repository stringclasses 11
values | repo_id stringlengths 1 3 | target_module_path stringlengths 16 72 | prompt stringlengths 298 21.7k | relavent_test_path stringlengths 50 99 | full_function stringlengths 336 33.8k | function_name stringlengths 2 51 | content_class stringclasses 3
values | external_dependencies stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|
seaborn | 0 | seaborn/_core/scales.py | def label(
self,
formatter: Formatter | None = None, *,
like: str | Callable | None = None,
base: int | None | Default = default,
unit: str | None = None,
) -> Continuous:
"""
Configure the appearance of tick labels for the scale's axis or legend.
... | /usr/src/app/target_test_cases/failed_tests_Continuous.label.txt | def label(
self,
formatter: Formatter | None = None, *,
like: str | Callable | None = None,
base: int | None | Default = default,
unit: str | None = None,
) -> Continuous:
"""
Configure the appearance of tick labels for the scale's axis or legend.
... | Continuous.label | repository-level | external |
seaborn | 1 | seaborn/_core/plot.py | def add(
self,
mark: Mark,
*transforms: Stat | Move,
orient: str | None = None,
legend: bool = True,
label: str | None = None,
data: DataSource = None,
**variables: VariableSpec,
) -> Plot:
"""
Specify a layer of the visualization i... | /usr/src/app/target_test_cases/failed_tests_Plot.add.txt | def add(
self,
mark: Mark,
*transforms: Stat | Move,
orient: str | None = None,
legend: bool = True,
label: str | None = None,
data: DataSource = None,
**variables: VariableSpec,
) -> Plot:
"""
Specify a layer of the visualization i... | Plot.add | repository-level | external |
seaborn | 2 | seaborn/_core/plot.py | def facet(
self,
col: VariableSpec = None,
row: VariableSpec = None,
order: OrderSpec | dict[str, OrderSpec] = None,
wrap: int | None = None,
) -> Plot:
"""
Produce subplots with conditional subsets of the data.
Parameters
----------
... | /usr/src/app/target_test_cases/failed_tests_Plot.facet.txt | def facet(
self,
col: VariableSpec = None,
row: VariableSpec = None,
order: OrderSpec | dict[str, OrderSpec] = None,
wrap: int | None = None,
) -> Plot:
"""
Produce subplots with conditional subsets of the data.
Parameters
----------
... | Plot.facet | repository-level | non_external |
seaborn | 3 | seaborn/_core/plot.py | def on(self, target: Axes | SubFigure | Figure) -> Plot:
"""
Provide existing Matplotlib figure or axes for drawing the plot.
When using this method, you will also need to explicitly call a method that
triggers compilation, such as :meth:`Plot.show` or :meth:`Plot.save`. If you
... | /usr/src/app/target_test_cases/failed_tests_Plot.on.txt | def on(self, target: Axes | SubFigure | Figure) -> Plot:
"""
Provide existing Matplotlib figure or axes for drawing the plot.
When using this method, you will also need to explicitly call a method that
triggers compilation, such as :meth:`Plot.show` or :meth:`Plot.save`. If you
... | Plot.on | file-level | external |
seaborn | 4 | seaborn/_core/plot.py | def pair(
self,
x: VariableSpecList = None,
y: VariableSpecList = None,
wrap: int | None = None,
cross: bool = True,
) -> Plot:
"""
Produce subplots by pairing multiple `x` and/or `y` variables.
Parameters
----------
x, y : sequenc... | /usr/src/app/target_test_cases/failed_tests_Plot.pair.txt | def pair(
self,
x: VariableSpecList = None,
y: VariableSpecList = None,
wrap: int | None = None,
cross: bool = True,
) -> Plot:
"""
Produce subplots by pairing multiple `x` and/or `y` variables.
Parameters
----------
x, y : sequenc... | Plot.pair | repository-level | non_external |
seaborn | 5 | seaborn/_base.py | def _attach(
self,
obj,
allowed_types=None,
log_scale=None,
):
"""Associate the plotter with an Axes manager and initialize its units.
Parameters
----------
obj : :class:`matplotlib.axes.Axes` or :class:'FacetGrid`
Structural object th... | /usr/src/app/target_test_cases/failed_tests__base.VectorPlotter._attach.txt | def _attach(
self,
obj,
allowed_types=None,
log_scale=None,
):
"""Associate the plotter with an Axes manager and initialize its units.
