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 __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 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 _apply_as_series_or_frame(self, func):
"""
Wraps a function that handles Spark column in order
to support it in both Koalas Series and DataFrame.
Note that the given `func` name should be same as the API's method name.
"""
from databricks.koalas import DataFrame
from databricks.koalas.series import _col
from databricks.koalas.groupby import SeriesGroupBy
kdf = self.kdf
sdf = self.kdf._sdf
# Here we need to include grouped key as an index, and shift previous index.
# [index_column0, index_column1] -> [grouped key, index_column0, index_column1]
new_index_scols = []
new_index_map = []
for groupkey in self._groupkeys:
new_index_scols.append(
# NOTE THAT this code intentionally uses `F.col` instead of `scol` in
# given series. This is because, in case of series, we convert it into
# DataFrame. So, if the given `groupkeys` is a series, they end up with
# being a different series.
F.col(name_like_string(groupkey.name)
).alias(SPARK_INDEX_NAME_FORMAT(len(new_index_scols))))
new_index_map.append((SPARK_INDEX_NAME_FORMAT(len(new_index_map)),
groupkey._internal.column_index[0]))
for new_index_scol, index_map in zip(kdf._internal.index_scols,
kdf._internal.index_map):
new_index_scols.append(
new_index_scol.alias(
SPARK_INDEX_NAME_FORMAT(len(new_index_scols))))
_, name = index_map
new_index_map.append(
(SPARK_INDEX_NAME_FORMAT(len(new_index_map)), name))
applied = []
for column in kdf.columns:
applied.append(kdf[column]._with_new_scol(func(
kdf[column]._scol)).rename(kdf[column].name))
# Seems like pandas filters out when grouped key is NA.
cond = self._groupkeys[0]._scol.isNotNull()
for c in self._groupkeys:
cond = cond | c._scol.isNotNull()
sdf = sdf.select(new_index_scols + [c._scol
for c in applied]).filter(cond)
internal = _InternalFrame(
sdf=sdf,
data_columns=[c._internal.data_columns[0] for c in applied],
index_map=new_index_map)
ret = DataFrame(internal)
if isinstance(self._groupby, SeriesGroupBy):
return _col(ret)
else:
return ret
def __setitem__(self, key, value):
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series, _col
if (not isinstance(key, tuple)) or (len(key) != 2):
raise SparkPandasNotImplementedError(
description="Only accepts pairs of candidates",
pandas_function=".loc[..., ...] = ...",
spark_target_function="withColumn, select")
rows_sel, cols_sel = key
if (not isinstance(rows_sel, slice)) or (rows_sel != slice(None)):
raise SparkPandasNotImplementedError(
description=
"""Can only assign value to the whole dataframe, the row index
has to be `slice(None)` or `:`""",
pandas_function=".loc[..., ...] = ...",
spark_target_function="withColumn, select")
if not isinstance(cols_sel, str):
raise ValueError("""only column names can be assigned""")
if isinstance(value, DataFrame):
if len(value.columns) == 1:
self._kdf[cols_sel] = _col(value)
else:
raise ValueError(
"Only a dataframe with one column can be assigned")
else:
self._kdf[cols_sel] = value
def from_pandas(self, pdf):
if isinstance(pdf, pd.Series):
return _col(self.from_pandas(pd.DataFrame(pdf)))
metadata = Metadata.from_pandas(pdf)
reset_index = pdf.reset_index()
reset_index.columns = metadata.all_fields
df = self.createDataFrame(reset_index)
df._metadata = metadata
return df
def __setitem__(self, key, value):
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series, _col
if (not isinstance(key, tuple)) or (len(key) != 2):
raise SparkPandasNotImplementedError(
description="Only accepts pairs of candidates",
pandas_function=".loc[..., ...] = ...",
spark_target_function="withColumn, select")
rows_sel, cols_sel = key
if (not isinstance(rows_sel, slice)) or (rows_sel != slice(None)):
if isinstance(rows_sel, list):
if isinstance(cols_sel, str):
cols_sel = [cols_sel]
kdf = self._kdf.copy()
for col_sel in cols_sel:
