[SPARK-7358] Move DataFrame mathfunctions into functions

Author: brkyvz

python/pyspark/sql/functions.py

@@ -51,6 +51,23 @@ def _(col):
     return _
 
 
+def _create_binary_mathfunction(name, doc=""):
+    """ Create a binary mathfunction by name"""
+    def _(col1, col2):
+        sc = SparkContext._active_spark_context
+        # users might write ints for simplicity. This would throw an error on the JVM side.
+        if type(col1) is int:
+            col1 = col1 * 1.0
+        if type(col2) is int:
+            col2 = col2 * 1.0
+        jc = getattr(sc._jvm.functions, name)(col1._jc if isinstance(col1, Column) else col1,
+                                              col2._jc if isinstance(col2, Column) else col2)
+        return Column(jc)
+    _.__name__ = name
+    _.__doc__ = doc
+    return _
+
+
 _functions = {
     'lit': 'Creates a :class:`Column` of literal value.',
     'col': 'Returns a :class:`Column` based on the given column name.',
@@ -63,6 +80,34 @@ def _(col):
     'sqrt': 'Computes the square root of the specified float value.',
     'abs': 'Computes the absolute value.',
 
+    # unary math functions
+    'acos': 'Computes the cosine inverse of the given value; the returned angle is in the range' +
+            '0.0 through pi.',
+    'asin': 'Computes the sine inverse of the given value; the returned angle is in the range' +
+            '-pi/2 through pi/2.',
+    'atan': 'Computes the tangent inverse of the given value.',
+    'cbrt': 'Computes the cube-root of the given value.',
+    'ceil': 'Computes the ceiling of the given value.',
+    'cos': 'Computes the cosine of the given value.',
+    'cosh': 'Computes the hyperbolic cosine of the given value.',
+    'exp': 'Computes the exponential of the given value.',
+    'expm1': 'Computes the exponential of the given value minus one.',
+    'floor': 'Computes the floor of the given value.',
+    'log': 'Computes the natural logarithm of the given value.',
+    'log10': 'Computes the logarithm of the given value in Base 10.',
+    'log1p': 'Computes the natural logarithm of the given value plus one.',
+    'rint': 'Returns the double value that is closest in value to the argument and' +
+            ' is equal to a mathematical integer.',
+    'signum': 'Computes the signum of the given value.',
+    'sin': 'Computes the sine of the given value.',
+    'sinh': 'Computes the hyperbolic sine of the given value.',
+    'tan': 'Computes the tangent of the given value.',
+    'tanh': 'Computes the hyperbolic tangent of the given value.',
+    'toDeg': 'Converts an angle measured in radians to an approximately equivalent angle ' +
+             'measured in degrees.',
+    'toRad': 'Converts an angle measured in degrees to an approximately equivalent angle ' +
+             'measured in radians.',
+
     'max': 'Aggregate function: returns the maximum value of the expression in a group.',
     'min': 'Aggregate function: returns the minimum value of the expression in a group.',
     'first': 'Aggregate function: returns the first value in a group.',
@@ -74,10 +119,21 @@ def _(col):
     'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.',
 }
 
+# math functions that take two arguments as input
+_binary_mathfunctions = {
+    'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' +
+             'polar coordinates (r, theta).',
+    'hypot': 'Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.',
+    'pow': 'Returns the value of the first argument raised to the power of the second argument.'
+}
+
 for _name, _doc in _functions.items():
     globals()[_name] = _create_function(_name, _doc)
+for _name, _doc in _binary_mathfunctions.items():
+    globals()[_name] = _create_binary_mathfunction(_name, _doc)
 del _name, _doc
 __all__ += _functions.keys()
+__all__ += _binary_mathfunctions.keys()
 __all__.sort()
 
 

python/pyspark/sql/mathfunctions.py

@@ -416,7 +416,7 @@ def test_crosstab(self):
 
     def test_math_functions(self):
         df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF()
-        from pyspark.sql import mathfunctions as functions
+        from pyspark.sql import functions
         import math
 
         def get_values(l):