def rank(self, method='average', ascending=True):
kdf = super(SeriesGroupBy, self).rank(method, ascending).to_dataframe()
return _col(
DataFrame(
kdf._internal.copy(sdf=kdf._sdf.select(
kdf._internal.data_scols),
index_map=[]))) # index is lost.
def drop(self, labels):
"""
Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like
Returns
-------
dropped : Index
Examples
--------
>>> index = ks.Index([1, 2, 3])
>>> index
Int64Index([1, 2, 3], dtype='int64')
>>> index.drop([1])
Int64Index([2, 3], dtype='int64')
"""
if not isinstance(labels, (tuple, list)):
labels = [labels]
sdf = self._internal.sdf[~self._internal.index_scols[0].isin(labels)]
return Index(
DataFrame(
_InternalFrame(sdf=sdf,
index_map=self._kdf._internal.index_map)))
def __setitem__(self, key, value):
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import _col
super(iLocIndexer, self).__setitem__(key, value)
if self._is_series:
internal = self._kdf_or_kser._internal
sdf = internal.spark_frame.select(internal.index_spark_columns +
[internal.spark_column])
internal = internal.copy(
spark_frame=sdf,
column_labels=[internal.column_labels[0] or ("0", )],
data_spark_columns=[
scol_for(sdf, internal.data_spark_column_names[0])
],
spark_column=None,
)
kser = _col(DataFrame(internal))
self._kdf_or_kser._internal = kser._internal
self._kdf_or_kser._kdf = kser._kdf
else:
assert self._is_df
# TODO: support DataFrame.
delattr(self, "_lazy__internal")
delattr(self, "_lazy__sequence_col")
def execute(self) -> DataFrame:
"""
Returns a DataFrame for which the SQL statement has been executed by
the underlying SQL engine.
>>> str0 = 'abc'
>>> ks.sql("select {str0}")
abc
0 abc
>>> str1 = 'abc"abc'
>>> str2 = "abc'abc"
>>> ks.sql("select {str0}, {str1}, {str2}")
abc abc"abc abc'abc
0 abc abc"abc abc'abc
>>> strs = ['a', 'b']
>>> ks.sql("select 'a' in {strs} as cond1, 'c' in {strs} as cond2")
cond1 cond2
0 True False
"""
blocks = _string.formatter_parser(self._statement)
# TODO: use a string builder
res = ""
try:
for (pre, inner, _, _) in blocks:
var_next = "" if inner is None else self._convert(inner)
res = res + pre + var_next
self._normalized_statement = res
sdf = self._session.sql(self._normalized_statement)
finally:
for v in self._temp_views:
self._session.catalog.dropTempView(v)
return DataFrame(sdf)
def aggregate(self, func_or_funcs, *args, **kwargs):
"""Compute aggregates and returns the result as a :class:`DataFrame`.
The available aggregate functions can be built-in aggregation functions, such as `avg`,
`max`, `min`, `sum`, `count`.
:param func_or_funcs: a dict mapping from column name (string) to aggregate functions
(string).
"""
if not isinstance(func_or_funcs, dict) or \
not all(isinstance(key, string_types) and isinstance(value, string_types)
for key, value in func_or_funcs.items()):
raise ValueError(
"aggs must be a dict mapping from column name (string) to aggregate "
"functions (string).")
sdf = self._groupdata.agg(func_or_funcs)
reorder = [
'%s(%s)' % (value, key)
for key, value in iter(func_or_funcs.items())
]
kdf = DataFrame(sdf.select(reorder))
kdf.columns = [key for key in iter(func_or_funcs.keys())]
return kdf
def _reduce_for_stat_function(self, sfun, only_numeric):
groupkeys = self._groupkeys
groupkey_cols = [
s._scol.alias('__index_level_{}__'.format(i))
for i, s in enumerate(groupkeys)
]
sdf = self._kdf._sdf
data_columns = []
if len(self._agg_columns) > 0:
stat_exprs = []
for ks in self._agg_columns:
