diff --git a/pandas/_libs/algos.pyx b/pandas/_libs/algos.pyx
index c2d6a3bc4906d..7a2fc9dc7845a 100644
--- a/pandas/_libs/algos.pyx
+++ b/pandas/_libs/algos.pyx
@@ -49,8 +49,9 @@ cdef inline bint are_diff(object left, object right):
 
 
 class Infinity:
-    """ provide a positive Infinity comparison method for ranking """
-
+    """
+    Provide a positive Infinity comparison method for ranking.
+    """
     __lt__ = lambda self, other: False
     __le__ = lambda self, other: isinstance(other, Infinity)
     __eq__ = lambda self, other: isinstance(other, Infinity)
@@ -61,8 +62,9 @@ class Infinity:
 
 
 class NegInfinity:
-    """ provide a negative Infinity comparison method for ranking """
-
+    """
+    Provide a negative Infinity comparison method for ranking.
+    """
     __lt__ = lambda self, other: (not isinstance(other, NegInfinity) and
                                   not missing.checknull(other))
     __le__ = lambda self, other: not missing.checknull(other)
@@ -158,20 +160,19 @@ def groupsort_indexer(const int64_t[:] index, Py_ssize_t ngroups):
     Parameters
     ----------
     index: int64 ndarray
-        mappings from group -> position
+        Mappings from group -> position.
     ngroups: int64
-        number of groups
+        Number of groups.
 
     Returns
     -------
     tuple
-        1-d indexer ordered by groups, group counts
+        1-d indexer ordered by groups, group counts.
 
     Notes
     -----
     This is a reverse of the label factorization process.
     """
-
     cdef:
         Py_ssize_t i, loc, label, n
         ndarray[int64_t] counts, where, result
@@ -679,7 +680,9 @@ def is_monotonic(ndarray[algos_t, ndim=1] arr, bint timelike):
     Returns
     -------
     tuple
-        is_monotonic_inc, is_monotonic_dec, is_unique
+        is_monotonic_inc : bool
+        is_monotonic_dec : bool
+        is_unique : bool
     """
     cdef:
         Py_ssize_t i, n
@@ -774,9 +777,8 @@ ctypedef fused rank_t:
 def rank_1d(rank_t[:] in_arr, ties_method='average',
             ascending=True, na_option='keep', pct=False):
     """
-    Fast NaN-friendly version of scipy.stats.rankdata
+    Fast NaN-friendly version of ``scipy.stats.rankdata``.
     """
-
     cdef:
         Py_ssize_t i, j, n, dups = 0, total_tie_count = 0, non_na_idx = 0
 
@@ -995,9 +997,8 @@ def rank_1d(rank_t[:] in_arr, ties_method='average',
 def rank_2d(rank_t[:, :] in_arr, axis=0, ties_method='average',
             ascending=True, na_option='keep', pct=False):
     """
-    Fast NaN-friendly version of scipy.stats.rankdata
+    Fast NaN-friendly version of ``scipy.stats.rankdata``.
     """
-
     cdef:
         Py_ssize_t i, j, z, k, n, dups = 0, total_tie_count = 0