.. _groupby:
{{ header }}
*****************************
Group by: split-apply-combine
*****************************
By "group by" we are referring to a process involving one or more of the following
steps:
* **Splitting** the data into groups based on some criteria.
* **Applying** a function to each group independently.
* **Combining** the results into a data structure.
Out of these, the split step is the most straightforward. In the apply step, we
might wish to do one of the following:
* **Aggregation**: compute a summary statistic (or statistics) for each
group. Some examples:
* Compute group sums or means.
* Compute group sizes / counts.
* **Transformation**: perform some group-specific computations and return a
like-indexed object. Some examples:
* Standardize data (zscore) within a group.
* Filling NAs within groups with a value derived from each group.
* **Filtration**: discard some groups, according to a group-wise computation
that evaluates to True or False. Some examples:
* Discard data that belong to groups with only a few members.
* Filter out data based on the group sum or mean.
Many of these operations are defined on GroupBy objects. These operations are similar
to those of the :ref:`aggregating API <basics.aggregate>`,
:ref:`window API <window.overview>`, and :ref:`resample API <timeseries.aggregate>`.
It is possible that a given operation does not fall into one of these categories or
is some combination of them. In such a case, it may be possible to compute the
operation using GroupBy's ``apply`` method. This method will examine the results of the
apply step and try to sensibly combine them into a single result if it doesn't fit into either
of the above three categories.
.. note::
An operation that is split into multiple steps using built-in GroupBy operations
will be more efficient than using the ``apply`` method with a user-defined Python
function.
The name GroupBy should be quite familiar to those who have used
a SQL-based tool (or ``itertools``), in which you can write code like:
.. code-block:: sql
SELECT Column1, Column2, mean(Column3), sum(Column4)
FROM SomeTable
GROUP BY Column1, Column2
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 the :ref:`cookbook<cookbook.grouping>` for some advanced strategies.
.. _groupby.split:
Splitting an object into groups
-------------------------------
The abstract definition of grouping is to provide a mapping of labels to
group names. To create a GroupBy object (more on what the GroupBy object is
later), you may do the following:
.. ipython:: python
speeds = pd.DataFrame(
[
("bird", "Falconiformes", 389.0),
("bird", "Psittaciformes", 24.0),
("mammal", "Carnivora", 80.2),
("mammal", "Primates", np.nan),
("mammal", "Carnivora", 58),
],
index=["falcon", "parrot", "lion", "monkey", "leopard"],
columns=("class", "order", "max_speed"),
)
speeds
grouped = speeds.groupby("class")
grouped = speeds.groupby(["class", "order"])
The mapping can be specified many different ways:
* A Python function, to be called on each of the index labels.
* A list or NumPy array of the same length as the index.
* A dict or ``Series``, providing a ``label -> group name`` mapping.
* For ``DataFrame`` objects, a string indicating either a column name or
an index level name to be used to group.
* A list of any of the above things.
Collectively we refer to the grouping objects as the **keys**. For example,
consider the following ``DataFrame``:
.. note::
A string passed to ``groupby`` may refer to either a column or an index level.
If a string matches both a column name and an index level name, a
``ValueError`` will be raised.
.. ipython:: python
df = pd.DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.randn(8),
"D": np.random.randn(8),
}
)
df
On a DataFrame, we obtain a GroupBy object by calling :meth:`~DataFrame.groupby`.
This method returns a ``pandas.api.typing.DataFrameGroupBy`` instance.
We could naturally group by either the ``A`` or ``B`` columns, or both:
.. ipython:: python
grouped = df.groupby("A")
grouped = df.groupby("B")
grouped = df.groupby(["A", "B"])
.. note::
``df.groupby('A')`` is just syntactic sugar for ``df.groupby(df['A'])``.
If we also have a MultiIndex on columns ``A`` and ``B``, we can group by all
the columns except the one we specify:
.. ipython:: python
df2 = df.set_index(["A", "B"])
grouped = df2.groupby(level=df2.index.names.difference(["B"]))
grouped.sum()
The above GroupBy will split the DataFrame on its index (rows). To split by columns, first do
a transpose:
.. ipython::
In [4]: def get_letter_type(letter):
...: if letter.lower() in 'aeiou':
...: return 'vowel'
...: else:
...: return 'consonant'
...:
In [5]: grouped = df.T.groupby(get_letter_type)
pandas :class:`~pandas.Index` objects support duplicate values. If a
non-unique index is used as the group key in a groupby operation, all values
for the same index value will be considered to be in one group and thus the
output of aggregation functions will only contain unique index values:
.. ipython:: python
index = [1, 2, 3, 1, 2, 3]
s = pd.Series([1, 2, 3, 10, 20, 30], index=index)
s
grouped = s.groupby(level=0)
grouped.first()
grouped.last()
grouped.sum()
Note that **no splitting occurs** until it's needed. Creating the GroupBy object
only verifies that you've passed a valid mapping.
.. note::
Many kinds of complicated data manipulations can be expressed in terms of
GroupBy operations (though it can't be guaranteed to be the most efficient implementation).
You can get quite creative with the label mapping functions.
.. _groupby.sorting:
GroupBy sorting
~~~~~~~~~~~~~~~~~~~~~~~~~
By default the group keys are sorted during the ``groupby`` operation. You may however pass ``sort=False`` for potential speedups. With ``sort=False`` the order among group-keys follows the order of appearance of the keys in the original dataframe:
.. ipython:: python
df2 = pd.DataFrame({"X": ["B", "B", "A", "A"], "Y": [1, 2, 3, 4]})
df2.groupby(["X"]).sum()
df2.groupby(["X"], sort=False).sum()
Note that ``groupby`` will preserve the order in which *observations* are sorted *within* each group.
For example, the groups created by ``groupby()`` below are in the order they appeared in the original ``DataFrame``:
.. ipython:: python
df3 = pd.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]})
df3.groupby("X").get_group("A")
df3.groupby(["X"]).get_group(("B",))
.. _groupby.dropna:
GroupBy dropna
^^^^^^^^^^^^^^
By default ``NA`` values are excluded from group keys during the ``groupby`` operation. However,
in case you want to include ``NA`` values in group keys, you could pass ``dropna=False`` to achieve it.
