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spark-instrumented-optimizer/python/pyspark/pandas/indexes/base.py
Takuya UESHIN d67d73b708 [SPARK-35505][PYTHON] Remove APIs which have been deprecated in Koalas 
### What changes were proposed in this pull request?

Removes APIs which have been deprecated in Koalas.

### Why are the changes needed?

There are some APIs that have been deprecated in Koalas. We shouldn't have those in pandas APIs on Spark.

### Does this PR introduce _any_ user-facing change?

Yes, the APIs deprecated in Koalas will be no longer available.

### How was this patch tested?

Modified some tests which use the deprecated APIs, and the other existing tests should pass.

Closes #32656 from ueshin/issues/SPARK-35505/remove_deprecated.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-05-25 11:16:27 -07:00

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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
#
from functools import partial
from typing import Any, Iterator, List, Optional, Tuple, Union, cast, no_type_check
import warnings
import pandas as pd
import numpy as np
from pandas.api.types import (
is_list_like,
is_interval_dtype,
is_bool_dtype,
is_categorical_dtype,
is_integer_dtype,
is_float_dtype,
is_numeric_dtype,
is_object_dtype,
)
from pandas.core.accessor import CachedAccessor
from pandas.io.formats.printing import pprint_thing
from pandas.api.types import CategoricalDtype, is_hashable
from pandas._libs import lib
from pyspark import sql as spark
from pyspark.sql import functions as F
from pyspark.sql.types import FractionalType, IntegralType, TimestampType
from pyspark import pandas as ps # For running doctests and reference resolution in PyCharm.
from pyspark.pandas.config import get_option, option_context
from pyspark.pandas.base import IndexOpsMixin
from pyspark.pandas.frame import DataFrame
from pyspark.pandas.missing.indexes import MissingPandasLikeIndex
from pyspark.pandas.series import Series, first_series
from pyspark.pandas.spark.accessors import SparkIndexMethods
from pyspark.pandas.utils import (
is_name_like_tuple,
is_name_like_value,
name_like_string,
same_anchor,
scol_for,
verify_temp_column_name,
validate_bool_kwarg,
ERROR_MESSAGE_CANNOT_COMBINE,
)
from pyspark.pandas.internal import (
InternalFrame,
DEFAULT_SERIES_NAME,
SPARK_DEFAULT_INDEX_NAME,
SPARK_INDEX_NAME_FORMAT,
)
from pyspark.pandas.typedef import Dtype, Scalar
class Index(IndexOpsMixin):
"""
pandas-on-Spark Index that corresponds to pandas Index logically. This might hold Spark Column
internally.
Parameters
----------
data : array-like (1-dimensional)
dtype : dtype, default None
If dtype is None, we find the dtype that best fits the data.
If an actual dtype is provided, we coerce to that dtype if it's safe.
Otherwise, an error will be raised.
copy : bool
Make a copy of input ndarray.
name : object
Name to be stored in the index.
tupleize_cols : bool (default: True)
When True, attempt to create a MultiIndex if possible.
See Also
--------
MultiIndex : A multi-level, or hierarchical, Index.
DatetimeIndex : Index of datetime64 data.
Int64Index : A special case of :class:`Index` with purely integer labels.
Float64Index : A special case of :class:`Index` with purely float labels.
Examples
--------
>>> ps.DataFrame({'a': ['a', 'b', 'c']}, index=[1, 2, 3]).index
Int64Index([1, 2, 3], dtype='int64')
>>> ps.DataFrame({'a': [1, 2, 3]}, index=list('abc')).index
Index(['a', 'b', 'c'], dtype='object')
>>> ps.Index([1, 2, 3])
Int64Index([1, 2, 3], dtype='int64')
>>> ps.Index(list('abc'))
Index(['a', 'b', 'c'], dtype='object')
From a Series:
>>> s = ps.Series([1, 2, 3], index=[10, 20, 30])
>>> ps.Index(s)
Int64Index([1, 2, 3], dtype='int64')
From an Index:
>>> idx = ps.Index([1, 2, 3])
>>> ps.Index(idx)
Int64Index([1, 2, 3], dtype='int64')
"""
def __new__(
cls,
data: Optional[Any] = None,
dtype: Optional[Union[str, Dtype]] = None,
copy: bool = False,
name: Optional[Union[Any, Tuple]] = None,
tupleize_cols: bool = True,
**kwargs: Any
) -> "Index":
if not is_hashable(name):
raise TypeError("Index.name must be a hashable type")
if isinstance(data, Series):
if dtype is not None:
data = data.astype(dtype)
if name is not None:
data = data.rename(name)
internal = InternalFrame(
spark_frame=data._internal.spark_frame,
index_spark_columns=data._internal.data_spark_columns,
index_names=data._internal.column_labels,
index_dtypes=data._internal.data_dtypes,
column_labels=[],
data_spark_columns=[],
data_dtypes=[],
)
return DataFrame(internal).index
elif isinstance(data, Index):
if copy:
data = data.copy()
if dtype is not None:
data = data.astype(dtype)
if name is not None:
data = data.rename(name)
return data
return cast(
Index,
ps.from_pandas(
pd.Index(
data=data,
dtype=dtype,
copy=copy,
name=name,
tupleize_cols=tupleize_cols,
**kwargs
)
),
)
@staticmethod
def _new_instance(anchor: DataFrame) -> "Index":
from pyspark.pandas.indexes.category import CategoricalIndex
from pyspark.pandas.indexes.datetimes import DatetimeIndex
from pyspark.pandas.indexes.multi import MultiIndex
from pyspark.pandas.indexes.numeric import Float64Index, Int64Index
if anchor._internal.index_level > 1:
instance = object.__new__(MultiIndex)
elif isinstance(anchor._internal.index_dtypes[0], CategoricalDtype):
instance = object.__new__(CategoricalIndex)
elif isinstance(
anchor._internal.spark_type_for(anchor._internal.index_spark_columns[0]), IntegralType
):
instance = object.__new__(Int64Index)
elif isinstance(
anchor._internal.spark_type_for(anchor._internal.index_spark_columns[0]), FractionalType
):
instance = object.__new__(Float64Index)
elif isinstance(
anchor._internal.spark_type_for(anchor._internal.index_spark_columns[0]), TimestampType
):
instance = object.__new__(DatetimeIndex)
else:
instance = object.__new__(Index)
instance._anchor = anchor
return instance
@property
def _psdf(self) -> DataFrame:
return self._anchor
@property
def _internal(self) -> InternalFrame:
internal = self._psdf._internal
return internal.copy(
column_labels=internal.index_names,
data_spark_columns=internal.index_spark_columns,
data_dtypes=internal.index_dtypes,
column_label_names=None,
)
@property
def _column_label(self) -> Optional[Tuple]:
return self._psdf._internal.index_names[0]
def _with_new_scol(self, scol: spark.Column, *, dtype: Optional[Dtype] = None) -> "Index":
"""
Copy pandas-on-Spark Index with the new Spark Column.
:param scol: the new Spark Column
:return: the copied Index
"""
internal = self._internal.copy(
index_spark_columns=[scol.alias(SPARK_DEFAULT_INDEX_NAME)],
index_dtypes=[dtype],
column_labels=[],
data_spark_columns=[],
data_dtypes=[],
)
return DataFrame(internal).index
spark = CachedAccessor("spark", SparkIndexMethods)
# This method is used via `DataFrame.info` API internally.
def _summary(self, name: Optional[str] = None) -> str:
"""
Return a summarized representation.
Parameters
----------
name : str
name to use in the summary representation
Returns
-------
String with a summarized representation of the index
"""
head, tail, total_count = tuple(
cast(
pd.DataFrame,
self._internal.spark_frame.select(
F.first(self.spark.column), F.last(self.spark.column), F.count(F.expr("*"))
).toPandas(),
).iloc[0]
)
if total_count > 0:
index_summary = ", %s to %s" % (pprint_thing(head), pprint_thing(tail))
else:
index_summary = ""
if name is None:
name = type(self).__name__
return "%s: %s entries%s" % (name, total_count, index_summary)
@property
def size(self) -> int:
"""
Return an int representing the number of elements in this object.
Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'],
... index=list('abcd'))
>>> df.index.size
4
>>> df.set_index('dogs', append=True).index.size
4
"""
return len(self)
@property
def shape(self) -> tuple:
"""
Return a tuple of the shape of the underlying data.
Examples
--------
>>> idx = ps.Index(['a', 'b', 'c'])
>>> idx
Index(['a', 'b', 'c'], dtype='object')
>>> idx.shape
(3,)
>>> midx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
>>> midx # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y'),
('c', 'z')],
)
>>> midx.shape
(3,)
"""
return (len(self._psdf),)
def identical(self, other: "Index") -> bool:
"""
Similar to equals, but check that other comparable attributes are
also equal.
Returns
-------
bool
If two Index objects have equal elements and same type True,
otherwise False.
Examples
--------
>>> from pyspark.pandas.config import option_context
>>> idx = ps.Index(['a', 'b', 'c'])
>>> midx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
For Index
>>> idx.identical(idx)
True
>>> with option_context('compute.ops_on_diff_frames', True):
... idx.identical(ps.Index(['a', 'b', 'c']))
True
>>> with option_context('compute.ops_on_diff_frames', True):
... idx.identical(ps.Index(['b', 'b', 'a']))
False
>>> idx.identical(midx)
False
For MultiIndex
>>> midx.identical(midx)
True
>>> with option_context('compute.ops_on_diff_frames', True):
... midx.identical(ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')]))
True
>>> with option_context('compute.ops_on_diff_frames', True):
... midx.identical(ps.MultiIndex.from_tuples([('c', 'z'), ('b', 'y'), ('a', 'x')]))
False
>>> midx.identical(idx)
False
"""
from pyspark.pandas.indexes.multi import MultiIndex
self_name = self.names if isinstance(self, MultiIndex) else self.name
other_name = other.names if isinstance(other, MultiIndex) else other.name
return (
self_name == other_name # to support non-index comparison by short-circuiting.
and self.equals(other)
)
def equals(self, other: "Index") -> bool:
"""
Determine if two Index objects contain the same elements.
Returns
-------
bool
True if "other" is an Index and it has the same elements as calling
index; False otherwise.
Examples
--------
>>> from pyspark.pandas.config import option_context
>>> idx = ps.Index(['a', 'b', 'c'])
>>> idx.name = "name"
>>> midx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
>>> midx.names = ("nameA", "nameB")
For Index
>>> idx.equals(idx)
True
>>> with option_context('compute.ops_on_diff_frames', True):
... idx.equals(ps.Index(['a', 'b', 'c']))
True
>>> with option_context('compute.ops_on_diff_frames', True):
... idx.equals(ps.Index(['b', 'b', 'a']))
False
>>> idx.equals(midx)
False
For MultiIndex
>>> midx.equals(midx)
True
>>> with option_context('compute.ops_on_diff_frames', True):
... midx.equals(ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')]))
True
>>> with option_context('compute.ops_on_diff_frames', True):
... midx.equals(ps.MultiIndex.from_tuples([('c', 'z'), ('b', 'y'), ('a', 'x')]))
False
>>> midx.equals(idx)
False
"""
if same_anchor(self, other):
return True
elif type(self) == type(other):
if get_option("compute.ops_on_diff_frames"):
# TODO: avoid using default index?
with option_context("compute.default_index_type", "distributed-sequence"):
# Directly using Series from both self and other seems causing
# some exceptions when 'compute.ops_on_diff_frames' is enabled.
# Working around for now via using frame.
return (
cast(Series, self.to_series("self").reset_index(drop=True))
== cast(Series, other.to_series("other").reset_index(drop=True))
).all()
else:
raise ValueError(ERROR_MESSAGE_CANNOT_COMBINE)
else:
return False
def transpose(self) -> "Index":
"""
Return the transpose, For index, It will be index itself.
Examples
--------
>>> idx = ps.Index(['a', 'b', 'c'])
>>> idx
Index(['a', 'b', 'c'], dtype='object')
>>> idx.transpose()
Index(['a', 'b', 'c'], dtype='object')
For MultiIndex
>>> midx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
>>> midx # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y'),
('c', 'z')],
)
>>> midx.transpose() # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y'),
('c', 'z')],
)
"""
return self
T = property(transpose)
def _to_internal_pandas(self) -> pd.Index:
"""
Return a pandas Index directly from _internal to avoid overhead of copy.
This method is for internal use only.
"""
return self._psdf._internal.to_pandas_frame.index
def to_pandas(self) -> pd.Index:
"""
Return a pandas Index.
.. note:: This method should only be used if the resulting pandas object is expected
to be small, as all the data is loaded into the driver's memory.
Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'],
... index=list('abcd'))
>>> df['dogs'].index.to_pandas()
Index(['a', 'b', 'c', 'd'], dtype='object')
"""
return self._to_internal_pandas().copy()
def to_numpy(self, dtype: Optional[Union[str, Dtype]] = None, copy: bool = False) -> np.ndarray:
"""
A NumPy ndarray representing the values in this Index or MultiIndex.
.. note:: This method should only be used if the resulting NumPy ndarray is expected
to be small, as all the data is loaded into the driver's memory.
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`
copy : bool, default False
Whether to ensure that the returned value is a not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
Returns
-------
numpy.ndarray
Examples
--------
>>> ps.Series([1, 2, 3, 4]).index.to_numpy()
array([0, 1, 2, 3])
>>> ps.DataFrame({'a': ['a', 'b', 'c']}, index=[[1, 2, 3], [4, 5, 6]]).index.to_numpy()
array([(1, 4), (2, 5), (3, 6)], dtype=object)
"""
result = np.asarray(self._to_internal_pandas()._values, dtype=dtype)
if copy:
result = result.copy()
return result
@property
def values(self) -> np.ndarray:
"""
Return an array representing the data in the Index.
.. warning:: We recommend using `Index.to_numpy()` instead.
.. note:: This method should only be used if the resulting NumPy ndarray is expected
to be small, as all the data is loaded into the driver's memory.
Returns
-------
numpy.ndarray
Examples
--------
>>> ps.Series([1, 2, 3, 4]).index.values
array([0, 1, 2, 3])
>>> ps.DataFrame({'a': ['a', 'b', 'c']}, index=[[1, 2, 3], [4, 5, 6]]).index.values
array([(1, 4), (2, 5), (3, 6)], dtype=object)
"""
warnings.warn("We recommend using `{}.to_numpy()` instead.".format(type(self).__name__))
return self.to_numpy()
@property
def asi8(self) -> np.ndarray:
"""
Integer representation of the values.
.. warning:: We recommend using `Index.to_numpy()` instead.
.. note:: This method should only be used if the resulting NumPy ndarray is expected
to be small, as all the data is loaded into the driver's memory.
Returns
-------
numpy.ndarray
An ndarray with int64 dtype.
Examples
--------
>>> ps.Index([1, 2, 3]).asi8
array([1, 2, 3])
Returns None for non-int64 dtype
>>> ps.Index(['a', 'b', 'c']).asi8 is None
True
"""
warnings.warn("We recommend using `{}.to_numpy()` instead.".format(type(self).__name__))
if isinstance(self.spark.data_type, IntegralType):
return self.to_numpy()
elif isinstance(self.spark.data_type, TimestampType):
return np.array(list(map(lambda x: x.astype(np.int64), self.to_numpy())))
else:
return None
@property
def has_duplicates(self) -> bool:
"""
If index has duplicates, return True, otherwise False.
Examples
--------
>>> idx = ps.Index([1, 5, 7, 7])
>>> idx.has_duplicates
True
>>> idx = ps.Index([1, 5, 7])
>>> idx.has_duplicates
False
>>> idx = ps.Index(["Watermelon", "Orange", "Apple",
... "Watermelon"])
>>> idx.has_duplicates
True
>>> idx = ps.Index(["Orange", "Apple",
... "Watermelon"])
>>> idx.has_duplicates
False
"""
sdf = self._internal.spark_frame.select(self.spark.column)
scol = scol_for(sdf, sdf.columns[0])
return sdf.select(F.count(scol) != F.countDistinct(scol)).first()[0]
@property
def is_unique(self) -> bool:
"""
Return if the index has unique values.
