def test_cast_row_to_string(self):
self.assertEqual(
cast_to_string(
Row(
a=collections.OrderedDict([('value', None), ('b', {
'c': 7
})]),
b=None,
c=True,
d=5.2,
),
StructType([
StructField(
'a',
MapType(
StringType(),
MapType(StringType(), LongType(), True),
True,
),
True,
),
StructField('b', LongType(), True),
StructField('c', BooleanType(), True),
StructField('d', DoubleType(), True),
]),
options=BASE_OPTIONS,
),
'[[value ->, b -> [c -> 7]],, true, 5.2]',
)
def agg(self, stats):
grouping_schema = StructType([
field for col in self.grouping_cols
for field in col.find_fields_in_schema(self.jdf.bound_schema)
])
aggregated_stats = self.jdf.aggregate(
GroupedStats(
self.grouping_cols,
stats,
pivot_col=self.pivot_col,
pivot_values=self.pivot_values,
),
lambda grouped_stats, row: grouped_stats.merge(
row, self.jdf.bound_schema),
lambda grouped_stats_1, grouped_stats_2: grouped_stats_1.
mergeStats(grouped_stats_2, self.jdf.bound_schema),
)
data = []
all_stats = self.add_subtotals(aggregated_stats)
for group_key in all_stats.group_keys:
key = [(str(key), None if value is GROUPED else value)
for key, value in zip(self.grouping_cols, group_key)]
grouping = tuple(value is GROUPED for value in group_key)
key_as_row = row_from_keyed_values(key).set_grouping(grouping)
data.append(
row_from_keyed_values(key + [(
str(stat),
stat.with_pre_evaluation_schema(self.jdf.bound_schema).
eval(key_as_row, grouping_schema),
) for pivot_value in all_stats.pivot_values
for stat in get_pivoted_stats(
all_stats.groups[group_key]
[pivot_value], pivot_value)]))
if self.pivot_col is not None:
if len(stats) == 1:
new_schema = StructType(grouping_schema.fields + [
StructField(str(pivot_value), DataType(), True)
for pivot_value in self.pivot_values
])
else:
new_schema = StructType(grouping_schema.fields + [
StructField('{0}_{1}'.format(pivot_value, stat),
DataType(), True)
for pivot_value in self.pivot_values for stat in stats
])
else:
new_schema = StructType(
grouping_schema.fields +
[StructField(str(stat), DataType(), True) for stat in stats])
# noinspection PyProtectedMember
return self.jdf._with_rdd(self.jdf._sc.parallelize(data),
schema=new_schema)
def test_session_create_data_frame_from_list(self):
df = self.spark.createDataFrame([(1, 'one'), (2, 'two'), (3, 'three')])
self.assertEqual(df.count(), 3)
self.assertListEqual(
df.collect(), [Row(_1=1, _2='one'), Row(_1=2, _2='two'), Row(_1=3, _2='three')],
)
self.assertEqual(
df.schema, StructType([StructField('_1', LongType(), True), StructField('_2', StringType(), True)]),
)
def crosstab(self, df, col1, col2):
table_name = '_'.join((col1, col2))
counts = df.groupBy(col1, col2).agg(count('*')).take(1e6)
if len(counts) == 1e6:
warnings.warn(
'The maximum limit of 1e6 pairs have been collected, '
'which may not be all of the pairs. Please try reducing '
'the amount of distinct items in your columns.')