Parameters
----------
obj : :class:`matplotlib.axes.Axes` or :class:'FacetGrid`
Structural object th... | VectorPlotter._attach | repository-level | external |
seaborn | 6 | seaborn/_base.py | def iter_data(
self, grouping_vars=None, *,
reverse=False, from_comp_data=False,
by_facet=True, allow_empty=False, dropna=True,
):
"""Generator for getting subsets of data defined by semantic variables.
Also injects "col" and "row" into grouping semantics.
Param... | /usr/src/app/target_test_cases/failed_tests_VectorPlotter.iter_data.txt | def iter_data(
self, grouping_vars=None, *,
reverse=False, from_comp_data=False,
by_facet=True, allow_empty=False, dropna=True,
):
"""Generator for getting subsets of data defined by semantic variables.
Also injects "col" and "row" into grouping semantics.
Param... | VectorPlotter.iter_data | repository-level | external |
seaborn | 7 | seaborn/_base.py | def scale_categorical(self, axis, order=None, formatter=None):
"""
Enforce categorical (fixed-scale) rules for the data on given axis.
Parameters
----------
axis : "x" or "y"
Axis of the plot to operate on.
order : list
Order that unique value... | /usr/src/app/target_test_cases/failed_tests__base.VectorPlotter.scale_categorical.txt | def scale_categorical(self, axis, order=None, formatter=None):
"""
Enforce categorical (fixed-scale) rules for the data on given axis.
Parameters
----------
axis : "x" or "y"
Axis of the plot to operate on.
order : list
Order that unique value... | VectorPlotter.scale_categorical | repository-level | external |
seaborn | 8 | seaborn/_base.py | def categorical_order(vector, order=None):
"""Return a list of unique data values.
Determine an ordered list of levels in ``values``.
Parameters
----------
vector : list, array, Categorical, or Series
Vector of "categorical" values
order : list-like, optional
Desired order of c... | /usr/src/app/target_test_cases/failed_tests__base.categorical_order.txt | def categorical_order(vector, order=None):
"""Return a list of unique data values.
Determine an ordered list of levels in ``values``.
Parameters
----------
vector : list, array, Categorical, or Series
Vector of "categorical" values
order : list-like, optional
Desired order of c... | _base.categorical_order | file-level | external |
seaborn | 9 | seaborn/_base.py | def infer_orient(x=None, y=None, orient=None, require_numeric=True):
"""Determine how the plot should be oriented based on the data.
For historical reasons, the convention is to call a plot "horizontally"
or "vertically" oriented based on the axis representing its dependent
variable. Practically, this ... | /usr/src/app/target_test_cases/failed_tests_infer_orient.txt | def infer_orient(x=None, y=None, orient=None, require_numeric=True):
"""Determine how the plot should be oriented based on the data.
For historical reasons, the convention is to call a plot "horizontally"
or "vertically" oriented based on the axis representing its dependent
variable. Practically, this ... | _base.infer_orient | file-level | external |
seaborn | 10 | seaborn/_base.py | def unique_dashes(n):
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings or tuples
Valid arguments for the ``dashes`` parameter on
:class:... | /usr/src/app/target_test_cases/failed_tests__base.unique_dashes.txt | def unique_dashes(n):
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings or tuples
Valid arguments for the ``dashes`` parameter on
:class:... | _base.unique_dashes | self-contained | non_external |
seaborn | 11 | seaborn/_base.py | def unique_markers(n):
"""Build an arbitrarily long list of unique marker styles for points.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
Values for defining :class:`matplotlib.markers.MarkerStyl... | /usr/src/app/target_test_cases/failed_tests_unique_markers.txt | def unique_markers(n):
"""Build an arbitrarily long list of unique marker styles for points.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
Values for defining :class:`matplotlib.markers.MarkerStyl... | _base.unique_markers | self-contained | non_external |
seaborn | 12 | seaborn/_base.py | def variable_type(vector, boolean_type="numeric"):
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in two ways:
- Python sequences or object-typed PyData objects are considered numeric if
all of their entries are nu... | /usr/src/app/target_test_cases/failed_tests_variable_type.txt | def variable_type(vector, boolean_type="numeric"):
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in two ways:
- Python sequences or object-typed PyData objects are considered numeric if
all of their entries are nu... | _base.variable_type | file-level | external |
seaborn | 13 | seaborn/_statistics.py | def __init__(self, k_depth, outlier_prop, trust_alpha):
"""
Compute percentiles of a distribution using various tail stopping rules.