spark_type = ks.spark_type
# TODO: we should have a function that takes dataframes and converts the numeric
# types. Converting the NaNs is used in a few places, it should be in utils.
# Special handle floating point types because Spark's count treats nan as a valid
# value, whereas Pandas count doesn't include nan.
if isinstance(spark_type, DoubleType) or isinstance(
spark_type, FloatType):
stat_exprs.append(
sfun(F.nanvl(ks._scol, F.lit(None))).alias(ks.name))
data_columns.append(ks.name)
elif isinstance(spark_type, NumericType) or not only_numeric:
stat_exprs.append(sfun(ks._scol).alias(ks.name))
data_columns.append(ks.name)
sdf = sdf.groupby(*groupkey_cols).agg(*stat_exprs)
else:
sdf = sdf.select(*groupkey_cols).distinct()
sdf = sdf.sort(*groupkey_cols)
internal = _InternalFrame(sdf=sdf,
data_columns=data_columns,
index_map=[('__index_level_{}__'.format(i),
s.name)
for i, s in enumerate(groupkeys)])
return DataFrame(internal)
def drop_duplicates(self):
"""
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 = ks.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])
>>> idx.drop_duplicates() # doctest: +SKIP
Index(['lama', 'cow', 'beetle', 'hippo'], dtype='object')
"""
sdf = self._internal.sdf.select(
self._internal.index_scols).drop_duplicates()
internal = _InternalFrame(sdf=sdf,
index_map=self._kdf._internal.index_map)
result = DataFrame(internal).index
return result
def _cum(self, func):
# This is used for cummin, cummax, cumxum, etc.
if func == F.min:
func = "cummin"
elif func == F.max:
func = "cummax"
elif func == F.sum:
func = "cumsum"
elif func.__name__ == "cumprod":
func = "cumprod"
if len(self._kdf._internal.index_columns) == 0:
raise ValueError("Index must be set.")
applied = []
kdf = self._kdf
groupkey_columns = [s.name for s in self._groupkeys]
for column in kdf._internal.data_columns:
# pandas groupby.cumxxx ignores the grouping key itself.
if column not in groupkey_columns:
applied.append(
getattr(kdf[column].groupby(self._groupkeys), func)())
sdf = kdf._sdf.select(kdf._internal.index_scols +
[c._scol for c in applied])
internal = kdf._internal.copy(sdf=sdf,
data_columns=[c.name for c in applied])
return DataFrame(internal)
def from_tuples(tuples, sortorder=None, names=None):
"""
Convert list of tuples to MultiIndex.
Parameters
----------
tuples : list / sequence of tuple-likes
Each tuple is the index of one row/column.
sortorder : int or None
Level of sortedness (must be lexicographically sorted by that level).
names : list / sequence of str, optional
Names for the levels in the index.
Returns
-------
index : MultiIndex
Examples
--------
>>> tuples = [(1, 'red'), (1, 'blue'),
... (2, 'red'), (2, 'blue')]
>>> ks.MultiIndex.from_tuples(tuples, names=('number', 'color')) # doctest: +SKIP
MultiIndex([(1, 'red'),
(1, 'blue'),
(2, 'red'),
(2, 'blue')],
names=['number', 'color'])
"""
return DataFrame(index=pd.MultiIndex.from_tuples(
tuples=tuples, sortorder=sortorder, names=names)).index
def from_arrays(arrays, sortorder=None, names=None):
"""
Convert arrays to MultiIndex.
Parameters
----------
arrays: list / sequence of array-likes
Each array-like gives one level’s value for each data point. len(arrays)
is the number of levels.
sortorder: int or None
Level of sortedness (must be lexicographically sorted by that level).
names: list / sequence of str, optional
Names for the levels in the index.
Returns
-------
index: MultiIndex
Examples
--------
>>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
>>> ks.MultiIndex.from_arrays(arrays, names=('number', 'color')) # doctest: +SKIP
MultiIndex([(1, 'red'),
(1, 'blue'),
(2, 'red'),
(2, 'blue')],
names=['number', 'color'])
"""
return DataFrame(index=pd.MultiIndex.from_arrays(
arrays=arrays, sortorder=sortorder, names=names)).index
def read_table(name: str) -> DataFrame:
"""
Read a Spark table and return a DataFrame.
