DOC: Split up the IO docs

doc/redirects.csv

@@ -24,7 +24,8 @@ gotchas,user_guide/gotchas
 groupby,user_guide/groupby
 indexing,user_guide/indexing
 integer_na,user_guide/integer_na
-io,user_guide/io
+io,user_guide/io/index
+user_guide/io,user_guide/io/index
 merging,user_guide/merging
 missing_data,user_guide/missing_data
 options,user_guide/options

doc/source/user_guide/index.rst

@@ -63,7 +63,7 @@ Guides
     10min
     dsintro
     basics
-    io
+    io/index
     pyarrow
     indexing
     advanced

doc/source/user_guide/io/clipboard.rst

@@ -0,0 +1,57 @@
+.. _io.clipboard:
+
+=========
+Clipboard
+=========
+
+A handy way to grab data is to use the :meth:`~DataFrame.read_clipboard` method,
+which takes the contents of the clipboard buffer and passes them to the
+``read_csv`` method. For instance, you can copy the following text to the
+clipboard (CTRL-C on many operating systems):
+
+.. code-block:: console
+
+     A B C
+   x 1 4 p
+   y 2 5 q
+   z 3 6 r
+
+And then import the data directly to a ``DataFrame`` by calling:
+
+.. code-block:: python
+
+    >>> clipdf = pd.read_clipboard()
+    >>> clipdf
+      A B C
+    x 1 4 p
+    y 2 5 q
+    z 3 6 r
+
+The ``to_clipboard`` method can be used to write the contents of a ``DataFrame`` to
+the clipboard. Following which you can paste the clipboard contents into other
+applications (CTRL-V on many operating systems). Here we illustrate writing a
+``DataFrame`` into clipboard and reading it back.
+
+.. code-block:: python
+
+    >>> df = pd.DataFrame(
+    ...     {"A": [1, 2, 3], "B": [4, 5, 6], "C": ["p", "q", "r"]}, index=["x", "y", "z"]
+    ... )
+
+    >>> df
+      A B C
+    x 1 4 p
+    y 2 5 q
+    z 3 6 r
+    >>> df.to_clipboard()
+    >>> pd.read_clipboard()
+      A B C
+    x 1 4 p
+    y 2 5 q
+    z 3 6 r
+
+We can see that we got the same content back, which we had earlier written to the clipboard.
+
+.. note::
+
+   You may need to install xclip or xsel (with PyQt5, PyQt4 or qtpy) on Linux to use these methods.

doc/source/user_guide/io/community_packages.rst

@@ -0,0 +1,27 @@
+.. _io.other:
+
+================================
+Community-supported file formats
+================================
+
+pandas itself only supports IO with a limited set of file formats that map
+cleanly to its tabular data model. For reading and writing other file formats
+into and from pandas, we recommend these packages from the broader community.
+
+.. _io.bigquery:
+
+Google BigQuery
+'''''''''''''''
+
+The pandas-gbq_ package provides functionality to read/write from Google BigQuery.
+
+.. _pandas-gbq: https://pandas-gbq.readthedocs.io/en/latest/
+
+netCDF
+''''''
+
+xarray_ provides data structures inspired by the pandas ``DataFrame`` for working
+with multi-dimensional datasets, with a focus on the netCDF file format and
+easy conversion to and from pandas.
+
+.. _xarray: https://xarray.pydata.org/en/stable/