.. ipython:: python
df_list = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
df_dropna = pd.DataFrame(df_list, columns=["a", "b", "c"])
df_dropna
.. ipython:: python
# Default ``dropna`` is set to True, which will exclude NaNs in keys
df_dropna.groupby(by=["b"], dropna=True).sum()
# In order to allow NaN in keys, set ``dropna`` to False
df_dropna.groupby(by=["b"], dropna=False).sum()
The default setting of ``dropna`` argument is ``True`` which means ``NA`` are not included in group keys.
.. _groupby.attributes:
GroupBy object attributes
~~~~~~~~~~~~~~~~~~~~~~~~~
The ``groups`` attribute is a dictionary whose keys are the computed unique groups
and corresponding values are the axis labels belonging to each group. In the
above example we have:
.. ipython:: python
df.groupby("A").groups
df.T.groupby(get_letter_type).groups
Calling the standard Python ``len`` function on the GroupBy object returns
the number of groups, which is the same as the length of the ``groups`` dictionary:
.. ipython:: python
grouped = df.groupby(["A", "B"])
grouped.groups
len(grouped)
.. _groupby.tabcompletion:
``GroupBy`` will tab complete column names, GroupBy operations, and other attributes:
.. ipython:: python
n = 10
weight = np.random.normal(166, 20, size=n)
height = np.random.normal(60, 10, size=n)
time = pd.date_range("1/1/2000", periods=n)
gender = np.random.choice(["male", "female"], size=n)
df = pd.DataFrame(
{"height": height, "weight": weight, "gender": gender}, index=time
)
df
gb = df.groupby("gender")
.. ipython::
@verbatim
In [1]: gb.<TAB> # noqa: E225, E999
gb.agg gb.boxplot gb.cummin gb.describe gb.filter gb.get_group gb.height gb.last gb.median gb.ngroups gb.plot gb.rank gb.std gb.transform
gb.aggregate gb.count gb.cumprod gb.dtype gb.first gb.groups gb.hist gb.max gb.min gb.nth gb.prod gb.resample gb.sum gb.var
gb.apply gb.cummax gb.cumsum gb.fillna gb.gender gb.head gb.indices gb.mean gb.name gb.ohlc gb.quantile gb.size gb.tail gb.weight
.. _groupby.multiindex:
GroupBy with MultiIndex
~~~~~~~~~~~~~~~~~~~~~~~
With :ref:`hierarchically-indexed data <advanced.hierarchical>`, it's quite
natural to group by one of the levels of the hierarchy.
Let's create a Series with a two-level ``MultiIndex``.
.. ipython:: python
arrays = [
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"])
s = pd.Series(np.random.randn(8), index=index)
s
We can then group by one of the levels in ``s``.
.. ipython:: python
grouped = s.groupby(level=0)
grouped.sum()
If the MultiIndex has names specified, these can be passed instead of the level
number:
.. ipython:: python
s.groupby(level="second").sum()
Grouping with multiple levels is supported.
.. ipython:: python
arrays = [
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
["doo", "doo", "bee", "bee", "bop", "bop", "bop", "bop"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
index = pd.MultiIndex.from_arrays(arrays, names=["first", "second", "third"])
s = pd.Series(np.random.randn(8), index=index)
s
s.groupby(level=["first", "second"]).sum()
Index level names may be supplied as keys.
.. ipython:: python
s.groupby(["first", "second"]).sum()
More on the ``sum`` function and aggregation later.
Grouping DataFrame with Index levels and columns
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A DataFrame may be grouped by a combination of columns and index levels. You
can specify both column and index names, or use a :class:`Grouper`.
Let's first create a DataFrame with a MultiIndex:
.. ipython:: python
arrays = [
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"])
df = pd.DataFrame({"A": [1, 1, 1, 1, 2, 2, 3, 3], "B": np.arange(8)}, index=index)
df
Then we group ``df`` by the ``second`` index level and the ``A`` column.
.. ipython:: python
df.groupby([pd.Grouper(level=1), "A"]).sum()
Index levels may also be specified by name.
.. ipython:: python
df.groupby([pd.Grouper(level="second"), "A"]).sum()
Index level names may be specified as keys directly to ``groupby``.
.. ipython:: python
df.groupby(["second", "A"]).sum()
DataFrame column selection in GroupBy
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Once you have created the GroupBy object from a DataFrame, you might want to do
something different for each of the columns. Thus, by using ``[]`` on the GroupBy
object in a similar way as the one used to get a column from a DataFrame, you can do:
.. ipython:: python
df = pd.DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.randn(8),
"D": np.random.randn(8),
}
)
df
grouped = df.groupby(["A"])
grouped_C = grouped["C"]
grouped_D = grouped["D"]
This is mainly syntactic sugar for the alternative, which is much more verbose:
.. ipython:: python
df["C"].groupby(df["A"])
Additionally, this method avoids recomputing the internal grouping information
derived from the passed key.
You can also include the grouping columns if you want to operate on them.
.. ipython:: python
grouped[["A", "B"]].sum()
.. _groupby.iterating-label:
Iterating through groups
------------------------
With the GroupBy object in hand, iterating through the grouped data is very
natural and functions similarly to :py:func:`itertools.groupby`:
.. ipython::
In [4]: grouped = df.groupby('A')
In [5]: for name, group in grouped:
...: print(name)
...: print(group)
...:
In the case of grouping by multiple keys, the group name will be a tuple:
.. ipython::
In [5]: for name, group in df.groupby(['A', 'B']):
...: print(name)
...: print(group)
...:
See :ref:`timeseries.iterating-label`.
Selecting a group
-----------------
A single group can be selected using
:meth:`.DataFrameGroupBy.get_group`:
.. ipython:: python
grouped.get_group("bar")
Or for an object grouped on multiple columns:
.. ipython:: python
df.groupby(["A", "B"]).get_group(("bar", "one"))
.. _groupby.aggregate:
Aggregation
-----------
An aggregation is a GroupBy operation that reduces the dimension of the grouping
object. The result of an aggregation is, or at least is treated as,
a scalar value for each column in a group. For example, producing the sum of each
column in a group of values.
