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:
df_dropna = self.to_dataframe()._spark_filter(self.notna())
else:
df_dropna = self.to_dataframe()
df = df_dropna._spark_groupby(self).count()
if sort:
if ascending:
df = df._spark_orderBy(F._spark_col('count'))
else:
df = df._spark_orderBy(F._spark_col('count')._spark_desc())
if normalize:
sum = df_dropna._spark_count()
df = df._spark_withColumn(
'count',
F._spark_col('count') / F._spark_lit(sum))
return _col(df.set_index([self.name]))
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:
df_dropna = self._pandas_anchor._spark_filter(self.notna())
else:
df_dropna = self._pandas_anchor
df = df_dropna._spark_groupby(self).count()
if sort:
if ascending:
df = df._spark_orderBy(F._spark_col('count'))
else:
df = df._spark_orderBy(F._spark_col('count')._spark_desc())
if normalize:
sum = df_dropna._spark_count()
df = df._spark_withColumn(
'count',
F._spark_col('count') / F._spark_lit(sum))
index_name = 'index' if self.name != 'index' else 'level_0'
df.columns = [index_name, self.name]
df._metadata = Metadata(column_fields=[self.name],
index_info=[(index_name, None)])
return _col(df)
def dropna(self,
axis=0,
how='any',
thresh=None,
subset=None,
inplace=False):
if axis == 0 or axis == 'index':
if subset is not None:
if isinstance(subset, string_types):
columns = [subset]
else:
columns = list(subset)
invalids = [
column for column in columns
if column not in self._metadata.column_fields
]
if len(invalids) > 0:
raise KeyError(invalids)
else:
columns = list(self.columns)
cnt = reduce(lambda x, y: x + y, [
F._spark_when(self[column].notna(), 1)._spark_otherwise(0)
for column in columns
], F._spark_lit(0))
if thresh is not None:
pred = cnt >= F._spark_lit(int(thresh))
elif how == 'any':
pred = cnt == F._spark_lit(len(columns))
elif how == 'all':
pred = cnt > F._spark_lit(0)
else:
if how is not None:
raise ValueError('invalid how option: {h}'.format(h=how))
else:
raise TypeError('must specify how or thresh')
df = self._spark_filter(pred)
df._metadata = self._metadata.copy()
if inplace:
_reassign_jdf(self, df)
else:
return df
else:
raise NotImplementedError(
"dropna currently only works for axis=0 or axis='index'")
def __invert__(self):
return anchor_wrap(self,
self._spark_cast("boolean") == F._spark_lit(False))
def __invert__(self):
return anchor_wrap(self, self.astype(bool) == F._spark_lit(False))
def __getitem__(self, key):
from pyspark.sql.functions import _spark_lit
def raiseNotImplemented(description):
raise SparkPandasNotImplementedError(
description=description,
pandas_function=".loc[..., ...]",
spark_target_function="select, where")
rows_sel, cols_sel = _unfold(key, self.col)
df = self.df
if isinstance(rows_sel, Column):
df_for_check_schema = self.df._spark_select(rows_sel)
assert isinstance(df_for_check_schema.schema.fields[0].dataType, BooleanType), \
(str(df_for_check_schema), df_for_check_schema.schema.fields[0].dataType)
df = df._spark_where(rows_sel)
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.df._index_columns) == 0:
raiseNotImplemented(
"Cannot use slice for Spark if no index provided.")
elif len(self.df._index_columns) == 1:
start = rows_sel.start
stop = rows_sel.stop
index_column = self.df._index_columns[0]
index_data_type = index_column.schema[0].dataType
cond = []
if start is not None:
cond.append(index_column >= _spark_lit(start)._spark_cast(
index_data_type))
if stop is not None:
cond.append(index_column <= _spark_lit(stop)._spark_cast(
index_data_type))
if len(cond) > 0:
df = df._spark_where(reduce(lambda x, y: x & y, cond))
else:
raiseNotImplemented(
"Cannot use slice for MultiIndex with Spark.")
elif isinstance(rows_sel, string_types):
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:
df = df._spark_where(_spark_lit(False))
elif len(self.df._index_columns) == 1:
index_column = self.df._index_columns[0]
index_data_type = index_column.schema[0].dataType
if len(rows_sel) == 1:
df = df._spark_where(index_column == _spark_lit(
rows_sel[0])._spark_cast(index_data_type))
else:
df = df._spark_where(
index_column._spark_isin([
_spark_lit(r)._spark_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.df._metadata.column_fields]
elif isinstance(cols_sel, Column):
columns = [cols_sel]
else:
columns = [_make_col(c) for c in cols_sel]
try:
df = df._spark_select(self.df._metadata.index_fields + columns)
except AnalysisException:
raise KeyError('[{}] don\'t exist in columns'.format(
[col._jc.toString() for col in columns]))
df._metadata = self.df._metadata.copy(
column_fields=df._metadata.column_fields[-len(columns):])
if cols_sel is not None and isinstance(cols_sel, Column):
from .series import _col
return _col(df)
else:
return df
