def test_numpy():
"""Test that ndarrays, Series work as well."""
assert len(array_like()) < 5 # In case we extend at some point
if len(array_like()) > 2: # Test numpy
import numpy as np
np_bounds = CategoricalBounds(np.array(["spam", "eggs"], dtype=object))
np_copy = loads(dumps(np_bounds))
assert np_copy == np_bounds
if len(array_like()) > 3: # Test numpy
import pandas as pd
pd_bounds = CategoricalBounds(pd.Series(["spam", "eggs"]))
pd_copy = loads(dumps(pd_bounds))
assert pd_copy == pd_bounds
def categories(self, categories):
if categories is None:
self._categories = set()
elif isinstance(categories, array_like()):
self._categories = set(categories)
elif isinstance(categories, set):
self._categories = categories
else:
raise ValueError("Categories must be a list, tuple, or set")
if not all(isinstance(x, str) for x in self.categories):
raise ValueError("All the categories must be strings")
def components(self, value):
if value is None:
self._components = set()
elif isinstance(value, array_like()):
self._components = set(value)
elif isinstance(value, set):
self._components = value
else:
raise ValueError(
"Components must be a list, tuple, or set: {}".format(value))
if not all(isinstance(x, str) for x in self.components):
raise ValueError("All the components must be strings")