Examples
--------
>>> idx = ps.Index([1, 5, 7, 7])
>>> idx.is_unique
False
>>> idx = ps.Index([1, 5, 7])
>>> idx.is_unique
True
>>> idx = ps.Index(["Watermelon", "Orange", "Apple",
... "Watermelon"])
>>> idx.is_unique
False
>>> idx = ps.Index(["Orange", "Apple",
... "Watermelon"])
>>> idx.is_unique
True
"""
return not self.has_duplicates
@property
def name(self) -> Union[Any, Tuple]:
"""Return name of the Index."""
return self.names[0]
@name.setter
def name(self, name: Union[Any, Tuple]) -> None:
self.names = [name]
@property
def names(self) -> List[Union[Any, Tuple]]:
"""Return names of the Index."""
return [
name if name is None or len(name) > 1 else name[0]
for name in self._internal.index_names # type: ignore
]
@names.setter
def names(self, names: List[Union[Any, Tuple]]) -> None:
if not is_list_like(names):
raise ValueError("Names must be a list-like")
if self._internal.index_level != len(names):
raise ValueError(
"Length of new names must be {}, got {}".format(
self._internal.index_level, len(names)
)
)
if self._internal.index_level == 1:
self.rename(names[0], inplace=True)
else:
self.rename(names, inplace=True)
@property
def nlevels(self) -> int:
"""
Number of levels in Index & MultiIndex.
Examples
--------
>>> psdf = ps.DataFrame({"a": [1, 2, 3]}, index=pd.Index(['a', 'b', 'c'], name="idx"))
>>> psdf.index.nlevels
1
>>> psdf = ps.DataFrame({'a': [1, 2, 3]}, index=[list('abc'), list('def')])
>>> psdf.index.nlevels
2
"""
return self._internal.index_level
def rename(
self, name: Union[Any, Tuple, List[Union[Any, Tuple]]], inplace: bool = False
) -> Optional["Index"]:
"""
Alter Index or MultiIndex name.
Able to set new names without level. Defaults to returning new index.
Parameters
----------
name : label or list of labels
Name(s) to set.
inplace : boolean, default False
Modifies the object directly, instead of creating a new Index or MultiIndex.
Returns
-------
Index or MultiIndex
The same type as the caller or None if inplace is True.
Examples
--------
>>> df = ps.DataFrame({'a': ['A', 'C'], 'b': ['A', 'B']}, columns=['a', 'b'])
>>> df.index.rename("c")
Int64Index([0, 1], dtype='int64', name='c')
>>> df.set_index("a", inplace=True)
>>> df.index.rename("d")
Index(['A', 'C'], dtype='object', name='d')
You can also change the index name in place.
>>> df.index.rename("e", inplace=True)
>>> df.index
Index(['A', 'C'], dtype='object', name='e')
>>> df # doctest: +NORMALIZE_WHITESPACE
b
e
A A
C B
Support for MultiIndex
>>> psidx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y')])
>>> psidx.names = ['hello', 'pandas-on-Spark']
>>> psidx # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y')],
names=['hello', 'pandas-on-Spark'])
>>> psidx.rename(['aloha', 'databricks']) # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y')],
names=['aloha', 'databricks'])
"""
names = self._verify_for_rename(name)
internal = self._psdf._internal.copy(index_names=names)
if inplace:
self._psdf._update_internal_frame(internal)
return None
else:
return DataFrame(internal).index
def _verify_for_rename(self, name: Union[Any, Tuple]) -> List[Tuple]:
if is_hashable(name):
if is_name_like_tuple(name):
return [name]
elif is_name_like_value(name):
return [(name,)]
raise TypeError("Index.name must be a hashable type")
# TODO: add downcast parameter for fillna function
def fillna(self, value: Scalar) -> "Index":
"""
Fill NA/NaN values with the specified value.
Parameters
----------
value : scalar
Scalar value to use to fill holes (example: 0). This value cannot be a list-likes.
Returns
-------
Index :
filled with value
Examples
--------
>>> ki = ps.DataFrame({'a': ['a', 'b', 'c']}, index=[1, 2, None]).index
>>> ki
Float64Index([1.0, 2.0, nan], dtype='float64')
>>> ki.fillna(0)
Float64Index([1.0, 2.0, 0.0], dtype='float64')
"""
if not isinstance(value, (float, int, str, bool)):
raise TypeError("Unsupported type %s" % type(value).__name__)
sdf = self._internal.spark_frame.fillna(value)
result = DataFrame(self._psdf._internal.with_new_sdf(sdf)).index # TODO: dtype?
return result
# TODO: ADD keep parameter
def drop_duplicates(self) -> "Index":
"""
Return Index with duplicate values removed.
Returns
-------
deduplicated : Index
See Also
--------
Series.drop_duplicates : Equivalent method on Series.
DataFrame.drop_duplicates : Equivalent method on DataFrame.
Examples
--------
Generate an pandas.Index with duplicate values.
>>> idx = ps.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])
>>> idx.drop_duplicates().sort_values()
Index(['beetle', 'cow', 'hippo', 'lama'], dtype='object')
"""
sdf = self._internal.spark_frame.select(
self._internal.index_spark_columns
).drop_duplicates()
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_dtypes=self._internal.index_dtypes,
)
return DataFrame(internal).index
def to_series(self, name: Optional[Union[Any, Tuple]] = None) -> Series:
"""
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
----------
name : string, optional
name of resulting Series. If None, defaults to name of original
index
Returns
-------
Series : dtype will be based on the type of the Index values.
Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'],
... index=list('abcd'))
>>> df['dogs'].index.to_series()
a a
b b
c c
d d
dtype: object
"""
if not is_hashable(name):
raise TypeError("Series.name must be a hashable type")
scol = self.spark.column
if name is not None:
scol = scol.alias(name_like_string(name))
elif self._internal.index_level == 1:
name = self.name
column_labels = [
name if is_name_like_tuple(name) else (name,)
] # type: List[Optional[Tuple]]
internal = self._internal.copy(
column_labels=column_labels, data_spark_columns=[scol], column_label_names=None
)
return first_series(DataFrame(internal))
def to_frame(self, index: bool = True, name: Optional[Union[Any, Tuple]] = None) -> DataFrame:
"""
Create a DataFrame with a column containing the Index.
Parameters
----------
index : boolean, 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
--------
>>> idx = ps.Index(['Ant', 'Bear', 'Cow'], name='animal')
>>> idx.to_frame() # doctest: +NORMALIZE_WHITESPACE
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)
animal
0 Ant
1 Bear
2 Cow
To override the name of the resulting column, specify `name`:
>>> idx.to_frame(name='zoo') # doctest: +NORMALIZE_WHITESPACE
zoo
animal
Ant Ant
Bear Bear
Cow Cow
"""
if name is None:
if self._internal.index_names[0] is None:
name = (DEFAULT_SERIES_NAME,)
else:
name = self._internal.index_names[0]
elif not is_name_like_tuple(name):
if is_name_like_value(name):
name = (name,)
else:
raise TypeError("unhashable type: '{}'".format(type(name).__name__))
return self._to_frame(index=index, names=[name])
def _to_frame(self, index: bool, names: List[Tuple]) -> DataFrame:
if index:
index_spark_columns = self._internal.index_spark_columns
index_names = self._internal.index_names
index_dtypes = self._internal.index_dtypes
else:
index_spark_columns = []
index_names = []
index_dtypes = []
internal = InternalFrame(
spark_frame=self._internal.spark_frame,
index_spark_columns=index_spark_columns,
index_names=index_names,
index_dtypes=index_dtypes,
column_labels=names,
data_spark_columns=self._internal.index_spark_columns,
data_dtypes=self._internal.index_dtypes,
)
return DataFrame(internal)
def is_boolean(self) -> bool:
"""
Return if the current index type is a boolean type.
Examples
--------
>>> ps.DataFrame({'a': [1]}, index=[True]).index.is_boolean()
True
"""
return is_bool_dtype(self.dtype)
def is_categorical(self) -> bool:
"""
Return if the current index type is a categorical type.
Examples
--------
>>> ps.DataFrame({'a': [1]}, index=[1]).index.is_categorical()
False
"""
return is_categorical_dtype(self.dtype)
def is_floating(self) -> bool:
"""
Return if the current index type is a floating type.
Examples
--------
>>> ps.DataFrame({'a': [1]}, index=[1]).index.is_floating()
False
"""
return is_float_dtype(self.dtype)
def is_integer(self) -> bool:
"""
Return if the current index type is a integer type.
Examples
--------
>>> ps.DataFrame({'a': [1]}, index=[1]).index.is_integer()
True
"""
return is_integer_dtype(self.dtype)
def is_interval(self) -> bool:
"""
Return if the current index type is an interval type.
Examples
--------
>>> ps.DataFrame({'a': [1]}, index=[1]).index.is_interval()
False
"""
return is_interval_dtype(self.dtype)
def is_numeric(self) -> bool:
"""
Return if the current index type is a numeric type.
Examples
--------
>>> ps.DataFrame({'a': [1]}, index=[1]).index.is_numeric()
True
"""
return is_numeric_dtype(self.dtype)
def is_object(self) -> bool:
"""
Return if the current index type is a object type.
Examples
--------
>>> ps.DataFrame({'a': [1]}, index=["a"]).index.is_object()
True
"""
return is_object_dtype(self.dtype)
def is_type_compatible(self, kind: str) -> bool:
"""
Whether the index type is compatible with the provided type.
Examples
--------
>>> psidx = ps.Index([1, 2, 3])
>>> psidx.is_type_compatible('integer')
True
>>> psidx = ps.Index([1.0, 2.0, 3.0])
>>> psidx.is_type_compatible('integer')
False
>>> psidx.is_type_compatible('floating')
True
"""
return kind == self.inferred_type
def dropna(self) -> "Index":
"""
Return Index or MultiIndex without NA/NaN values
Examples
--------
>>> df = ps.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=['cobra', 'viper', None],
... columns=['max_speed', 'shield'])
>>> df
max_speed shield
cobra 1 2
viper 4 5
NaN 7 8
>>> df.index.dropna()
Index(['cobra', 'viper'], dtype='object')
Also support for MultiIndex
>>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
... [None, 'weight', 'length']],
... [[0, 1, 1, 1, 1, 1, 2, 2, 2],
... [0, 1, 1, 0, 1, 2, 1, 1, 2]])
>>> s = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, None],
... index=midx)
>>> s
lama NaN 45.0
cow weight 200.0
weight 1.2
NaN 30.0
weight 250.0
length 1.5
falcon weight 320.0
weight 1.0
length NaN
dtype: float64
>>> s.index.dropna() # doctest: +SKIP
MultiIndex([( 'cow', 'weight'),
( 'cow', 'weight'),
( 'cow', 'weight'),
( 'cow', 'length'),
('falcon', 'weight'),
('falcon', 'weight'),
('falcon', 'length')],
)
"""
sdf = self._internal.spark_frame.select(self._internal.index_spark_columns).dropna()
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_dtypes=self._internal.index_dtypes,
)
return DataFrame(internal).index
def unique(self, level: Optional[Union[int, Any, Tuple]] = None) -> "Index":
"""
Return unique values in the index.
Be aware the order of unique values might be different than pandas.Index.unique
Parameters
----------
level : int or str, optional, default is None
Returns
-------
Index without duplicates
See Also
--------
Series.unique
groupby.SeriesGroupBy.unique
Examples
--------
>>> ps.DataFrame({'a': ['a', 'b', 'c']}, index=[1, 1, 3]).index.unique().sort_values()
Int64Index([1, 3], dtype='int64')
>>> ps.DataFrame({'a': ['a', 'b', 'c']}, index=['d', 'e', 'e']).index.unique().sort_values()
Index(['d', 'e'], dtype='object')
MultiIndex
>>> ps.MultiIndex.from_tuples([("A", "X"), ("A", "Y"), ("A", "X")]).unique()
... # doctest: +SKIP
MultiIndex([('A', 'X'),
('A', 'Y')],
)
"""
if level is not None:
self._validate_index_level(level)
scols = self._internal.index_spark_columns
sdf = self._psdf._internal.spark_frame.select(scols).distinct()
return DataFrame(
InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_dtypes=self._internal.index_dtypes,
)
).index
# TODO: add error parameter
def drop(self, labels: List[Any]) -> "Index":
"""
Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like
Returns
-------
dropped : Index
Examples
--------
>>> index = ps.Index([1, 2, 3])
>>> index
Int64Index([1, 2, 3], dtype='int64')
>>> index.drop([1])
Int64Index([2, 3], dtype='int64')
"""
internal = self._internal.resolved_copy
sdf = internal.spark_frame[~internal.index_spark_columns[0].isin(labels)]
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_dtypes=self._internal.index_dtypes,
column_labels=[],
data_spark_columns=[],
data_dtypes=[],
)
return DataFrame(internal).index
def _validate_index_level(self, level: Union[int, Any, Tuple]) -> None:
"""
Validate index level.
For single-level Index getting level number is a no-op, but some
verification must be done like in MultiIndex.
"""
if isinstance(level, int):
if level < 0 and level != -1:
raise IndexError(
"Too many levels: Index has only 1 level,"
" %d is not a valid level number" % (level,)
)
elif level > 0:
raise IndexError("Too many levels:" " Index has only 1 level, not %d" % (level + 1))
elif level != self.name:
raise KeyError(
"Requested level ({}) does not match index name ({})".format(level, self.name)
)
def get_level_values(self, level: Union[int, Any, Tuple]) -> "Index":
"""
Return Index if a valid level is given.
Examples:
--------
>>> psidx = ps.Index(['a', 'b', 'c'], name='ks')
>>> psidx.get_level_values(0)
Index(['a', 'b', 'c'], dtype='object', name='ks')
>>> psidx.get_level_values('ks')
Index(['a', 'b', 'c'], dtype='object', name='ks')
"""
self._validate_index_level(level)
return self
def copy(
self, name: Optional[Union[Any, Tuple]] = None, deep: Optional[bool] = None
) -> "Index":
"""
Make a copy of this object. name sets those attributes on the new object.
Parameters
----------
name : string, optional
to set name of index
deep : None
this parameter is not supported but just dummy parameter to match pandas.
Examples
--------
>>> df = ps.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=['cobra', 'viper', 'sidewinder'],
... columns=['max_speed', 'shield'])
>>> df
max_speed shield
cobra 1 2
viper 4 5
sidewinder 7 8
>>> df.index
Index(['cobra', 'viper', 'sidewinder'], dtype='object')
Copy index
>>> df.index.copy()
Index(['cobra', 'viper', 'sidewinder'], dtype='object')
Copy index with name
>>> df.index.copy(name='snake')
Index(['cobra', 'viper', 'sidewinder'], dtype='object', name='snake')
"""
result = self._psdf.copy().index
if name:
result.name = name
return result
def droplevel(self, level: Union[int, Any, Tuple, List[Union[int, Any, Tuple]]]) -> "Index":
"""
Return index with requested level(s) removed.
If resulting index has only 1 level left, the result will be
of Index type, not MultiIndex.
Parameters
----------
level : int, str, tuple, or list-like, default 0
If a string is given, must be the name of a level
If list-like, elements must be names or indexes of levels.
Returns
-------
Index or MultiIndex
Examples
--------
>>> midx = ps.DataFrame({'a': ['a', 'b']}, index=[['a', 'x'], ['b', 'y'], [1, 2]]).index
>>> midx # doctest: +SKIP
MultiIndex([('a', 'b', 1),
('x', 'y', 2)],
)
>>> midx.droplevel([0, 1]) # doctest: +SKIP
Int64Index([1, 2], dtype='int64')
>>> midx.droplevel(0) # doctest: +SKIP
MultiIndex([('b', 1),
('y', 2)],
)
>>> midx.names = [("a", "b"), "b", "c"]
>>> midx.droplevel([('a', 'b')]) # doctest: +SKIP
MultiIndex([('b', 1),
('y', 2)],
names=['b', 'c'])
"""
names = self.names
nlevels = self.nlevels
if not is_list_like(level):
levels = [cast(Union[int, Any, Tuple], level)]
else:
levels = cast(List[Union[int, Any, Tuple]], level)
int_level = set()
for n in levels:
if isinstance(n, int):
if n < 0:
n = n + nlevels
if n < 0:
raise IndexError(
"Too many levels: Index has only {} levels, "
"{} is not a valid level number".format(nlevels, (n - nlevels))
)
if n >= nlevels:
raise IndexError(
"Too many levels: Index has only {} levels, not {}".format(nlevels, n + 1)
)
else:
if n not in names:
raise KeyError("Level {} not found".format(n))
n = names.index(n)
int_level.add(n)
if len(levels) >= nlevels:
raise ValueError(
"Cannot remove {} levels from an index with {} "
"levels: at least one level must be "
"left.".format(len(levels), nlevels)
)
index_spark_columns, index_names, index_dtypes = zip(
*[
item
for i, item in enumerate(
zip(
self._internal.index_spark_columns,
self._internal.index_names,
self._internal.index_dtypes,
)
)
if i not in int_level
]
)
internal = self._internal.copy(
index_spark_columns=list(index_spark_columns),
index_names=list(index_names),
index_dtypes=list(index_dtypes),
column_labels=[],
data_spark_columns=[],
data_dtypes=[],
)
return DataFrame(internal).index
def symmetric_difference(
self,
other: "Index",
result_name: Optional[Union[Any, Tuple]] = None,
sort: Optional[bool] = None,
) -> "Index":
"""
Compute the symmetric difference of two Index objects.
Parameters
----------
other : Index or array-like
result_name : str
sort : True or None, default None
Whether to sort the resulting index.
* True : Attempt to sort the result.
* None : Do not sort the result.
Returns
-------
symmetric_difference : Index
Notes
-----
``symmetric_difference`` contains elements that appear in either
``idx1`` or ``idx2`` but not both. Equivalent to the Index created by
``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates
dropped.
Examples
--------
>>> s1 = ps.Series([1, 2, 3, 4], index=[1, 2, 3, 4])
>>> s2 = ps.Series([1, 2, 3, 4], index=[2, 3, 4, 5])
>>> s1.index.symmetric_difference(s2.index) # doctest: +SKIP
Int64Index([5, 1], dtype='int64')
You can set name of result Index.
>>> s1.index.symmetric_difference(s2.index, result_name='pandas-on-Spark') # doctest: +SKIP
Int64Index([5, 1], dtype='int64', name='pandas-on-Spark')
You can set sort to `True`, if you want to sort the resulting index.
>>> s1.index.symmetric_difference(s2.index, sort=True)
Int64Index([1, 5], dtype='int64')
You can also use the ``^`` operator:
>>> s1.index ^ s2.index # doctest: +SKIP
Int64Index([5, 1], dtype='int64')
"""
if type(self) != type(other):
raise NotImplementedError(
"Doesn't support symmetric_difference between Index & MultiIndex for now"
)
sdf_self = self._psdf._internal.spark_frame.select(self._internal.index_spark_columns)
sdf_other = other._psdf._internal.spark_frame.select(other._internal.index_spark_columns)
sdf_symdiff = sdf_self.union(sdf_other).subtract(sdf_self.intersect(sdf_other))
if sort:
sdf_symdiff = sdf_symdiff.sort(*self._internal.index_spark_column_names)
internal = InternalFrame(
spark_frame=sdf_symdiff,
index_spark_columns=[
scol_for(sdf_symdiff, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_dtypes=self._internal.index_dtypes,
)
result = DataFrame(internal).index
if result_name:
result.name = result_name
return result
# TODO: return_indexer
def sort_values(self, ascending: bool = True) -> "Index":
"""
Return a sorted copy of the index.
.. note:: This method is not supported for pandas when index has NaN value.
pandas raises unexpected TypeError, but we support treating NaN
as the smallest value.
Parameters
----------
ascending : bool, default True
Should the index values be sorted in an ascending order.
Returns
-------
sorted_index : ps.Index or ps.MultiIndex
Sorted copy of the index.
See Also
--------
Series.sort_values : Sort values of a Series.
DataFrame.sort_values : Sort values in a DataFrame.
Examples
--------
>>> idx = ps.Index([10, 100, 1, 1000])
>>> idx
Int64Index([10, 100, 1, 1000], dtype='int64')
Sort values in ascending order (default behavior).
>>> idx.sort_values()
Int64Index([1, 10, 100, 1000], dtype='int64')
Sort values in descending order.
>>> idx.sort_values(ascending=False)
Int64Index([1000, 100, 10, 1], dtype='int64')
Support for MultiIndex.
>>> psidx = ps.MultiIndex.from_tuples([('a', 'x', 1), ('c', 'y', 2), ('b', 'z', 3)])
>>> psidx # doctest: +SKIP
MultiIndex([('a', 'x', 1),
('c', 'y', 2),
('b', 'z', 3)],
)
>>> psidx.sort_values() # doctest: +SKIP
MultiIndex([('a', 'x', 1),
('b', 'z', 3),
('c', 'y', 2)],
)
>>> psidx.sort_values(ascending=False) # doctest: +SKIP
MultiIndex([('c', 'y', 2),
('b', 'z', 3),
('a', 'x', 1)],
)
"""
sdf = self._internal.spark_frame
sdf = sdf.orderBy(*self._internal.index_spark_columns, ascending=ascending).select(
self._internal.index_spark_columns
)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_dtypes=self._internal.index_dtypes,
)
return DataFrame(internal).index
@no_type_check
def sort(self, *args, **kwargs) -> None:
"""
Use sort_values instead.
"""
raise TypeError("cannot sort an Index object in-place, use sort_values instead")
def min(self) -> Union[Scalar, Tuple[Scalar, ...]]:
"""
Return the minimum value of the Index.
Returns
-------
scalar
Minimum value.
See Also
--------
Index.max : Return the maximum value of the object.
Series.min : Return the minimum value in a Series.
DataFrame.min : Return the minimum values in a DataFrame.
Examples
--------
>>> idx = ps.Index([3, 2, 1])
>>> idx.min()
1
>>> idx = ps.Index(['c', 'b', 'a'])
>>> idx.min()
'a'
For a MultiIndex, the maximum is determined lexicographically.
>>> idx = ps.MultiIndex.from_tuples([('a', 'x', 1), ('b', 'y', 2)])
>>> idx.min()
('a', 'x', 1)
"""
sdf = self._internal.spark_frame
min_row = cast(
pd.DataFrame,
sdf.select(F.min(F.struct(*self._internal.index_spark_columns)).alias("min_row"))
.select("min_row.*")
.toPandas(),
)
result = tuple(min_row.iloc[0])
return result if len(result) > 1 else result[0]
def max(self) -> Union[Scalar, Tuple[Scalar, ...]]:
"""
Return the maximum value of the Index.
Returns
-------
scalar
Maximum value.
See Also
--------
Index.min : Return the minimum value in an Index.
Series.max : Return the maximum value in a Series.
DataFrame.max : Return the maximum values in a DataFrame.
Examples
--------
>>> idx = ps.Index([3, 2, 1])
>>> idx.max()
3
>>> idx = ps.Index(['c', 'b', 'a'])
>>> idx.max()
'c'
For a MultiIndex, the maximum is determined lexicographically.
>>> idx = ps.MultiIndex.from_tuples([('a', 'x', 1), ('b', 'y', 2)])
>>> idx.max()
('b', 'y', 2)
"""
sdf = self._internal.spark_frame
max_row = cast(
pd.DataFrame,
sdf.select(F.max(F.struct(*self._internal.index_spark_columns)).alias("max_row"))
.select("max_row.*")
.toPandas(),
)
result = tuple(max_row.iloc[0])
return result if len(result) > 1 else result[0]
def delete(self, loc: Union[int, List[int]]) -> "Index":
"""
Make new Index with passed location(-s) deleted.
.. note:: this API can be pretty expensive since it is based on
a global sequence internally.