# Uses `kdf` to allow operations on different DataFrames.
# TODO: avoid temp column name or declare `__` prefix is
# reserved for Koalas' internal columns.
kdf["__indexing_temp_col__"] = value
new_col = kdf["__indexing_temp_col__"]._scol
kdf[col_sel] = Series(
kdf[col_sel]._internal.copy(scol=F.when(
kdf._internal.index_scols[0].isin(rows_sel),
new_col).otherwise(kdf[col_sel]._scol)),
anchor=kdf)
kdf = kdf.drop(labels=['__indexing_temp_col__'])
self._kdf._internal = kdf._internal.copy()
else:
raise SparkPandasNotImplementedError(
description=
"""Can only assign value to the whole dataframe, the row index
has to be `slice(None)` or `:`""",
pandas_function=".loc[..., ...] = ...",
spark_target_function="withColumn, select")
if not isinstance(cols_sel, (str, list)):
raise ValueError(
"""only column names or list of column names can be assigned"""
)
if isinstance(value, DataFrame):
if len(value.columns) == 1:
self._kdf[cols_sel] = _col(value)
else:
raise ValueError(
"Only a dataframe with one column can be assigned")
else:
if isinstance(cols_sel, str):
cols_sel = [cols_sel]
if (not isinstance(rows_sel, list)) and (isinstance(
cols_sel, list)):
for col_sel in cols_sel:
self._kdf[col_sel] = value
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 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 _col(from_pandas(pd.DataFrame(pdf)))
metadata = Metadata.from_pandas(pdf)
reset_index = pdf.reset_index()
reset_index.columns = metadata.all_fields
df = default_session().createDataFrame(reset_index)
df._metadata = metadata
return df
def __getitem__(self, key):
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series
def raiseNotImplemented(description):
raise SparkPandasNotImplementedError(
description=description,
pandas_function=".loc[..., ...]",
spark_target_function="select, where")
rows_sel, cols_sel = _unfold(key, self._ks)
sdf = self._kdf._sdf
if isinstance(rows_sel, Series):
sdf_for_check_schema = sdf.select(rows_sel._scol)
assert isinstance(sdf_for_check_schema.schema.fields[0].dataType, BooleanType), \
(str(sdf_for_check_schema), sdf_for_check_schema.schema.fields[0].dataType)
sdf = sdf.where(rows_sel._scol)
elif isinstance(rows_sel, slice):
if rows_sel.step is not None:
raiseNotImplemented("Cannot use step with Spark.")
if rows_sel == slice(None):
# If slice is None - select everything, so nothing to do
pass
elif len(self._kdf._internal.index_columns) == 0:
raiseNotImplemented(
"Cannot use slice for Spark if no index provided.")
elif len(self._kdf._internal.index_columns) == 1:
start = rows_sel.start
stop = rows_sel.stop
index_column = self._kdf.index.to_series()
index_data_type = index_column.schema[0].dataType
cond = []
if start is not None:
cond.append(index_column._scol >= F.lit(start).cast(
index_data_type))
if stop is not None:
cond.append(
index_column._scol <= F.lit(stop).cast(index_data_type)
)
if len(cond) > 0:
sdf = sdf.where(reduce(lambda x, y: x & y, cond))
else:
raiseNotImplemented(
"Cannot use slice for MultiIndex with Spark.")
elif isinstance(rows_sel, str):
raiseNotImplemented(
"Cannot use a scalar value for row selection with Spark.")
else:
try:
rows_sel = list(rows_sel)
except TypeError:
raiseNotImplemented(
"Cannot use a scalar value for row selection with Spark.")
if len(rows_sel) == 0:
sdf = sdf.where(F.lit(False))
elif len(self._kdf._internal.index_columns) == 1:
index_column = self._kdf.index.to_series()
index_data_type = index_column.schema[0].dataType
if len(rows_sel) == 1:
sdf = sdf.where(index_column._scol == F.lit(
rows_sel[0]).cast(index_data_type))
else:
sdf = sdf.where(
index_column._scol.isin([
F.lit(r).cast(index_data_type) for r in rows_sel
]))
else:
raiseNotImplemented(
"Cannot select with MultiIndex with Spark.")
# make cols_sel a 1-tuple of string if a single string
column_index = self._kdf._internal.column_index
if isinstance(cols_sel, str):
if column_index is not None:
return self[rows_sel, [cols_sel]]._get_from_multiindex_column(
(cols_sel, ))
else:
cols_sel = _make_col(cols_sel)
elif isinstance(cols_sel, Series):
cols_sel = _make_col(cols_sel)
elif isinstance(cols_sel, slice) and cols_sel != slice(None):
raise raiseNotImplemented(
"Can only select columns either by name or reference or all")
elif isinstance(cols_sel, slice) and cols_sel == slice(None):
cols_sel = None
if cols_sel is None:
columns = self._kdf._internal.data_scols
elif isinstance(cols_sel, spark.Column):
columns = [cols_sel]
else:
if column_index is not None:
column_to_index = list(
zip(self._kdf._internal.data_columns,
self._kdf._internal.column_index))
columns, column_index = zip(
*[(_make_col(column), idx) for key in cols_sel
for column, idx in column_to_index if idx[0] == key])
columns, column_index = list(columns), list(column_index)
else:
columns = [_make_col(c) for c in cols_sel]
try:
kdf = DataFrame(
sdf.select(self._kdf._internal.index_scols + columns))
except AnalysisException:
raise KeyError('[{}] don\'t exist in columns'.format(
[col._jc.toString() for col in columns]))
kdf._internal = kdf._internal.copy(
data_columns=kdf._internal.data_columns[-len(columns):],
index_map=self._kdf._internal.index_map,
column_index=column_index)
if cols_sel is not None and isinstance(cols_sel, spark.Column):
from databricks.koalas.series import _col
return _col(kdf)
else:
return kdf
def __getitem__(self, key):
from pyspark.sql.functions import lit
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series
def raiseNotImplemented(description):
raise SparkPandasNotImplementedError(
description=description,
pandas_function=".loc[..., ...]",
spark_target_function="select, where")
rows_sel, cols_sel = _unfold(key, self._ks)
sdf = self._kdf._sdf
if isinstance(rows_sel, Series):
sdf_for_check_schema = sdf.select(rows_sel._scol)
assert isinstance(sdf_for_check_schema.schema.fields[0].dataType, BooleanType), \
(str(sdf_for_check_schema), sdf_for_check_schema.schema.fields[0].dataType)
sdf = sdf.where(rows_sel._scol)
elif isinstance(rows_sel, slice):
if rows_sel.step is not None:
raiseNotImplemented("Cannot use step with Spark.")
