diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx index 93ea94f7b18fc..edc44f1c94589 100644 --- a/pandas/_libs/groupby.pyx +++ b/pandas/_libs/groupby.pyx @@ -1143,7 +1143,7 @@ def group_rank(float64_t[:, :] out, # Update out only when there is a transition of values or labels. # When a new value or group is encountered, go back #dups steps( # the number of occurrence of current value) and assign the ranks - # based on the the starting index of the current group (grp_start) + # based on the starting index of the current group (grp_start) # and the current index if (i == N - 1 or (masked_vals[_as[i]] != masked_vals[_as[i+1]]) or diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py index d890c0c16aecc..4574cd7bea1ae 100644 --- a/pandas/core/arrays/interval.py +++ b/pandas/core/arrays/interval.py @@ -729,11 +729,11 @@ def _shallow_copy(self, left=None, right=None, closed=None): Parameters ---------- left : array-like - Values to be used for the left-side of the the intervals. + Values to be used for the left-side of the intervals. If None, the existing left and right values will be used. right : array-like - Values to be used for the right-side of the the intervals. + Values to be used for the right-side of the intervals. If None and left is IntervalArray-like, the left and right of the IntervalArray-like will be used. diff --git a/pandas/core/generic.py b/pandas/core/generic.py index f8ee47de94edd..3b1d7e4c50be5 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2681,7 +2681,7 @@ def to_clipboard( ... # 0,1,2,3 ... # 1,4,5,6 - We can omit the the index by passing the keyword `index` and setting + We can omit the index by passing the keyword `index` and setting it to false. >>> df.to_clipboard(sep=',', index=False) diff --git a/pandas/plotting/_core.py b/pandas/plotting/_core.py index 139e0f2bbad8b..1fe383706f74d 100644 --- a/pandas/plotting/_core.py +++ b/pandas/plotting/_core.py @@ -1426,7 +1426,7 @@ def pie(self, **kwargs): Examples -------- In the example below we have a DataFrame with the information about - planet's mass and radius. We pass the the 'mass' column to the + planet's mass and radius. We pass the 'mass' column to the pie function to get a pie plot. .. plot::