""" 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 functionality. """ from contextlib import contextmanager import datetime from functools import partial, wraps import inspect from textwrap import dedent import types from typing import ( Callable, Dict, FrozenSet, Generic, Hashable, Iterable, Iterator, List, Mapping, Optional, Sequence, Set, Tuple, Type, TypeVar, Union, ) import numpy as np from pandas._config.config import option_context from pandas._libs import Timestamp, lib import pandas._libs.groupby as libgroupby from pandas._typing import ( F, FrameOrSeries, FrameOrSeriesUnion, IndexLabel, Label, Scalar, final, ) from pandas.compat.numpy import function as nv from pandas.errors import AbstractMethodError from pandas.util._decorators import Appender, Substitution, cache_readonly, doc from pandas.core.dtypes.cast import maybe_downcast_to_dtype from pandas.core.dtypes.common import ( ensure_float, is_bool_dtype, is_datetime64_dtype, is_extension_array_dtype, is_integer_dtype, is_numeric_dtype, is_object_dtype, is_scalar, is_timedelta64_dtype, ) from pandas.core.dtypes.missing import isna, notna from pandas.core import nanops import pandas.core.algorithms as algorithms from pandas.core.arrays import Categorical, DatetimeArray from pandas.core.base import DataError, PandasObject, SelectionMixin import pandas.core.common as com from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame from pandas.core.groupby import base, numba_, ops from pandas.core.indexes.api import CategoricalIndex, Index, MultiIndex from pandas.core.series import Series from pandas.core.sorting import get_group_index_sorter from pandas.core.util.numba_ import NUMBA_FUNC_CACHE _common_see_also = """ See Also -------- Series.%(name)s : Apply a function %(name)s to a Series. DataFrame.%(name)s : Apply a function %(name)s to each row or column of a DataFrame. """ _apply_docs = { "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} """, } _groupby_agg_method_template = """ Compute {fname} of group values. Parameters ---------- numeric_only : bool, default {no} Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. min_count : int, default {mc} The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. Returns ------- Series or DataFrame Computed {fname} of values within each group. """ _pipe_template = """ Apply a function `func` with arguments to this %(klass)s object and return the function's result. 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) # doctest: +SKIP You can write >>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=a) ... .pipe(h, arg2=b, arg3=c)) # doctest: +SKIP which is much more readable. Parameters ---------- func : callable or tuple of (callable, str) 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 `_ 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. Can also accept a Numba JIT function with ``engine='numba'`` specified. If the ``'numba'`` engine is chosen, the function must be a user defined function with ``values`` and ``index`` as the first and second arguments respectively in the function signature. Each group's index will be passed to the user defined function and optionally available for use. .. versionchanged:: 1.1.0 *args Positional arguments to pass to func. engine : str, default None * ``'cython'`` : Runs the function through C-extensions from cython. * ``'numba'`` : Runs the function through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` .. versionadded:: 1.1.0 engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to the function .. versionadded:: 1.1.0 **kwargs Keyword arguments to be passed into func. Returns ------- %(klass)s See Also -------- %(klass)s.groupby.apply : Apply function func group-wise and combine the results together. %(klass)s.groupby.aggregate : Aggregate using one or more operations over the specified axis. %(klass)s.transform : Transforms the Series on each group based on the given function. 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, if `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. When using ``engine='numba'``, there will be no "fall back" behavior internally. The group data and group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried. Examples -------- >>> 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 Broadcast result of the transformation >>> 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 """ _agg_template = """ Aggregate using one or more operations over the specified axis. Parameters ---------- func : function, str, list or dict Function to use for aggregating the data. If a function, must either work when passed a {klass} or when passed to {klass}.apply. Accepted combinations are: - function - string function name - list of functions and/or function names, e.g. ``[np.sum, 'mean']`` - dict of axis labels -> functions, function names or list of such. Can also accept a Numba JIT function with ``engine='numba'`` specified. Only passing a single function is supported with this engine. If the ``'numba'`` engine is chosen, the function must be a user defined function with ``values`` and ``index`` as the first and second arguments respectively in the function signature. Each group's index will be passed to the user defined function and