#
# Copyright (C) 2019 Databricks, Inc.
#
# 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.
#
"""
Base and utility classes for Koalas objects.
"""
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
from functools import wraps, partial
from typing import Union, Callable, Any
import warnings
import numpy as np
import pandas as pd
from pandas.api.types import is_list_like
from pandas.core.accessor import CachedAccessor
from pyspark.sql import functions as F, Window, Column
from pyspark.sql.types import DateType, DoubleType, FloatType, LongType, StringType, TimestampType
from databricks import koalas as ks # For running doctests and reference resolution in PyCharm.
from databricks.koalas import numpy_compat
from databricks.koalas.internal import (
InternalFrame,
NATURAL_ORDER_COLUMN_NAME,
SPARK_DEFAULT_INDEX_NAME,
)
from databricks.koalas.spark.accessors import SparkIndexOpsMethods
from databricks.koalas.typedef import spark_type_to_pandas_dtype
from databricks.koalas.utils import align_diff_series, same_anchor, scol_for, validate_axis
from databricks.koalas.frame import DataFrame
def booleanize_null(left_scol, scol, f):
"""
Booleanize Null in Spark Column
"""
comp_ops = [
getattr(Column, "__{}__".format(comp_op))
for comp_op in ["eq", "ne", "lt", "le", "ge", "gt"]
]
if f in comp_ops:
# if `f` is "!=", fill null with True otherwise False
filler = f == Column.__ne__
scol = F.when(scol.isNull(), filler).otherwise(scol)
elif f == Column.__or__:
scol = F.when(left_scol.isNull() | scol.isNull(), False).otherwise(scol)
elif f == Column.__and__:
scol = F.when(scol.isNull(), False).otherwise(scol)
return scol
def column_op(f):
"""
A decorator that wraps APIs taking/returning Spark Column so that Koalas Series can be
supported too. If this decorator is used for the `f` function that takes Spark Column and
returns Spark Column, decorated `f` takes Koalas Series as well and returns Koalas
Series.
:param f: a function that takes Spark Column and returns Spark Column.
:param self: Koalas Series
:param args: arguments that the function `f` takes.
"""
@wraps(f)
def wrapper(self, *args):
# It is possible for the function `f` takes other arguments than Spark Column.
# To cover this case, explicitly check if the argument is Koalas Series and
# extract Spark Column. For other arguments, they are used as are.
cols = [arg for arg in args if isinstance(arg, IndexOpsMixin)]
if all(same_anchor(self, col) for col in cols):
# Same DataFrame anchors
args = [arg.spark.column if isinstance(arg, IndexOpsMixin) else arg for arg in args]
scol = f(self.spark.column, *args)
scol = booleanize_null(self.spark.column, scol, f)
return self._with_new_scol(scol)
else:
# Different DataFrame anchors
def apply_func(this_column, *that_columns):
scol = f(this_column, *that_columns)
return booleanize_null(this_column, scol, f)
return align_diff_series(apply_func, self, *args, how="full")
return wrapper
def numpy_column_op(f):
@wraps(f)
def wrapper(self, *args):
# PySpark does not support NumPy type out of the box. For now, we convert NumPy types
# into some primitive types understandable in PySpark.
new_args = []
for arg in args:
# TODO: This is a quick hack to support NumPy type. We should revisit this.
if isinstance(self.spark.data_type, LongType) and isinstance(arg, np.timedelta64):
new_args.append(float(arg / np.timedelta64(1, "s")))
else:
new_args.append(arg)
return column_op(f)(self, *new_args)
return wrapper
class IndexOpsMixin(object, metaclass=ABCMeta):
"""common ops mixin to support a unified interface / docs for Series / Index
Assuming there are following attributes or properties and function.
:ivar _anchor: Parent's Koalas DataFrame
:type _anchor: ks.DataFrame
"""
def __init__(self, anchor: DataFrame):
assert anchor is not None
self._anchor = anchor
@property
@abstractmethod
def _internal(self) -> InternalFrame:
pass
@property
def _kdf(self) -> DataFrame:
return self._anchor
spark = CachedAccessor("spark", SparkIndexOpsMethods)
@property
def spark_column(self):
warnings.warn(
"Series.spark_column is deprecated as of Series.spark.column. "
"Please use the API instead.",
FutureWarning,
)
return self.spark.column
spark_column.__doc__ = SparkIndexOpsMethods.column.__doc__
# arithmetic operators
__neg__ = column_op(Column.__neg__)
def __add__(self, other):
if isinstance(self.spark.data_type, StringType):
# Concatenate string columns
if isinstance(other, IndexOpsMixin) and isinstance(other.spark.data_type, StringType):
return column_op(F.concat)(self, other)
# Handle df['col'] + 'literal'
elif isinstance(other, str):
return column_op(F.concat)(self, F.lit(other))
else:
raise TypeError("string addition can only be applied to string series or literals.")