Parameters
----------
k_depth: "tukey", "proportion", "trustworthy", or "full"
Stopping rule for choosing tail percentiled to show... | /usr/src/app/target_test_cases/failed_tests__statistics.LetterValues.__init__.txt | def __init__(self, k_depth, outlier_prop, trust_alpha):
"""
Compute percentiles of a distribution using various tail stopping rules.
Parameters
----------
k_depth: "tukey", "proportion", "trustworthy", or "full"
Stopping rule for choosing tail percentiled to show... | _statistics.LetterValues.__init__ | repository-level | non_external |
seaborn | 14 | seaborn/_statistics.py | def __init__(self, estimator, errorbar=None, **boot_kws):
"""
Data aggregator that produces a weighted estimate and error bar interval.
Parameters
----------
estimator : string
Function (or method name) that maps a vector to a scalar. Currently
suppor... | /usr/src/app/target_test_cases/failed_tests__statistics.WeightedAggregator.__init__.txt | def __init__(self, estimator, errorbar=None, **boot_kws):
"""
Data aggregator that produces a weighted estimate and error bar interval.
Parameters
----------
estimator : string
Function (or method name) that maps a vector to a scalar. Currently
suppor... | _statistics.WeightedAggregator.__init__ | file-level | non_external |
seaborn | 15 | seaborn/algorithms.py | def bootstrap(*args, **kwargs):
"""Resample one or more arrays with replacement and store aggregate values.
Positional arguments are a sequence of arrays to bootstrap along the first
axis and pass to a summary function.
Keyword arguments:
n_boot : int, default=10000
Number of itera... | /usr/src/app/target_test_cases/failed_tests_algorithms.bootstrap.txt | def bootstrap(*args, **kwargs):
"""Resample one or more arrays with replacement and store aggregate values.
Positional arguments are a sequence of arrays to bootstrap along the first
axis and pass to a summary function.
Keyword arguments:
n_boot : int, default=10000
Number of itera... | algorithms.bootstrap | file-level | external |
seaborn | 16 | seaborn/axisgrid.py | def facet_data(self):
"""Generator for name indices and data subsets for each facet.
Yields
------
(i, j, k), data_ijk : tuple of ints, DataFrame
The ints provide an index into the {row, col, hue}_names attribute,
and the dataframe contains a subset of the fu... | /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.facet_data.txt | def facet_data(self):
"""Generator for name indices and data subsets for each facet.
Yields
------
(i, j, k), data_ijk : tuple of ints, DataFrame
The ints provide an index into the {row, col, hue}_names attribute,
and the dataframe contains a subset of the fu... | axisgrid.FacetGrid.facet_data | file-level | external |
seaborn | 17 | seaborn/axisgrid.py | def map(self, func, *args, **kwargs):
"""Apply a plotting function to each facet's subset of the data.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. It
must plot to the currently active matplotlib Axes and ta... | /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.map.txt | def map(self, func, *args, **kwargs):
"""Apply a plotting function to each facet's subset of the data.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. It
must plot to the currently active matplotlib Axes and ta... | axisgrid.FacetGrid.map | repository-level | external |
seaborn | 18 | seaborn/axisgrid.py | def map_dataframe(self, func, *args, **kwargs):
"""Like ``.map`` but passes args as strings and inserts data in kwargs.
This method is suitable for plotting with functions that accept a
long-form DataFrame as a `data` keyword argument and access the
data in that DataFrame using stri... | /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.map_dataframe.txt | def map_dataframe(self, func, *args, **kwargs):
"""Like ``.map`` but passes args as strings and inserts data in kwargs.
