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Remove time unit and column from python and make time the first column #18

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Remove time unit and column from python and make time the first column #18

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7e73caa
Remove time_column and unit from Python API
heimir-sverrisson Aug 15, 2017
34caa45
Fix issue# 8 and verify time is first column
heimir-sverrisson Aug 15, 2017
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113 changes: 113 additions & 0 deletions python/tests/ts/test_dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,6 +120,46 @@ def fn_2(rows):
"with key 7"
)

def test_addColumnsForCycleTimeNotFirst(self):
import pyspark.sql.types as pyspark_types
price = self.flintContext.read.pandas(test_utils.make_pdf([
[7, 1000, 0.5],
[3, 1000, 1.0],
[3, 1050, 1.5],
[7, 1050, 2.0],
[3, 1100, 2.5],
[7, 1100, 3.0],
[3, 1150, 3.5],
[7, 1150, 4.0],
[3, 1200, 4.5],
[7, 1200, 5.0],
[3, 1250, 5.5],
[7, 1250, 6.0],
], ["id", "time", "price"]))
expected_pdf = test_utils.make_pdf([
[1000, 7, 0.5, 1.0],
[1000, 3, 1.0, 2.0],
[1050, 3, 1.5, 3.0],
[1050, 7, 2.0, 4.0],
[1100, 3, 2.5, 5.0],
[1100, 7, 3.0, 6.0],
[1150, 3, 3.5, 7.0],
[1150, 7, 4.0, 8.0],
[1200, 3, 4.5, 9.0],
[1200, 7, 5.0, 10.0],
[1250, 3, 5.5, 11.0],
[1250, 7, 6.0, 12.0],
], ["time", "id", "price", "adjustedPrice"])

def fn_1(rows):
size = len(rows)
return {row: row.price*size for row in rows}

new_pdf = price.addColumnsForCycle(
{"adjustedPrice": (pyspark_types.DoubleType(), fn_1)}
).toPandas()
test_utils.assert_same(new_pdf, expected_pdf)

def test_merge(self):
price = self.price()
price1 = price.filter(price.time > 1100)
Expand Down Expand Up @@ -171,6 +211,79 @@ def test_leftJoin(self):
).toPandas()
test_utils.assert_same(new_pdf, expected_pdf)

def test_leftJoinTimeNotFirst(self):
# Note that in price we have time as the second column
price = self.flintContext.read.pandas(test_utils.make_pdf([
[7, 1000, 0.5],
[3, 1000, 1.0],
[3, 1050, 1.5],
[7, 1050, 2.0],
[3, 1100, 2.5],
[7, 1100, 3.0],
[3, 1150, 3.5],
[7, 1150, 4.0],
[3, 1200, 4.5],
[7, 1200, 5.0],
[3, 1250, 5.5],
[7, 1250, 6.0],
], ["id", "time", "price"]))
# Time is also the second column of vol
vol = self.flintContext.read.pandas(test_utils.make_pdf([
[7, 1000, 100],
[3, 1000, 200],
[3, 1050, 300],
[7, 1050, 400],
[3, 1100, 500],
[7, 1100, 600],
[3, 1150, 700],
[7, 1150, 800],
[3, 1200, 900],
[7, 1200, 1000],
[3, 1250, 1100],
[7, 1250, 1200],
], ["id", "time", "volume"]))
# We expect to get the result with time as the first column
expected_pdf = test_utils.make_pdf([
(1000, 7, 0.5, 100,),
(1000, 3, 1.0, 200,),
(1050, 3, 1.5, 300,),
(1050, 7, 2.0, 400,),
(1100, 3, 2.5, 500,),
(1100, 7, 3.0, 600,),
(1150, 3, 3.5, 700,),
(1150, 7, 4.0, 800,),
(1200, 3, 4.5, 900,),
(1200, 7, 5.0, 1000,),
(1250, 3, 5.5, 1100,),
(1250, 7, 6.0, 1200,)
], ["time", "id", "price", "volume"])

new_pdf = price.leftJoin(vol, key=["id"]).toPandas()
test_utils.assert_same(new_pdf, expected_pdf)
test_utils.assert_same(
new_pdf, price.leftJoin(vol, key="id").toPandas()
)

expected_pdf = test_utils.make_pdf([
(1000, 7, 0.5, 100),
(1000, 3, 1.0, 200),
(1050, 3, 1.5, None),
(1050, 7, 2.0, None),
(1100, 3, 2.5, 500),
(1100, 7, 3.0, 600),
(1150, 3, 3.5, 700),
(1150, 7, 4.0, 800),
(1200, 3, 4.5, 900),
(1200, 7, 5.0, 1000),
(1250, 3, 5.5, 1100),
(1250, 7, 6.0, 1200),
], ["time", "id", "price", "volume"])

new_pdf = price.leftJoin(
vol.filter(vol.time != 1050), key="id"
).toPandas()
test_utils.assert_same(new_pdf, expected_pdf)

def test_futureLeftJoin(self):
import pyspark.sql.types as pyspark_types
price = self.price()
Expand Down
29 changes: 10 additions & 19 deletions python/ts/flint/dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,11 +85,9 @@ class TimeSeriesDataFrame(pyspark.sql.DataFrame):
DEFAULT_UNIT = "ns"
'''The units of the timestamps present in :attr:`DEFAULT_TIME_COLUMN`.

