Merge branch 'master' into bugfix/zip

Author: minggli

ci/travis-36.yaml

@@ -18,12 +18,10 @@ dependencies:
   - numexpr
   - numpy
   - openpyxl
-  - pandas-datareader
   - psycopg2
   - pyarrow
   - pymysql
   - pytables
-  - python-dateutil
   - python-snappy
   - python=3.6*
   - pytz
@@ -45,3 +43,5 @@ dependencies:
   - pip:
     - brotlipy
     - coverage
+    - pandas-datareader
+    - python-dateutil

doc/source/advanced.rst

@@ -924,6 +924,55 @@ bins, with ``NaN`` representing a missing value similar to other dtypes.
 
    pd.cut([0, 3, 5, 1], bins=c.categories)
 
+
+Generating Ranges of Intervals
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If we need intervals on a regular frequency, we can use the :func:`interval_range` function
+to create an ``IntervalIndex`` using various combinations of ``start``, ``end``, and ``periods``.
+The default frequency for ``interval_range`` is a 1 for numeric intervals, and calendar day for
+datetime-like intervals:
+
+.. ipython:: python
+
+   pd.interval_range(start=0, end=5)
+
+   pd.interval_range(start=pd.Timestamp('2017-01-01'), periods=4)
+
+   pd.interval_range(end=pd.Timedelta('3 days'), periods=3)
+
+The ``freq`` parameter can used to specify non-default frequencies, and can utilize a variety
+of :ref:`frequency aliases <timeseries.offset_aliases>` with datetime-like intervals:
+
+.. ipython:: python
+
+   pd.interval_range(start=0, periods=5, freq=1.5)
+
+   pd.interval_range(start=pd.Timestamp('2017-01-01'), periods=4, freq='W')
+
+   pd.interval_range(start=pd.Timedelta('0 days'), periods=3, freq='9H')
+
+Additionally, the ``closed`` parameter can be used to specify which side(s) the intervals
+are closed on.  Intervals are closed on the right side by default.
+
+.. ipython:: python
+
+   pd.interval_range(start=0, end=4, closed='both')
+
+   pd.interval_range(start=0, end=4, closed='neither')
+
+.. versionadded:: 0.23.0
+
+Specifying ``start``, ``end``, and ``periods`` will generate a range of evenly spaced
+intervals from ``start`` to ``end`` inclusively, with ``periods`` number of elements
+in the resulting ``IntervalIndex``:
+
+.. ipython:: python
+
+   pd.interval_range(start=0, end=6, periods=4)
+
+   pd.interval_range(pd.Timestamp('2018-01-01'), pd.Timestamp('2018-02-28'), periods=3)
+
 Miscellaneous indexing FAQ
 --------------------------
 

doc/source/api.rst

@@ -1459,7 +1459,6 @@ Modifying and Computations
    Index.is_floating
    Index.is_integer
    Index.is_interval
-   Index.is_lexsorted_for_tuple
    Index.is_mixed
    Index.is_numeric
    Index.is_object
@@ -1471,11 +1470,19 @@ Modifying and Computations
    Index.where
    Index.take
    Index.putmask
-   Index.set_names
    Index.unique
    Index.nunique
    Index.value_counts
 
+Compatibility with MultiIndex
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: generated/
+
+   Index.set_names
+   Index.is_lexsorted_for_tuple
+   Index.droplevel
+
 Missing Values
 ~~~~~~~~~~~~~~
 .. autosummary::

doc/source/timedeltas.rst

@@ -352,8 +352,8 @@ You can convert a ``Timedelta`` to an `ISO 8601 Duration`_ string with the
 TimedeltaIndex
 --------------
 
-To generate an index with time delta, you can use either the ``TimedeltaIndex`` or
-the ``timedelta_range`` constructor.
+To generate an index with time delta, you can use either the :class:`TimedeltaIndex` or
+the :func:`timedelta_range` constructor.
 
 Using ``TimedeltaIndex`` you can pass string-like, ``Timedelta``, ``timedelta``,
 or ``np.timedelta64`` objects. Passing ``np.nan/pd.NaT/nat`` will represent missing values.
@@ -363,13 +363,47 @@ or ``np.timedelta64`` objects. Passing ``np.nan/pd.NaT/nat`` will represent miss
    pd.TimedeltaIndex(['1 days', '1 days, 00:00:05',
                      np.timedelta64(2,'D'), datetime.timedelta(days=2,seconds=2)])
 
-Similarly to ``date_range``, you can construct regular ranges of a ``TimedeltaIndex``:
+Generating Ranges of Time Deltas
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Similar to :func:`date_range`, you can construct regular ranges of a ``TimedeltaIndex``
+using :func:`timedelta_range`.  The default frequency for ``timedelta_range`` is 
+calendar day:
+
+.. ipython:: python
+
+   pd.timedelta_range(start='1 days', periods=5)
+
+Various combinations of ``start``, ``end``, and ``periods`` can be used with
+``timedelta_range``:
+
+.. ipython:: python
+
+   pd.timedelta_range(start='1 days', end='5 days')
+
+   pd.timedelta_range(end='10 days', periods=4)
+
+The ``freq`` parameter can passed a variety of :ref:`frequency aliases <timeseries.offset_aliases>`:
 
 .. ipython:: python
 
-   pd.timedelta_range(start='1 days', periods=5, freq='D')
    pd.timedelta_range(start='1 days', end='2 days', freq='30T')
 
+   pd.timedelta_range(start='1 days', periods=5, freq='2D5H')
+
+
+.. versionadded:: 0.23.0
+
+Specifying ``start``, ``end``, and ``periods`` will generate a range of evenly spaced
+timedeltas from ``start`` to ``end`` inclusively, with ``periods`` number of elements
+in the resulting ``TimedeltaIndex``:
+
+.. ipython:: python
+
+   pd.timedelta_range('0 days', '4 days', periods=5)
+
+   pd.timedelta_range('0 days', '4 days', periods=10)
+
 Using the TimedeltaIndex
 ~~~~~~~~~~~~~~~~~~~~~~~~
 