optionally available for use. .. versionchanged:: 1.1.0 *args Positional arguments to pass to func. engine : str, default None * ``'cython'`` : Runs the function through C-extensions from cython. * ``'numba'`` : Runs the function through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` .. versionadded:: 1.1.0 engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be applied to the function .. versionadded:: 1.1.0 **kwargs Keyword arguments to be passed into func. Returns ------- {klass} See Also -------- {klass}.groupby.apply : Apply function func group-wise and combine the results together. {klass}.groupby.transform : Aggregate using one or more operations over the specified axis. {klass}.aggregate : Transforms the Series on each group based on the given function. Notes ----- When using ``engine='numba'``, there will be no "fall back" behavior internally. The group data and group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried. {examples} """ @final 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: str): 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: "BaseGroupBy") -> Iterator["BaseGroupBy"]: """ Set / reset the group_selection_context. """ groupby._set_group_selection() try: yield groupby finally: groupby._reset_group_selection() _KeysArgType = Union[ Hashable, List[Hashable], Callable[[Hashable], Hashable], List[Callable[[Hashable], Hashable]], Mapping[Hashable, Hashable], ] class BaseGroupBy(PandasObject, SelectionMixin, Generic[FrameOrSeries]): _group_selection: Optional[IndexLabel] = None _apply_allowlist: FrozenSet[str] = frozenset() _hidden_attrs = PandasObject._hidden_attrs | { "as_index", "axis", "dropna", "exclusions", "grouper", "group_keys", "keys", "level", "mutated", "obj", "observed", "sort", "squeeze", } def __init__( self, obj: FrameOrSeries, keys: Optional[_KeysArgType] = None, axis: int = 0, level: Optional[IndexLabel] = None, grouper: Optional["ops.BaseGrouper"] = None, exclusions: Optional[Set[Label]] = None, selection: Optional[IndexLabel] = None, as_index: bool = True, sort: bool = True, group_keys: bool = True, squeeze: bool = False, observed: bool = False, mutated: bool = False, dropna: bool = True, ): self._selection = selection assert isinstance(obj, NDFrame), type(obj) 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 = mutated self.dropna = dropna 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, dropna=self.dropna, ) self.obj = obj self.axis = obj._get_axis_number(axis) self.grouper = grouper self.exclusions = exclusions or set() @final def __len__(self) -> int: return len(self.groups) @final def __repr__(self) -> str: # TODO: Better repr for GroupBy object return object.__repr__(self) def _assure_grouper(self) -> None: """ We create the grouper on instantiation sub-classes may have a different policy. """ pass @final @property def groups(self) -> Dict[Hashable, np.ndarray]: """ Dict {group name -> group labels}. """ self._assure_grouper() return self.grouper.groups @final @property def ngroups(self) -> int: self._assure_grouper() return self.grouper.ngroups @final @property def indices(self): """ Dict {group name -> group indices}. """ self._assure_grouper() return self.grouper.indices @final 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, 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 as err: # turns out it wasn't a tuple msg = ( "must supply a same-length tuple to get_group " "with multiple grouping keys" ) raise ValueError(msg) from err 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] @final def _get_index(self, name): """ Safe get index, translate keys for datelike to underlying repr. """ return self._get_indices([name])[0] @final @cache_readonly def _selected_obj(self): # Note: _selected_obj is always just `self.obj` for SeriesGroupBy 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] @final def _reset_group_selection(self) -> None: """ 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") @final def _set_group_selection(self) -> None: """ 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 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 ax = self.obj._info_axis self._group_selection = ax.difference(Index(groupers), sort=False).tolist() self._reset_cache("_selected_obj") @final def _set_result_index_ordered( self, result: "OutputFrameOrSeries" ) -> "OutputFrameOrSeries": # 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 @final def _dir_additions(self) -> Set[str]: return self.obj._dir_additions() | self._apply_allowlist def __getattr__(self, attr: str): if attr in self._internal_names_set: return object.__getattribute__(self, attr) if attr in self.obj: return self[attr] raise AttributeError( f"'{type(self).__name__}' object has no attribute '{attr}'" ) @Substitution( klass="GroupBy", examples=dedent( """\ >>> 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) @final def _make_wrapper(self, name: str) -> Callable: assert name in self._apply_allowlist with group_selection_context(self): # need to setup the selection # as are not passed directly but in the grouper f = getattr(self._obj_with_exclusions, name) if not isinstance(f, types.MethodType): return self.apply(lambda self: getattr(self, name)) f = getattr(type(self._obj_with_exclusions), name) sig = inspect.signature(f) def wrapper(*args, **kwargs): # a little trickery for aggregation functions that need an axis # argument if "axis" in sig.parameters: if kwargs.get("axis", None) is None: kwargs["axis"] = self.