/
groupby.py
526 lines (421 loc) · 17.1 KB
/
groupby.py
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import pandas
import pandas.core.groupby
from pandas.core.dtypes.common import is_list_like
import pandas.core.common as com
from modin.error_message import ErrorMessage
from .utils import _inherit_docstrings
@_inherit_docstrings(
pandas.core.groupby.DataFrameGroupBy,
excluded=[
pandas.core.groupby.DataFrameGroupBy,
pandas.core.groupby.DataFrameGroupBy.__init__,
],
)
class DataFrameGroupBy(object):
def __init__(
self,
df,
by,
axis,
level,
as_index,
sort,
group_keys,
squeeze,
idx_name,
**kwargs
):
self._axis = axis
self._idx_name = idx_name
self._df = df
self._query_compiler = self._df._query_compiler
self._index = self._query_compiler.index
self._columns = self._query_compiler.columns
self._by = by
if (
level is None
and not isinstance(by, type(self._query_compiler))
and is_list_like(by)
):
# This tells us whether or not there are multiple columns/rows in the groupby
self._is_multi_by = all(obj in self._df for obj in self._by) and axis == 0
else:
self._is_multi_by = False
self._level = level
self._kwargs = {
"level": level,
"sort": sort,
"as_index": as_index,
"group_keys": group_keys,
"squeeze": squeeze,
}
self._kwargs.update(kwargs)
@property
def _sort(self):
return self._kwargs.get("sort")
@property
def _as_index(self):
return self._kwargs.get("as_index")
def __getattr__(self, key):
"""Afer regular attribute access, looks up the name in the columns
Args:
key (str): Attribute name.
Returns:
The value of the attribute.
"""
try:
return object.__getattribute__(self, key)
except AttributeError as e:
if key in self._columns:
return self._default_to_pandas(lambda df: df.__getitem__(key))
raise e
_index_grouped_cache = None
@property
def _index_grouped(self):
if self._index_grouped_cache is None:
if self._is_multi_by:
# Because we are doing a collect (to_pandas) here and then groupby, we
# end up using pandas implementation. Add the warning so the user is
# aware.
ErrorMessage.catch_bugs_and_request_email(self._axis == 1)
ErrorMessage.default_to_pandas("Groupby with multiple columns")
self._index_grouped_cache = {
k: v.index
for k, v in self._df._query_compiler.getitem_column_array(self._by)
.to_pandas()
.groupby(by=self._by)
}
else:
if isinstance(self._by, type(self._query_compiler)):
by = self._by.to_pandas().squeeze()
else:
by = self._by
if self._axis == 0:
self._index_grouped_cache = self._index.groupby(by)
else:
self._index_grouped_cache = self._columns.groupby(by)
return self._index_grouped_cache
@property
def _iter(self):
from .dataframe import DataFrame
group_ids = self._index_grouped.keys()
if self._axis == 0:
return (
(
k,
DataFrame(
query_compiler=self._query_compiler.getitem_row_array(
self._index.get_indexer_for(self._index_grouped[k].unique())
)
),
)
for k in (sorted(group_ids) if self._sort else group_ids)
)
else:
return (
(
k,
DataFrame(
query_compiler=self._query_compiler.getitem_column_array(
self._index_grouped[k].unique()
)
),
)
for k in (sorted(group_ids) if self._sort else group_ids)
)
@property
def ngroups(self):
return len(self)
def skew(self, **kwargs):
return self._apply_agg_function(lambda df: df.skew(**kwargs))
def ffill(self, limit=None):
return self._default_to_pandas(lambda df: df.ffill(limit=limit))
def sem(self, ddof=1):
return self._default_to_pandas(lambda df: df.sem(ddof=ddof))
def mean(self, *args, **kwargs):
return self._apply_agg_function(lambda df: df.mean(*args, **kwargs))
def any(self, **kwargs):
return self._groupby_reduce(
lambda df: df.any(**kwargs), None, numeric_only=False
)
@property
def plot(self): # pragma: no cover
return self._default_to_pandas(lambda df: df.plot)
def ohlc(self):
return self._default_to_pandas(lambda df: df.ohlc())
def __bytes__(self):
return self._default_to_pandas(lambda df: df.__bytes__())
@property
def tshift(self):
return self._default_to_pandas(lambda df: df.tshift)
@property
def groups(self):
return self._index_grouped
def min(self, **kwargs):
return self._groupby_reduce(
lambda df: df.min(**kwargs), None, numeric_only=False
)
def idxmax(self):
return self._default_to_pandas(lambda df: df.idxmax())
@property
def ndim(self):
return 2 # ndim is always 2 for DataFrames
def shift(self, periods=1, freq=None, axis=0):
return self._default_to_pandas(
lambda df: df.shift(periods=periods, freq=freq, axis=axis)
)
def nth(self, n, dropna=None):
return self._default_to_pandas(lambda df: df.nth(n, dropna=dropna))
def cumsum(self, axis=0, *args, **kwargs):
return self._apply_agg_function(lambda df: df.cumsum(axis, *args, **kwargs))
@property
def indices(self):
return self._index_grouped
def pct_change(self):
return self._default_to_pandas(lambda df: df.pct_change())
