.. currentmodule:: pandas
.. ipython:: python
:suppress:
import numpy as np
np.random.seed(123456)
np.set_printoptions(precision=4, suppress=True)
import pandas as pd
pd.options.display.max_rows = 15
import matplotlib
# matplotlib.style.use('default')
import matplotlib.pyplot as plt
plt.close('all')
from collections import OrderedDict
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 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:
# default is axis=0
>>> grouped = obj.groupby(key)
>>> grouped = obj.groupby(key, axis=1)
>>> grouped = obj.groupby([key1, key2])
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 a column to be used to group. Of coursedf.groupby('A')is just syntactic sugar fordf.groupby(df['A']), but it makes life simpler. - For
DataFrameobjects, a string indicating an index level 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
.. versionadded:: 0.20
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 then a warning is
issued and the column takes precedence. This will result in an ambiguity error
in a future version.
.. 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'])
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')
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> 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()
The aggregation functions such as sum will take the level parameter
directly. Additionally, the resulting index will be named according to the
chosen level:
.. ipython:: python s.sum(level='second')
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()
.. versionadded:: 0.20
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()
.. versionadded:: 0.20
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
:suppress:
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)})
.. ipython:: python 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)
...:
It's standard Python-fu but remember you can unpack the tuple in the for loop
statement if you wish: for (k1, k2), group in grouped:.
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 functions API <stats.aggregate>`, 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.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).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()
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.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.agg([np.sum, np.mean, np.std])
.rename(columns={'sum': 'foo',
'mean': 'bar',
'std': 'baz'})
)
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'})
Note
If you pass a dict to aggregate, the ordering of the output columns is
non-deterministic. If you want to be sure the output columns will be in a specific
order, you can use an OrderedDict. Compare the output of the following two commands:
.. ipython:: python
grouped.agg({'D': 'std', 'C': 'mean'})
grouped.agg(OrderedDict([('D', 'std'), ('C', 'mean')]))
Some common aggregations, currently only sum, mean, std, and sem, have
optimized Cython implementations:
.. ipython:: python
df.groupby('A').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).
The transform method returns an object that is indexed the same (same size)
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.
For example, 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()
key = lambda x: x.year
zscore = lambda x: (x - x.mean()) / x.std()
transformed = ts.groupby(key).transform(zscore)
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(key) grouped.mean() grouped.std() # Transformed Data grouped_trans = transformed.groupby(key) 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 data_range = lambda x: x.max() - x.min() ts.groupby(key).transform(data_range)
Alternatively, the built-in methods could be used to produce the same outputs.
.. ipython:: python
ts.groupby(key).transform('max') - ts.groupby(key).transform('min')
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() f = lambda x: x.fillna(x.mean()) transformed = grouped.transform(f)
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()
.. versionadded:: 0.18.1
Working with the resample, expanding or rolling operations on the groupby
level used to require the application of helper functions. However,
now 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
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 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, for example:
.. 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::
In [8]: grouped = df.groupby('A')['C']
In [10]: def f(group):
....: return pd.DataFrame({'original' : group,
....: 'demeaned' : group - group.mean()})
....:
In [11]: 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
apply can act as a reducer, transformer, or filter function, depending on exactly what is passed to it.
So depending on the path taken, and exactly what you are grouping. Thus the grouped columns(s) may be included in
the output as well as set the indices.
Warning
In the current implementation apply calls func twice on the first group to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first group.
.. ipython:: python
d = pd.DataFrame({"a":["x", "y"], "b":[1,2]})
def identity(df):
print(df)
return df
d.groupby("a").apply(identity)
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. If the passed
aggregation function can't be applied to some columns, the troublesome columns
will be (silently) dropped. Thus, this does not pose any problems:
.. ipython:: python
df.groupby('A').std()
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.
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, .25, .5, .75, 1.]) 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']).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']).sum()
df.groupby([pd.Grouper(freq='6M',level='Date'),'Buyer']).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)
.. versionadded:: 0.20.2
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.
.. versionadded:: 0.21.0
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
import numpy as np
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:
(df.groupby(['Store', 'Product']).pipe(report_func)where report_func takes a GroupBy object and creates a report
from that.
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.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()