{{ header }}
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 fact, in many situations we may wish to split the data set into groups and do something with those groups. 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 True or False. Some examples:
- Discard data that belongs to groups with only a few members.
- Filter out data based on the group sum or mean.
Some combination of the above: GroupBy will examine the results of the apply step and try to return a sensibly combined result if it doesn't fit into either of the above two categories.
Since the set of object instance methods on pandas data structures are generally
rich and expressive, we often simply want to invoke, say, a DataFrame function
on each group. 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:
SELECT Column1, Column2, mean(Column3), sum(Column4)
FROM SomeTable
GROUP BY Column1, Column2We 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.
pandas objects can be split on any of their axes. 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
df = 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"),
)
df
# default is axis=0
grouped = df.groupby("class")
grouped = df.groupby("order", axis="columns")
grouped = df.groupby(["class", "order"])
The mapping can be specified many different ways:
- A Python function, to be called on each of the axis labels.
- A list or NumPy array of the same length as the selected axis.
- A dict or
Series, providing alabel -> group namemapping. - For
DataFrameobjects, a string indicating either a column name or an index level name to be used to group. df.groupby('A')is just syntactic sugar fordf.groupby(df['A']).- 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`.
We could naturally group by either the A or B columns, or both:
.. ipython:: python
grouped = df.groupby("A")
grouped = df.groupby(["A", "B"])
If we also have a MultiIndex on columns A and B, we can group by all
but the specified columns
.. ipython:: python df2 = df.set_index(["A", "B"]) grouped = df2.groupby(level=df2.index.names.difference(["B"])) grouped.sum()
These will split the DataFrame on its index (rows). We could also split by the columns:
.. ipython::
In [4]: def get_letter_type(letter):
...: if letter.lower() in 'aeiou':
...: return 'vowel'
...: else:
...: return 'consonant'
...:
In [5]: grouped = df.groupby(get_letter_type, axis=1)
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 lst = [1, 2, 3, 1, 2, 3] s = pd.Series([1, 2, 3, 10, 20, 30], lst) 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 can't be guaranteed to be the most efficient). You can get quite creative with the label mapping functions.
By default the group keys are sorted during the groupby operation. You may however pass sort=False for potential speedups:
.. 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")
.. versionadded:: 1.1.0
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.
The groups attribute is a dict whose keys are the computed unique groups
and corresponding values being the axis labels belonging to each group. In the
above example we have:
.. ipython:: python
df.groupby("A").groups
df.groupby(get_letter_type, axis=1).groups
Calling the standard Python len function on the GroupBy object just returns
the length of the groups dict, so it is largely just a convenience:
.. ipython:: python grouped = df.groupby(["A", "B"]) grouped.groups len(grouped)
GroupBy will tab complete column names (and other attributes):
.. ipython:: python
:suppress:
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
)
.. ipython:: python
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
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
:suppress:
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"],
]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=["first", "second", "third"])
s = pd.Series(np.random.randn(8), index=index)
.. ipython:: python 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.
A DataFrame may be grouped by a combination of columns and index levels by
specifying the column names as strings and the index levels as pd.Grouper
objects.
.. 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
The following example groups 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()
Once you have created the GroupBy object from a DataFrame, you might want to do
something different for each of the columns. Thus, using [] similar to
getting 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 and much more verbose:
.. ipython:: python df["C"].groupby(df["A"])
Additionally this method avoids recomputing the internal grouping information derived from the passed key.
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`.
A single group can be selected using :meth:`~pandas.core.groupby.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"))
Once the GroupBy object has been created, several methods are available to perform a computation on the grouped data. These operations are similar to the :ref:`aggregating API <basics.aggregate>`, :ref:`window API <window.overview>`, and :ref:`resample API <timeseries.aggregate>`.