def filter(self, func, dropna=True, *args, **kwargs):
return self._default_to_pandas(
lambda df: df.filter(func, dropna=dropna, *args, **kwargs)
)
def cummax(self, axis=0, **kwargs):
return self._apply_agg_function(lambda df: df.cummax(axis, **kwargs))
def apply(self, func, *args, **kwargs):
return self._apply_agg_function(lambda df: df.apply(func, *args, **kwargs))
@property
def dtypes(self):
if self._axis == 1:
raise ValueError("Cannot call dtypes on groupby with axis=1")
return self._apply_agg_function(lambda df: df.dtypes)
def first(self, **kwargs):
return self._default_to_pandas(lambda df: df.first(**kwargs))
def backfill(self, limit=None):
return self.bfill(limit)
def __getitem__(self, key):
return SeriesGroupBy(self._default_to_pandas(lambda df: df.__getitem__(key)))
def cummin(self, axis=0, **kwargs):
return self._apply_agg_function(lambda df: df.cummin(axis=axis, **kwargs))
def bfill(self, limit=None):
return self._default_to_pandas(lambda df: df.bfill(limit=limit))
def idxmin(self):
return self._default_to_pandas(lambda df: df.idxmin())
def prod(self, **kwargs):
return self._groupby_reduce(lambda df: df.prod(**kwargs), None)
def std(self, ddof=1, *args, **kwargs):
return self._apply_agg_function(lambda df: df.std(ddof, *args, **kwargs))
def aggregate(self, arg, *args, **kwargs):
if self._axis != 0:
# This is not implemented in pandas,
# so we throw a different message
raise NotImplementedError("axis other than 0 is not supported")
if is_list_like(arg):
return self._default_to_pandas(
lambda df: df.aggregate(arg, *args, **kwargs)
)
return self._apply_agg_function(lambda df: df.aggregate(arg, *args, **kwargs))
def last(self, **kwargs):
return self._default_to_pandas(lambda df: df.last(**kwargs))
def mad(self):
return self._default_to_pandas(lambda df: df.mad())
def rank(self, **kwargs):
return self._apply_agg_function(lambda df: df.rank(**kwargs))
@property
def corrwith(self):
return self._default_to_pandas(lambda df: df.corrwith)
def pad(self, limit=None):
return self._default_to_pandas(lambda df: df.pad(limit=limit))
def max(self, **kwargs):
return self._groupby_reduce(
lambda df: df.max(**kwargs), None, numeric_only=False
)
def var(self, ddof=1, *args, **kwargs):
return self._apply_agg_function(lambda df: df.var(ddof, *args, **kwargs))
def get_group(self, name, obj=None):
return self._default_to_pandas(lambda df: df.get_group(name, obj=obj))
def __len__(self):
return len(self._index_grouped)
def all(self, **kwargs):
return self._groupby_reduce(
lambda df: df.all(**kwargs), None, numeric_only=False
)
def size(self):
return pandas.Series({k: len(v) for k, v in self._index_grouped.items()})
def sum(self, **kwargs):
return self._groupby_reduce(lambda df: df.sum(**kwargs), None)
def describe(self, **kwargs):
return self._default_to_pandas(lambda df: df.describe(**kwargs))
def boxplot(
self,
grouped,
subplots=True,
column=None,
fontsize=None,
rot=0,
grid=True,
ax=None,
figsize=None,
layout=None,
**kwargs
):
return self._default_to_pandas(
lambda df: df.boxplot(
grouped,
subplots=subplots,
column=column,
fontsize=fontsize,
rot=rot,
grid=grid,
ax=ax,
figsize=figsize,
layout=layout,
**kwargs
)
)
def ngroup(self, ascending=True):
return self._default_to_pandas(lambda df: df.ngroup(ascending))
def nunique(self, dropna=True):
return self._apply_agg_function(lambda df: df.nunique(dropna), drop=False)
def resample(self, rule, *args, **kwargs):
return self._default_to_pandas(lambda df: df.resample(rule, *args, **kwargs))
def median(self, **kwargs):
return self._apply_agg_function(lambda df: df.median(**kwargs))
def head(self, n=5):
return self._default_to_pandas(lambda df: df.head(n))
def cumprod(self, axis=0, *args, **kwargs):
return self._apply_agg_function(lambda df: df.cumprod(axis, *args, **kwargs))
def __iter__(self):
return self._iter.__iter__()
def agg(self, arg, *args, **kwargs):
return self.aggregate(arg, *args, **kwargs)
def cov(self):
return self._default_to_pandas(lambda df: df.cov())
def transform(self, func, *args, **kwargs):
return self._apply_agg_function(lambda df: df.transform(func, *args, **kwargs))
def corr(self, **kwargs):
return self._default_to_pandas(lambda df: df.corr(**kwargs))
def fillna(self, **kwargs):
return self._apply_agg_function(lambda df: df.fillna(**kwargs))
def count(self, **kwargs):
return self._groupby_reduce(
lambda df: df.count(**kwargs),
lambda df: df.sum(**kwargs),
numeric_only=False,
)
def pipe(self, func, *args, **kwargs):
return com._pipe(self, func, *args, **kwargs)
def cumcount(self, ascending=True):
return self._default_to_pandas(lambda df: df.cumcount(ascending=ascending))
def tail(self, n=5):
return self._default_to_pandas(lambda df: df.tail(n))
