idxmax(self, axis: 'Axis' = 0, skipna: 'bool' = True) -> 'Series'

NA/null values are excluded.

Notes

This method is the DataFrame version of ndarray.argmax .

Parameters

axis : {0 or 'index', 1 or 'columns'}, default 0

The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.

skipna : bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Raises

ValueError

• If the row/column is empty

Returns

Series

Indexes of maxima along the specified axis.

Return index of first occurrence of maximum over requested axis.

See Also

Series.idxmax

Return index of the maximum element.

Examples

Consider a dataset containing food consumption in Argentina.

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])

This example is valid syntax, but we were not able to check execution

>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

This example is valid syntax, but we were not able to check execution

>>> df.idxmax()
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns" .

This example is valid syntax, but we were not able to check execution

>>> df.idxmax(axis="columns")
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.generic.NDFrame._add_numeric_operations.<locals>.min pandas.core.generic.NDFrame._add_numeric_operations.<locals>.prod pandas.core.series.Series.idxmax pandas.core.generic.NDFrame._add_numeric_operations.<locals>.sum pandas.core.generic.NDFrame._add_numeric_operations.<locals>.max

Local connectivity graph

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Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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