.. ipython:: python
animals = pd.DataFrame(
{
"kind": ["cat", "dog", "cat", "dog"],
"height": [9.1, 6.0, 9.5, 34.0],
"weight": [7.9, 7.5, 9.9, 198.0],
}
)
animals
animals.groupby("kind").sum()
In the result, the keys of the groups appear in the index by default. They can be
instead included in the columns by passing ``as_index=False``.
.. ipython:: python
animals.groupby("kind", as_index=False).sum()
.. _groupby.aggregate.builtin:
Built-in aggregation methods
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Many common aggregations are built-in to GroupBy objects as methods. Of the methods
listed below, those with a ``*`` do *not* have an efficient, GroupBy-specific, implementation.
.. csv-table::
:header: "Method", "Description"
:widths: 20, 80
:meth:`~.DataFrameGroupBy.any`,Compute whether any of the values in the groups are truthy
:meth:`~.DataFrameGroupBy.all`,Compute whether all of the values in the groups are truthy
:meth:`~.DataFrameGroupBy.count`,Compute the number of non-NA values in the groups
:meth:`~.DataFrameGroupBy.cov` * ,Compute the covariance of the groups
:meth:`~.DataFrameGroupBy.first`,Compute the first occurring value in each group
:meth:`~.DataFrameGroupBy.idxmax`,Compute the index of the maximum value in each group
:meth:`~.DataFrameGroupBy.idxmin`,Compute the index of the minimum value in each group
:meth:`~.DataFrameGroupBy.last`,Compute the last occurring value in each group
:meth:`~.DataFrameGroupBy.max`,Compute the maximum value in each group
:meth:`~.DataFrameGroupBy.mean`,Compute the mean of each group
:meth:`~.DataFrameGroupBy.median`,Compute the median of each group
:meth:`~.DataFrameGroupBy.min`,Compute the minimum value in each group
:meth:`~.DataFrameGroupBy.nunique`,Compute the number of unique values in each group
:meth:`~.DataFrameGroupBy.prod`,Compute the product of the values in each group
:meth:`~.DataFrameGroupBy.quantile`,Compute a given quantile of the values in each group
:meth:`~.DataFrameGroupBy.sem`,Compute the standard error of the mean of the values in each group
:meth:`~.DataFrameGroupBy.size`,Compute the number of values in each group
:meth:`~.DataFrameGroupBy.skew` * ,Compute the skew of the values in each group
:meth:`~.DataFrameGroupBy.std`,Compute the standard deviation of the values in each group
:meth:`~.DataFrameGroupBy.sum`,Compute the sum of the values in each group
:meth:`~.DataFrameGroupBy.var`,Compute the variance of the values in each group
Some examples:
.. ipython:: python
df.groupby("A")[["C", "D"]].max()
df.groupby(["A", "B"]).mean()
Another aggregation example is to compute the size of each group.
This is included in GroupBy as the ``size`` method. It returns a Series whose
index consists of the group names and the values are the sizes of each group.
.. ipython:: python
grouped = df.groupby(["A", "B"])
grouped.size()
While the :meth:`.DataFrameGroupBy.describe` method is not itself a reducer, it
can be used to conveniently produce a collection of summary statistics about each of
the groups.
.. ipython:: python
grouped.describe()
Another aggregation example is to compute the number of unique values of each group.
This is similar to the :meth:`.DataFrameGroupBy.value_counts` function, except that it only counts the
number of unique values.
.. ipython:: python
ll = [['foo', 1], ['foo', 2], ['foo', 2], ['bar', 1], ['bar', 1]]
df4 = pd.DataFrame(ll, columns=["A", "B"])
df4
df4.groupby("A")["B"].nunique()
.. note::
Aggregation functions **will not** return the groups that you are aggregating over
as named *columns* when ``as_index=True``, the default. The grouped columns will
be the **indices** of the returned object.
Passing ``as_index=False`` **will** return the groups that you are aggregating over as
named columns, regardless if they are named **indices** or *columns* in the inputs.
.. _groupby.aggregate.agg:
The :meth:`~.DataFrameGroupBy.aggregate` method
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. note::
The :meth:`~.DataFrameGroupBy.aggregate` method can accept many different types of
inputs. This section details using string aliases for various GroupBy methods; other
inputs are detailed in the sections below.
Any reduction method that pandas implements can be passed as a string to
:meth:`~.DataFrameGroupBy.aggregate`. Users are encouraged to use the shorthand,
``agg``. It will operate as if the corresponding method was called.
.. ipython:: python
grouped = df.groupby("A")
grouped[["C", "D"]].aggregate("sum")
grouped = df.groupby(["A", "B"])
grouped.agg("sum")
The result of the aggregation will have the group names as the
new index. In the case of multiple keys, the result is a
:ref:`MultiIndex <advanced.hierarchical>` by default. As mentioned above, this can be
changed by using the ``as_index`` option:
.. ipython:: python
grouped = df.groupby(["A", "B"], as_index=False)
grouped.agg("sum")
df.groupby("A", as_index=False)[["C", "D"]].agg("sum")
Note that you could use the :meth:`DataFrame.reset_index` DataFrame function to achieve
the same result as the column names are stored in the resulting ``MultiIndex``, although
this will make an extra copy.
.. ipython:: python
df.groupby(["A", "B"]).agg("sum").reset_index()
.. _groupby.aggregate.udf:
Aggregation with User-Defined Functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Users can also provide their own User-Defined Functions (UDFs) for custom aggregations.
.. warning::
When aggregating with a UDF, the UDF should not mutate the
provided ``Series``. See :ref:`gotchas.udf-mutation` for more information.
.. note::
Aggregating with a UDF is often less performant than using
the pandas built-in methods on GroupBy. Consider breaking up a complex operation
into a chain of operations that utilize the built-in methods.
.. ipython:: python
animals
animals.groupby("kind")[["height"]].agg(lambda x: set(x))
The resulting dtype will reflect that of the aggregating function. If the results from different groups have
different dtypes, then a common dtype will be determined in the same way as ``DataFrame`` construction.
.. ipython:: python
animals.groupby("kind")[["height"]].agg(lambda x: x.astype(int).sum())
.. _groupby.aggregate.multifunc:
Applying multiple functions at once
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
On a grouped ``Series``, you can pass a list or dict of functions to
:meth:`SeriesGroupBy.agg`, outputting a DataFrame:
.. ipython:: python
grouped = df.groupby("A")
grouped["C"].agg(["sum", "mean", "std"])
On a grouped ``DataFrame``, you can pass a list of functions to
:meth:`DataFrameGroupBy.agg` to aggregate each
column, which produces an aggregated result with a hierarchical column index:
.. ipython:: python
grouped[["C", "D"]].agg(["sum", "mean", "std"])
The resulting aggregations are named after the functions themselves. If you
need to rename, then you can add in a chained operation for a ``Series`` like this:
.. ipython:: python
(
grouped["C"]
.agg(["sum", "mean", "std"])
.rename(columns={"sum": "foo", "mean": "bar", "std": "baz"})
)
For a grouped ``DataFrame``, you can rename in a similar manner:
.. ipython:: python
(
grouped[["C", "D"]].agg(["sum", "mean", "std"]).rename(
columns={"sum": "foo", "mean": "bar", "std": "baz"}
)
)
.. note::
In general, the output column names should be unique, but pandas will allow
you apply to the same function (or two functions with the same name) to the same
column.
.. ipython:: python
grouped["C"].agg(["sum", "sum"])
pandas also allows you to provide multiple lambdas. In this case, pandas
will mangle the name of the (nameless) lambda functions, appending ``_<i>``
to each subsequent lambda.
.. ipython:: python
grouped["C"].agg([lambda x: x.max() - x.min(), lambda x: x.median() - x.mean()])
.. _groupby.aggregate.named:
Named aggregation
~~~~~~~~~~~~~~~~~
To support column-specific aggregation *with control over the output column names*, pandas
accepts the special syntax in :meth:`.DataFrameGroupBy.agg` and :meth:`.SeriesGroupBy.agg`, known as "named aggregation", where
- The keywords are the *output* column names
- The values are tuples whose first element is the column to select
and the second element is the aggregation to apply to that column. pandas
provides the :class:`NamedAgg` namedtuple with the fields ``['column', 'aggfunc']``
to make it clearer what the arguments are. As usual, the aggregation can
be a callable or a string alias.
.. ipython:: python
animals
animals.groupby("kind").agg(
min_height=pd.NamedAgg(column="height", aggfunc="min"),
max_height=pd.NamedAgg(column="height", aggfunc="max"),
average_weight=pd.NamedAgg(column="weight", aggfunc="mean"),
)
:class:`NamedAgg` is just a ``namedtuple``. Plain tuples are allowed as well.
.. ipython:: python
animals.groupby("kind").agg(
min_height=("height", "min"),
max_height=("height", "max"),
average_weight=("weight", "mean"),
)
If the column names you want are not valid Python keywords, construct a dictionary
and unpack the keyword arguments
.. ipython:: python
animals.groupby("kind").agg(
**{
"total weight": pd.NamedAgg(column="weight", aggfunc="sum")
}
)
When using named aggregation, additional keyword arguments are not passed through
to the aggregation functions; only pairs
of ``(column, aggfunc)`` should be passed as ``**kwargs``. If your aggregation functions
require additional arguments, apply them partially with :meth:`functools.partial`.
Named aggregation is also valid for Series groupby aggregations. In this case there's
no column selection, so the values are just the functions.
.. ipython:: python
animals.groupby("kind").height.agg(
min_height="min",
max_height="max",
)
Applying different functions to DataFrame columns
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
By passing a dict to ``aggregate`` you can apply a different aggregation to the
columns of a DataFrame:
.. ipython:: python
grouped.agg({"C": "sum", "D": lambda x: np.std(x, ddof=1)})
The function names can also be strings. In order for a string to be valid it
must be implemented on GroupBy:
.. ipython:: python
grouped.agg({"C": "sum", "D": "std"})
.. _groupby.transform:
Transformation
--------------
A transformation is a GroupBy operation whose result is indexed the same
as the one being grouped. Common examples include :meth:`~.DataFrameGroupBy.cumsum` and
:meth:`~.DataFrameGroupBy.diff`.
.. ipython:: python
speeds
grouped = speeds.groupby("class")["max_speed"]
grouped.cumsum()
grouped.diff()
Unlike aggregations, the groupings that are used to split
the original object are not included in the result.
.. note::
Since transformations do not include the groupings that are used to split the result,
the arguments ``as_index`` and ``sort`` in :meth:`DataFrame.groupby` and
:meth:`Series.groupby` have no effect.
A common use of a transformation is to add the result back into the original DataFrame.
.. ipython:: python
result = speeds.copy()
result["cumsum"] = grouped.cumsum()
result["diff"] = grouped.diff()
result
Built-in transformation methods
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The following methods on GroupBy act as transformations.
.. csv-table::
:header: "Method", "Description"
:widths: 20, 80
:meth:`~.DataFrameGroupBy.bfill`,Back fill NA values within each group
:meth:`~.DataFrameGroupBy.cumcount`,Compute the cumulative count within each group
:meth:`~.DataFrameGroupBy.cummax`,Compute the cumulative max within each group
:meth:`~.DataFrameGroupBy.cummin`,Compute the cumulative min within each group
:meth:`~.DataFrameGroupBy.cumprod`,Compute the cumulative product within each group
:meth:`~.DataFrameGroupBy.cumsum`,Compute the cumulative sum within each group
:meth:`~.DataFrameGroupBy.diff`,Compute the difference between adjacent values within each group
:meth:`~.DataFrameGroupBy.ffill`,Forward fill NA values within each group
:meth:`~.DataFrameGroupBy.pct_change`,Compute the percent change between adjacent values within each group
:meth:`~.DataFrameGroupBy.rank`,Compute the rank of each value within each group
:meth:`~.DataFrameGroupBy.shift`,Shift values up or down within each group
In addition, passing any built-in aggregation method as a string to
:meth:`~.DataFrameGroupBy.transform` (see the next section) will broadcast the result
across the group, producing a transformed result. If the aggregation method has an efficient
implementation, this will be performant as well.
.. _groupby.transformation.transform:
The :meth:`~.DataFrameGroupBy.transform` method
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Similar to the :ref:`aggregation method <groupby.aggregate.agg>`, the
:meth:`~.DataFrameGroupBy.transform` method can accept string aliases to the built-in
transformation methods in the previous section. It can *also* accept string aliases to
the built-in aggregation methods. When an aggregation method is provided, the result
will be broadcast across the group.
