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DOC: fix PR07 for pandas.pivot_table (#58896)
* DOC: fix PR07 for pandas.pivot_table * DOC: remove redundant comments Co-authored-by: mroeschke <[email protected]> --------- Co-authored-by: mroeschke <[email protected]>
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‎ci/code_checks.sh

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@@ -470,7 +470,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.merge_ordered PR07" \
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-i "pandas.period_range RT03,SA01" \
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-i "pandas.pivot PR07" \
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-i "pandas.pivot_table PR07" \
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-i "pandas.plotting.andrews_curves RT03,SA01" \
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-i "pandas.plotting.lag_plot RT03,SA01" \
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-i "pandas.plotting.scatter_matrix PR07,SA01" \

‎pandas/core/reshape/pivot.py

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@@ -54,10 +54,6 @@
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from pandas import DataFrame
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# Note: We need to make sure `frame` is imported before `pivot`, otherwise
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# _shared_docs['pivot_table'] will not yet exist. TODO: Fix this dependency
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@Substitution("\ndata : DataFrame")
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@Appender(_shared_docs["pivot_table"], indents=1)
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def pivot_table(
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data: DataFrame,
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values=None,
@@ -71,6 +67,171 @@ def pivot_table(
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observed: bool = True,
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sort: bool = True,
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) -> DataFrame:
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"""
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Create a spreadsheet-style pivot table as a DataFrame.
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The levels in the pivot table will be stored in MultiIndex objects
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(hierarchical indexes) on the index and columns of the result DataFrame.
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Parameters
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----------
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data : DataFrame
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Input pandas DataFrame object.
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values : list-like or scalar, optional
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Column or columns to aggregate.
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index : column, Grouper, array, or list of the previous
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Keys to group by on the pivot table index. If a list is passed,
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it can contain any of the other types (except list). If an array is
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passed, it must be the same length as the data and will be used in
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the same manner as column values.
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columns : column, Grouper, array, or list of the previous
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Keys to group by on the pivot table column. If a list is passed,
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it can contain any of the other types (except list). If an array is
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passed, it must be the same length as the data and will be used in
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the same manner as column values.
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aggfunc : function, list of functions, dict, default "mean"
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If a list of functions is passed, the resulting pivot table will have
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hierarchical columns whose top level are the function names
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(inferred from the function objects themselves).
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If a dict is passed, the key is column to aggregate and the value is
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function or list of functions. If ``margin=True``, aggfunc will be
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used to calculate the partial aggregates.
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fill_value : scalar, default None
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Value to replace missing values with (in the resulting pivot table,
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after aggregation).
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margins : bool, default False
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If ``margins=True``, special ``All`` columns and rows
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will be added with partial group aggregates across the categories
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on the rows and columns.
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dropna : bool, default True
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Do not include columns whose entries are all NaN. If True,
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rows with a NaN value in any column will be omitted before
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computing margins.
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margins_name : str, default 'All'
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Name of the row / column that will contain the totals
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when margins is True.
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observed : bool, default False
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This only applies if any of the groupers are Categoricals.
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If True: only show observed values for categorical groupers.
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If False: show all values for categorical groupers.
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.. versionchanged:: 3.0.0
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The default value is now ``True``.
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sort : bool, default True
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Specifies if the result should be sorted.
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.. versionadded:: 1.3.0
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Returns
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-------
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DataFrame
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An Excel style pivot table.
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See Also
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--------
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DataFrame.pivot : Pivot without aggregation that can handle
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non-numeric data.
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DataFrame.melt: Unpivot a DataFrame from wide to long format,
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optionally leaving identifiers set.
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wide_to_long : Wide panel to long format. Less flexible but more
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user-friendly than melt.
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Notes
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-----
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Reference :ref:`the user guide <reshaping.pivot>` for more examples.
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Examples
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--------
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>>> df = pd.DataFrame(
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... {
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... "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
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... "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
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... "C": [
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... "small",
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... "large",
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... "large",
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... "small",
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... "small",
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... "large",
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... "small",
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... "small",
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... "large",
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... ],
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... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
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... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
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... }
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... )
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>>> df
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A B C D E
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0 foo one small 1 2
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1 foo one large 2 4
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2 foo one large 2 5
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3 foo two small 3 5
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4 foo two small 3 6
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5 bar one large 4 6
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6 bar one small 5 8
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7 bar two small 6 9
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8 bar two large 7 9
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This first example aggregates values by taking the sum.
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>>> table = pd.pivot_table(
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... df, values="D", index=["A", "B"], columns=["C"], aggfunc="sum"
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... )
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>>> table
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C large small
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A B
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bar one 4.0 5.0
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two 7.0 6.0
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foo one 4.0 1.0
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two NaN 6.0
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We can also fill missing values using the `fill_value` parameter.
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>>> table = pd.pivot_table(
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... df, values="D", index=["A", "B"], columns=["C"], aggfunc="sum", fill_value=0
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... )
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>>> table
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C large small
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A B
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bar one 4 5
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two 7 6
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foo one 4 1
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two 0 6
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The next example aggregates by taking the mean across multiple columns.
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>>> table = pd.pivot_table(
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... df, values=["D", "E"], index=["A", "C"], aggfunc={"D": "mean", "E": "mean"}
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... )
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>>> table
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D E
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A C
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bar large 5.500000 7.500000
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small 5.500000 8.500000
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foo large 2.000000 4.500000
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small 2.333333 4.333333
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We can also calculate multiple types of aggregations for any given
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value column.
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>>> table = pd.pivot_table(
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... df,
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... values=["D", "E"],
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... index=["A", "C"],
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... aggfunc={"D": "mean", "E": ["min", "max", "mean"]},
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... )
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>>> table
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D E
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mean max mean min
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A C
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bar large 5.500000 9 7.500000 6
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small 5.500000 9 8.500000 8
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foo large 2.000000 5 4.500000 4
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small 2.333333 6 4.333333 2
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"""
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index = _convert_by(index)
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columns = _convert_by(columns)
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