def pivot(
data: DataFrame,
index: IndexLabel | None = None,
columns: IndexLabel | None = None,
values: IndexLabel | None = None,
) -> DataFrame:
if columns is None:
raise TypeError("pivot() missing 1 required argument: 'columns'")
columns_listlike = com.convert_to_list_like(columns)
indexed: DataFrame | Series
if values is None:
if index is not None:
cols = com.convert_to_list_like(index)
else:
cols = []
append = index is None
# error: Unsupported operand types for + ("List[Any]" and "ExtensionArray")
# error: Unsupported left operand type for + ("ExtensionArray")
indexed = data.set_index(
cols + columns_listlike,
append=append # type: ignore[operator]
)
else:
if index is None:
if isinstance(data.index, MultiIndex):
# GH 23955
index_list = [
data.index.get_level_values(i)
for i in range(data.index.nlevels)
]
else:
index_list = [Series(data.index, name=data.index.name)]
else:
index_list = [data[idx] for idx in com.convert_to_list_like(index)]
data_columns = [data[col] for col in columns_listlike]
index_list.extend(data_columns)
multiindex = MultiIndex.from_arrays(index_list)
if is_list_like(values) and not isinstance(values, tuple):
# Exclude tuple because it is seen as a single column name
values = cast(Sequence[Hashable], values)
indexed = data._constructor(data[values]._values,
index=multiindex,
columns=values)
else:
indexed = data._constructor_sliced(data[values]._values,
index=multiindex)
# error: Argument 1 to "unstack" of "DataFrame" has incompatible type "Union
# [List[Any], ExtensionArray, ndarray[Any, Any], Index, Series]"; expected
# "Hashable"
return indexed.unstack(columns_listlike) # type: ignore[arg-type]
def pivot(
data: DataFrame,
index: IndexLabel | None = None,
columns: IndexLabel | None = None,
values: IndexLabel | None = None,
) -> DataFrame:
if columns is None:
raise TypeError("pivot() missing 1 required argument: 'columns'")
columns = com.convert_to_list_like(columns)
if values is None:
if index is not None:
cols = com.convert_to_list_like(index)
else:
cols = []
append = index is None
# error: Unsupported operand types for + ("List[Any]" and "ExtensionArray")
# error: Unsupported left operand type for + ("ExtensionArray")
indexed = data.set_index(
cols + columns,
append=append # type: ignore[operator]
)
else:
if index is None:
index = [Series(data.index, name=data.index.name)]
else:
index = com.convert_to_list_like(index)
index = [data[idx] for idx in index]
data_columns = [data[col] for col in columns]
index.extend(data_columns)
index = MultiIndex.from_arrays(index)
if is_list_like(values) and not isinstance(values, tuple):
# Exclude tuple because it is seen as a single column name
values = cast(Sequence[Hashable], values)
indexed = data._constructor(data[values]._values,
index=index,
columns=values)
else:
indexed = data._constructor_sliced(data[values]._values,
index=index)
return indexed.unstack(columns)
def pivot(
data: "DataFrame",
index: Optional[Union[Label, Sequence[Label]]] = None,
columns: Optional[Union[Label, Sequence[Label]]] = None,
values: Optional[Union[Label, Sequence[Label]]] = None,
) -> "DataFrame":
if columns is None:
raise TypeError("pivot() missing 1 required argument: 'columns'")
columns = com.convert_to_list_like(columns)
if values is None:
if index is not None:
cols = com.convert_to_list_like(index)
else:
cols = []
cols.extend(columns)
append = index is None
indexed = data.set_index(cols, append=append)
else:
if index is None:
index = [Series(data.index, name=data.index.name)]
else:
index = com.convert_to_list_like(index)
index = [data[idx] for idx in index]
data_columns = [data[col] for col in columns]
index.extend(data_columns)
index = MultiIndex.from_arrays(index)
if is_list_like(values) and not isinstance(values, tuple):
# Exclude tuple because it is seen as a single column name
values = cast(Sequence[Label], values)
indexed = data._constructor(data[values]._values,
index=index,
columns=values)
else:
indexed = data._constructor_sliced(data[values]._values,
index=index)
return indexed.unstack(columns)
def boxplot_frame_groupby(
grouped,
subplots=True,
column=None,
fontsize=None,
rot=0,
grid=True,
ax=None,
figsize=None,
layout=None,
sharex=False,
sharey=True,
**kwds,
):
if subplots is True:
naxes = len(grouped)
fig, axes = create_subplots(
naxes=naxes,
squeeze=False,
ax=ax,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout,
)
axes = flatten_axes(axes)
ret = pd.Series(dtype=object)
for (key, group), ax in zip(grouped, axes):
d = group.boxplot(
ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, **kwds
)
ax.set_title(pprint_thing(key))
ret.loc[key] = d
maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
else:
keys, frames = zip(*grouped)
if grouped.axis == 0:
df = pd.concat(frames, keys=keys, axis=1)
else:
if len(frames) > 1:
df = frames[0].join(frames[1::])
else:
df = frames[0]
# GH 16748, DataFrameGroupby fails when subplots=False and `column` argument
# is assigned, and in this case, since `df` here becomes MI after groupby,
# so we need to couple the keys (grouped values) and column (original df
# column) together to search for subset to plot
if column is not None:
column = com.convert_to_list_like(column)
multi_key = pd.MultiIndex.from_product([keys, column])
column = list(multi_key.values)
ret = df.boxplot(
column=column,
fontsize=fontsize,
rot=rot,
grid=grid,
ax=ax,
figsize=figsize,
layout=layout,
**kwds,
)
return ret
