DOC: Creating top-level user guide section, and moving pages inside (#24677)

Author: datapythonista

doc/source/index.rst.template

@@ -131,25 +131,7 @@ See the package overview for more detail about what's in the library.
     cookbook
     dsintro
     basics
-    text
-    options
-    indexing
-    advanced
-    computation
-    missing_data
-    groupby
-    merging
-    reshaping
-    timeseries
-    timedeltas
-    categorical
-    integer_na
-    visualization
-    style
-    io
-    enhancingperf
-    sparse
-    gotchas
+    user_guide/index
     r_interface
     ecosystem
     comparison_with_r

doc/source/advanced.rst renamed to doc/source/user_guide/advanced.rst

@@ -783,15 +783,15 @@ significant performance benefit.  Here is a plot showing the running time of
 computation. The two lines are two different engines.
 
 
-.. image:: _static/eval-perf.png
+.. image:: ../_static/eval-perf.png
 
 
 .. note::
 
    Operations with smallish objects (around 15k-20k rows) are faster using
    plain Python:
 
-       .. image:: _static/eval-perf-small.png
+       .. image:: ../_static/eval-perf-small.png
 
 
 This plot was created using a ``DataFrame`` with 3 columns each containing

doc/source/categorical.rst renamed to doc/source/user_guide/categorical.rst

@@ -0,0 +1,39 @@
+{{ header }}
+
+.. _user_guide:
+
+==========
+User Guide
+==========
+
+The User Guide covers all of pandas by topic area. Each of the subsections
+introduces a topic (such as "working with missing data"), and discusses how
+pandas approaches the problem, with many examples throughout.
+
+Users brand-new to pandas should start with :ref:`10min`.
+
+Further information on any specific method can be obtained in the
+:ref:`api`.
+
+.. toctree::
+    :maxdepth: 2
+
+    io
+    indexing
+    advanced
+    merging
+    reshaping
+    text
+    missing_data
+    categorical
+    integer_na
+    visualization
+    computation
+    groupby
+    timeseries
+    timedeltas
+    style
+    options
+    enhancingperf
+    sparse
+    gotchas

doc/source/computation.rst renamed to doc/source/user_guide/computation.rst

@@ -1392,15 +1392,15 @@ Performance of :meth:`~pandas.DataFrame.query`
 ``DataFrame.query()`` using ``numexpr`` is slightly faster than Python for
 large frames.
 
-.. image:: _static/query-perf.png
+.. image:: ../_static/query-perf.png
 
 .. note::
 
    You will only see the performance benefits of using the ``numexpr`` engine
    with ``DataFrame.query()`` if your frame has more than approximately 200,000
    rows.
 
-      .. image:: _static/query-perf-small.png
+      .. image:: ../_static/query-perf-small.png
 
 This plot was created using a ``DataFrame`` with 3 columns each containing
 floating point values generated using ``numpy.random.randn()``.