doc/source/timeseries.rst

@@ -393,6 +393,18 @@ of those specified will not be generated:
 
    pd.bdate_range(start=start, periods=20)
 
+.. versionadded:: 0.23.0
+
+Specifying ``start``, ``end``, and ``periods`` will generate a range of evenly spaced
+dates from ``start`` to ``end`` inclusively, with ``periods`` number of elements in the
+resulting ``DatetimeIndex``:
+
+.. ipython:: python
+
+   pd.date_range('2018-01-01', '2018-01-05', periods=5)
+
+   pd.date_range('2018-01-01', '2018-01-05', periods=10)
+
 .. _timeseries.custom-freq-ranges:
 
 Custom Frequency Ranges

doc/source/whatsnew/v0.23.1.txt

@@ -15,6 +15,8 @@ and bug fixes. We recommend that all users upgrade to this version.
 New features
 ~~~~~~~~~~~~
 
+- :meth:`Index.droplevel` is now implemented also for flat indexes, for compatibility with MultiIndex (:issue:`21115`)
+
 
 .. _whatsnew_0231.deprecations:
 
@@ -44,6 +46,8 @@ Documentation Changes
 Bug Fixes
 ~~~~~~~~~
 
+- tab completion on :class:`Index` in IPython no longer outputs deprecation warnings (:issue:`21125`)
+
 Groupby/Resample/Rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -53,7 +57,10 @@ Strings
 ^^^^^^^
 
 - Bug in :meth:`Series.str.replace()` where the method throws `TypeError` on Python 3.5.2 (:issue: `21078`)
--
+
+Timedelta
+^^^^^^^^^
+- Bug in :class:`Timedelta`: where passing a float with a unit would prematurely round the float precision (:issue: `14156`)
 
 Categorical
 ^^^^^^^^^^^
@@ -70,12 +77,14 @@ Indexing
 ^^^^^^^^
 
 - Bug in :meth:`Series.reset_index` where appropriate error was not raised with an invalid level name (:issue:`20925`)
+- Bug in :func:`interval_range` when ``start``/``periods`` or ``end``/``periods`` are specified with float ``start`` or ``end`` (:issue:`21161`)
 -
 
 I/O
 ^^^
 
 - Bug in :class:`pandas.io.common.BytesZipFile` where zip compression produces uncompressed zip archive (:issue:`17778`)
+- Bug in :meth:`DataFrame.to_stata` which prevented exporting DataFrames to buffers and most file-like objects (:issue:`21041`)
 -
 
 Plotting
@@ -87,10 +96,5 @@ Plotting
 Reshaping
 ^^^^^^^^^
 
--
--
-
-Categorical
-^^^^^^^^^^^
-
+- Bug in :func:`concat` where error was raised in concatenating :class:`Series` with numpy scalar and tuple names (:issue:`21015`)
 -

pandas/_libs/tslibs/timedeltas.pyx

@@ -202,22 +202,22 @@ cpdef inline int64_t cast_from_unit(object ts, object unit) except? -1:
 
