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DOC: Docstring EX03 fixes for pandas.DataFrame methods #56898

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Merged
merged 3 commits into from
Jan 16, 2024
Merged

DOC: Docstring EX03 fixes for pandas.DataFrame methods #56898

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359a8ff
Docstring fixes for groupby and values methods
alpakpinar Jan 15, 2024
e2787ee
Fix indentation
alpakpinar Jan 15, 2024
5b0eb03
Fix docstring for sort_values
alpakpinar Jan 16, 2024
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3 changes: 0 additions & 3 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -91,9 +91,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
pandas.io.formats.style.Styler.highlight_quantile \
pandas.io.formats.style.Styler.background_gradient \
pandas.io.formats.style.Styler.text_gradient \
pandas.DataFrame.values \
pandas.DataFrame.groupby \
pandas.DataFrame.sort_values \
pandas.DataFrame.plot.hexbin \
pandas.DataFrame.plot.line \
RET=$(($RET + $?)) ; echo $MSG "DONE"
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20 changes: 10 additions & 10 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7087,8 +7087,8 @@ def sort_values(
using the `natsort <https://github.com/SethMMorton/natsort>` package.

>>> df = pd.DataFrame({
... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
... "value": [10, 20, 30, 40, 50]
... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
... "value": [10, 20, 30, 40, 50]
... })
>>> df
time value
Expand Down Expand Up @@ -9035,8 +9035,8 @@ def update(
We can also choose to include NA in group keys or not by setting
`dropna` parameter, the default setting is `True`.

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> arr = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(arr, columns=["a", "b", "c"])

>>> df.groupby(by=["b"]).sum()
a c
Expand All @@ -9051,8 +9051,8 @@ def update(
2.0 2 5
NaN 1 4

>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> arr = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(arr, columns=["a", "b", "c"])

>>> df.groupby(by="a").sum()
b c
Expand Down Expand Up @@ -12569,7 +12569,7 @@ def values(self) -> np.ndarray:
A DataFrame where all columns are the same type (e.g., int64) results
in an array of the same type.

>>> df = pd.DataFrame({'age': [ 3, 29],
>>> df = pd.DataFrame({'age': [3, 29],
... 'height': [94, 170],
... 'weight': [31, 115]})
>>> df
Expand All @@ -12589,10 +12589,10 @@ def values(self) -> np.ndarray:
results in an ndarray of the broadest type that accommodates these
mixed types (e.g., object).

>>> df2 = pd.DataFrame([('parrot', 24.0, 'second'),
... ('lion', 80.5, 1),
>>> df2 = pd.DataFrame([('parrot', 24.0, 'second'),
... ('lion', 80.5, 1),
... ('monkey', np.nan, None)],
... columns=('name', 'max_speed', 'rank'))
... columns=('name', 'max_speed', 'rank'))
>>> df2.dtypes
name object
max_speed float64
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