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__ = name # special case otherwise extra plots are created when catching the # exception below if name in base.plotting_methods: return self.apply(curried) return self._python_apply_general(curried, self._obj_with_exclusions) wrapper.__name__ = name return wrapper @final 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_with_is_copy(inds, axis=self.axis) def __iter__(self) -> Iterator[Tuple[Label, FrameOrSeries]]: """ 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) elif hasattr(nanops, "nan" + func): # TODO: should we wrap this in to e.g. _is_builtin_func? f = getattr(nanops, "nan" + func) 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, self._selected_obj) except TypeError: # 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, self._selected_obj) return result @final def _python_apply_general( self, f: F, data: FrameOrSeriesUnion ) -> FrameOrSeriesUnion: """ Apply function f in python space Parameters ---------- f : callable Function to apply data : Series or DataFrame Data to apply f to Returns ------- Series or DataFrame data after applying f """ keys, values, mutated = self.grouper.apply(f, data, self.axis) return self._wrap_applied_output( keys, values, not_indexed_same=mutated or self.mutated ) def _iterate_slices(self) -> Iterable[Series]: raise AbstractMethodError(self) def transform(self, func, *args, **kwargs): raise AbstractMethodError(self) @final def _cumcount_array(self, ascending: bool = 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) @final def _cython_transform( self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs ): output: Dict[base.OutputKey, np.ndarray] = {} for idx, obj in enumerate(self._iterate_slices()): name = obj.name is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result = self.grouper._cython_operation( "transform", obj._values, how, axis, **kwargs ) except NotImplementedError: continue key = base.OutputKey(label=name, position=idx) output[key] = result if not output: raise DataError("No numeric types to aggregate") return self._wrap_transformed_output(output) def _wrap_aggregated_output( self, output: Mapping[base.OutputKey, np.ndarray], index: Optional[Index] ): raise AbstractMethodError(self) def _wrap_transformed_output(self, output: Mapping[base.OutputKey, np.ndarray]): raise AbstractMethodError(self) def _wrap_applied_output(self, keys, values, not_indexed_same: bool = False): raise AbstractMethodError(self) @final def _agg_general( self, numeric_only: bool = True, min_count: int = -1, *, alias: str, npfunc: Callable, ): with group_selection_context(self): # try a cython aggregation if we can result = None try: result = self._cython_agg_general( how=alias, alt=npfunc, numeric_only=numeric_only, min_count=min_count, ) except DataError: pass except NotImplementedError as err: if "function is not implemented for this dtype" in str( err ) or "category dtype not supported" in str(err): # raised in _get_cython_function, in some cases can # be trimmed by implementing cython funcs for more dtypes pass else: raise # apply a non-cython aggregation if result is None: result = self.aggregate(lambda x: npfunc(x, axis=self.axis)) return result.__finalize__(self.obj, method="groupby") def _cython_agg_general( self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1 ): output: Dict[base.OutputKey, Union[np.ndarray, DatetimeArray]] = {} # Ideally we would be able to enumerate self._iterate_slices and use # the index from enumeration as the key of output, but ohlc in particular # returns a (n x 4) array. Output requires 1D ndarrays as values, so we # need to slice that up into 1D arrays idx = 0 for obj in self._iterate_slices(): name = obj.name is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue result = self.grouper._cython_operation( "aggregate", obj._values, how, axis=0, min_count=min_count ) if how == "ohlc": # e.g. ohlc agg_names = ["open", "high", "low", "close"] assert len(agg_names) == result.shape[1] for result_column, result_name in zip(result.T, agg_names): key = base.OutputKey(label=result_name, position=idx) output[key] = result_column idx += 1 else: assert result.ndim == 1 key = base.OutputKey(label=name, position=idx) output[key] = result idx += 1 if not output: raise DataError("No numeric types to aggregate") return self._wrap_aggregated_output(output, index=self.grouper.result_index) @final def _transform_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs): """ Perform groupby transform routine with the numba engine. This routine mimics the data splitting routine of the DataSplitter class to generate the indices of each group in the sorted data and then passes the data and indices into a Numba jitted function. """ if not callable(func): raise NotImplementedError( "Numba engine can only be used with a single function." ) group_keys = self.grouper._get_group_keys() labels, _, n_groups = self.grouper.group_info sorted_index = get_group_index_sorter(labels, n_groups) sorted_labels = algorithms.take_nd(labels, sorted_index, allow_fill=False) sorted_data = data.take(sorted_index, axis=self.axis).to_numpy() starts, ends = lib.generate_slices(sorted_labels, n_groups) numba_transform_func = numba_.generate_numba_transform_func( tuple(args), kwargs, func, engine_kwargs ) result = numba_transform_func( sorted_data, sorted_index, starts, ends, len(group_keys), len(data.columns) ) cache_key = (func, "groupby_transform") if cache_key not in NUMBA_FUNC_CACHE: NUMBA_FUNC_CACHE[cache_key] = numba_transform_func # result values needs to be resorted to their original positions since we # evaluated the data sorted by group return result.take(np.argsort(sorted_index), axis=0) @final def _aggregate_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs): """ Perform groupby aggregation routine with the numba engine. This routine mimics the data splitting routine of the DataSplitter class to generate the indices of each group in the sorted data and then passes the data and indices into a Numba jitted function. """ if not callable(func): raise NotImplementedError( "Numba engine can only be used with a single function." ) group_keys = self.grouper._get_group_keys() labels, _, n_groups = self.grouper.group_info sorted_index = get_group_index_sorter(labels, n_groups) sorted_labels = algorithms.take_nd(labels, sorted_index, allow_fill=False) sorted_data = data.take(sorted_index, axis=self.axis).to_numpy() starts, ends = lib.generate_slices(sorted_labels, n_groups) numba_agg_func = numba_.generate_numba_agg_func( tuple(args), kwargs, func, engine_kwargs ) result = numba_agg_func( sorted_data, sorted_index, starts, ends, len(group_keys), len(data.columns) ) cache_key = (func, "groupby_agg") if cache_key not in NUMBA_FUNC_CACHE: NUMBA_FUNC_CACHE[cache_key] = numba_agg_func if self.grouper.nkeys > 1: index = MultiIndex.from_tuples(group_keys, names=self.grouper.names) else: index = Index(group_keys, name=self.grouper.names[0]) return result, index @final 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: Dict[base.OutputKey, np.ndarray] = {} for idx, obj in enumerate(self._iterate_slices()): name = obj.name if self.grouper.ngroups == 0: # agg_series below assumes ngroups > 0 continue try: # if this function is invalid for this dtype, we will ignore it. result, counts = self.grouper.agg_series(obj, f) except TypeError: continue assert result is not None key = base.OutputKey(label=name, position=idx) if is_numeric_dtype(obj.dtype): result = maybe_downcast_to_dtype(result, obj.dtype) if self.grouper._filter_empty_groups: mask = counts.ravel() > 0 # since we are masking, make sure that we have a float object values = result if is_numeric_dtype(values.dtype): values = ensure_float(values) result = maybe_downcast_to_dtype(values[mask], result.dtype) output[key] = result if not output: return self._python_apply_general(f, self._selected_obj) return self._wrap_aggregated_output(output, index=self.grouper.result_index) @final def _concat_objects(self, keys, values, not_indexed_same: bool = 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.filter(lambda x: True).axes[self.axis] # this is a very unfortunate situation # we can't use reindex to restore the original order # when the ax has duplicates # so we resort to this # GH 14776, 30667 if ax.has_duplicates and not result.axes[self.axis].equals(ax): indexer, _ = result.index.get_indexer_non_unique(ax.values) indexer = algorithms.unique1d(indexer) result = result.take(indexer, axis=self.axis) else: result = result.reindex(ax, axis=self.axis, copy=False) 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 self._selection_name is not None: result.name = self._selection_name return result @final 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 # To track operations that expand dimensions, like ohlc OutputFrameOrSeries = TypeVar("OutputFrameOrSeries", bound=NDFrame) class GroupBy(BaseGroupBy[FrameOrSeries]): """ 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 : str 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 """ @final @property def _obj_1d_constructor(self) -> Type["Series"]: # GH28330 preserve subclassed Series/DataFrames if isinstance(self.obj, DataFrame): return self.obj._constructor_sliced assert isinstance(self.obj, Series) return self.obj._constructor @final 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(bool) return vals.view(np.uint8), 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", aggregate=True, numeric_only=False, cython_dtype=np.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, ) @final @Substitution(name="groupby") @Appender(_common_see_also) def any(self, skipna: bool = 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 ------- Series or DataFrame DataFrame or Series of boolean values, where a value is True if any element is True within its respective group, False otherwise. """ return self._bool_agg("any", skipna) @final @Substitution(name="groupby") @Appender(_common_see_also) def all(self, skipna: bool = 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 ------- Series or DataFrame DataFrame or Series of boolean values, where a value is True if all elements are True within its respective group, False otherwise. """ 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 @final @Substitution(name="groupby") @Substitution(see_also=_common_see_also) def mean(self, numeric_only: bool = True): """ Compute mean of groups, excluding missing values. Parameters ---------- numeric_only : bool, default True Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. 