if rows_sel == slice(None):
# If slice is None - select everything, so nothing to do
pass
elif len(self._kdf._index_columns) == 0:
raiseNotImplemented(
"Cannot use slice for Spark if no index provided.")
elif len(self._kdf._index_columns) == 1:
start = rows_sel.start
stop = rows_sel.stop
index_column = self._kdf.index
index_data_type = index_column.schema[0].dataType
cond = []
if start is not None:
cond.append(
index_column._scol >= lit(start).cast(index_data_type))
if stop is not None:
cond.append(
index_column._scol <= lit(stop).cast(index_data_type))
if len(cond) > 0:
sdf = sdf.where(reduce(lambda x, y: x & y, cond))
else:
raiseNotImplemented(
"Cannot use slice for MultiIndex with Spark.")
elif isinstance(rows_sel, str):
raiseNotImplemented(
"Cannot use a scalar value for row selection with Spark.")
else:
try:
rows_sel = list(rows_sel)
except TypeError:
raiseNotImplemented(
"Cannot use a scalar value for row selection with Spark.")
if len(rows_sel) == 0:
sdf = sdf.where(lit(False))
elif len(self._kdf._index_columns) == 1:
index_column = self._kdf.index
index_data_type = index_column.schema[0].dataType
if len(rows_sel) == 1:
sdf = sdf.where(index_column._scol == lit(
rows_sel[0]).cast(index_data_type))
else:
sdf = sdf.where(
index_column._scol.isin(
[lit(r).cast(index_data_type) for r in rows_sel]))
else:
raiseNotImplemented(
"Cannot select with MultiIndex with Spark.")
if cols_sel is None:
columns = [_make_col(c) for c in self._kdf._metadata.data_columns]
elif isinstance(cols_sel, spark.Column):
columns = [cols_sel]
else:
columns = [_make_col(c) for c in cols_sel]
try:
kdf = DataFrame(
sdf.select(self._kdf._metadata.index_columns + columns))
except AnalysisException:
raise KeyError('[{}] don\'t exist in columns'.format(
[col._jc.toString() for col in columns]))
kdf._metadata = self._kdf._metadata.copy(
data_columns=kdf._metadata.data_columns[-len(columns):])
if cols_sel is not None and isinstance(cols_sel, spark.Column):
from databricks.koalas.series import _col
return _col(kdf)
else:
return kdf
def _reduce_for_stat_function(self, sfun, only_numeric):
return _col(
super(SeriesGroupBy,
self)._reduce_for_stat_function(sfun, only_numeric))
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 _col
if bins is not None:
raise NotImplementedError("value_counts currently does not support bins")
if dropna:
sdf_dropna = self._internal._sdf.select(self.spark_column).dropna()
else:
sdf_dropna = self._internal._sdf.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 _col(DataFrame(internal))
def __getitem__(self, key):
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series
def raiseNotImplemented(description):
raise SparkPandasNotImplementedError(
description=description,
pandas_function=".loc[..., ...]",
spark_target_function="select, where")
rows_sel, cols_sel = _unfold(key, self._kser)
sdf = self._kdf._sdf
if isinstance(rows_sel, Series):
sdf_for_check_schema = sdf.select(rows_sel._scol)
assert isinstance(sdf_for_check_schema.schema.fields[0].dataType, BooleanType), \
(str(sdf_for_check_schema), sdf_for_check_schema.schema.fields[0].dataType)
sdf = sdf.where(rows_sel._scol)
elif isinstance(rows_sel, slice):
assert len(self._kdf._internal.index_columns) > 0
if rows_sel.step is not None:
raiseNotImplemented("Cannot use step with Spark.")
if rows_sel == slice(None):
# If slice is None - select everything, so nothing to do
pass
elif len(self._kdf._internal.index_columns) == 1:
start = rows_sel.start
stop = rows_sel.stop
index_column = self._kdf.index.to_series()
index_data_type = index_column.spark_type
cond = []
if start is not None:
cond.append(index_column._scol >= F.lit(start).cast(
index_data_type))
if stop is not None:
cond.append(
index_column._scol <= F.lit(stop).cast(index_data_type)
)
if len(cond) > 0:
sdf = sdf.where(reduce(lambda x, y: x & y, cond))
else:
raiseNotImplemented(
"Cannot use slice for MultiIndex with Spark.")
elif isinstance(rows_sel, str):
raiseNotImplemented(
"Cannot use a scalar value for row selection with Spark.")
else:
try:
rows_sel = list(rows_sel)
except TypeError:
raiseNotImplemented(
"Cannot use a scalar value for row selection with Spark.")
if len(rows_sel) == 0:
sdf = sdf.where(F.lit(False))
elif len(self._kdf._internal.index_columns) == 1:
index_column = self._kdf.index.to_series()
index_data_type = index_column.spark_type
if len(rows_sel) == 1:
sdf = sdf.where(index_column._scol == F.lit(
rows_sel[0]).cast(index_data_type))
else:
sdf = sdf.where(
index_column._scol.isin([
F.lit(r).cast(index_data_type) for r in rows_sel
]))
else:
raiseNotImplemented(
"Cannot select with MultiIndex with Spark.")
