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"""
Provide the groupby split-apply-combine paradigm. Define the GroupBy
class providing the base-class of operations.
The SeriesGroupBy and DataFrameGroupBy sub-class
(defined in pandas.core.groupby.generic)
expose these user-facing objects to provide specific functionailty.
"""
import collections
from contextlib import contextmanager
import datetime
from functools import partial, wraps
import types
from typing import FrozenSet, List, Optional, Tuple, Type, Union
import numpy as np
from pandas._config.config import option_context
from pandas._libs import Timestamp
import pandas._libs.groupby as libgroupby
from pandas.compat import set_function_name
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
from pandas.util._decorators import Appender, Substitution, cache_readonly
from pandas.util._validators import validate_kwargs
from pandas.core.dtypes.cast import maybe_downcast_to_dtype
from pandas.core.dtypes.common import (
ensure_float,
is_datetime64tz_dtype,
is_extension_array_dtype,
is_numeric_dtype,
is_scalar,
)
from pandas.core.dtypes.missing import isna, notna
from pandas.api.types import is_datetime64_dtype, is_integer_dtype, is_object_dtype
import pandas.core.algorithms as algorithms
from pandas.core.arrays import Categorical
from pandas.core.base import (
DataError,
GroupByError,
PandasObject,
SelectionMixin,
SpecificationError,
)
import pandas.core.common as com
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
from pandas.core.groupby import base
from pandas.core.index import CategoricalIndex, Index, MultiIndex
from pandas.core.series import Series
from pandas.core.sorting import get_group_index_sorter
_common_see_also = """
See Also
--------
Series.%(name)s
DataFrame.%(name)s
"""
_apply_docs = dict(
template="""
Apply function `func` group-wise and combine the results together.
The function passed to `apply` must take a {input} as its first
argument and return a DataFrame, Series or scalar. `apply` will
then take care of combining the results back together into a single
dataframe or series. `apply` is therefore a highly flexible
grouping method.
While `apply` is a very flexible method, its downside is that
using it can be quite a bit slower than using more specific methods
like `agg` or `transform`. Pandas offers a wide range of method that will
be much faster than using `apply` for their specific purposes, so try to
use them before reaching for `apply`.
Parameters
----------
func : callable
A callable that takes a {input} as its first argument, and
returns a dataframe, a series or a scalar. In addition the
callable may take positional and keyword arguments.
args, kwargs : tuple and dict
Optional positional and keyword arguments to pass to `func`.
Returns
-------
applied : Series or DataFrame
See Also
--------
pipe : Apply function to the full GroupBy object instead of to each
group.
aggregate : Apply aggregate function to the GroupBy object.
transform : Apply function column-by-column to the GroupBy object.
Series.apply : Apply a function to a Series.
DataFrame.apply : Apply a function to each row or column of a DataFrame.
""",
dataframe_examples="""
>>> df = pd.DataFrame({'A': 'a a b'.split(),
'B': [1,2,3],
'C': [4,6, 5]})
>>> g = df.groupby('A')
Notice that ``g`` has two groups, ``a`` and ``b``.
Calling `apply` in various ways, we can get different grouping results:
Example 1: below the function passed to `apply` takes a DataFrame as
its argument and returns a DataFrame. `apply` combines the result for
each group together into a new DataFrame:
>>> g[['B', 'C']].apply(lambda x: x / x.sum())
B C
0 0.333333 0.4
1 0.666667 0.6
2 1.000000 1.0
Example 2: The function passed to `apply` takes a DataFrame as
its argument and returns a Series. `apply` combines the result for
each group together into a new DataFrame:
>>> g[['B', 'C']].apply(lambda x: x.max() - x.min())
B C
A
a 1 2
b 0 0
Example 3: The function passed to `apply` takes a DataFrame as
its argument and returns a scalar. `apply` combines the result for
each group together into a Series, including setting the index as
appropriate:
>>> g.apply(lambda x: x.C.max() - x.B.min())
A
a 5
b 2
dtype: int64
""",
series_examples="""
>>> s = pd.Series([0, 1, 2], index='a a b'.split())
>>> g = s.groupby(s.index)
From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``.
Calling `apply` in various ways, we can get different grouping results:
Example 1: The function passed to `apply` takes a Series as
its argument and returns a Series. `apply` combines the result for
each group together into a new Series:
>>> g.apply(lambda x: x*2 if x.name == 'b' else x/2)
0 0.0
1 0.5
2 4.0
dtype: float64
Example 2: The function passed to `apply` takes a Series as
its argument and returns a scalar. `apply` combines the result for
each group together into a Series, including setting the index as
appropriate:
>>> g.apply(lambda x: x.max() - x.min())
a 1
b 0
dtype: int64
Notes
-----
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.
Examples
--------
{examples}
""",
)
_pipe_template = """
Apply a function `func` with arguments to this %(klass)s object and return
the function's result.
%(versionadded)s
Use `.pipe` when you want to improve readability by chaining together
functions that expect Series, DataFrames, GroupBy or Resampler objects.
Instead of writing
>>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)
You can write
>>> (df.groupby('group')
... .pipe(f)
... .pipe(g, arg1=a)
... .pipe(h, arg2=b, arg3=c))
which is much more readable.
Parameters
----------
func : callable or tuple of (callable, string)
Function to apply to this %(klass)s object or, alternatively,
a `(callable, data_keyword)` tuple where `data_keyword` is a
string indicating the keyword of `callable` that expects the
%(klass)s object.
args : iterable, optional
positional arguments passed into `func`.
kwargs : dict, optional
a dictionary of keyword arguments passed into `func`.
Returns
-------
object : the return type of `func`.
See Also
--------
Series.pipe : Apply a function with arguments to a series.
DataFrame.pipe: Apply a function with arguments to a dataframe.
apply : Apply function to each group instead of to the
full %(klass)s object.
Notes
-----
See more `here
<http://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#piping-function-calls>`_
Examples
--------
%(examples)s
"""
_transform_template = """
Call function producing a like-indexed %(klass)s on each group and
return a %(klass)s having the same indexes as the original object
filled with the transformed values
Parameters
----------
f : function
Function to apply to each group
Returns
-------
%(klass)s
See Also
--------
aggregate, transform
Notes
-----
Each group is endowed the attribute 'name' in case you need to know
which group you are working on.
The current implementation imposes three requirements on f:
* f must return a value that either has the same shape as the input
subframe or can be broadcast to the shape of the input subframe.
For example, f returns a scalar it will be broadcast to have the
same shape as the input subframe.
* if this is a DataFrame, f must support application column-by-column
in the subframe. If f also supports application to the entire subframe,
then a fast path is used starting from the second chunk.
* f must not mutate groups. Mutation is not supported and may
produce unexpected results.
Examples
--------
# Same shape
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
... 'foo', 'bar'],
... 'B' : ['one', 'one', 'two', 'three',
... 'two', 'two'],
... 'C' : [1, 5, 5, 2, 5, 5],
... 'D' : [2.0, 5., 8., 1., 2., 9.]})
>>> grouped = df.groupby('A')
>>> grouped.transform(lambda x: (x - x.mean()) / x.std())
C D
0 -1.154701 -0.577350
1 0.577350 0.000000
2 0.577350 1.154701
3 -1.154701 -1.000000
4 0.577350 -0.577350
5 0.577350 1.000000
# Broadcastable
>>> grouped.transform(lambda x: x.max() - x.min())
C D
0 4 6.0
1 3 8.0
2 4 6.0
3 3 8.0
4 4 6.0
5 3 8.0
"""
class GroupByPlot(PandasObject):
"""
Class implementing the .plot attribute for groupby objects.
