Return the index of the maximum over the requested axis. ... >>> idx = pd.MultiIndex.from_arrays( ... [["warm", "warm", "cold", "cold"], ["dog", "falcon", "fish", "spider"]], ... names=["blooded", "animal"], ... ) >>> s = pd.Series([4, 2, 0, 8], name="legs", index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64

June 24, 2025 - The max() function in Pandas Series is used to find the maximum value within a Series. It returns the highest value present in the Series. It returns a float value representing the maximum value within the series.

Return index of first occurrence of maximum over requested axis. ... Return index label of the first occurrence of minimum of values. ... This method is the Series version of ndarray.argmax. This method returns the label of the maximum, while ndarray.argmax returns the position.

Return the index of the maximum over the requested axis. ... >>> idx = pd.MultiIndex.from_arrays( ... [["warm", "warm", "cold", "cold"], ["dog", "falcon", "fish", "spider"]], ... names=["blooded", "animal"], ... ) >>> s = pd.Series([4, 2, 0, 8], name="legs", index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64

>>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64 · >>> s.max() 8 · Max using level names, as well as indices. >>> s.max(level='blooded') blooded warm 4 cold 8 Name: legs, dtype: int64 · >>> s.max(level=0) blooded warm 4 cold 8 Name: legs, dtype: int64 · index · modules | next | previous | pandas 0.24.2 documentation » ·

Series.max(axis=_NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)[source]#

Return the maximum of the values. ... Axis for the function to be applied on. ... Exclude NA/null values when computing the result. Changed in version 3.4.0: Supported including NA/null values. ... If True, include only float, int, boolean columns. This parameter is mainly for pandas compatibility.

September 15, 2022 - Pandas Series - max() function: The max() function is used to return the maximum of the values for the requested axis.

February 11, 2019 - ... # importing pandas as pd import ... Print the series print(sr) Output : Now we will use Series.max() function to find the maximum value of the given series object....

Series.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)¶ · This method returns the maximum of the values in the object. If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax. index · modules | next | previous | pandas ...

The maximum of the values in the Series.

Return the index of the maximum over the requested axis. ... >>> idx = pd.MultiIndex.from_arrays( ... [["warm", "warm", "cold", "cold"], ["dog", "falcon", "fish", "spider"]], ... names=["blooded", "animal"], ... ) >>> s = pd.Series([4, 2, 0, 8], name="legs", index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64

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Return index of first occurrence of maximum over requested axis. ... Return index label of the first occurrence of minimum of values. ... This method is the Series version of ndarray.argmax. This method returns the label of the maximum, while ndarray.argmax returns the position.

Getting the maximum value of Series elements¶ · import pandas as pd from numba import njit @njit def series_max(): s = pd.Series([1, 4, 2, 0]) out_series = s.max() return out_series # Expect maximum value 4 print(series_max()) $ python ./series/series_max.py 4 ·

Series.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)[source]¶ · This method returns the maximum of the values in the object. If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax. index · modules | next | previous ...

import pandas as pd data = [[10, 18, 11], [13, 15, 8], [9, 20, 3]] df = pd.DataFrame(data) print(df.max()) Try it Yourself » · The max() method returns a Series with the maximum value of each column.

Return the index of the maximum over the requested axis. ... >>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64

>>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64 · >>> s.max() 8 · Max using level names, as well as indices. >>> s.max(level='blooded') blooded warm 4 cold 8 Name: legs, dtype: int64 · >>> s.max(level=0) blooded warm 4 cold 8 Name: legs, dtype: int64 · index · modules | next | previous | pandas 0.25.3 documentation » ·