This method is suitable for plotting with functions that accept a
long-form DataFrame as a `data` keyword argument and access the
data in that DataFrame using stri... | axisgrid.FacetGrid.map_dataframe | file-level | non_external |
seaborn | 19 | seaborn/axisgrid.py | def refline(self, *, x=None, y=None, color='.5', linestyle='--', **line_kws):
"""Add a reference line(s) to each facet.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies... | /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.refline.txt | def refline(self, *, x=None, y=None, color='.5', linestyle='--', **line_kws):
"""Add a reference line(s) to each facet.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies... | axisgrid.FacetGrid.refline | file-level | external |
seaborn | 20 | seaborn/axisgrid.py | def set_titles(self, template=None, row_template=None, col_template=None, **kwargs):
"""Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for all titles with the formatting keys {col_var} and
{col_nam... | /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.set_titles.txt | def set_titles(self, template=None, row_template=None, col_template=None, **kwargs):
"""Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for all titles with the formatting keys {col_var} and
{col_nam... | axisgrid.FacetGrid.set_titles | repository-level | external |
seaborn | 21 | seaborn/axisgrid.py | def add_legend(self, legend_data=None, title=None, label_order=None,
adjust_subtitles=False, **kwargs):
"""Draw a legend, maybe placing it outside axes and resizing the figure.
Parameters
----------
legend_data : dict
Dictionary mapping label names (or... | /usr/src/app/target_test_cases/failed_tests_axisgrid.Grid.add_legend.txt | def add_legend(self, legend_data=None, title=None, label_order=None,
adjust_subtitles=False, **kwargs):
"""Draw a legend, maybe placing it outside axes and resizing the figure.
Parameters
----------
legend_data : dict
Dictionary mapping label names (or... | axisgrid.Grid.add_legend | repository-level | external |
seaborn | 22 | seaborn/axisgrid.py | def tick_params(self, axis='both', **kwargs):
"""Modify the ticks, tick labels, and gridlines.
Parameters
----------
axis : {'x', 'y', 'both'}
The axis on which to apply the formatting.
kwargs : keyword arguments
Additional keyword arguments to pass t... | /usr/src/app/target_test_cases/failed_tests_axisgrid.Grid.tick_params.txt | def tick_params(self, axis='both', **kwargs):
"""Modify the ticks, tick labels, and gridlines.
Parameters
----------
axis : {'x', 'y', 'both'}
The axis on which to apply the formatting.
kwargs : keyword arguments
Additional keyword arguments to pass t... | axisgrid.Grid.tick_params | file-level | non_external |
seaborn | 23 | seaborn/axisgrid.py | def plot(self, joint_func, marginal_func, **kwargs):
"""Draw the plot by passing functions for joint and marginal axes.
This method passes the ``kwargs`` dictionary to both functions. If you
need more control, call :meth:`JointGrid.plot_joint` and
:meth:`JointGrid.plot_marginals` di... | /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot.txt | def plot(self, joint_func, marginal_func, **kwargs):
"""Draw the plot by passing functions for joint and marginal axes.
This method passes the ``kwargs`` dictionary to both functions. If you
need more control, call :meth:`JointGrid.plot_joint` and
:meth:`JointGrid.plot_marginals` di... | axisgrid.JointGrid.plot | file-level | non_external |
seaborn | 24 | seaborn/axisgrid.py | def plot_joint(self, func, **kwargs):
"""Draw a bivariate plot on the joint axes of the grid.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y``. Otherwise,
it must accept ``x`` and ``y`` vectors of data as ... | /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot_joint.txt | def plot_joint(self, func, **kwargs):
"""Draw a bivariate plot on the joint axes of the grid.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y``. Otherwise,
it must accept ``x`` and ``y`` vectors of data as ... | axisgrid.JointGrid.plot_joint | file-level | external |
seaborn | 25 | seaborn/axisgrid.py | def plot_marginals(self, func, **kwargs):
"""Draw univariate plots on each marginal axes.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y`` and plot
when only one of them is defined. Otherwise, it must acc... | /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot_marginals.txt | def plot_marginals(self, func, **kwargs):
"""Draw univariate plots on each marginal axes.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y`` and plot
when only one of them is defined. Otherwise, it must acc... | axisgrid.JointGrid.plot_marginals | file-level | external |
seaborn | 26 | seaborn/axisgrid.py | def refline(
self, *, x=None, y=None, joint=True, marginal=True,
color='.5', linestyle='--', **line_kws
):
"""Add a reference line(s) to joint and/or marginal axes.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
joint, m... | /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.refline.txt | def refline(
self, *, x=None, y=None, joint=True, marginal=True,
color='.5', linestyle='--', **line_kws
):
"""Add a reference line(s) to joint and/or marginal axes.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
joint, m... | axisgrid.JointGrid.refline | file-level | non_external |
seaborn | 27 | seaborn/axisgrid.py | def set_axis_labels(self, xlabel="", ylabel="", **kwargs):
"""Set axis labels on the bivariate axes.
Parameters
----------
xlabel, ylabel : strings
Label names for the x and y variables.
kwargs : key, value mappings
Other keyword arguments are passed ... | /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.set_axis_labels.txt | def set_axis_labels(self, xlabel="", ylabel="", **kwargs):
"""Set axis labels on the bivariate axes.
Parameters
----------
xlabel, ylabel : strings
Label names for the x and y variables.
kwargs : key, value mappings
Other keyword arguments are passed ... | axisgrid.JointGrid.set_axis_labels | file-level | non_external |
seaborn | 28 | seaborn/axisgrid.py | def __init__(
self, data, *, hue=None, vars=None, x_vars=None, y_vars=None,
hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True,
height=2.5, aspect=1, layout_pad=.5, despine=True, dropna=False,
):
"""Initialize the plot figure and PairGrid object.
... | /usr/src/app/target_test_cases/failed_tests_axisgrid.PairGrid.__init__.txt | def __init__(
self, data, *, hue=None, vars=None, x_vars=None, y_vars=None,
hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True,
height=2.5, aspect=1, layout_pad=.5, despine=True, dropna=False,
):
"""Initialize the plot figure and PairGrid object.
... | axisgrid.PairGrid.__init__ | repository-level | external |
seaborn | 29 | seaborn/axisgrid.py | def pairplot(
data, *,
hue=None, hue_order=None, palette=None,
vars=None, x_vars=None, y_vars=None,
kind="scatter", diag_kind="auto", markers=None,
height=2.5, aspect=1, corner=False, dropna=False,
plot_kws=None, diag_kws=None, grid_kws=None, size=None,
):
"""Plot pairwise relationships in a... | /usr/src/app/target_test_cases/failed_tests_axisgrid.pairplot.txt | def pairplot(
data, *,
hue=None, hue_order=None, palette=None,
vars=None, x_vars=None, y_vars=None,
kind="scatter", diag_kind="auto", markers=None,
height=2.5, aspect=1, corner=False, dropna=False,
plot_kws=None, diag_kws=None, grid_kws=None, size=None,
):
"""Plot pairwise relationships in a... | axisgrid.pairplot | repository-level | external |
seaborn | 30 | seaborn/_marks/base.py | def _resolve(
self,
data: DataFrame | dict[str, Any],
name: str,
scales: dict[str, Scale] | None = None,
) -> Any:
"""Obtain default, specified, or mapped value for a named feature.
Parameters
----------
data : DataFrame or dict with scalar values... | /usr/src/app/target_test_cases/failed_tests_base.Mark._resolve.txt | def _resolve(
self,
data: DataFrame | dict[str, Any],
name: str,
scales: dict[str, Scale] | None = None,
) -> Any:
"""Obtain default, specified, or mapped value for a named feature.
Parameters
----------
data : DataFrame or dict with scalar values... | base.Mark._resolve | repository-level | external |
seaborn | 31 | seaborn/_marks/base.py | def resolve_color(
mark: Mark,
data: DataFrame | dict,
prefix: str = "",
scales: dict[str, Scale] | None = None,
) -> RGBATuple | ndarray:
"""
Obtain a default, specified, or mapped value for a color feature.
This method exists separately to support the relationship between a
color and ... | /usr/src/app/target_test_cases/failed_tests_resolve_color.txt | def resolve_color(
mark: Mark,
data: DataFrame | dict,
prefix: str = "",
scales: dict[str, Scale] | None = None,
) -> RGBATuple | ndarray:
"""
Obtain a default, specified, or mapped value for a color feature.