An obvious one is aggregation via the :meth:`~pandas.core.groupby.DataFrameGroupBy.aggregate` or equivalently :meth:`~pandas.core.groupby.DataFrameGroupBy.agg` method:
.. ipython:: python
grouped = df.groupby("A")
grouped[["C", "D"]].aggregate(np.sum)
grouped = df.groupby(["A", "B"])
grouped.aggregate(np.sum)
As you can see, the result of the aggregation will have the group names as the
new index along the grouped axis. In the case of multiple keys, the result is a
:ref:`MultiIndex <advanced.hierarchical>` by default, though this can be
changed by using the as_index option:
.. ipython:: python
grouped = df.groupby(["A", "B"], as_index=False)
grouped.aggregate(np.sum)
df.groupby("A", as_index=False)[["C", "D"]].sum()
Note that you could use the reset_index DataFrame function to achieve the
same result as the column names are stored in the resulting MultiIndex:
.. ipython:: python df.groupby(["A", "B"]).sum().reset_index()
Another simple 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 are the group names and whose values are the sizes of each group.
.. ipython:: python grouped.size()
.. ipython:: python grouped.describe()
Another aggregation example is to compute the number of unique values of each group. This is similar to the value_counts function, except that it only counts 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
if they are 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, if they are
named columns.
Aggregating functions are the ones that reduce the dimension of the returned objects. Some common aggregating functions are tabulated below:
The aggregating functions above will exclude NA values. Any function which
reduces a :class:`Series` to a scalar value is an aggregation function and will work,
a trivial example is df.groupby('A').agg(lambda ser: 1). Note that
:meth:`~pd.core.groupby.DataFrameGroupBy.nth` can act as a reducer or a
filter, see :ref:`here <groupby.nth>`.
With grouped Series you can also pass a list or dict of functions to do
aggregation with, outputting a DataFrame:
.. ipython:: python
grouped = df.groupby("A")
grouped["C"].agg([np.sum, np.mean, np.std])
On a grouped DataFrame, you can pass a list of functions to apply to each
column, which produces an aggregated result with a hierarchical index:
.. ipython:: python grouped[["C", "D"]].agg([np.sum, np.mean, np.std])
The resulting aggregations are named for 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([np.sum, np.mean, np.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([np.sum, np.mean, np.std]).rename(
columns={"sum": "foo", "mean": "bar", "std": "baz"}
)
)
Note
In general, the output column names should be unique. You can't apply the same function (or two functions with the same name) to the same column.
.. ipython:: python :okexcept: grouped["C"].agg(["sum", "sum"])
pandas does allow 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()])
.. versionadded:: 0.25.0
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
pandas.NamedAggnamedtuple 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 = 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").agg(
min_height=pd.NamedAgg(column="height", aggfunc="min"),
max_height=pd.NamedAgg(column="height", aggfunc="max"),
average_weight=pd.NamedAgg(column="weight", aggfunc=np.mean),
)
pandas.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", np.mean),
)
If your desired output column names 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)
}
)
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
requires additional arguments, partially apply them with :meth:`functools.partial`.
Note
For Python 3.5 and earlier, the order of **kwargs in a functions was not
preserved. This means that the output column ordering would not be
consistent. To ensure consistent ordering, the keys (and so output columns)
will always be sorted for Python 3.5.
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",
)
By passing a dict to aggregate you can apply a different aggregation to the
columns of a DataFrame:
.. ipython:: python
grouped.agg({"C": np.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 either implemented on GroupBy or available via :ref:`dispatching <groupby.dispatch>`:
.. ipython:: python
grouped.agg({"C": "sum", "D": "std"})
Some common aggregations, currently only sum, mean, std, and sem, have
optimized Cython implementations:
.. ipython:: python
df.groupby("A")[["C", "D"]].sum()
df.groupby(["A", "B"]).mean()
Of course sum and mean are implemented on pandas objects, so the above
code would work even without the special versions via dispatching (see below).
Users can also provide their own functions for custom aggregations. When aggregating
with a User-Defined Function (UDF), the UDF should not mutate the provided Series, see
:ref:`gotchas.udf-mutation` for more information.
.. ipython:: python
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())
The transform method returns an object that is indexed the same
as the one being grouped. The transform function 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. For example, when using
fillna,inplacemust beFalse(grouped.transform(lambda x: x.fillna(inplace=False))). - (Optionally) operates on the entire group chunk. If this is supported, a fast path is used starting from the second chunk.
