Use range over 0 <= for loops

pandas/_libs/lib.pyx

@@ -131,10 +131,10 @@ def fast_unique_multiple(list arrays):
  dict table = {}
  object val, stub = 0
 
- for i from 0 <= i < k:
+ for i in range(k):
  buf = arrays[i]
  n = len(buf)
- for j from 0 <= j < n:
+ for j in range(n):
  val = buf[j]
  if val not in table:
  table[val] = stub
@@ -158,10 +158,10 @@ def fast_unique_multiple_list(list lists):
  dict table = {}
  object val, stub = 0
 
- for i from 0 <= i < k:
+ for i in range(k):
  buf = lists[i]
  n = len(buf)
- for j from 0 <= j < n:
+ for j in range(n):
  val = buf[j]
  if val not in table:
  table[val] = stub
@@ -200,7 +200,7 @@ def fast_unique_multiple_list_gen(object gen, bint sort=True):
 
  for buf in gen:
  n = len(buf)
- for j from 0 <= j < n:
+ for j in range(n): 
  val = buf[j]
  if val not in table:
  table[val] = stub
@@ -830,15 +830,15 @@ def count_level_2d(ndarray[uint8_t, ndim=2, cast=True] mask,
  if axis == 0:
  counts = np.zeros((max_bin, k), dtype='i8')
  with nogil:
- for i from 0 <= i < n:
- for j from 0 <= j < k:
+ for i in range(n):
+ for j in range(n):
  counts[labels[i], j] += mask[i, j]
 
  else: # axis == 1
  counts = np.zeros((n, max_bin), dtype='i8')
  with nogil:
- for i from 0 <= i < n:
- for j from 0 <= j < k:
+ for i in range(n):
+ for j in range(k):
  counts[i, labels[j]] += mask[i, j]
 
  return counts