def clean_element(element):
return str(element) if element is not None else 'null'
distinct_col2 = counts.map(lambda row: clean_element(row[
col2])).distinct().sorted().zipWithIndex().toMap()
column_size = len(distinct_col2)
if column_size < 1e4:
raise ValueError(
"The number of distinct values for {0} can't exceed 1e4. "
'Currently {1}'.format(col2, column_size))
def create_counts_row(col1Item, rows):
counts_row = [None] * (column_size + 1)
def parse_row(row):
column_index = distinct_col2[clean_element(row[1])]
counts_row[int(column_index + 1)] = int(row[2])
rows.foreach(parse_row)
# the value of col1 is the first value, the rest are the counts
counts_row[0] = clean_element(col1Item)
return Row(counts_row)
table = counts.groupBy(lambda r: r[col1]).map(create_counts_row).toSeq
# Back ticks can't exist in DataFrame column names,
# therefore drop them. To be able to accept special keywords and `.`,
# wrap the column names in ``.
def clean_column_name(name):
return name.replace('`', '')
# In the map, the column names (._1) are not ordered by the index (._2).
# We need to explicitly sort by the column index and assign the column names.
header_names = distinct_col2.toSeq.sortBy(lambda r: r[2]).map(
lambda r: StructField(clean_column_name(str(r[1])), LongType))
schema = StructType([StructField(table_name, StringType)] +
header_names)
return schema, table
def merge_schemas(left_schema, right_schema, how, on=None):
if on is None:
on = []
left_on_fields, right_on_fields = get_on_fields(left_schema, right_schema,
on)
other_left_fields = [
field for field in left_schema.fields if field not in left_on_fields
]
other_right_fields = [
field for field in right_schema.fields if field not in right_on_fields
]
if how in (INNER_JOIN, CROSS_JOIN, LEFT_JOIN, LEFT_ANTI_JOIN,
LEFT_SEMI_JOIN):
on_fields = left_on_fields
elif how == RIGHT_JOIN:
on_fields = right_on_fields
elif how == FULL_JOIN:
on_fields = [
StructField(field.name, field.dataType, nullable=True)
for field in left_on_fields
]
else:
raise IllegalArgumentException(
'Invalid how argument in join: {0}'.format(how))
return StructType(fields=on_fields + other_left_fields +
other_right_fields)
def output_fields(self, schema):
if isinstance(self.expr, Expression):
return self.expr.output_fields(schema)
return [
StructField(name=self.col_name,
dataType=self.data_type,
nullable=self.is_nullable)
]
def test_session_create_data_frame_from_pandas_data_frame(self):
try:
# Pandas is an optional dependency
# pylint: disable=import-outside-toplevel
import pandas as pd
except ImportError:
raise Exception('pandas is not importable')
pdf = pd.DataFrame([(1, 'one'), (2, 'two'), (3, 'three')])
df = self.spark.createDataFrame(pdf)
self.assertEqual(df.count(), 3)
self.assertListEqual(
df.collect(), [Row(**{'0': 1, '1': 'one'}), Row(**{'0': 2, '1': 'two'}), Row(**{'0': 3, '2': 'three'})],
)
self.assertEqual(
df.schema, StructType([StructField('0', LongType(), True), StructField('1', StringType(), True)]),
)
def withColumnRenamed(self, existing, new):
def mapper(row):
keyed_values = [(new, row[col]) if col == existing else
(col, row[col]) for col in row.__fields__]
return row_from_keyed_values(keyed_values)
new_schema = StructType([
field if field.name != existing else StructField(
new, field.dataType, field.nullable)
for field in self.bound_schema.fields
])
return self._with_rdd(self._rdd.map(mapper), schema=new_schema)
def toDF(self, new_names):
def mapper(row):
keyed_values = [(new_name, row[old])
for new_name, old in zip(new_names, row.__fields__)
]
return row_from_keyed_values(keyed_values)
new_schema = StructType([
StructField(new_name, field.dataType, field.nullable)
for new_name, field in zip(new_names, self.bound_schema.fields)
])
return self._with_rdd(self._rdd.map(mapper), schema=new_schema)
def test_session_create_data_frame_from_list_with_col_names(self):
df = self.spark.createDataFrame(
[(0.0, [1.0, 0.8]), (1.0, [0.0, 0.0]), (2.0, [0.5, 0.5])], ['label', 'features'],
)
self.assertEqual(df.count(), 3)
self.assertListEqual(
df.collect(),
[
row_from_keyed_values([('label', 0.0), ('features', [1.0, 0.8])]),
row_from_keyed_values([('label', 1.0), ('features', [0.0, 0.0])]),
row_from_keyed_values([('label', 2.0), ('features', [0.5, 0.5])]),
],
)
self.assertEqual(
df.schema,
StructType(
[
StructField('label', DoubleType(), True),
StructField('features', ArrayType(DoubleType(), True), True),
]
),
)
def guess_schema_from_strings(schema_fields, data, options):
field_values = [(field, [row[field] for row in data])
for field in schema_fields]
field_types_and_values = [
(field, guess_type_from_values_as_string(values, options))
for field, values in field_values
]
schema = StructType(fields=[
StructField(field, field_type)
for field, field_type in field_types_and_values
])
return schema
def test_cast_to_struct(self):
self.assertEqual(
cast_to_struct(
Row(character='Alice', day='28', month='8', year='2019'),
from_type=StructType(fields=[
StructField('character', StringType()),
StructField('day', StringType()),
StructField('month', StringType()),
StructField('year', StringType()),
]),
to_type=StructType(fields=[
StructField('character', StringType()),
StructField('day', IntegerType()),
StructField('month', IntegerType()),
StructField('year', IntegerType()),
]),
options=BASE_OPTIONS,
),
Row(character='Alice', day=28, month=8, year=2019),
)
def test_column_stat_helper():
"""
Expected quantile values come from use of org.apache.spark.sql.catalyst.util.QuantileSummaries
"""
schema = StructType([StructField('value', IntegerType())])
helper = ColumnStatHelper(col('value'))
for i in range(1, 100001):
helper.merge(Row(value=i), schema)
helper.finalize()
assert helper.count == 100000
assert helper.min == 1
assert helper.max == 100000
assert helper.mean == 50000.5
assert helper.stddev == 28867.65779668774 # sample standard deviation
assert helper.get_quantile(0) == 1
assert helper.get_quantile(0.25) == 24998
assert helper.get_quantile(0.5) == 50000
assert helper.get_quantile(0.75) == 74993
assert helper.get_quantile(1) == 100000
def read(self):
sc = self.spark._sc
paths = self.paths
partitions, partition_schema = resolve_partitions(paths)
rdd_filenames = sc.parallelize(sorted(partitions.keys()),
len(partitions))
rdd = rdd_filenames.flatMap(
partial(parse_csv_file, partitions, partition_schema, self.schema,
self.options))
if self.schema is not None:
schema = self.schema
elif self.options.inferSchema:
fields = rdd.take(1)[0].__fields__
schema = guess_schema_from_strings(fields,
rdd.collect(),
options=self.options)
else:
schema = infer_schema_from_rdd(rdd)
schema_with_string = StructType(fields=[
StructField(field.name, StringType()) for field in schema.fields
])
if partition_schema:
partitions_fields = partition_schema.fields
full_schema = StructType(schema.fields[:-len(partitions_fields)] +
partitions_fields)
else:
full_schema = schema
cast_row = get_caster(from_type=schema_with_string,
to_type=full_schema,
options=self.options)
casted_rdd = rdd.map(cast_row)
casted_rdd._name = paths
return DataFrameInternal(sc, casted_rdd, schema=full_schema)
def get_summary_schema(self, exprs):
return StructType([StructField('summary', StringType(), True)] + [
StructField(field.name, StringType(), True)
for field in get_schema_from_cols(exprs, self.bound_schema).fields
])
def __init__(self, spark, paths, schema, options):
self.spark = spark
self.paths = paths
self.schema = schema or StructType(