.. deprecated:: 1.5.0
When using ``.transform`` on a grouped DataFrame and the transformation function
returns a DataFrame, currently pandas does not align the result's index
with the input's index. This behavior is deprecated and alignment will
be performed in a future version of pandas. You can apply ``.to_numpy()`` to the
result of the transformation function to avoid alignment.
Similar to :ref:`groupby.aggregate.udfs`, 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 wished 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, 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())
Alternatively, the built-in methods could be used to produce the same outputs.
.. ipython:: python
max_ts = ts.groupby(lambda x: x.year).transform("max")
min_ts = ts.groupby(lambda x: x.year).transform("min")
max_ts - min_ts
Another common data transform is to replace missing data with the group mean.
.. ipython:: python :suppress: 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)
.. ipython:: python 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
Note
Some functions will automatically transform the input when applied to a
GroupBy object, but returning an object of the same shape as the original.
Passing as_index=False will not affect these transformation methods.
For example: fillna, ffill, bfill, shift..
.. ipython:: python grouped.ffill()
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").ffill()
The filter method returns a subset of the original object. 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)
The argument of filter must be a function that, applied to the group as a
whole, returns True or False.
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)
Note
Some functions when applied to a groupby object will act as a filter on the input, returning
a reduced shape of the original (and potentially eliminating groups), but with the index unchanged.
Passing as_index=False will not affect these transformation methods.
For example: head, tail.
.. ipython:: python
dff.groupby("B").head(2)
When doing an aggregation or transformation, you might just want to call an instance method on each data group. This is pretty easy to do by passing lambda functions:
.. ipython:: python
:okwarning:
grouped = df.groupby("A")
grouped.agg(lambda x: x.std())
But, it's rather verbose and can be untidy if you need to pass additional arguments. Using a bit of metaprogramming cleverness, GroupBy now has the ability to "dispatch" method calls to the groups:
.. ipython:: python :okwarning: grouped.std()
What is actually happening here is that a function wrapper is being
generated. When invoked, it takes any passed arguments and invokes the function
with any arguments on each group (in the above example, the std
function). The results are then combined together much in the style of agg
and transform (it actually uses apply to infer the gluing, documented
next). This enables some operations to be carried out rather succinctly:
.. ipython:: python
tsdf = pd.DataFrame(
np.random.randn(1000, 3),
index=pd.date_range("1/1/2000", periods=1000),
columns=["A", "B", "C"],
)
tsdf.iloc[::2] = np.nan
grouped = tsdf.groupby(lambda x: x.year)
grouped.fillna(method="pad")
In this example, we chopped the collection of time series into yearly chunks then independently called :ref:`fillna <missing_data.fillna>` on the groups.
The nlargest and nsmallest methods work on Series style groupbys:
.. ipython:: python
s = pd.Series([9, 8, 7, 5, 19, 1, 4.2, 3.3])
g = pd.Series(list("abababab"))
gb = s.groupby(g)
gb.nlargest(3)
gb.nsmallest(3)
Some operations on the grouped data might not fit into either the aggregate or
transform categories. Or, you may simply want GroupBy to infer how to combine
the results. For these, use the apply function, which can be substituted
for both aggregate and transform in many standard use cases. However,
apply can handle some exceptional use cases.
Note
apply can act as a reducer, transformer, or filter function, depending
on exactly what is passed to it. It can depend on the passed function and
exactly what you are grouping. Thus the grouped column(s) may be included in
the output as well as set the indices.
.. 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)
Note
If group_keys=True is specified when calling :meth:`~DataFrame.groupby`,
functions passed to apply that return like-indexed outputs will have the
group keys added to the result index. Previous versions of pandas would add
the group keys only when the result from the applied function had a different
index than the input. If group_keys is not specified, the group keys will
not be added for like-indexed outputs. In the future this behavior
will change to always respect group_keys, which defaults to True.
.. versionchanged:: 1.5.0
To control whether the grouped column(s) are included in the indices, you can use
the argument group_keys. Compare
.. ipython:: python
df.groupby("A", group_keys=True).apply(lambda x: x)
with
.. ipython:: python
df.groupby("A", group_keys=False).apply(lambda x: x)
Similar to :ref:`groupby.aggregate.udfs`, 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.