"""
def __init__(self, groupby):
self._groupby = groupby
def __call__(self, *args, **kwargs):
def f(self):
return self.plot(*args, **kwargs)
f.__name__ = "plot"
return self._groupby.apply(f)
def __getattr__(self, name):
def attr(*args, **kwargs):
def f(self):
return getattr(self.plot, name)(*args, **kwargs)
return self._groupby.apply(f)
return attr
@contextmanager
def _group_selection_context(groupby):
"""
Set / reset the _group_selection_context.
"""
groupby._set_group_selection()
yield groupby
groupby._reset_group_selection()
class _GroupBy(PandasObject, SelectionMixin):
_group_selection = None
_apply_whitelist = frozenset() # type: FrozenSet[str]
def __init__(
self,
obj,
keys=None,
axis=0,
level=None,
grouper=None,
exclusions=None,
selection=None,
as_index=True,
sort=True,
group_keys=True,
squeeze=False,
observed=False,
**kwargs
):
self._selection = selection
if isinstance(obj, NDFrame):
obj._consolidate_inplace()
self.level = level
if not as_index:
if not isinstance(obj, DataFrame):
raise TypeError("as_index=False only valid with DataFrame")
if axis != 0:
raise ValueError("as_index=False only valid for axis=0")
self.as_index = as_index
self.keys = keys
self.sort = sort
self.group_keys = group_keys
self.squeeze = squeeze
self.observed = observed
self.mutated = kwargs.pop("mutated", False)
if grouper is None:
from pandas.core.groupby.grouper import _get_grouper
grouper, exclusions, obj = _get_grouper(
obj,
keys,
axis=axis,
level=level,
sort=sort,
observed=observed,
mutated=self.mutated,
)
self.obj = obj
self.axis = obj._get_axis_number(axis)
self.grouper = grouper
self.exclusions = set(exclusions) if exclusions else set()
# we accept no other args
validate_kwargs("group", kwargs, {})
def __len__(self):
return len(self.groups)
def __repr__(self):
# TODO: Better repr for GroupBy object
return object.__repr__(self)
def _assure_grouper(self):
"""
We create the grouper on instantiation sub-classes may have a
different policy.
"""
pass
@property
def groups(self):
"""
Dict {group name -> group labels}.
"""
self._assure_grouper()
return self.grouper.groups
@property
def ngroups(self):
self._assure_grouper()
return self.grouper.ngroups
@property
def indices(self):
"""
Dict {group name -> group indices}.
"""
self._assure_grouper()
return self.grouper.indices
def _get_indices(self, names):
"""
Safe get multiple indices, translate keys for
datelike to underlying repr.
"""
def get_converter(s):
# possibly convert to the actual key types
# in the indices, could be a Timestamp or a np.datetime64
if isinstance(s, (Timestamp, datetime.datetime)):
return lambda key: Timestamp(key)
elif isinstance(s, np.datetime64):
return lambda key: Timestamp(key).asm8
else:
return lambda key: key
if len(names) == 0:
return []
if len(self.indices) > 0:
index_sample = next(iter(self.indices))
else:
index_sample = None # Dummy sample
name_sample = names[0]
if isinstance(index_sample, tuple):
if not isinstance(name_sample, tuple):
msg = "must supply a tuple to get_group with multiple" " grouping keys"
raise ValueError(msg)
if not len(name_sample) == len(index_sample):
try:
# If the original grouper was a tuple
return [self.indices[name] for name in names]
except KeyError:
# turns out it wasn't a tuple
msg = (
"must supply a same-length tuple to get_group"
" with multiple grouping keys"
)
raise ValueError(msg)
converters = [get_converter(s) for s in index_sample]
names = (tuple(f(n) for f, n in zip(converters, name)) for name in names)
else:
converter = get_converter(index_sample)
names = (converter(name) for name in names)
return [self.indices.get(name, []) for name in names]
def _get_index(self, name):
"""
Safe get index, translate keys for datelike to underlying repr.
"""
return self._get_indices([name])[0]
@cache_readonly
def _selected_obj(self):
if self._selection is None or isinstance(self.obj, Series):
if self._group_selection is not None:
return self.obj[self._group_selection]
return self.obj
else:
return self.obj[self._selection]
def _reset_group_selection(self):
"""
Clear group based selection.
Used for methods needing to return info on each group regardless of
whether a group selection was previously set.
"""
if self._group_selection is not None:
# GH12839 clear cached selection too when changing group selection
self._group_selection = None
self._reset_cache("_selected_obj")
def _set_group_selection(self):
"""
Create group based selection.
Used when selection is not passed directly but instead via a grouper.
NOTE: this should be paired with a call to _reset_group_selection
"""
grp = self.grouper
if not (
self.as_index
and getattr(grp, "groupings", None) is not None
and self.obj.ndim > 1
and self._group_selection is None
):
return
ax = self.obj._info_axis
groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis]
if len(groupers):
# GH12839 clear selected obj cache when group selection changes
self._group_selection = ax.difference(Index(groupers), sort=False).tolist()
self._reset_cache("_selected_obj")
def _set_result_index_ordered(self, result):
# set the result index on the passed values object and
# return the new object, xref 8046
# the values/counts are repeated according to the group index
# shortcut if we have an already ordered grouper
if not self.grouper.is_monotonic:
index = Index(np.concatenate(self._get_indices(self.grouper.result_index)))
result.set_axis(index, axis=self.axis, inplace=True)
result = result.sort_index(axis=self.axis)
result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True)
return result
def _dir_additions(self):
return self.obj._dir_additions() | self._apply_whitelist
def __getattr__(self, attr):
if attr in self._internal_names_set:
return object.__getattribute__(self, attr)
if attr in self.obj:
return self[attr]
if hasattr(self.obj, attr):
return self._make_wrapper(attr)
raise AttributeError(
"%r object has no attribute %r" % (type(self).__name__, attr)
)
@Substitution(
klass="GroupBy",
versionadded=".. versionadded:: 0.21.0",
examples="""\
>>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]})
>>> df
A B
0 a 1
1 b 2
2 a 3
3 b 4
To get the difference between each groups maximum and minimum value in one
pass, you can do
>>> df.groupby('A').pipe(lambda x: x.max() - x.min())
B
A
a 2
b 2""",
)
@Appender(_pipe_template)
def pipe(self, func, *args, **kwargs):
return com._pipe(self, func, *args, **kwargs)
plot = property(GroupByPlot)
def _make_wrapper(self, name):
if name not in self._apply_whitelist:
is_callable = callable(getattr(self._selected_obj, name, None))
kind = " callable " if is_callable else " "
msg = (
"Cannot access{0}attribute {1!r} of {2!r} objects, try "
"using the 'apply' method".format(kind, name, type(self).__name__)
)
raise AttributeError(msg)
self._set_group_selection()
# need to setup the selection
# as are not passed directly but in the grouper
f = getattr(self._selected_obj, name)
if not isinstance(f, types.MethodType):
return self.apply(lambda self: getattr(self, name))
f = getattr(type(self._selected_obj), name)
def wrapper(*args, **kwargs):
# a little trickery for aggregation functions that need an axis
# argument
kwargs_with_axis = kwargs.copy()
if "axis" not in kwargs_with_axis or kwargs_with_axis["axis"] is None:
kwargs_with_axis["axis"] = self.axis
def curried_with_axis(x):
return f(x, *args, **kwargs_with_axis)
def curried(x):
return f(x, *args, **kwargs)
# preserve the name so we can detect it when calling plot methods,
# to avoid duplicates
curried.__name__ = curried_with_axis.__name__ = name
# special case otherwise extra plots are created when catching the
# exception below
if name in base.plotting_methods:
return self.apply(curried)
try:
return self.apply(curried_with_axis)
except Exception:
try:
return self.apply(curried)
except Exception:
# related to : GH3688
# try item-by-item
# this can be called recursively, so need to raise
# ValueError
# if we don't have this method to indicated to aggregate to
# mark this column as an error
try:
return self._aggregate_item_by_item(name, *args, **kwargs)
except (AttributeError):
raise ValueError
return wrapper
def get_group(self, name, obj=None):
"""
Construct DataFrame from group with provided name.
