""" 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 import warnings import numpy as np from pandas._libs import Timestamp, groupby as libgroupby import pandas.compat as compat from pandas.compat import callable, range, set_function_name, zip 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_extension_array_dtype, is_numeric_dtype, is_scalar) from pandas.core.dtypes.missing import isna, notna import pandas.core.algorithms as algorithms from pandas.core.base import ( DataError, GroupByError, PandasObject, SelectionMixin, SpecificationError) import pandas.core.common as com from pandas.core.config import option_context from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame from pandas.core.groupby import base from pandas.core.index import Index, MultiIndex from pandas.core.series import Series from pandas.core.sorting import get_group_index_sorter _common_see_also = """ See Also -------- pandas.Series.%(name)s pandas.DataFrame.%(name)s pandas.Panel.%(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 -------- pandas.Series.pipe : Apply a function with arguments to a series. pandas.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 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() 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 __unicode__(self): # TODO: Better unicode/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): """ Constructs NDFrame from group with provided name. Parameters ---------- name : object the name of the group to get as a DataFrame obj : NDFrame, default None the NDFrame 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_extension_array_dtype(dtype): # The function can return something of any type, so check # if the type is compatible with the calling EA. 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 compat.iteritems(output): # 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): try: vals = vals.astype(np.bool) except ValueError: # for objects vals = np.array([bool(x) for x in vals]) return vals.view(np.uint8) def result_to_bool(result): return result.astype(np.bool, 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): """ Returns True if any value in the group is truthful, else False. Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing """ return self._bool_agg('any', skipna) @Substitution(name='groupby') @Appender(_common_see_also) def all(self, skipna=True): """ Returns True if all values in the group are truthful, else False. Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing """ return self._bool_agg('all', skipna) @Substitution(name='groupby') @Appender(_common_see_also) def count(self): """ Compute count of group, excluding missing values. """ # defined here for API doc raise NotImplementedError @Substitution(name='groupby', 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', **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 """ try: return self._cython_agg_general('median', **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 """ # 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 """ nv.validate_groupby_func('var', args, kwargs) if ddof == 1: try: return self._cython_agg_general('var', **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 """ return self.std(ddof=ddof) / np.sqrt(self.count()) @Substitution(name='groupby') @Appender(_common_see_also) def size(self): """ Compute group sizes. """ 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, _convert=False, min_count=-1): _local_template = "Compute %(f)s of group values" @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: return self._cython_agg_general( alias, alt=npfunc, **kwargs) except AssertionError as e: raise SpecificationError(str(e)) except Exception: result = self.aggregate( lambda x: npfunc(x, axis=self.axis)) if _convert: result = result._convert(datetime=True) 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 """ 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 ` 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 -------- pandas.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 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 See Also -------- Series.backfill DataFrame.backfill Series.fillna DataFrame.fillna """ return self._fill('bfill', limit=limit) bfill = backfill @Substitution(name='groupby', see_also=_common_see_also) def nth(self, n, dropna=None): """ 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 Truthy (if a Series) or 'all', 'any' (if a DataFrame); 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' %(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 """ if isinstance(n, int): nth_values = [n] elif isinstance(n, (set, list, tuple)): nth_values = list(set(n)) if dropna is not None: raise ValueError( "dropna option with a list of nth values is not supported") else: raise TypeError("n needs to be an int or a list/set/tuple of ints") nth_values = np.array(nth_values, dtype=np.intp) self._set_group_selection() if not dropna: mask_left = np.in1d(self._cumcount_array(), nth_values) mask_right = np.in1d(self._cumcount_array(ascending=False) + 1, -nth_values) mask = mask_left | mask_right out = self._selected_obj[mask] if not self.as_index: return out ids, _, _ = self.grouper.group_info out.index = self.grouper.result_index[ids[mask]] return out.sort_index() if self.sort else out if dropna not in ['any', 'all']: if isinstance(self._selected_obj, Series) and dropna is True: warnings.warn("the dropna={dropna} keyword is deprecated," "use dropna='all' instead. " "For a Series groupby, dropna must be " "either None, 'any' or 'all'.".format( dropna=dropna), FutureWarning, stacklevel=2) dropna = 'all' else: # 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 the 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 @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. 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. 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): """ Provides 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. """ 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. """ 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. """ 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. """ 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 Raises if `needs_values` is False post_processing : function, default None Function to be applied to result of Cython function **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) if needs_values: vals = obj.values if pre_processing: vals = 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) 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 """ 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. """ 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) filled = filled.drop(self.grouper.names, axis=1) fill_grp = filled.groupby(self.grouper.labels) shifted = fill_grp.shift(periods=periods, freq=freq) return (filled / shifted) - 1 @Substitution(name='groupby', see_also=_common_see_also) def head(self, n=5): """ Returns first n rows of each group. Essentially equivalent to ``.apply(lambda x: x.head(n))``, except ignores as_index flag. %(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', see_also=_common_see_also) def tail(self, n=5): """ Returns last n rows of each group. Essentially equivalent to ``.apply(lambda x: x.tail(n))``, except ignores as_index flag. %(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] 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)