else:
return column_op(Column.__add__)(self, other)
def __sub__(self, other):
# Note that timestamp subtraction casts arguments to integer. This is to mimic pandas's
# behaviors. pandas returns 'timedelta64[ns]' from 'datetime64[ns]'s subtraction.
if isinstance(other, IndexOpsMixin) and isinstance(self.spark.data_type, TimestampType):
if not isinstance(other.spark.data_type, TimestampType):
raise TypeError("datetime subtraction can only be applied to datetime series.")
return self.astype("bigint") - other.astype("bigint")
elif isinstance(other, IndexOpsMixin) and isinstance(self.spark.data_type, DateType):
if not isinstance(other.spark.data_type, DateType):
raise TypeError("date subtraction can only be applied to date series.")
return column_op(F.datediff)(self, other)
else:
return column_op(Column.__sub__)(self, other)
__mul__ = column_op(Column.__mul__)
def __truediv__(self, other):
"""
__truediv__ has different behaviour between pandas and PySpark for several cases.
1. When divide np.inf by zero, PySpark returns null whereas pandas returns np.inf
2. When divide positive number by zero, PySpark returns null whereas pandas returns np.inf
3. When divide -np.inf by zero, PySpark returns null whereas pandas returns -np.inf
4. When divide negative number by zero, PySpark returns null whereas pandas returns -np.inf
+-------------------------------------------+
| dividend (divisor: 0) | PySpark | pandas |
|-----------------------|---------|---------|
| np.inf | null | np.inf |
| -np.inf | null | -np.inf |
| 10 | null | np.inf |
| -10 | null | -np.inf |
+-----------------------|---------|---------+
"""
def truediv(left, right):
return F.when(F.lit(right != 0) | F.lit(right).isNull(), left.__div__(right)).otherwise(
F.when(F.lit(left == np.inf) | F.lit(left == -np.inf), left).otherwise(
F.lit(np.inf).__div__(left)
)
)
return numpy_column_op(truediv)(self, other)
def __mod__(self, other):
def mod(left, right):
return ((left % right) + right) % right
return column_op(mod)(self, other)
def __radd__(self, other):
# Handle 'literal' + df['col']
if isinstance(self.spark.data_type, StringType) and isinstance(other, str):
return self._with_new_scol(F.concat(F.lit(other), self.spark.column))
else:
return column_op(Column.__radd__)(self, other)
__rsub__ = column_op(Column.__rsub__)
__rmul__ = column_op(Column.__rmul__)
def __rtruediv__(self, other):
def rtruediv(left, right):
return F.when(left == 0, F.lit(np.inf).__div__(right)).otherwise(
F.lit(right).__truediv__(left)
)
return numpy_column_op(rtruediv)(self, other)
def __floordiv__(self, other):
"""
__floordiv__ has different behaviour between pandas and PySpark for several cases.
1. When divide np.inf by zero, PySpark returns null whereas pandas returns np.inf
2. When divide positive number by zero, PySpark returns null whereas pandas returns np.inf
3. When divide -np.inf by zero, PySpark returns null whereas pandas returns -np.inf
4. When divide negative number by zero, PySpark returns null whereas pandas returns -np.inf
+-------------------------------------------+
| dividend (divisor: 0) | PySpark | pandas |
|-----------------------|---------|---------|
| np.inf | null | np.inf |
| -np.inf | null | -np.inf |
| 10 | null | np.inf |
| -10 | null | -np.inf |
+-----------------------|---------|---------+
"""
def floordiv(left, right):
return F.when(F.lit(right is np.nan), np.nan).otherwise(
F.when(
F.lit(right != 0) | F.lit(right).isNull(), F.floor(left.__div__(right))
).otherwise(
F.when(F.lit(left == np.inf) | F.lit(left == -np.inf), left).otherwise(
F.lit(np.inf).__div__(left)
)
)
)
return numpy_column_op(floordiv)(self, other)
def __rfloordiv__(self, other):
def rfloordiv(left, right):
return F.when(F.lit(left == 0), F.lit(np.inf).__div__(right)).otherwise(
F.when(F.lit(left) == np.nan, np.nan).otherwise(F.floor(F.lit(right).__div__(left)))
)
return numpy_column_op(rfloordiv)(self, other)
def __rmod__(self, other):
def rmod(left, right):
return ((right % left) + left) % left
return column_op(rmod)(self, other)
__pow__ = column_op(Column.__pow__)
__rpow__ = column_op(Column.__rpow__)
__abs__ = column_op(F.abs)
# comparison operators
__eq__ = column_op(Column.__eq__)
__ne__ = column_op(Column.__ne__)
__lt__ = column_op(Column.__lt__)
__le__ = column_op(Column.__le__)
__ge__ = column_op(Column.__ge__)
__gt__ = column_op(Column.__gt__)
# `and`, `or`, `not` cannot be overloaded in Python,
# so use bitwise operators as boolean operators
__and__ = column_op(Column.__and__)
__or__ = column_op(Column.__or__)
__invert__ = column_op(Column.__invert__)
__rand__ = column_op(Column.__rand__)
__ror__ = column_op(Column.__ror__)
# NDArray Compat
def __array_ufunc__(self, ufunc: Callable, method: str, *inputs: Any, **kwargs: Any):