.. ipython:: python
speeds
grouped = speeds.groupby("class")[["max_speed"]]
grouped.transform("cumsum")
grouped.transform("sum")
In addition to string aliases, the :meth:`~.DataFrameGroupBy.transform` method can
also accept User-Defined Functions (UDFs). The UDF must:
* Return a result that is either the same size as the group chunk or
broadcastable to the size of the group chunk (e.g., a scalar,
``grouped.transform(lambda x: x.iloc[-1])``).
* Operate column-by-column on the group chunk. The transform is applied to
the first group chunk using chunk.apply.
* Not perform in-place operations on the group chunk. Group chunks should
be treated as immutable, and changes to a group chunk may produce unexpected
results. See :ref:`gotchas.udf-mutation` for more information.
* (Optionally) operates on all columns of the entire group chunk at once. If this is
supported, a fast path is used starting from the *second* chunk.
.. note::
Transforming by supplying ``transform`` with a UDF is
often less performant than using the built-in methods on GroupBy.
Consider breaking up a complex operation into a chain of operations that utilize
the built-in methods.
All of the examples in this section can be made more performant by calling
built-in methods instead of using UDFs.
See :ref:`below for examples <groupby_efficient_transforms>`.
.. versionchanged:: 2.0.0
When using ``.transform`` on a grouped DataFrame and the transformation function
returns a DataFrame, pandas now aligns the result's index
with the input's index. You can call ``.to_numpy()`` within the transformation
function to avoid alignment.
Similar to :ref:`groupby.aggregate.agg`, the resulting dtype will reflect that of the
transformation function. If the results from different groups have different dtypes, then
a common dtype will be determined in the same way as ``DataFrame`` construction.
Suppose we wish to standardize the data within each group:
.. ipython:: python
index = pd.date_range("10/1/1999", periods=1100)
ts = pd.Series(np.random.normal(0.5, 2, 1100), index)
ts = ts.rolling(window=100, min_periods=100).mean().dropna()
ts.head()
ts.tail()
transformed = ts.groupby(lambda x: x.year).transform(
lambda x: (x - x.mean()) / x.std()
)
We would expect the result to now have mean 0 and standard deviation 1 within
each group (up to floating-point error), which we can easily check:
.. ipython:: python
# Original Data
grouped = ts.groupby(lambda x: x.year)
grouped.mean()
grouped.std()
# Transformed Data
grouped_trans = transformed.groupby(lambda x: x.year)
grouped_trans.mean()
grouped_trans.std()
We can also visually compare the original and transformed data sets.
.. ipython:: python
compare = pd.DataFrame({"Original": ts, "Transformed": transformed})
@savefig groupby_transform_plot.png
compare.plot()
Transformation functions that have lower dimension outputs are broadcast to
match the shape of the input array.
.. ipython:: python
ts.groupby(lambda x: x.year).transform(lambda x: x.max() - x.min())
Another common data transform is to replace missing data with the group mean.
.. ipython:: python
cols = ["A", "B", "C"]
values = np.random.randn(1000, 3)
values[np.random.randint(0, 1000, 100), 0] = np.nan
values[np.random.randint(0, 1000, 50), 1] = np.nan
values[np.random.randint(0, 1000, 200), 2] = np.nan
data_df = pd.DataFrame(values, columns=cols)
data_df
countries = np.array(["US", "UK", "GR", "JP"])
key = countries[np.random.randint(0, 4, 1000)]
grouped = data_df.groupby(key)
# Non-NA count in each group
grouped.count()
transformed = grouped.transform(lambda x: x.fillna(x.mean()))
We can verify that the group means have not changed in the transformed data,
and that the transformed data contains no NAs.
.. ipython:: python
grouped_trans = transformed.groupby(key)
grouped.mean() # original group means
grouped_trans.mean() # transformation did not change group means
grouped.count() # original has some missing data points
grouped_trans.count() # counts after transformation
grouped_trans.size() # Verify non-NA count equals group size
.. _groupby_efficient_transforms:
As mentioned in the note above, each of the examples in this section can be computed
more efficiently using built-in methods. In the code below, the inefficient way
using a UDF is commented out and the faster alternative appears below.
.. ipython:: python
# result = ts.groupby(lambda x: x.year).transform(
# lambda x: (x - x.mean()) / x.std()
# )
grouped = ts.groupby(lambda x: x.year)
result = (ts - grouped.transform("mean")) / grouped.transform("std")
# result = ts.groupby(lambda x: x.year).transform(lambda x: x.max() - x.min())
grouped = ts.groupby(lambda x: x.year)
result = grouped.transform("max") - grouped.transform("min")
# grouped = data_df.groupby(key)
# result = grouped.transform(lambda x: x.fillna(x.mean()))
grouped = data_df.groupby(key)
result = data_df.fillna(grouped.transform("mean"))
.. _groupby.transform.window_resample:
Window and resample operations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
It is possible to use ``resample()``, ``expanding()`` and
``rolling()`` as methods on groupbys.
The example below will apply the ``rolling()`` method on the samples of
the column B, based on the groups of column A.
.. ipython:: python
df_re = pd.DataFrame({"A": [1] * 10 + [5] * 10, "B": np.arange(20)})
df_re
df_re.groupby("A").rolling(4).B.mean()
The ``expanding()`` method will accumulate a given operation
(``sum()`` in the example) for all the members of each particular
group.
.. ipython:: python
df_re.groupby("A").expanding().sum()
Suppose you want to use the ``resample()`` method to get a daily
frequency in each group of your dataframe, and wish to complete the
missing values with the ``ffill()`` method.
.. ipython:: python
df_re = pd.DataFrame(
{
"date": pd.date_range(start="2016-01-01", periods=4, freq="W"),
"group": [1, 1, 2, 2],
"val": [5, 6, 7, 8],
}
).set_index("date")
df_re
df_re.groupby("group").resample("1D", include_groups=False).ffill()
.. _groupby.filter:
Filtration
----------
A filtration is a GroupBy operation that subsets the original grouping object. It
may either filter out entire groups, part of groups, or both. Filtrations return
a filtered version of the calling object, including the grouping columns when provided.
In the following example, ``class`` is included in the result.
