#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2025 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import operator
import weakref
from collections.abc import Iterable
from io import StringIO
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
from ..core import (
ENTITY_TYPE,
HasShapeTileable,
HasShapeTileableData,
OutputType,
Tileable,
_ExecuteAndFetchMixin,
is_build_mode,
register_output_types,
)
from ..core.entity.utils import refresh_tileable_shape
from ..protocol import DataFrameTableMeta
from ..serialization.serializables import (
AnyField,
BoolField,
DataTypeField,
DictField,
Int32Field,
IntervalArrayField,
ListField,
NDArrayField,
OneOfField,
ReferenceField,
Serializable,
SeriesField,
SliceField,
StringField,
)
from ..session import get_default_session
from ..utils import (
calc_nsplits,
ceildiv,
estimate_pandas_size,
on_serialize_numpy_type,
tokenize,
)
from .utils import (
ReprSeries,
apply_if_callable,
fetch_corner_data,
merge_index_value,
parse_index,
)
class IndexValue(Serializable):
"""
Meta class for index, held by IndexData, SeriesData and DataFrameData
"""
__slots__ = ()
class IndexBase(Serializable):
_key = StringField("key") # to identify if the index is the same
_is_monotonic_increasing = BoolField("is_monotonic_increasing")
_is_monotonic_decreasing = BoolField("is_monotonic_decreasing")
_is_unique = BoolField("is_unique")
_max_val = AnyField("max_val", on_serialize=on_serialize_numpy_type)
_max_val_close = BoolField("max_val_close")
_min_val = AnyField("min_val", on_serialize=on_serialize_numpy_type)
_min_val_close = BoolField("min_val_close")
@property
def is_monotonic_increasing(self):
return self._is_monotonic_increasing
@property
def is_monotonic_decreasing(self):
return self._is_monotonic_decreasing
@property
def is_unique(self):
return self._is_unique
@property
def min_val(self):
return self._min_val
@property
def min_val_close(self):
return self._min_val_close
@property
def max_val(self):
return self._max_val
@property
def max_val_close(self):
return self._max_val_close
@property
def key(self):
return self._key
@property
def inferred_type(self):
return None
def to_pandas(self):
kw = {
field.tag: getattr(self, attr, None)
for attr, field in self._FIELDS.items()
if attr not in super(type(self), self)._FIELDS
}
kw = {k: v for k, v in kw.items() if v is not None}
if kw.get("data") is None:
kw["data"] = []
pd_initializer = getattr(self, "_pd_initializer", None)
if pd_initializer is None:
pd_initializer = getattr(pd, type(self).__name__)
return pd_initializer(**kw)
class Index(IndexBase):
_name = AnyField("name")
_data = NDArrayField("data")
_dtype = DataTypeField("dtype")
@property
def dtype(self):
return getattr(self, "_dtype", None)
@property
def inferred_type(self):
return "floating" if self.dtype.kind == "f" else "integer"
class RangeIndex(IndexBase):
_name = AnyField("name")
_slice = SliceField("slice")
_dtype = DataTypeField("dtype")
@property
def slice(self):
return self._slice
@property
def dtype(self):
return getattr(self, "_dtype", np.dtype(np.intc))
def to_pandas(self):
slc = self._slice
return pd.RangeIndex(
slc.start, slc.stop, slc.step, name=getattr(self, "_name", None)
)
class CategoricalIndex(IndexBase):
_name = AnyField("name")
_data = NDArrayField("data")
_categories = AnyField("categories")
_ordered = BoolField("ordered")
@property
def inferred_type(self):
return "categorical"
class IntervalIndex(IndexBase):
_name = AnyField("name")
_data = IntervalArrayField("data")
_closed = StringField("closed")
@property
def inferred_type(self):
return "interval"
class DatetimeIndex(IndexBase):
_name = AnyField("name")
_data = NDArrayField("data")
_freq = AnyField("freq")
_start = AnyField("start")
_periods = AnyField("periods")
_end = AnyField("end")
_closed = AnyField("closed")
_tz = AnyField("tz")
_ambiguous = AnyField("ambiguous")
_dayfirst = BoolField("dayfirst")
_yearfirst = BoolField("yearfirst")
@property
def inferred_type(self):
return "datetime64"
@property
def freq(self):
return getattr(self, "_freq", None)
class TimedeltaIndex(IndexBase):
_name = AnyField("name")
_data = NDArrayField("data")
_unit = AnyField("unit")
_freq = AnyField("freq")
_start = AnyField("start")
_periods = AnyField("periods")
_end = AnyField("end")
_closed = AnyField("closed")
@property
def inferred_type(self):
return "timedelta64"
class PeriodIndex(IndexBase):
_name = AnyField("name")
_data = NDArrayField("data")
_freq = AnyField("freq")
_start = AnyField("start")
_periods = AnyField("periods")
_end = AnyField("end")
_year = AnyField("year")
_month = AnyField("month")
_quarter = AnyField("quarter")
_day = AnyField("day")
_hour = AnyField("hour")
_minute = AnyField("minute")
_second = AnyField("second")
_tz = AnyField("tz")
_dtype = DataTypeField("dtype")
@property
def inferred_type(self):
return "period"
class Int64Index(IndexBase):
_pd_initializer = pd.Index
_name = AnyField("name")
_data = NDArrayField("data")
_dtype = DataTypeField("dtype")
@property
def dtype(self):
return getattr(self, "_dtype", None)
@property
def inferred_type(self):
return "integer"
class UInt64Index(IndexBase):
_pd_initializer = pd.Index
_name = AnyField("name")
_data = NDArrayField("data")
_dtype = DataTypeField("dtype")
@property
def dtype(self):
return getattr(self, "_dtype", None)
@property
def inferred_type(self):
return "integer"
class Float64Index(IndexBase):
_pd_initializer = pd.Index
_name = AnyField("name")
_data = NDArrayField("data")
_dtype = DataTypeField("dtype")
@property
def dtype(self):
return getattr(self, "_dtype", None)
@property
def inferred_type(self):
return "floating"
class MultiIndex(IndexBase):
_names = ListField("names", on_serialize=list)
_dtypes = ListField("dtypes", on_serialize=list)
_data = NDArrayField("data")
_sortorder = Int32Field("sortorder")
@property
def inferred_type(self):
return "mixed"
@property
def names(self) -> list:
return self._names
def to_pandas(self):
data = getattr(self, "_data", None)
sortorder = getattr(self, "_sortorder", None)
def _build_empty_array(dtype):
try:
return np.array([], dtype=dtype)
except TypeError: # pragma: no cover
return pd.array([], dtype=dtype)
if data is None:
return pd.MultiIndex.from_arrays(
[_build_empty_array(dtype) for dtype in self._dtypes],
sortorder=sortorder,
names=self._names,
)
return pd.MultiIndex.from_tuples(
[tuple(d) for d in data], sortorder=sortorder, names=self._names
)
_index_value = OneOfField(
"index_value",
index=Index,
range_index=RangeIndex,
categorical_index=CategoricalIndex,
interval_index=IntervalIndex,
datetime_index=DatetimeIndex,
timedelta_index=TimedeltaIndex,
period_index=PeriodIndex,
int64_index=Int64Index,
uint64_index=UInt64Index,
float64_index=Float64Index,
multi_index=MultiIndex,
)
def __maxframe_tokenize__(self):
# return object for tokenize
v = self._index_value
return v._key
@property
def value(self):
return self._index_value
@property
def key(self):
return self._index_value.key
@property
def is_monotonic_increasing(self):
return self._index_value.is_monotonic_increasing
@property
def is_monotonic_decreasing(self):
return self._index_value.is_monotonic_decreasing
@property
def is_monotonic_increasing_or_decreasing(self):
return self.is_monotonic_increasing or self.is_monotonic_decreasing
@property
def is_unique(self):
return self._index_value.is_unique
@property
def min_val(self):
return self._index_value.min_val
@property
def min_val_close(self):
return self._index_value.min_val_close
@property
def max_val(self):
return self._index_value.max_val
@property
def max_val_close(self):
return self._index_value.max_val_close
@property
def min_max(self):
return (
self._index_value.min_val,
self._index_value.min_val_close,
self._index_value.max_val,
self._index_value.max_val_close,
)
@property
def name(self):
return getattr(self._index_value, "_name", None)
@property
def names(self):
return getattr(self._index_value, "_names", [self.name])
@property
def inferred_type(self):
return self._index_value.inferred_type
def has_value(self):
if isinstance(self._index_value, self.RangeIndex):
if np.isnan(self._index_value.max_val):
return False
else:
return True
elif getattr(self._index_value, "_data", None) is not None:
return True
return False
def to_pandas(self):
return self._index_value.to_pandas()
class DtypesValue(Serializable):
"""
Meta class for dtypes.
"""
__slots__ = ()
_key = StringField("key")
_value = SeriesField("value")
def __init__(self, key=None, value=None, **kw):
super().__init__(_key=key, _value=value, **kw)
if self._key is None:
self._key = tokenize(self._value)
@property
def key(self):
return self._key
@property
def value(self):
return self._value
def refresh_index_value(tileable: ENTITY_TYPE):
index_to_index_values = dict()
for chunk in tileable.chunks:
if chunk.ndim == 1:
index_to_index_values[chunk.index] = chunk.index_value
elif chunk.index[1] == 0:
index_to_index_values[chunk.index] = chunk.index_value
index_value = merge_index_value(index_to_index_values, store_data=False)
# keep key as original index_value's
index_value._index_value._key = tileable.index_value.key
tileable._index_value = index_value
def refresh_dtypes(tileable: ENTITY_TYPE):
all_dtypes = [c.dtypes_value.value for c in tileable.chunks if c.index[0] == 0]
dtypes = pd.concat(all_dtypes)
tileable._dtypes = dtypes
columns_values = parse_index(dtypes.index, store_data=True)
tileable._columns_value = columns_values
tileable._dtypes_value = DtypesValue(key=tokenize(dtypes), value=dtypes)
_tileable_key_property = "_tileable_key"
_tileable_dtypes_property = "_tileable_dtypes"
_tileable_index_value_property = "_tileable_index_value"
_tileable_columns_value_property = "_tileable_columns_value"
_nsplits_property = "_tileable_nsplits"
_lazy_chunk_meta_properties = (
_tileable_key_property,
_tileable_dtypes_property,
_tileable_index_value_property,
_tileable_columns_value_property,
_nsplits_property,
)
@functools.lru_cache(maxsize=128)
def _get_cum_nsplit(nsplit: Tuple[int]) -> List[int]:
return [0] + np.cumsum(nsplit).tolist()
def _calc_axis_slice(nsplit: Tuple[int], index: int) -> slice:
if not isinstance(nsplit, tuple):
nsplit = tuple(nsplit)
cum_nsplit = _get_cum_nsplit(nsplit)
return slice(cum_nsplit[index], cum_nsplit[index + 1])
def _on_deserialize_index_value(index_value):
if index_value is None:
return
try:
getattr(index_value, "value")
return index_value
except AttributeError:
return
class _ToPandasMixin(_ExecuteAndFetchMixin):
__slots__ = ()
def to_pandas(self, session=None, **kw):
return self._execute_and_fetch(session=session, **kw)
class _BatchedFetcher:
__slots__ = ()
def _iter(self, batch_size=None, session=None, **kw):
from .indexing.iloc import iloc
if batch_size is not None:
size = self.shape[0]
n_batch = ceildiv(size, batch_size)
if n_batch > 1:
for i in range(n_batch):
batch_data = iloc(self)[batch_size * i : batch_size * (i + 1)]
yield batch_data._fetch(session=session, **kw)
else:
yield self._fetch(session=session, **kw)
else:
# if batch_size is not specified, use first batch to estimate
# batch_size.
default_batch_bytes = 50 * 1024**2
first_batch = 1000
size = self.shape[0]
if size >= first_batch:
batch_data = iloc(self)[:first_batch]
first_batch_data = batch_data._fetch(session=session, **kw)
yield first_batch_data
data_size = estimate_pandas_size(first_batch_data)
batch_size = int(default_batch_bytes / data_size * first_batch)
n_batch = ceildiv(size - 1000, batch_size)
for i in range(n_batch):
batch_data = iloc(self)[
first_batch
+ batch_size * i : first_batch
+ batch_size * (i + 1)
]
yield batch_data._fetch(session=session, **kw)
else:
yield self._fetch(session=session, **kw)
def iterbatch(self, batch_size=None, session=None, **kw):
# stop triggering execution under build mode
if is_build_mode():
raise ValueError("Cannot fetch data under build mode")
# trigger execution
self.execute(session=session, **kw)
return self._iter(batch_size=batch_size, session=session)
def fetch(self, session=None, **kw):
from .indexing.iloc import DataFrameIlocGetItem, SeriesIlocGetItem
batch_size = kw.pop("batch_size", None)
if isinstance(self.op, (DataFrameIlocGetItem, SeriesIlocGetItem)):
# see GH#1871
# already iloc, do not trigger batch fetch
return self._fetch(session=session, **kw)
else:
batches = list(self._iter(batch_size=batch_size, session=session, **kw))
return pd.concat(batches) if len(batches) > 1 else batches[0]
def fetch_infos(self, fields=None, session=None, **kw):
return self._fetch_infos(fields=fields, session=session, **kw)
class IndexData(HasShapeTileableData, _ToPandasMixin):
__slots__ = ()
type_name = "Index"
# optional field
_dtype = DataTypeField("dtype")
_name = AnyField("name")
_names = AnyField("names")
_index_value = ReferenceField(
"index_value", IndexValue, on_deserialize=_on_deserialize_index_value
)
def __init__(
self,
op=None,
shape=None,
nsplits=None,
dtype=None,
name=None,
names=None,
index_value=None,
**kw,
):
super().__init__(
_op=op,
_shape=shape,
_nsplits=nsplits,
_dtype=dtype,
_name=name,
_names=names,
_index_value=index_value,
**kw,
)
@property
def params(self) -> Dict[str, Any]:
# params return the properties which useful to rebuild a new tileable object
return {
"shape": self.shape,
"dtype": self.dtype,
"name": self.name,
"index_value": self.index_value,
}
@params.setter
def params(self, new_params: Dict[str, Any]):
params = new_params.copy()
new_shape = params.pop("shape", None)
if new_shape is not None:
self._shape = new_shape
dtype = params.pop("dtype", None)
if dtype is not None:
self._dtype = dtype
index_value = params.pop("index_value", None)
if index_value is not None:
self._index_value = index_value
name = params.pop("name", None)
if name is not None:
self._name = name
if params: # pragma: no cover
raise TypeError(f"Unknown params: {list(params)}")
def refresh_params(self):
# refresh params when chunks updated
refresh_tileable_shape(self)
refresh_index_value(self)
if self._dtype is None:
self._dtype = self.chunks[0].dtype
if self._name is None:
self._name = self.chunks[0].name
def refresh_from_table_meta(self, table_meta: DataFrameTableMeta) -> None:
pass
def _to_str(self, representation=False):
if is_build_mode() or len(self._executed_sessions) == 0:
# in build mode, or not executed, just return representation
if representation:
return f"Index <op={type(self._op).__name__}, key={self.key}"
else:
return f"Index(op={type(self._op).__name__})"
else:
data = self.fetch(session=self._executed_sessions[-1])
return repr(data) if repr(data) else str(data)
def __str__(self):
return self._to_str(representation=False)
def __repr__(self):
return self._to_str(representation=True)
def _to_maxframe_tensor(self, dtype=None, order="K", extract_multi_index=False):
tensor = self.to_tensor(extract_multi_index=extract_multi_index)
dtype = dtype if dtype is not None else tensor.dtype
return tensor.astype(dtype=dtype, order=order, copy=False)
def __maxframe_tensor__(self, dtype=None, order="K"):
return self._to_maxframe_tensor(dtype=dtype, order=order)
@property
def dtype(self):
return getattr(self, "_dtype", None) or self.op.dtype
@property
def name(self):
return self._name
@property
def names(self):
return getattr(self, "_names", None) or [self.name]
@property
def index_value(self) -> IndexValue:
return self._index_value
@property
def inferred_type(self):
return self._index_value.inferred_type
def to_tensor(self, dtype=None, extract_multi_index=False):
from ..tensor.datasource.from_dataframe import from_index
return from_index(self, dtype=dtype, extract_multi_index=extract_multi_index)
class Index(HasShapeTileable, _ToPandasMixin):
__slots__ = "_df_or_series", "_parent_key", "_axis"
_allow_data_type_ = (IndexData,)
type_name = "Index"
def __new__(cls, data: Union[pd.Index, IndexData] = None, **_):
if data is not None and not isinstance(data, pd.Index):
# create corresponding Index class
# according to type of index_value
clz = globals()[type(data.index_value.value).__name__]
else:
clz = cls
return object.__new__(clz)
def __len__(self):
return len(self._data)
def __maxframe_tensor__(self, dtype=None, order="K"):
return self._data.__maxframe_tensor__(dtype=dtype, order=order)
def _get_df_or_series(self):
obj = getattr(self, "_df_or_series", None)
if obj is not None:
return obj()
return None
def _set_df_or_series(self, df_or_series, axis):
self._df_or_series = weakref.ref(df_or_series)
self._parent_key = df_or_series.key
self._axis = axis
@property
def T(self):
"""Return the transpose, which is by definition self."""
return self
@property
def name(self):
return self._data.name
@name.setter
def name(self, value):
df_or_series = self._get_df_or_series()
if df_or_series is not None and df_or_series.key == self._parent_key:
df_or_series.rename_axis(value, axis=self._axis, inplace=True)
self.data = df_or_series.axes[self._axis].data
else:
self.rename(value, inplace=True)
@property
def names(self):
return self._data.names
@names.setter
def names(self, value):
df_or_series = self._get_df_or_series()
if df_or_series is not None:
df_or_series.rename_axis(value, axis=self._axis, inplace=True)
self.data = df_or_series.axes[self._axis].data
else:
self.rename(value, inplace=True)
@property
def values(self):
return self.to_tensor()
def to_frame(self, index: bool = True, name=None):
"""
Create a DataFrame with a column containing the Index.
Parameters
----------
index : bool, default True
Set the index of the returned DataFrame as the original Index.
name : object, default None
The passed name should substitute for the index name (if it has
one).
Returns
-------
DataFrame
DataFrame containing the original Index data.
See Also
--------
Index.to_series : Convert an Index to a Series.
Series.to_frame : Convert Series to DataFrame.
Examples
--------
>>> import maxframe.dataframe as md
>>> idx = md.Index(['Ant', 'Bear', 'Cow'], name='animal')
>>> idx.to_frame().execute()
animal
animal
Ant Ant
Bear Bear
Cow Cow
By default, the original Index is reused. To enforce a new Index:
>>> idx.to_frame(index=False).execute()
animal
0 Ant
1 Bear
2 Cow
To override the name of the resulting column, specify `name`:
>>> idx.to_frame(index=False, name='zoo').execute()
zoo
0 Ant
1 Bear
2 Cow
"""
from . import dataframe_from_tensor
if isinstance(self.index_value.value, IndexValue.MultiIndex):
old_names = self.index_value.value.names
if (
name is not None
and not isinstance(name, Iterable)
or isinstance(name, str)
):
raise TypeError("'name' must be a list / sequence of column names.")
name = list(name if name is not None else old_names)
if len(name) != len(old_names):
raise ValueError(
"'name' should have same length as number of levels on index."
)
columns = [
old or new or idx for idx, (old, new) in enumerate(zip(old_names, name))
]
else:
columns = [name or self.name or 0]
index_ = self if index else None
return dataframe_from_tensor(
self._data._to_maxframe_tensor(self, extract_multi_index=True),
index=index_,
columns=columns,
)
def to_series(self, index=None, name=None):
"""
Create a Series with both index and values equal to the index keys.
Useful with map for returning an indexer based on an index.
Parameters
----------
index : Index, optional
Index of resulting Series. If None, defaults to original index.
name : str, optional
Dame of resulting Series. If None, defaults to name of original
index.
Returns
-------
Series
The dtype will be based on the type of the Index values.