# Try dunder methods first.
result = numpy_compat.maybe_dispatch_ufunc_to_dunder_op(
self, ufunc, method, *inputs, **kwargs
)
# After that, we try with PySpark APIs.
if result is NotImplemented:
result = numpy_compat.maybe_dispatch_ufunc_to_spark_func(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
else:
# TODO: support more APIs?
raise NotImplementedError("Koalas objects currently do not support %s." % ufunc)
@property
def dtype(self):
"""Return the dtype object of the underlying data.
Examples
--------
>>> s = ks.Series([1, 2, 3])
>>> s.dtype
dtype('int64')
>>> s = ks.Series(list('abc'))
>>> s.dtype
dtype('O')
>>> s = ks.Series(pd.date_range('20130101', periods=3))
>>> s.dtype
dtype('<M8[ns]')
>>> s.rename("a").to_frame().set_index("a").index.dtype
dtype('<M8[ns]')
"""
return spark_type_to_pandas_dtype(self.spark.data_type)
@property
def empty(self):
"""
Returns true if the current object is empty. Otherwise, returns false.
>>> ks.range(10).id.empty
False
>>> ks.range(0).id.empty
True
>>> ks.DataFrame({}, index=list('abc')).index.empty
False
"""
return self._internal.resolved_copy.spark_frame.rdd.isEmpty()
@property
def hasnans(self):
"""
Return True if it has any missing values. Otherwise, it returns False.
>>> ks.DataFrame({}, index=list('abc')).index.hasnans
False
>>> ks.Series(['a', None]).hasnans
True
>>> ks.Series([1.0, 2.0, np.nan]).hasnans
True
>>> ks.Series([1, 2, 3]).hasnans
False
>>> (ks.Series([1.0, 2.0, np.nan]) + 1).hasnans
True
>>> ks.Series([1, 2, 3]).rename("a").to_frame().set_index("a").index.hasnans
False
"""
sdf = self._internal.spark_frame
scol = self.spark.column
return sdf.select(F.max(scol.isNull() | F.isnan(scol))).collect()[0][0]
@property
def is_monotonic(self):
"""
Return boolean if values in the object are monotonically increasing.
.. note:: the current implementation of is_monotonic requires to shuffle
and aggregate multiple times to check the order locally and globally,
which is potentially expensive. In case of multi-index, all data are
transferred to single node which can easily cause out-of-memory error currently.
Returns
-------
is_monotonic : boolean
Examples
--------
>>> ser = ks.Series(['1/1/2018', '3/1/2018', '4/1/2018'])
>>> ser.is_monotonic
True
>>> df = ks.DataFrame({'dates': [None, '1/1/2018', '2/1/2018', '3/1/2018']})
>>> df.dates.is_monotonic
False
>>> df.index.is_monotonic
True
>>> ser = ks.Series([1])
>>> ser.is_monotonic
True
>>> ser = ks.Series([])
>>> ser.is_monotonic
True
>>> ser.rename("a").to_frame().set_index("a").index.is_monotonic
True
>>> ser = ks.Series([5, 4, 3, 2, 1], index=[1, 2, 3, 4, 5])
>>> ser.is_monotonic
False
>>> ser.index.is_monotonic
True
Support for MultiIndex
>>> midx = ks.MultiIndex.from_tuples(
... [('x', 'a'), ('x', 'b'), ('y', 'c'), ('y', 'd'), ('z', 'e')])
>>> midx # doctest: +SKIP
MultiIndex([('x', 'a'),
('x', 'b'),
('y', 'c'),
('y', 'd'),
('z', 'e')],
)
>>> midx.is_monotonic
True
>>> midx = ks.MultiIndex.from_tuples(
... [('z', 'a'), ('z', 'b'), ('y', 'c'), ('y', 'd'), ('x', 'e')])
>>> midx # doctest: +SKIP
MultiIndex([('z', 'a'),
('z', 'b'),
('y', 'c'),
('y', 'd'),
('x', 'e')],
)
>>> midx.is_monotonic
False
"""
return self._is_monotonic("increasing")
is_monotonic_increasing = is_monotonic
@property
def is_monotonic_decreasing(self):
"""
Return boolean if values in the object are monotonically decreasing.
