ENH: pandas read_* wildcard #15904

doc/source/cookbook.rst

@@ -910,9 +910,6 @@ The :ref:`CSV <io.read_csv_table>` docs
 `appending to a csv
 <http://stackoverflow.com/questions/17134942/pandas-dataframe-output-end-of-csv>`__
 
-`how to read in multiple files, appending to create a single dataframe
-<http://stackoverflow.com/questions/25210819/speeding-up-data-import-function-pandas-and-appending-to-dataframe/25210900#25210900>`__
-
 `Reading a csv chunk-by-chunk
 <http://stackoverflow.com/questions/11622652/large-persistent-dataframe-in-pandas/12193309#12193309>`__
 
@@ -943,6 +940,43 @@ using that handle to read.
 `Write a multi-row index CSV without writing duplicates
 <http://stackoverflow.com/questions/17349574/pandas-write-multiindex-rows-with-to-csv>`__
 
+.. _cookbook.csv.multiple_files:
+
+Reading multiple files to create a single DataFrame
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The best way to combine multiple files into a single DataFrame is to read the individual frames one by one, put all
+of the individual frames into a list, and then combine the frames in the list using :func:`pd.concat`:
+
+.. ipython:: python
+
+    for i in range(3):
+        data = pd.DataFrame(np.random.randn(10, 4))
+        data.to_csv('file_{}.csv'.format(i))
+
+    frames = []
+    files = ['file_0.csv', 'file_1.csv', 'file_2.csv']
+    result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)
+
+You can use the same approach to read all files matching a pattern.  Here is an example using ``glob``:
+
+.. ipython:: python
+
+    import glob
+    frames = []
+    files = glob.glob('file_*.csv')
+    result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)
+
+Finally, this strategy will work with the other ``read_*(...)`` functions described in the :ref:`io docs<io>`.
+
+.. ipython:: python
+    :supress:
+    for i in range(3):
+        os.remove('file_{}.csv'.format(i))
+
+Parsing date components in multi-columns
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 Parsing date components in multi-columns is faster with a format
 
 .. code-block:: python

doc/source/io.rst

@@ -1439,6 +1439,14 @@ class of the csv module. For this, you have to specify ``sep=None``.
     print(open('tmp2.sv').read())
     pd.read_csv('tmp2.sv', sep=None, engine='python')
 
+.. _io.multiple_files:
+
+Reading multiple files to create a single DataFrame
+'''''''''''''''''''''''''''''''''''''''''''''''''''
+
+It's best to use :func:`~pandas.concat` to combine multiple files.
+See the :ref:`cookbook<cookbook.csv.multiple_files>` for an example.
+
 .. _io.chunking:
 
 Iterating through files chunk by chunk