diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index 1d8701368558f..12140bcc66403 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -127,12 +127,18 @@ Splitting `Splitting a frame `__ +.. _cookbook.pivot: + Pivot ~~~~~ +The :ref:`Pivot ` docs. `Partial sums and subtotals `__ +`Frequency table like plyr in R +`__ + Timeseries ---------- diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 107200a82903a..c18cfbd01bbca 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -59,7 +59,7 @@ We aim to make operations like this natural and easy to express using pandas. We'll address each area of GroupBy functionality then provide some non-trivial examples / use cases. -See some :ref:`cookbook examples ` for some advanced strategies +See the :ref:`cookbook` for some advanced strategies .. _groupby.split: diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 272a2243125ec..3222176af2ee3 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -32,7 +32,7 @@ attention in this area. Expect more work to be invested higher-dimensional data structures (including Panel) in the future, especially in label-based advanced indexing. -See some :ref:`cookbook examples ` for some advanced strategies +See the :ref:`cookbook` for some advanced strategies Choice ------ @@ -920,7 +920,7 @@ described above and in prior sections. Later, when discussing :ref:`group by non-trivial applications to illustrate how it aids in structuring data for analysis. -See some :ref:`cookbook examples ` for some advanced strategies +See the :ref:`cookbook` for some advanced strategies .. note:: diff --git a/doc/source/io.rst b/doc/source/io.rst index a244b9c545d88..1bcaf047561a5 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -35,10 +35,10 @@ CSV & Text files The two workhorse functions for reading text files (a.k.a. flat files) are :func:`~pandas.io.parsers.read_csv` and :func:`~pandas.io.parsers.read_table`. They both use the same parsing code to intelligently convert tabular -data into a DataFrame object. They can take a number of arguments: +data into a DataFrame object. See the :ref:`cookbook` +for some advanced strategies -See some :ref:`cookbook examples ` for some advanced strategies -See some :ref:`cookbook examples ` for some advanced strategies +They can take a number of arguments: - ``filepath_or_buffer``: Either a string path to a file, or any object with a ``read`` method (such as an open file or ``StringIO``). @@ -917,9 +917,10 @@ Excel files The ``ExcelFile`` class can read an Excel 2003 file using the ``xlrd`` Python module and use the same parsing code as the above to convert tabular data into -a DataFrame. To use it, create the ``ExcelFile`` object: +a DataFrame. See the :ref:`cookbook` for some +advanced strategies -See some :ref:`cookbook examples ` for some advanced strategies +To use it, create the ``ExcelFile`` object: .. code-block:: python @@ -985,9 +986,8 @@ HDF5 (PyTables) ``HDFStore`` is a dict-like object which reads and writes pandas using the high performance HDF5 format using the excellent `PyTables -`__ library. - -See some :ref:`cookbook examples ` for some advanced strategies +`__ library. See the :ref:`cookbook` +for some advanced strategies .. ipython:: python :suppress: @@ -1696,9 +1696,8 @@ SQL Queries The :mod:`pandas.io.sql` module provides a collection of query wrappers to both facilitate data retrieval and to reduce dependency on DB-specific API. There wrappers only support the Python database adapters which respect the `Python -DB-API `_. - -See some :ref:`cookbook examples ` for some advanced strategies +DB-API `_. See some +:ref:`cookbook examples ` for some advanced strategies Suppose you want to query some data with different types from a table such as: diff --git a/doc/source/merging.rst b/doc/source/merging.rst index 1495e9218ac41..b719f0c24e3f9 100644 --- a/doc/source/merging.rst +++ b/doc/source/merging.rst @@ -304,7 +304,7 @@ better) than other open source implementations (like ``base::merge.data.frame`` in R). The reason for this is careful algorithmic design and internal layout of the data in DataFrame. -See some :ref:`cookbook examples ` for some advanced strategies +See the :ref:`cookbook` for some advanced strategies pandas provides a single function, ``merge``, as the entry point for all standard database join operations between DataFrame objects: diff --git a/doc/source/reshaping.rst b/doc/source/reshaping.rst index 7f799a8e8927f..9a7a9c2a87e52 100644 --- a/doc/source/reshaping.rst +++ b/doc/source/reshaping.rst @@ -239,7 +239,9 @@ Pivot tables and cross-tabulations .. _reshaping.pivot: The function ``pandas.pivot_table`` can be used to create spreadsheet-style pivot -tables. It takes a number of arguments +tables. See the :ref:`cookbook` for some advanced strategies + +It takes a number of arguments - ``data``: A DataFrame object - ``values``: a column or a list of columns to aggregate diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 97e7661cd1152..da1f092a1881f 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -33,7 +33,7 @@ We use the standard convention for referencing the matplotlib API: Basic plotting: ``plot`` ------------------------ -See some :ref:`cookbook examples ` for some advanced strategies +See the :ref:`cookbook` for some advanced strategies The ``plot`` method on Series and DataFrame is just a simple wrapper around ``plt.plot``: