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NaN, or not-a-number, values indicate non-numeric results that come from computations on a dataset.
Deephaven supports constants for NaN values.
NaNs typically come from computations on datasets. The following example shows how NaNs might end up in a table.
NaN values can be detected using the isNaN filter.
from deephaven import new_table
from deephaven.column import float_col, double_col
NAN_VALUE = float("nan")
result = new_table(
[float_col("Floats", [NAN_VALUE]), double_col("Doubles", [NAN_VALUE])]
)
from deephaven import new_table
from deephaven.column import float_col, double_col
from deephaven.constants import NULL_DOUBLE, NULL_FLOAT
source = new_table(
[float_col("NaNFloat", [-1.0]), double_col("NaNDouble", [-1.0])]
).update(
formulas=[
"NaNFloat = java.lang.Math.sqrt(NaNFloat)",
"NaNDouble = java.lang.Math.sqrt(NaNDouble)",
]
)
result = source.update(
formulas=[
"NaNFloatNaN = isNaN(NaNFloat)",
"NaNDoubleNaN = isNaN(NaNDouble)",
]
)