# make cols_sel a 1-tuple of string if a single string
column_index = self._kdf._internal.column_index
if isinstance(cols_sel, str):
kdf = DataFrame(self._kdf._internal.copy(sdf=sdf))
return kdf._get_from_multiindex_column((cols_sel, ))
elif isinstance(cols_sel, Series):
cols_sel = _make_col(cols_sel)
elif isinstance(cols_sel, slice) and cols_sel != slice(None):
raise raiseNotImplemented(
"Can only select columns either by name or reference or all")
elif isinstance(cols_sel, slice) and cols_sel == slice(None):
cols_sel = None
if cols_sel is None:
columns = self._kdf._internal.column_scols
elif isinstance(cols_sel, spark.Column):
columns = [cols_sel]
column_index = None
elif all(isinstance(key, Series) for key in cols_sel):
columns = [_make_col(key) for key in cols_sel]
column_index = [key._internal.column_index[0] for key in cols_sel]
elif all(isinstance(key, spark.Column) for key in cols_sel):
columns = cols_sel
column_index = None
elif (any(isinstance(key, str) for key in cols_sel)
and any(isinstance(key, tuple) for key in cols_sel)):
raise TypeError('Expected tuple, got str')
else:
if all(isinstance(key, tuple) for key in cols_sel):
level = self._kdf._internal.column_index_level
if any(len(key) != level for key in cols_sel):
raise ValueError(
'All the key level should be the same as column index level.'
)
column_to_index = list(
zip(self._kdf._internal.data_columns,
self._kdf._internal.column_index))
columns = []
column_index = []
for key in cols_sel:
found = False
for column, idx in column_to_index:
if idx == key or idx[0] == key:
columns.append(_make_col(column))
column_index.append(idx)
found = True
if not found:
raise KeyError("['{}'] not in index".format(key))
try:
sdf = sdf.select(self._kdf._internal.index_scols + columns)
index_columns = self._kdf._internal.index_columns
data_columns = [
column for column in sdf.columns if column not in index_columns
]
column_scols = [scol_for(sdf, col) for col in data_columns]
internal = _InternalFrame(sdf=sdf,
index_map=self._kdf._internal.index_map,
column_index=column_index,
column_scols=column_scols)
kdf = DataFrame(internal)
except AnalysisException:
raise KeyError('[{}] don\'t exist in columns'.format(
[col._jc.toString() for col in columns]))
if cols_sel is not None and isinstance(cols_sel, spark.Column):
from databricks.koalas.series import _col
return _col(kdf)
else:
return kdf
def value_counts(self, sort=None, ascending=None, dropna=True):
"""
Compute group sizes.
Parameters
----------
sort : boolean, default None
Sort by frequencies.
ascending : boolean, default False
Sort in ascending order.
dropna : boolean, default True
Don't include counts of NaN.
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')['B'].value_counts().sort_index() # doctest: +NORMALIZE_WHITESPACE
A B
1 1 1
2 1 1
2 1
3 3 3
Name: B, dtype: int64
"""
groupkeys = self._groupkeys + self._agg_columns
groupkey_cols = [
s._scol.alias('__index_level_{}__'.format(i))
for i, s in enumerate(groupkeys)
]
sdf = self._kdf._sdf
agg_column = self._agg_columns[0].name
sdf = sdf.groupby(*groupkey_cols).count().withColumnRenamed(
'count', agg_column)
if sort:
if ascending:
sdf = sdf.orderBy(F.col(agg_column).asc())
else:
sdf = sdf.orderBy(F.col(agg_column).desc())
internal = _InternalFrame(sdf=sdf,
data_columns=[agg_column],
index_map=[('__index_level_{}__'.format(i),
s.name)
for i, s in enumerate(groupkeys)])
return _col(DataFrame(internal))
def __getitem__(self, key):
from databricks.koalas.frame import DataFrame
from databricks.koalas.indexes import Index
from databricks.koalas.series import Series
def raiseNotImplemented(description):
raise SparkPandasNotImplementedError(
description=description,
pandas_function=".iloc[..., ...]",
spark_target_function="select, where")
rows_sel, cols_sel = _unfold(key, self._ks)
sdf = self._kdf._sdf
if isinstance(rows_sel, Index):
sdf_for_check_schema = sdf.select(rows_sel._scol)
assert isinstance(sdf_for_check_schema.schema.fields[0].dataType, BooleanType), \
(str(sdf_for_check_schema), sdf_for_check_schema.schema.fields[0].dataType)
sdf = sdf.where(rows_sel._scol)
elif isinstance(rows_sel, slice):
if rows_sel == slice(None):
# If slice is None - select everything, so nothing to do
pass
elif (rows_sel.start is not None) or (rows_sel.step is not None):
raiseNotImplemented("Cannot use start or step with Spark.")