Parameters
----------
name : string
Table name in Spark.
Returns
-------
DataFrame
See Also
--------
DataFrame.to_table
read_delta
read_parquet
read_spark_io
Examples
--------
>>> ks.range(1).to_table('%s.my_table' % db)
>>> ks.read_table('%s.my_table' % db)
id
0 0
"""
sdf = default_session().read.table(name)
return DataFrame(sdf)
def from_pandas(
pobj: Union['pd.DataFrame',
'pd.Series']) -> Union['Series', 'DataFrame']:
"""Create a Koalas DataFrame or Series from a pandas DataFrame or Series.
This is similar to Spark's `SparkSession.createDataFrame()` with pandas DataFrame,
but this also works with pandas Series and picks the index.
Parameters
----------
pobj : pandas.DataFrame or pandas.Series
pandas DataFrame or Series to read.
Returns
-------
Series or DataFrame
If a pandas Series is passed in, this function returns a Koalas Series.
If a pandas DataFrame is passed in, this function returns a Koalas DataFrame.
"""
if isinstance(pobj, pd.Series):
return Series(pobj)
elif isinstance(pobj, pd.DataFrame):
return DataFrame(pobj)
else:
raise ValueError("Unknown data type: {}".format(type(pobj)))
def value_counts(self,
normalize=False,
sort=True,
ascending=False,
bins=None,
dropna=True):
if bins is not None:
raise NotImplementedError(
"value_counts currently does not support bins")
if dropna:
sdf_dropna = self._kdf._sdf.filter(self.notna()._scol)
else:
sdf_dropna = self._kdf._sdf
sdf = sdf_dropna.groupby(self._scol).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))
index_name = 'index' if self.name != 'index' else 'level_0'
kdf = DataFrame(sdf)
kdf.columns = [index_name, self.name]
kdf._metadata = Metadata(column_fields=[self.name],
index_info=[(index_name, None)])
return _col(kdf)
def read_parquet(path, columns=None):
"""Load a parquet object from the file path, returning a DataFrame.
Parameters
----------
path : string
File path
columns : list, default=None
If not None, only these columns will be read from the file.
Returns
-------
DataFrame
Examples
--------
>>> ks.read_parquet('data.parquet', columns=['name', 'gender']) # doctest: +SKIP
"""
if columns is not None:
columns = list(columns)
if columns is None or len(columns) > 0:
sdf = default_session().read.parquet(path)
if columns is not None:
fields = [field.name for field in sdf.schema]
cols = [col for col in columns if col in fields]
if len(cols) > 0:
sdf = sdf.select(cols)
else:
sdf = default_session().createDataFrame([],
schema=StructType())
else:
sdf = default_session().createDataFrame([], schema=StructType())
return DataFrame(sdf)
def to_pandas(self) -> pd.Index:
"""
Return a pandas Series.
.. 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. If the input
is large, set max_rows parameter.
Examples
--------
>>> df = ks.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')
"""
sdf = self._kdf._sdf.select(self._scol)
internal = self._kdf._internal.copy(
sdf=sdf,
index_map=[(sdf.schema[0].name, self._kdf._internal.index_names[0])
],
data_columns=[],
column_index=[],
column_index_names=None)
return DataFrame(internal)._to_internal_pandas().index
def fillna(self, value):
"""
Fill NA/NaN values with the specified value.
Parameters
----------
value : scalar
Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes.
Returns
-------
Index :
filled with value
Examples
--------
>>> ki = ks.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))
sdf = self._internal.sdf.fillna(value)
result = DataFrame(self._kdf._internal.copy(sdf=sdf)).index
return result
def __setitem__(self, key, value):
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import _col
super(iLocIndexer, self).__setitem__(key, value)
if self._is_series:
internal = self._kdf_or_kser._internal
sdf = internal.spark_frame.select(internal.index_spark_columns +
[internal.spark_column])
internal = internal.copy(
spark_frame=sdf,
column_labels=[internal.column_labels[0] or ("0", )],
data_spark_columns=[
scol_for(sdf, internal.data_spark_column_names[0])
],
spark_column=None,
)
kser = _col(DataFrame(internal))
self._kdf_or_kser._internal = kser._internal
self._kdf_or_kser._kdf = kser._kdf
else:
assert self._is_df
# Clean up implicitly cached properties to be able to reuse the indexer.
del self._internal
del self._sequence_col
def unique(self):
"""
Return unique values of Series object.