Parameters
----------
name : object
the name of the group to get as a DataFrame
obj : DataFrame, default None
the DataFrame to take the DataFrame out of. If
it is None, the object groupby was called on will
be used
Returns
-------
group : same type as obj
"""
if obj is None:
obj = self._selected_obj
inds = self._get_index(name)
if not len(inds):
raise KeyError(name)
return obj.take(inds, axis=self.axis)
def __iter__(self):
"""
Groupby iterator.
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group
"""
return self.grouper.get_iterator(self.obj, axis=self.axis)
@Appender(
_apply_docs["template"].format(
input="dataframe", examples=_apply_docs["dataframe_examples"]
)
)
def apply(self, func, *args, **kwargs):
func = self._is_builtin_func(func)
# this is needed so we don't try and wrap strings. If we could
# resolve functions to their callable functions prior, this
# wouldn't be needed
if args or kwargs:
if callable(func):
@wraps(func)
def f(g):
with np.errstate(all="ignore"):
return func(g, *args, **kwargs)
else:
raise ValueError(
"func must be a callable if args or " "kwargs are supplied"
)
else:
f = func
# ignore SettingWithCopy here in case the user mutates
with option_context("mode.chained_assignment", None):
try:
result = self._python_apply_general(f)
except Exception:
# gh-20949
# try again, with .apply acting as a filtering
# operation, by excluding the grouping column
# This would normally not be triggered
# except if the udf is trying an operation that
# fails on *some* columns, e.g. a numeric operation
# on a string grouper column
with _group_selection_context(self):
return self._python_apply_general(f)
return result
def _python_apply_general(self, f):
keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
return self._wrap_applied_output(
keys, values, not_indexed_same=mutated or self.mutated
)
def _iterate_slices(self):
yield self._selection_name, self._selected_obj
def transform(self, func, *args, **kwargs):
raise AbstractMethodError(self)
def _cumcount_array(self, ascending=True):
"""
Parameters
----------
ascending : bool, default True
If False, number in reverse, from length of group - 1 to 0.
Notes
-----
this is currently implementing sort=False
(though the default is sort=True) for groupby in general
"""
ids, _, ngroups = self.grouper.group_info
sorter = get_group_index_sorter(ids, ngroups)
ids, count = ids[sorter], len(ids)
if count == 0:
return np.empty(0, dtype=np.int64)
run = np.r_[True, ids[:-1] != ids[1:]]
rep = np.diff(np.r_[np.nonzero(run)[0], count])
out = (~run).cumsum()
if ascending:
out -= np.repeat(out[run], rep)
else:
out = np.repeat(out[np.r_[run[1:], True]], rep) - out
rev = np.empty(count, dtype=np.intp)
rev[sorter] = np.arange(count, dtype=np.intp)
return out[rev].astype(np.int64, copy=False)
def _try_cast(self, result, obj, numeric_only=False):
"""
Try to cast the result to our obj original type,
we may have roundtripped through object in the mean-time.
If numeric_only is True, then only try to cast numerics
and not datetimelikes.
"""
if obj.ndim > 1:
dtype = obj._values.dtype
else:
dtype = obj.dtype
if not is_scalar(result):
if is_datetime64tz_dtype(dtype):
# GH 23683
# Prior results _may_ have been generated in UTC.
# Ensure we localize to UTC first before converting
# to the target timezone
try:
result = obj._values._from_sequence(
result, dtype="datetime64[ns, UTC]"
)
result = result.astype(dtype)
except TypeError:
# _try_cast was called at a point where the result
# was already tz-aware
pass
elif is_extension_array_dtype(dtype):
# The function can return something of any type, so check
# if the type is compatible with the calling EA.
# return the same type (Series) as our caller
try:
result = obj._values._from_sequence(result, dtype=dtype)
except Exception:
