PyKX to Pythonic data type mapping
A breakdown of each of the pykx.K types and their analogous numpy, pandas, and pyarrow types.
Cheat Sheet
| PyKX type | Python type | Numpy dtype | Pandas dtype | PyArrow type |
|---|---|---|---|---|
| List | list | object | object | Not Supported |
| Boolean | bool | bool | bool | Not Supported |
| GUID | uuid4 | uuid4 | uuid4 | uuid4 |
| Byte | int | uint8 | uint8 | uint8 |
| Short | int | int16 | int16 | int16 |
| Int | int | int32 | int32 | int32 |
| Long | int | int64 | int64 | int64 |
| Real | float | float32 | float32 | FloatArray |
| Float | float | float64 | float64 | DoubleArray |
| Char | bytes | |S1 | bytes8 | BinaryArray |
| Symbol | str | object | object | StringArray |
| Timestamp | datetime | datetime64[ns] | datetime64[ns] | TimestampArray |
| Month | date | datetime64[M] | datetime64[ns] | Not Supported |
| Date | date | datetime64[D] | datetime64[ns] | Date32Array |
| Timespan | timedelta | timedelta[ns] | timedelta64[ns] | DurationArray |
| Minute | timedelta | timedelta64[m] | timedelta64[ns] | Not Supported |
| Second | timedelta | timedelta64[s] | timedelta64[ns] | DurationArray |
| Time | timedelta | timedelta64[ms] | timedelta64[ns] | DurationArray |
| Dictionary | dict | Not Supported | Not Supported | Not Supported |
| Table | dict | records | DataFrame | Table |
pykx.List
A python list of mixed types will be converted into a pykx.List.
>>> pykx.List([1, b'foo', 'bar', 4.5])
pykx.List(pykx.q('
1
"foo"
`bar
4.5
'))Calling .py() on a pykx.List will return a generic python list object where each object is converted into its analogous python type.
>>> pykx.List([1, b'foo', 'bar', 4.5]).py()
[1, b'foo', 'bar', 4.5]A numpy list with dtype==object containing data of mixed types will be converted into a pykx.List
>>> pykx.List(np.array([1, b'foo', 'bar', 4.5], dtype=object))
pykx.List(pykx.q('
1
"foo"
`bar
4.5
'))Calling .np() on a pykx.List object will return a numpy ndarray with dtype==object where each element has been converted into its closest analogous python type.
>>> pykx.List([1, b'foo', 'bar', 4.5]).np()
array([1, b'foo', 'bar', 4.5], dtype=object)Calling .pd() on a pykx.List object will return a pandas Series with dtype==object where each element has been converted into its closest analogous python type.
>>> pykx.List([1, b'foo', 'bar', 4.5]).pd()
0 1
1 b'foo'
2 bar
3 4.5
dtype: objectpykx.BooleanAtom
Python
The python bool type will be converted into a pykx.BooleanAtom.
>>> pykx.BooleanAtom(True)
pykx.BooleanAtom(pykx.q('1b'))Calling .py() on a pykx.BooleanAtom will return a python bool object.
>>> pykx.BooleanAtom(True).py()
Truepykx.BooleanVector
A list of python bool types will be converted into a pykx.BooleanVector.
>>> pykx.BooleanVector([True, False, True])
pykx.BooleanVector(pykx.q('101b'))Calling .py() on a pykx.BooleanVector will return a list of python bool objects.
>>> pykx.BooleanVector([True, False, True]).py()
[True, False, True]Converting a pykx.BoolVector will result in an array of objects with the bool dtype, arrays of that dtype can also be converted into pykx.BoolVector objects.
pykx.GUIDAtom
The python uuid4 type from the uuid library will be converted into a pykx.GUIDAtom.
>>> from uuid import uuid4
>>> pykx.GUIDAtom(uuid4())
pykx.GUIDAtom(pykx.q('012e8fb7-52c4-49e6-9b4e-93aa625ca3d7'))Calling .py() on a pykx.GUIDAtom will return a python uuid4 object.
