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Version: Python

How to handle PyObjects in tables

This guide will cover how to deal with an org.jpy.PyObject column in tables. For the sake of brevity, these columns will be called PyObjects for the remainder of this guide.

A PyObject is an artifact of jpy, the bi-directional Python-Java bridge that connects Deephaven's Python API to its Java backend. For more background information on Deephaven data types -- Python, Java, and jpy -- see the following links:

PyObject columns should typically be avoided, as their usage will almost always result in downstream errors in queries, as well as degraded performance. This guide will present strategies to avoid creating PyObject columns.

What is a PyObject?

A PyObject is a generic Java object that holds a Python object of some kind. It gets used when the engine hasn't been told enough about the type of data returned by a Python function or other Python process. The type is used because it's safe. A PyObject can hold any arbitrary Python object such as a list, dictionary, int, float, etc. Unfortunately, that flexibility comes at the cost of compatibility and speed.

Limitations

The code below produces a table with three columns using empty_table. The three columns are as follows:

  • X is 1/10th of the row index created using i.
  • SinX is created using the built-in sin function.
  • NumpySinX is created using NumPy's sin function.

As a result, X and SinX are double columns, and NumpySinX is a PyObject column.

from deephaven import empty_table
import numpy as np

source = empty_table(10).update(
["X = 0.1 * i", "SinX = sin(X)", "NumpySinX = np.sin(X)"]
)
source_meta = source.meta_table

This seems fine at first. But, what if we try to calculate the difference between the SinX and NumpySinX columns?

result = source.update(["Difference = SinX - NumpySinX"])

The code raises an exception with the message Cannot find method plus(int, org.jpy.PyObject). In Java, there is no addition operator that can handle those two data types. It makes sense that this doesn't work. As stated before, a PyObject is so generic that it can hold any Python data type. So, if it holds a dictionary, what is the correct way to add a dictionary and an integer together? There isn't one. This limitation extends to far more than just integer values. They are incompatible with a wide range of operations.

Thankfully, the built-in sin function is always available. For operations where no built-in method exists, a typecast or a type hint can do the trick. In the example below, the TypecastSinX and TypehintSinX columns use those, respectively:

from deephaven import empty_table
import numpy as np


def np_sin_typehint(val) -> np.double:
return np.sin(val)


source = empty_table(10).update(
[
"X = 0.1 * i",
"SinX = sin(X)",
"TypecastSinX = (double)np.sin(X)",
"TypehintSinX = np_sin_typehint(X)",
]
)
source_meta = source.meta_table

source_meta shows that all four columns in source are now double columns.

The rest of this guide will show how to avoid creating PyObject columns in your queries.

Scalar columns

The previous example showed how a column of PyObject scalar values (e.g., integers and decimal numbers) can affect queries. There are three ways to avoid creating PyObject columns full of scalar values.

Built-in query language methods

Deephaven's query language has a large number of built-in methods that can be used in place of Python functions.

from deephaven import empty_table
import numpy as np

source = empty_table(10).update(
["X = 0.2 * i", "Y_PyObject = np.sin(X)", "Y_Double = sin(X)"]
)
source_meta = source.meta_table

Python type hints

If the query language doesn't have a function to perform a specific operation, a Python type hint will cast the result to the proper type. We recommend using NumPy data types over Python built-in types for type hints, as they have a one-to-one translation to the Java primitives Deephaven tables typically use.

from deephaven import empty_table
import numpy as np


def bessel(value) -> np.double:
return np.i0(value)


source = empty_table(10).update(["X = i", "Y = bessel(X)"])
source_meta = source.meta_table
caution

As of Deephaven Community Core v0.32.0, type hints in functions must match the data type they recieved, or an error will result. See Community Questions for more information.

Type casts

If all else fails, an explicit typecast can be performed in the query string.

from deephaven import empty_table
import numpy as np


def bessel(value):
return np.i0(value)


source = empty_table(10).update(
["X = i", "Y_PyObject = bessel(X)", "Y_TypeCast = (double)Y_PyObject"]
)
source_meta = source.meta_table

String columns

Python functions that return string values can lead to PyObject columns.

Python type hints

Like with scalar columns, type hints work the same way. For strings, Python's built-in string type works great as the type hint.

from deephaven import empty_table


def str_from_num(value) -> str:
if value == 1:
return "one"
elif value == 2:
return "two"
elif value == 3:
return "three"
else:
return "Out Of Range"


source = empty_table(10).update(["X = i", "Y = str_from_num(X)"])
source_meta = source.meta_table
caution

As of Deephaven Community Core v0.32.0, type hints in functions must match the data type they recieved, or an error will result. See Community Questions for more information.

Type casts

An explicit type cast in the query string works as well. You can use the abbreviated String or full name java.lang.String to the same effect.

from deephaven import empty_table


def str_from_num(value):
if value == 1:
return "one"
elif value == 2:
return "two"
elif value == 3:
return "three"
else:
return "Out Of Range"


source = empty_table(10).update(["X = i", "Y = (String)str_from_num(X)"])
source_meta = source.meta_table

Array columns

Typehints using typing and numpy.typing and typing are the best and most flexible ways to handle arrays of data. Alternatively, Python functions can use jpy directly to return a Java array, but the query string must cast the result to the appropriate array type.

PyObject columns that store arrays of data can be a bit trickier to deal with than scalar and string columns. Thankfully, Python modules like numpy.typing and typing allow type hints to be used to return array columns of the desired type. Alternatively, jpy can be invoked directly to return a Java array, which the query engine will understand by default.

from deephaven import empty_table
from numpy import typing as npt
import numpy as np
import typing
import jpy


def return_py_array(idx):
return [idx, idx + 1]


def return_j_array(idx):
return jpy.array("int", [idx, idx + 1])


def array_typing(idx) -> typing.List[np.intc]:
return [idx, idx + 1]


def numpy_arr_typing(idx) -> npt.NDArray[np.intc]:
return np.array([idx, idx + 1])


source = empty_table(10).update(
[
"PyObj = return_py_array(i)",
"IntArrFromJpy = (int[])return_j_array(i)",
"IntArrFromTyping = array_typing(i)",
"IntArrFromNumPy = numpy_arr_typing(i)",
]
)
source_meta = source.meta_table