Merge branch 'master' into fix-skip-error

* master:
  DOC: Update link and description of the Spyder IDE in Ecosystem docs (pandas-dev#22136)
  BUG: resample with TimedeltaIndex, fenceposts are off (pandas-dev#22488)
  Use dispatch_to_series where possible (pandas-dev#22572)
  BLD: Fix openpyxl to 2.5.5 (pandas-dev#22601)

Author: rhysparry

ci/appveyor-27.yaml

@@ -13,7 +13,7 @@ dependencies:
   - matplotlib
   - numexpr
   - numpy=1.12*
-  - openpyxl
+  - openpyxl=2.5.5
   - pytables
   - python=2.7.*
   - pytz

ci/appveyor-36.yaml

@@ -10,7 +10,7 @@ dependencies:
   - matplotlib
   - numexpr
   - numpy=1.14*
-  - openpyxl
+  - openpyxl=2.5.5
   - pyarrow
   - pytables
   - python-dateutil

ci/circle-27-compat.yaml

@@ -8,7 +8,7 @@ dependencies:
   - jinja2=2.8
   - numexpr=2.4.4 # we test that we correctly don't use an unsupported numexpr
   - numpy=1.9.3
-  - openpyxl
+  - openpyxl=2.5.5
   - psycopg2
   - pytables=3.2.2
   - python-dateutil=2.5.0

ci/circle-36-locale.yaml

@@ -13,7 +13,7 @@ dependencies:
   - nomkl
   - numexpr
   - numpy
-  - openpyxl
+  - openpyxl=2.5.5
   - psycopg2
   - pymysql
   - pytables

ci/circle-36-locale_slow.yaml

@@ -14,7 +14,7 @@ dependencies:
   - nomkl
   - numexpr
   - numpy
-  - openpyxl
+  - openpyxl=2.5.5
   - psycopg2
   - pymysql
   - pytables

ci/requirements-optional-conda.txt

@@ -12,7 +12,7 @@ lxml
 matplotlib
 nbsphinx
 numexpr
-openpyxl
+openpyxl=2.5.5
 pyarrow
 pymysql
 pytables

ci/requirements-optional-pip.txt

@@ -14,7 +14,7 @@ lxml
 matplotlib
 nbsphinx
 numexpr
-openpyxl
+openpyxl=2.5.5
 pyarrow
 pymysql
 tables
@@ -28,4 +28,4 @@ statsmodels
 xarray
 xlrd
 xlsxwriter
-xlwt
+xlwt

ci/travis-35-osx.yaml

@@ -12,7 +12,7 @@ dependencies:
   - nomkl
   - numexpr
   - numpy=1.10.4
-  - openpyxl
+  - openpyxl=2.5.5
   - pytables
   - python=3.5*
   - pytz

ci/travis-36-doc.yaml

@@ -22,7 +22,7 @@ dependencies:
   - notebook
   - numexpr
   - numpy=1.13*
-  - openpyxl
+  - openpyxl=2.5.5
   - pandoc
   - pyqt
   - pytables

ci/travis-36-slow.yaml

@@ -10,7 +10,7 @@ dependencies:
   - matplotlib
   - numexpr
   - numpy
-  - openpyxl
+  - openpyxl=2.5.5
   - patsy
   - psycopg2
   - pymysql

ci/travis-36.yaml

@@ -18,7 +18,7 @@ dependencies:
   - nomkl
   - numexpr
   - numpy
-  - openpyxl
+  - openpyxl=2.5.5
   - psycopg2
   - pyarrow
   - pymysql

doc/source/ecosystem.rst

@@ -14,7 +14,7 @@ development to remain focused around it's original requirements.
 
 This is an inexhaustive list of projects that build on pandas in order to provide
 tools in the PyData space. For a list of projects that depend on pandas,
-see the 
+see the
 `libraries.io usage page for pandas <https://libraries.io/pypi/pandas/usage>`_
 or `search pypi for pandas <https://pypi.org/search/?q=pandas>`_.
 
@@ -44,7 +44,7 @@ ML pipeline.
 `Featuretools <https://github.com/featuretools/featuretools/>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Featuretools is a Python library for automated feature engineering built on top of pandas. It excels at transforming temporal and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives". Users can contribute their own primitives in Python and share them with the rest of the community. 
+Featuretools is a Python library for automated feature engineering built on top of pandas. It excels at transforming temporal and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives". Users can contribute their own primitives in Python and share them with the rest of the community.
 