This method exists separately to support the relationship between a
color and ... | base.resolve_color | repository-level | external |
seaborn | 32 | seaborn/_core/rules.py | def categorical_order(vector: Series, order: list | None = None) -> list:
"""
Return a list of unique data values using seaborn's ordering rules.
Parameters
----------
vector : Series
Vector of "categorical" values
order : list
Desired order of category levels to override the or... | /usr/src/app/target_test_cases/failed_tests_rules.categorical_order.txt | def categorical_order(vector: Series, order: list | None = None) -> list:
"""
Return a list of unique data values using seaborn's ordering rules.
Parameters
----------
vector : Series
Vector of "categorical" values
order : list
Desired order of category levels to override the or... | categorical_order | file-level | external |
seaborn | 33 | seaborn/palettes.py | def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
"""Return a list of colors or continuous colormap defining a palette.
Possible ``palette`` values include:
- Name of a seaborn palette (deep, muted, bright, pastel, dark, colorblind)
- Name of matplotlib colormap
... | /usr/src/app/target_test_cases/failed_tests_color_palette.txt | def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
"""Return a list of colors or continuous colormap defining a palette.
Possible ``palette`` values include:
- Name of a seaborn palette (deep, muted, bright, pastel, dark, colorblind)
- Name of matplotlib colormap
... | color_palette | repository-level | external |
seaborn | 34 | seaborn/utils.py | def desaturate(color, prop):
"""Decrease the saturation channel of a color by some percent.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
prop : float
saturation channel of color will be multiplied by this value
Returns
-------
new_co... | /usr/src/app/target_test_cases/failed_tests_desaturate.txt | def desaturate(color, prop):
"""Decrease the saturation channel of a color by some percent.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
prop : float
saturation channel of color will be multiplied by this value
Returns
-------
new_co... | desaturate | self-contained | external |
seaborn | 35 | seaborn/_core/groupby.py | def __init__(self, order: list[str] | dict[str, list | None]):
"""
Initialize the GroupBy from grouping variables and optional level orders.
Parameters
----------
order
List of variable names or dict mapping names to desired level orders.
Level order ... | /usr/src/app/target_test_cases/failed_tests_groupby.GroupBy.__init__.txt | def __init__(self, order: list[str] | dict[str, list | None]):
"""
Initialize the GroupBy from grouping variables and optional level orders.
Parameters
----------
order
List of variable names or dict mapping names to desired level orders.
Level order ... | groupby.GroupBy.__init__ | file-level | non_external |
seaborn | 36 | seaborn/external/kde.py | def evaluate(self, points):
"""Evaluate the estimated pdf on a set of points.
Parameters
----------
points : (# of dimensions, # of points)-array
Alternatively, a (# of dimensions,) vector can be passed in and
treated as a single point.
Returns
... | /usr/src/app/target_test_cases/failed_tests_kde.gaussian_kde.evaluate.txt | def evaluate(self, points):
"""Evaluate the estimated pdf on a set of points.
Parameters
----------
points : (# of dimensions, # of points)-array
Alternatively, a (# of dimensions,) vector can be passed in and
treated as a single point.
Returns
... | kde.gaussian_kde.evaluate | file-level | external |
seaborn | 37 | seaborn/external/kde.py | def set_bandwidth(self, bw_method=None):
"""Compute the estimator bandwidth with given method.
The new bandwidth calculated after a call to `set_bandwidth` is used
for subsequent evaluations of the estimated density.
Parameters
----------
bw_method : str, scalar or ... | /usr/src/app/target_test_cases/failed_tests_kde.gaussian_kde.set_bandwidth.txt | def set_bandwidth(self, bw_method=None):
"""Compute the estimator bandwidth with given method.
The new bandwidth calculated after a call to `set_bandwidth` is used
for subsequent evaluations of the estimated density.
Parameters
----------
bw_method : str, scalar or ... | kde.gaussian_kde.set_bandwidth | file-level | external |
seaborn | 38 | seaborn/utils.py | def load_dataset(name, cache=True, data_home=None, **kws):
"""Load an example dataset from the online repository (requires internet).
This function provides quick access to a small number of example datasets
that are useful for documenting seaborn or generating reproducible examples
for bug reports. It... | /usr/src/app/target_test_cases/failed_tests_load_dataset.txt | def load_dataset(name, cache=True, data_home=None, **kws):
"""Load an example dataset from the online repository (requires internet).