.. ipython:: python
speeds
speeds.groupby("class").nth(1)
.. note::
Unlike aggregations, filtrations do not add the group keys to the index of the
result. Because of this, passing ``as_index=False`` or ``sort=True`` will not
affect these methods.
Filtrations will respect subsetting the columns of the GroupBy object.
.. ipython:: python
speeds.groupby("class")[["order", "max_speed"]].nth(1)
Built-in filtrations
~~~~~~~~~~~~~~~~~~~~
The following methods on GroupBy act as filtrations. All these methods have an
efficient, GroupBy-specific, implementation.
.. csv-table::
:header: "Method", "Description"
:widths: 20, 80
:meth:`~.DataFrameGroupBy.head`,Select the top row(s) of each group
:meth:`~.DataFrameGroupBy.nth`,Select the nth row(s) of each group
:meth:`~.DataFrameGroupBy.tail`,Select the bottom row(s) of each group
Users can also use transformations along with Boolean indexing to construct complex
filtrations within groups. For example, suppose we are given groups of products and
their volumes, and we wish to subset the data to only the largest products capturing no
more than 90% of the total volume within each group.
.. ipython:: python
product_volumes = pd.DataFrame(
{
"group": list("xxxxyyy"),
"product": list("abcdefg"),
"volume": [10, 30, 20, 15, 40, 10, 20],
}
)
product_volumes
# Sort by volume to select the largest products first
product_volumes = product_volumes.sort_values("volume", ascending=False)
grouped = product_volumes.groupby("group")["volume"]
cumpct = grouped.cumsum() / grouped.transform("sum")
cumpct
significant_products = product_volumes[cumpct <= 0.9]
significant_products.sort_values(["group", "product"])
The :class:`~DataFrameGroupBy.filter` method
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. note::
Filtering by supplying ``filter`` with a User-Defined Function (UDF) is
often less performant than using the built-in methods on GroupBy.
Consider breaking up a complex operation into a chain of operations that utilize
the built-in methods.
The ``filter`` method takes a User-Defined Function (UDF) that, when applied to
an entire group, returns either ``True`` or ``False``. The result of the ``filter``
method is then the subset of groups for which the UDF returned ``True``.
Suppose we want to take only elements that belong to groups with a group sum greater
than 2.
.. ipython:: python
sf = pd.Series([1, 1, 2, 3, 3, 3])
sf.groupby(sf).filter(lambda x: x.sum() > 2)
Another useful operation is filtering out elements that belong to groups
with only a couple members.
.. ipython:: python
dff = pd.DataFrame({"A": np.arange(8), "B": list("aabbbbcc")})
dff.groupby("B").filter(lambda x: len(x) > 2)
Alternatively, instead of dropping the offending groups, we can return a
like-indexed objects where the groups that do not pass the filter are filled
with NaNs.
.. ipython:: python
dff.groupby("B").filter(lambda x: len(x) > 2, dropna=False)
For DataFrames with multiple columns, filters should explicitly specify a column as the filter criterion.
.. ipython:: python
dff["C"] = np.arange(8)
dff.groupby("B").filter(lambda x: len(x["C"]) > 2)
.. _groupby.apply:
Flexible ``apply``
------------------
Some operations on the grouped data might not fit into the aggregation,
transformation, or filtration categories. For these, you can use the ``apply``
function.
.. warning::
``apply`` has to try to infer from the result whether it should act as a reducer,
transformer, *or* filter, depending on exactly what is passed to it. Thus the
grouped column(s) may be included in the output or not. While
it tries to intelligently guess how to behave, it can sometimes guess wrong.
.. note::
All of the examples in this section can be more reliably, and more efficiently,
computed using other pandas functionality.
.. ipython:: python
df
grouped = df.groupby("A")
# could also just call .describe()
grouped["C"].apply(lambda x: x.describe())
The dimension of the returned result can also change:
.. ipython:: python
grouped = df.groupby('A')['C']
def f(group):
return pd.DataFrame({'original': group,
'demeaned': group - group.mean()})
grouped.apply(f)
``apply`` on a Series can operate on a returned value from the applied function
that is itself a series, and possibly upcast the result to a DataFrame:
.. ipython:: python
def f(x):
return pd.Series([x, x ** 2], index=["x", "x^2"])
s = pd.Series(np.random.rand(5))
s
s.apply(f)
Similar to :ref:`groupby.aggregate.agg`, the resulting dtype will reflect that of the
apply function. If the results from different groups have different dtypes, then
a common dtype will be determined in the same way as ``DataFrame`` construction.
Control grouped column(s) placement with ``group_keys``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To control whether the grouped column(s) are included in the indices, you can use
the argument ``group_keys`` which defaults to ``True``. Compare
.. ipython:: python
df.groupby("A", group_keys=True).apply(lambda x: x, include_groups=False)
with
.. ipython:: python
df.groupby("A", group_keys=False).apply(lambda x: x, include_groups=False)
Numba Accelerated Routines
--------------------------
.. versionadded:: 1.1
If `Numba <https://numba.pydata.org/>`__ is installed as an optional dependency, the ``transform`` and
``aggregate`` methods support ``engine='numba'`` and ``engine_kwargs`` arguments.
See :ref:`enhancing performance with Numba <enhancingperf.numba>` for general usage of the arguments
and performance considerations.
The function signature must start with ``values, index`` **exactly** as the data belonging to each group
will be passed into ``values``, and the group index will be passed into ``index``.
.. warning::
When using ``engine='numba'``, there will be no "fall back" behavior internally. The group
data and group index will be passed as NumPy arrays to the JITed user defined function, and no
alternative execution attempts will be tried.
Other useful features
---------------------
Exclusion of non-numeric columns
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Again consider the example DataFrame we've been looking at:
.. ipython:: python
df
Suppose we wish to compute the standard deviation grouped by the ``A``
column. There is a slight problem, namely that we don't care about the data in
column ``B`` because it is not numeric. You can avoid non-numeric columns by
specifying ``numeric_only=True``:
.. ipython:: python
df.groupby("A").std(numeric_only=True)
Note that ``df.groupby('A').colname.std().`` is more efficient than
``df.groupby('A').std().colname``. So if the result of an aggregation function
is only needed over one column (here ``colname``), it may be filtered
*before* applying the aggregation function.