>>> pykx.GUIDAtom(uuid4()).py()
UUID('d16f9f3f-2a57-4dfd-818e-04c9c7a53584')pykx.GUIDVector
A list of python uuid4 types from the uuid library will be converted into a pykx.GUIDVector.
>>> pykx.GUIDVector([uuid4(), uuid4()])
pykx.GUIDVector(pykx.q('542ccbef-8aa1-4433-804a-7928172ec2d4 ff6f89fb-1aec-4073-821a-ce281ca6263e'))Calling .py() on a pykx.GUIDVector will return a list of python uuid4 objects.
>>> pykx.GUIDVector([uuid4(), uuid4()]).py()
[UUID('a3b284fc-5f31-4ba2-b521-fa8b5c309e02'), UUID('95ee9044-3930-492c-96f2-e336110de023')]Each of these will return an array of their respective object types around a list of uuid4 objects.
pykx.ByteAtom
The python int type will be converted into a pykx.ByteAtom.
Float types will also be converted but the decimal will be truncated away and no rounding done.
>>> pykx.ByteAtom(1.0)
pykx.ByteAtom(pykx.q('0x01'))
>>> pykx.ByteAtom(1.5)
pykx.ByteAtom(pykx.q('0x01'))Calling .py() on a pykx.ByteAtom will return a python int object.
>>> pykx.ByteAtom(1.5).py()
1pykx.ByteVector
A list of python int types will be converted into a pykx.ByteVector.
Float types will also be converted but the decimal will be truncated away and no rounding done.
>>> pykx.ByteVector([1, 2.5])
pykx.ByteVector(pykx.q('0x0102'))Calling .py() on a pykx.ByteVector will return a list of python int objects.
>>> pykx.ByteVector([1, 2.5]).py()
[1, 2]Converting a pykx.ByteVector will result in an array of objects with the uint8 dtype, arrays of that dtype can also be converted into pykx.ByteVector objects.
pykx.ShortAtom
The python int type will be converted into a pykx.ShortAtom.
Float types will also be converted but the decimal will be truncated away and no rounding done.
>>> pykx.ShortAtom(1)
pykx.ShortAtom(pykx.q('1h'))
>>> pykx.ShortAtom(1.5)
pykx.ShortAtom(pykx.q('1h'))Calling .py() on a pykx.ShortAtom will return a python int object.
>>> pykx.ShortAtom(1.5).py()
1pykx.ShortVector
A list of python int types will be converted into a pykx.ShortVector.
Float types will also be converted but the decimal will be truncated away and no rounding done.
>>> pykx.ShortVector([1, 2.5])
pykx.ShortVector(pykx.q('1 2h'))Calling .py() on a pykx.ShortVector will return a list of python int objects.
>>> pykx.ShortVector([1, 2.5]).py()
[1, 2]Converting a pykx.ShortVector will result in an array of objects with the int16 dtype, arrays of that dtype can also be converted into pykx.ShortVector objects.
pykx.IntAtom
The python int type will be converted into a pykx.IntAtom.
Float types will also be converted but the decimal will be truncated away and no rounding done.
>>> pykx.IntAtom(1)
pykx.IntAtom(pykx.q('1i'))
>>> pykx.IntAtom(1.5)
pykx.IntAtom(pykx.q('1i'))Calling .py() on a pykx.IntAtom will return a python int object.
>>> pykx.IntAtom(1.5).py()
1pykx.IntVector
A list of python int types will be converted into a pykx.IntVector.
Float types will also be converted but the decimal will be truncated away and no rounding done.
>>> pykx.IntVector([1, 2.5])
pykx.IntVector(pykx.q('1 2i'))Calling .py() on a pykx.IntVector will return a list of python int objects.
>>> pykx.IntVector([1, 2.5]).py()
[1, 2]Converting a pykx.IntVector will result in an array of objects with the int32 dtype, arrays of that dtype can also be converted into pykx.IntVector objects.
pykx.LongAtom
The python int type will be converted into a pykx.LongAtom.
Float types will also be converted but the decimal will be truncated away and no rounding done.
>>> pykx.LongAtom(1)
pykx.LongAtom(pykx.q('1'))
>>> pykx.LongAtom(1.5)
pykx.LongAtom(pykx.q('1'))Calling .py() on a pykx.LongAtom will return a python int object.
>>> pykx.LongAtom(1.5).py()
1pykx.LongVector
A list of python int types will be converted into a pykx.LongVector.
Float types will also be converted but the decimal will be truncated away and no rounding done.
>>> pykx.LongVector([1, 2.5])
pykx.LongVector(pykx.q('1 2'))Calling .py() on a pykx.LongVector will return a list of python int objects.
>>> pykx.LongVector([1, 2.5]).py()
[1, 2]Converting a pykx.LongVector will result in an array of objects with the int64 dtype, arrays of that dtype can also be converted into pykx.LongVector objects.
pykx.RealAtom
The python float and int types will be converted into a pykx.RealAtom.
>>> pykx.RealAtom(2.5)
pykx.RealAtom(pykx.q('2.5e'))Calling .py() on a pykx.RealAtom will return a python float object.
>>> pykx.RealAtom(2.5).py()
2.5pykx.RealVector
A list of python int and float types will be converted into a pykx.RealVector.
>>> pykx.RealVector([1, 2.5])
pykx.RealVector(pykx.q('1 2.5e'))Calling .py() on a pykx.RealVector will return a list of python float objects.
>>> pykx.RealVector([1, 2.5]).py()
[1.0, 2.5]Converting a pykx.RealVector will result in an array of objects with the float32 dtype, arrays of that dtype can also be converted into pykx.RealVector objects.
This will return a PyArrow array with the FloatArray type.
pykx.FloatAtom
The python float and int types will be converted into a pykx.FloatAtom.
>>> pykx.FloatAtom(2.5)
pykx.FloatAtom(pykx.q('2.5'))Calling .py() on a pykx.FloatAtom will return a python float object.
>>> pykx.FloatAtom(2.5).py()
2.5pykx.FloatVector
A list of python int and float types will be converted into a pykx.FloatVector.
>>> pykx.FloatVector([1, 2.5])
pykx.FloatVector(pykx.q('1 2.5'))Calling .py() on a pykx.FloatVector will return a list of python float objects.
>>> pykx.FloatVector([1, 2.5]).py()
[1.0, 2.5]Converting a pykx.FloatVector will result in an array of objects with the float64 dtype, arrays of that dtype can also be converted into pykx.FloatVector objects.
This will return a PyArrow array with the DoubleArray type.
pykx.CharAtom
The python bytes type with length 1 will be converted into a pykx.CharAtom.
>>> pykx.CharAtom(b'a')
pykx.CharAtom(pykx.q('"a"'))Calling .py() on a pykx.CharAtom will return a python bytes object.
>>> pykx.CharAtom(b'a').py()
b'a'pykx.CharVector
The python bytes type with length greater than 1 will be converted into a pykx.CharVector.
>>> pykx.CharVector(b'abc')
pykx.CharVector(pykx.q('"abc"'))Calling .py() on a pykx.CharVector will return a python bytes object.
>>> pykx.CharVector(b'abc').py()
b'abc'Calling .np() on a pykx.CharVector will return a numpy ndarray with dtype '|S1'.
>>> pykx.CharVector(b'abc').np()
array([b'a', b'b', b'c'], dtype='|S1')Converting a ndarray of this dtype will create a pykx.CharVector.
>>> pykx.CharVector(np.array([b'a', b'b', b'c'], dtype='|S1'))
pykx.CharVector(pykx.q('"abc"'))Calling .pd() on a pykx.CharVector will return a pandas Series with dtype bytes8.
>>> pykx.CharVector(b'abc').pd()
0 b'a'
1 b'b'
2 b'c'
dtype: bytes8Converting a Series of this dtype will create a pykx.CharVector.
>>> pykx.CharVector(pd.Series([b'a', b'b', b'c'], dtype=bytes))
pykx.CharVector(pykx.q('"abc"'))Calling .pa() on a pykx.CharVector will return a pyarrow BinaryArray.
<pyarrow.lib.BinaryArray object at 0x7f44cc099c00>
[
61,
62,
63
]pykx.SymbolAtom
The python string type will be converted into a pykx.SymbolAtom.
