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DOC: Remove deprecated methods from code_checks.sh (pandas-dev#58115)
* Update style for see also * Remove deprecated methods from code_checks.sh * Fix merge conflict * Update pandas.DataFrame.assign See Also section
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ci/code_checks.sh

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@@ -81,14 +81,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.CategoricalIndex.ordered SA01" \
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-i "pandas.DataFrame.__dataframe__ SA01" \
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-i "pandas.DataFrame.__iter__ SA01" \
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-i "pandas.DataFrame.assign SA01" \
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-i "pandas.DataFrame.at_time PR01" \
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-i "pandas.DataFrame.bfill SA01" \
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-i "pandas.DataFrame.columns SA01" \
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-i "pandas.DataFrame.copy SA01" \
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-i "pandas.DataFrame.droplevel SA01" \
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-i "pandas.DataFrame.dtypes SA01" \
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-i "pandas.DataFrame.ffill SA01" \
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-i "pandas.DataFrame.first_valid_index SA01" \
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-i "pandas.DataFrame.get SA01" \
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-i "pandas.DataFrame.hist RT03" \
@@ -303,7 +300,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.Series.add PR07" \
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-i "pandas.Series.at_time PR01" \
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-i "pandas.Series.backfill PR01,SA01" \
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-i "pandas.Series.bfill SA01" \
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-i "pandas.Series.case_when RT03" \
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-i "pandas.Series.cat PR07,SA01" \
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-i "pandas.Series.cat.add_categories PR01,PR02" \
@@ -361,7 +357,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.Series.dtypes SA01" \
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-i "pandas.Series.empty GL08" \
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-i "pandas.Series.eq PR07,SA01" \
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-i "pandas.Series.ffill SA01" \
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-i "pandas.Series.first_valid_index SA01" \
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-i "pandas.Series.floordiv PR07" \
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-i "pandas.Series.ge PR07,SA01" \

pandas/core/frame.py

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@@ -4924,6 +4924,11 @@ def assign(self, **kwargs) -> DataFrame:
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A new DataFrame with the new columns in addition to
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all the existing columns.
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See Also
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--------
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DataFrame.loc : Select a subset of a DataFrame by labels.
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DataFrame.iloc : Select a subset of a DataFrame by positions.
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Notes
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-----
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Assigning multiple columns within the same ``assign`` is possible.

pandas/core/generic.py

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@@ -7086,6 +7086,11 @@ def ffill(
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{klass} or None
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Object with missing values filled or None if ``inplace=True``.
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See Also
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--------
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DataFrame.bfill : Fill NA/NaN values by using the next valid observation
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to fill the gap.
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Examples
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--------
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>>> df = pd.DataFrame(
@@ -7214,6 +7219,11 @@ def bfill(
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{klass} or None
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Object with missing values filled or None if ``inplace=True``.
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See Also
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--------
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DataFrame.ffill : Fill NA/NaN values by propagating the last valid
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observation to next valid.
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Examples
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--------
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For Series:

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