Cythonized GroupBy Fill

asv_bench/benchmarks/groupby.py

@@ -370,11 +370,11 @@ class GroupByMethods(object):
 
     param_names = ['dtype', 'method']
     params = [['int', 'float'],
-              ['all', 'any', 'count', 'cumcount', 'cummax', 'cummin',
-               'cumprod', 'cumsum', 'describe', 'first', 'head', 'last', 'mad',
-               'max', 'min', 'median', 'mean', 'nunique', 'pct_change', 'prod',
-               'rank', 'sem', 'shift', 'size', 'skew', 'std', 'sum', 'tail',
-               'unique', 'value_counts', 'var']]
+              ['all', 'any', 'bfill', 'count', 'cumcount', 'cummax', 'cummin',
+               'cumprod', 'cumsum', 'describe', 'ffill', 'first', 'head',
+               'last', 'mad', 'max', 'min', 'median', 'mean', 'nunique',
+               'pct_change', 'prod', 'rank', 'sem', 'shift', 'size', 'skew',
+               'std', 'sum', 'tail', 'unique', 'value_counts', 'var']]
 
     def setup(self, dtype, method):
         ngroups = 1000

doc/source/whatsnew/v0.23.0.txt

@@ -689,6 +689,7 @@ Performance Improvements
 - Improved performance of pairwise ``.rolling()`` and ``.expanding()`` with ``.cov()`` and ``.corr()`` operations (:issue:`17917`)
 - Improved performance of :func:`DataFrameGroupBy.rank` (:issue:`15779`)
 - Improved performance of variable ``.rolling()`` on ``.min()`` and ``.max()`` (:issue:`19521`)
+- Improved performance of ``GroupBy.ffill`` and ``GroupBy.bfill`` (:issue:`11296`)
 
 .. _whatsnew_0230.docs:
 

pandas/_libs/groupby.pyx

@@ -94,5 +94,221 @@ cdef inline float64_t kth_smallest_c(float64_t* a,
     return a[k]
 