 .. _ecosystem.visualization:
 
@@ -149,13 +149,30 @@ for pandas ``display.`` settings.
 qgrid is "an interactive grid for sorting and filtering
 DataFrames in IPython Notebook" built with SlickGrid.
 
-`Spyder <https://github.com/spyder-ide/spyder/>`__
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+`Spyder <https://www.spyder-ide.org/>`__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Spyder is a cross-platform PyQt-based IDE combining the editing, analysis,
+debugging and profiling functionality of a software development tool with the
+data exploration, interactive execution, deep inspection and rich visualization
+capabilities of a scientific environment like MATLAB or Rstudio.
+
+Its `Variable Explorer <https://docs.spyder-ide.org/variableexplorer.html>`__
+allows users to view, manipulate and edit pandas ``Index``, ``Series``,
+and ``DataFrame`` objects like a "spreadsheet", including copying and modifying
+values, sorting, displaying a "heatmap", converting data types and more.
+Pandas objects can also be renamed, duplicated, new columns added,
+copyed/pasted to/from the clipboard (as TSV), and saved/loaded to/from a file.
+Spyder can also import data from a variety of plain text and binary files
+or the clipboard into a new pandas DataFrame via a sophisticated import wizard.
+
+Most pandas classes, methods and data attributes can be autocompleted in
+Spyder's `Editor <https://docs.spyder-ide.org/editor.html>`__ and
+`IPython Console <https://docs.spyder-ide.org/ipythonconsole.html>`__,
+and Spyder's `Help pane<https://docs.spyder-ide.org/help.html>`__ can retrieve
+and render Numpydoc documentation on pandas objects in rich text with Sphinx
+both automatically and on-demand.
 
-Spyder is a cross-platform Qt-based open-source Python IDE with
-editing, testing, debugging, and introspection features.
-Spyder can now introspect and display Pandas DataFrames and show
-both "column wise min/max and global min/max coloring."
 
 .. _ecosystem.api:
 
@@ -205,12 +222,12 @@ This package requires valid credentials for this API (non free).
 pandaSDMX is a library to retrieve and acquire statistical data
 and metadata disseminated in
 `SDMX <http://www.sdmx.org>`_ 2.1, an ISO-standard
-widely used by institutions such as statistics offices, central banks,   
-and international organisations. pandaSDMX can expose datasets and related 
+widely used by institutions such as statistics offices, central banks,
+and international organisations. pandaSDMX can expose datasets and related
 structural metadata including data flows, code-lists,
 and data structure definitions as pandas Series
 or MultiIndexed DataFrames.
-   
+
 `fredapi <https://github.com/mortada/fredapi>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 fredapi is a Python interface to the `Federal Reserve Economic Data (FRED) <http://research.stlouisfed.org/fred2/>`__

doc/source/whatsnew/v0.24.0.txt

@@ -709,6 +709,7 @@ Groupby/Resample/Rolling
   datetime-like index leading to incorrect results and also segfault. (:issue:`21704`)
 - Bug in :meth:`Resampler.apply` when passing postiional arguments to applied func (:issue:`14615`).
 - Bug in :meth:`Series.resample` when passing ``numpy.timedelta64`` to `loffset` kwarg (:issue:`7687`).
+- Bug in :meth:`Resampler.asfreq` when frequency of ``TimedeltaIndex`` is a subperiod of a new frequency (:issue:`13022`).
 
 Sparse
 ^^^^^^

pandas/core/frame.py

@@ -4832,13 +4832,14 @@ def _arith_op(left, right):
             return ops.dispatch_to_series(this, other, _arith_op)
         else:
             result = _arith_op(this.values, other.values)
-
-        return self._constructor(result, index=new_index, columns=new_columns,
-                                 copy=False)
+            return self._constructor(result,
+                                     index=new_index, columns=new_columns,
+                                     copy=False)
 
     def _combine_match_index(self, other, func, level=None):
         left, right = self.align(other, join='outer', axis=0, level=level,
                                  copy=False)
+        assert left.index.equals(right.index)
         new_data = func(left.values.T, right.values).T
         return self._constructor(new_data,
                                  index=left.index, columns=self.columns,
@@ -4847,6 +4848,7 @@ def _combine_match_index(self, other, func, level=None):
     def _combine_match_columns(self, other, func, level=None, try_cast=True):
         left, right = self.align(other, join='outer', axis=1, level=level,
                                  copy=False)
+        assert left.columns.equals(right.index)
 
