Merge remote-tracking branch 'upstream/master' into remove-panel

ci/deps/azure-35-compat.yaml

@@ -11,7 +11,7 @@ dependencies:
   - openpyxl=2.4.8
   - pytables=3.4.2
   - python-dateutil=2.6.1
-  - python=3.5.*
+  - python=3.5.3
   - pytz=2017.2
   - scipy=0.19.0
   - xlrd=1.1.0

ci/deps/azure-37-locale.yaml

@@ -10,6 +10,7 @@ dependencies:
   - jinja2
   - lxml
   - matplotlib
+  - moto
   - nomkl
   - numexpr
   - numpy
@@ -32,4 +33,3 @@ dependencies:
   - pip
   - pip:
     - hypothesis>=3.58.0
-    - moto  # latest moto in conda-forge fails with 3.7, move to conda dependencies when this is fixed

ci/deps/azure-windows-37.yaml

@@ -10,6 +10,7 @@ dependencies:
   - jinja2
   - lxml
   - matplotlib=2.2.*
+  - moto
   - numexpr
   - numpy=1.14.*
   - openpyxl
@@ -29,6 +30,5 @@ dependencies:
   - pytest-xdist
   - pytest-mock
   - pytest-azurepipelines
-  - moto
   - hypothesis>=3.58.0
   - pyreadstat

ci/deps/travis-36-cov.yaml

@@ -12,6 +12,7 @@ dependencies:
   - geopandas
   - html5lib
   - matplotlib
+  - moto
   - nomkl
   - numexpr
   - numpy=1.15.*
@@ -46,6 +47,5 @@ dependencies:
   - pip:
     - brotlipy
     - coverage
-    - moto
     - pandas-datareader
     - python-dateutil

ci/deps/travis-36-locale.yaml

@@ -14,6 +14,7 @@ dependencies:
   - jinja2
   - lxml=3.8.0
   - matplotlib=3.0.*
+  - moto
   - nomkl
   - numexpr
   - numpy
@@ -36,7 +37,6 @@ dependencies:
   - pytest>=4.0.2
   - pytest-xdist
   - pytest-mock
-  - moto
   - pip
   - pip:
     - hypothesis>=3.58.0

doc/source/development/contributing.rst

@@ -178,7 +178,6 @@ We'll now kick off a three-step process:
    # Create and activate the build environment
    conda env create -f environment.yml
    conda activate pandas-dev
-   conda uninstall --force pandas
 
    # or with older versions of Anaconda:
    source activate pandas-dev

doc/source/getting_started/10min.rst

@@ -712,7 +712,6 @@ See the :ref:`Plotting <visualization>` docs.
    plt.close('all')
 
 .. ipython:: python
-   :okwarning:
 
    ts = pd.Series(np.random.randn(1000),
                   index=pd.date_range('1/1/2000', periods=1000))

doc/source/reference/frame.rst

@@ -198,7 +198,6 @@ Reindexing / selection / label manipulation
    DataFrame.idxmin
    DataFrame.last
    DataFrame.reindex
-   DataFrame.reindex_axis
    DataFrame.reindex_like
    DataFrame.rename
    DataFrame.rename_axis
@@ -337,7 +336,6 @@ Serialization / IO / conversion
 .. autosummary::
    :toctree: api/
 
-   DataFrame.from_csv
    DataFrame.from_dict
    DataFrame.from_items
    DataFrame.from_records

doc/source/user_guide/advanced.rst

@@ -965,21 +965,26 @@ If you select a label *contained* within an interval, this will also select the
    df.loc[2.5]
    df.loc[[2.5, 3.5]]
 
-``Interval`` and ``IntervalIndex`` are used by ``cut`` and ``qcut``:
+:func:`cut` and :func:`qcut` both return a ``Categorical`` object, and the bins they
+create are stored as an ``IntervalIndex`` in its ``.categories`` attribute.
 
 .. ipython:: python
 
    c = pd.cut(range(4), bins=2)
    c
    c.categories
 
-Furthermore, ``IntervalIndex`` allows one to bin *other* data with these same
-bins, with ``NaN`` representing a missing value similar to other dtypes.
+:func:`cut` also accepts an ``IntervalIndex`` for its ``bins`` argument, which enables
+a useful pandas idiom. First, We call :func:`cut` with some data and ``bins`` set to a
+fixed number, to generate the bins. Then, we pass the values of ``.categories`` as the
+``bins`` argument in subsequent calls to :func:`cut`, supplying new data which will be
+binned into the same bins.
 
 .. ipython:: python
 
    pd.cut([0, 3, 5, 1], bins=c.categories)
 
+Any value which falls outside all bins will be assigned a ``NaN`` value.
 
