def test_can_create_a_series_of_any_dtype(data):
dtype = np.dtype(data.draw(npst.scalar_dtypes()))
assume(supported_by_pandas(dtype))
# Use raw data to work around pandas bug in repr. See
# https://github.com/pandas-dev/pandas/issues/27484
series = data.conjecture_data.draw(pdst.series(dtype=dtype))
assert series.dtype == pandas.Series([], dtype=dtype).dtype
def test_generate_arbitrary_indices(data):
min_size = data.draw(st.integers(0, 10), 'min_size')
max_size = data.draw(st.none() | st.integers(min_size, min_size + 10),
'max_size')
unique = data.draw(st.booleans(), 'unique')
dtype = data.draw(npst.scalar_dtypes(), 'dtype')
assume(supported_by_pandas(dtype))
# Pandas bug: https://github.com/pandas-dev/pandas/pull/14916 until 0.20;
# then int64 indexes are inferred from uint64 values.
assume(dtype.kind != 'u')
pass_elements = data.draw(st.booleans(), 'pass_elements')
converted_dtype = pandas.Index([], dtype=dtype).dtype
try:
inferred_dtype = pandas.Index([data.draw(npst.from_dtype(dtype))
]).dtype
if pass_elements:
elements = npst.from_dtype(dtype)
dtype = None
else:
elements = None
index = data.draw(
pdst.indexes(
elements=elements,
dtype=dtype,
min_size=min_size,
max_size=max_size,
unique=unique,
))
except Exception as e:
if type(e).__name__ == 'OutOfBoundsDatetime':
# See https://github.com/HypothesisWorks/hypothesis-python/pull/826
reject()
else:
raise
if dtype is None:
if pandas.__version__ >= '0.19':
assert index.dtype == inferred_dtype
else:
assert index.dtype == converted_dtype
if unique:
assert len(set(index.values)) == len(index)
def test_generate_arbitrary_indices(data):
min_size = data.draw(st.integers(0, 10), "min_size")
max_size = data.draw(st.none() | st.integers(min_size, min_size + 10), "max_size")
unique = data.draw(st.booleans(), "unique")
dtype = data.draw(npst.scalar_dtypes(), "dtype")
assume(supported_by_pandas(dtype))
# Pandas bug: https://github.com/pandas-dev/pandas/pull/14916 until 0.20;
# then int64 indexes are inferred from uint64 values.
assume(dtype.kind != "u")
pass_elements = data.draw(st.booleans(), "pass_elements")
converted_dtype = pandas.Index([], dtype=dtype).dtype
try:
inferred_dtype = pandas.Index([data.draw(npst.from_dtype(dtype))]).dtype
if pass_elements:
elements = npst.from_dtype(dtype)
dtype = None
else:
elements = None
index = data.draw(
pdst.indexes(
elements=elements,
dtype=dtype,
min_size=min_size,
max_size=max_size,
unique=unique,
)
)
except Exception as e:
if type(e).__name__ == "OutOfBoundsDatetime":
# See https://github.com/HypothesisWorks/hypothesis-python/pull/826
reject()
else:
raise
if dtype is None:
if pandas.__version__ >= "0.19":
assert index.dtype == inferred_dtype
else:
assert index.dtype == converted_dtype
if unique:
assert len(set(index.values)) == len(index)
def test_generate_arbitrary_indices(data):
min_size = data.draw(st.integers(0, 10), 'min_size')
max_size = data.draw(
st.none() | st.integers(min_size, min_size + 10), 'max_size')
unique = data.draw(st.booleans(), 'unique')
dtype = data.draw(npst.scalar_dtypes(), 'dtype')
assume(supported_by_pandas(dtype))
pass_elements = data.draw(st.booleans(), 'pass_elements')
converted_dtype = pandas.Index([], dtype=dtype).dtype
try:
inferred_dtype = pandas.Index(
[data.draw(npst.from_dtype(dtype))]).dtype
if pass_elements:
elements = npst.from_dtype(dtype)
dtype = None
else:
elements = None
index = data.draw(pdst.indexes(
elements=elements, dtype=dtype, min_size=min_size,
max_size=max_size, unique=unique,
))
except Exception as e:
if type(e).__name__ == 'OutOfBoundsDatetime':
# See https://github.com/HypothesisWorks/hypothesis-python/pull/826
reject()
else:
raise
if dtype is None:
assert index.dtype == inferred_dtype
else:
assert index.dtype == converted_dtype
if unique:
assert len(set(index.values)) == len(index)
def test_can_create_a_series_of_any_dtype(data):
dtype = np.dtype(data.draw(npst.scalar_dtypes()))
assume(supported_by_pandas(dtype))
series = data.draw(pdst.series(dtype=dtype))
assert series.dtype == pandas.Series([], dtype=dtype).dtype
def test_can_create_a_series_of_any_dtype(data):
dtype = np.dtype(data.draw(npst.scalar_dtypes()))
assume(supported_by_pandas(dtype))
series = data.draw(pdst.series(dtype=dtype))
assert series.dtype == pandas.Series([], dtype=dtype).dtype
