Categorical.searchsorted(v, side='left', sorter=None)

Find indices where elements should be inserted to maintain order.

Find the indices into a sorted Categorical self such that, if the corresponding elements in v were inserted before the indices, the order of self would be preserved.

Parameters :

v : array_like

Array-like values or a scalar value, to insert/search for in self.

side : {‘left’, ‘right’}, optional

If ‘left’, the index of the first suitable location found is given. If ‘right’, return the last such index. If there is no suitable index, return either 0 or N (where N is the length of a).

sorter : 1-D array_like, optional

Optional array of integer indices that sort self into ascending order. They are typically the result of np.argsort.

Returns :

indices : array of ints

Array of insertion points with the same shape as v.

See also

Series.searchsorted, numpy.searchsorted


Binary search is used to find the required insertion points.


>>> x = pd.Categorical(['apple', 'bread', 'bread', 'cheese', 'milk' ])
[apple, bread, bread, cheese, milk]
Categories (4, object): [apple < bread < cheese < milk]
>>> x.searchsorted('bread')
array([1])     # Note: an array, not a scalar
>>> x.searchsorted(['bread'])
>>> x.searchsorted(['bread', 'eggs'])
array([1, 4])
>>> x.searchsorted(['bread', 'eggs'], side='right')
array([3, 4])       # eggs before milk
>>> x = pd.Categorical(['apple', 'bread', 'bread', 'cheese', 'milk', 'donuts' ])
>>> x.searchsorted(['bread', 'eggs'], side='right', sorter=[0, 1, 2, 3, 5, 4])
array([3, 5])       # eggs after donuts, after switching milk and donuts