elif not isinstance(rows_sel.stop, int):
raise TypeError(
"cannot do slice indexing with these indexers [{}] of {}".
format(rows_sel.stop, type(rows_sel.stop)))
elif rows_sel.stop >= 0:
sdf = sdf.limit(rows_sel.stop)
else:
sdf = sdf.limit(sdf.count() + rows_sel.stop)
else:
raiseNotImplemented(
".iloc requires numeric slice or conditional boolean Index, "
"got {}".format(rows_sel))
# make cols_sel a 1-tuple of string if a single string
if isinstance(cols_sel, Series):
columns = [cols_sel._scol]
elif isinstance(cols_sel, int):
columns = [self._kdf._internal.data_scols[cols_sel]]
elif cols_sel is None or cols_sel == slice(None):
columns = self._kdf._internal.data_scols
elif isinstance(cols_sel, slice):
if all(s is None or isinstance(s, int)
for s in (cols_sel.start, cols_sel.stop, cols_sel.step)):
columns = self._kdf._internal.data_scols[cols_sel]
else:
not_none = cols_sel.start if cols_sel.start is not None \
else cols_sel.stop if cols_sel.stop is not None else cols_sel.step
raise TypeError(
'cannot do slice indexing with these indexers {} of {}'.
format(not_none, type(not_none)))
elif is_list_like(cols_sel):
if all(isinstance(s, int) for s in cols_sel):
columns = [
self._kdf._internal.scol_for(col)
for col in self._kdf.columns[cols_sel]
]
else:
raise TypeError('cannot perform reduce with flexible type')
else:
raise ValueError(
"Location based indexing can only have [integer, integer slice, "
"listlike of integers, boolean array] types, got {}".format(
cols_sel))
try:
kdf = DataFrame(
sdf.select(self._kdf._internal.index_scols + columns))
except AnalysisException:
raise KeyError('[{}] don\'t exist in columns'.format(
[col._jc.toString() for col in columns]))
column_index = self._kdf._internal.column_index
if column_index is not None:
if cols_sel is not None and isinstance(cols_sel, (Series, int)):
column_index = None
else:
column_index = pd.MultiIndex.from_tuples(
column_index)[cols_sel].tolist()
kdf._internal = kdf._internal.copy(
data_columns=kdf._internal.data_columns[-len(columns):],
index_map=self._kdf._internal.index_map,
column_index=column_index)
if cols_sel is not None and isinstance(cols_sel, (Series, int)):
from databricks.koalas.series import _col
return _col(kdf)
else:
return kdf
def _reduce_for_stat_function(self, sfun, only_numeric):
return _col(super(SeriesGroupBy, self)._reduce_for_stat_function(sfun, only_numeric))
def __getitem__(self, key):
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series
rows_sel, cols_sel = _unfold(key, self._kdf_or_kser if self._is_series else None)
sdf = self._internal.sdf
cond, limit = self._select_rows(rows_sel)
if cond is not None:
sdf = sdf.where(cond)
if limit is not None:
if limit >= 0:
sdf = sdf.limit(limit)
else:
sdf = sdf.limit(sdf.count() + limit)
# make cols_sel a 1-tuple of string if a single string
if isinstance(cols_sel, Series) and cols_sel._equals(self._kdf_or_kser):
columns = cols_sel._internal.column_scols
column_index = cols_sel._internal.column_index
elif isinstance(cols_sel, int):
columns = [self._internal.column_scols[cols_sel]]
column_index = [self._internal.column_index[cols_sel]]
elif cols_sel is None or cols_sel == slice(None):
columns = self._internal.column_scols
column_index = self._internal.column_index
elif isinstance(cols_sel, slice):
if all(s is None or isinstance(s, int)
for s in (cols_sel.start, cols_sel.stop, cols_sel.step)):
columns = self._internal.column_scols[cols_sel]
column_index = self._internal.column_index[cols_sel]
else:
not_none = cols_sel.start if cols_sel.start is not None \
else cols_sel.stop if cols_sel.stop is not None else cols_sel.step
raise TypeError('cannot do slice indexing with these indexers {} of {}'
.format(not_none, type(not_none)))
elif is_list_like(cols_sel):
if all(isinstance(s, bool) for s in cols_sel):
cols_sel = [i for i, s in enumerate(cols_sel) if s]
if all(isinstance(s, int) for s in cols_sel):
columns = [self._internal.column_scols[s] for s in cols_sel]
column_index = [self._internal.column_index[s] for s in cols_sel]
else:
raise TypeError('cannot perform reduce with flexible type')
else:
raise ValueError("Location based indexing can only have [integer, integer slice, "
"listlike of integers, boolean array] types, got {}".format(cols_sel))
try:
sdf = sdf.select(self._internal.index_scols + columns)
internal = _InternalFrame(sdf=sdf,
index_map=self._internal.index_map,
column_index=column_index,
column_index_names=self._internal.column_index_names)
kdf = DataFrame(internal)
except AnalysisException:
raise KeyError('[{}] don\'t exist in columns'
.format([col._jc.toString() for col in columns]))
if cols_sel is not None and isinstance(cols_sel, (Series, int)):
from databricks.koalas.series import _col
return _col(kdf)
else:
return kdf
def transform(self, func):
return _col(super(SeriesGroupBy, self).transform(func))
def __getitem__(self, key):
from pyspark.sql.functions import lit
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series
def raiseNotImplemented(description):
raise SparkPandasNotImplementedError(
description=description,
pandas_function=".loc[..., ...]",
spark_target_function="select, where")
rows_sel, cols_sel = _unfold(key, self._ks)
sdf = self._kdf._sdf
if isinstance(rows_sel, Series):
sdf_for_check_schema = sdf.select(rows_sel._scol)
assert isinstance(sdf_for_check_schema.schema.fields[0].dataType, BooleanType), \
(str(sdf_for_check_schema), sdf_for_check_schema.schema.fields[0].dataType)
sdf = sdf.where(rows_sel._scol)
elif isinstance(rows_sel, slice):
if rows_sel.step is not None:
raiseNotImplemented("Cannot use step with Spark.")
