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# pandas.Index.searchsorted

Index.searchsorted(value, side='left', sorter=None)

  Find indices where elements should be inserted to maintain order.

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

  Note

  The Index must be monotonically sorted, otherwise wrong locations will likely be returned. Pandas does not check this for you.

  Parameters:
    value array-like or scalar

      Values to insert into 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 self).

    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:
    int or array of int

      A scalar or array of insertion points with the same shape as value.

  See also

  `sort_values`

    Sort by the values along either axis.

  `numpy.searchsorted`

    Similar method from NumPy.

  Notes

  Binary search is used to find the required insertion points.

  Examples

      >>> ser = pd.Series([1, 2, 3])
      >>> ser
      0    1
      1    2
      2    3
      dtype: int64

      >>> ser.searchsorted(4)
      np.int64(3)

      >>> ser.searchsorted([0, 4])
      array([0, 3])

      >>> ser.searchsorted([1, 3], side="left")
      array([0, 2])

      >>> ser.searchsorted([1, 3], side="right")
      array([1, 3])

      >>> ser = pd.Series(pd.to_datetime(["3/11/2000", "3/12/2000", "3/13/2000"]))
      >>> ser
      0   2000-03-11
      1   2000-03-12
      2   2000-03-13
      dtype: datetime64[us]

      >>> ser.searchsorted("3/14/2000")
      np.int64(3)

      >>> ser = pd.Categorical(
      ...     ["apple", "bread", "bread", "cheese", "milk"], ordered=True
      ... )
      >>> ser
      ['apple', 'bread', 'bread', 'cheese', 'milk']
      Categories (4, str): ['apple' < 'bread' < 'cheese' < 'milk']

      >>> ser.searchsorted("bread")
      np.int64(1)

      >>> ser.searchsorted(["bread"], side="right")
      array([3])

  If the values are not monotonically sorted, wrong locations may be returned:

      >>> ser = pd.Series([2, 1, 3])
      >>> ser
      0    2
      1    1
      2    3
      dtype: int64

      >>> ser.searchsorted(1)
      0  # wrong result, correct would be 1