"""
from . import series_from_index
return series_from_index(self, index=index, name=name)
class RangeIndex(Index):
__slots__ = ()
class CategoricalIndex(Index):
__slots__ = ()
class IntervalIndex(Index):
__slots__ = ()
class DatetimeIndex(Index):
__slots__ = ()
class TimedeltaIndex(Index):
__slots__ = ()
class PeriodIndex(Index):
__slots__ = ()
class Int64Index(Index):
__slots__ = ()
class UInt64Index(Index):
__slots__ = ()
class Float64Index(Index):
__slots__ = ()
class MultiIndex(Index):
__slots__ = ()
class BaseSeriesData(HasShapeTileableData, _ToPandasMixin):
__slots__ = "_cache", "_accessors"
# optional field
_dtype = DataTypeField("dtype")
_name = AnyField("name")
_index_value = ReferenceField(
"index_value", IndexValue, on_deserialize=_on_deserialize_index_value
)
def __init__(
self,
op=None,
shape=None,
nsplits=None,
dtype=None,
name=None,
index_value=None,
**kw,
):
super().__init__(
_op=op,
_shape=shape,
_nsplits=nsplits,
_dtype=dtype,
_name=name,
_index_value=index_value,
**kw,
)
self._accessors = dict()
def _get_params(self) -> Dict[str, Any]:
# params return the properties which useful to rebuild a new tileable object
return {
"shape": self.shape,
"dtype": self.dtype,
"name": self.name,
"index_value": self.index_value,
}
def _set_params(self, new_params: Dict[str, Any]):
params = new_params.copy()
new_shape = params.pop("shape", None)
if new_shape is not None:
self._shape = new_shape
dtype = params.pop("dtype", None)
if dtype is not None:
self._dtype = dtype
index_value = params.pop("index_value", None)
if index_value is not None:
self._index_value = index_value
name = params.pop("name", None)
if name is not None:
self._name = name
if params: # pragma: no cover
raise TypeError(f"Unknown params: {list(params)}")
params = property(_get_params, _set_params)
def refresh_params(self):
# refresh params when chunks updated
refresh_tileable_shape(self)
refresh_index_value(self)
if self._dtype is None:
self._dtype = self.chunks[0].dtype
if self._name is None:
self._name = self.chunks[0].name
def refresh_from_table_meta(self, table_meta: DataFrameTableMeta) -> None:
pass
def _to_str(self, representation=False):
if is_build_mode() or len(self._executed_sessions) == 0:
# in build mode, or not executed, just return representation
if representation:
return (
f"{self.type_name} <op={type(self._op).__name__}, key={self.key}>"
)
else:
return f"{self.type_name}(op={type(self._op).__name__})"
else:
corner_data = fetch_corner_data(self, session=self._executed_sessions[-1])
buf = StringIO()
max_rows = pd.get_option("display.max_rows")
corner_max_rows = (
max_rows
if self.shape[0] <= max_rows or corner_data.shape[0] == 0
else corner_data.shape[0] - 1
) # make sure max_rows < corner_data
with pd.option_context("display.max_rows", corner_max_rows):
if self.shape[0] <= max_rows:
corner_series = corner_data
else:
corner_series = ReprSeries(corner_data, self.shape)
buf.write(repr(corner_series) if representation else str(corner_series))
return buf.getvalue()
def __str__(self):
return self._to_str(representation=False)
def __repr__(self):
return self._to_str(representation=True)
@property
def dtype(self):
return getattr(self, "_dtype", None) or getattr(self.op, "dtype", None)
@property
def name(self):
return self._name
@property
def index_value(self):
return self._index_value
@property
def index(self):
from .datasource.index import from_tileable
return from_tileable(self)
@property
def axes(self):
return [self.index]
@property
def empty(self):
shape = getattr(self, "_shape")
if np.any(np.isnan(shape)):
raise ValueError("Tileable object must be executed first")
return shape == (0,)
def to_tensor(self, dtype=None):
from ..tensor.datasource.from_dataframe import from_series
return from_series(self, dtype=dtype)
@staticmethod
def from_tensor(in_tensor, index=None, name=None):
from .datasource.from_tensor import series_from_tensor
return series_from_tensor(in_tensor, index=index, name=name)
class SeriesData(_BatchedFetcher, BaseSeriesData):
type_name = "Series"
def __maxframe_tensor__(self, dtype=None, order="K"):
tensor = self.to_tensor()
dtype = dtype if dtype is not None else tensor.dtype
return tensor.astype(dtype=dtype, order=order, copy=False)
def iteritems(self, batch_size=10000, session=None):
for batch_data in self.iterbatch(batch_size=batch_size, session=session):
yield from getattr(batch_data, "iteritems")()
items = iteritems
def to_dict(self, into=dict, batch_size=10000, session=None):
fetch_kwargs = dict(batch_size=batch_size)
return self.to_pandas(session=session, fetch_kwargs=fetch_kwargs).to_dict(
into=into
)
class Series(HasShapeTileable, _ToPandasMixin):
__slots__ = ("_cache",)
_allow_data_type_ = (SeriesData,)
type_name = "Series"
def to_tensor(self, dtype=None):
return self._data.to_tensor(dtype=dtype)
def from_tensor(self, in_tensor, index=None, name=None):
return self._data.from_tensor(in_tensor, index=index, name=name)
@property
def T(self):
"""Return the transpose, which is by definition self."""
return self
@property
def ndim(self):
"""
Return an int representing the number of axes / array dimensions.
Return 1 if Series. Otherwise return 2 if DataFrame.
See Also
--------
ndarray.ndim : Number of array dimensions.
Examples
--------
>>> import maxframe.dataframe as md
>>> s = md.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.ndim
1
>>> df = md.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.ndim
2
"""
return super().ndim
@property
def index(self):
"""
The index (axis labels) of the Series.
"""
idx = self._data.index
idx._set_df_or_series(self, 0)
return idx
@index.setter
def index(self, new_index):
self.set_axis(new_index, axis=0, inplace=True)
@property
def name(self):
return self._data.name
@name.setter
def name(self, val):
from .indexing.rename import DataFrameRename
op = DataFrameRename(new_name=val, output_types=[OutputType.series])
new_series = op(self)
self.data = new_series.data
@property
def dtype(self):
"""
Return the dtype object of the underlying data.
"""
return self._data.dtype
[docs]
def copy(self, deep=True): # pylint: disable=arguments-differ
"""
Make a copy of this object's indices and data.
When ``deep=True`` (default), a new object will be created with a
copy of the calling object's data and indices. Modifications to
the data or indices of the copy will not be reflected in the
original object (see notes below).
When ``deep=False``, a new object will be created without copying
the calling object's data or index (only references to the data
and index are copied). Any changes to the data of the original
will be reflected in the shallow copy (and vice versa).
Parameters
----------
deep : bool, default True
Make a deep copy, including a copy of the data and the indices.
With ``deep=False`` neither the indices nor the data are copied.
Returns
-------
copy : Series or DataFrame
Object type matches caller.
"""
if deep:
return super().copy()
else:
return super()._view()
def __len__(self):
return len(self._data)
def __maxframe_tensor__(self, dtype=None, order="K"):
return self._data.__maxframe_tensor__(dtype=dtype, order=order)
def keys(self):
"""
Return alias for index.
Returns
-------
Index
Index of the Series.
"""
return self.index
@property
def values(self):
return self.to_tensor()
def iteritems(self, batch_size=10000, session=None):
"""
Lazily iterate over (index, value) tuples.
This method returns an iterable tuple (index, value). This is
convenient if you want to create a lazy iterator.
Returns
-------
iterable
Iterable of tuples containing the (index, value) pairs from a
Series.
See Also
--------
DataFrame.items : Iterate over (column name, Series) pairs.
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
Examples
--------
>>> import maxframe.dataframe as md
>>> s = md.Series(['A', 'B', 'C'])
>>> for index, value in s.items():
... print(f"Index : {index}, Value : {value}")
Index : 0, Value : A
Index : 1, Value : B
Index : 2, Value : C
"""
return self._data.iteritems(batch_size=batch_size, session=session)
items = iteritems
def to_dict(self, into=dict, batch_size=10000, session=None):
"""
Convert Series to {label -> value} dict or dict-like object.
Parameters
----------
into : class, default dict
The collections.abc.Mapping subclass to use as the return
object. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
Returns
-------
collections.abc.Mapping
Key-value representation of Series.
Examples
--------
>>> import maxframe.dataframe as md
>>> s = md.Series([1, 2, 3, 4])
>>> s.to_dict()
{0: 1, 1: 2, 2: 3, 3: 4}
>>> from collections import OrderedDict, defaultdict
>>> s.to_dict(OrderedDict)
OrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)])
>>> dd = defaultdict(list)
>>> s.to_dict(dd)
defaultdict(<class 'list'>, {0: 1, 1: 2, 2: 3, 3: 4})
"""
return self._data.to_dict(into=into, batch_size=batch_size, session=session)
def to_frame(self, name=None):
"""
Convert Series to DataFrame.