Uniques are returned in order of appearance. Hash table-based unique,
therefore does NOT sort.
.. note:: This method returns newly creased Series whereas Pandas returns
the unique values as a NumPy array.
Returns
-------
Returns the unique values as a Series.
See Examples section.
Examples
--------
>>> ks.Series([2, 1, 3, 3], name='A').unique()
0 1
1 3
2 2
Name: A, dtype: int64
>>> ks.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique()
0 2016-01-01
Name: 0, dtype: datetime64[ns]
"""
sdf = self.to_dataframe()._sdf
return _col(DataFrame(sdf.select(self._scol).distinct()))
def intersection(self, other) -> "MultiIndex":
"""
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 : MultiIndex
Examples
--------
>>> midx1 = ks.MultiIndex.from_tuples([("a", "x"), ("b", "y"), ("c", "z")])
>>> midx2 = ks.MultiIndex.from_tuples([("c", "z"), ("d", "w")])
>>> midx1.intersection(midx2).sort_values() # doctest: +SKIP
MultiIndex([('c', 'z')],
)
"""
if isinstance(other, Series) or not is_list_like(other):
raise TypeError("other must be a MultiIndex or a list of tuples")
elif isinstance(other, DataFrame):
raise ValueError("Index data must be 1-dimensional")
elif isinstance(other, MultiIndex):
spark_frame_other = other.to_frame().to_spark()
keep_name = self.names == other.names
elif isinstance(other, Index):
# Always returns an empty MultiIndex if `other` is Index.
return self.to_frame().head(0).index # type: ignore
elif not all(isinstance(item, tuple) for item in other):
raise TypeError("other must be a MultiIndex or a list of tuples")
else:
other = MultiIndex.from_tuples(list(other))
spark_frame_other = other.to_frame().to_spark()
keep_name = True
default_name = [
SPARK_INDEX_NAME_FORMAT(i) for i in range(self.nlevels)
]
spark_frame_self = self.to_frame(name=default_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, col) for col in default_name
],
index_names=index_names,
)
return cast(MultiIndex, DataFrame(internal).index)
def size(self):
"""
Compute group sizes.
See Also
--------
databricks.koalas.Series.groupby
databricks.koalas.DataFrame.groupby
Examples
--------
>>> df = ks.DataFrame({'A': [1, 2, 2, 3, 3, 3],
... 'B': [1, 1, 2, 3, 3, 3]},
... columns=['A', 'B'])
>>> df
A B
0 1 1
1 2 1
2 2 2
3 3 3
4 3 3
5 3 3
>>> df.groupby('A').size().sort_index() # doctest: +NORMALIZE_WHITESPACE
A
1 1
2 2
3 3
Name: count, dtype: int64
>>> df.groupby(['A', 'B']).size().sort_index() # doctest: +NORMALIZE_WHITESPACE
A B
1 1 1
2 1 1
2 1
3 3 3
Name: count, dtype: int64
"""
groupkeys = self._groupkeys
groupkey_cols = [
s._scol.alias('__index_level_{}__'.format(i))
for i, s in enumerate(groupkeys)
]
sdf = self._kdf._sdf
sdf = sdf.groupby(*groupkey_cols).count()
if (len(self._agg_columns) > 0) and (self._have_agg_columns):
name = self._agg_columns[0].name
sdf = sdf.withColumnRenamed('count', name)
else:
name = 'count'
internal = _InternalFrame(sdf=sdf,
data_columns=[name],
index_map=[('__index_level_{}__'.format(i),
s.name)
for i, s in enumerate(groupkeys)])
return _col(DataFrame(internal))
def _init_from_pandas(self, s, *args):
"""
Creates Koalas Series from Pandas Series.
