ENH: Cython Reducer, speed up DataFrame.apply significantly, GH #309

RELEASE.rst

@@ -54,8 +54,11 @@ pandas 0.5.1
     for fast conversion to DataFrame (GH #357)
   - Can pass multiple levels to groupby, e.g. `df.groupby(level=[0, 1])` (GH
     #103)
+  - Can sort by multiple columns in `DataFrame.sort_index` (GH #92, PR #362)
   - Add fast `get_value` and `put_value` methods to DataFrame and
     micro-performance tweaks (GH #360)
+  - Add `cov` instance methods to Series and DataFrame (GH #194, PR #362)
+
 
 **Improvements to existing features**
 

pandas/core/frame.py

@@ -1520,7 +1520,6 @@ def sort_index(self, axis=0, by=None, ascending=True):
             else:
                 to_sort = self[by].values
 
-            # stable sort
             indexer = to_sort.argsort()
         else:
             indexer = labels.argsort()
@@ -2187,7 +2186,10 @@ def apply(self, func, axis=0, broadcast=False, raw=False):
                 return self._apply_broadcast(func, axis)
 
     def _apply_raw(self, func, axis):
-        result = np.apply_along_axis(func, axis, self.values)
+        try:
+            result = lib.reduce(self.values, func, axis=axis)
+        except Exception:
+            result = np.apply_along_axis(func, axis, self.values)
 
         # TODO: mixed type case
         if result.ndim == 2:
@@ -2197,6 +2199,15 @@ def _apply_raw(self, func, axis):
             return Series(result, index=self._get_agg_axis(axis))
 
     def _apply_standard(self, func, axis, ignore_failures=False):
+        try:
+            values = self.values
+            dummy = Series(np.nan, index=self._get_axis(axis),
+                           dtype=values.dtype)
+            result = lib.reduce(values, func, axis=axis, dummy=dummy)
+            return Series(result, index=self._get_agg_axis(axis))
+        except Exception:
+            pass
+
         if axis == 0:
             series_gen = ((c, self[c]) for c in self.columns)
             res_index = self.columns

pandas/src/reduce.pyx

@@ -0,0 +1,83 @@
+from numpy cimport *
+import numpy as np
+
+cdef class Reducer:
+    '''
+    Performs generic reduction operation on a C or Fortran-contiguous ndarray
+    while avoiding ndarray construction overhead
+    '''
+    cdef:
+        Py_ssize_t increment, chunksize, nresults
+        object arr, dummy, f
+
+    def __init__(self, object arr, object f, axis=1, dummy=None):
+        n, k = arr.shape
+
+        if axis == 0:
+            if not arr.flags.f_contiguous:
+                arr = arr.copy('F')
+
+            self.nresults = k
+            self.chunksize = n
+            self.increment = n * arr.dtype.itemsize
+        else:
+            if not arr.flags.c_contiguous:
+                arr = arr.copy('C')
+
+            self.nresults = n
+            self.chunksize = k
+            self.increment = k * arr.dtype.itemsize
+
+        self.f = f
+        self.arr = arr
+        self.dummy = self._check_dummy(dummy)
+
+    def _check_dummy(self, dummy=None):
+        if dummy is None:
+            dummy = np.empty(self.chunksize, dtype=self.arr.dtype)
+        else:
+            if dummy.dtype != self.arr.dtype:
+                raise ValueError('Dummy array must be same dtype')
+            if len(dummy) != self.chunksize:
+                raise ValueError('Dummy array must be length %d' %
+                                 self.chunksize)
+
+        return dummy
+
+    def get_result(self):
+        cdef:
+            char* dummy_buf
+            ndarray arr, result, chunk
+            Py_ssize_t i
+            flatiter it
+
+        arr = self.arr
+        chunk = self.dummy
+
+        result = np.empty(self.nresults, dtype=self.arr.dtype)
+        it = <flatiter> PyArray_IterNew(result)
+
+        test = self.f(self.chunk)
+        try:
+            result[0] = test
+        except Exception:
+            raise ValueError('function does not reduce')
+
+        dummy_buf = chunk.data
+        chunk.data = arr.data
+
+        try:
+            for i in range(self.nresults):
+                PyArray_SETITEM(result, PyArray_ITER_DATA(it),
+                                self.f(self.dummy))
+                chunk.data = chunk.data + self.increment
+                PyArray_ITER_NEXT(it)
+        finally:
+            # so we don't free the wrong memory
+            chunk.data = dummy_buf
+
+        return result
+
+def reduce(arr, f, axis=0, dummy=None):
+    reducer = Reducer(arr, f, axis=axis, dummy=dummy)
+    return reducer.get_result()

pandas/src/sandbox.pyx

@@ -3,46 +3,10 @@ import numpy as np
 
 import_array()
 
-cdef class ArrayCruncher:
+cdef class SeriesIterator:
 
-    cdef:
-        ndarray arr
-        object f
-        bint raw
-        Py_ssize_t N, K
+    def __init__(self, arr):
+        pass
 
-    def __init__(self, arr, f, axis=0, raw=True):
-        self.arr = arr
-        self.f = f
-        self.raw = raw
-        self.N, self.K = arr.shape
-
-    def reduce(self):
-        cdef:
-            char* dummy_buf
-            ndarray arr, result, chunk
-            Py_ssize_t i, increment
-            flatiter it
-
-        if not self.arr.flags.c_contiguous:
-            arr = self.arr.copy('C')
-        else:
-            arr = self.arr
-
-        increment = self.K * self.arr.dtype.itemsize
-        chunk = np.empty(self.K, dtype=arr.dtype)
-        result = np.empty(self.N, dtype=arr.dtype)
-        it = <flatiter> PyArray_IterNew(result)
-
-        dummy_buf = chunk.data
-        chunk.data = arr.data
-
-        for i in range(self.N):
-            PyArray_SETITEM(result, PyArray_ITER_DATA(it), self.f(chunk))
-            chunk.data = chunk.data + increment
-            PyArray_ITER_NEXT(it)
-
-        # so we don't free the wrong memory
-        chunk.data = dummy_buf
-
-        return result
+    def next(self):
+        pass

pandas/src/tseries.pyx

@@ -473,4 +473,4 @@ include "moments.pyx"
 include "reindex.pyx"
 include "generated.pyx"
 include "parsing.pyx"
-
+include "reduce.pyx"

setup.py

@@ -275,7 +275,7 @@ def run(self):
     cmdclass['sdist'] =  CheckSDist
 
 tseries_depends = ['reindex', 'groupby', 'skiplist', 'moments',
-                   'generated', 'parsing']
+                   'generated', 'parsing', 'reduce']
 def srcpath(name=None, suffix='.pyx', subdir='src'):
     return pjoin('pandas', subdir, name+suffix)