# https://github.com/pandas-dev/pandas/issues/22850
# pandas has no control over what 3rd-party ExtensionArrays
# do in _values_from_sequence. We still want ops to work
# though, so we catch any regular Exception.
pass
elif numeric_only and is_numeric_dtype(dtype) or not numeric_only:
result = maybe_downcast_to_dtype(result, dtype)
return result
def _transform_should_cast(self, func_nm):
"""
Parameters
----------
func_nm: str
The name of the aggregation function being performed
Returns
-------
bool
Whether transform should attempt to cast the result of aggregation
"""
return (self.size().fillna(0) > 0).any() and (
func_nm not in base.cython_cast_blacklist
)
def _cython_transform(self, how, numeric_only=True, **kwargs):
output = collections.OrderedDict()
for name, obj in self._iterate_slices():
is_numeric = is_numeric_dtype(obj.dtype)
if numeric_only and not is_numeric:
continue
try:
result, names = self.grouper.transform(obj.values, how, **kwargs)
except NotImplementedError:
continue
except AssertionError as e:
raise GroupByError(str(e))
if self._transform_should_cast(how):
output[name] = self._try_cast(result, obj)
else:
output[name] = result
if len(output) == 0:
raise DataError("No numeric types to aggregate")
return self._wrap_transformed_output(output, names)
def _cython_agg_general(self, how, alt=None, numeric_only=True, min_count=-1):
output = {}
for name, obj in self._iterate_slices():
is_numeric = is_numeric_dtype(obj.dtype)
if numeric_only and not is_numeric:
continue
try:
result, names = self.grouper.aggregate(
obj.values, how, min_count=min_count
)
except AssertionError as e:
raise GroupByError(str(e))
output[name] = self._try_cast(result, obj)
if len(output) == 0:
raise DataError("No numeric types to aggregate")
return self._wrap_aggregated_output(output, names)
def _python_agg_general(self, func, *args, **kwargs):
func = self._is_builtin_func(func)
f = lambda x: func(x, *args, **kwargs)
# iterate through "columns" ex exclusions to populate output dict
output = {}
for name, obj in self._iterate_slices():
try:
result, counts = self.grouper.agg_series(obj, f)
output[name] = self._try_cast(result, obj, numeric_only=True)
except TypeError:
continue
if len(output) == 0:
return self._python_apply_general(f)
if self.grouper._filter_empty_groups:
mask = counts.ravel() > 0
for name, result in output.items():
# since we are masking, make sure that we have a float object
values = result
if is_numeric_dtype(values.dtype):
values = ensure_float(values)
output[name] = self._try_cast(values[mask], result)
return self._wrap_aggregated_output(output)
def _wrap_applied_output(self, *args, **kwargs):
raise AbstractMethodError(self)
def _concat_objects(self, keys, values, not_indexed_same=False):
from pandas.core.reshape.concat import concat
def reset_identity(values):
# reset the identities of the components
# of the values to prevent aliasing
for v in com._not_none(*values):
ax = v._get_axis(self.axis)
ax._reset_identity()
return values
if not not_indexed_same:
result = concat(values, axis=self.axis)
ax = self._selected_obj._get_axis(self.axis)
if isinstance(result, Series):
result = result.reindex(ax)
else:
# this is a very unfortunate situation
# we have a multi-index that is NOT lexsorted
# and we have a result which is duplicated
# we can't reindex, so we resort to this
# GH 14776
if isinstance(ax, MultiIndex) and not ax.is_unique:
indexer = algorithms.unique1d(
result.index.get_indexer_for(ax.values)
)
result = result.take(indexer, axis=self.axis)
else:
result = result.reindex(ax, axis=self.axis)
elif self.group_keys:
values = reset_identity(values)
if self.as_index:
# possible MI return case
group_keys = keys
group_levels = self.grouper.levels
group_names = self.grouper.names
result = concat(
values,
axis=self.axis,
keys=group_keys,
levels=group_levels,
names=group_names,
sort=False,
)
else:
# GH5610, returns a MI, with the first level being a
# range index
keys = list(range(len(values)))
result = concat(values, axis=self.axis, keys=keys)
else:
values = reset_identity(values)
result = concat(values, axis=self.axis)
if (
isinstance(result, Series)
and getattr(self, "_selection_name", None) is not None
):
result.name = self._selection_name
return result
def _apply_filter(self, indices, dropna):
if len(indices) == 0:
indices = np.array([], dtype="int64")
else:
indices = np.sort(np.concatenate(indices))
if dropna:
filtered = self._selected_obj.take(indices, axis=self.axis)
else:
mask = np.empty(len(self._selected_obj.index), dtype=bool)
mask.fill(False)
mask[indices.astype(int)] = True
# mask fails to broadcast when passed to where; broadcast manually.
mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T
filtered = self._selected_obj.where(mask) # Fill with NaNs.
return filtered
class GroupBy(_GroupBy):
"""
Class for grouping and aggregating relational data.
See aggregate, transform, and apply functions on this object.
It's easiest to use obj.groupby(...) to use GroupBy, but you can also do:
::
grouped = groupby(obj, ...)
Parameters
----------
obj : pandas object
axis : int, default 0
level : int, default None
Level of MultiIndex
groupings : list of Grouping objects
Most users should ignore this
exclusions : array-like, optional
List of columns to exclude
name : string
Most users should ignore this
Returns
-------
**Attributes**
groups : dict
{group name -> group labels}
len(grouped) : int
Number of groups
Notes
-----
After grouping, see aggregate, apply, and transform functions. Here are
some other brief notes about usage. When grouping by multiple groups, the
result index will be a MultiIndex (hierarchical) by default.
Iteration produces (key, group) tuples, i.e. chunking the data by group. So
you can write code like:
::
grouped = obj.groupby(keys, axis=axis)
for key, group in grouped:
# do something with the data
Function calls on GroupBy, if not specially implemented, "dispatch" to the
grouped data. So if you group a DataFrame and wish to invoke the std()
method on each group, you can simply do:
::
df.groupby(mapper).std()
rather than
::
df.groupby(mapper).aggregate(np.std)
You can pass arguments to these "wrapped" functions, too.
See the online documentation for full exposition on these topics and much
more
"""
def _bool_agg(self, val_test, skipna):
"""
Shared func to call any / all Cython GroupBy implementations.
"""
def objs_to_bool(vals: np.ndarray) -> Tuple[np.ndarray, Type]:
if is_object_dtype(vals):
vals = np.array([bool(x) for x in vals])
else:
vals = vals.astype(np.bool)
return vals.view(np.uint8), np.bool
def result_to_bool(result: np.ndarray, inference: Type) -> np.ndarray:
return result.astype(inference, copy=False)
return self._get_cythonized_result(
"group_any_all",
self.grouper,
aggregate=True,
cython_dtype=np.uint8,
needs_values=True,
needs_mask=True,
pre_processing=objs_to_bool,
post_processing=result_to_bool,
val_test=val_test,
skipna=skipna,
)
@Substitution(name="groupby")
@Appender(_common_see_also)
def any(self, skipna=True):
"""
Return True if any value in the group is truthful, else False.
Parameters
----------
skipna : bool, default True
Flag to ignore nan values during truth testing
Returns
-------
bool
"""
return self._bool_agg("any", skipna)
@Substitution(name="groupby")
@Appender(_common_see_also)
def all(self, skipna=True):
"""
Return True if all values in the group are truthful, else False.
Parameters
----------
skipna : bool, default True
Flag to ignore nan values during truth testing
Returns
-------
bool
"""
return self._bool_agg("all", skipna)
@Substitution(name="groupby")
@Appender(_common_see_also)
def count(self):
"""
Compute count of group, excluding missing values.
Returns
-------
Series or DataFrame
Count of values within each group.
"""
# defined here for API doc
raise NotImplementedError
@Substitution(name="groupby")
@Substitution(see_also=_common_see_also)
def mean(self, *args, **kwargs):
"""
Compute mean of groups, excluding missing values.
Returns
-------
pandas.Series or pandas.DataFrame
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5],
... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])
Groupby one column and return the mean of the remaining columns in
each group.
>>> df.groupby('A').mean()
B C
A
1 3.0 1.333333
2 4.0 1.500000
Groupby two columns and return the mean of the remaining column.
>>> df.groupby(['A', 'B']).mean()
C
A B
1 2.0 2
4.0 1
2 3.0 1
5.0 2
Groupby one column and return the mean of only particular column in
the group.
>>> df.groupby('A')['B'].mean()
A
1 3.0
2 4.0
Name: B, dtype: float64
"""
nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
try:
return self._cython_agg_general(
"mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
)
except GroupByError:
raise
except Exception: # pragma: no cover
with _group_selection_context(self):
f = lambda x: x.mean(axis=self.axis, **kwargs)
return self._python_agg_general(f)
@Substitution(name="groupby")
@Appender(_common_see_also)
def median(self, **kwargs):
"""
Compute median of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex
Returns
-------
Series or DataFrame
Median of values within each group.