.. note:: the current implementation of is_monotonic_decreasing requires to shuffle
and aggregate multiple times to check the order locally and globally,
which is potentially expensive. In case of multi-index, all data are transferred
to single node which can easily cause out-of-memory error currently.
Returns
-------
is_monotonic : boolean
Examples
--------
>>> ser = ks.Series(['4/1/2018', '3/1/2018', '1/1/2018'])
>>> ser.is_monotonic_decreasing
True
>>> df = ks.DataFrame({'dates': [None, '3/1/2018', '2/1/2018', '1/1/2018']})
>>> df.dates.is_monotonic_decreasing
False
>>> df.index.is_monotonic_decreasing
False
>>> ser = ks.Series([1])
>>> ser.is_monotonic_decreasing
True
>>> ser = ks.Series([])
>>> ser.is_monotonic_decreasing
True
>>> ser.rename("a").to_frame().set_index("a").index.is_monotonic_decreasing
True
>>> ser = ks.Series([5, 4, 3, 2, 1], index=[1, 2, 3, 4, 5])
>>> ser.is_monotonic_decreasing
True
>>> ser.index.is_monotonic_decreasing
False
Support for MultiIndex
>>> midx = ks.MultiIndex.from_tuples(
... [('x', 'a'), ('x', 'b'), ('y', 'c'), ('y', 'd'), ('z', 'e')])
>>> midx # doctest: +SKIP
MultiIndex([('x', 'a'),
('x', 'b'),
('y', 'c'),
('y', 'd'),
('z', 'e')],
)
>>> midx.is_monotonic_decreasing
False
>>> midx = ks.MultiIndex.from_tuples(
... [('z', 'e'), ('z', 'd'), ('y', 'c'), ('y', 'b'), ('x', 'a')])
>>> midx # doctest: +SKIP
MultiIndex([('z', 'a'),
('z', 'b'),
('y', 'c'),
('y', 'd'),
('x', 'e')],
)
>>> midx.is_monotonic_decreasing
True
"""
return self._is_monotonic("decreasing")
def _is_locally_monotonic_spark_column(self, order):
window = (
Window.partitionBy(F.col("__partition_id"))
.orderBy(NATURAL_ORDER_COLUMN_NAME)
.rowsBetween(-1, -1)
)
if order == "increasing":
return (F.col("__origin") >= F.lag(F.col("__origin"), 1).over(window)) & F.col(
"__origin"
).isNotNull()
else:
return (F.col("__origin") <= F.lag(F.col("__origin"), 1).over(window)) & F.col(
"__origin"
).isNotNull()
def _is_monotonic(self, order):
assert order in ("increasing", "decreasing")
sdf = self._internal.spark_frame
sdf = (
sdf.select(
F.spark_partition_id().alias(
"__partition_id"
), # Make sure we use the same partition id in the whole job.
F.col(NATURAL_ORDER_COLUMN_NAME),
self.spark.column.alias("__origin"),
)
.select(
F.col("__partition_id"),
F.col("__origin"),
self._is_locally_monotonic_spark_column(order).alias(
"__comparison_within_partition"
),
)
.groupby(F.col("__partition_id"))
.agg(
F.min(F.col("__origin")).alias("__partition_min"),
F.max(F.col("__origin")).alias("__partition_max"),
F.min(F.coalesce(F.col("__comparison_within_partition"), F.lit(True))).alias(
"__comparison_within_partition"
),
)
)
# Now we're windowing the aggregation results without partition specification.
# The number of rows here will be as the same of partitions, which is expected
# to be small.
window = Window.orderBy(F.col("__partition_id")).rowsBetween(-1, -1)
if order == "increasing":
comparison_col = F.col("__partition_min") >= F.lag(F.col("__partition_max"), 1).over(
window
)
else:
comparison_col = F.col("__partition_min") <= F.lag(F.col("__partition_max"), 1).over(
window
)
sdf = sdf.select(
comparison_col.alias("__comparison_between_partitions"),
F.col("__comparison_within_partition"),
)
ret = sdf.select(
F.min(F.coalesce(F.col("__comparison_between_partitions"), F.lit(True)))
& F.min(F.coalesce(F.col("__comparison_within_partition"), F.lit(True)))
).collect()[0][0]
if ret is None:
return True
else:
return ret
@property
def ndim(self):
"""
Return an int representing the number of array dimensions.
