/
accessors.py
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/
accessors.py
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"""
datetimelike delegation
"""
import numpy as np
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_datetime64_dtype,
is_datetime64tz_dtype,
is_datetime_arraylike,
is_integer_dtype,
is_list_like,
is_period_arraylike,
is_timedelta64_dtype,
)
from pandas.core.dtypes.generic import ABCSeries
from pandas.core.accessor import PandasDelegate, delegate_names
from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray
from pandas.core.base import NoNewAttributesMixin, PandasObject
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.timedeltas import TimedeltaIndex
class Properties(PandasDelegate, PandasObject, NoNewAttributesMixin):
def __init__(self, data, orig):
if not isinstance(data, ABCSeries):
raise TypeError(
f"cannot convert an object of type {type(data)} to a datetimelike index"
)
self._parent = data
self.orig = orig
self.name = getattr(data, "name", None)
self._freeze()
def _get_values(self):
data = self._parent
if is_datetime64_dtype(data.dtype):
return DatetimeIndex(data, copy=False, name=self.name)
elif is_datetime64tz_dtype(data.dtype):
return DatetimeIndex(data, copy=False, name=self.name)
elif is_timedelta64_dtype(data.dtype):
return TimedeltaIndex(data, copy=False, name=self.name)
else:
if is_period_arraylike(data):
# TODO: use to_period_array
return PeriodArray(data, copy=False)
if is_datetime_arraylike(data):
return DatetimeIndex(data, copy=False, name=self.name)
raise TypeError(
f"cannot convert an object of type {type(data)} to a datetimelike index"
)
def _delegate_property_get(self, name):
from pandas import Series
values = self._get_values()
result = getattr(values, name)
# maybe need to upcast (ints)
if isinstance(result, np.ndarray):
if is_integer_dtype(result):
result = result.astype("int64")
elif not is_list_like(result):
return result
result = np.asarray(result)
if self.orig is not None:
index = self.orig.index
else:
index = self._parent.index
# return the result as a Series, which is by definition a copy
result = Series(result, index=index, name=self.name)
# setting this object will show a SettingWithCopyWarning/Error
result._is_copy = (
"modifications to a property of a datetimelike "
"object are not supported and are discarded. "
"Change values on the original."
)
return result
def _delegate_property_set(self, name, value, *args, **kwargs):
raise ValueError(
"modifications to a property of a datetimelike object are not supported. "
"Change values on the original."
)
def _delegate_method(self, name, *args, **kwargs):
from pandas import Series
values = self._get_values()
method = getattr(values, name)
result = method(*args, **kwargs)
if not is_list_like(result):
return result
result = Series(result, index=self._parent.index, name=self.name)
# setting this object will show a SettingWithCopyWarning/Error
result._is_copy = (
"modifications to a method of a datetimelike "
"object are not supported and are discarded. "
"Change values on the original."
)
return result
@delegate_names(
delegate=DatetimeArray, accessors=DatetimeArray._datetimelike_ops, typ="property"
)
@delegate_names(
delegate=DatetimeArray, accessors=DatetimeArray._datetimelike_methods, typ="method"
)
class DatetimeProperties(Properties):
"""
Accessor object for datetimelike properties of the Series values.
Examples
--------
>>> s.dt.hour
>>> s.dt.second
>>> s.dt.quarter
Returns a Series indexed like the original Series.
Raises TypeError if the Series does not contain datetimelike values.
"""
def to_pydatetime(self):
"""
Return the data as an array of native Python datetime objects.
Timezone information is retained if present.
.. warning::
Python's datetime uses microsecond resolution, which is lower than
pandas (nanosecond). The values are truncated.
Returns
-------
numpy.ndarray
Object dtype array containing native Python datetime objects.
See Also
--------
datetime.datetime : Standard library value for a datetime.
Examples
--------
>>> s = pd.Series(pd.date_range('20180310', periods=2))
>>> s
0 2018-03-10
1 2018-03-11
dtype: datetime64[ns]
>>> s.dt.to_pydatetime()
array([datetime.datetime(2018, 3, 10, 0, 0),
datetime.datetime(2018, 3, 11, 0, 0)], dtype=object)
pandas' nanosecond precision is truncated to microseconds.