"""
try:
return self._cython_agg_general(
"median",
alt=lambda x, axis: Series(x).median(axis=axis, **kwargs),
**kwargs
)
except GroupByError:
raise
except Exception: # pragma: no cover
def f(x):
if isinstance(x, np.ndarray):
x = Series(x)
return x.median(axis=self.axis, **kwargs)
with _group_selection_context(self):
return self._python_agg_general(f)
@Substitution(name="groupby")
@Appender(_common_see_also)
def std(self, ddof=1, *args, **kwargs):
"""
Compute standard deviation of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
Returns
-------
Series or DataFrame
Standard deviation of values within each group.
"""
# TODO: implement at Cython level?
nv.validate_groupby_func("std", args, kwargs)
return np.sqrt(self.var(ddof=ddof, **kwargs))
@Substitution(name="groupby")
@Appender(_common_see_also)
def var(self, ddof=1, *args, **kwargs):
"""
Compute variance of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
Returns
-------
Series or DataFrame
Variance of values within each group.
"""
nv.validate_groupby_func("var", args, kwargs)
if ddof == 1:
try:
return self._cython_agg_general(
"var",
alt=lambda x, axis: Series(x).var(ddof=ddof, **kwargs),
**kwargs
)
except Exception:
f = lambda x: x.var(ddof=ddof, **kwargs)
with _group_selection_context(self):
return self._python_agg_general(f)
else:
f = lambda x: x.var(ddof=ddof, **kwargs)
with _group_selection_context(self):
return self._python_agg_general(f)
@Substitution(name="groupby")
@Appender(_common_see_also)
def sem(self, ddof=1):
"""
Compute standard error of the mean of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
Returns
-------
Series or DataFrame
Standard error of the mean of values within each group.
"""
return self.std(ddof=ddof) / np.sqrt(self.count())
@Substitution(name="groupby")
@Appender(_common_see_also)
def size(self):
"""
Compute group sizes.
Returns
-------
Series
Number of rows in each group.
"""
result = self.grouper.size()
if isinstance(self.obj, Series):
result.name = getattr(self.obj, "name", None)
return result
@classmethod
def _add_numeric_operations(cls):
"""
Add numeric operations to the GroupBy generically.
"""
def groupby_function(name, alias, npfunc, numeric_only=True, min_count=-1):
_local_template = """
Compute %(f)s of group values.
Returns
-------
Series or DataFrame
Computed %(f)s of values within each group.
"""
@Substitution(name="groupby", f=name)
@Appender(_common_see_also)
@Appender(_local_template)
def f(self, **kwargs):
if "numeric_only" not in kwargs:
kwargs["numeric_only"] = numeric_only
if "min_count" not in kwargs:
kwargs["min_count"] = min_count
self._set_group_selection()
# try a cython aggregation if we can
try:
return self._cython_agg_general(alias, alt=npfunc, **kwargs)
except AssertionError as e:
raise SpecificationError(str(e))
except Exception:
pass
# apply a non-cython aggregation
result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
# coerce the resulting columns if we can
if isinstance(result, DataFrame):
for col in result.columns:
result[col] = self._try_cast(result[col], self.obj[col])
else:
result = self._try_cast(result, self.obj)
return result
set_function_name(f, name, cls)
return f
def first_compat(x, axis=0):
def first(x):
x = x.to_numpy()
x = x[notna(x)]
if len(x) == 0:
return np.nan
return x[0]
if isinstance(x, DataFrame):
return x.apply(first, axis=axis)
else:
return first(x)
def last_compat(x, axis=0):
def last(x):
x = x.to_numpy()
x = x[notna(x)]
if len(x) == 0:
return np.nan
return x[-1]
if isinstance(x, DataFrame):
return x.apply(last, axis=axis)
else:
return last(x)
cls.sum = groupby_function("sum", "add", np.sum, min_count=0)
cls.prod = groupby_function("prod", "prod", np.prod, min_count=0)
cls.min = groupby_function("min", "min", np.min, numeric_only=False)
cls.max = groupby_function("max", "max", np.max, numeric_only=False)
cls.first = groupby_function("first", "first", first_compat, numeric_only=False)
cls.last = groupby_function("last", "last", last_compat, numeric_only=False)
@Substitution(name="groupby")
@Appender(_common_see_also)
def ohlc(self):
"""
Compute sum of values, excluding missing values.
For multiple groupings, the result index will be a MultiIndex
Returns
-------
DataFrame
Open, high, low and close values within each group.
"""
return self._apply_to_column_groupbys(lambda x: x._cython_agg_general("ohlc"))
@Appender(DataFrame.describe.__doc__)
def describe(self, **kwargs):
with _group_selection_context(self):
result = self.apply(lambda x: x.describe(**kwargs))
if self.axis == 1:
return result.T
return result.unstack()
def resample(self, rule, *args, **kwargs):
"""
Provide resampling when using a TimeGrouper.
Given a grouper, the function resamples it according to a string
"string" -> "frequency".
See the :ref:`frequency aliases <timeseries.offset_aliases>`
documentation for more details.
Parameters
----------
rule : str or DateOffset
The offset string or object representing target grouper conversion.
*args, **kwargs
Possible arguments are `how`, `fill_method`, `limit`, `kind` and
`on`, and other arguments of `TimeGrouper`.
Returns
-------
Grouper
Return a new grouper with our resampler appended.
See Also
--------
Grouper : Specify a frequency to resample with when
grouping by a key.
DatetimeIndex.resample : Frequency conversion and resampling of
time series.
Examples
--------
>>> idx = pd.date_range('1/1/2000', periods=4, freq='T')
>>> df = pd.DataFrame(data=4 * [range(2)],
... index=idx,
... columns=['a', 'b'])
>>> df.iloc[2, 0] = 5
>>> df
a b
2000-01-01 00:00:00 0 1
2000-01-01 00:01:00 0 1
2000-01-01 00:02:00 5 1
2000-01-01 00:03:00 0 1
Downsample the DataFrame into 3 minute bins and sum the values of
the timestamps falling into a bin.
>>> df.groupby('a').resample('3T').sum()
a b
a
0 2000-01-01 00:00:00 0 2
2000-01-01 00:03:00 0 1
5 2000-01-01 00:00:00 5 1
Upsample the series into 30 second bins.
>>> df.groupby('a').resample('30S').sum()
a b
a
0 2000-01-01 00:00:00 0 1
2000-01-01 00:00:30 0 0
2000-01-01 00:01:00 0 1
2000-01-01 00:01:30 0 0
2000-01-01 00:02:00 0 0
2000-01-01 00:02:30 0 0
2000-01-01 00:03:00 0 1
5 2000-01-01 00:02:00 5 1
Resample by month. Values are assigned to the month of the period.
>>> df.groupby('a').resample('M').sum()
a b
a
0 2000-01-31 0 3
5 2000-01-31 5 1
Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.
>>> df.groupby('a').resample('3T', closed='right').sum()
a b
a
0 1999-12-31 23:57:00 0 1
2000-01-01 00:00:00 0 2
5 2000-01-01 00:00:00 5 1
Downsample the series into 3 minute bins and close the right side of
the bin interval, but label each bin using the right edge instead of
the left.
>>> df.groupby('a').resample('3T', closed='right', label='right').sum()
a b
a
0 2000-01-01 00:00:00 0 1
2000-01-01 00:03:00 0 2
5 2000-01-01 00:03:00 5 1
Add an offset of twenty seconds.