Return 1 for Series / Index / MultiIndex.
Examples
--------
For Series
>>> s = ks.Series([None, 1, 2, 3, 4], index=[4, 5, 2, 1, 8])
>>> s.ndim
1
For Index
>>> s.index.ndim
1
For MultiIndex
>>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... [[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [1, 1, 1, 1, 1, 2, 1, 2, 2]])
>>> s = ks.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)
>>> s.index.ndim
1
"""
return 1
def astype(self, dtype):
"""
Cast a Koalas object to a specified dtype ``dtype``.
Parameters
----------
dtype : data type
Use a numpy.dtype or Python type to cast entire pandas object to
the same type.
Returns
-------
casted : same type as caller
See Also
--------
to_datetime : Convert argument to datetime.
Examples
--------
>>> ser = ks.Series([1, 2], dtype='int32')
>>> ser
0 1
1 2
Name: 0, dtype: int32
>>> ser.astype('int64')
0 1
1 2
Name: 0, dtype: int64
>>> ser.rename("a").to_frame().set_index("a").index.astype('int64')
Int64Index([1, 2], dtype='int64', name='a')
"""
from databricks.koalas.typedef import as_spark_type
spark_type = as_spark_type(dtype)
if not spark_type:
raise ValueError("Type {} not understood".format(dtype))
return self._with_new_scol(self.spark.column.cast(spark_type))
def isin(self, values):
"""
Check whether `values` are contained in Series.
Return a boolean Series showing whether each element in the Series
matches an element in the passed sequence of `values` exactly.
Parameters
----------
values : list or set
The sequence of values to test.
Returns
-------
isin : Series (bool dtype)
Examples
--------
>>> s = ks.Series(['lama', 'cow', 'lama', 'beetle', 'lama',
... 'hippo'], name='animal')
>>> s.isin(['cow', 'lama'])
0 True
1 True
2 True
3 False
4 True
5 False
Name: animal, dtype: bool
Passing a single string as ``s.isin('lama')`` will raise an error. Use
a list of one element instead:
>>> s.isin(['lama'])
0 True
1 False
2 True
3 False
4 True
5 False
Name: animal, dtype: bool
>>> s.rename("a").to_frame().set_index("a").index.isin(['lama'])
Index([True, False, True, False, True, False], dtype='object', name='a')
"""
if not is_list_like(values):
raise TypeError(
"only list-like objects are allowed to be passed"
" to isin(), you passed a [{values_type}]".format(values_type=type(values).__name__)
)
return self._with_new_scol(self.spark.column.isin(list(values))).rename(self.name)
def isnull(self):
"""
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are NA.
NA values, such as None or numpy.NaN, gets mapped to True values.
Everything else gets mapped to False values. Characters such as empty strings '' or
numpy.inf are not considered NA values
(unless you set pandas.options.mode.use_inf_as_na = True).
Returns
-------
Series : Mask of bool values for each element in Series
that indicates whether an element is not an NA value.
Examples
--------
>>> ser = ks.Series([5, 6, np.NaN])
>>> ser.isna() # doctest: +NORMALIZE_WHITESPACE
0 False
1 False
2 True
Name: 0, dtype: bool
>>> ser.rename("a").to_frame().set_index("a").index.isna()
Index([False, False, True], dtype='object', name='a')
"""
from databricks.koalas.indexes import MultiIndex
if isinstance(self, MultiIndex):
raise NotImplementedError("isna is not defined for MultiIndex")
if isinstance(self.spark.data_type, (FloatType, DoubleType)):
return self._with_new_scol(
self.spark.column.isNull() | F.isnan(self.spark.column)
).rename(self.name)
else:
return self._with_new_scol(self.spark.column.isNull()).rename(self.name)
isna = isnull
def notnull(self):
"""
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True.
Characters such as empty strings '' or numpy.inf are not considered NA values
(unless you set pandas.options.mode.use_inf_as_na = True).
NA values, such as None or numpy.NaN, get mapped to False values.
Returns
-------
Series : Mask of bool values for each element in Series
that indicates whether an element is not an NA value.
Examples
--------
Show which entries in a Series are not NA.
>>> ser = ks.Series([5, 6, np.NaN])
>>> ser
0 5.0
1 6.0
2 NaN
Name: 0, dtype: float64
>>> ser.notna()
0 True
1 True
2 False
Name: 0, dtype: bool
>>> ser.rename("a").to_frame().set_index("a").index.notna()
Index([True, True, False], dtype='object', name='a')
"""
from databricks.koalas.indexes import MultiIndex
if isinstance(self, MultiIndex):
raise NotImplementedError("notna is not defined for MultiIndex")
return (~self.isnull()).rename(self.name)
notna = notnull
# TODO: axis, skipna, and many arguments should be implemented.
def all(self, axis: Union[int, str] = 0) -> bool:
"""
Return whether all elements are True.