 
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def group_median_float64(ndarray[float64_t, ndim=2] out,
+                         ndarray[int64_t] counts,
+                         ndarray[float64_t, ndim=2] values,
+                         ndarray[int64_t] labels,
+                         Py_ssize_t min_count=-1):
+    """
+    Only aggregates on axis=0
+    """
+    cdef:
+        Py_ssize_t i, j, N, K, ngroups, size
+        ndarray[int64_t] _counts
+        ndarray data
+        float64_t* ptr
+
+    assert min_count == -1, "'min_count' only used in add and prod"
+
+    ngroups = len(counts)
+    N, K = (<object> values).shape
+
+    indexer, _counts = groupsort_indexer(labels, ngroups)
+    counts[:] = _counts[1:]
+
+    data = np.empty((K, N), dtype=np.float64)
+    ptr = <float64_t*> data.data
+
+    take_2d_axis1_float64_float64(values.T, indexer, out=data)
+
+    with nogil:
+
+        for i in range(K):
+            # exclude NA group
+            ptr += _counts[0]
+            for j in range(ngroups):
+                size = _counts[j + 1]
+                out[j, i] = median_linear(ptr, size)
+                ptr += size
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def group_cumprod_float64(float64_t[:, :] out,
+                          float64_t[:, :] values,
+                          int64_t[:] labels,
+                          bint is_datetimelike):
+    """
+    Only transforms on axis=0
+    """
+    cdef:
+        Py_ssize_t i, j, N, K, size
+        float64_t val
+        float64_t[:, :] accum
+        int64_t lab
+
+    N, K = (<object> values).shape
+    accum = np.ones_like(values)
+
+    with nogil:
+        for i in range(N):
+            lab = labels[i]
+
+            if lab < 0:
+                continue
+            for j in range(K):
+                val = values[i, j]
+                if val == val:
+                    accum[lab, j] *= val
+                    out[i, j] = accum[lab, j]
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def group_cumsum(numeric[:, :] out,
+                 numeric[:, :] values,
+                 int64_t[:] labels,
+                 is_datetimelike):
+    """
+    Only transforms on axis=0
+    """
+    cdef:
+        Py_ssize_t i, j, N, K, size
+        numeric val
+        numeric[:, :] accum
+        int64_t lab
+
+    N, K = (<object> values).shape
+    accum = np.zeros_like(values)
+
+    with nogil:
+        for i in range(N):
+            lab = labels[i]
+
+            if lab < 0:
+                continue
+            for j in range(K):
+                val = values[i, j]
+
+                if numeric == float32_t or numeric == float64_t:
+                    if val == val:
+                        accum[lab, j] += val
+                        out[i, j] = accum[lab, j]
+                else:
+                    accum[lab, j] += val
+                    out[i, j] = accum[lab, j]
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def group_shift_indexer(ndarray[int64_t] out, ndarray[int64_t] labels,
+                        int ngroups, int periods):
+    cdef:
+        Py_ssize_t N, i, j, ii
+        int offset, sign
+        int64_t lab, idxer, idxer_slot
+        int64_t[:] label_seen = np.zeros(ngroups, dtype=np.int64)
+        int64_t[:, :] label_indexer
+
+    N, = (<object> labels).shape
+
+    if periods < 0:
+        periods = -periods
+        offset = N - 1
+        sign = -1
+    elif periods > 0:
+        offset = 0
+        sign = 1
+
+    if periods == 0:
+        with nogil:
+            for i in range(N):
+                out[i] = i
+    else:
+        # array of each previous indexer seen
+        label_indexer = np.zeros((ngroups, periods), dtype=np.int64)
+        with nogil:
+            for i in range(N):
+                ## reverse iterator if shifting backwards
+                ii = offset + sign * i
+                lab = labels[ii]
+
+                # Skip null keys
+                if lab == -1:
+                    out[ii] = -1
+                    continue
+
+                label_seen[lab] += 1
+
+                idxer_slot = label_seen[lab] % periods
+                idxer = label_indexer[lab, idxer_slot]
+
+                if label_seen[lab] > periods:
+                    out[ii] = idxer
+                else:
+                    out[ii] = -1
+
+                label_indexer[lab, idxer_slot] = ii
+
+
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def group_fillna_indexer(ndarray[int64_t] out, ndarray[int64_t] labels,
+                         ndarray[uint8_t] mask, object direction,
+                         int64_t limit):
+    """Indexes how to fill values forwards or backwards within a group
+
+    Parameters
+    ----------
+    out : array of int64_t values which this method will write its results to
+        Missing values will be written to with a value of -1
+    labels : array containing unique label for each group, with its ordering
+        matching up to the corresponding record in `values`
+    mask : array of int64_t values where a 1 indicates a missing value
+    direction : {'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[int64_t] sorted_labels
+        int64_t idx, curr_fill_idx=-1, filled_vals=0
+
+    N = len(out)
+
+    # Make sure all arrays are the same size
+    assert N == len(labels) == len(mask)
+
+    sorted_labels = np.argsort(labels).astype(np.int64, copy=False)
+    if direction == 'bfill':
+        sorted_labels = sorted_labels[::-1]
+
+    with nogil:
+        for i in range(N):
+            idx = sorted_labels[i]
+            if mask[idx] == 1:  # is missing
+                # Stop filling once we've hit the limit
+                if filled_vals >= limit and limit != -1:
+                    curr_fill_idx = -1
+                filled_vals += 1
+            else:  # reset items when not missing
+                filled_vals = 0
+                curr_fill_idx = idx
+
+            out[idx] = curr_fill_idx
+
+            # If we move to the next group, reset
+            # the fill_idx and counter
+            if i == N - 1 or labels[idx] != labels[sorted_labels[i+1]]:
+                curr_fill_idx = -1
+                filled_vals = 0
+
+
 # generated from template
 include "groupby_helper.pxi"