DOC: Adding redirects to API moved pages

.gitignore

@@ -101,7 +101,7 @@ asv_bench/pandas/
 # Documentation generated files #
 #################################
 doc/source/generated
-doc/source/api/generated
+doc/source/reference/api
 doc/source/_static
 doc/source/vbench
 doc/source/vbench.rst

doc/make.py

@@ -53,7 +53,7 @@ def __init__(self, num_jobs=0, include_api=True, single_doc=None,
         if single_doc and single_doc.endswith('.rst'):
             self.single_doc_html = os.path.splitext(single_doc)[0] + '.html'
         elif single_doc:
-            self.single_doc_html = 'api/generated/pandas.{}.html'.format(
+            self.single_doc_html = 'reference/api/pandas.{}.html'.format(
                 single_doc)
 
     def _process_single_doc(self, single_doc):
@@ -63,7 +63,7 @@ def _process_single_doc(self, single_doc):
 
         For example, categorial.rst or pandas.DataFrame.head. For the latter,
         return the corresponding file path
-        (e.g. generated/pandas.DataFrame.head.rst).
+        (e.g. reference/api/pandas.DataFrame.head.rst).
         """
         base_name, extension = os.path.splitext(single_doc)
         if extension in ('.rst', '.ipynb'):
@@ -258,7 +258,7 @@ def clean():
         Clean documentation generated files.
         """
         shutil.rmtree(BUILD_PATH, ignore_errors=True)
-        shutil.rmtree(os.path.join(SOURCE_PATH, 'api', 'generated'),
+        shutil.rmtree(os.path.join(SOURCE_PATH, 'reference', 'api'),
                       ignore_errors=True)
 
     def zip_html(self):

doc/source/index.rst.template

@@ -113,7 +113,7 @@ See the package overview for more detail about what's in the library.
     {{ single_doc[:-4] }}
 {% elif single_doc %}
 .. autosummary::
-    :toctree: api/generated/
+    :toctree: reference/api/
 
     {{ single_doc }}
 {% else -%}
@@ -135,7 +135,7 @@ See the package overview for more detail about what's in the library.
     comparison_with_stata
     {% endif -%}
     {% if include_api -%}
-    api/index
+    reference/index
     {% endif -%}
     {% if not single_doc -%}
     development/index

doc/source/api/arrays.rst → doc/source/reference/arrays.rst

@@ -31,7 +31,7 @@ The top-level :meth:`array` method can be used to create a new array, which may
 stored in a :class:`Series`, :class:`Index`, or as a column in a :class:`DataFrame`.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    array
 
@@ -48,14 +48,14 @@ or timezone-aware values.
 scalar type for timezone-naive or timezone-aware datetime data.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Timestamp
 
 Properties
 ~~~~~~~~~~
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Timestamp.asm8
    Timestamp.day
@@ -91,7 +91,7 @@ Properties
 Methods
 ~~~~~~~
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Timestamp.astimezone
    Timestamp.ceil
@@ -142,7 +142,7 @@ is used.
 If the data are tz-aware, then every value in the array must have the same timezone.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    arrays.DatetimeArray
    DatetimeTZDtype
@@ -156,14 +156,14 @@ NumPy can natively represent timedeltas. Pandas provides :class:`Timedelta`
 for symmetry with :class:`Timestamp`.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Timedelta
 
 Properties
 ~~~~~~~~~~
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Timedelta.asm8
    Timedelta.components
@@ -183,7 +183,7 @@ Properties
 Methods
 ~~~~~~~
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Timedelta.ceil
    Timedelta.floor
@@ -196,7 +196,7 @@ Methods
 A collection of timedeltas may be stored in a :class:`TimedeltaArray`.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    arrays.TimedeltaArray
 
@@ -210,14 +210,14 @@ Pandas represents spans of times as :class:`Period` objects.
 Period
 ------
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Period
 
 Properties
 ~~~~~~~~~~
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Period.day
    Period.dayofweek
@@ -244,7 +244,7 @@ Properties
 Methods
 ~~~~~~~
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Period.asfreq
    Period.now
@@ -255,7 +255,7 @@ A collection of timedeltas may be stored in a :class:`arrays.PeriodArray`.
 Every period in a ``PeriodArray`` must have the same ``freq``.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    arrays.DatetimeArray
    PeriodDtype
@@ -268,14 +268,14 @@ Interval Data
 Arbitrary intervals can be represented as :class:`Interval` objects.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
     Interval
 
 Properties
 ~~~~~~~~~~
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Interval.closed
    Interval.closed_left
@@ -291,7 +291,7 @@ Properties
 A collection of intervals may be stored in an :class:`IntervalArray`.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    IntervalArray
    IntervalDtype
@@ -305,7 +305,7 @@ Nullable Integer
 Pandas provides this through :class:`arrays.IntegerArray`.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    arrays.IntegerArray
    Int8Dtype
@@ -327,21 +327,21 @@ limited, fixed set of values. The dtype of a ``Categorical`` can be described by
 a :class:`pandas.api.types.CategoricalDtype`.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
    :template: autosummary/class_without_autosummary.rst
 
    CategoricalDtype
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    CategoricalDtype.categories
    CategoricalDtype.ordered
 
 Categorical data can be stored in a :class:`pandas.Categorical`
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
    :template: autosummary/class_without_autosummary.rst
 
    Categorical
@@ -350,14 +350,14 @@ The alternative :meth:`Categorical.from_codes` constructor can be used when you
 have the categories and integer codes already:
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Categorical.from_codes
 
 The dtype information is available on the ``Categorical``
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Categorical.dtype
    Categorical.categories
@@ -368,7 +368,7 @@ The dtype information is available on the ``Categorical``
 the Categorical back to a NumPy array, so categories and order information is not preserved!
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    Categorical.__array__
 
@@ -391,7 +391,7 @@ Data where a single value is repeated many times (e.g. ``0`` or ``NaN``) may
 be stored efficiently as a :class:`SparseArray`.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    SparseArray
    SparseDtype

doc/source/api/extensions.rst → doc/source/reference/extensions.rst

@@ -11,7 +11,7 @@ These are primarily intended for library authors looking to extend pandas
 objects.
 
 .. autosummary::
-   :toctree: generated/
+   :toctree: api/
 
    api.extensions.register_extension_dtype
    api.extensions.register_dataframe_accessor