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DOC: Enhancing pivot / reshape docs (pandas-dev#21038)
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@VincentLa @Pingviinituutti
VincentLa authored and Pingviinituutti committed Feb 28, 2019
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110 changes: 104 additions & 6 deletions doc/source/reshaping.rst
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Expand Up @@ -17,6 +17,8 @@ Reshaping and Pivot Tables
Reshaping by pivoting DataFrame objects
---------------------------------------

.. image:: _static/reshaping_pivot.png

.. ipython::
:suppress:

Expand All @@ -33,8 +35,7 @@ Reshaping by pivoting DataFrame objects

In [3]: df = unpivot(tm.makeTimeDataFrame())

Data is often stored in CSV files or databases in so-called "stacked" or
"record" format:
Data is often stored in so-called "stacked" or "record" format:

.. ipython:: python
Expand Down Expand Up @@ -66,8 +67,6 @@ To select out everything for variable ``A`` we could do:
df[df['variable'] == 'A']
.. image:: _static/reshaping_pivot.png

But suppose we wish to do time series operations with the variables. A better
representation would be where the ``columns`` are the unique variables and an
``index`` of dates identifies individual observations. To reshape the data into
Expand All @@ -87,7 +86,7 @@ column:
.. ipython:: python
df['value2'] = df['value'] * 2
pivoted = df.pivot('date', 'variable')
pivoted = df.pivot(index='date', columns='variable')
pivoted
You can then select subsets from the pivoted ``DataFrame``:
Expand All @@ -99,6 +98,12 @@ You can then select subsets from the pivoted ``DataFrame``:
Note that this returns a view on the underlying data in the case where the data
are homogeneously-typed.

.. note::
:func:`~pandas.pivot` will error with a ``ValueError: Index contains duplicate
entries, cannot reshape`` if the index/column pair is not unique. In this
case, consider using :func:`~pandas.pivot_table` which is a generalization
of pivot that can handle duplicate values for one index/column pair.

.. _reshaping.stacking:

Reshaping by stacking and unstacking
Expand Down Expand Up @@ -704,10 +709,103 @@ handling of NaN:
In [3]: np.unique(x, return_inverse=True)[::-1]
Out[3]: (array([3, 3, 0, 4, 1, 2]), array([nan, 3.14, inf, 'A', 'B'], dtype=object))
.. note::
If you just want to handle one column as a categorical variable (like R's factor),
you can use ``df["cat_col"] = pd.Categorical(df["col"])`` or
``df["cat_col"] = df["col"].astype("category")``. For full docs on :class:`~pandas.Categorical`,
see the :ref:`Categorical introduction <categorical>` and the
:ref:`API documentation <api.categorical>`.

Examples
--------

In this section, we will review frequently asked questions and examples. The
column names and relevant column values are named to correspond with how this
DataFrame will be pivoted in the answers below.

.. ipython:: python
np.random.seed([3, 1415])
n = 20
cols = np.array(['key', 'row', 'item', 'col'])
df = cols + pd.DataFrame((np.random.randint(5, size=(n, 4)) // [2, 1, 2, 1]).astype(str))
df.columns = cols
df = df.join(pd.DataFrame(np.random.rand(n, 2).round(2)).add_prefix('val'))
df
Pivoting with Single Aggregations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Suppose we wanted to pivot ``df`` such that the ``col`` values are columns,
``row`` values are the index, and the mean of ``val0`` are the values? In
particular, the resulting DataFrame should look like:

.. code-block:: ipython
col col0 col1 col2 col3 col4
row
row0 0.77 0.605 NaN 0.860 0.65
row2 0.13 NaN 0.395 0.500 0.25
row3 NaN 0.310 NaN 0.545 NaN
row4 NaN 0.100 0.395 0.760 0.24
This solution uses :func:`~pandas.pivot_table`. Also note that
``aggfunc='mean'`` is the default. It is included here to be explicit.

.. ipython:: python
df.pivot_table(
values='val0', index='row', columns='col', aggfunc='mean')
Note that we can also replace the missing values by using the ``fill_value``
parameter.

.. ipython:: python
df.pivot_table(
values='val0', index='row', columns='col', aggfunc='mean', fill_value=0)
Also note that we can pass in other aggregation functions as well. For example,
we can also pass in ``sum``.

.. ipython:: python
df.pivot_table(
values='val0', index='row', columns='col', aggfunc='sum', fill_value=0)
Another aggregation we can do is calculate the frequency in which the columns
and rows occur together a.k.a. "cross tabulation". To do this, we can pass
``size`` to the ``aggfunc`` parameter.

.. ipython:: python
df.pivot_table(index='row', columns='col', fill_value=0, aggfunc='size')
Pivoting with Multiple Aggregations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We can also perform multiple aggregations. For example, to perform both a
``sum`` and ``mean``, we can pass in a list to the ``aggfunc`` argument.

.. ipython:: python
df.pivot_table(
values='val0', index='row', columns='col', aggfunc=['mean', 'sum'])
Note to aggregate over multiple value columns, we can pass in a list to the
``values`` parameter.

.. ipython:: python
df.pivot_table(
values=['val0', 'val1'], index='row', columns='col', aggfunc=['mean'])
Note to subdivide over multiple columns we can pass in a list to the
``columns`` parameter.

.. ipython:: python
df.pivot_table(
values=['val0'], index='row', columns=['item', 'col'], aggfunc=['mean'])
72 changes: 47 additions & 25 deletions pandas/core/frame.py
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Expand Up @@ -5518,50 +5518,72 @@ def pivot(self, index=None, columns=None, values=None):
... "C": ["small", "large", "large", "small",
... "small", "large", "small", "small",
... "large"],
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7]})
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
A B C D
0 foo one small 1
1 foo one large 2
2 foo one large 2
3 foo two small 3
4 foo two small 3
5 bar one large 4
6 bar one small 5
7 bar two small 6
8 bar two large 7
A B C D E
0 foo one small 1 2
1 foo one large 2 4
2 foo one large 2 5
3 foo two small 3 5
4 foo two small 3 6
5 bar one large 4 6
6 bar one small 5 8
7 bar two small 6 9
8 bar two large 7 9
This first example aggregates values by taking the sum.
>>> table = pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0
bar one 4 5
two 7 6
foo one 4 1
two NaN 6
We can also fill missing values using the `fill_value` parameter.
>>> table = pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
... columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0
bar one 4 5
two 7 6
foo one 4 1
two 0 6
The next example aggregates by taking the mean across multiple columns.
>>> table = pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': np.mean})
>>> table
D E
mean mean
A C
bar large 5.500000 7.500000
small 5.500000 8.500000
foo large 2.000000 4.500000
small 2.333333 4.333333
We can also calculate multiple types of aggregations for any given
value column.
>>> table = pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': [min, max, np.mean]})
>>> table
D E
mean max median min
mean max mean min
A C
bar large 5.500000 16 14.5 13
small 5.500000 15 14.5 14
foo large 2.000000 10 9.5 9
small 2.333333 12 11.0 8
bar large 5.500000 9 7.500000 6
small 5.500000 9 8.500000 8
foo large 2.000000 5 4.500000 4
small 2.333333 6 4.333333 2
Returns
-------
Expand Down

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