| Current File : //usr/local/lib64/python3.6/site-packages/pandas/core/apply.py |
import abc
import inspect
from typing import TYPE_CHECKING, Any, Dict, Iterator, Optional, Tuple, Type, Union
import numpy as np
from pandas._config import option_context
from pandas._typing import Axis
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import (
is_dict_like,
is_extension_array_dtype,
is_list_like,
is_sequence,
)
from pandas.core.dtypes.generic import ABCSeries
from pandas.core.construction import create_series_with_explicit_dtype
if TYPE_CHECKING:
from pandas import DataFrame, Index, Series
ResType = Dict[int, Any]
def frame_apply(
obj: "DataFrame",
func,
axis: Axis = 0,
raw: bool = False,
result_type: Optional[str] = None,
ignore_failures: bool = False,
args=None,
kwds=None,
):
""" construct and return a row or column based frame apply object """
axis = obj._get_axis_number(axis)
klass: Type[FrameApply]
if axis == 0:
klass = FrameRowApply
elif axis == 1:
klass = FrameColumnApply
return klass(
obj,
func,
raw=raw,
result_type=result_type,
ignore_failures=ignore_failures,
args=args,
kwds=kwds,
)
class FrameApply(metaclass=abc.ABCMeta):
# ---------------------------------------------------------------
# Abstract Methods
axis: int
@property
@abc.abstractmethod
def result_index(self) -> "Index":
pass
@property
@abc.abstractmethod
def result_columns(self) -> "Index":
pass
@property
@abc.abstractmethod
def series_generator(self) -> Iterator["Series"]:
pass
@abc.abstractmethod
def wrap_results_for_axis(
self, results: ResType, res_index: "Index"
) -> Union["Series", "DataFrame"]:
pass
# ---------------------------------------------------------------
def __init__(
self,
obj: "DataFrame",
func,
raw: bool,
result_type: Optional[str],
ignore_failures: bool,
args,
kwds,
):
self.obj = obj
self.raw = raw
self.ignore_failures = ignore_failures
self.args = args or ()
self.kwds = kwds or {}
if result_type not in [None, "reduce", "broadcast", "expand"]:
raise ValueError(
"invalid value for result_type, must be one "
"of {None, 'reduce', 'broadcast', 'expand'}"
)
self.result_type = result_type
# curry if needed
if (kwds or args) and not isinstance(func, (np.ufunc, str)):
def f(x):
return func(x, *args, **kwds)
else:
f = func
self.f = f
@property
def res_columns(self) -> "Index":
return self.result_columns
@property
def columns(self) -> "Index":
return self.obj.columns
@property
def index(self) -> "Index":
return self.obj.index
@cache_readonly
def values(self):
return self.obj.values
@cache_readonly
def dtypes(self) -> "Series":
return self.obj.dtypes
@property
def agg_axis(self) -> "Index":
return self.obj._get_agg_axis(self.axis)
def get_result(self):
""" compute the results """
# dispatch to agg
if is_list_like(self.f) or is_dict_like(self.f):
return self.obj.aggregate(self.f, axis=self.axis, *self.args, **self.kwds)
# all empty
if len(self.columns) == 0 and len(self.index) == 0:
return self.apply_empty_result()
# string dispatch
if isinstance(self.f, str):
# Support for `frame.transform('method')`
# Some methods (shift, etc.) require the axis argument, others
# don't, so inspect and insert if necessary.
func = getattr(self.obj, self.f)
sig = inspect.getfullargspec(func)
if "axis" in sig.args:
self.kwds["axis"] = self.axis
return func(*self.args, **self.kwds)
# ufunc
elif isinstance(self.f, np.ufunc):
with np.errstate(all="ignore"):
results = self.obj._mgr.apply("apply", func=self.f)
# _constructor will retain self.index and self.columns
return self.obj._constructor(data=results)
# broadcasting
if self.result_type == "broadcast":
return self.apply_broadcast(self.obj)
# one axis empty
elif not all(self.obj.shape):
return self.apply_empty_result()
# raw
elif self.raw:
return self.apply_raw()
return self.apply_standard()
def apply_empty_result(self):
"""
we have an empty result; at least 1 axis is 0
we will try to apply the function to an empty
series in order to see if this is a reduction function
"""
# we are not asked to reduce or infer reduction
# so just return a copy of the existing object
if self.result_type not in ["reduce", None]:
return self.obj.copy()
# we may need to infer
should_reduce = self.result_type == "reduce"
from pandas import Series
if not should_reduce:
try:
r = self.f(Series([], dtype=np.float64))
except Exception:
pass
else:
should_reduce = not isinstance(r, Series)
if should_reduce:
if len(self.agg_axis):
r = self.f(Series([], dtype=np.float64))
else:
r = np.nan
return self.obj._constructor_sliced(r, index=self.agg_axis)
else:
return self.obj.copy()
def apply_raw(self):
""" apply to the values as a numpy array """
def wrap_function(func):
"""
Wrap user supplied function to work around numpy issue.