Returns True unless there at least one element within a series that is
False or equivalent (e.g. zero or empty)
Parameters
----------
axis : {0 or 'index'}, default 0
Indicate which axis or axes should be reduced.
* 0 / 'index' : reduce the index, return a Series whose index is the
original column labels.
Examples
--------
>>> ks.Series([True, True]).all()
True
>>> ks.Series([True, False]).all()
False
>>> ks.Series([0, 1]).all()
False
>>> ks.Series([1, 2, 3]).all()
True
>>> ks.Series([True, True, None]).all()
True
>>> ks.Series([True, False, None]).all()
False
>>> ks.Series([]).all()
True
>>> ks.Series([np.nan]).all()
True
>>> df = ks.Series([True, False, None]).rename("a").to_frame()
>>> df.set_index("a").index.all()
False
"""
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
sdf = self._internal.spark_frame.select(self.spark.column)
col = scol_for(sdf, sdf.columns[0])
# Note that we're ignoring `None`s here for now.
# any and every was added as of Spark 3.0
# ret = sdf.select(F.expr("every(CAST(`%s` AS BOOLEAN))" % sdf.columns[0])).collect()[0][0]
# Here we use min as its alternative:
ret = sdf.select(F.min(F.coalesce(col.cast("boolean"), F.lit(True)))).collect()[0][0]
if ret is None:
return True
else:
return ret
# TODO: axis, skipna, and many arguments should be implemented.
def any(self, axis: Union[int, str] = 0) -> bool:
"""
Return whether any element is True.
Returns False unless there at least one element within a series that is
True or equivalent (e.g. non-zero or non-empty).
Parameters
----------
axis : {0 or 'index'}, default 0
Indicate which axis or axes should be reduced.
* 0 / 'index' : reduce the index, return a Series whose index is the
original column labels.
Examples
--------
>>> ks.Series([False, False]).any()
False
>>> ks.Series([True, False]).any()
True
>>> ks.Series([0, 0]).any()
False
>>> ks.Series([0, 1, 2]).any()
True
>>> ks.Series([False, False, None]).any()
False
>>> ks.Series([True, False, None]).any()
True
>>> ks.Series([]).any()
False
>>> ks.Series([np.nan]).any()
False
>>> df = ks.Series([True, False, None]).rename("a").to_frame()
>>> df.set_index("a").index.any()
True
"""
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
sdf = self._internal.spark_frame.select(self.spark.column)
col = scol_for(sdf, sdf.columns[0])
# Note that we're ignoring `None`s here for now.
# any and every was added as of Spark 3.0
# ret = sdf.select(F.expr("any(CAST(`%s` AS BOOLEAN))" % sdf.columns[0])).collect()[0][0]
# Here we use max as its alternative:
ret = sdf.select(F.max(F.coalesce(col.cast("boolean"), F.lit(False)))).collect()[0][0]
if ret is None:
return False
else:
return ret
# TODO: add frep and axis parameter
def shift(self, periods=1, fill_value=None):
"""
Shift Series/Index by desired number of periods.
.. note:: the current implementation of shift uses Spark's Window without
specifying partition specification. This leads to move all data into
single partition in single machine and could cause serious
performance degradation. Avoid this method against very large dataset.
Parameters
----------
periods : int
Number of periods to shift. Can be positive or negative.
fill_value : object, optional
The scalar value to use for newly introduced missing values.
The default depends on the dtype of self. For numeric data, np.nan is used.
Returns
-------
Copy of input Series/Index, shifted.
Examples
--------
>>> df = ks.DataFrame({'Col1': [10, 20, 15, 30, 45],
... 'Col2': [13, 23, 18, 33, 48],
... 'Col3': [17, 27, 22, 37, 52]},
... columns=['Col1', 'Col2', 'Col3'])
>>> df.Col1.shift(periods=3)
0 NaN
1 NaN
2 NaN
3 10.0
4 20.0
Name: Col1, dtype: float64
>>> df.Col2.shift(periods=3, fill_value=0)
0 0
1 0
2 0
3 13
4 23
Name: Col2, dtype: int64
>>> df.index.shift(periods=3, fill_value=0)
Int64Index([0, 0, 0, 0, 1], dtype='int64')
"""
return self._shift(periods, fill_value)
def _shift(self, periods, fill_value, part_cols=()):
if not isinstance(periods, int):
raise ValueError("periods should be an int; however, got [%s]" % type(periods))
col = self.spark.column
window = (
Window.partitionBy(*part_cols)
.orderBy(NATURAL_ORDER_COLUMN_NAME)
.rowsBetween(-periods, -periods)
)
lag_col = F.lag(col, periods).over(window)
col = F.when(lag_col.isNull() | F.isnan(lag_col), fill_value).otherwise(lag_col)
return self._with_new_scol(col).rename(self.name)
# TODO: Update Documentation for Bins Parameter when its supported
def value_counts(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True):
"""
Return a Series containing counts of unique values.
