Python pandas.DatetimeTZDtype() Examples
The following are 6
code examples of pandas.DatetimeTZDtype().
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Example #1
| Source File: test_merge.py From recruit with Apache License 2.0 | 6 votes |
|
def test_merge_on_datetime64tz_empty(self):
# https://github.com/pandas-dev/pandas/issues/25014
dtz = pd.DatetimeTZDtype(tz='UTC')
right = pd.DataFrame({'date': [pd.Timestamp('2018', tz=dtz.tz)],
'value': [4.0],
'date2': [pd.Timestamp('2019', tz=dtz.tz)]},
columns=['date', 'value', 'date2'])
left = right[:0]
result = left.merge(right, on='date')
expected = pd.DataFrame({
'value_x': pd.Series(dtype=float),
'date2_x': pd.Series(dtype=dtz),
'date': pd.Series(dtype=dtz),
'value_y': pd.Series(dtype=float),
'date2_y': pd.Series(dtype=dtz),
}, columns=['value_x', 'date2_x', 'date', 'value_y', 'date2_y'])
tm.assert_frame_equal(result, expected)
Example #2
| Source File: test_merge.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_merge_on_datetime64tz_empty(self):
# https://github.com/pandas-dev/pandas/issues/25014
dtz = pd.DatetimeTZDtype(tz='UTC')
right = pd.DataFrame({'date': [pd.Timestamp('2018', tz=dtz.tz)],
'value': [4.0],
'date2': [pd.Timestamp('2019', tz=dtz.tz)]},
columns=['date', 'value', 'date2'])
left = right[:0]
result = left.merge(right, on='date')
expected = pd.DataFrame({
'value_x': pd.Series(dtype=float),
'date2_x': pd.Series(dtype=dtz),
'date': pd.Series(dtype=dtz),
'value_y': pd.Series(dtype=float),
'date2_y': pd.Series(dtype=dtz),
}, columns=['value_x', 'date2_x', 'date', 'value_y', 'date2_y'])
tm.assert_frame_equal(result, expected)
Example #3
| Source File: filtering_fe_autotype.py From dash-docs with MIT License | 6 votes |
|
def table_type(df_column):
# Note - this only works with Pandas >= 1.0.0
if sys.version_info < (3, 0): # Pandas 1.0.0 does not support Python 2
return 'any'
if isinstance(df_column.dtype, pd.DatetimeTZDtype):
return 'datetime',
elif (isinstance(df_column.dtype, pd.StringDtype) or
isinstance(df_column.dtype, pd.BooleanDtype) or
isinstance(df_column.dtype, pd.CategoricalDtype) or
isinstance(df_column.dtype, pd.PeriodDtype)):
return 'text'
elif (isinstance(df_column.dtype, pd.SparseDtype) or
isinstance(df_column.dtype, pd.IntervalDtype) or
isinstance(df_column.dtype, pd.Int8Dtype) or
isinstance(df_column.dtype, pd.Int16Dtype) or
isinstance(df_column.dtype, pd.Int32Dtype) or
isinstance(df_column.dtype, pd.Int64Dtype)):
return 'numeric'
else:
return 'any'
Example #4
| Source File: test_merge.py From coffeegrindsize with MIT License | 6 votes |
|
def test_merge_on_datetime64tz_empty(self):
# https://github.com/pandas-dev/pandas/issues/25014
dtz = pd.DatetimeTZDtype(tz='UTC')
right = pd.DataFrame({'date': [pd.Timestamp('2018', tz=dtz.tz)],
'value': [4.0],
'date2': [pd.Timestamp('2019', tz=dtz.tz)]},
columns=['date', 'value', 'date2'])
left = right[:0]
result = left.merge(right, on='date')
expected = pd.DataFrame({
'value_x': pd.Series(dtype=float),
'date2_x': pd.Series(dtype=dtz),
'date': pd.Series(dtype=dtz),
'value_y': pd.Series(dtype=float),
'date2_y': pd.Series(dtype=dtz),
}, columns=['value_x', 'date2_x', 'date', 'value_y', 'date2_y'])
tm.assert_frame_equal(result, expected)
Example #5
| Source File: core.py From mars with Apache License 2.0 | 5 votes |
|
def assert_dtype_consistent(expected_dtype, real_dtype):
if isinstance(real_dtype, pd.DatetimeTZDtype):
real_dtype = real_dtype.base
if expected_dtype != real_dtype:
if expected_dtype == np.dtype('O') and real_dtype.type is np.str_:
# real dtype is string, this matches expectation
return
if expected_dtype is None:
raise AssertionError('Expected dtype cannot be None')
if not np.can_cast(real_dtype, expected_dtype) and not np.can_cast(expected_dtype, real_dtype):
raise AssertionError('cannot cast between dtype of real dtype %r and dtype %r defined in metadata'
% (real_dtype, expected_dtype))
Example #6
| Source File: test_dtypes.py From pandera with MIT License | 4 votes |
|
def test_pandas_extension_types():
"""Test pandas extension data type happy path."""
# pylint: disable=no-member
test_params = [
(
pd.CategoricalDtype(),
pd.Series(["a", "a", "b", "b", "c", "c"], dtype="category"),
None
),
(
pd.DatetimeTZDtype(tz='UTC'),
pd.Series(
pd.date_range(start="20200101", end="20200301"),
dtype="datetime64[ns, utc]"
),
None
),
(pd.Int64Dtype(), pd.Series(range(10), dtype="Int64"), None),
(pd.StringDtype(), pd.Series(["foo", "bar", "baz"], dtype="string"), None),
(
pd.PeriodDtype(freq='D'),
pd.Series(pd.period_range('1/1/2019', '1/1/2020', freq='D')),
None
),
(
pd.SparseDtype("float"),
pd.Series(range(100)).where(
lambda s: s < 5, other=np.nan).astype("Sparse[float]"),
{"nullable": True},
),
(
pd.BooleanDtype(),
pd.Series([1, 0, 0, 1, 1], dtype="boolean"),
None
),
(
pd.IntervalDtype(subtype="int64"),
pd.Series(pd.IntervalIndex.from_breaks([0, 1, 2, 3, 4])),
None,
)
]
for dtype, data, series_kwargs in test_params:
series_kwargs = {} if series_kwargs is None else series_kwargs
series_schema = SeriesSchema(pandas_dtype=dtype, **series_kwargs)
assert isinstance(series_schema.validate(data), pd.Series)