 Generating ranges of intervals
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -1108,6 +1113,8 @@ the :meth:`~Index.is_unique` attribute.
    weakly_monotonic.is_monotonic_increasing
    weakly_monotonic.is_monotonic_increasing & weakly_monotonic.is_unique
 
+.. _advanced.endpoints_are_inclusive:
+
 Endpoints are inclusive
 ~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -1137,7 +1144,7 @@ index can be somewhat complicated. For example, the following does not work:
     s.loc['c':'e' + 1]
 
 A very common use case is to limit a time series to start and end at two
-specific dates. To enable this, we made the design to make label-based
+specific dates. To enable this, we made the design choice to make label-based
 slicing include both endpoints:
 
 .. ipython:: python

doc/source/user_guide/indexing.rst

@@ -61,8 +61,8 @@ of multi-axis indexing.
     * A list or array of labels ``['a', 'b', 'c']``.
     * A slice object with labels ``'a':'f'`` (Note that contrary to usual python
       slices, **both** the start and the stop are included, when present in the
-      index! See :ref:`Slicing with labels
-      <indexing.slicing_with_labels>`.).
+      index! See :ref:`Slicing with labels <indexing.slicing_with_labels>`
+      and :ref:`Endpoints are inclusive <advanced.endpoints_are_inclusive>`.)
     * A boolean array
     * A ``callable`` function with one argument (the calling Series or DataFrame) and
       that returns valid output for indexing (one of the above).
@@ -335,8 +335,7 @@ The ``.loc`` attribute is the primary access method. The following are valid inp
 * A list or array of labels ``['a', 'b', 'c']``.
 * A slice object with labels ``'a':'f'`` (Note that contrary to usual python
   slices, **both** the start and the stop are included, when present in the
-  index! See :ref:`Slicing with labels
-  <indexing.slicing_with_labels>`.).
+  index! See :ref:`Slicing with labels <indexing.slicing_with_labels>`.
 * A boolean array.
 * A ``callable``, see :ref:`Selection By Callable <indexing.callable>`.
 
@@ -418,6 +417,9 @@ error will be raised (since doing otherwise would be computationally expensive,
 as well as potentially ambiguous for mixed type indexes). For instance, in the
 above example, ``s.loc[1:6]`` would raise ``KeyError``.
 
+For the rationale behind this behavior, see
+:ref:`Endpoints are inclusive <advanced.endpoints_are_inclusive>`.
+
 .. _indexing.integer:
 
 Selection by position

doc/source/user_guide/io.rst

@@ -340,13 +340,6 @@ dialect : str or :class:`python:csv.Dialect` instance, default ``None``
   `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
   override values, a ParserWarning will be issued. See :class:`python:csv.Dialect`
   documentation for more details.
-tupleize_cols : boolean, default ``False``
-    .. deprecated:: 0.21.0
-
-    This argument will be removed and will always convert to MultiIndex
-
-  Leave a list of tuples on columns as is (default is to convert to a MultiIndex
-  on the columns).
 
 Error handling
 ++++++++++++++
@@ -1718,8 +1711,6 @@ function takes a number of arguments. Only the first is required.
 * ``escapechar``: Character used to escape ``sep`` and ``quotechar`` when
   appropriate (default None)
 * ``chunksize``: Number of rows to write at a time
-* ``tupleize_cols``: If False (default), write as a list of tuples, otherwise
-  write in an expanded line format suitable for ``read_csv``
 * ``date_format``: Format string for datetime objects
 
 Writing a formatted string
@@ -3393,15 +3384,15 @@ both on the writing (serialization), and reading (deserialization).
 
 .. warning::
 
-   This is a very new feature of pandas. We intend to provide certain
-   optimizations in the io of the ``msgpack`` data. Since this is marked
-   as an EXPERIMENTAL LIBRARY, the storage format may not be stable until a future release.
+   The msgpack format is deprecated as of 0.25 and will be removed in a future version.
+   It is recommended to use pyarrow for on-the-wire transmission of pandas objects.
 
 .. warning::
 
    :func:`read_msgpack` is only guaranteed backwards compatible back to pandas version 0.20.3
 
 .. ipython:: python
+   :okwarning:
 
    df = pd.DataFrame(np.random.rand(5, 2), columns=list('AB'))
    df.to_msgpack('foo.msg')
@@ -3411,20 +3402,23 @@ both on the writing (serialization), and reading (deserialization).
 You can pass a list of objects and you will receive them back on deserialization.
 