if rows_sel == slice(None):
# If slice is None - select everything, so nothing to do
pass
elif len(self._kdf._index_columns) == 0:
raiseNotImplemented("Cannot use slice for Spark if no index provided.")
elif len(self._kdf._index_columns) == 1:
start = rows_sel.start
stop = rows_sel.stop
index_column = self._kdf.index
index_data_type = index_column.schema[0].dataType
cond = []
if start is not None:
cond.append(index_column._scol >= lit(start).cast(index_data_type))
if stop is not None:
cond.append(index_column._scol <= lit(stop).cast(index_data_type))
if len(cond) > 0:
sdf = sdf.where(reduce(lambda x, y: x & y, cond))
else:
raiseNotImplemented("Cannot use slice for MultiIndex with Spark.")
elif isinstance(rows_sel, str):
raiseNotImplemented("Cannot use a scalar value for row selection with Spark.")
else:
try:
rows_sel = list(rows_sel)
except TypeError:
raiseNotImplemented("Cannot use a scalar value for row selection with Spark.")
if len(rows_sel) == 0:
sdf = sdf.where(lit(False))
elif len(self._kdf._index_columns) == 1:
index_column = self._kdf.index
index_data_type = index_column.schema[0].dataType
if len(rows_sel) == 1:
sdf = sdf.where(
index_column._scol == lit(rows_sel[0]).cast(index_data_type))
else:
sdf = sdf.where(index_column._scol.isin(
[lit(r).cast(index_data_type) for r in rows_sel]))
else:
raiseNotImplemented("Cannot select with MultiIndex with Spark.")
if cols_sel is None:
columns = [_make_col(c) for c in self._kdf._metadata.data_columns]
elif isinstance(cols_sel, spark.Column):
columns = [cols_sel]
else:
columns = [_make_col(c) for c in cols_sel]
try:
kdf = DataFrame(sdf.select(self._kdf._metadata.index_columns + columns))
except AnalysisException:
raise KeyError('[{}] don\'t exist in columns'
.format([col._jc.toString() for col in columns]))
kdf._metadata = self._kdf._metadata.copy(
data_columns=kdf._metadata.data_columns[-len(columns):])
if cols_sel is not None and isinstance(cols_sel, spark.Column):
from databricks.koalas.series import _col
return _col(kdf)
else:
return kdf
def concat(objs, axis=0, join='outer', ignore_index=False):
"""
Concatenate pandas objects along a particular axis with optional set logic
along the other axes.
Parameters
----------
objs : a sequence of Series or DataFrame
Any None objects will be dropped silently unless
they are all None in which case a ValueError will be raised
axis : {0/'index'}, default 0
The axis to concatenate along.
join : {'inner', 'outer'}, default 'outer'
How to handle indexes on other axis(es)
ignore_index : boolean, default False
If True, do not use the index values along the concatenation axis. The
resulting axis will be labeled 0, ..., n - 1. This is useful if you are
concatenating objects where the concatenation axis does not have
meaningful indexing information. Note the index values on the other
axes are still respected in the join.
Returns
-------
concatenated : object, type of objs
When concatenating all ``Series`` along the index (axis=0), a
``Series`` is returned. When ``objs`` contains at least one
``DataFrame``, a ``DataFrame`` is returned.
See Also
--------
DataFrame.merge
Examples
--------
Combine two ``Series``.
>>> s1 = ks.Series(['a', 'b'])
>>> s2 = ks.Series(['c', 'd'])
>>> ks.concat([s1, s2])
0 a
1 b
0 c
1 d
Name: 0, dtype: object
Clear the existing index and reset it in the result
by setting the ``ignore_index`` option to ``True``.
>>> ks.concat([s1, s2], ignore_index=True)
0 a
1 b
2 c
3 d
Name: 0, dtype: object
Combine two ``DataFrame`` objects with identical columns.
>>> df1 = ks.DataFrame([['a', 1], ['b', 2]],
... columns=['letter', 'number'])
>>> df1
letter number
0 a 1
1 b 2
>>> df2 = ks.DataFrame([['c', 3], ['d', 4]],
... columns=['letter', 'number'])
>>> df2
letter number
0 c 3
1 d 4
>>> ks.concat([df1, df2])
letter number
0 a 1
1 b 2
0 c 3
1 d 4
Combine ``DataFrame`` and ``Series`` objects with different columns.