         new_data = left._data.eval(func=func, other=right,
                                    axes=[left.columns, self.index],
@@ -4855,12 +4857,7 @@ def _combine_match_columns(self, other, func, level=None, try_cast=True):
 
     def _combine_const(self, other, func, errors='raise', try_cast=True):
         if lib.is_scalar(other) or np.ndim(other) == 0:
-            new_data = {i: func(self.iloc[:, i], other)
-                        for i, col in enumerate(self.columns)}
-
-            result = self._constructor(new_data, index=self.index, copy=False)
-            result.columns = self.columns
-            return result
+            return ops.dispatch_to_series(self, other, func)
 
         new_data = self._data.eval(func=func, other=other,
                                    errors=errors,

pandas/core/ops.py

@@ -1638,6 +1638,7 @@ def dispatch_to_series(left, right, func):
     """
     # Note: we use iloc to access columns for compat with cases
     #       with non-unique columns.
+    right = lib.item_from_zerodim(right)
     if lib.is_scalar(right):
         new_data = {i: func(left.iloc[:, i], right)
                     for i in range(len(left.columns))}

pandas/core/resample.py

@@ -963,7 +963,10 @@ def _downsample(self, how, **kwargs):
         return self._wrap_result(result)
 
     def _adjust_binner_for_upsample(self, binner):
-        """ adjust our binner when upsampling """
+        """
+        Adjust our binner when upsampling.
+        The range of a new index should not be outside specified range
+        """
         if self.closed == 'right':
             binner = binner[1:]
         else:
@@ -1156,17 +1159,11 @@ def _get_binner_for_time(self):
         return self.groupby._get_time_delta_bins(self.ax)
 
     def _adjust_binner_for_upsample(self, binner):
-        """ adjust our binner when upsampling """
-        ax = self.ax
-
-        if is_subperiod(ax.freq, self.freq):
-            # We are actually downsampling
-            # but are in the asfreq path
-            # GH 12926
-            if self.closed == 'right':
-                binner = binner[1:]
-            else:
-                binner = binner[:-1]
+        """
+        Adjust our binner when upsampling.
+        The range of a new index is allowed to be greater than original range
+        so we don't need to change the length of a binner, GH 13022
+        """
         return binner
 
 

pandas/tests/test_resample.py

@@ -26,7 +26,6 @@
 from pandas.compat import range, lrange, zip, OrderedDict
 from pandas.errors import UnsupportedFunctionCall
 import pandas.tseries.offsets as offsets
-from pandas.tseries.frequencies import to_offset
 from pandas.tseries.offsets import Minute, BDay
 
 from pandas.core.groupby.groupby import DataError
@@ -626,12 +625,7 @@ def test_asfreq(self, series_and_frame, freq):
         obj = series_and_frame
 
         result = obj.resample(freq).asfreq()
-        if freq == '2D':
-            new_index = obj.index.take(np.arange(0, len(obj.index), 2))
-            new_index.freq = to_offset('2D')
-        else:
-            new_index = self.create_index(obj.index[0], obj.index[-1],
-                                          freq=freq)
+        new_index = self.create_index(obj.index[0], obj.index[-1], freq=freq)
         expected = obj.reindex(new_index)
         assert_almost_equal(result, expected)
 
@@ -2932,6 +2926,17 @@ def test_resample_with_nat(self):
                                                    freq='1S'))
         assert_frame_equal(result, expected)
 
+    def test_resample_as_freq_with_subperiod(self):
+        # GH 13022
+        index = timedelta_range('00:00:00', '00:10:00', freq='5T')
+        df = DataFrame(data={'value': [1, 5, 10]}, index=index)
+        result = df.resample('2T').asfreq()
+        expected_data = {'value': [1, np.nan, np.nan, np.nan, np.nan, 10]}
+        expected = DataFrame(data=expected_data,
+                             index=timedelta_range('00:00:00',
+                                                   '00:10:00', freq='2T'))
+        tm.assert_frame_equal(result, expected)
+
 
 class TestResamplerGrouper(object):