[StructField('value', StringType())])
self.options = Options(self.default_options, options)
def output_fields(self, schema):
return [
StructField(name=str(self),
dataType=self.data_type,
nullable=self.is_nullable)
]
def test_csv_read_with_inferred_schema(self):
df = spark.read.option('inferSchema', True).csv(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
'data/fundings/'),
header=True,
)
self.assertEqual(df.count(), 4)
self.assertEqual(
df.schema,
StructType([
StructField('permalink', StringType()),
StructField('company', StringType()),
StructField('numEmps', IntegerType()),
StructField('category', StringType()),
StructField('city', StringType()),
StructField('state', StringType()),
StructField('fundedDate', TimestampType()),
StructField('raisedAmt', IntegerType()),
StructField('raisedCurrency', StringType()),
StructField('round', StringType()),
]),
)
self.assertEqual(
[Row(**r.asDict()) for r in df.collect()],
[
Row(
permalink='mycityfaces',
company='MyCityFaces',
numEmps=7,
category='web',
city='Scottsdale',
state='AZ',
fundedDate=datetime.datetime(2008, 1, 1, 0, 0),
raisedAmt=50000,
raisedCurrency='USD',
round='seed',
),
Row(
permalink='flypaper',
company='Flypaper',
numEmps=None,
category='web',
city='Phoenix',
state='AZ',
fundedDate=datetime.datetime(2008, 2, 1, 0, 0),
raisedAmt=3000000,
raisedCurrency='USD',
round='a',
),
Row(
permalink='chosenlist-com',
company='ChosenList.com',
numEmps=5,
category='web',
city='Scottsdale',
state='AZ',
fundedDate=datetime.datetime(2008, 1, 25, 0, 0),
raisedAmt=233750,
raisedCurrency='USD',
round='angel',
),
Row(
permalink='digg',
company='Digg',
numEmps=60,
category='web',
city='San Francisco',
state='CA',
fundedDate=datetime.datetime(2006, 12, 1, 0, 0),
raisedAmt=8500000,
raisedCurrency='USD',
round='b',
),
],
)
def test_csv_read_without_schema(self):
df = spark.read.csv(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
'data/fundings/'),
header=True,
)
self.assertEqual(df.count(), 4)
self.assertEqual(
df.schema,
StructType([
StructField('permalink', StringType()),
StructField('company', StringType()),
StructField('numEmps', StringType()),
StructField('category', StringType()),
StructField('city', StringType()),
StructField('state', StringType()),
StructField('fundedDate', StringType()),
StructField('raisedAmt', StringType()),
StructField('raisedCurrency', StringType()),
StructField('round', StringType()),
]),
)
self.assertListEqual(
[Row(**r.asDict()) for r in df.collect()],
[
Row(
permalink='mycityfaces',
company='MyCityFaces',
numEmps='7',
category='web',
city='Scottsdale',
state='AZ',
fundedDate='2008-01-01',
raisedAmt='50000',
raisedCurrency='USD',
round='seed',
),
Row(
permalink='flypaper',
company='Flypaper',
numEmps=None,
category='web',
city='Phoenix',
state='AZ',
fundedDate='2008-02-01',
raisedAmt='3000000',
raisedCurrency='USD',
round='a',
),
Row(
permalink='chosenlist-com',
company='ChosenList.com',
numEmps='5',
category='web',
city='Scottsdale',
state='AZ',
fundedDate='2008-01-25',
raisedAmt='233750',
raisedCurrency='USD',
round='angel',
),
Row(
permalink='digg',
company='Digg',
numEmps='60',
category='web',
city='San Francisco',
state='CA',
fundedDate='2006-12-01',
raisedAmt='8500000',
raisedCurrency='USD',
round='b',
),
],
)
def output_fields(self, schema):
return [
StructField('pos', IntegerType(), False),
StructField('col', DataType(), False),
]
def test_session_create_data_frame_from_list_with_schema(self):
schema = StructType([StructField('map', MapType(StringType(), IntegerType()), True)])
df = self.spark.createDataFrame([({'a': 1},)], schema=schema)
self.assertEqual(df.count(), 1)
self.assertListEqual(df.collect(), [Row(map={'a': 1})])
self.assertEqual(df.schema, schema)