>>> ks.concat([df2, s1, s2])
0 letter number
0 None c 3.0
1 None d 4.0
0 a None NaN
1 b None NaN
0 c None NaN
1 d None NaN
Combine ``DataFrame`` objects with overlapping columns
and return everything. Columns outside the intersection will
be filled with ``None`` values.
>>> df3 = ks.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
... columns=['letter', 'number', 'animal'])
>>> df3
letter number animal
0 c 3 cat
1 d 4 dog
>>> ks.concat([df1, df3])
animal letter number
0 None a 1
1 None b 2
0 cat c 3
1 dog d 4
Combine ``DataFrame`` objects with overlapping columns
and return only those that are shared by passing ``inner`` to
the ``join`` keyword argument.
>>> ks.concat([df1, df3], join="inner")
letter number
0 a 1
1 b 2
0 c 3
1 d 4
"""
if not isinstance(objs, (dict, Iterable)):
raise TypeError('first argument must be an iterable of koalas '
'objects, you passed an object of type '
'"{name}"'.format(name=type(objs).__name__))
if axis not in [0, 'index']:
raise ValueError('axis should be either 0 or "index" currently.')
if all(map(lambda obj: obj is None, objs)):
raise ValueError("All objects passed were None")
objs = list(filter(lambda obj: obj is not None, objs))
for obj in objs:
if not isinstance(obj, (Series, DataFrame)):
raise TypeError(
'cannot concatenate object of type '
"'{name}"
'; only ks.Series '
'and ks.DataFrame are valid'.format(name=type(objs).__name__))
# Series, Series ...
# We should return Series if objects are all Series.
should_return_series = all(map(lambda obj: isinstance(obj, Series), objs))
# DataFrame, Series ... & Series, Series ...
# In this case, we should return DataFrame.
new_objs = []
for obj in objs:
if isinstance(obj, Series):
obj = obj.to_dataframe()
new_objs.append(obj)
objs = new_objs
# DataFrame, DataFrame, ...
# All Series are converted into DataFrame and then compute concat.
if not ignore_index:
indices_of_kdfs = [kdf._internal.index_map for kdf in objs]
index_of_first_kdf = indices_of_kdfs[0]
for index_of_kdf in indices_of_kdfs:
if index_of_first_kdf != index_of_kdf:
raise ValueError(
'Index type and names should be same in the objects to concatenate. '
'You passed different indices '
'{index_of_first_kdf} and {index_of_kdf}'.format(
index_of_first_kdf=index_of_first_kdf,
index_of_kdf=index_of_kdf))
columns_of_kdfs = [kdf._internal.columns for kdf in objs]
first_kdf = objs[0]
if ignore_index:
columns_of_first_kdf = first_kdf._internal.data_columns
else:
columns_of_first_kdf = first_kdf._internal.columns
if all(current_kdf == columns_of_first_kdf
for current_kdf in columns_of_kdfs):
# If all columns are in the same order and values, use it.
kdfs = objs
else:
if ignore_index:
columns_to_apply = [kdf._internal.data_columns for kdf in objs]
else:
columns_to_apply = [kdf._internal.columns for kdf in objs]
if join == "inner":
interested_columns = set.intersection(*map(set, columns_to_apply))
# Keep the column order with its firsts DataFrame.
interested_columns = list(
map(
lambda c: columns_of_first_kdf[columns_of_first_kdf.index(
c)], interested_columns))
kdfs = []
for kdf in objs:
sdf = kdf._sdf.select(interested_columns)
if ignore_index:
kdfs.append(DataFrame(sdf))
else:
kdfs.append(DataFrame(first_kdf._internal.copy(sdf=sdf)))
elif join == "outer":
# If there are columns unmatched, just sort the column names.
merged_columns = set(
itertools.chain.from_iterable(columns_to_apply))
kdfs = []
for kdf in objs:
if ignore_index:
columns_to_add = merged_columns - set(
kdf._internal.data_columns)
else:
columns_to_add = merged_columns - set(
kdf._internal.columns)
# TODO: NaN and None difference for missing values. pandas seems filling NaN.
kdf = kdf.assign(
**dict(zip(columns_to_add, [None] * len(columns_to_add))))
if ignore_index:
sdf = kdf._sdf.select(sorted(kdf._internal.data_columns))
else:
sdf = kdf._sdf.select(kdf._internal.index_columns +
sorted(kdf._internal.data_columns))
kdf = DataFrame(
kdf._internal.copy(sdf=sdf,
data_columns=sorted(
kdf._internal.data_columns)))
kdfs.append(kdf)
else:
raise ValueError(
"Only can inner (intersect) or outer (union) join the other axis."
