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14 changes: 2 additions & 12 deletions pandas/core/frame.py
Original file line number Diff line number Diff line change
Expand Up @@ -9038,16 +9038,6 @@ def combine(
0 0 -5.0
1 0 4.0

However, if the same element in both dataframes is None, that None
is preserved

>>> df1 = pd.DataFrame({"A": [0, 0], "B": [None, 4]})
>>> df2 = pd.DataFrame({"A": [1, 1], "B": [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 3.0

Example that demonstrates the use of `overwrite` and behavior when
the axis differ between the dataframes.

Expand Down Expand Up @@ -13820,8 +13810,8 @@ def quantile(
0.1 1 1
0.5 3 100

Specifying `numeric_only=False` will also compute the quantile of
datetime and timedelta data.
Specifying `numeric_only=False` will compute the quantiles for all
columns.

>>> df = pd.DataFrame(
... {
Expand Down
4 changes: 2 additions & 2 deletions pandas/core/series.py
Original file line number Diff line number Diff line change
Expand Up @@ -3112,8 +3112,8 @@ def combine(

Combine the Series and `other` using `func` to perform elementwise
selection for combined Series.
`fill_value` is assumed when value is missing at some index
from one of the two objects being combined.
`fill_value` is assumed when value is not present at some index
from one of the two Series being combined.

Parameters
----------
Expand Down
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