Acceptable values are: ``'s'``, ``'ms'``, ``'us'``, ``'ns'``.

'''

def __init__(self, df, sql_ctx, *, time_column=DEFAULT_TIME_COLUMN, is_sorted=True, unit=DEFAULT_UNIT, tsrdd_part_info=None):
def __init__(self, df, sql_ctx, *, is_sorted=True, tsrdd_part_info=None):
'''
:type df: pyspark.sql.DataFrame
:type sql_ctx: pyspark.sql.SqlContext
Expand All @@ -102,7 +100,7 @@ def __init__(self, df, sql_ctx, *, time_column=DEFAULT_TIME_COLUMN, is_sorted=Tr
:param tsrdd_part_info: Partition info
:type tsrdd_part_info: Option[com.twosigma.flint.timeseries.PartitionInfo]
'''
self._time_column = time_column
self._time_column = self.DEFAULT_TIME_COLUMN
self._is_sorted = is_sorted
self._tsrdd_part_info = tsrdd_part_info

Expand All @@ -112,7 +110,7 @@ def __init__(self, df, sql_ctx, *, time_column=DEFAULT_TIME_COLUMN, is_sorted=Tr
super().__init__(self._jdf, sql_ctx)

self._jpkg = java.Packages(self._sc)
self._junit = utils.junit(self._sc, unit) if isinstance(unit,str) else unit
self._junit = utils.junit(self._sc, self.DEFAULT_UNIT)

if tsrdd_part_info:
if not is_sorted:
Expand Down Expand Up @@ -169,9 +167,7 @@ def _new_method(self, *args, **kwargs):
if self._jpkg.OrderPreservingOperation.isDerivedFrom(self._jdf, df._jdf):
tsdf_args = {
"df": df,
"sql_ctx": df.sql_ctx,
"time_column": self._time_column,
"unit": self._junit
"sql_ctx": df.sql_ctx
}

tsdf_args['is_sorted'] = self._is_sorted and self._jpkg.OrderPreservingOperation.isOrderPreserving(self._jdf, df._jdf)
Expand Down Expand Up @@ -226,17 +222,14 @@ def _from_df(df, *, time_column, is_sorted, unit):
return TimeSeriesDataFrame(df,
df.sql_ctx,
time_column=time_column,
is_sorted=is_sorted,
unit=unit)
is_sorted=is_sorted)

@staticmethod
def _from_pandas(df, schema, sql_ctx, *, time_column, is_sorted, unit):
def _from_pandas(df, schema, sql_ctx, *, is_sorted):
df = sql_ctx.createDataFrame(df, schema)
return TimeSeriesDataFrame(df,
sql_ctx,
time_column=time_column,
is_sorted=is_sorted,
unit=unit)
is_sorted=is_sorted)

def _timedelta_ns(self, varname, timedelta, *, default=None):
"""Transforms pandas.Timedelta to a ns string with appropriate checks
Expand Down Expand Up @@ -308,10 +301,10 @@ def addColumnsForCycle(self, columns, *, key=None):
:returns: a new dataframe with the columns added
:rtype: :class:`TimeSeriesDataFrame`
"""
# Need to make a new StructType to prevent from modifying the original schema object
schema = pyspark_types.StructType.fromJson(self.schema.jsonValue())
tsdf = self.groupByCycle(key)
# Don't pickle the whole schema, just the names for the lambda
# Last element of tsdf.schema describes the 'rows' returned
# which does differ from self.schema if the first column is not 'time'
schema = tsdf.schema[len(tsdf.schema)-1].dataType.elementType
schema_names = list(schema.names)

def flatmap_fn():
Expand All @@ -335,8 +328,6 @@ def _(orig_row):

return TimeSeriesDataFrame(df,
df.sql_ctx,
time_column=self._time_column,
unit=self._junit,
tsrdd_part_info=tsdf._tsrdd_part_info)

def merge(self, other):
Expand Down
5 changes: 1 addition & 4 deletions python/ts/flint/readwriter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,6 @@
#

from pyspark.sql import DataFrame
from pyspark.sql.readwriter import DataFrameReader, DataFrameWriter

from . import java
from . import utils
Expand Down Expand Up @@ -57,9 +56,7 @@ def pandas(self, df, schema=None, *,

return TimeSeriesDataFrame._from_pandas(
df, schema, self._flintContext._sqlContext,
time_column=time_column,
is_sorted=is_sorted,
unit=unit)
is_sorted=is_sorted)

def _df_between(self, df, begin, end, time_column, unit):
"""Filter a Python dataframe to contain data between begin (inclusive) and end (exclusive)
Expand Down