a sentinel that suits your application.
else:
index = 0
value = values[0]
for i, candidate in enumerate(values[1:], start=1):
if candidate > value:
index = i
value = candidateBecause the loop updates only for a strictly larger value, it keeps the first index when values tie. The empty-list branch uses None only as an example; choose an empty-input convention appropriate for your application.
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Find a maximum in a NumPy array
One-dimensional arrays
For a NumPy array, np.argmax() gives the index of the maximum. Use that index to retrieve the value:
import numpy as np
array = np.array([4, 12, 7, 12, 3])
index = np.argmax(array)
value = array[index]
print(value) # 12
print(index) # 1NumPy 2.0 documents that argmax() returns an index into the flattened array by default, and returns the first occurrence when the maximum appears more than once. See the NumPy 2.0 argmax() reference.
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Multidimensional arrays
With a multidimensional array, a default call to np.argmax(array) returns one flattened index, not a row-and-column coordinate. Convert that index to coordinates with np.unravel_index():
flat_index = np.argmax(array)
coordinates = np.unravel_index(flat_index, array.shape)
value = array[coordinates]To find indices along a particular dimension, pass axis= to np.argmax(). The result then represents indices along that axis rather than one index for the fully flattened array. Consult the NumPy 2.0 unravel_index() reference for the coordinate conversion pattern.
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Handle empty inputs, ties, and NaN values
Empty Python lists
Calling max() on an empty iterable without a default raises ValueError. If you need both a value and an index, check for emptiness before unpacking the result:
if values:
index, value = max(enumerate(values), key=lambda pair: pair[1])
else:
index = value = None # Application-specific choiceThe default argument to max() supplies a value for an empty iterable; it does not supply an index-value pair. The right empty-input result depends on what the rest of your program expects.
Tied maximum values
The list recipe returns the first maximum because Python’s max() chooses the first maximal item encountered. NumPy’s argmax() also returns the first occurrence of a tied maximum, as stated in the NumPy reference.
NaN values in NumPy
NaNs need an explicit policy. NumPy’s max() propagates NaNs, while nanmax() ignores them, according to the NumPy 2.0 max() reference. Do not assume ordinary argmax() ignores NaNs. If you need a NaN-aware index, consult the documentation for nanargmax() for your installed NumPy version and decide what should happen for an all-NaN or empty slice.
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