CLN: datetimelike arrays: isort, small reorg

pandas/core/arrays/datetimelike.py

@@ -124,8 +124,12 @@ def asi8(self):
         # do not cache or you'll create a memory leak
         return self._data.view('i8')
 
-    # ------------------------------------------------------------------
-    # Array-like Methods
+    # ----------------------------------------------------------------
+    # Array-Like / EA-Interface Methods
+
+    @property
+    def nbytes(self):
+        return self._data.nbytes
 
     @property
     def shape(self):

pandas/core/arrays/datetimes.py

@@ -385,7 +385,7 @@ def _resolution(self):
         return libresolution.resolution(self.asi8, self.tz)
 
     # ----------------------------------------------------------------
-    # Array-like Methods
+    # Array-Like / EA-Interface Methods
 
     def __array__(self, dtype=None):
         if is_object_dtype(dtype):

pandas/core/arrays/period.py

@@ -272,10 +272,6 @@ def _concat_same_type(cls, to_concat):
 
     # --------------------------------------------------------------------
     # Data / Attributes
-    @property
-    def nbytes(self):
-        # TODO(DatetimeArray): remove
-        return self._data.nbytes
 
     @cache_readonly
     def dtype(self):
@@ -286,10 +282,6 @@ def _ndarray_values(self):
         # Ordinals
         return self._data
 
-    @property
-    def asi8(self):
-        return self._data
-
     @property
     def freq(self):
         """Return the frequency object for this PeriodArray."""
@@ -330,6 +322,50 @@ def start_time(self):
     def end_time(self):
         return self.to_timestamp(how='end')
 
+    def to_timestamp(self, freq=None, how='start'):
+        """
+        Cast to DatetimeArray/Index.
+
+        Parameters
+        ----------
+        freq : string or DateOffset, optional
+            Target frequency. The default is 'D' for week or longer,
+            'S' otherwise
+        how : {'s', 'e', 'start', 'end'}
+
+        Returns
+        -------
+        DatetimeArray/Index
+        """
+        from pandas.core.arrays import DatetimeArrayMixin
+
+        how = libperiod._validate_end_alias(how)
+
+        end = how == 'E'
+        if end:
+            if freq == 'B':
+                # roll forward to ensure we land on B date
+                adjust = Timedelta(1, 'D') - Timedelta(1, 'ns')
+                return self.to_timestamp(how='start') + adjust
+            else:
+                adjust = Timedelta(1, 'ns')
+                return (self + self.freq).to_timestamp(how='start') - adjust
+
+        if freq is None:
+            base, mult = frequencies.get_freq_code(self.freq)
+            freq = frequencies.get_to_timestamp_base(base)
+        else:
+            freq = Period._maybe_convert_freq(freq)
+
+        base, mult = frequencies.get_freq_code(freq)
+        new_data = self.asfreq(freq, how=how)
+
+        new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
+        return DatetimeArrayMixin(new_data, freq='infer')
+
+    # --------------------------------------------------------------------
+    # Array-like / EA-Interface Methods
+
     def __repr__(self):
         return '<{}>\n{}\nLength: {}, dtype: {}'.format(
             self.__class__.__name__,
@@ -456,6 +492,8 @@ def value_counts(self, dropna=False):
                             name=result.index.name)
         return Series(result.values, index=index, name=result.name)
 
+    # --------------------------------------------------------------------
+
     def shift(self, periods=1):
         """
         Shift values by desired number.
@@ -567,49 +605,9 @@ def asfreq(self, freq=None, how='E'):
 
         return type(self)(new_data, freq=freq)
 
-    def to_timestamp(self, freq=None, how='start'):
-        """
-        Cast to DatetimeArray/Index
-
-        Parameters
-        ----------
-        freq : string or DateOffset, optional
-            Target frequency. The default is 'D' for week or longer,
-            'S' otherwise
-        how : {'s', 'e', 'start', 'end'}
-
-        Returns
-        -------
-        DatetimeArray/Index
-        """
-        from pandas.core.arrays import DatetimeArrayMixin
-
-        how = libperiod._validate_end_alias(how)
-
-        end = how == 'E'
-        if end:
-            if freq == 'B':
-                # roll forward to ensure we land on B date
-                adjust = Timedelta(1, 'D') - Timedelta(1, 'ns')
-                return self.to_timestamp(how='start') + adjust
-            else:
-                adjust = Timedelta(1, 'ns')
-                return (self + self.freq).to_timestamp(how='start') - adjust
-
-        if freq is None:
-            base, mult = frequencies.get_freq_code(self.freq)
-            freq = frequencies.get_to_timestamp_base(base)
-        else:
-            freq = Period._maybe_convert_freq(freq)
-
-        base, mult = frequencies.get_freq_code(freq)
-        new_data = self.asfreq(freq, how=how)
-
-        new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
-        return DatetimeArrayMixin(new_data, freq='infer')
-
     # ------------------------------------------------------------------
     # Formatting
+
     def _format_native_types(self, na_rep=u'NaT', date_format=None, **kwargs):
         """ actually format my specific types """
         # TODO(DatetimeArray): remove
@@ -630,9 +628,13 @@ def _format_native_types(self, na_rep=u'NaT', date_format=None, **kwargs):
             values = np.array([formatter(dt) for dt in values])
         return values
 
+    # Delegation...
+    def strftime(self, date_format):
+        return self._format_native_types(date_format=date_format)
+
     def repeat(self, repeats, *args, **kwargs):
         """
-        Repeat elements of a Categorical.
+        Repeat elements of a PeriodArray.
 