.. ipython:: python
from decimal import Decimal
df_dec = pd.DataFrame(
{
"id": [1, 2, 1, 2],
"int_column": [1, 2, 3, 4],
"dec_column": [
Decimal("0.50"),
Decimal("0.15"),
Decimal("0.25"),
Decimal("0.40"),
],
}
)
df_dec.groupby(["id"])[["dec_column"]].sum()
.. _groupby.observed:
Handling of (un)observed Categorical values
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
When using a ``Categorical`` grouper (as a single grouper, or as part of multiple groupers), the ``observed`` keyword
controls whether to return a cartesian product of all possible groupers values (``observed=False``) or only those
that are observed groupers (``observed=True``).
Show all values:
.. ipython:: python
pd.Series([1, 1, 1]).groupby(
pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=False
).count()
Show only the observed values:
.. ipython:: python
pd.Series([1, 1, 1]).groupby(
pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=True
).count()
The returned dtype of the grouped will *always* include *all* of the categories that were grouped.
.. ipython:: python
s = (
pd.Series([1, 1, 1])
.groupby(pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=True)
.count()
)
s.index.dtype
.. _groupby.missing:
NA group handling
~~~~~~~~~~~~~~~~~
By ``NA``, we are referring to any ``NA`` values, including
:class:`NA`, ``NaN``, ``NaT``, and ``None``. If there are any ``NA`` values in the
grouping key, by default these will be excluded. In other words, any
"``NA`` group" will be dropped. You can include NA groups by specifying ``dropna=False``.
.. ipython:: python
df = pd.DataFrame({"key": [1.0, 1.0, np.nan, 2.0, np.nan], "A": [1, 2, 3, 4, 5]})
df
df.groupby("key", dropna=True).sum()
df.groupby("key", dropna=False).sum()
Grouping with ordered factors
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Categorical variables represented as instances of pandas's ``Categorical`` class
can be used as group keys. If so, the order of the levels will be preserved. When
``observed=False`` and ``sort=False``, any unobserved categories will be at the
end of the result in order.
.. ipython:: python
days = pd.Categorical(
values=["Wed", "Mon", "Thu", "Mon", "Wed", "Sat"],
categories=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
)
data = pd.DataFrame(
{
"day": days,
"workers": [3, 4, 1, 4, 2, 2],
}
)
data
data.groupby("day", observed=False, sort=True).sum()
data.groupby("day", observed=False, sort=False).sum()
.. _groupby.specify:
Grouping with a grouper specification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You may need to specify a bit more data to properly group. You can
use the ``pd.Grouper`` to provide this local control.
.. ipython:: python
import datetime
df = pd.DataFrame(
{
"Branch": "A A A A A A A B".split(),
"Buyer": "Carl Mark Carl Carl Joe Joe Joe Carl".split(),
"Quantity": [1, 3, 5, 1, 8, 1, 9, 3],
"Date": [
datetime.datetime(2013, 1, 1, 13, 0),
datetime.datetime(2013, 1, 1, 13, 5),
datetime.datetime(2013, 10, 1, 20, 0),
datetime.datetime(2013, 10, 2, 10, 0),
datetime.datetime(2013, 10, 1, 20, 0),
datetime.datetime(2013, 10, 2, 10, 0),
datetime.datetime(2013, 12, 2, 12, 0),
datetime.datetime(2013, 12, 2, 14, 0),
],
}
)
df
Groupby a specific column with the desired frequency. This is like resampling.
.. ipython:: python
df.groupby([pd.Grouper(freq="1ME", key="Date"), "Buyer"])[["Quantity"]].sum()
When ``freq`` is specified, the object returned by ``pd.Grouper`` will be an
instance of ``pandas.api.typing.TimeGrouper``. When there is a column and index
with the same name, you can use ``key`` to group by the column and ``level``
to group by the index.
.. ipython:: python
df = df.set_index("Date")
df["Date"] = df.index + pd.offsets.MonthEnd(2)
df.groupby([pd.Grouper(freq="6ME", key="Date"), "Buyer"])[["Quantity"]].sum()
df.groupby([pd.Grouper(freq="6ME", level="Date"), "Buyer"])[["Quantity"]].sum()
Taking the first rows of each group
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Just like for a DataFrame or Series you can call head and tail on a groupby:
.. ipython:: python
df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"])
df
g = df.groupby("A")
g.head(1)
g.tail(1)
This shows the first or last n rows from each group.
.. _groupby.nth:
Taking the nth row of each group
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To select the nth item from each group, use :meth:`.DataFrameGroupBy.nth` or
:meth:`.SeriesGroupBy.nth`. Arguments supplied can be any integer, lists of integers,
slices, or lists of slices; see below for examples. When the nth element of a group
does not exist an error is *not* raised; instead no corresponding rows are returned.
In general this operation acts as a filtration. In certain cases it will also return
one row per group, making it also a reduction. However because in general it can
return zero or multiple rows per group, pandas treats it as a filtration in all cases.
.. ipython:: python
df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"])
g = df.groupby("A")
g.nth(0)
g.nth(-1)
g.nth(1)
If the nth element of a group does not exist, then no corresponding row is included
in the result. In particular, if the specified ``n`` is larger than any group, the
result will be an empty DataFrame.
.. ipython:: python
g.nth(5)
If you want to select the nth not-null item, use the ``dropna`` kwarg. For a DataFrame this should be either ``'any'`` or ``'all'`` just like you would pass to dropna:
.. ipython:: python
# nth(0) is the same as g.first()
g.nth(0, dropna="any")
g.first()
# nth(-1) is the same as g.last()
g.nth(-1, dropna="any")
g.last()
g.B.nth(0, dropna="all")
You can also select multiple rows from each group by specifying multiple nth values as a list of ints.
.. ipython:: python
business_dates = pd.date_range(start="4/1/2014", end="6/30/2014", freq="B")
df = pd.DataFrame(1, index=business_dates, columns=["a", "b"])
# get the first, 4th, and last date index for each month
df.groupby([df.index.year, df.index.month]).nth([0, 3, -1])
You may also use slices or lists of slices.