Parameters
----------
name : object, default None
The passed name should substitute for the series name (if it has
one).
Returns
-------
DataFrame
DataFrame representation of Series.
Examples
--------
>>> import maxframe.dataframe as md
>>> s = md.Series(["a", "b", "c"], name="vals")
>>> s.to_frame().execute()
vals
0 a
1 b
2 c
"""
from . import dataframe_from_tensor
name = name or self.name or 0
return dataframe_from_tensor(self, columns=[name])
def between(self, left, right, inclusive="both"):
"""
Return boolean Series equivalent to left <= series <= right.
This function returns a boolean vector containing `True` wherever the
corresponding Series element is between the boundary values `left` and
`right`. NA values are treated as `False`.
Parameters
----------
left : scalar or list-like
Left boundary.
right : scalar or list-like
Right boundary.
inclusive : {"both", "neither", "left", "right"}
Include boundaries. Whether to set each bound as closed or open.
Returns
-------
Series
Series representing whether each element is between left and
right (inclusive).
See Also
--------
Series.gt : Greater than of series and other.
Series.lt : Less than of series and other.
Notes
-----
This function is equivalent to ``(left <= ser) & (ser <= right)``
Examples
--------
>>> import maxframe.dataframe as md
>>> s = md.Series([2, 0, 4, 8, np.nan])
Boundary values are included by default:
>>> s.between(1, 4).execute()
0 True
1 False
2 True
3 False
4 False
dtype: bool
With `inclusive` set to ``"neither"`` boundary values are excluded:
>>> s.between(1, 4, inclusive="neither").execute()
0 True
1 False
2 False
3 False
4 False
dtype: bool
`left` and `right` can be any scalar value:
>>> s = md.Series(['Alice', 'Bob', 'Carol', 'Eve'])
>>> s.between('Anna', 'Daniel').execute()
0 False
1 True
2 True
3 False
dtype: bool
"""
if isinstance(inclusive, bool): # pragma: no cover
# for pandas < 1.3.0
if inclusive:
inclusive = "both"
else:
inclusive = "neither"
if inclusive == "both":
lmask = self >= left
rmask = self <= right
elif inclusive == "left":
lmask = self >= left
rmask = self < right
elif inclusive == "right":
lmask = self > left
rmask = self <= right
elif inclusive == "neither":
lmask = self > left
rmask = self < right
else:
raise ValueError(
"Inclusive has to be either string of 'both',"
"'left', 'right', or 'neither'."
)
return lmask & rmask
# def median(
# self, axis=None, skipna=True, out=None, overwrite_input=False, keepdims=False
# ):
# """
# Return the median of the values over the requested axis.
#
# Parameters
# ----------
# axis : {index (0)}
# Axis or axes along which the medians are computed. The default
# is to compute the median along a flattened version of the tensor.
# A sequence of axes is supported since version 1.9.0.
# skipna : bool, optional, default True
# Exclude NA/null values when computing the result.
# out : Tensor, default None
# Output tensor in which to place the result. It must
# have the same shape and buffer length as the expected output,
# but the type (of the output) will be cast if necessary.
# overwrite_input : bool, default False
# Just for compatibility with Numpy, would not take effect.
# keepdims : bool, default False
# If this is set to True, the axes which are reduced are left
# in the result as dimensions with size one. With this option,
# the result will broadcast correctly against the original `arr`.
#
# Returns
# -------
# median : scalar
# Return the median of the values over the requested axis.
#
# See Also
# --------
# tensor.mean, tensor.percentile
#
# Notes
# -----
# Given a vector ``V`` of length ``N``, the median of ``V`` is the
# middle value of a sorted copy of ``V``, ``V_sorted`` - i
# e., ``V_sorted[(N-1)/2]``, when ``N`` is odd, and the average of the
# two middle values of ``V_sorted`` when ``N`` is even.
#
# Examples
# --------
# >>> import maxframe.dataframe as md
# >>> a = md.Series([10, 7, 4, 3, 2, 1])
# >>> a.median().execute()
# 2.0
# >>> mt.median(a).execute()
# 3.5
# >>> a = md.Series([10, 7, 4, None, 2, 1])
# >>> a.median().execute()
# 4.0
# >>> a.median(skipna=False).execute()
# nan
# """
# if skipna:
# return statistics.median(
# self.dropna(),
# axis=None,
# out=None,
# overwrite_input=False,
# keepdims=False,
# )
# else:
# return statistics.median(
# self, axis=None, out=None, overwrite_input=False, keepdims=False
# )
class BaseDataFrameData(HasShapeTileableData, _ToPandasMixin):
__slots__ = "_accessors", "_dtypes_value", "_dtypes_dict"
# optional fields
_dtypes = SeriesField("dtypes")
_index_value = ReferenceField(
"index_value", IndexValue, on_deserialize=_on_deserialize_index_value
)
_columns_value = ReferenceField("columns_value", IndexValue)
def __init__(
self,
op=None,
shape=None,
nsplits=None,
dtypes=None,
index_value=None,
columns_value=None,
**kw,
):
super().__init__(
_op=op,
_shape=shape,
_nsplits=nsplits,
_dtypes=dtypes,
_index_value=index_value,
_columns_value=columns_value,
**kw,
)
self._accessors = dict()
self._dtypes_value = None
self._dtypes_dict = None
def __on_deserialize__(self):
super().__on_deserialize__()
self._accessors = dict()
self._dtypes_value = None
self._dtypes_dict = None
def _get_params(self) -> Dict[str, Any]:
# params return the properties which useful to rebuild a new tileable object
return {
"shape": self.shape,
"dtypes": self.dtypes,
"index_value": self.index_value,
"columns_value": self.columns_value,
"dtypes_value": self.dtypes_value,
}
def _set_params(self, new_params: Dict[str, Any]):
params = new_params.copy()
new_shape = params.pop("shape", None)
if new_shape is not None:
self._shape = new_shape
index_value = params.pop("index_value", None)
if index_value is not None:
self._index_value = index_value
dtypes = params.pop("dtypes", None)
if dtypes is not None:
self._dtypes = dtypes
columns_value = params.pop("columns_value", None)
if columns_value is not None:
self._columns_value = columns_value
dtypes_value = params.pop("dtypes_value", None)
if dtypes_value is not None:
if dtypes is None:
self._dtypes = dtypes_value.value
if columns_value is None:
self._columns_value = parse_index(self._dtypes.index, store_data=True)
self._dtypes_value = dtypes_value
if params: # pragma: no cover
raise TypeError(f"Unknown params: {list(params)}")
params = property(_get_params, _set_params)
def refresh_params(self):
# refresh params when chunks updated
refresh_tileable_shape(self)
refresh_index_value(self)
refresh_dtypes(self)
def refresh_from_dtypes(self, dtypes: pd.Series) -> None:
self._dtypes = dtypes
self._columns_value = parse_index(dtypes.index, store_data=True)
self._dtypes_value = DtypesValue(key=tokenize(dtypes), value=dtypes)
new_shape = list(self._shape)
new_shape[-1] = len(dtypes)
self._shape = tuple(new_shape)
def refresh_from_table_meta(self, table_meta: DataFrameTableMeta) -> None:
self.refresh_from_dtypes(table_meta.pd_column_dtypes)
@property
def dtypes(self):
dt = getattr(self, "_dtypes", None)
if dt is not None:
return dt
