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

Author: WillAyd

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