>>> pykx.toq('symbol')
pykx.SymbolAtom(pykx.q('`symbol'))Calling .py() on a pykx.SymbolAtom will return a python string object.
>>> pykx.toq('symbol').py()
'symbol'pykx.SymbolVector
A list of python string types will be converted into a pykx.SymbolVector.
>>> pykx.SymbolVector(['a', 'b', 'c'])
pykx.SymbolVector(pykx.q('`a`b`c'))Calling .py() on a pykx.SymbolVector will return a list of python string objects.
>>> pykx.SymbolVector(['a', 'b', 'c']).py()
['a', 'b', 'c']Calling .np() on a pykx.SymbolVector will return a numpy ndarray of python strings with dtype object.
>>> pykx.SymbolVector(['a', 'b', 'c']).np()
array(['a', 'b', 'c'], dtype=object)Converting a ndarray of dtype object will create a pykx.SymbolVector.
>>> pykx.SymbolVector(np.array(['a', 'b', 'c'], dtype=object))
pykx.SymbolVector(pykx.q('`a`b`c'))Calling .pd() on a pykx.SymbolVector will return a pandas Series with dtype object.
>>> pykx.SymbolVector(['a', 'b', 'c']).pd()
0 a
1 b
2 c
dtype: objectCalling .pa() on a pykx.SymbolVector will return a pyarrow StringArray.
>>> pykx.SymbolVector(['a', 'b', 'c']).pa()
<pyarrow.lib.StringArray object at 0x7f44cc323fa0>
[
"a",
"b",
"c"
]pykx.TimestampAtom
The python datetime type will be converted into a pykx.TimestampAtom.
>>> kx.TimestampAtom(datetime(2150, 10, 22, 20, 31, 15, 70713))
pykx.TimestampAtom(pykx.q('2150.10.22D20:31:15.070713000'))Calling .py() on a pykx.TimestampAtom will return a python datetime object.
>>> kx.TimestampAtom(datetime(2150, 10, 22, 20, 31, 15, 70713)).py()
datetime.datetime(2150, 10, 22, 20, 31, 15, 70713)pykx.TimestampVector
A list of python datetime types will be converted into a pykx.TimestampVector.
>>> kx.TimestampVector([datetime(2150, 10, 22, 20, 31, 15, 70713), datetime(2050, 10, 22, 20, 31, 15, 70713)])
pykx.TimestampVector(pykx.q('2150.10.22D20:31:15.070713000 2050.10.22D20:31:15.070713000'))Calling .py() on a pykx.TimestampVector will return a list of python datetime objects.
>>> kx.TimestampVector([datetime(2150, 10, 22, 20, 31, 15, 70713), datetime(2050, 10, 22, 20, 31, 15, 70713)]).py()
[datetime.datetime(2150, 10, 22, 20, 31, 15, 70713), datetime.datetime(2050, 10, 22, 20, 31, 15, 70713)]Calling .np() on a pykx.TimestampVector will return a numpy ndarray with dtype datetime64[ns].
>>> kx.TimestampVector([datetime(2150, 10, 22, 20, 31, 15, 70713), datetime(2050, 10, 22, 20, 31, 15, 70713)]).np()
array(['2150-10-22T20:31:15.070713000', '2050-10-22T20:31:15.070713000'],
dtype='datetime64[ns]')Converting a ndarray of dtype datetime64[ns] will create a pykx.TimestampVector.
>>> kx.TimestampVector(np.array(['2150-10-22T20:31:15.070713000', '2050-10-22T20:31:15.070713000'], dtype='datetime64[ns]'))
pykx.TimestampVector(pykx.q('2150.10.22D20:31:15.070713000 2050.10.22D20:31:15.070713000'))Calling .pd() on a pykx.TimestampVector will return a pandas Series with dtype datetime64[ns].
>>> kx.TimestampVector([datetime(2150, 10, 22, 20, 31, 15, 70713), datetime(2050, 10, 22, 20, 31, 15, 70713)]).pd()
0 2150-10-22 20:31:15.070713
1 2050-10-22 20:31:15.070713
dtype: datetime64[ns]Calling .pa() on a pykx.TimestampVector will return a pyarrow TimestampArray.