The resulting object will be in descending order so that the
first element is the most frequently-occurring element.
Excludes NA values by default.
Parameters
----------
normalize : boolean, default False
If True then the object returned will contain the relative
frequencies of the unique values.
sort : boolean, default True
Sort by values.
ascending : boolean, default False
Sort in ascending order.
bins : Not Yet Supported
dropna : boolean, default True
Don't include counts of NaN.
Returns
-------
counts : Series
See Also
--------
Series.count: Number of non-NA elements in a Series.
Examples
--------
For Series
>>> df = ks.DataFrame({'x':[0, 0, 1, 1, 1, np.nan]})
>>> df.x.value_counts() # doctest: +NORMALIZE_WHITESPACE
1.0 3
0.0 2
Name: x, dtype: int64
With `normalize` set to `True`, returns the relative frequency by
dividing all values by the sum of values.
>>> df.x.value_counts(normalize=True) # doctest: +NORMALIZE_WHITESPACE
1.0 0.6
0.0 0.4
Name: x, dtype: float64
**dropna**
With `dropna` set to `False` we can also see NaN index values.
>>> df.x.value_counts(dropna=False) # doctest: +NORMALIZE_WHITESPACE
1.0 3
0.0 2
NaN 1
Name: x, dtype: int64
For Index
>>> from databricks.koalas.indexes import Index
>>> idx = Index([3, 1, 2, 3, 4, np.nan])
>>> idx
Float64Index([3.0, 1.0, 2.0, 3.0, 4.0, nan], dtype='float64')
>>> idx.value_counts().sort_index()
1.0 1
2.0 1
3.0 2
4.0 1
Name: count, dtype: int64
**sort**
With `sort` set to `False`, the result wouldn't be sorted by number of count.
>>> idx.value_counts(sort=True).sort_index()
1.0 1
2.0 1
3.0 2
4.0 1
Name: count, dtype: int64
**normalize**
With `normalize` set to `True`, returns the relative frequency by
dividing all values by the sum of values.
>>> idx.value_counts(normalize=True).sort_index()
1.0 0.2
2.0 0.2
3.0 0.4
4.0 0.2
Name: count, dtype: float64
**dropna**
With `dropna` set to `False` we can also see NaN index values.
>>> idx.value_counts(dropna=False).sort_index() # doctest: +SKIP
1.0 1
2.0 1
3.0 2
4.0 1
NaN 1
Name: count, dtype: int64
For MultiIndex.
>>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... [[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [1, 1, 1, 1, 1, 2, 1, 2, 2]])
>>> s = ks.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)
>>> s.index # doctest: +SKIP
MultiIndex([( 'lama', 'weight'),
( 'lama', 'weight'),
( 'lama', 'weight'),
( 'cow', 'weight'),
( 'cow', 'weight'),
( 'cow', 'length'),
('falcon', 'weight'),
('falcon', 'length'),
('falcon', 'length')],
)
>>> s.index.value_counts().sort_index()
(cow, length) 1
(cow, weight) 2
(falcon, length) 2
(falcon, weight) 1
(lama, weight) 3
Name: count, dtype: int64
>>> s.index.value_counts(normalize=True).sort_index()
(cow, length) 0.111111
(cow, weight) 0.222222
(falcon, length) 0.222222
(falcon, weight) 0.111111
(lama, weight) 0.333333
Name: count, dtype: float64
If Index has name, keep the name up.