Returns
-------
new_index : Index
Examples
--------
>>> psidx = ps.Index([10, 10, 9, 8, 4, 2, 4, 4, 2, 2, 10, 10])
>>> psidx
Int64Index([10, 10, 9, 8, 4, 2, 4, 4, 2, 2, 10, 10], dtype='int64')
>>> psidx.delete(0).sort_values()
Int64Index([2, 2, 2, 4, 4, 4, 8, 9, 10, 10, 10], dtype='int64')
>>> psidx.delete([0, 1, 2, 3, 10, 11]).sort_values()
Int64Index([2, 2, 2, 4, 4, 4], dtype='int64')
MultiIndex
>>> psidx = ps.MultiIndex.from_tuples([('a', 'x', 1), ('b', 'y', 2), ('c', 'z', 3)])
>>> psidx # doctest: +SKIP
MultiIndex([('a', 'x', 1),
('b', 'y', 2),
('c', 'z', 3)],
)
>>> psidx.delete([0, 2]).sort_values() # doctest: +SKIP
MultiIndex([('b', 'y', 2)],
)
"""
length = len(self)
def is_len_exceeded(index: int) -> bool:
"""Check if the given index is exceeded the length or not"""
return index >= length if index >= 0 else abs(index) > length
if not is_list_like(loc):
if is_len_exceeded(cast(int, loc)):
raise IndexError(
"index {} is out of bounds for axis 0 with size {}".format(loc, length)
)
locs = [cast(int, loc)]
else:
for index in cast(List[int], loc):
if is_len_exceeded(index):
raise IndexError(
"index {} is out of bounds for axis 0 with size {}".format(index, length)
)
locs = cast(List[int], loc)
locs = [int(item) for item in locs]
locs = [item if item >= 0 else length + item for item in locs]
# we need a temporary column such as '__index_value_0__'
# since 'InternalFrame.attach_default_index' will be failed
# when self._scol has name of '__index_level_0__'
index_value_column_format = "__index_value_{}__"
sdf = self._internal._sdf
index_value_column_names = [
verify_temp_column_name(sdf, index_value_column_format.format(i))
for i in range(self._internal.index_level)
]
index_value_columns = [
index_scol.alias(index_vcol_name)
for index_scol, index_vcol_name in zip(
self._internal.index_spark_columns, index_value_column_names
)
]
sdf = sdf.select(index_value_columns)
sdf = InternalFrame.attach_default_index(sdf, default_index_type="distributed-sequence")
# sdf here looks as below
# +-----------------+-----------------+-----------------+-----------------+
# |__index_level_0__|__index_value_0__|__index_value_1__|__index_value_2__|
# +-----------------+-----------------+-----------------+-----------------+
# | 0| a| x| 1|
# | 1| b| y| 2|
# | 2| c| z| 3|
# +-----------------+-----------------+-----------------+-----------------+
# delete rows which are matched with given `loc`
sdf = sdf.where(~F.col(SPARK_INDEX_NAME_FORMAT(0)).isin(locs))
sdf = sdf.select(index_value_column_names)
# sdf here looks as below, we should alias them back to origin spark column names
# +-----------------+-----------------+-----------------+
# |__index_value_0__|__index_value_1__|__index_value_2__|
# +-----------------+-----------------+-----------------+
# | c| z| 3|
# +-----------------+-----------------+-----------------+
index_origin_columns = [
F.col(index_vcol_name).alias(index_scol_name)
for index_vcol_name, index_scol_name in zip(
index_value_column_names, self._internal.index_spark_column_names
)
]
sdf = sdf.select(index_origin_columns)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_dtypes=self._internal.index_dtypes,
)
return DataFrame(internal).index
def append(self, other: "Index") -> "Index":
"""
Append a collection of Index options together.
Parameters
----------
other : Index
Returns
-------
appended : Index
Examples
--------
>>> psidx = ps.Index([10, 5, 0, 5, 10, 5, 0, 10])
>>> psidx
Int64Index([10, 5, 0, 5, 10, 5, 0, 10], dtype='int64')
>>> psidx.append(psidx)
Int64Index([10, 5, 0, 5, 10, 5, 0, 10, 10, 5, 0, 5, 10, 5, 0, 10], dtype='int64')
Support for MiltiIndex
>>> psidx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y')])
>>> psidx # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y')],
)
>>> psidx.append(psidx) # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y'),
('a', 'x'),
('b', 'y')],
)
"""
from pyspark.pandas.indexes.multi import MultiIndex
if type(self) is not type(other):
raise NotImplementedError(
"append() between Index & MultiIndex currently is not supported"
)
sdf_self = self._internal.spark_frame.select(self._internal.index_spark_columns)
sdf_other = other._internal.spark_frame.select(other._internal.index_spark_columns)
sdf_appended = sdf_self.union(sdf_other)
# names should be kept when MultiIndex, but Index wouldn't keep its name.
if isinstance(self, MultiIndex):
index_names = self._internal.index_names
else:
index_names = None
internal = InternalFrame( # TODO: dtypes?
spark_frame=sdf_appended,
index_spark_columns=[
scol_for(sdf_appended, col) for col in self._internal.index_spark_column_names
],
index_names=index_names,
)
return DataFrame(internal).index
def argmax(self) -> int:
"""
Return a maximum argument indexer.
Parameters
----------
skipna : bool, default True
Returns
-------
maximum argument indexer
Examples
--------
>>> psidx = ps.Index([10, 9, 8, 7, 100, 5, 4, 3, 100, 3])
>>> psidx
Int64Index([10, 9, 8, 7, 100, 5, 4, 3, 100, 3], dtype='int64')
>>> psidx.argmax()
4
"""
sdf = self._internal.spark_frame.select(self.spark.column)
sequence_col = verify_temp_column_name(sdf, "__distributed_sequence_column__")
sdf = InternalFrame.attach_distributed_sequence_column(sdf, column_name=sequence_col)
# spark_frame here looks like below
# +-----------------+---------------+
# |__index_level_0__|__index_value__|
# +-----------------+---------------+
# | 0| 10|
# | 4| 100|
# | 2| 8|
# | 3| 7|
# | 6| 4|
# | 5| 5|
# | 7| 3|
# | 8| 100|
# | 1| 9|
# +-----------------+---------------+
return (
sdf.orderBy(
scol_for(sdf, self._internal.data_spark_column_names[0]).desc(),
F.col(sequence_col).asc(),
)
.select(sequence_col)
.first()[0]
)
def argmin(self) -> int:
"""
Return a minimum argument indexer.
Parameters
----------
skipna : bool, default True
Returns
-------
minimum argument indexer
Examples
--------
>>> psidx = ps.Index([10, 9, 8, 7, 100, 5, 4, 3, 100, 3])
>>> psidx
Int64Index([10, 9, 8, 7, 100, 5, 4, 3, 100, 3], dtype='int64')
>>> psidx.argmin()
7
"""
sdf = self._internal.spark_frame.select(self.spark.column)
sequence_col = verify_temp_column_name(sdf, "__distributed_sequence_column__")
sdf = InternalFrame.attach_distributed_sequence_column(sdf, column_name=sequence_col)
return (
sdf.orderBy(
scol_for(sdf, self._internal.data_spark_column_names[0]).asc(),
F.col(sequence_col).asc(),
)
.select(sequence_col)
.first()[0]
)
def set_names(
self,
names: Union[Any, Tuple, List[Union[Any, Tuple]]],
level: Optional[Union[int, Any, Tuple, List[Union[int, Any, Tuple]]]] = None,
inplace: bool = False,
) -> Optional["Index"]:
"""
Set Index or MultiIndex name.
Able to set new names partially and by level.
Parameters
----------
names : label or list of label
Name(s) to set.
level : int, label or list of int or label, optional
If the index is a MultiIndex, level(s) to set (None for all
levels). Otherwise level must be None.
inplace : bool, default False
Modifies the object directly, instead of creating a new Index or
MultiIndex.
Returns
-------
Index
The same type as the caller or None if inplace is True.
See Also
--------
Index.rename : Able to set new names without level.
Examples
--------
>>> idx = ps.Index([1, 2, 3, 4])
>>> idx
Int64Index([1, 2, 3, 4], dtype='int64')
>>> idx.set_names('quarter')
Int64Index([1, 2, 3, 4], dtype='int64', name='quarter')
For MultiIndex
>>> idx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y')])
>>> idx # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y')],
)
>>> idx.set_names(['kind', 'year'], inplace=True)
>>> idx # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y')],
names=['kind', 'year'])
>>> idx.set_names('species', level=0) # doctest: +SKIP
MultiIndex([('a', 'x'),
('b', 'y')],
names=['species', 'year'])
"""
from pyspark.pandas.indexes.multi import MultiIndex
if isinstance(self, MultiIndex):
if level is not None:
self_names = self.names
self_names[level] = names # type: ignore
names = self_names
return self.rename(name=names, inplace=inplace)
def difference(self, other: "Index", sort: Optional[bool] = None) -> "Index":
"""
Return a new Index with elements from the index that are not in
`other`.
