Added code for group_fillna

Author: WillAyd

pandas/_libs/groupby_helper.pxi.in

@@ -273,7 +273,7 @@ def group_ohlc_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
 {{endfor}}
 
 #----------------------------------------------------------------------
-# group_nth, group_last, group_rank
+# group_nth, group_last, group_rank, group_fillna
 #----------------------------------------------------------------------
 
 {{py:
@@ -574,6 +574,75 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
             for i in range(N):
                 out[i, 0] = out[i, 0] / grp_sizes[i, 0]
 {{endif}}
+
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def group_fillna_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
+                          ndarray[{{c_type}}, ndim=2] values,
+                          ndarray[int64_t] labels,
+                          object method,
+                          int64_t limit):
+    """Fills values forwards or backwards within a group
+
+    Parameters
+    ----------
+    out : array of {{dest_type2}} values which this method will write its
+        results to
+    values : array of {{c_type}} values which may require filling
+    labels : array containing unique label for each group, with its ordering
+        matching up to the corresponding record in `values`
+    method : {'ffill', 'bfill'}
+        Direction for fill to be applied (forwards or backwards, respectively)
+    limit : Consecutive values to fill before stopping, or -1 for no limit
+
+    Notes
+    -----
+    This method modifies the `out` parameter rather than returning an object
+    """
+    cdef:
+        Py_ssize_t i, N
+        ndarray[uint8_t] mask
+        ndarray[int64_t] sorted_labels
+        {{dest_type2}} curr_fill_val = {{nan_val}}
+        int64_t idx, filled_vals=0
+
+    N, K = (<object> values).shape
+
+    {{if name=='int64'}}
+    mask = (values[:, 0] == {{nan_val}}).astype(np.uint8)
+    {{elif name=='object'}}
+    mask = np.array([x != x for x in values[:, 0]]).astype(np.uint8)
+    {{else}}
+    mask = np.isnan(values[:, 0]).astype(np.uint8)
+    {{endif}}
+
+    sorted_labels = np.argsort(labels)
+    if method == 'bfill':
+        sorted_labels[::-1].sort()
+
+    {{if name == 'object'}}
+    if True:  # make templating happy
+    {{else}}
+    with nogil:
+    {{endif}}
+        for i in range(N):
+            idx = sorted_labels[i]
+            if mask[idx]:  # is missing
+                if limit == -1 or filled_vals < limit:
+                    out[idx, 0] = curr_fill_val
+                else:
+                    out[idx, 0] == {{nan_val}}
+                filled_vals += 1
+            else:  # reset items when not missing
+                filled_vals = 0
+                curr_fill_val = values[idx, 0]
+                out[idx, 0] = values[idx, 0]
+
+            # If we move to the next group, reset
+            # the fill_val and counter
+            if i == N - 1 or labels[idx] != labels[sorted_labels[i+1]]:
+                curr_fill_val = {{nan_val}}
+                filled_vals = 0
 {{endfor}}