:param s: Pandas Series
"""
kdf = DataFrame(pd.DataFrame(s))
self._init_from_spark(kdf._sdf[kdf._metadata.column_fields[0]],
kdf, kdf._metadata.index_info)
def range(start: int,
end: Optional[int] = None,
step: int = 1,
num_partitions: Optional[int] = None) -> DataFrame:
"""
Create a DataFrame with some range of numbers.
The resulting DataFrame has a single int64 column named `id`, containing elements in a range
from ``start`` to ``end`` (exclusive) with step value ``step``. If only the first parameter
(i.e. start) is specified, we treat it as the end value with the start value being 0.
This is similar to the range function in SparkSession and is used primarily for testing.
Parameters
----------
start : int
the start value (inclusive)
end : int, optional
the end value (exclusive)
step : int, optional, default 1
the incremental step
num_partitions : int, optional
the number of partitions of the DataFrame
Returns
-------
DataFrame
Examples
--------
When the first parameter is specified, we generate a range of values up till that number.
>>> ks.range(5)
id
0 0
1 1
2 2
3 3
4 4
When start, end, and step are specified:
>>> ks.range(start = 100, end = 200, step = 20)
id
0 100
1 120
2 140
3 160
4 180
"""
sdf = default_session().range(start=start,
end=end,
step=step,
numPartitions=num_partitions)
return DataFrame(sdf)
def dropna(self):
"""
Return Index or MultiIndex without NA/NaN values
Examples
--------
>>> df = ks.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 = ks.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
Name: 0, dtype: float64
>>> s.index.dropna() # doctest: +SKIP
MultiIndex([( 'cow', 'weight'),
( 'cow', 'weight'),
( 'cow', 'weight'),
( 'cow', 'length'),
('falcon', 'weight'),
('falcon', 'weight'),
('falcon', 'length')],
)
"""
kdf = self._kdf.copy()
sdf = kdf._internal.sdf.select(self._internal.index_scols).dropna()
internal = _InternalFrame(sdf=sdf, index_map=self._internal.index_map)
kdf = DataFrame(internal)
return Index(kdf) if type(self) == Index else MultiIndex(kdf)
def rename(self, name, inplace=False):
"""
Alter Index 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.
Returns
-------
Index
The same type as the caller or None if inplace is True.
Examples
--------
>>> df = ks.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)
Index(['A', 'C'], dtype='object', name='e')
>>> df # doctest: +NORMALIZE_WHITESPACE
b
e
A A
C B
"""
index_columns = self._kdf._internal.index_columns
assert len(index_columns) == 1
sdf = self._kdf._sdf.select([self._scol] +
self._kdf._internal.data_scols)
internal = self._kdf._internal.copy(sdf=sdf,
index_map=[(sdf.schema[0].name,
name)])
if inplace:
self._kdf._internal = internal
return self
else:
return DataFrame(internal).index
def __repr__(self):
sdf = self._kdf._sdf.select(self._scol).limit(max_display_count + 1)
internal = self._kdf._internal.copy(
sdf=sdf,
index_map=[(sdf.schema[0].name, self._kdf._internal.index_names[0])],
data_columns=[])
pindex = DataFrame(internal).index.to_pandas()
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 analyzed(self) -> "ks.Series":
"""
Returns a new Series with the analyzed Spark DataFrame.
After multiple operations, the underlying Spark plan could grow huge
and make the Spark planner take a long time to finish the planning.
This function is for the workaround to avoid it.
.. note:: After analyzed, operations between the analyzed Series and the original one
will **NOT** work without setting a config `compute.ops_on_diff_frames` to `True`.
Returns
-------
Series
Examples
--------
>>> ser = ks.Series([1, 2, 3])
>>> ser
0 1
1 2
2 3
dtype: int64
The analyzed one should return the same value.
>>> ser.spark.analyzed
0 1
1 2
2 3
dtype: int64
However, it won't work with the same anchor Series.
>>> ser + ser.spark.analyzed
Traceback (most recent call last):
...
ValueError: ... enable 'compute.ops_on_diff_frames' option.