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 """ return self._cython_agg_general( "mean", alt=lambda x, axis: Series(x).mean(numeric_only=numeric_only), numeric_only=numeric_only, ) @final @Substitution(name="groupby") @Appender(_common_see_also) def median(self, numeric_only=True): """ Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex Parameters ---------- numeric_only : bool, default True Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Returns ------- Series or DataFrame Median of values within each group. """ return self._cython_agg_general( "median", alt=lambda x, axis: Series(x).median(axis=axis, numeric_only=numeric_only), numeric_only=numeric_only, ) @final @Substitution(name="groupby") @Appender(_common_see_also) def std(self, ddof: int = 1): """ Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. Returns ------- Series or DataFrame Standard deviation of values within each group. """ return self._get_cythonized_result( "group_var_float64", aggregate=True, needs_counts=True, needs_values=True, needs_2d=True, cython_dtype=np.dtype(np.float64), post_processing=lambda vals, inference: np.sqrt(vals), ddof=ddof, ) @final @Substitution(name="groupby") @Appender(_common_see_also) def var(self, ddof: int = 1): """ Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. Returns ------- Series or DataFrame Variance of values within each group. """ if ddof == 1: return self._cython_agg_general( "var", alt=lambda x, axis: Series(x).var(ddof=ddof) ) else: func = lambda x: x.var(ddof=ddof) with group_selection_context(self): return self._python_agg_general(func) @final @Substitution(name="groupby") @Appender(_common_see_also) def sem(self, ddof: int = 1): """ Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. Returns ------- Series or DataFrame Standard error of the mean of values within each group. """ result = self.std(ddof=ddof) if result.ndim == 1: result /= np.sqrt(self.count()) else: cols = result.columns.difference(self.exclusions).unique() counts = self.count() result_ilocs = result.columns.get_indexer_for(cols) count_ilocs = counts.columns.get_indexer_for(cols) result.iloc[:, result_ilocs] /= np.sqrt(counts.iloc[:, count_ilocs]) return result @final @Substitution(name="groupby") @Appender(_common_see_also) def size(self) -> FrameOrSeriesUnion: """ Compute group sizes. Returns ------- DataFrame or Series Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False. """ result = self.grouper.size() # GH28330 preserve subclassed Series/DataFrames through calls if issubclass(self.obj._constructor, Series): result = self._obj_1d_constructor(result, name=self.obj.name) else: result = self._obj_1d_constructor(result) if not self.as_index: result = result.rename("size").reset_index() return self._reindex_output(result, fill_value=0) @final @doc(_groupby_agg_method_template, fname="sum", no=True, mc=0) def sum(self, numeric_only: bool = True, min_count: int = 0): # If we are grouping on categoricals we want unobserved categories to # return zero, rather than the default of NaN which the reindexing in # _agg_general() returns. GH #31422 with com.temp_setattr(self, "observed", True): result = self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="add", npfunc=np.sum, ) return self._reindex_output(result, fill_value=0) @final @doc(_groupby_agg_method_template, fname="prod", no=True, mc=0) def prod(self, numeric_only: bool = True, min_count: int = 0): return self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod ) @final @doc(_groupby_agg_method_template, fname="min", no=False, mc=-1) def min(self, numeric_only: bool = False, min_count: int = -1): return self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="min", npfunc=np.min ) @final @doc(_groupby_agg_method_template, fname="max", no=False, mc=-1) def max(self, numeric_only: bool = False, min_count: int = -1): return self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="max", npfunc=np.max ) @final @doc(_groupby_agg_method_template, fname="first", no=False, mc=-1) def first(self, numeric_only: bool = False, min_count: int = -1): def first_compat(obj: FrameOrSeries, axis: int = 0): def first(x: Series): """Helper function for first item that isn't NA.""" arr = x.array[notna(x.array)] if not len(arr): return np.nan return arr[0] if isinstance(obj, DataFrame): return obj.apply(first, axis=axis) elif isinstance(obj, Series): return first(obj) else: raise TypeError(type(obj)) return self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="first", npfunc=first_compat, ) @final @doc(_groupby_agg_method_template, fname="last", no=False, mc=-1) def last(self, numeric_only: bool = False, min_count: int = -1): def last_compat(obj: FrameOrSeries, axis: int = 0): def last(x: Series): """Helper function for last item that isn't NA.""" arr = x.array[notna(x.array)] if not len(arr): return np.nan return arr[-1] if isinstance(obj, DataFrame): return