move map to numpy_to_pandas

src/snowflake/snowpark/modin/plugin/compiler/snowflake_query_compiler.py

@@ -185,7 +185,6 @@
     APPLY_LABEL_COLUMN_QUOTED_IDENTIFIER,
     APPLY_VALUE_COLUMN_QUOTED_IDENTIFIER,
     DEFAULT_UDTF_PARTITION_SIZE,
-    NUMPY_FUNCTION_TO_SNOWFLAKE_FUNCTION,
     GroupbyApplySortMethod,
     check_return_variant_and_get_return_type,
     create_udf_for_series_apply,
@@ -373,6 +372,9 @@
 from snowflake.snowpark.modin.plugin.utils.error_message import ErrorMessage
 from snowflake.snowpark.modin.plugin.utils.warning_message import WarningMessage
 from snowflake.snowpark.modin.utils import MODIN_UNNAMED_SERIES_LABEL
+from snowflake.snowpark.modin.plugin.utils.numpy_to_pandas import (
+    NUMPY_FUNCTION_TO_SNOWFLAKE_FUNCTION,
+)
 from snowflake.snowpark.session import Session
 from snowflake.snowpark.types import (
     ArrayType,
@@ -8760,7 +8762,10 @@ def applymap(
         # Check if the function is a known numpy function that can be translated to Snowflake function.
         sf_func = NUMPY_FUNCTION_TO_SNOWFLAKE_FUNCTION.get(func)
         if sf_func is not None:
-            return self._apply_snowpark_python_function_to_columns(sf_func, kwargs)
+            # TODO SNOW-1739034: remove pragma no cover when apply tests are enabled in CI
+            return self._apply_snowpark_python_function_to_columns(
+                sf_func, kwargs
+            )  # pragma: no cover
 
         # Currently, NULL values are always passed into the udtf even if strict=True,
         # which is a bug on the server side SNOW-880105.

src/snowflake/snowpark/modin/plugin/utils/numpy_to_pandas.py

@@ -6,6 +6,7 @@
 import modin.pandas as pd
 from modin.pandas.base import BasePandasDataset
 from modin.pandas.utils import is_scalar
+import numpy as np
 
 from snowflake.snowpark import functions as sp_func
 from snowflake.snowpark.modin.plugin.utils.warning_message import WarningMessage
@@ -288,3 +289,48 @@ def map_to_bools(inputs: Any) -> Any:
         sp_func.trunc
     ),  # df.truncate not supported in snowpandas yet
 }
+
+
+NUMPY_FUNCTION_TO_SNOWFLAKE_FUNCTION = {
+    # Math operations
+    np.absolute: sp_func.abs,
+    np.sign: sp_func.sign,
+    np.negative: sp_func.negate,
+    np.positive: lambda col: col,
+    np.sqrt: sp_func.sqrt,
+    np.square: lambda col: sp_func.builtin("square")(col),
+    np.cbrt: lambda col: sp_func.builtin("cbrt")(col),
+    np.reciprocal: lambda col: 1 / col,
+    np.exp: sp_func.exp,
+    np.exp2: lambda col: sp_func.pow(2, col),
+    np.expm1: lambda col: sp_func.exp(col) - 1,
+    np.log: sp_func.ln,
+    np.log2: sp_func._log2,
+    np.log10: sp_func._log10,
+    np.log1p: lambda col: sp_func.ln(col + 1),
+    # Aggregate functions translate to identity functions when applied element wise
+    np.sum: lambda col: col,
+    np.min: lambda col: col,
+    np.max: lambda col: col,
+    # Trigonometric functions
+    np.sin: sp_func.sin,
+    np.cos: sp_func.cos,
+    np.tan: sp_func.tan,
+    np.sinh: sp_func.sinh,
+    np.cosh: sp_func.cosh,
+    np.tanh: sp_func.tanh,
+    np.arcsin: lambda col: sp_func.builtin("asin")(col),
+    np.arccos: lambda col: sp_func.builtin("acos")(col),
+    np.arctan: lambda col: sp_func.builtin("atan")(col),
+    np.arctan2: lambda col: sp_func.builtin("atan2")(col),
+    np.arcsinh: lambda col: sp_func.builtin("asinh")(col),
+    np.arccosh: lambda col: sp_func.builtin("acosh")(col),
+    np.arctanh: lambda col: sp_func.builtin("atanh")(col),
+    np.degrees: lambda col: sp_func.builtin("degrees")(col),
+    np.radians: lambda col: sp_func.builtin("radians")(col),
+    # Floating functions
+    np.ceil: sp_func.ceil,
+    np.floor: sp_func.floor,
+    np.trunc: sp_func.trunc,
+    np.isnan: sp_func.is_null,
+}