         See also
         --------
@@ -643,10 +645,6 @@ def repeat(self, repeats, *args, **kwargs):
         values = self._data.repeat(repeats)
         return type(self)(values, self.freq)
 
-    # Delegation...
-    def strftime(self, date_format):
-        return self._format_native_types(date_format=date_format)
-
     def astype(self, dtype, copy=True):
         # TODO: Figure out something better here...
         # We have DatetimeLikeArrayMixin ->

pandas/core/arrays/timedeltas.py

@@ -190,6 +190,9 @@ def _generate_range(cls, start, end, periods, freq, closed=None):
 
         return cls._simple_new(index, freq=freq)
 
+    # ----------------------------------------------------------------
+    # Array-Like / EA-Interface Methods
+
     # ----------------------------------------------------------------
     # Arithmetic Methods
 
@@ -412,20 +415,25 @@ def sequence_to_td64ns(data, copy=False, unit="ns", errors="raise"):
     array : list-like
     copy : bool, default False
     unit : str, default "ns"
+        The timedelta unit to treat integers as multiples of.
     errors : {"raise", "coerce", "ignore"}, default "raise"
+        How to handle elements that cannot be converted to timedelta64[ns].
+        See ``pandas.to_timedelta`` for details.
 
     Returns
     -------
-    ndarray[timedelta64[ns]]
+    converted : numpy.ndarray
+        The sequence converted to a numpy array with dtype ``timedelta64[ns]``.
     inferred_freq : Tick or None
+        The inferred frequency of the sequence.
 
     Raises
     ------
-    ValueError : data cannot be converted to timedelta64[ns]
+    ValueError : Data cannot be converted to timedelta64[ns].
 
     Notes
     -----
-    Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause
+    Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause
     errors to be ignored; they are caught and subsequently ignored at a
     higher level.
     """
@@ -497,12 +505,13 @@ def ints_to_td64ns(data, unit="ns"):
 
     Parameters
     ----------
-    data : np.ndarray with integer-dtype
+    data : numpy.ndarray with integer-dtype
     unit : str, default "ns"
+        The timedelta unit to treat integers as multiples of.
 
     Returns
     -------
-    ndarray[timedelta64[ns]]
+    numpy.ndarray : timedelta64[ns] array converted from data
     bool : whether a copy was made
     """
     copy_made = False
@@ -538,15 +547,18 @@ def objects_to_td64ns(data, unit="ns", errors="raise"):
     ----------
     data : ndarray or Index
     unit : str, default "ns"
+        The timedelta unit to treat integers as multiples of.
     errors : {"raise", "coerce", "ignore"}, default "raise"
+        How to handle elements that cannot be converted to timedelta64[ns].
+        See ``pandas.to_timedelta`` for details.
 
     Returns
     -------
-    ndarray[timedelta64[ns]]
+    numpy.ndarray : timedelta64[ns] array converted from data
 
     Raises
     ------
-    ValueError : data cannot be converted to timedelta64[ns]
+    ValueError : Data cannot be converted to timedelta64[ns].
 
     Notes
     -----

pandas/core/indexes/datetimelike.py

@@ -4,44 +4,32 @@
 """
 import warnings
 
-from pandas import compat
-from pandas.compat.numpy import function as nv
-from pandas.core.tools.timedeltas import to_timedelta
-
 import numpy as np
 
-from pandas._libs import lib, iNaT, NaT
-from pandas._libs.tslibs.timestamps import round_nsint64, RoundTo
+from pandas._libs import NaT, iNaT, lib
+from pandas._libs.tslibs.timestamps import RoundTo, round_nsint64
+import pandas.compat as compat
+from pandas.compat.numpy import function as nv
+from pandas.util._decorators import Appender, cache_readonly
 
 from pandas.core.dtypes.common import (
-    ensure_int64,
-    is_dtype_equal,
-    is_float,
-    is_integer,
-    is_list_like,
-    is_scalar,
-    is_bool_dtype,
-    is_period_dtype,
-    is_categorical_dtype,
-    is_datetime_or_timedelta_dtype,
-    is_float_dtype,
-    is_integer_dtype,
-    is_object_dtype,
-    is_string_dtype)
-from pandas.core.dtypes.generic import (
-    ABCIndex, ABCSeries, ABCIndexClass)
+    ensure_int64, is_bool_dtype, is_categorical_dtype,
+    is_datetime_or_timedelta_dtype, is_dtype_equal, is_float, is_float_dtype,
+    is_integer, is_integer_dtype, is_list_like, is_object_dtype,
+    is_period_dtype, is_scalar, is_string_dtype)
+import pandas.core.dtypes.concat as _concat
+from pandas.core.dtypes.generic import ABCIndex, ABCIndexClass, ABCSeries
 from pandas.core.dtypes.missing import isna
-from pandas.core import common as com, algorithms, ops
-
-import pandas.io.formats.printing as printing
 
+from pandas.core import algorithms, common as com, ops
 from pandas.core.arrays import PeriodArray
 from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
+import pandas.core.indexes.base as ibase
 from pandas.core.indexes.base import Index, _index_shared_docs
-from pandas.util._decorators import Appender, cache_readonly
-import pandas.core.dtypes.concat as _concat
+from pandas.core.tools.timedeltas import to_timedelta
+
+import pandas.io.formats.printing as printing
 
-import pandas.core.indexes.base as ibase
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)