This is the set difference of two Index objects.
Parameters
----------
other : Index or array-like
sort : True or None, default None
Whether to sort the resulting index.
* True : Attempt to sort the result.
* None : Do not sort the result.
Returns
-------
difference : Index
Examples
--------
>>> idx1 = ps.Index([2, 1, 3, 4])
>>> idx2 = ps.Index([3, 4, 5, 6])
>>> idx1.difference(idx2, sort=True)
Int64Index([1, 2], dtype='int64')
MultiIndex
>>> midx1 = ps.MultiIndex.from_tuples([('a', 'x', 1), ('b', 'y', 2), ('c', 'z', 3)])
>>> midx2 = ps.MultiIndex.from_tuples([('a', 'x', 1), ('b', 'z', 2), ('k', 'z', 3)])
>>> midx1.difference(midx2) # doctest: +SKIP
MultiIndex([('b', 'y', 2),
('c', 'z', 3)],
)
"""
from pyspark.pandas.indexes.multi import MultiIndex
if not is_list_like(other):
raise TypeError("Input must be Index or array-like")
if not isinstance(sort, (type(None), type(True))):
raise ValueError(
"The 'sort' keyword only takes the values of None or True; {} was passed.".format(
sort
)
)
# Handling MultiIndex
if isinstance(self, MultiIndex) and not isinstance(other, MultiIndex):
if not all([isinstance(item, tuple) for item in other]):
raise TypeError("other must be a MultiIndex or a list of tuples")
other = MultiIndex.from_tuples(other)
if not isinstance(other, Index):
other = Index(other)
sdf_self = self._internal.spark_frame
sdf_other = other._internal.spark_frame
idx_self = self._internal.index_spark_columns
idx_other = other._internal.index_spark_columns
sdf_diff = sdf_self.select(idx_self).subtract(sdf_other.select(idx_other))
internal = InternalFrame(
spark_frame=sdf_diff,
index_spark_columns=[
scol_for(sdf_diff, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_dtypes=self._internal.index_dtypes,
)
result = DataFrame(internal).index
# Name(s) will be kept when only name(s) of (Multi)Index are the same.
if isinstance(self, type(other)) and isinstance(self, MultiIndex):
if self.names == other.names:
result.names = self.names
elif isinstance(self, type(other)) and not isinstance(self, MultiIndex):
if self.name == other.name:
result.name = self.name
return result if sort is None else result.sort_values()
@property
def is_all_dates(self) -> bool:
"""
Return if all data types of the index are datetime.
remember that since pandas-on-Spark does not support multiple data types in an index,
so it returns True if any type of data is datetime.
Examples
--------
>>> from datetime import datetime
>>> idx = ps.Index([datetime(2019, 1, 1, 0, 0, 0), datetime(2019, 2, 3, 0, 0, 0)])
>>> idx
DatetimeIndex(['2019-01-01', '2019-02-03'], dtype='datetime64[ns]', freq=None)
>>> idx.is_all_dates
True
>>> idx = ps.Index([datetime(2019, 1, 1, 0, 0, 0), None])
>>> idx
DatetimeIndex(['2019-01-01', 'NaT'], dtype='datetime64[ns]', freq=None)
>>> idx.is_all_dates
True
>>> idx = ps.Index([0, 1, 2])
>>> idx
Int64Index([0, 1, 2], dtype='int64')
>>> idx.is_all_dates
False
"""
return isinstance(self.spark.data_type, TimestampType)
def repeat(self, repeats: int) -> "Index":
"""
Repeat elements of a Index/MultiIndex.
Returns a new Index/MultiIndex where each element of the current Index/MultiIndex
is repeated consecutively a given number of times.
Parameters
----------
repeats : int
The number of repetitions for each element. This should be a
non-negative integer. Repeating 0 times will return an empty
Index.
Returns
-------
repeated_index : Index/MultiIndex
Newly created Index/MultiIndex with repeated elements.
See Also
--------
Series.repeat : Equivalent function for Series.
Examples
--------
>>> idx = ps.Index(['a', 'b', 'c'])
>>> idx
Index(['a', 'b', 'c'], dtype='object')
>>> idx.repeat(2)
Index(['a', 'b', 'c', 'a', 'b', 'c'], dtype='object')
For MultiIndex,
>>> midx = ps.MultiIndex.from_tuples([('x', 'a'), ('x', 'b'), ('y', 'c')])
>>> midx # doctest: +SKIP
MultiIndex([('x', 'a'),
('x', 'b'),
('y', 'c')],
)
>>> midx.repeat(2) # doctest: +SKIP
MultiIndex([('x', 'a'),
('x', 'b'),
('y', 'c'),
('x', 'a'),
('x', 'b'),
('y', 'c')],
)
>>> midx.repeat(0) # doctest: +SKIP
MultiIndex([], )
"""
if not isinstance(repeats, int):
raise TypeError(
"`repeats` argument must be integer, but got {}".format(type(repeats).__name__)
)
elif repeats < 0:
raise ValueError("negative dimensions are not allowed")
psdf = DataFrame(self._internal.resolved_copy) # type: DataFrame
if repeats == 0:
return DataFrame(psdf._internal.with_filter(F.lit(False))).index
else:
return ps.concat([psdf] * repeats).index
def asof(self, label: Any) -> Scalar:
"""
Return the label from the index, or, if not present, the previous one.
Assuming that the index is sorted, return the passed index label if it
is in the index, or return the previous index label if the passed one
is not in the index.
.. note:: This API is dependent on :meth:`Index.is_monotonic_increasing`
which can be expensive.
Parameters
----------
label : object
The label up to which the method returns the latest index label.
Returns
-------
object
The passed label if it is in the index. The previous label if the
passed label is not in the sorted index or `NaN` if there is no
such label.
Examples
--------
`Index.asof` returns the latest index label up to the passed label.
>>> idx = ps.Index(['2013-12-31', '2014-01-02', '2014-01-03'])
>>> idx.asof('2014-01-01')
'2013-12-31'
If the label is in the index, the method returns the passed label.
>>> idx.asof('2014-01-02')
'2014-01-02'
If all of the labels in the index are later than the passed label,
NaN is returned.
>>> idx.asof('1999-01-02')
nan
"""
sdf = self._internal.spark_frame
if self.is_monotonic_increasing:
sdf = sdf.where(self.spark.column <= F.lit(label).cast(self.spark.data_type)).select(
F.max(self.spark.column)
)
elif self.is_monotonic_decreasing:
sdf = sdf.where(self.spark.column >= F.lit(label).cast(self.spark.data_type)).select(
F.min(self.spark.column)
)
else:
raise ValueError("index must be monotonic increasing or decreasing")
result = cast(pd.DataFrame, sdf.toPandas()).iloc[0, 0]
return result if result is not None else np.nan
def union(
self, other: Union[DataFrame, Series, "Index", List], sort: Optional[bool] = None
) -> "Index":
"""
Form the union of two Index objects.
Parameters
----------
other : Index or array-like
sort : bool or None, default None
Whether to sort the resulting Index.