>>> kx.TimestampVector([datetime(2150, 10, 22, 20, 31, 15, 70713), datetime(2050, 10, 22, 20, 31, 15, 70713)]).pa()
<pyarrow.lib.TimestampArray object at 0x7f6428f0dde0>
[
2150-10-22 20:31:15.070713000,
2050-10-22 20:31:15.070713000
]pykx.MonthAtom
The python date type will be converted into a pykx.MonthAtom.
>>> from datetime import date
>>> kx.MonthAtom(date(1972, 5, 1))
pykx.MonthAtom(pykx.q('1972.05m'))Calling .py() on a pykx.MonthAtom will return a python date object.
>>> kx.MonthAtom(date(1972, 5, 1)).py()
datetime.date(1972, 5, 1)pykx.MonthVector
A list of python date types will be converted into a pykx.MonthVector.
>>> kx.MonthVector([date(1972, 5, 1), date(1999, 5, 1)])
pykx.MonthVector(pykx.q('1972.05 1999.05m'))Calling .py() on a pykx.MonthVector will return a list of python date objects.
>>> kx.MonthVector([date(1972, 5, 1), date(1999, 5, 1)]).py()
[datetime.date(1972, 5, 1), datetime.date(1999, 5, 1)]Calling .np() on a pykx.MonthVector will return a numpy ndarray with dtype datetime64[M].
>>> kx.MonthVector([date(1972, 5, 1), date(1999, 5, 1)]).np()
array(['1972-05', '1999-05'], dtype='datetime64[M]')Converting a ndarray of dtype datetime64[M] will create a pykx.MonthVector.
>>> kx.MonthVector(np.array(['1972-05', '1999-05'], dtype='datetime64[M]'))
pykx.MonthVector(pykx.q('1972.05 1999.05m'))Calling .pd() on a pykx.MonthVector will return a pandas Series with dtype datetime64[ns].
>>> kx.MonthVector([date(1972, 5, 1), date(1999, 5, 1)]).pd()
0 1972-05-01
1 1999-05-01
dtype: datetime64[ns]pykx.DateAtom
The python date type will be converted into a pykx.DateAtom.
>>> kx.DateAtom(date(1972, 5, 31))
pykx.DateAtom(pykx.q('1972.05.31'))Calling .py() on a pykx.DateAtom will return a python date object.
>>> kx.DateAtom(date(1972, 5, 31)).py()
datetime.date(1972, 5, 31)pykx.DateVector
A list of python date types will be converted into a pykx.DateVector.
>>> kx.DateVector([date(1972, 5, 1), date(1999, 5, 1)])
pykx.DateVector(pykx.q('1972.05.01 1999.05.01'))Calling .py() on a pykx.DateVector will return a list of python date objects.
>>> kx.DateVector([date(1972, 5, 1), date(1999, 5, 1)]).py()
[datetime.date(1972, 5, 1), datetime.date(1999, 5, 1)]Calling .np() on a pykx.DateVector will return a numpy ndarray of python strings with dtype datetime64[D].
>>> kx.DateVector([date(1972, 5, 1), date(1999, 5, 1)]).np()
array(['1972-05-01', '1999-05-01'], dtype='datetime64[D]')Converting a ndarray of dtype datetime64[D] will create a pykx.DateVector.
>>> kx.DateVector(np.array(['1972-05-01', '1999-05-01'], dtype='datetime64[D]'))
pykx.DateVector(pykx.q('1972.05.01 1999.05.01'))Calling .pd() on a pykx.DateVector will return a pandas Series with dtype datetime64[ns].
>>> kx.DateVector([date(1972, 5, 1), date(1999, 5, 1)]).pd()
0 1972-05-01
1 1999-05-01
dtype: datetime64[ns]Calling .pa() on a pykx.DateVector will return a pyarrow Date32Array.
>>> kx.DateVector([date(1972, 5, 1), date(1999, 5, 1)]).pa()
<pyarrow.lib.Date32Array object at 0x7f6428f0de40>
[
1972-05-01,
1999-05-01
]pykx.Datetime types
These types are deprecated and can only be accessed using the raw key word argument.