return getattr(self.op, "dtypes", None)
@property
def dtypes_value(self):
if self._dtypes_value is not None:
return self._dtypes_value
# TODO(qinxuye): when creating Dataframe,
# dtypes_value instead of dtypes later must be passed into
dtypes = self.dtypes
if dtypes is not None:
self._dtypes_value = DtypesValue(key=tokenize(dtypes), value=dtypes)
return self._dtypes_value
@property
def index_value(self):
return self._index_value
@property
def columns_value(self):
return self._columns_value
@property
def empty(self):
shape = getattr(self, "_shape")
if np.any(np.isnan(shape)):
raise ValueError("Tileable object must be executed first")
return 0 in shape
def to_tensor(self, dtype=None):
from ..tensor.datasource.from_dataframe import from_dataframe
return from_dataframe(self, dtype=dtype)
@staticmethod
def from_tensor(in_tensor, index=None, columns=None):
from .datasource.from_tensor import dataframe_from_tensor
return dataframe_from_tensor(in_tensor, index=index, columns=columns)
@staticmethod
def from_records(records, **kw):
from .datasource.from_records import from_records
return from_records(records, **kw)
@property
def index(self):
from .datasource.index import from_tileable
return from_tileable(self)
@property
def columns(self):
from .datasource.index import from_pandas as from_pandas_index
return from_pandas_index(self.dtypes.index, store_data=True)
@property
def axes(self):
return [self.index, self.columns]
def _get_dtypes_dict(self):
if self._dtypes_dict is None:
self._dtypes_dict = d = dict()
for k, v in self.dtypes.items():
try:
obj_list = d[k]
except KeyError:
obj_list = d[k] = []
obj_list.append(v)
return self._dtypes_dict
def _get_dtypes_by_columns(self, columns: list):
dtypes_dict = self._get_dtypes_dict()
return functools.reduce(operator.add, (dtypes_dict[c] for c in columns), [])
def _get_columns_by_columns(self, columns: list):
dtypes_dict = self._get_dtypes_dict()
return functools.reduce(
operator.add, ([c] * len(dtypes_dict[c]) for c in columns), []
)
class DataFrameData(_BatchedFetcher, BaseDataFrameData):
type_name = "DataFrame"
def _to_str(self, representation=False):
if is_build_mode() or len(self._executed_sessions) == 0:
# in build mode, or not executed, just return representation
if representation:
return (
f"{self.type_name} <op={type(self._op).__name__}, key={self.key}>"
)
else:
return f"{self.type_name}(op={type(self._op).__name__})"
else:
corner_data = fetch_corner_data(self, session=self._executed_sessions[-1])
buf = StringIO()
max_rows = pd.get_option("display.max_rows")
if self.shape[0] <= max_rows or corner_data.shape[0] == 0:
buf.write(repr(corner_data) if representation else str(corner_data))
else:
# remember we cannot directly call repr(df),
# because the [... rows x ... columns] may show wrong rows
with pd.option_context(
"display.show_dimensions",
False,
"display.max_rows",
corner_data.shape[0] - 1,
):
if representation:
s = repr(corner_data)
else:
s = str(corner_data)
buf.write(s)
if pd.get_option("display.show_dimensions"):
n_rows, n_cols = self.shape
buf.write(f"\n\n[{n_rows} rows x {n_cols} columns]")
return buf.getvalue()
def __str__(self):
return self._to_str(representation=False)
def __repr__(self):
return self._to_str(representation=True)
def __maxframe_tensor__(self, dtype=None, order="K"):
return self.to_tensor().astype(dtype=dtype, order=order, copy=False)
def _repr_html_(self):
if len(self._executed_sessions) == 0:
# not executed before, fall back to normal repr
raise NotImplementedError
corner_data = fetch_corner_data(self, session=self._executed_sessions[-1])
if corner_data is None:
return
buf = StringIO()
max_rows = pd.get_option("display.max_rows")
if self.shape[0] <= max_rows:
buf.write(corner_data._repr_html_())
else:
with pd.option_context(
"display.show_dimensions",
False,
"display.max_rows",
corner_data.shape[0] - 1,
):
buf.write(corner_data._repr_html_().rstrip().rstrip("</div>"))
if pd.get_option("display.show_dimensions"):
n_rows, n_cols = self.shape
buf.write(f"<p>{n_rows} rows × {n_cols} columns</p>\n")
buf.write("</div>")
return buf.getvalue()
def items(self):
for col_name in self.dtypes.index:
yield col_name, self[col_name]
iteritems = items
def iterrows(self, batch_size=1000, session=None):
for batch_data in self.iterbatch(batch_size=batch_size, session=session):
yield from getattr(batch_data, "iterrows")()
def itertuples(self, index=True, name="Pandas", batch_size=1000, session=None):
for batch_data in self.iterbatch(batch_size=batch_size, session=session):
yield from getattr(batch_data, "itertuples")(index=index, name=name)
def _need_execution(self):
if self._dtypes is None:
return True
return False
class DataFrame(HasShapeTileable, _ToPandasMixin):
__slots__ = ("_cache",)
_allow_data_type_ = (DataFrameData,)
type_name = "DataFrame"
def __len__(self):
return len(self._data)
def to_tensor(self):
return self._data.to_tensor()
def from_tensor(self, in_tensor, index=None, columns=None):
return self._data.from_tensor(in_tensor, index=index, columns=columns)
def from_records(self, records, **kw):
return self._data.from_records(records, **kw)
def __maxframe_tensor__(self, dtype=None, order="K"):
return self._data.__maxframe_tensor__(dtype=dtype, order=order)
def __getattr__(self, key):
try:
return getattr(self._data, key)
except AttributeError:
if key in self.dtypes:
return self[key]
else:
raise
def __dir__(self):
result = list(super().__dir__())
return sorted(
result
+ [k for k in self.dtypes.index if isinstance(k, str) and k.isidentifier()]
)
@property
def T(self):
return self.transpose()
@property
def ndim(self):
"""
Return an int representing the number of axes / array dimensions.
Return 1 if Series. Otherwise return 2 if DataFrame.
See Also
--------
ndarray.ndim : Number of array dimensions.
Examples
--------
>>> import maxframe.dataframe as md
>>> s = md.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.ndim
1
>>> df = md.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.ndim
2
"""
return super().ndim
@property
def index(self):
idx = self._data.index
idx._set_df_or_series(self, 0)
return idx
@index.setter
def index(self, new_index):
self.set_axis(new_index, axis=0, inplace=True)
@property
def columns(self):
col = self._data.columns
col._set_df_or_series(self, 1)
return col
@columns.setter
def columns(self, new_columns):
self.set_axis(new_columns, axis=1, inplace=True)
def keys(self):
"""
Get the 'info axis' (see Indexing for more).
This is index for Series, columns for DataFrame.
Returns
-------
Index
Info axis.
"""
return self.columns
@property
def values(self):
return self.to_tensor()
@property
def dtypes(self):
"""
Return the dtypes in the DataFrame.
This returns a Series with the data type of each column.
The result's index is the original DataFrame's columns. Columns
with mixed types are stored with the ``object`` dtype. See
:ref:`the User Guide <basics.dtypes>` for more.
Returns
-------
pandas.Series
The data type of each column.