>>> df.groupby('a').resample('3T', loffset='20s').sum()
a b
a
0 2000-01-01 00:00:20 0 2
2000-01-01 00:03:20 0 1
5 2000-01-01 00:00:20 5 1
"""
from pandas.core.resample import get_resampler_for_grouping
return get_resampler_for_grouping(self, rule, *args, **kwargs)
@Substitution(name="groupby")
@Appender(_common_see_also)
def rolling(self, *args, **kwargs):
"""
Return a rolling grouper, providing rolling functionality per group.
"""
from pandas.core.window import RollingGroupby
return RollingGroupby(self, *args, **kwargs)
@Substitution(name="groupby")
@Appender(_common_see_also)
def expanding(self, *args, **kwargs):
"""
Return an expanding grouper, providing expanding
functionality per group.
"""
from pandas.core.window import ExpandingGroupby
return ExpandingGroupby(self, *args, **kwargs)
def _fill(self, direction, limit=None):
"""
Shared function for `pad` and `backfill` to call Cython method.
Parameters
----------
direction : {'ffill', 'bfill'}
Direction passed to underlying Cython function. `bfill` will cause
values to be filled backwards. `ffill` and any other values will
default to a forward fill
limit : int, default None
Maximum number of consecutive values to fill. If `None`, this
method will convert to -1 prior to passing to Cython
Returns
-------
`Series` or `DataFrame` with filled values
See Also
--------
pad
backfill
"""
# Need int value for Cython
if limit is None:
limit = -1
return self._get_cythonized_result(
"group_fillna_indexer",
self.grouper,
needs_mask=True,
cython_dtype=np.int64,
result_is_index=True,
direction=direction,
limit=limit,
)
@Substitution(name="groupby")
def pad(self, limit=None):
"""
Forward fill the values.
Parameters
----------
limit : integer, optional
limit of how many values to fill
Returns
-------
Series or DataFrame
Object with missing values filled.
See Also
--------
Series.pad
DataFrame.pad
Series.fillna
DataFrame.fillna
"""
return self._fill("ffill", limit=limit)
ffill = pad
@Substitution(name="groupby")
def backfill(self, limit=None):
"""
Backward fill the values.
Parameters
----------
limit : integer, optional
limit of how many values to fill
Returns
-------
Series or DataFrame
Object with missing values filled.
See Also
--------
Series.backfill
DataFrame.backfill
Series.fillna
DataFrame.fillna
"""
return self._fill("bfill", limit=limit)
bfill = backfill
@Substitution(name="groupby")
@Substitution(see_also=_common_see_also)
def nth(self, n: Union[int, List[int]], dropna: Optional[str] = None) -> DataFrame:
"""
Take the nth row from each group if n is an int, or a subset of rows
if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either
'all' or 'any'; this is equivalent to calling dropna(how=dropna)
before the groupby.
Parameters
----------
n : int or list of ints
a single nth value for the row or a list of nth values
dropna : None or str, optional
apply the specified dropna operation before counting which row is
the nth row. Needs to be None, 'any' or 'all'
Returns
-------
Series or DataFrame
N-th value within each group.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
>>> g = df.groupby('A')
>>> g.nth(0)
B
A
1 NaN
2 3.0
>>> g.nth(1)
B
A
1 2.0
2 5.0
>>> g.nth(-1)
B
A
1 4.0
2 5.0
>>> g.nth([0, 1])
B
A
1 NaN
1 2.0
2 3.0
2 5.0
Specifying `dropna` allows count ignoring ``NaN``
>>> g.nth(0, dropna='any')
B
A
1 2.0
2 3.0
NaNs denote group exhausted when using dropna
>>> g.nth(3, dropna='any')
B
A
1 NaN
2 NaN
Specifying `as_index=False` in `groupby` keeps the original index.
>>> df.groupby('A', as_index=False).nth(1)
A B
1 1 2.0
4 2 5.0
"""
valid_containers = (set, list, tuple)
if not isinstance(n, (valid_containers, int)):
raise TypeError("n needs to be an int or a list/set/tuple of ints")
if not dropna:
if isinstance(n, int):
nth_values = [n]
elif isinstance(n, valid_containers):
nth_values = list(set(n))
nth_array = np.array(nth_values, dtype=np.intp)
self._set_group_selection()
mask_left = np.in1d(self._cumcount_array(), nth_array)
mask_right = np.in1d(self._cumcount_array(ascending=False) + 1, -nth_array)
mask = mask_left | mask_right
ids, _, _ = self.grouper.group_info
# Drop NA values in grouping
mask = mask & (ids != -1)
out = self._selected_obj[mask]
if not self.as_index:
return out
result_index = self.grouper.result_index
out.index = result_index[ids[mask]]
if not self.observed and isinstance(result_index, CategoricalIndex):
out = out.reindex(result_index)
return out.sort_index() if self.sort else out
# dropna is truthy
if isinstance(n, valid_containers):
raise ValueError("dropna option with a list of nth values is not supported")
if dropna not in ["any", "all"]:
# Note: when agg-ing picker doesn't raise this, just returns NaN
raise ValueError(
"For a DataFrame groupby, dropna must be "
"either None, 'any' or 'all', "
"(was passed {dropna}).".format(dropna=dropna)
)
# old behaviour, but with all and any support for DataFrames.
# modified in GH 7559 to have better perf
max_len = n if n >= 0 else -1 - n
dropped = self.obj.dropna(how=dropna, axis=self.axis)
# get a new grouper for our dropped obj
if self.keys is None and self.level is None:
# we don't have the grouper info available
# (e.g. we have selected out
# a column that is not in the current object)
axis = self.grouper.axis
grouper = axis[axis.isin(dropped.index)]
else:
# create a grouper with the original parameters, but on dropped
# object
from pandas.core.groupby.grouper import _get_grouper
grouper, _, _ = _get_grouper(
dropped,
key=self.keys,
axis=self.axis,
level=self.level,
sort=self.sort,
mutated=self.mutated,
)
grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
sizes, result = grb.size(), grb.nth(n)
mask = (sizes < max_len).values
# set the results which don't meet the criteria
if len(result) and mask.any():
result.loc[mask] = np.nan
# reset/reindex to the original groups
if len(self.obj) == len(dropped) or len(result) == len(
self.grouper.result_index
):
result.index = self.grouper.result_index
else:
result = result.reindex(self.grouper.result_index)
return result
def quantile(self, q=0.5, interpolation="linear"):
"""
Return group values at the given quantile, a la numpy.percentile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value(s) between 0 and 1 providing the quantile(s) to compute.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
Method to use when the desired quantile falls between two points.
Returns
-------
Series or DataFrame
Return type determined by caller of GroupBy object.
See Also
--------
Series.quantile : Similar method for Series.
DataFrame.quantile : Similar method for DataFrame.
numpy.percentile : NumPy method to compute qth percentile.
Examples
--------
>>> df = pd.DataFrame([
... ['a', 1], ['a', 2], ['a', 3],
... ['b', 1], ['b', 3], ['b', 5]
... ], columns=['key', 'val'])
>>> df.groupby('key').quantile()
val
key
a 2.0
b 3.0
"""
from pandas import concat
def pre_processor(vals: np.ndarray) -> Tuple[np.ndarray, Optional[Type]]:
if is_object_dtype(vals):
raise TypeError(
"'quantile' cannot be performed against " "'object' dtypes!"