obj.apply(last, axis=axis) elif isinstance(obj, Series): return last(obj) else: raise TypeError(type(obj)) return self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="last", npfunc=last_compat, ) @final @Substitution(name="groupby") @Appender(_common_see_also) def ohlc(self) -> DataFrame: """ Compute open, high, low and close values of a group, 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")) @final @doc(DataFrame.describe) 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() @final 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 ` 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 """ from pandas.core.resample import get_resampler_for_grouping return get_resampler_for_grouping(self, rule, *args, **kwargs) @final @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) @final @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) @final @Substitution(name="groupby") @Appender(_common_see_also) def ewm(self, *args, **kwargs): """ Return an ewm grouper, providing ewm functionality per group. """ from pandas.core.window import ExponentialMovingWindowGroupby return ExponentialMovingWindowGroupby(self, *args, **kwargs) @final 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 : Returns Series with minimum number of char in object. backfill : Backward fill the missing values in the dataset. """ # Need int value for Cython if limit is None: limit = -1 return self._get_cythonized_result( "group_fillna_indexer", numeric_only=False, needs_mask=True, cython_dtype=np.dtype(np.int64), result_is_index=True, direction=direction, limit=limit, dropna=self.dropna, ) @final @Substitution(name="groupby") def pad(self, limit=None): """ Forward fill the values. Parameters ---------- limit : int, optional Limit of how many values to fill. Returns ------- Series or DataFrame Object with missing values filled. See Also -------- Series.pad: Returns Series with minimum number of char in object. DataFrame.pad: Object with missing values filled or None if inplace=True. Series.fillna: Fill NaN values of a Series. DataFrame.fillna: Fill NaN values of a DataFrame. """ return self._fill("ffill", limit=limit) ffill = pad @final @Substitution(name="groupby") def backfill(self, limit=None): """ Backward fill the values. Parameters ---------- limit : int, optional Limit of how many values to fill. Returns ------- Series or DataFrame Object with missing values filled. See Also -------- Series.backfill : Backward fill the missing values in the dataset. DataFrame.backfill: Backward fill the missing values in the dataset. Series.fillna: Fill NaN values of a Series. DataFrame.fillna: Fill NaN values of a DataFrame. """ return self._fill("bfill", limit=limit) bfill = backfill @final @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) with group_selection_context(self): 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) out = self._reindex_output(out) 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', " f"(was passed {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 @final def quantile(self, q=0.5, interpolation: str = "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.dtype): if is_extension_array_dtype(vals.dtype): vals = vals.to_numpy(dtype=float, na_value=np.nan) inference = np.int64 elif is_bool_dtype(vals.dtype) and is_extension_array_dtype(vals.dtype): vals = vals.to_numpy(dtype=float, na_value=np.nan) elif is_datetime64_dtype(vals.dtype): inference = "datetime64[ns]" vals = np.asarray(vals).astype(float) elif is_timedelta64_dtype(vals.dtype): inference = "timedelta64[ns]" vals = np.asarray(vals).astype(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", aggregate=True, numeric_only=False, needs_values=True, needs_mask=True, cython_dtype=np.dtype(np.float64), pre_processing=pre_processor, post_processing=post_processor, q=q, interpolation=interpolation, ) else: results = [ self._get_cythonized_result( "group_quantile", aggregate=True, needs_values=True, needs_mask=True, cython_dtype=np.dtype(np.float64), pre_processing=pre_processor, post_processing=post_processor, q=qi, interpolation=interpolation, ) for qi in q ] result = concat(results, axis=self.axis, 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 = list(range(1, result.axes[self.axis].nlevels)) + [0] # temporarily saves the index names index_names = np.array(result.axes[self.axis].names) # set index names to positions to avoid confusion result.axes[self.axis].names = np.arange(len(index_names)) # place quantiles on the inside if isinstance(result, Series): result = result.reorder_levels(order) else: result = result.reorder_levels(order, axis=self.axis) # restore the index names in order result.axes[self.axis].names = index_names[order] # reorder rows to keep things sorted indices = ( np.arange(result.shape[self.axis]) .reshape([len(q), self.ngroups]) .T.flatten() ) return result.take(indices, axis=self.axis) @final @Substitution(name="groupby") def ngroup(self, ascending: bool = 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. 