Examples
--------
>>> import maxframe.dataframe as md
>>> df = md.DataFrame({'float': [1.0],
... 'int': [1],
... 'datetime': [md.Timestamp('20180310')],
... 'string': ['foo']})
>>> df.dtypes
float float64
int int64
datetime datetime64[ns]
string object
dtype: object
"""
return self._data.dtypes
def iterrows(self, batch_size=1000, session=None):
"""
Iterate over DataFrame rows as (index, Series) pairs.
Yields
------
index : label or tuple of label
The index of the row. A tuple for a `MultiIndex`.
data : Series
The data of the row as a Series.
it : generator
A generator that iterates over the rows of the frame.
See Also
--------
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
DataFrame.items : Iterate over (column name, Series) pairs.
Notes
-----
1. Because ``iterrows`` returns a Series for each row,
it does **not** preserve dtypes across the rows (dtypes are
preserved across columns for DataFrames). For example,
>>> import maxframe.dataframe as md
>>> df = md.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int 1.0
float 1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64
To preserve dtypes while iterating over the rows, it is better
to use :meth:`itertuples` which returns namedtuples of the values
and which is generally faster than ``iterrows``.
2. You should **never modify** something you are iterating over.
This is not guaranteed to work in all cases. Depending on the
data types, the iterator returns a copy and not a view, and writing
to it will have no effect.
"""
return self._data.iterrows(batch_size=batch_size, session=session)
def itertuples(self, index=True, name="Pandas", batch_size=1000, session=None):
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
The name of the returned namedtuples or None to return regular
tuples.
Returns
-------
iterator
An object to iterate over namedtuples for each row in the
DataFrame with the first field possibly being the index and
following fields being the column values.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
pairs.
DataFrame.items : Iterate over (column name, Series) pairs.
Notes
-----
The column names will be renamed to positional names if they are
invalid Python identifiers, repeated, or start with an underscore.
On python versions < 3.7 regular tuples are returned for DataFrames
with a large number of columns (>254).
Examples
--------
>>> import maxframe.dataframe as md
>>> df = md.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
... index=['dog', 'hawk'])
>>> df.execute()
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)
By setting the `index` parameter to False we can remove the index
as the first element of the tuple:
>>> for row in df.itertuples(index=False):
... print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)
With the `name` parameter set we set a custom name for the yielded
namedtuples:
>>> for row in df.itertuples(name='Animal'):
... print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
"""
return self._data.itertuples(
batch_size=batch_size, session=session, index=index, name=name
)
def assign(self, **kwargs):
"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable or Series}
The column names are keywords. If the values are
callable, they are computed on the DataFrame and
assigned to the new columns. The callable must not
change input DataFrame (though pandas doesn't check it).
If the values are not callable, (e.g. a Series, scalar, or array),
they are simply assigned.
Returns
-------
DataFrame
A new DataFrame with the new columns in addition to
all the existing columns.
Notes
-----
Assigning multiple columns within the same ``assign`` is possible.
Later items in 'kwargs' may refer to newly created or modified
columns in 'df'; items are computed and assigned into 'df' in order.
Examples
--------
>>> import maxframe.dataframe as md
>>> df = md.DataFrame({'temp_c': [17.0, 25.0]},
... index=['Portland', 'Berkeley'])
>>> df.execute()
temp_c
Portland 17.0
Berkeley 25.0
Where the value is a callable, evaluated on `df`:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32).execute()
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly
referencing an existing Series or sequence:
>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32).execute()
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
You can create multiple columns within the same assign where one
of the columns depends on another one defined within the same assign:
>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9).execute()
temp_c temp_f temp_k
Portland 17.0 62.6 290.15
Berkeley 25.0 77.0 298.15
"""
data = self.copy()
for k, v in kwargs.items():
data[k] = apply_if_callable(v, data)
return data
class DataFrameGroupByData(BaseDataFrameData):
type_name = "DataFrameGroupBy"
_key_dtypes = SeriesField("key_dtypes")
_selection = AnyField("selection")
@property
def key_dtypes(self):
return self._key_dtypes
@property
def selection(self):
return self._selection
def _get_params(self) -> Dict[str, Any]:
p = super()._get_params()
p.update(dict(key_dtypes=self.key_dtypes, selection=self.selection))
return p
def _set_params(self, new_params: Dict[str, Any]):
params = new_params.copy()
key_dtypes = params.pop("key_dtypes", None)
if key_dtypes is not None:
self._key_dtypes = key_dtypes
selection = params.pop("selection", None)
if selection is not None:
self._selection = selection
super()._set_params(params)
params = property(_get_params, _set_params)
def __init__(self, key_dtypes=None, selection=None, **kw):
super().__init__(_key_dtypes=key_dtypes, _selection=selection, **kw)
def _equal(self, o):
# FIXME We need to implemented a true `==` operator for DataFrameGroupby
if is_build_mode():
return self is o
else:
return self == o
class SeriesGroupByData(BaseSeriesData):
type_name = "SeriesGroupBy"
_key_dtypes = AnyField("key_dtypes")
@property
def key_dtypes(self):
return self._key_dtypes
def _get_params(self) -> Dict[str, Any]:
p = super()._get_params()
p["key_dtypes"] = self.key_dtypes
return p
def _set_params(self, new_params: Dict[str, Any]):
params = new_params.copy()
key_dtypes = params.pop("key_dtypes", None)
if key_dtypes is not None:
self._key_dtypes = key_dtypes
super()._set_params(params)
params = property(_get_params, _set_params)
def __init__(self, key_dtypes=None, **kw):
super().__init__(_key_dtypes=key_dtypes, **kw)
def _equal(self, o):
# FIXME We need to implemented a true `==` operator for DataFrameGroupby
if is_build_mode():
return self is o
else:
return self == o
class GroupBy(Tileable, _ToPandasMixin):
__slots__ = ()
class DataFrameGroupBy(GroupBy):
__slots__ = ()
_allow_data_type_ = (DataFrameGroupByData,)
type_name = "DataFrameGroupBy"
def __eq__(self, other):
return self._equal(other)
def __hash__(self):
# NB: we have customized __eq__ explicitly, thus we need define __hash__ explicitly as well.
return super().__hash__()
def __getattr__(self, item):
try:
return super().__getattr__(item)
except AttributeError:
if item in self.dtypes:
return self[item]
else:
raise
def __dir__(self):
result = list(super().__dir__())
return sorted(
result
+ [k for k in self.dtypes.index if isinstance(k, str) and k.isidentifier()]
)
class SeriesGroupBy(GroupBy):
__slots__ = ()
_allow_data_type_ = (SeriesGroupByData,)
type_name = "SeriesGroupBy"
def __eq__(self, other):
return self._equal(other)
def __hash__(self):