# expanding and rolling are unique cases and need to likely be handled
# separately. They do not appear to be commonly used.
def expanding(self, *args, **kwargs):
return self._default_to_pandas(lambda df: df.expanding(*args, **kwargs))
def rolling(self, *args, **kwargs):
return self._default_to_pandas(lambda df: df.rolling(*args, **kwargs))
def hist(self):
return self._default_to_pandas(lambda df: df.hist())
def quantile(self, q=0.5, **kwargs):
import numpy as np
if self._df.dtypes.map(lambda x: x == np.dtype("O")).any():
raise TypeError("'quantile' cannot be performed against 'object' dtypes!")
if is_list_like(q):
return self._default_to_pandas(lambda df: df.quantile(q=q, **kwargs))
return self._apply_agg_function(lambda df: df.quantile(q, **kwargs))
def diff(self):
return self._default_to_pandas(lambda df: df.diff())
def take(self, **kwargs):
return self._default_to_pandas(lambda df: df.take(**kwargs))
def _groupby_reduce(
self, map_func, reduce_func, drop=True, numeric_only=True, **kwargs
):
if self._is_multi_by:
return self._default_to_pandas(map_func, **kwargs)
if not isinstance(self._by, type(self._query_compiler)):
return self._apply_agg_function(map_func, drop=drop, **kwargs)
# For aggregations, pandas behavior does this for the result.
# For other operations it does not, so we wait until there is an aggregation to
# actually perform this operation.
if self._idx_name is not None and drop:
groupby_qc = self._query_compiler.drop(columns=[self._idx_name])
else:
groupby_qc = self._query_compiler
from .dataframe import DataFrame
return DataFrame(
query_compiler=groupby_qc.groupby_reduce(
self._by,
self._axis,
self._kwargs,
map_func,
kwargs,
reduce_func=reduce_func,
reduce_args=kwargs,
numeric_only=numeric_only,
)
)
def _apply_agg_function(self, f, drop=True, **kwargs):
"""Perform aggregation and combine stages based on a given function.
Args:
f: The function to apply to each group.
Returns:
A new combined DataFrame with the result of all groups.
"""
assert callable(f), "'{0}' object is not callable".format(type(f))
from .dataframe import DataFrame
if isinstance(self._by, type(self._query_compiler)):
by = self._by.to_pandas().squeeze()
else:
by = self._by
if self._is_multi_by:
return self._default_to_pandas(f, **kwargs)
# For aggregations, pandas behavior does this for the result.
# For other operations it does not, so we wait until there is an aggregation to
# actually perform this operation.
if self._idx_name is not None and drop:
groupby_qc = self._query_compiler.drop(columns=[self._idx_name])
else:
groupby_qc = self._query_compiler
new_manager = groupby_qc.groupby_agg(by, self._axis, f, self._kwargs, kwargs)
if self._idx_name is not None and self._as_index:
new_manager.index.name = self._idx_name
return DataFrame(query_compiler=new_manager)
def _default_to_pandas(self, f, **kwargs):
"""Defailts the execution of this function to pandas.
Args:
f: The function to apply to each group.
Returns:
A new Modin DataFrame with the result of the pandas function.
"""
if isinstance(self._by, type(self._query_compiler)):
by = self._by.to_pandas().squeeze()
else:
by = self._by
def groupby_on_multiple_columns(df):
return f(df.groupby(by=by, axis=self._axis, **self._kwargs), **kwargs)
return self._df._default_to_pandas(groupby_on_multiple_columns)
class SeriesGroupBy(object): # pragma: no cover
def __init__(self, pandas_groupby_obj):
self._pandas_groupby_obj = pandas_groupby_obj
def __getattribute__(self, item):
if item in ["_pandas_groupby_obj"]:
return object.__getattribute__(self, item)
return_val = self._pandas_groupby_obj.__getattribute__(item)
from .series import Series
from .dataframe import DataFrame
if isinstance(return_val, pandas.Series):
return Series(return_val)
elif isinstance(return_val, pandas.DataFrame):
return DataFrame(return_val)
elif callable(return_val):
# This wraps the pandas callable and intercepts the return value before it
# is given back to the user to re-distribute it.
def wrapper(*args, **kwargs):
pandas_return_val = return_val(*args, **kwargs)
if isinstance(pandas_return_val, pandas.Series):
return Series(pandas_return_val)
elif isinstance(pandas_return_val, pandas.DataFrame):
return DataFrame(pandas_return_val)
else:
return pandas_return_val
return wrapper
else:
return return_val