piso.contains#

piso.contains(interval_array, x, include_index=True, result='cartesian', how='any')#

Evaluates the intersection of a set of intervals with a set of points.

The format of the result is dependent on the result parameter. If result = “cartesian” then the the function returns a 2-dimensional boolean mask M of shape (m,n) where m is the number of intervals, and n is the number of points. The element in the i-th row and j-th column is True if the i-th interval contains the j-th point.

If result = “points” then the result is a 1-dimensional boolean mask of length n. If result = “intervals” then the result is a 1-dimensional boolean mask of length m.

Parameters

interval_arraypandas.IntervalIndex or pandas.arrays.IntervalArray: Contains the intervals. May be left-closed, right-closed, both, or neither.
xscalar, or array-like of scalars: Values in x should belong to the same domain as the intervals in interval_array.
include_indexboolean, default True: Indicates whether to return a numpy.ndarray or pandas.DataFrame indexed by interval_array and column names equal to x
result{“cartesian”, “points”, “intervals”}, default “cartesian”: If result = “cartesian” then the result will be two dimensional, otherwise it will be one dimensional.
how{“any”, “all”}, default “any”: Only relevant if result is not “cartesian”. This parameter indicates either: - a True value means any or all points are contained within an interval, or - a True value means any or all intervals contained a point. Which of these interpretations is dependent on the result parameter.

Returns

numpy.ndarray, pandas.DataFrame or pandas.Series: One, or two, dimensional and boolean valued. Return type dependent on include_index and result.

Examples

>>> import pandas as pd
>>> import piso

>>> arr = pd.arrays.IntervalArray.from_tuples(
...     [(0, 4), (2, 5)],
... )

>>> piso.contains(arr, 1)
            1
(0, 4]   True
(2, 5]  False

>>> piso.contains(arr, [0, 1, 3, 4])
            0      1     3     4
(0, 4]  False   True  True  True
(2, 5]  False  False  True  True

>>> piso.contains(arr, [0, 1, 3, 4], include_index=False)
array([[False,  True,  True,  True],
       [False, False,  True,  True]])

>>> piso.contains(arr, [0, 1, 3, 4], result="points")
0    False
1     True
3     True
4     True
dtype: bool

>>> piso.contains(arr, [0, 1, 3, 4], result="points", how="all")
0    False
1    False
3     True
4     True
dtype: bool

>>> piso.contains(arr, [0, 1, 3, 4], result="intervals")
(0, 4]    True
(2, 5]    True
dtype: bool

>>> piso.contains(pd.IntervalIndex.from_tuples([(0,2)]), 1, include_index=False)
array([[ True]])