Converting these types to python will return a float object or a float64 object in numpy's case.
>>> kx.q('0001.02.03T04:05:06.007 0001.02.03T04:05:06.007').py(raw=True)
[-730085.8297915857, -730085.8297915857]
>>> kx.q('0001.02.03T04:05:06.007 0001.02.03T04:05:06.007').np(raw=True)
array([-730085.82979159, -730085.82979159])
>>> kx.q('0001.02.03T04:05:06.007 0001.02.03T04:05:06.007').np(raw=True).dtype
dtype('float64')pykx.TimespanAtom
The python timedelta type will be converted into a pykx.TimespanAtom.
>>> from datetime import timedelta
>>> kx.TimespanAtom(timedelta(days=43938, seconds=68851, microseconds=664551))
pykx.TimespanAtom(pykx.q('43938D19:07:31.664551000'))Calling .py() on a pykx.TimespanAtom will return a python timedelta object.
>>> kx.TimespanAtom(timedelta(days=43938, seconds=68851, microseconds=664551)).py()
datetime.timedelta(days=43938, seconds=68851, microseconds=664551)pykx.TimespanVector
A list of python timedelta types will be converted into a pykx.TimespanVector.
>>> kx.TimespanVector([timedelta(days=43938, seconds=68851, microseconds=664551), timedelta(days=43938, seconds=68851, microseconds=664551)])
pykx.TimespanVector(pykx.q('43938D19:07:31.664551000 43938D19:07:31.664551000'))Calling .py() on a pykx.TimespanVector will return a list of python timedelta objects.
>>> kx.TimespanVector([timedelta(days=43938, seconds=68851, microseconds=664551), timedelta(days=43938, seconds=68851, microseconds=664551)]).py()
[datetime.timedelta(days=43938, seconds=68851, microseconds=664551), datetime.timedelta(days=43938, seconds=68851, microseconds=664551)]Calling .np() on a pykx.TimespanVector will return a numpy ndarray of python strings with dtype timedelta64[ns].
>>> kx.TimespanVector([timedelta(days=43938, seconds=68851, microseconds=664551), timedelta(days=43938, seconds=68851, microseconds=664551)]).np()
array([3796312051664551000, 3796312051664551000], dtype='timedelta64[ns]')Converting a ndarray of dtype datetime64[ns] will create a pykx.TimespanVector.
>>> kx.TimespanVector(np.array([3796312051664551000, 3796312051664551000], dtype='timedelta64[ns]'))
pykx.TimespanVector(pykx.q('43938D19:07:31.664551000 43938D19:07:31.664551000'))Calling .pd() on a pykx.TimespanVector will return a pandas Series with dtype timedelta64[ns].
>>> kx.TimespanVector([timedelta(days=43938, seconds=68851, microseconds=664551), timedelta(days=43938, seconds=68851, microseconds=664551)]).pd()
0 43938 days 19:07:31.664551
1 43938 days 19:07:31.664551
dtype: timedelta64[ns]Calling .pa() on a pykx.TimespanVector will return a pyarrow DurationArray.
>>> kx.TimespanVector([timedelta(days=43938, seconds=68851, microseconds=664551), timedelta(days=43938, seconds=68851, microseconds=664551)]).pa()
<pyarrow.lib.DurationArray object at 0x7f6428f0dea0>
[
3796312051664551000,
3796312051664551000
]pykx.MinuteAtom
The python timedelta type will be converted into a pykx.MinuteAtom.
>>> kx.MinuteAtom(timedelta(minutes=216))
pykx.MinuteAtom(pykx.q('03:36'))Calling .py() on a pykx.MinuteAtom will return a python timedelta object.
>>> kx.MinuteAtom(timedelta(minutes=216)).py()
datetime.timedelta(seconds=12960)pykx.MinuteVector
A list of python timedelta types will be converted into a pykx.MinuteVector.
>>> kx.MinuteVector([timedelta(minutes=216), timedelta(minutes=67)])
pykx.MinuteVector(pykx.q('03:36 01:07'))Calling .py() on a pykx.MinuteVector will return a list of python timedelta objects.