see https://github.com/numpy/numpy/issues/8352
"""
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if isinstance(result, str):
result = np.array(result, dtype=object)
return result
return wrapper
result = np.apply_along_axis(wrap_function(self.f), self.axis, self.values)
# TODO: mixed type case
if result.ndim == 2:
return self.obj._constructor(result, index=self.index, columns=self.columns)
else:
return self.obj._constructor_sliced(result, index=self.agg_axis)
def apply_broadcast(self, target: "DataFrame") -> "DataFrame":
result_values = np.empty_like(target.values)
# axis which we want to compare compliance
result_compare = target.shape[0]
for i, col in enumerate(target.columns):
res = self.f(target[col])
ares = np.asarray(res).ndim
# must be a scalar or 1d
if ares > 1:
raise ValueError("too many dims to broadcast")
elif ares == 1:
# must match return dim
if result_compare != len(res):
raise ValueError("cannot broadcast result")
result_values[:, i] = res
# we *always* preserve the original index / columns
result = self.obj._constructor(
result_values, index=target.index, columns=target.columns
)
return result
def apply_standard(self):
results, res_index = self.apply_series_generator()
# wrap results
return self.wrap_results(results, res_index)
def apply_series_generator(self) -> Tuple[ResType, "Index"]:
series_gen = self.series_generator
res_index = self.result_index
results = {}
if self.ignore_failures:
successes = []
for i, v in enumerate(series_gen):
try:
results[i] = self.f(v)
except Exception:
pass
else:
successes.append(i)
# so will work with MultiIndex
if len(successes) < len(res_index):
res_index = res_index.take(successes)
else:
with option_context("mode.chained_assignment", None):
for i, v in enumerate(series_gen):
# ignore SettingWithCopy here in case the user mutates
results[i] = self.f(v)
if isinstance(results[i], ABCSeries):
# If we have a view on v, we need to make a copy because
# series_generator will swap out the underlying data
results[i] = results[i].copy(deep=False)
return results, res_index
def wrap_results(
self, results: ResType, res_index: "Index"
) -> Union["Series", "DataFrame"]:
from pandas import Series
# see if we can infer the results
if len(results) > 0 and 0 in results and is_sequence(results[0]):
return self.wrap_results_for_axis(results, res_index)
# dict of scalars
# the default dtype of an empty Series will be `object`, but this
# code can be hit by df.mean() where the result should have dtype
# float64 even if it's an empty Series.
constructor_sliced = self.obj._constructor_sliced
if constructor_sliced is Series:
result = create_series_with_explicit_dtype(
results, dtype_if_empty=np.float64
)
else:
result = constructor_sliced(results)
result.index = res_index
return result
class FrameRowApply(FrameApply):
axis = 0
def apply_broadcast(self, target: "DataFrame") -> "DataFrame":
return super().apply_broadcast(target)
@property
def series_generator(self):
return (self.obj._ixs(i, axis=1) for i in range(len(self.columns)))
@property
def result_index(self) -> "Index":
return self.columns
@property
def result_columns(self) -> "Index":
return self.index
def wrap_results_for_axis(
self, results: ResType, res_index: "Index"
) -> Union["Series", "DataFrame"]:
""" return the results for the rows """
if self.result_type == "reduce":
# e.g. test_apply_dict GH#8735
res = self.obj._constructor_sliced(results)
res.index = res_index
return res
elif self.result_type is None and all(
isinstance(x, dict) for x in results.values()
):
# Our operation was a to_dict op e.g.
# test_apply_dict GH#8735, test_apply_reduce_rows_to_dict GH#25196
return self.obj._constructor_sliced(results)
try:
result = self.obj._constructor(data=results)
except ValueError as err:
if "arrays must all be same length" in str(err):
# e.g. result = [[2, 3], [1.5], ['foo', 'bar']]
# see test_agg_listlike_result GH#29587
res = self.obj._constructor_sliced(results)
res.index = res_index
return res
else:
raise
if not isinstance(results[0], ABCSeries):
if len(result.index) == len(self.res_columns):
result.index = self.res_columns
if len(result.columns) == len(res_index):
result.columns = res_index
return result
class FrameColumnApply(FrameApply):
axis = 1
def apply_broadcast(self, target: "DataFrame") -> "DataFrame":
result = super().apply_broadcast(target.T)
return result.T
@property
def series_generator(self):
values = self.values
assert len(values) > 0
# We create one Series object, and will swap out the data inside
# of it. Kids: don't do this at home.
ser = self.obj._ixs(0, axis=0)
mgr = ser._mgr
blk = mgr.blocks[0]
if is_extension_array_dtype(blk.dtype):
# values will be incorrect for this block
# TODO(EA2D): special case would be unnecessary with 2D EAs
obj = self.obj
for i in range(len(obj)):
yield obj._ixs(i, axis=0)
else:
for (arr, name) in zip(values, self.index):
# GH#35462 re-pin mgr in case setitem changed it
ser._mgr = mgr
blk.values = arr
ser.name = name
yield ser
@property
def result_index(self) -> "Index":
return self.index
@property
def result_columns(self) -> "Index":
return self.columns
def wrap_results_for_axis(
self, results: ResType, res_index: "Index"
) -> Union["Series", "DataFrame"]:
""" return the results for the columns """
result: Union["Series", "DataFrame"]
# we have requested to expand
if self.result_type == "expand":
result = self.infer_to_same_shape(results, res_index)
# we have a non-series and don't want inference
elif not isinstance(results[0], ABCSeries):
result = self.obj._constructor_sliced(results)
result.index = res_index
# we may want to infer results
else:
result = self.infer_to_same_shape(results, res_index)
return result
def infer_to_same_shape(self, results: ResType, res_index: "Index") -> "DataFrame":
""" infer the results to the same shape as the input object """
result = self.obj._constructor(data=results)
result = result.T
# set the index
result.index = res_index
# infer dtypes
result = result.infer_objects()
return result