)
concatenated = kdfs[0]._sdf
for kdf in kdfs[1:]:
concatenated = concatenated.unionByName(kdf._sdf)
if ignore_index:
result_kdf = DataFrame(
concatenated.select(kdfs[0]._internal.data_columns))
else:
result_kdf = DataFrame(kdfs[0]._internal.copy(sdf=concatenated))
if should_return_series:
# If all input were Series, we should return Series.
return _col(result_kdf)
else:
return result_kdf
def __setitem__(self, key, value):
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import Series, _col
if self._is_series:
if (isinstance(key, Series)
and key._kdf is not self._kdf_or_kser._kdf) or (
isinstance(value, Series)
and value._kdf is not self._kdf_or_kser._kdf):
kdf = self._kdf_or_kser.to_frame()
temp_natural_order = verify_temp_column_name(
kdf, "__temp_natural_order__")
temp_key_col = verify_temp_column_name(kdf, "__temp_key_col__")
temp_value_col = verify_temp_column_name(
kdf, "__temp_value_col__")
kdf[temp_natural_order] = F.monotonically_increasing_id()
if isinstance(key, Series):
kdf[temp_key_col] = key
if isinstance(value, Series):
kdf[temp_value_col] = value
kdf = kdf.sort_values(temp_natural_order).drop(
temp_natural_order)
kser = kdf[self._kdf_or_kser.name]
if isinstance(key, Series):
key = kdf[temp_key_col]
if isinstance(value, Series):
value = kdf[temp_value_col]
type(self)(kser)[key] = value
self._kdf_or_kser._internal = kser._internal
self._kdf_or_kser._kdf = kser._kdf
return
if isinstance(value, DataFrame):
raise ValueError("Incompatible indexer with DataFrame")
cond, limit, remaining_index = self._select_rows(key)
if cond is None:
cond = F.lit(True)
if limit is not None:
cond = cond & (self._internal.spark_frame[self._sequence_col] <
F.lit(limit))
if isinstance(value, Series):
if remaining_index is not None and remaining_index == 0:
raise ValueError(
"No axis named {} for object type {}".format(
key, type(value)))
value = value._scol
else:
value = F.lit(value)
scol = (F.when(cond,
value).otherwise(self._internal.spark_column).alias(
name_like_string(self._kdf_or_kser.name
or "0")))
internal = self._internal.copy(spark_column=scol)
self._kdf_or_kser._internal = internal
else:
assert self._is_df
if isinstance(key, tuple):
if len(key) != 2:
raise SparkPandasIndexingError(
"Only accepts pairs of candidates")
rows_sel, cols_sel = key
else:
rows_sel = key
cols_sel = None
if isinstance(value, DataFrame):
if len(value.columns) == 1:
value = _col(value)
else:
raise ValueError(
"Only a dataframe with one column can be assigned")
if (isinstance(rows_sel, Series)
and rows_sel._kdf is not self._kdf_or_kser) or (
isinstance(value, Series)
and value._kdf is not self._kdf_or_kser):
kdf = self._kdf_or_kser.copy()
temp_natural_order = verify_temp_column_name(
kdf, "__temp_natural_order__")
temp_key_col = verify_temp_column_name(kdf, "__temp_key_col__")
temp_value_col = verify_temp_column_name(
kdf, "__temp_value_col__")
kdf[temp_natural_order] = F.monotonically_increasing_id()
if isinstance(rows_sel, Series):
kdf[temp_key_col] = rows_sel
if isinstance(value, Series):
kdf[temp_value_col] = value
kdf = kdf.sort_values(temp_natural_order)
if isinstance(rows_sel, Series):
rows_sel = kdf[temp_key_col]
if isinstance(value, Series):
value = kdf[temp_value_col]
type(self)(kdf)[rows_sel, cols_sel] = value
self._kdf_or_kser._internal = kdf[list(
self._kdf_or_kser.columns)]._internal
return
cond, limit, remaining_index = self._select_rows(rows_sel)
missing_keys = []
_, data_spark_columns, _ = self._select_cols(
cols_sel, missing_keys=missing_keys)
if cond is None:
cond = F.lit(True)
if limit is not None:
cond = cond & (self._internal.spark_frame[self._sequence_col] <
F.lit(limit))
if isinstance(value, Series):
if remaining_index is not None and remaining_index == 0:
raise ValueError("Incompatible indexer with Series")
if len(data_spark_columns) > 1:
raise ValueError("shape mismatch")
value = value._scol
else:
value = F.lit(value)
new_data_spark_columns = []
for new_scol, spark_column_name in zip(
self._internal.data_spark_columns,
self._internal.data_spark_column_names):
for scol in data_spark_columns:
if new_scol._jc.equals(scol._jc):
new_scol = F.when(
cond,
value).otherwise(scol).alias(spark_column_name)
break
new_data_spark_columns.append(new_scol)
column_labels = self._internal.column_labels.copy()
for label in missing_keys:
if isinstance(label, str):
label = (label, )
if len(label) < self._internal.column_labels_level:
label = tuple(
list(label) +
([""] *
(self._internal.column_labels_level - len(label))))
elif len(label) > self._internal.column_labels_level:
raise KeyError(
"Key length ({}) exceeds index depth ({})".format(
len(label), self._internal.column_labels_level))
column_labels.append(label)
new_data_spark_columns.append(
F.when(cond, value).alias(name_like_string(label)))
internal = self._internal.with_new_columns(new_data_spark_columns,
column_labels)
self._kdf_or_kser._internal = internal