.. versionadded:: 1.1
If Numba 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.
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. We refer to this as a "nuisance" column. You can avoid nuisance
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 interesting over one column (here colname), it may be filtered
before applying the aggregation function.
Note
Any object column, also if it contains numerical values such as Decimal
objects, is considered as a "nuisance" columns. They are excluded from
aggregate functions automatically in groupby.
If you do wish to include decimal or object columns in an aggregation with other non-nuisance data types, you must do so explicitly.
Warning
The automatic dropping of nuisance columns has been deprecated and will be removed
in a future version of pandas. If columns are included that cannot be operated
on, pandas will instead raise an error. In order to avoid this, either select
the columns you wish to operate on or specify numeric_only=True.
.. ipython:: python
:okwarning:
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"),
],
}
)
# Decimal columns can be sum'd explicitly by themselves...
df_dec.groupby(["id"])[["dec_column"]].sum()
# ...but cannot be combined with standard data types or they will be excluded
df_dec.groupby(["id"])[["int_column", "dec_column"]].sum()
# Use .agg function to aggregate over standard and "nuisance" data types
# at the same time
df_dec.groupby(["id"]).agg({"int_column": "sum", "dec_column": "sum"})
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=False)
.count()
)
s.index.dtype
If there are any NaN or NaT values in the grouping key, these will be automatically excluded. In other words, there will never be an "NA group" or "NaT group". This was not the case in older versions of pandas, but users were generally discarding the NA group anyway (and supporting it was an implementation headache).
Categorical variables represented as instance of pandas's Categorical class
can be used as group keys. If so, the order of the levels will be preserved:
.. ipython:: python data = pd.Series(np.random.randn(100)) factor = pd.qcut(data, [0, 0.25, 0.5, 0.75, 1.0]) data.groupby(factor).mean()
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="1M", key="Date"), "Buyer"])[["Quantity"]].sum()
You have an ambiguous specification in that you have a named index and a column that could be potential groupers.
.. ipython:: python
df = df.set_index("Date")
df["Date"] = df.index + pd.offsets.MonthEnd(2)
df.groupby([pd.Grouper(freq="6M", key="Date"), "Buyer"])[["Quantity"]].sum()
df.groupby([pd.Grouper(freq="6M", level="Date"), "Buyer"])[["Quantity"]].sum()
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.
To select from a DataFrame or Series the nth item, use :meth:`~pd.core.groupby.DataFrameGroupBy.nth`. This is a reduction method, and will return a single row (or no row) per group if you pass an int for n:
.. 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 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") # NaNs denote group exhausted when using dropna g.last() g.B.nth(0, dropna="all")
As with other methods, passing as_index=False, will achieve a filtration, which returns the grouped row.
.. ipython:: python
df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"])
g = df.groupby("A", as_index=False)
g.nth(0)
g.nth(-1)
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])
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)
To see the ordering of the groups (as opposed to the order of rows
within a group given by cumcount) you can use
:meth:`~pandas.core.groupby.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)
Groupby also works with some plotting methods. For example, suppose we suspect that some features in a DataFrame may differ by group, in this case, the values in column 1 where the group is "B" are 3 higher on average.
.. 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.
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)
Now, to find prices per store/product, we can simply do:
.. 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)
where 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.
Regroup columns of a DataFrame according to their sum, and sum the aggregated ones.
.. ipython:: python
df = pd.DataFrame({"a": [1, 0, 0], "b": [0, 1, 0], "c": [1, 0, 0], "d": [2, 3, 4]})
df
df.groupby(df.sum(), axis=1).sum()
By using :meth:`~pandas.core.groupby.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()
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 to resample to work on indices that are non-datetimelike, the following procedure can be utilized.
In the following examples, df.index // 5 returns a binary array which is used to determine what gets selected for the groupby operation.
Note
The below example shows how we can downsample by consolidation of samples into fewer samples. 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()
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)
result
result.stack()