ENH: rolling rank - address pre-commit errors

asv_bench/benchmarks/rolling.py

@@ -189,7 +189,14 @@ class Rank:
  [True, False],
  ["min", "max", "average"],
  )
- param_names = ["constructor", "window", "dtype", "percentile", "ascending", "method"]
+ param_names = [
+ "constructor",
+ "window",
+ "dtype",
+ "percentile",
+ "ascending",
+ "method",
+ ]
 
  def setup(self, constructor, window, dtype, percentile, ascending, method):
  N = 10 ** 5

pandas/_libs/src/skiplist.h

@@ -195,8 +195,8 @@ PANDAS_INLINE int skiplist_min_rank(skiplist_t *skp, double value) {
  return rank + 1;
 }
 
-// Returns the rank of the inserted element. When there are duplicates, `rank` is the highest of
-// the group, i.e. the 'max' method of
+// Returns the rank of the inserted element. When there are duplicates,
+// `rank` is the highest of the group, i.e. the 'max' method of
 // https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rank.html
 PANDAS_INLINE int skiplist_insert(skiplist_t *skp, double value) {
  node_t *node, *prevnode, *newnode, *next_at_level;

pandas/_libs/window/aggregations.pyx

@@ -1155,7 +1155,8 @@ rank_types = {
 
 
 def roll_rank(const float64_t[:] values, ndarray[int64_t] start,
- ndarray[int64_t] end, int64_t minp, bint percentile, str method, bint ascending) -> np.ndarray:
+ ndarray[int64_t] end, int64_t minp, bint percentile,
+ str method, bint ascending) -> np.ndarray:
  """
  O(N log(window)) implementation using skip list
 
@@ -1211,7 +1212,9 @@ def roll_rank(const float64_t[:] values, ndarray[int64_t] start,
  raise MemoryError("skiplist_insert failed")
  if rank_type == AVERAGE:
  rank_min = skiplist_min_rank(skiplist, val)
- rank = ((rank * (rank + 1) / 2) - ((rank_min - 1) * rank_min / 2)) / (rank - rank_min + 1)
+ rank = ((rank * (rank + 1) / 2)
+ - ((rank_min - 1) * rank_min / 2)) \
+ / (rank - rank_min + 1)
  elif rank_type == MIN:
  rank = skiplist_min_rank(skiplist, val)
 
@@ -1233,7 +1236,9 @@ def roll_rank(const float64_t[:] values, ndarray[int64_t] start,
  raise MemoryError("skiplist_insert failed")
  if rank_type == AVERAGE:
  rank_min = skiplist_min_rank(skiplist, val)
- rank = ((rank * (rank + 1) / 2) - ((rank_min - 1) * rank_min / 2)) / (rank - rank_min + 1)
+ rank = ((rank * (rank + 1) / 2)
+ - ((rank_min - 1) * rank_min / 2)) \
+ / (rank - rank_min + 1)
  elif rank_type == MIN:
  rank = skiplist_min_rank(skiplist, val)
  if nobs >= minp:

pandas/core/window/rolling.py

@@ -1409,12 +1409,18 @@ def quantile(self, quantile: float, interpolation: str = "linear", **kwargs):
 
  return self._apply(window_func, name="quantile", **kwargs)
 
- def rank(self, method: str = "average", ascending: bool = True, pct: bool = False, **kwargs):
+ def rank(
+ self,
+ method: str = "average",
+ ascending: bool = True,
+ pct: bool = False,
+ **kwargs,
+ ):
  window_func = partial(
  window_aggregations.roll_rank,
- percentile=pct,
  method=method,
  ascending=ascending,
+ percentile=pct,
  )
 
  return self._apply(window_func, name="rank", **kwargs)

pandas/tests/window/test_rolling.py

@@ -1516,7 +1516,9 @@ def test_rank(window, method, pct, ascending, test_data):
  elif test_data == "nans":
  ser = Series(data=np.random.choice([1.0, 0.25, 0.75, np.nan], length))
 
- expected = ser.rolling(window).apply(lambda x: x.rank(method=method, pct=pct, ascending=ascending).iloc[-1])
+ expected = ser.rolling(window).apply(
+ lambda x: x.rank(method=method, pct=pct, ascending=ascending).iloc[-1]
+ )
  result = ser.rolling(window).rank(method=method, pct=pct, ascending=ascending)
 
  tm.assert_series_equal(result, expected)