     if unit == 'D' or unit == 'd':
         m = 1000000000L * 86400
-        p = 6
+        p = 9
     elif unit == 'h':
         m = 1000000000L * 3600
-        p = 6
+        p = 9
     elif unit == 'm':
         m = 1000000000L * 60
-        p = 6
+        p = 9
     elif unit == 's':
         m = 1000000000L
-        p = 6
+        p = 9
     elif unit == 'ms':
         m = 1000000L
-        p = 3
+        p = 6
     elif unit == 'us':
         m = 1000L
-        p = 0
+        p = 3
     elif unit == 'ns' or unit is None:
         m = 1L
         p = 0
@@ -231,10 +231,10 @@ cpdef inline int64_t cast_from_unit(object ts, object unit) except? -1:
     # cast the unit, multiply base/frace separately
     # to avoid precision issues from float -> int
     base = <int64_t> ts
-    frac = ts -base
+    frac = ts - base
     if p:
         frac = round(frac, p)
-    return <int64_t> (base *m) + <int64_t> (frac *m)
+    return <int64_t> (base * m) + <int64_t> (frac * m)
 
 
 cdef inline _decode_if_necessary(object ts):
@@ -760,7 +760,32 @@ cdef class _Timedelta(timedelta):
 
     @property
     def delta(self):
-        """ return out delta in ns (for internal compat) """
+        """
+        Return the timedelta in nanoseconds (ns), for internal compatibility.
+
+        Returns
+        -------
+        int
+            Timedelta in nanoseconds.
+
+        Examples
+        --------
+        >>> td = pd.Timedelta('1 days 42 ns')
+        >>> td.delta
+        86400000000042
+
+        >>> td = pd.Timedelta('3 s')
+        >>> td.delta
+        3000000000
+
+        >>> td = pd.Timedelta('3 ms 5 us')
+        >>> td.delta
+        3005000
+
+        >>> td = pd.Timedelta(42, unit='ns')
+        >>> td.delta
+        42
+        """
         return self.value
 
     @property
@@ -1221,7 +1246,7 @@ class Timedelta(_Timedelta):
                 deprecated. Use 'array // timedelta.value' instead.
                 If you want to obtain epochs from an array of timestamps,
                 you can rather use
-                'array - pd.Timestamp("1970-01-01")) // pd.Timedelta("1s")'.
+                '(array - pd.Timestamp("1970-01-01")) // pd.Timedelta("1s")'.
                 """)
                 warnings.warn(msg, FutureWarning)
                 return other // self.value

pandas/core/accessor.py

@@ -12,7 +12,8 @@
 
 class DirNamesMixin(object):
     _accessors = frozenset([])
-    _deprecations = frozenset(['asobject'])
+    _deprecations = frozenset(
+        ['asobject', 'base', 'data', 'flags', 'itemsize', 'strides'])
 
     def _dir_deletions(self):
         """ delete unwanted __dir__ for this object """

pandas/core/common.py

@@ -55,8 +55,11 @@ def flatten(l):
 def _consensus_name_attr(objs):
     name = objs[0].name
     for obj in objs[1:]:
-        if obj.name != name:
-            return None
+        try:
+            if obj.name != name:
+                name = None
+        except ValueError:
+            name = None
     return name
 
 

pandas/core/frame.py

@@ -1774,8 +1774,11 @@ def to_stata(self, fname, convert_dates=None, write_index=True,
 
         Parameters
         ----------
-        fname : str or buffer
-            String path of file-like object.
+        fname : path (string), buffer or path object
+            string, path object (pathlib.Path or py._path.local.LocalPath) or
+            object implementing a binary write() functions. If using a buffer
+            then the buffer will not be automatically closed after the file
+            data has been written.
         convert_dates : dict
             Dictionary mapping columns containing datetime types to stata
             internal format to use when writing the dates. Options are 'tc',
@@ -4096,9 +4099,8 @@ def _maybe_casted_values(index, labels=None):
             if not isinstance(level, (tuple, list)):
                 level = [level]
             level = [self.index._get_level_number(lev) for lev in level]
-            if isinstance(self.index, MultiIndex):
-                if len(level) < self.index.nlevels:
-                    new_index = self.index.droplevel(level)
+            if len(level) < self.index.nlevels:
+                new_index = self.index.droplevel(level)
 
         if not drop:
             if isinstance(self.index, MultiIndex):
@@ -4175,8 +4177,9 @@ def dropna(self, axis=0, how='any', thresh=None, subset=None,
             * 0, or 'index' : Drop rows which contain missing values.
             * 1, or 'columns' : Drop columns which contain missing value.
 
-            .. deprecated:: 0.23.0: Pass tuple or list to drop on multiple
-            axes.
+            .. deprecated:: 0.23.0
+                Pass tuple or list to drop on multiple axes.
+
         how : {'any', 'all'}, default 'any'
             Determine if row or column is removed from DataFrame, when we have
             at least one NA or all NA.