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 = self._obj_1d_constructor(self.grouper.group_info[0], index) if not ascending: result = self.ngroups - 1 - result return result @final @Substitution(name="groupby") def cumcount(self, ascending: bool = True): """ Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to .. code-block:: python 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._get_axis(self.axis) cumcounts = self._cumcount_array(ascending=ascending) return self._obj_1d_constructor(cumcounts, index) @final @Substitution(name="groupby") @Appender(_common_see_also) def rank( self, method: str = "average", ascending: bool = True, na_option: str = "keep", pct: bool = False, axis: int = 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 : bool, 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 : bool, 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, ) @final @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) @final @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) @final @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) @final @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) @final def _get_cythonized_result( self, how: str, cython_dtype: np.dtype, aggregate: bool = False, numeric_only: bool = True, needs_counts: bool = False, needs_values: bool = False, needs_2d: bool = False, min_count: Optional[int] = None, needs_mask: bool = False, needs_ngroups: bool = False, result_is_index: bool = False, pre_processing=None, post_processing=None, **kwargs, ): """ Get result for Cythonized functions. Parameters ---------- how : str, Cythonized function name to be called cython_dtype : np.dtype Type of the array that will be modified by the Cython call. aggregate : bool, default False Whether the result should be aggregated to match the number of groups numeric_only : bool, default True Whether only numeric datatypes should be computed needs_counts : bool, default False Whether the counts should be a part of the Cython call needs_values : bool, default False Whether the values should be a part of the Cython call signature needs_2d : bool, default False Whether the values and result of the Cython call signature are 2-dimensional. min_count : int, default None When not None, min_count for the Cython call 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. This function is also responsible for raising a TypeError if the values have an invalid type. 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 and not callable(post_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'!" ) grouper = self.grouper labels, _, ngroups = grouper.group_info output: Dict[base.OutputKey, np.ndarray] = {} base_func = getattr(libgroupby, how) error_msg = "" for idx, obj in enumerate(self._iterate_slices()): name = obj.name values = obj._values if numeric_only and not is_numeric_dtype(values): continue if aggregate: result_sz = ngroups else: result_sz = len(values) result = np.zeros(result_sz, dtype=cython_dtype) if needs_2d: result = result.reshape((-1, 1)) func = partial(base_func, result) inferences = None if needs_counts: counts = np.zeros(self.ngroups, dtype=np.int64) func = partial(func, counts) if needs_values: vals = values if pre_processing: try: vals, inferences = pre_processing(vals) except TypeError as e: error_msg = str(e) continue vals = vals.astype(cython_dtype, copy=False) if needs_2d: vals = vals.reshape((-1, 1)) func = partial(func, vals) func = partial(func, labels) if min_count is not None: func = partial(func, min_count) if needs_mask: mask = isna(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 needs_2d: result = result.reshape(-1) if result_is_index: result = algorithms.take_nd(values, result) if post_processing: result = post_processing(result, inferences) key = base.OutputKey(label=name, position=idx) output[key] = result # error_msg is "" on an frame/series with no rows or columns if not output and error_msg != "": raise TypeError(error_msg) if aggregate: return self._wrap_aggregated_output(output, index=self.grouper.result_index) else: return self._wrap_transformed_output(output) @final @Substitution(name="groupby") def shift(self, periods=1, freq=None, axis=0, fill_value=None): """ Shift each group by periods observations. If freq is passed, the index will be increased using the periods and the freq. Parameters ---------- periods : int, default 1 Number of periods to shift. freq : str, optional Frequency string. axis : axis to shift, default 0 Shift direction. fill_value : optional The scalar value to use for newly introduced missing values. .. versionadded:: 0.24.0 Returns ------- Series or DataFrame Object shifted within each group. See Also -------- Index.shift : Shift values of Index. tshift : Shift the time index, using the index’s frequency if available. """ 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", numeric_only=False, cython_dtype=np.dtype(np.int64), needs_ngroups=True, result_is_index=True, periods=periods, ) @final @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, ) ) if fill_method is None: # GH30463 fill_method = "pad" limit = 0 filled = getattr(self, fill_method)(limit=limit) fill_grp = filled.groupby(self.grouper.codes, axis=self.axis) shifted = fill_grp.shift(periods=periods, freq=freq, axis=self.axis) return (filled / shifted) - 1 @final @Substitution(name="groupby") @Substitution(see_also=_common_see_also) def head(self, n=5): """ Return first n rows of each group. Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows from the original DataFrame with original index and order preserved (``as_index`` flag is ignored). Does not work for negative values of `n`. Returns ------- Series or DataFrame %(see_also)s Examples -------- >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], ... columns=['A', 'B']) >>> df.groupby('A').head(1) A B 0 1 2 2 5 6 >>> df.groupby('A').head(-1) Empty DataFrame Columns: [A, B] Index: [] """ self._reset_group_selection() mask = self._cumcount_array() < n if self.axis == 0: return self._selected_obj[mask] else: return self._selected_obj.iloc[:, mask] @final @Substitution(name="groupby") @Substitution(see_also=_common_see_also) def tail(self, n=5): """ Return last n rows of each group. Similar to ``.apply(lambda x: x.tail(n))``, but it returns a subset of rows from the original DataFrame with original index and order preserved (``as_index`` flag is ignored). Does not work for negative values of `n`. 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').tail(-1) Empty DataFrame Columns: [A, B] Index: [] """ self._reset_group_selection() mask = self._cumcount_array(ascending=False) < n if self.axis == 0: return self._selected_obj[mask] else: return self._selected_obj.iloc[:, mask] @final def _reindex_output( self, output: OutputFrameOrSeries, fill_value: Scalar = np.NaN ) -> OutputFrameOrSeries: """ 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. fill_value : scalar, default np.NaN Value to use for unobserved categories if self.observed is False. 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, "fill_value": fill_value, } 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, fill_value=fill_value ) # 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) @final def sample( self, n: Optional[int] = None, frac: Optional[float] = None, replace: bool = False, weights: Optional[Union[Sequence, Series]] = None, random_state=None, ): """ Return a random sample of items from each group. You can use `random_state` for reproducibility. .. versionadded:: 1.1.0 Parameters ---------- n : int, optional Number of items to return for each group. Cannot be used with `frac` and must be no larger than the smallest group unless `replace` is True. Default is one if `frac` is None. frac : float, optional Fraction of items to return. Cannot be used with `n`. replace : bool, default False Allow or disallow sampling of the same row more than once. weights : list-like, optional Default None results in equal probability weighting. If passed a list-like then values must have the same length as the underlying DataFrame or Series object and will be used as sampling probabilities after normalization within each group. Values must be non-negative with at least one positive element within each group. random_state : int, array-like, BitGenerator, np.random.RandomState, optional If int, array-like, or BitGenerator (NumPy>=1.17), seed for random number generator If np.random.RandomState, use as numpy RandomState object. Returns ------- Series or DataFrame A new object of same type as caller containing items randomly sampled within each group from the caller object. See Also -------- DataFrame.sample: Generate random samples from a DataFrame object. numpy.random.choice: Generate a random sample from a given 1-D numpy array. Examples -------- >>> df = pd.DataFrame( ... {"a": ["red"] * 2 + ["blue"] * 2 + ["black"] * 2, "b": range(6)} ... ) >>> df a b 0 red 0 1 red 1 2 blue 2 3 blue 3 4 black 4 5 black 5 Select one row at random for each distinct value in column a. The `random_state` argument can be used to guarantee reproducibility: >>> df.groupby("a").sample(n=1, random_state=1) a b 4 black 4 2 blue 2 1 red 1 Set `frac` to sample fixed proportions rather than counts: >>> df.groupby("a")["b"].sample(frac=0.5, random_state=2) 5 5 2 2 0 0 Name: b, dtype: int64 Control sample probabilities within groups by setting weights: >>> df.groupby("a").sample( ... n=1, ... weights=[1, 1, 1, 0, 0, 1], ... random_state=1, ... ) a b 5 black 5 2 blue 2 0 red 0 """ from pandas.core.reshape.concat import concat if weights is not None: weights = Series(weights, index=self._selected_obj.index) ws = [weights[idx] for idx in self.indices.values()] else: ws = [None] * self.ngroups if random_state is not None: random_state = com.random_state(random_state) samples = [ obj.sample( n=n, frac=frac, replace=replace, weights=w, random_state=random_state ) for (_, obj), w in zip(self, ws) ] return concat(samples, axis=self.axis) @doc(GroupBy) def get_groupby( obj: NDFrame, by: Optional[_KeysArgType] = None, axis: int = 0, level=None, grouper: "Optional[ops.BaseGrouper]" = None, exclusions=None, selection=None, as_index: bool = True, sort: bool = True, group_keys: bool = True, squeeze: bool = False, observed: bool = False, mutated: bool = False, dropna: bool = True, ) -> GroupBy: klass: Type[GroupBy] 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: raise TypeError(f"invalid type: {obj}") return klass( obj=obj, keys=by, axis=axis, level=level, grouper=grouper, exclusions=exclusions, selection=selection, as_index=as_index, sort=sort, group_keys=group_keys, squeeze=squeeze, observed=observed, mutated=mutated, dropna=dropna, )