Returns
-------
union : Index
Examples
--------
Index
>>> idx1 = ps.Index([1, 2, 3, 4])
>>> idx2 = ps.Index([3, 4, 5, 6])
>>> idx1.union(idx2).sort_values()
Int64Index([1, 2, 3, 4, 5, 6], dtype='int64')
MultiIndex
>>> midx1 = ps.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("x", "c"), ("x", "d")])
>>> midx2 = ps.MultiIndex.from_tuples([("x", "c"), ("x", "d"), ("x", "e"), ("x", "f")])
>>> midx1.union(midx2).sort_values() # doctest: +SKIP
MultiIndex([('x', 'a'),
('x', 'b'),
('x', 'c'),
('x', 'd'),
('x', 'e'),
('x', 'f')],
)
"""
from pyspark.pandas.indexes.multi import MultiIndex
sort = True if sort is None else sort
sort = validate_bool_kwarg(sort, "sort")
if type(self) is not type(other):
if isinstance(self, MultiIndex):
if not isinstance(other, list) or not all(
[isinstance(item, tuple) for item in other]
):
raise TypeError("other must be a MultiIndex or a list of tuples")
other_idx = MultiIndex.from_tuples(other) # type: Index
else:
if isinstance(other, MultiIndex):
# TODO: We can't support different type of values in a single column for now.
raise NotImplementedError(
"Union between Index and MultiIndex is not yet supported"
)
elif isinstance(other, Series):
other = other.to_frame()
other_idx = other.set_index(other.columns[0]).index
elif isinstance(other, DataFrame):
raise ValueError("Index data must be 1-dimensional")
else:
other_idx = Index(other)
else:
other_idx = cast(Index, other)
sdf_self = self._internal.spark_frame.select(self._internal.index_spark_columns)
sdf_other = other_idx._internal.spark_frame.select(other_idx._internal.index_spark_columns)
sdf = sdf_self.union(sdf_other.subtract(sdf_self))
if isinstance(self, MultiIndex):
sdf = sdf.drop_duplicates()
if sort:
sdf = sdf.sort(*self._internal.index_spark_column_names)
internal = InternalFrame( # TODO: dtypes?
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
)
return DataFrame(internal).index
def holds_integer(self) -> bool:
"""
Whether the type is an integer type.
Always return False for MultiIndex.
Notes
-----
When Index contains null values the result can be different with pandas
since pandas-on-Spark cast integer to float when Index contains null values.
>>> ps.Index([1, 2, 3, None])
Float64Index([1.0, 2.0, 3.0, nan], dtype='float64')
Examples
--------
>>> psidx = ps.Index([1, 2, 3, 4])
>>> psidx.holds_integer()
True
Returns False for string type.
>>> psidx = ps.Index(["A", "B", "C", "D"])
>>> psidx.holds_integer()
False
Returns False for float type.
>>> psidx = ps.Index([1.1, 2.2, 3.3, 4.4])
>>> psidx.holds_integer()
False
"""
return isinstance(self.spark.data_type, IntegralType)
def intersection(self, other: Union[DataFrame, Series, "Index", List]) -> "Index":
"""
Form the intersection of two Index objects.
This returns a new Index with elements common to the index and `other`.
Parameters
----------
other : Index or array-like
Returns
-------
intersection : Index
Examples
--------
>>> idx1 = ps.Index([1, 2, 3, 4])
>>> idx2 = ps.Index([3, 4, 5, 6])
>>> idx1.intersection(idx2).sort_values()
Int64Index([3, 4], dtype='int64')
"""
from pyspark.pandas.indexes.multi import MultiIndex
if isinstance(other, DataFrame):
raise ValueError("Index data must be 1-dimensional")
elif isinstance(other, MultiIndex):
# Always returns a no-named empty Index if `other` is MultiIndex.
return self._psdf.head(0).index.rename(None)
elif isinstance(other, Index):
spark_frame_other = other.to_frame().to_spark()
keep_name = self.name == other.name
elif isinstance(other, Series):
spark_frame_other = other.to_frame().to_spark()
keep_name = True
elif is_list_like(other):
other = Index(other)
if isinstance(other, MultiIndex):
return other.to_frame().head(0).index
spark_frame_other = other.to_frame().to_spark()
keep_name = True
else:
raise TypeError("Input must be Index or array-like")
spark_frame_self = self.to_frame(name=SPARK_DEFAULT_INDEX_NAME).to_spark()
spark_frame_intersected = spark_frame_self.intersect(spark_frame_other)
if keep_name:
index_names = self._internal.index_names
else:
index_names = None
internal = InternalFrame( # TODO: dtypes?
spark_frame=spark_frame_intersected,
index_spark_columns=[scol_for(spark_frame_intersected, SPARK_DEFAULT_INDEX_NAME)],
index_names=index_names,
)
return DataFrame(internal).index
def item(self) -> Union[Scalar, Tuple[Scalar, ...]]:
"""
Return the first element of the underlying data as a python scalar.
Returns
-------
scalar
The first element of Index.
Raises
------
ValueError
If the data is not length-1.
Examples
--------
>>> psidx = ps.Index([10])
>>> psidx.item()
10
"""
return self.to_series().item()
def insert(self, loc: int, item: Any) -> "Index":
"""
Make new Index inserting new item at location.
Follows Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
Examples
--------
>>> psidx = ps.Index([1, 2, 3, 4, 5])
>>> psidx.insert(3, 100)
Int64Index([1, 2, 3, 100, 4, 5], dtype='int64')
For negative values
>>> psidx = ps.Index([1, 2, 3, 4, 5])
>>> psidx.insert(-3, 100)
Int64Index([1, 2, 100, 3, 4, 5], dtype='int64')
"""
if loc < 0:
length = len(self)
loc = loc + length
loc = 0 if loc < 0 else loc
index_name = self._internal.index_spark_column_names[0]
sdf_before = self.to_frame(name=index_name)[:loc].to_spark()
sdf_middle = Index([item]).to_frame(name=index_name).to_spark()
sdf_after = self.to_frame(name=index_name)[loc:].to_spark()
sdf = sdf_before.union(sdf_middle).union(sdf_after)
internal = self._internal.with_new_sdf(sdf) # TODO: dtype?
return DataFrame(internal).index
def view(self) -> "Index":
"""
this is defined as a copy with the same identity
"""
return self.copy()
def to_list(self) -> List:
"""
Return a list of the values.
These are each a scalar type, which is a Python scalar
(for str, int, float) or a pandas scalar
(for Timestamp/Timedelta/Interval/Period)
.. note:: This method should only be used if the resulting list is expected
to be small, as all the data is loaded into the driver's memory.
Examples
--------
Index
>>> idx = ps.Index([1, 2, 3, 4, 5])
>>> idx.to_list()
[1, 2, 3, 4, 5]
MultiIndex
>>> tuples = [(1, 'red'), (1, 'blue'), (2, 'red'), (2, 'green')]
>>> midx = ps.MultiIndex.from_tuples(tuples)
>>> midx.to_list()
[(1, 'red'), (1, 'blue'), (2, 'red'), (2, 'green')]
"""
return self._to_internal_pandas().tolist()
tolist = to_list
@property
def inferred_type(self) -> str:
"""
Return a string of the type inferred from the values.
Examples
--------
>>> from datetime import datetime
>>> ps.Index([1, 2, 3]).inferred_type
'integer'
>>> ps.Index([1.0, 2.0, 3.0]).inferred_type
'floating'
>>> ps.Index(['a', 'b', 'c']).inferred_type
'string'
>>> ps.Index([True, False, True, False]).inferred_type
'boolean'
"""
return lib.infer_dtype([self.to_series().head(1).item()])
def __getattr__(self, item: str) -> Any:
if hasattr(MissingPandasLikeIndex, item):
property_or_func = getattr(MissingPandasLikeIndex, item)
if isinstance(property_or_func, property):
return property_or_func.fget(self) # type: ignore
else:
return partial(property_or_func, self)
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
def __repr__(self) -> str:
max_display_count = get_option("display.max_rows")
if max_display_count is None:
return repr(self._to_internal_pandas())
pindex = self._psdf._get_or_create_repr_pandas_cache(max_display_count).index
pindex_length = len(pindex)
repr_string = repr(pindex[:max_display_count])
if pindex_length > max_display_count:
footer = "\nShowing only the first {}".format(max_display_count)
return repr_string + footer
return repr_string
def __iter__(self) -> Iterator:
return MissingPandasLikeIndex.__iter__(self)
def __xor__(self, other: "Index") -> "Index":
return self.symmetric_difference(other)
def __bool__(self) -> bool:
raise ValueError(
"The truth value of a {0} is ambiguous. "
"Use a.empty, a.bool(), a.item(), a.any() or a.all().".format(self.__class__.__name__)
)
def _test() -> None:
import os
import doctest
import sys
from pyspark.sql import SparkSession
import pyspark.pandas.indexes.base
os.chdir(os.environ["SPARK_HOME"])
globs = pyspark.pandas.indexes.base.__dict__.copy()
globs["ps"] = pyspark.pandas
spark = (
SparkSession.builder.master("local[4]")
.appName("pyspark.pandas.indexes.base tests")
.getOrCreate()
)
(failure_count, test_count) = doctest.testmod(
pyspark.pandas.indexes.base,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()
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