.. ipython:: python
df.groupby([df.index.year, df.index.month]).nth[1:]
df.groupby([df.index.year, df.index.month]).nth[1:, :-1]
Enumerate group items
~~~~~~~~~~~~~~~~~~~~~
To see the order in which each row appears within its group, use the
``cumcount`` method:
.. ipython:: python
dfg = pd.DataFrame(list("aaabba"), columns=["A"])
dfg
dfg.groupby("A").cumcount()
dfg.groupby("A").cumcount(ascending=False)
.. _groupby.ngroup:
Enumerate groups
~~~~~~~~~~~~~~~~
To see the ordering of the groups (as opposed to the order of rows
within a group given by ``cumcount``) you can use
:meth:`.DataFrameGroupBy.ngroup`.
Note that the numbers given to the groups match the order in which the
groups would be seen when iterating over the groupby object, not the
order they are first observed.
.. ipython:: python
dfg = pd.DataFrame(list("aaabba"), columns=["A"])
dfg
dfg.groupby("A").ngroup()
dfg.groupby("A").ngroup(ascending=False)
Plotting
~~~~~~~~
Groupby also works with some plotting methods. In this case, suppose we
suspect that the values in column 1 are 3 times higher on average in group "B".
.. ipython:: python
np.random.seed(1234)
df = pd.DataFrame(np.random.randn(50, 2))
df["g"] = np.random.choice(["A", "B"], size=50)
df.loc[df["g"] == "B", 1] += 3
We can easily visualize this with a boxplot:
.. ipython:: python
:okwarning:
@savefig groupby_boxplot.png
df.groupby("g").boxplot()
The result of calling ``boxplot`` is a dictionary whose keys are the values
of our grouping column ``g`` ("A" and "B"). The values of the resulting dictionary
can be controlled by the ``return_type`` keyword of ``boxplot``.
See the :ref:`visualization documentation<visualization.box>` for more.
.. warning::
For historical reasons, ``df.groupby("g").boxplot()`` is not equivalent
to ``df.boxplot(by="g")``. See :ref:`here<visualization.box.return>` for
an explanation.
.. _groupby.pipe:
Piping function calls
~~~~~~~~~~~~~~~~~~~~~
Similar to the functionality provided by ``DataFrame`` and ``Series``, functions
that take ``GroupBy`` objects can be chained together using a ``pipe`` method to
allow for a cleaner, more readable syntax. To read about ``.pipe`` in general terms,
see :ref:`here <basics.pipe>`.
Combining ``.groupby`` and ``.pipe`` is often useful when you need to reuse
GroupBy objects.
As an example, imagine having a DataFrame with columns for stores, products,
revenue and quantity sold. We'd like to do a groupwise calculation of *prices*
(i.e. revenue/quantity) per store and per product. We could do this in a
multi-step operation, but expressing it in terms of piping can make the
code more readable. First we set the data:
.. ipython:: python
n = 1000
df = pd.DataFrame(
{
"Store": np.random.choice(["Store_1", "Store_2"], n),
"Product": np.random.choice(["Product_1", "Product_2"], n),
"Revenue": (np.random.random(n) * 50 + 10).round(2),
"Quantity": np.random.randint(1, 10, size=n),
}
)
df.head(2)
We now find the prices per store/product.
.. ipython:: python
(
df.groupby(["Store", "Product"])
.pipe(lambda grp: grp.Revenue.sum() / grp.Quantity.sum())
.unstack()
.round(2)
)
Piping can also be expressive when you want to deliver a grouped object to some
arbitrary function, for example:
.. ipython:: python
def mean(groupby):
return groupby.mean()
df.groupby(["Store", "Product"]).pipe(mean)
Here ``mean`` takes a GroupBy object and finds the mean of the Revenue and Quantity
columns respectively for each Store-Product combination. The ``mean`` function can
be any function that takes in a GroupBy object; the ``.pipe`` will pass the GroupBy
object as a parameter into the function you specify.
Examples
--------
.. _groupby.multicolumn_factorization:
Multi-column factorization
~~~~~~~~~~~~~~~~~~~~~~~~~~
By using :meth:`.DataFrameGroupBy.ngroup`, we can extract
information about the groups in a way similar to :func:`factorize` (as described
further in the :ref:`reshaping API <reshaping.factorize>`) but which applies
naturally to multiple columns of mixed type and different
sources. This can be useful as an intermediate categorical-like step
in processing, when the relationships between the group rows are more
important than their content, or as input to an algorithm which only
accepts the integer encoding. (For more information about support in
pandas for full categorical data, see the :ref:`Categorical
introduction <categorical>` and the
:ref:`API documentation <api.arrays.categorical>`.)
.. ipython:: python
dfg = pd.DataFrame({"A": [1, 1, 2, 3, 2], "B": list("aaaba")})
dfg
dfg.groupby(["A", "B"]).ngroup()
dfg.groupby(["A", [0, 0, 0, 1, 1]]).ngroup()
Groupby by indexer to 'resample' data
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Resampling produces new hypothetical samples (resamples) from already existing observed data or from a model that generates data. These new samples are similar to the pre-existing samples.
In order for resample to work on indices that are non-datetimelike, the following procedure can be utilized.
In the following examples, **df.index // 5** returns an integer array which is used to determine what gets selected for the groupby operation.
.. note::
The example below shows how we can downsample by consolidation of samples into fewer ones.
Here by using **df.index // 5**, we are aggregating the samples in bins. By applying **std()**
function, we aggregate the information contained in many samples into a small subset of values
which is their standard deviation thereby reducing the number of samples.
.. ipython:: python
df = pd.DataFrame(np.random.randn(10, 2))
df
df.index // 5
df.groupby(df.index // 5).std()
Returning a Series to propagate names
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Group DataFrame columns, compute a set of metrics and return a named Series.
The Series name is used as the name for the column index. This is especially
useful in conjunction with reshaping operations such as stacking, in which the
column index name will be used as the name of the inserted column:
.. ipython:: python
df = pd.DataFrame(
{
"a": [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2],
"b": [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1],
"c": [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
"d": [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
}
)
def compute_metrics(x):
result = {"b_sum": x["b"].sum(), "c_mean": x["c"].mean()}
return pd.Series(result, name="metrics")
result = df.groupby("a").apply(compute_metrics, include_groups=False)
result
result.stack(future_stack=True)