>>> kx.MinuteVector([timedelta(minutes=216), timedelta(minutes=67)]).py()
[datetime.timedelta(seconds=12960), datetime.timedelta(seconds=4020)]Calling .np() on a pykx.MinuteVector will return a numpy ndarray of python strings with dtype timedelta64[m].
>>> kx.MinuteVector([timedelta(minutes=216), timedelta(minutes=67)]).np()
array([216, 67], dtype='timedelta64[m]')Converting a ndarray of dtype timedelta64[m] will create a pykx.MinuteVector.
>>> kx.MinuteVector(np.array([216, 67], dtype='timedelta64[m]'))
pykx.MinuteVector(pykx.q('03:36 01:07'))Calling .pd() on a pykx.MinuteVector will return a pandas Series with dtype timedelta64[ns].
>>> kx.MinuteVector([timedelta(minutes=216), timedelta(minutes=67)]).pd()
0 0 days 03:36:00
1 0 days 01:07:00
dtype: timedelta64[ns]pykx.SecondAtom
The python timedelta type will be converted into a pykx.SecondAtom.
>>> kx.SecondAtom(timedelta(seconds=13019))
pykx.SecondAtom(pykx.q('03:36:59'))Calling .py() on a pykx.SecondAtom will return a python timedelta object.
>>> kx.SecondAtom(timedelta(seconds=13019)).py()
datetime.timedelta(seconds=13019)pykx.SecondVector
A list of python timedelta types will be converted into a pykx.SecondVector.
>>> kx.SecondVector([timedelta(seconds=13019), timedelta(seconds=1019)])
pykx.SecondVector(pykx.q('03:36:59 00:16:59'))Calling .py() on a pykx.SecondVector will return a list of python timedelta objects.
>>> kx.SecondVector([timedelta(seconds=13019), timedelta(seconds=1019)]).py()
[datetime.timedelta(seconds=13019), datetime.timedelta(seconds=1019)]Calling .np() on a pykx.SecondVector will return a numpy ndarray of python strings with dtype timedelta64[s].
>>> kx.SecondVector([timedelta(seconds=13019), timedelta(seconds=1019)]).np()
array([13019, 1019], dtype='timedelta64[s]')Converting a ndarray of dtype timedelta64[s] will create a pykx.SecondVector.
>>> kx.SecondVector(np.array([13019, 1019], dtype='timedelta64[s]'))
pykx.SecondVector(pykx.q('03:36:59 00:16:59'))Calling .pd() on a pykx.SecondVector will return a pandas Series with dtype timedelta64[ns].
>>> kx.SecondVector([timedelta(seconds=13019), timedelta(seconds=1019)]).pd()
0 0 days 03:36:59
1 0 days 00:16:59
dtype: timedelta64[ns]Calling .pa() on a pykx.SecondVector will return a pyarrow DurationArray.
>>> kx.SecondVector([timedelta(seconds=13019), timedelta(seconds=1019)]).pa()
<pyarrow.lib.DurationArray object at 0x7f6428f0e020>
[
13019,
1019
]pykx.TimeAtom
The python timedelta type will be converted into a pykx.TimeAtom.
>>> kx.TimeAtom(timedelta(seconds=59789, microseconds=214000))
pykx.TimeAtom(pykx.q('16:36:29.214'))Calling .py() on a pykx.TimeAtom will return a python timedelta object.
>>> kx.TimeAtom(timedelta(seconds=59789, microseconds=214000)).py()
datetime.timedelta(seconds=59789, microseconds=214000)pykx.TimeVector
A list of python timedelta types will be converted into a pykx.TimeVector.
>>> kx.TimeVector([timedelta(seconds=59789, microseconds=214000), timedelta(seconds=23789, microseconds=214000)])
pykx.TimeVector(pykx.q('16:36:29.214 06:36:29.214'))Calling .py() on a pykx.TimeVector will return a list of python timedelta objects.