 .. ipython:: python
+   :okwarning:
 
    pd.to_msgpack('foo.msg', df, 'foo', np.array([1, 2, 3]), s)
    pd.read_msgpack('foo.msg')
 
 You can pass ``iterator=True`` to iterate over the unpacked results:
 
 .. ipython:: python
+   :okwarning:
 
    for o in pd.read_msgpack('foo.msg', iterator=True):
        print(o)
 
 You can pass ``append=True`` to the writer to append to an existing pack:
 
 .. ipython:: python
+   :okwarning:
 
    df.to_msgpack('foo.msg', append=True)
    pd.read_msgpack('foo.msg')
@@ -3435,6 +3429,7 @@ can pack arbitrary collections of Python lists, dicts, scalars, while intermixin
 pandas objects.
 
 .. ipython:: python
+   :okwarning:
 
    pd.to_msgpack('foo2.msg', {'dict': [{'df': df}, {'string': 'foo'},
                                        {'scalar': 1.}, {'s': s}]})
@@ -3453,14 +3448,16 @@ Read/write API
 Msgpacks can also be read from and written to strings.
 
 .. ipython:: python
+   :okwarning:
 
    df.to_msgpack()
 
 Furthermore you can concatenate the strings to produce a list of the original objects.
 
 .. ipython:: python
+   :okwarning:
 
-  pd.read_msgpack(df.to_msgpack() + s.to_msgpack())
+   pd.read_msgpack(df.to_msgpack() + s.to_msgpack())
 
 .. _io.hdf5:
 

doc/source/user_guide/missing_data.rst

@@ -458,7 +458,6 @@ You can mix pandas' ``reindex`` and ``interpolate`` methods to interpolate
 at the new values.
 
 .. ipython:: python
-   :okexcept:
 
    ser = pd.Series(np.sort(np.random.uniform(size=100)))
 

doc/source/user_guide/timeseries.rst

@@ -474,16 +474,6 @@ resulting ``DatetimeIndex``:
 Custom frequency ranges
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-.. warning::
-
-   This functionality was originally exclusive to ``cdate_range``, which is
-   deprecated as of version 0.21.0 in favor of ``bdate_range``.  Note that
-   ``cdate_range`` only utilizes the ``weekmask`` and ``holidays`` parameters
-   when custom business day, 'C', is passed as the frequency string. Support has
-   been expanded with ``bdate_range`` to work with any custom frequency string.
-
-.. versionadded:: 0.21.0
-
 ``bdate_range`` can also generate a range of custom frequency dates by using
 the ``weekmask`` and ``holidays`` parameters.  These parameters will only be
 used if a custom frequency string is passed.

doc/source/whatsnew/v0.13.0.rst

@@ -829,6 +829,7 @@ Experimental
      Since this is an EXPERIMENTAL LIBRARY, the storage format may not be stable until a future release.
 
   .. ipython:: python
+     :okwarning:
 
      df = pd.DataFrame(np.random.rand(5, 2), columns=list('AB'))
      df.to_msgpack('foo.msg')
@@ -841,6 +842,7 @@ Experimental
   You can pass ``iterator=True`` to iterator over the unpacked results
 
   .. ipython:: python
+     :okwarning:
 
      for o in pd.read_msgpack('foo.msg', iterator=True):
          print(o)

doc/source/whatsnew/v0.24.0.rst

@@ -1298,7 +1298,7 @@ Deprecations
 - :meth:`Series.compress` is deprecated. Use ``Series[condition]`` instead (:issue:`18262`)
 - The signature of :meth:`Series.to_csv` has been uniformed to that of :meth:`DataFrame.to_csv`: the name of the first argument is now ``path_or_buf``, the order of subsequent arguments has changed, the ``header`` argument now defaults to ``True``. (:issue:`19715`)
 - :meth:`Categorical.from_codes` has deprecated providing float values for the ``codes`` argument. (:issue:`21767`)
-- :func:`pandas.read_table` is deprecated. Instead, use :func:`read_csv` passing ``sep='\t'`` if necessary (:issue:`21948`)
+- :func:`pandas.read_table` is deprecated. Instead, use :func:`read_csv` passing ``sep='\t'`` if necessary. This deprecation has been removed in 0.25.0. (:issue:`21948`)
 - :meth:`Series.str.cat` has deprecated using arbitrary list-likes *within* list-likes. A list-like container may still contain
   many ``Series``, ``Index`` or 1-dimensional ``np.ndarray``, or alternatively, only scalar values. (:issue:`21950`)
 - :meth:`FrozenNDArray.searchsorted` has deprecated the ``v`` parameter in favor of ``value`` (:issue:`14645`)