>>> idx = Index([0, 0, 0, 1, 1, 2, 3], name='koalas')
>>> idx.value_counts().sort_index()
0 3
1 2
2 1
3 1
Name: koalas, dtype: int64
"""
from databricks.koalas.series import first_series
if bins is not None:
raise NotImplementedError("value_counts currently does not support bins")
if dropna:
sdf_dropna = self._internal.spark_frame.select(self.spark.column).dropna()
else:
sdf_dropna = self._internal.spark_frame.select(self.spark.column)
index_name = SPARK_DEFAULT_INDEX_NAME
column_name = self._internal.data_spark_column_names[0]
sdf = sdf_dropna.groupby(scol_for(sdf_dropna, column_name).alias(index_name)).count()
if sort:
if ascending:
sdf = sdf.orderBy(F.col("count"))
else:
sdf = sdf.orderBy(F.col("count").desc())
if normalize:
sum = sdf_dropna.count()
sdf = sdf.withColumn("count", F.col("count") / F.lit(sum))
column_labels = self._internal.column_labels
if (column_labels[0] is None) or (None in column_labels[0]):
internal = InternalFrame(
spark_frame=sdf,
index_map=OrderedDict({index_name: None}),
data_spark_columns=[scol_for(sdf, "count")],
)
else:
internal = InternalFrame(
spark_frame=sdf,
index_map=OrderedDict({index_name: None}),
column_labels=column_labels,
data_spark_columns=[scol_for(sdf, "count")],
column_label_names=self._internal.column_label_names,
)
return first_series(DataFrame(internal))
def nunique(self, dropna: bool = True, approx: bool = False, rsd: float = 0.05) -> int:
"""
Return number of unique elements in the object.
Excludes NA values by default.
Parameters
----------
dropna : bool, default True
Don’t include NaN in the count.
approx: bool, default False
If False, will use the exact algorithm and return the exact number of unique.
If True, it uses the HyperLogLog approximate algorithm, which is significantly faster
for large amount of data.
Note: This parameter is specific to Koalas and is not found in pandas.
rsd: float, default 0.05
Maximum estimation error allowed in the HyperLogLog algorithm.
Note: Just like ``approx`` this parameter is specific to Koalas.
Returns
-------
int
See Also
--------
DataFrame.nunique: Method nunique for DataFrame.
Series.count: Count non-NA/null observations in the Series.
Examples
--------
>>> ks.Series([1, 2, 3, np.nan]).nunique()
3
>>> ks.Series([1, 2, 3, np.nan]).nunique(dropna=False)
4
On big data, we recommend using the approximate algorithm to speed up this function.
The result will be very close to the exact unique count.
>>> ks.Series([1, 2, 3, np.nan]).nunique(approx=True)
3
>>> idx = ks.Index([1, 1, 2, None])
>>> idx
Float64Index([1.0, 1.0, 2.0, nan], dtype='float64')
>>> idx.nunique()
2
>>> idx.nunique(dropna=False)
3
"""
res = self._internal.spark_frame.select([self._nunique(dropna, approx, rsd)])
return res.collect()[0][0]
def _nunique(self, dropna=True, approx=False, rsd=0.05):
colname = self._internal.data_spark_column_names[0]
count_fn = partial(F.approx_count_distinct, rsd=rsd) if approx else F.countDistinct
if dropna:
return count_fn(self.spark.column).alias(colname)
else:
return (
count_fn(self.spark.column)
+ F.when(
F.count(F.when(self.spark.column.isNull(), 1).otherwise(None)) >= 1, 1
).otherwise(0)
).alias(colname)
def take(self, indices):
"""
Return the elements in the given *positional* indices along an axis.
This means that we are not indexing according to actual values in
the index attribute of the object. We are indexing according to the
actual position of the element in the object.
Parameters
----------
indices : array-like
An array of ints indicating which positions to take.
Returns
-------
taken : same type as caller
An array-like containing the elements taken from the object.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by labels.
DataFrame.iloc : Select a subset of a DataFrame by positions.
numpy.take : Take elements from an array along an axis.
Examples
--------
Series
>>> kser = ks.Series([100, 200, 300, 400, 500])
>>> kser
0 100
1 200
2 300
3 400
4 500
Name: 0, dtype: int64
>>> kser.take([0, 2, 4]).sort_index()
0 100
2 300
4 500
Name: 0, dtype: int64
Index
>>> kidx = ks.Index([100, 200, 300, 400, 500])
>>> kidx
Int64Index([100, 200, 300, 400, 500], dtype='int64')
>>> kidx.take([0, 2, 4]).sort_values()
Int64Index([100, 300, 500], dtype='int64')
MultiIndex
>>> kmidx = ks.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("x", "c")])
>>> kmidx # doctest: +SKIP
MultiIndex([('x', 'a'),
('x', 'b'),
('x', 'c')],
)
>>> kmidx.take([0, 2]) # doctest: +SKIP
MultiIndex([('x', 'a'),
('x', 'c')],
)
"""
if not is_list_like(indices) or isinstance(indices, (dict, set)):
raise ValueError("`indices` must be a list-like except dict or set")
if isinstance(self, ks.Series):
result = self.iloc[indices]
elif isinstance(self, ks.Index):
result = self._kdf.iloc[indices].index
return result
