Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Exclude NA/null values when showing the result. *args, **k
(self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs)
| 7744 | return maybe_unbox_numpy_scalar(nanops.nanmin(self._values, skipna=skipna)) |
| 7745 | |
| 7746 | def max(self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs): |
| 7747 | """ |
| 7748 | Return the maximum value of the Index. |
| 7749 | |
| 7750 | Parameters |
| 7751 | ---------- |
| 7752 | axis : int, optional |
| 7753 | For compatibility with NumPy. Only 0 or None are allowed. |
| 7754 | skipna : bool, default True |
| 7755 | Exclude NA/null values when showing the result. |
| 7756 | *args, **kwargs |
| 7757 | Additional arguments and keywords for compatibility with NumPy. |
| 7758 | |
| 7759 | Returns |
| 7760 | ------- |
| 7761 | scalar |
| 7762 | Maximum value. |
| 7763 | |
| 7764 | See Also |
| 7765 | -------- |
| 7766 | Index.min : Return the minimum value in an Index. |
| 7767 | Series.max : Return the maximum value in a Series. |
| 7768 | DataFrame.max : Return the maximum values in a DataFrame. |
| 7769 | |
| 7770 | Examples |
| 7771 | -------- |
| 7772 | >>> idx = pd.Index([3, 2, 1]) |
| 7773 | >>> idx.max() |
| 7774 | 3 |
| 7775 | |
| 7776 | >>> idx = pd.Index(["c", "b", "a"]) |
| 7777 | >>> idx.max() |
| 7778 | 'c' |
| 7779 | |
| 7780 | For a MultiIndex, the maximum is determined lexicographically. |
| 7781 | |
| 7782 | >>> idx = pd.MultiIndex.from_product([("a", "b"), (2, 1)]) |
| 7783 | >>> idx.max() |
| 7784 | ('b', 2) |
| 7785 | """ |
| 7786 | |
| 7787 | nv.validate_max(args, kwargs) |
| 7788 | nv.validate_minmax_axis(axis) |
| 7789 | |
| 7790 | if not len(self): |
| 7791 | return self._na_value |
| 7792 | |
| 7793 | if len(self) and self.is_monotonic_increasing: |
| 7794 | # quick check |
| 7795 | last = self[-1] |
| 7796 | if not isna(last): |
| 7797 | return maybe_unbox_numpy_scalar(last) |
| 7798 | |
| 7799 | if not self._is_multi and self.hasnans: |
| 7800 | # Take advantage of cache |
| 7801 | mask = self._isnan |
| 7802 | if not skipna or mask.all(): |
| 7803 | return maybe_unbox_numpy_scalar(self._na_value) |
no test coverage detected