)
inference = None
if is_integer_dtype(vals):
inference = np.int64
elif is_datetime64_dtype(vals):
inference = "datetime64[ns]"
vals = vals.astype(np.float)
return vals, inference
def post_processor(vals: np.ndarray, inference: Optional[Type]) -> np.ndarray:
if inference:
# Check for edge case
if not (
is_integer_dtype(inference)
and interpolation in {"linear", "midpoint"}
):
vals = vals.astype(inference)
return vals
if is_scalar(q):
return self._get_cythonized_result(
"group_quantile",
self.grouper,
aggregate=True,
needs_values=True,
needs_mask=True,
cython_dtype=np.float64,
pre_processing=pre_processor,
post_processing=post_processor,
q=q,
interpolation=interpolation,
)
else:
results = [
self._get_cythonized_result(
"group_quantile",
self.grouper,
aggregate=True,
needs_values=True,
needs_mask=True,
cython_dtype=np.float64,
pre_processing=pre_processor,
post_processing=post_processor,
q=qi,
interpolation=interpolation,
)
for qi in q
]
result = concat(results, axis=0, keys=q)
# fix levels to place quantiles on the inside
# TODO(GH-10710): Ideally, we could write this as
# >>> result.stack(0).loc[pd.IndexSlice[:, ..., q], :]
# but this hits https://github.com/pandas-dev/pandas/issues/10710
# which doesn't reorder the list-like `q` on the inner level.
order = np.roll(list(range(result.index.nlevels)), -1)
result = result.reorder_levels(order)
result = result.reindex(q, level=-1)
# fix order.
hi = len(q) * self.ngroups
arr = np.arange(0, hi, self.ngroups)
arrays = []
for i in range(self.ngroups):
arr2 = arr + i
arrays.append(arr2)
indices = np.concatenate(arrays)
assert len(indices) == len(result)
return result.take(indices)
@Substitution(name="groupby")
def ngroup(self, ascending=True):
"""
Number each group from 0 to the number of groups - 1.
This is the enumerative complement of cumcount. 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.
.. versionadded:: 0.20.2
Parameters
----------
ascending : bool, default True
If False, number in reverse, from number of group - 1 to 0.
Returns
-------
Series
Unique numbers for each group.
See Also
--------
.cumcount : Number the rows in each group.
Examples
--------
>>> df = pd.DataFrame({"A": list("aaabba")})
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').ngroup()
0 0
1 0
2 0
3 1
4 1
5 0
dtype: int64
>>> df.groupby('A').ngroup(ascending=False)
0 1
1 1
2 1
3 0
4 0
5 1
dtype: int64
>>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
0 0
1 0
2 1
3 3
4 2
5 0
dtype: int64
"""
with _group_selection_context(self):
index = self._selected_obj.index
result = Series(self.grouper.group_info[0], index)
if not ascending:
result = self.ngroups - 1 - result
return result
@Substitution(name="groupby")
def cumcount(self, ascending=True):
"""
Number each item in each group from 0 to the length of that group - 1.
Essentially this is equivalent to
>>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Parameters
----------
ascending : bool, default True
If False, number in reverse, from length of group - 1 to 0.
Returns
-------
Series
Sequence number of each element within each group.
See Also
--------
.ngroup : Number the groups themselves.
Examples
--------
>>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
... columns=['A'])
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').cumcount()
0 0
1 1
2 2
3 0
4 1
5 3
dtype: int64
>>> df.groupby('A').cumcount(ascending=False)
0 3
1 2
2 1
3 1
4 0
5 0
dtype: int64
"""
with _group_selection_context(self):
index = self._selected_obj.index
cumcounts = self._cumcount_array(ascending=ascending)
return Series(cumcounts, index)
@Substitution(name="groupby")
@Appender(_common_see_also)
def rank(
self, method="average", ascending=True, na_option="keep", pct=False, axis=0
):
"""
Provide the rank of values within each group.
Parameters
----------
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups
ascending : boolean, default True
False for ranks by high (1) to low (N)
na_option : {'keep', 'top', 'bottom'}, default 'keep'
* keep: leave NA values where they are
* top: smallest rank if ascending
* bottom: smallest rank if descending
pct : boolean, default False
Compute percentage rank of data within each group
axis : int, default 0
The axis of the object over which to compute the rank.
Returns
-------
DataFrame with ranking of values within each group
"""
if na_option not in {"keep", "top", "bottom"}:
msg = "na_option must be one of 'keep', 'top', or 'bottom'"
raise ValueError(msg)
return self._cython_transform(
"rank",
numeric_only=False,
ties_method=method,
ascending=ascending,
na_option=na_option,
pct=pct,
axis=axis,
)
@Substitution(name="groupby")
@Appender(_common_see_also)
def cumprod(self, axis=0, *args, **kwargs):
"""
Cumulative product for each group.
Returns
-------
Series or DataFrame
"""
nv.validate_groupby_func("cumprod", args, kwargs, ["numeric_only", "skipna"])
if axis != 0:
return self.apply(lambda x: x.cumprod(axis=axis, **kwargs))
return self._cython_transform("cumprod", **kwargs)
@Substitution(name="groupby")
@Appender(_common_see_also)
def cumsum(self, axis=0, *args, **kwargs):
"""
Cumulative sum for each group.
Returns
-------
Series or DataFrame
"""
nv.validate_groupby_func("cumsum", args, kwargs, ["numeric_only", "skipna"])
if axis != 0:
return self.apply(lambda x: x.cumsum(axis=axis, **kwargs))
return self._cython_transform("cumsum", **kwargs)
@Substitution(name="groupby")
@Appender(_common_see_also)
def cummin(self, axis=0, **kwargs):
"""
Cumulative min for each group.
Returns
-------
Series or DataFrame
"""
if axis != 0:
return self.apply(lambda x: np.minimum.accumulate(x, axis))
return self._cython_transform("cummin", numeric_only=False)
@Substitution(name="groupby")
@Appender(_common_see_also)
def cummax(self, axis=0, **kwargs):
"""
Cumulative max for each group.
Returns
-------
Series or DataFrame
"""
if axis != 0:
return self.apply(lambda x: np.maximum.accumulate(x, axis))
return self._cython_transform("cummax", numeric_only=False)
def _get_cythonized_result(
self,
how,
grouper,
aggregate=False,
cython_dtype=None,
needs_values=False,
needs_mask=False,
needs_ngroups=False,
result_is_index=False,
pre_processing=None,
post_processing=None,
**kwargs
):
"""
Get result for Cythonized functions.
Parameters
----------
how : str, Cythonized function name to be called
grouper : Grouper object containing pertinent group info
aggregate : bool, default False
Whether the result should be aggregated to match the number of
groups
cython_dtype : default None
Type of the array that will be modified by the Cython call. If
`None`, the type will be inferred from the values of each slice
needs_values : bool, default False
Whether the values should be a part of the Cython call
signature
needs_mask : bool, default False
Whether boolean mask needs to be part of the Cython call
signature
needs_ngroups : bool, default False
Whether number of groups is part of the Cython call signature
result_is_index : bool, default False
Whether the result of the Cython operation is an index of
values to be retrieved, instead of the actual values themselves
pre_processing : function, default None
Function to be applied to `values` prior to passing to Cython.