>>> s = pd.Series(pd.date_range('20180310', periods=2, freq='ns'))
>>> s
0 2018-03-10 00:00:00.000000000
1 2018-03-10 00:00:00.000000001
dtype: datetime64[ns]
>>> s.dt.to_pydatetime()
array([datetime.datetime(2018, 3, 10, 0, 0),
datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)
"""
return self._get_values().to_pydatetime()
@property
def freq(self):
return self._get_values().inferred_freq
@delegate_names(
delegate=TimedeltaArray, accessors=TimedeltaArray._datetimelike_ops, typ="property"
)
@delegate_names(
delegate=TimedeltaArray,
accessors=TimedeltaArray._datetimelike_methods,
typ="method",
)
class TimedeltaProperties(Properties):
"""
Accessor object for datetimelike properties of the Series values.
Examples
--------
>>> s.dt.hours
>>> s.dt.seconds
Returns a Series indexed like the original Series.
Raises TypeError if the Series does not contain datetimelike values.
"""
def to_pytimedelta(self):
"""
Return an array of native `datetime.timedelta` objects.
Python's standard `datetime` library uses a different representation
timedelta's. This method converts a Series of pandas Timedeltas
to `datetime.timedelta` format with the same length as the original
Series.
Returns
-------
numpy.ndarray
Array of 1D containing data with `datetime.timedelta` type.
See Also
--------
datetime.timedelta
Examples
--------
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='d'))
>>> s
0 0 days
1 1 days
2 2 days
3 3 days
4 4 days
dtype: timedelta64[ns]
>>> s.dt.to_pytimedelta()
array([datetime.timedelta(0), datetime.timedelta(1),
datetime.timedelta(2), datetime.timedelta(3),
datetime.timedelta(4)], dtype=object)
"""
return self._get_values().to_pytimedelta()
@property
def components(self):
"""
Return a Dataframe of the components of the Timedeltas.
Returns
-------
DataFrame
Examples
--------
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))
>>> s
0 00:00:00
1 00:00:01
2 00:00:02
3 00:00:03
4 00:00:04
dtype: timedelta64[ns]
>>> s.dt.components
days hours minutes seconds milliseconds microseconds nanoseconds
0 0 0 0 0 0 0 0
1 0 0 0 1 0 0 0
2 0 0 0 2 0 0 0
3 0 0 0 3 0 0 0
4 0 0 0 4 0 0 0
""" # noqa: E501
return self._get_values().components.set_index(self._parent.index)
@property
def freq(self):
return self._get_values().inferred_freq
@delegate_names(
delegate=PeriodArray, accessors=PeriodArray._datetimelike_ops, typ="property"
)
@delegate_names(
delegate=PeriodArray, accessors=PeriodArray._datetimelike_methods, typ="method"
)
class PeriodProperties(Properties):
"""
Accessor object for datetimelike properties of the Series values.
Examples
--------
>>> s.dt.hour
>>> s.dt.second
>>> s.dt.quarter
Returns a Series indexed like the original Series.
Raises TypeError if the Series does not contain datetimelike values.
"""
class CombinedDatetimelikeProperties(
DatetimeProperties, TimedeltaProperties, PeriodProperties
):
def __new__(cls, data):
# CombinedDatetimelikeProperties isn't really instantiated. Instead
# we need to choose which parent (datetime or timedelta) is
# appropriate. Since we're checking the dtypes anyway, we'll just
# do all the validation here.
from pandas import Series
if not isinstance(data, ABCSeries):
raise TypeError(
f"cannot convert an object of type {type(data)} to a datetimelike index"
)
orig = data if is_categorical_dtype(data) else None
if orig is not None:
data = Series(
orig.array,
name=orig.name,
copy=False,
dtype=orig.values.categories.dtype,
)
if is_datetime64_dtype(data.dtype):
return DatetimeProperties(data, orig)
elif is_datetime64tz_dtype(data.dtype):
return DatetimeProperties(data, orig)
elif is_timedelta64_dtype(data.dtype):
return TimedeltaProperties(data, orig)
elif is_period_arraylike(data):
return PeriodProperties(data, orig)
elif is_datetime_arraylike(data):
return DatetimeProperties(data, orig)
raise AttributeError("Can only use .dt accessor with datetimelike values")