>>> with ks.option_context('compute.ops_on_diff_frames', True):
... (ser + ser.spark.analyzed).sort_index()
0 2
1 4
2 6
dtype: int64
"""
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import first_series
return first_series(DataFrame(self._data._internal.resolved_copy))
def from_pandas(pdf):
"""Create DataFrame from pandas DataFrame.
This is similar to `DataFrame.createDataFrame()` with pandas DataFrame, but this also picks
the index in the given pandas DataFrame.
:param pdf: :class:`pandas.DataFrame`
"""
if isinstance(pdf, pd.Series):
return Series(pdf)
elif isinstance(pdf, pd.DataFrame):
return DataFrame(pdf)
else:
raise ValueError("Unknown data type: {}".format(type(pdf)))
def _spark_col_apply(kdf_or_ks, sfun):
"""
Performs a function to all cells on a dataframe, the function being a known sql function.
"""
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series
if isinstance(kdf_or_ks, Series):
ks = kdf_or_ks
return Series(ks._kdf._internal.copy(scol=sfun(kdf_or_ks._scol)), anchor=ks._kdf)
assert isinstance(kdf_or_ks, DataFrame)
kdf = kdf_or_ks
sdf = kdf._sdf
sdf = sdf.select([sfun(sdf[col]).alias(col) for col in kdf.columns])
return DataFrame(sdf)
def coalesce(self, num_partitions: int) -> "ks.DataFrame":
"""
Returns a new DataFrame that has exactly `num_partitions` partitions.
.. note:: This operation results in a narrow dependency, e.g. if you go from 1000
partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new
partitions will claim 10 of the current partitions. If a larger number of partitions is
requested, it will stay at the current number of partitions. However, if you're doing a
drastic coalesce, e.g. to num_partitions = 1, this may result in your computation taking
place on fewer nodes than you like (e.g. one node in the case of num_partitions = 1). To
avoid this, you can call repartition(). This will add a shuffle step, but means the
current upstream partitions will be executed in parallel (per whatever the current
partitioning is).
Parameters
----------
num_partitions : int
The target number of partitions.
Returns
-------
DataFrame
Examples
--------
>>> kdf = ks.DataFrame({"age": [5, 5, 2, 2],
... "name": ["Bob", "Bob", "Alice", "Alice"]}).set_index("age")
>>> kdf.sort_index() # doctest: +NORMALIZE_WHITESPACE
name
age
2 Alice
2 Alice
5 Bob
5 Bob
>>> new_kdf = kdf.spark.coalesce(1)
>>> new_kdf.to_spark().rdd.getNumPartitions()
1
>>> new_kdf.sort_index() # doctest: +NORMALIZE_WHITESPACE
name
age
2 Alice
2 Alice
5 Bob
5 Bob
"""
from databricks.koalas.frame import DataFrame
internal = self._kdf._internal.resolved_copy
coalesced_sdf = internal.spark_frame.coalesce(num_partitions)
return DataFrame(internal.with_new_sdf(coalesced_sdf))
def analyzed(self) -> "ks.DataFrame":
"""
Returns a new DataFrame with the analyzed Spark DataFrame.
After multiple operations, the underlying Spark plan could grow huge
and make the Spark planner take a long time to finish the planning.
This function is for the workaround to avoid it.
.. note:: After analyzed, operations between the analyzed DataFrame and the original one
will **NOT** work without setting a config `compute.ops_on_diff_frames` to `True`.
Returns
-------
DataFrame
Examples
--------
>>> df = ks.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, columns=["a", "b"])
>>> df
a b
0 1 4
1 2 5
2 3 6
The analyzed one should return the same value.
>>> df.spark.analyzed
a b
0 1 4
1 2 5
2 3 6
However, it won't work with the same anchor Series.
>>> df + df.spark.analyzed
Traceback (most recent call last):
...
ValueError: ... enable 'compute.ops_on_diff_frames' option.
>>> with ks.option_context('compute.ops_on_diff_frames', True):
... (df + df.spark.analyzed).sort_index()
a b
0 2 8
1 4 10
2 6 12
"""
from databricks.koalas.frame import DataFrame
return DataFrame(self._kdf._internal.resolved_copy)