Function should return a tuple where the first element is the
values to be passed to Cython and the second element is an optional
type which the values should be converted to after being returned
by the Cython operation. Raises if `needs_values` is False.
post_processing : function, default None
Function to be applied to result of Cython function. Should accept
an array of values as the first argument and type inferences as its
second argument, i.e. the signature should be
(ndarray, Type).
**kwargs : dict
Extra arguments to be passed back to Cython funcs
Returns
-------
`Series` or `DataFrame` with filled values
"""
if result_is_index and aggregate:
raise ValueError(
"'result_is_index' and 'aggregate' cannot both " "be True!"
)
if post_processing:
if not callable(pre_processing):
raise ValueError("'post_processing' must be a callable!")
if pre_processing:
if not callable(pre_processing):
raise ValueError("'pre_processing' must be a callable!")
if not needs_values:
raise ValueError(
"Cannot use 'pre_processing' without " "specifying 'needs_values'!"
)
labels, _, ngroups = grouper.group_info
output = collections.OrderedDict()
base_func = getattr(libgroupby, how)
for name, obj in self._iterate_slices():
if aggregate:
result_sz = ngroups
else:
result_sz = len(obj.values)
if not cython_dtype:
cython_dtype = obj.values.dtype
result = np.zeros(result_sz, dtype=cython_dtype)
func = partial(base_func, result, labels)
inferences = None
if needs_values:
vals = obj.values
if pre_processing:
vals, inferences = pre_processing(vals)
func = partial(func, vals)
if needs_mask:
mask = isna(obj.values).view(np.uint8)
func = partial(func, mask)
if needs_ngroups:
func = partial(func, ngroups)
func(**kwargs) # Call func to modify indexer values in place
if result_is_index:
result = algorithms.take_nd(obj.values, result)
if post_processing:
result = post_processing(result, inferences)
output[name] = result
if aggregate:
return self._wrap_aggregated_output(output)
else:
return self._wrap_transformed_output(output)
@Substitution(name="groupby")
@Appender(_common_see_also)
def shift(self, periods=1, freq=None, axis=0, fill_value=None):
"""
Shift each group by periods observations.
Parameters
----------
periods : integer, default 1
number of periods to shift
freq : frequency string
axis : axis to shift, default 0
fill_value : optional
.. versionadded:: 0.24.0
Returns
-------
Series or DataFrame
Object shifted within each group.
"""
if freq is not None or axis != 0 or not isna(fill_value):
return self.apply(lambda x: x.shift(periods, freq, axis, fill_value))
return self._get_cythonized_result(
"group_shift_indexer",
self.grouper,
cython_dtype=np.int64,
needs_ngroups=True,
result_is_index=True,
periods=periods,
)
@Substitution(name="groupby")
@Appender(_common_see_also)
def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None, axis=0):
"""
Calculate pct_change of each value to previous entry in group.
Returns
-------
Series or DataFrame
Percentage changes within each group.
"""
if freq is not None or axis != 0:
return self.apply(
lambda x: x.pct_change(
periods=periods,
fill_method=fill_method,
limit=limit,
freq=freq,
axis=axis,
)
)
filled = getattr(self, fill_method)(limit=limit)
fill_grp = filled.groupby(self.grouper.labels)
shifted = fill_grp.shift(periods=periods, freq=freq)
return (filled / shifted) - 1
@Substitution(name="groupby")
@Substitution(see_also=_common_see_also)
def head(self, n=5):
"""
Return first n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.head(n))``,
except ignores as_index flag.
Returns
-------
Series or DataFrame
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
... columns=['A', 'B'])
>>> df.groupby('A', as_index=False).head(1)
A B
0 1 2
2 5 6
>>> df.groupby('A').head(1)
A B
0 1 2
2 5 6
"""
self._reset_group_selection()
mask = self._cumcount_array() < n
return self._selected_obj[mask]
@Substitution(name="groupby")
@Substitution(see_also=_common_see_also)
def tail(self, n=5):
"""
Return last n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.tail(n))``,
except ignores as_index flag.
Returns
-------
Series or DataFrame
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
... columns=['A', 'B'])
>>> df.groupby('A').tail(1)
A B
1 a 2
3 b 2
>>> df.groupby('A').head(1)
A B
0 a 1
2 b 1
"""
self._reset_group_selection()
mask = self._cumcount_array(ascending=False) < n
return self._selected_obj[mask]
def _reindex_output(self, output):
"""
If we have categorical groupers, then we might want to make sure that
we have a fully re-indexed output to the levels. This means expanding
the output space to accommodate all values in the cartesian product of
our groups, regardless of whether they were observed in the data or
not. This will expand the output space if there are missing groups.
The method returns early without modifying the input if the number of
groupings is less than 2, self.observed == True or none of the groupers
are categorical.
Parameters
----------
output: Series or DataFrame
Object resulting from grouping and applying an operation.
Returns
-------
Series or DataFrame
Object (potentially) re-indexed to include all possible groups.
"""
groupings = self.grouper.groupings
if groupings is None:
return output
elif len(groupings) == 1:
return output
# if we only care about the observed values
# we are done
elif self.observed:
return output
# reindexing only applies to a Categorical grouper
elif not any(
isinstance(ping.grouper, (Categorical, CategoricalIndex))
for ping in groupings
):
return output
levels_list = [ping.group_index for ping in groupings]
index, _ = MultiIndex.from_product(
levels_list, names=self.grouper.names
).sortlevel()
if self.as_index:
d = {self.obj._get_axis_name(self.axis): index, "copy": False}
return output.reindex(**d)
# GH 13204
# Here, the categorical in-axis groupers, which need to be fully
# expanded, are columns in `output`. An idea is to do:
# output = output.set_index(self.grouper.names)
# .reindex(index).reset_index()
# but special care has to be taken because of possible not-in-axis
# groupers.
# So, we manually select and drop the in-axis grouper columns,
# reindex `output`, and then reset the in-axis grouper columns.
# Select in-axis groupers
in_axis_grps = (
(i, ping.name) for (i, ping) in enumerate(groupings) if ping.in_axis
)
g_nums, g_names = zip(*in_axis_grps)
output = output.drop(labels=list(g_names), axis=1)
# Set a temp index and reindex (possibly expanding)
output = output.set_index(self.grouper.result_index).reindex(index, copy=False)
# Reset in-axis grouper columns
# (using level numbers `g_nums` because level names may not be unique)
output = output.reset_index(level=g_nums)
return output.reset_index(drop=True)
GroupBy._add_numeric_operations()
@Appender(GroupBy.__doc__)
def groupby(obj, by, **kwds):
if isinstance(obj, Series):
from pandas.core.groupby.generic import SeriesGroupBy
klass = SeriesGroupBy
elif isinstance(obj, DataFrame):
from pandas.core.groupby.generic import DataFrameGroupBy
klass = DataFrameGroupBy
else: # pragma: no cover
raise TypeError("invalid type: {}".format(obj))
return klass(obj, by, **kwds)