# NB: we have customized __eq__ explicitly, thus we need define __hash__ explicitly as well.
return super().__hash__()
class CategoricalData(HasShapeTileableData, _ToPandasMixin):
__slots__ = ("_cache",)
type_name = "Categorical"
# optional field
_dtype = DataTypeField("dtype")
_categories_value = ReferenceField(
"categories_value", IndexValue, on_deserialize=_on_deserialize_index_value
)
def __init__(
self,
op=None,
shape=None,
nsplits=None,
dtype=None,
categories_value=None,
**kw,
):
super().__init__(
_op=op,
_shape=shape,
_nsplits=nsplits,
_dtype=dtype,
_categories_value=categories_value,
**kw,
)
@property
def params(self) -> Dict[str, Any]:
# params return the properties which useful to rebuild a new tileable object
return {
"shape": self.shape,
"dtype": self.dtype,
"categories_value": self.categories_value,
}
@params.setter
def params(self, new_params: Dict[str, Any]):
params = new_params.copy()
new_shape = params.pop("shape", None)
if new_shape is not None:
self._shape = new_shape
dtype = params.pop("dtype", None)
if dtype is not None:
self._dtype = dtype
categories_value = params.pop("categories_value", None)
if categories_value is not None:
self._categories_value = categories_value
if params: # pragma: no cover
raise TypeError(f"Unknown params: {list(params)}")
def refresh_params(self):
# refresh params when chunks updated
refresh_tileable_shape(self)
if self._dtype is None:
self._dtype = self.chunks[0].dtype
if self._categories_value is None:
categories = []
for chunk in self.chunks:
categories.extend(chunk.categories_value.to_pandas())
self._categories_value = parse_index(
pd.Categorical(categories).categories, store_data=True
)
def refresh_from_table_meta(self, table_meta: DataFrameTableMeta) -> None:
pass
def _to_str(self, representation=False):
if is_build_mode() or len(self._executed_sessions) == 0:
# in build mode, or not executed, just return representation
if representation:
return f"{self.type_name} <op={type(self.op).__name__}, key={self.key}>"
else:
return f"{self.type_name}(op={type(self.op).__name__})"
else:
data = self.fetch(session=self._executed_sessions[-1])
return repr(data) if repr(data) else str(data)
def __str__(self):
return self._to_str(representation=False)
def __repr__(self):
return self._to_str(representation=True)
def _equal(self, o):
# FIXME We need to implemented a true `==` operator for DataFrameGroupby
if is_build_mode():
return self is o
else: # pragma: no cover
return self == o
@property
def dtype(self):
return getattr(self, "_dtype", None) or self.op.dtype
@property
def categories_value(self):
return self._categories_value
def __eq__(self, other):
return self._equal(other)
def __hash__(self):
# NB: we have customized __eq__ explicitly, thus we need define __hash__ explicitly as well.
return super().__hash__()
class Categorical(HasShapeTileable, _ToPandasMixin):
__slots__ = ()
_allow_data_type_ = (CategoricalData,)
type_name = "Categorical"
def __len__(self):
return len(self._data)
def __eq__(self, other):
return self._equal(other)
def __hash__(self):
# NB: we have customized __eq__ explicitly, thus we need define __hash__ explicitly as well.
return super().__hash__()
class DataFrameOrSeriesData(HasShapeTileableData, _ToPandasMixin):
__slots__ = ()
_data_type = StringField("data_type")
_data_params = DictField("data_params")
def __init__(
self,
op=None,
data_type=None,
data_params=None,
**kw,
):
self._data_type = data_type
self._data_params = data_params or dict()
super().__init__(
_op=op,
**kw,
)
def __getattr__(self, item):
if item in self._data_params:
return self._data_params[item]
raise AttributeError(f"'{type(self)}' object has no attribute '{item}'")
@property
def shape(self):
return self._data_params.get("shape", None)
@property
def nsplits(self):
return self._data_params.get("nsplits", None)
@property
def data_type(self):
return self._data_type
@property
def data_params(self):
return self._data_params
@property
def params(self) -> Dict[str, Any]:
return {"data_type": self._data_type, "data_params": self._data_params}
@params.setter
def params(self, new_params: Dict[str, Any]):
# After execution, create DataFrameFetch, and the data
# corresponding to the original key is still DataFrameOrSeries type,
# so when restoring DataFrameOrSeries type,
# there is no "data_type" field in params.
if "data_type" not in new_params:
if "dtype" in new_params:
self._data_type = "series"
else:
self._data_type = "dataframe"
self._data_params = new_params.copy()
else:
self._data_type = new_params.get("data_type")
self._data_params = {
k: v for k, v in new_params.get("data_params", {}).items()
}
def refresh_params(self):
index_to_index_values = dict()
for chunk in self.chunks:
if chunk.ndim == 1:
index_to_index_values[chunk.index] = chunk.index_value
elif chunk.index[1] == 0:
index_to_index_values[chunk.index] = chunk.index_value
index_value = merge_index_value(index_to_index_values, store_data=False)
nsplits = calc_nsplits({c.index: c.shape for c in self.chunks})
shape = tuple(sum(ns) for ns in nsplits)
data_params = dict()
data_params["nsplits"] = nsplits
data_params["shape"] = shape
data_params["index_value"] = index_value
self._data_type = self._chunks[0]._data_type
if self.data_type == "dataframe":
all_dtypes = [c.dtypes_value.value for c in self.chunks if c.index[0] == 0]
dtypes = pd.concat(all_dtypes)
data_params["dtypes"] = dtypes
columns_values = parse_index(dtypes.index, store_data=True)
data_params["columns_value"] = columns_values
data_params["dtypes_value"] = DtypesValue(
key=tokenize(dtypes), value=dtypes
)
else:
data_params["dtype"] = self.chunks[0].dtype
data_params["name"] = self.chunks[0].name
self._data_params.update(data_params)
def refresh_from_table_meta(self, table_meta: DataFrameTableMeta) -> None:
pass
def ensure_data(self):
from .fetch.core import DataFrameFetch
self.execute()
default_sess = get_default_session()
self._detach_session(default_sess._session)
fetch_tileable = default_sess._session._tileable_to_fetch[self]
new = DataFrameFetch(
output_types=[getattr(OutputType, self.data_type)]
).new_tileable(
[],
_key=self.key,
chunks=fetch_tileable.chunks,
nsplits=fetch_tileable.nsplits,
**self.data_params,
)
new._attach_session(default_sess._session)
return new
class DataFrameOrSeries(HasShapeTileable, _ToPandasMixin):
__slots__ = ()
_allow_data_type_ = (DataFrameOrSeriesData,)
type_name = "DataFrameOrSeries"
INDEX_TYPE = (Index, IndexData)
SERIES_TYPE = (Series, SeriesData)
DATAFRAME_OR_SERIES_TYPE = (DataFrameOrSeries, DataFrameOrSeriesData)
DATAFRAME_TYPE = (DataFrame, DataFrameData)
DATAFRAME_GROUPBY_TYPE = (DataFrameGroupBy, DataFrameGroupByData)
SERIES_GROUPBY_TYPE = (SeriesGroupBy, SeriesGroupByData)
GROUPBY_TYPE = (GroupBy,) + DATAFRAME_GROUPBY_TYPE + SERIES_GROUPBY_TYPE
CATEGORICAL_TYPE = (Categorical, CategoricalData)
TILEABLE_TYPE = (
INDEX_TYPE + SERIES_TYPE + DATAFRAME_TYPE + GROUPBY_TYPE + CATEGORICAL_TYPE
)
register_output_types(OutputType.dataframe, DATAFRAME_TYPE)
register_output_types(OutputType.series, SERIES_TYPE)
register_output_types(OutputType.df_or_series, DATAFRAME_OR_SERIES_TYPE)
register_output_types(OutputType.index, INDEX_TYPE)
register_output_types(OutputType.categorical, CATEGORICAL_TYPE)
register_output_types(OutputType.dataframe_groupby, DATAFRAME_GROUPBY_TYPE)
register_output_types(OutputType.series_groupby, SERIES_GROUPBY_TYPE)