>>> kx.TimeVector([timedelta(seconds=59789, microseconds=214000), timedelta(seconds=23789, microseconds=214000)]).py()
[datetime.timedelta(seconds=59789, microseconds=214000), datetime.timedelta(seconds=23789, microseconds=214000)]Calling .np() on a pykx.TimeVector will return a numpy ndarray of python strings with dtype timedelta64[ms].
>>> kx.TimeVector([timedelta(seconds=59789, microseconds=214000), timedelta(seconds=23789, microseconds=214000)]).np()
array([59789214, 23789214], dtype='timedelta64[ms]')Converting a ndarray of dtype timedelta64[ms] will create a pykx.TimeVector.
>>> kx.TimeVector(np.array([59789214, 23789214], dtype='timedelta64[ms]'))
pykx.TimeVector(pykx.q('16:36:29.214 06:36:29.214'))Calling .pd() on a pykx.TimeVector will return a pandas Series with dtype timedelta64[ns].
>>> kx.TimeVector([timedelta(seconds=59789, microseconds=214000), timedelta(seconds=23789, microseconds=214000)]).pd()
0 0 days 16:36:29.214000
1 0 days 06:36:29.214000
dtype: timedelta64[ns]Calling .pa() on a pykx.TimeVector will return a pyarrow DurationArray.
>>> kx.TimeVector([timedelta(seconds=59789, microseconds=214000), timedelta(seconds=23789, microseconds=214000)]).pa()
<pyarrow.lib.DurationArray object at 0x7f643021ff40>
[
59789214,
23789214
]pykx.Dictionary
A python dict type will be converted into a pykx.Dictionary.
>>> kx.Dictionary({'foo': b'bar', 'baz': 3.5, 'z': 'prime'})
pykx.Dictionary(pykx.q('
foo| "bar"
baz| 3.5
z | `prime
'))Calling .py() on a pykx.Dictionary will return a python dict object.
>>> kx.Dictionary({'foo': b'bar', 'baz': 3.5, 'z': 'prime'}).py()
{'foo': b'bar', 'baz': 3.5, 'z': 'prime'}pykx.Table
Calling .py() on a pykx.Table will return a python dict object.
>>> kx.Table(data={
... 'a': kx.random.random(10, 10),
... 'b': kx.random.random(10, 10)}).py()
{'a': [5, 6, 4, 1, 3, 3, 7, 8, 2, 1], 'b': [8, 1, 7, 2, 4, 5, 4, 2, 7, 8]}Calling .np() on a pykx.Table will return a numpy record array of the rows of the table with each type converted to it closest analogous numpy type.
>>> kx.Table(data={
... 'a': kx.random.random(10, 10),
... 'b': kx.random.random(10, 10)}).np()
rec.array([(9, 9), (9, 7), (2, 6), (5, 6), (4, 4), (2, 7), (5, 8), (8, 4),
(7, 4), (9, 6)],
dtype=[('a', '<i8'), ('b', '<i8')])Calling .pd() on a pykx.Table will return a pandas DataFrame with each column being converted to its closest pandas dtype.
>>> kx.Table(data={
... 'a': kx.random.random(10, 10),
... 'b': kx.random.random(10, 10)}).pd()
a b
0 1 9
1 0 7
2 5 7
3 1 1
4 0 9
5 0 1
6 1 0
7 7 8
8 6 8
9 3 3Converting a pandas DataFrame object will result in a pykx.Table object.
>>> kx.Table(pd.DataFrame({'a': [x for x in range(10)], 'b': [float(x) for x in range(10)]}))
pykx.Table(pykx.q('
a b
---
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
'))Calling .pa() on a pykx.Table will return a pyarrow Table.
>>> kx.Table(data={
... 'a': kx.random.random(10, 10),
... 'b': kx.random.random(10, 10)}).pa()
pyarrow.Table
a: int64
b: int64
----
a: [[0,7,3,3,6,8,2,3,8,9]]
b: [[5,7,5,6,7,0,2,1,8,1]]Converting a pyarow Table object will result in a pykx.Table object.
>>> kx.Table(pa.Table.from_arrays([[1, 2, 3, 4], [5, 6, 7, 8]], names=['a', 'b']))
pykx.Table(pykx.q('
a b
---
1 5
2 6
3 7
4 8
'))