This function provides quick access to a small number of example datasets
that are useful for documenting seaborn or generating reproducible examples
for bug reports. It... | load_dataset | file-level | external |
seaborn | 39 | seaborn/matrix.py | def clustermap(
data, *,
pivot_kws=None, method='average', metric='euclidean',
z_score=None, standard_scale=None, figsize=(10, 10),
cbar_kws=None, row_cluster=True, col_cluster=True,
row_linkage=None, col_linkage=None,
row_colors=None, col_colors=None, mask=None,
dendrogram_ratio=.2, colors_... | /usr/src/app/target_test_cases/failed_tests_matrix.clustermap.txt | def clustermap(
data, *,
pivot_kws=None, method='average', metric='euclidean',
z_score=None, standard_scale=None, figsize=(10, 10),
cbar_kws=None, row_cluster=True, col_cluster=True,
row_linkage=None, col_linkage=None,
row_colors=None, col_colors=None, mask=None,
dendrogram_ratio=.2, colors_... | matrix.clustermap | file-level | non_external |
seaborn | 40 | seaborn/matrix.py | def dendrogram(
data, *,
linkage=None, axis=1, label=True, metric='euclidean',
method='average', rotate=False, tree_kws=None, ax=None
):
"""Draw a tree diagram of relationships within a matrix
Parameters
----------
data : pandas.DataFrame
Rectangular data
linkage : numpy.array, ... | /usr/src/app/target_test_cases/failed_tests_matrix.dendrogram.txt | def dendrogram(
data, *,
linkage=None, axis=1, label=True, metric='euclidean',
method='average', rotate=False, tree_kws=None, ax=None
):
"""Draw a tree diagram of relationships within a matrix
Parameters
----------
data : pandas.DataFrame
Rectangular data
linkage : numpy.array, ... | matrix.dendrogram | file-level | external |
seaborn | 41 | seaborn/matrix.py | def heatmap(
data, *,
vmin=None, vmax=None, cmap=None, center=None, robust=False,
annot=None, fmt=".2g", annot_kws=None,
linewidths=0, linecolor="white",
cbar=True, cbar_kws=None, cbar_ax=None,
square=False, xticklabels="auto", yticklabels="auto",
mask=None, ax=None,
**kwargs
):
"""P... | /usr/src/app/target_test_cases/failed_tests_matrix.heatmap.txt | def heatmap(
data, *,
vmin=None, vmax=None, cmap=None, center=None, robust=False,
annot=None, fmt=".2g", annot_kws=None,
linewidths=0, linecolor="white",
cbar=True, cbar_kws=None, cbar_ax=None,
square=False, xticklabels="auto", yticklabels="auto",
mask=None, ax=None,
**kwargs
):
"""P... | matrix.heatmap | file-level | external |
seaborn | 42 | seaborn/palettes.py | def blend_palette(colors, n_colors=6, as_cmap=False, input="rgb"):
"""Make a palette that blends between a list of colors.
Parameters
----------
colors : sequence of colors in various formats interpreted by `input`
hex code, html color name, or tuple in `input` space.
n_colors : int, option... | /usr/src/app/target_test_cases/failed_tests_palettes.blend_palette.txt | def blend_palette(colors, n_colors=6, as_cmap=False, input="rgb"):
"""Make a palette that blends between a list of colors.
Parameters
----------
colors : sequence of colors in various formats interpreted by `input`
hex code, html color name, or tuple in `input` space.
n_colors : int, option... | palettes.blend_palette | file-level | external |
seaborn | 43 | seaborn/palettes.py | def crayon_palette(colors):
"""Make a palette with color names from Crayola crayons.
Colors are taken from here:
https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors
This is just a simple wrapper around the `seaborn.crayons` dictionary.
Parameters
----------
colors : list of string... | /usr/src/app/target_test_cases/failed_tests_palettes.crayon_palette.txt | def crayon_palette(colors):
"""Make a palette with color names from Crayola crayons.
Colors are taken from here:
https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors
This is just a simple wrapper around the `seaborn.crayons` dictionary.
Parameters
----------
colors : list of string... | palettes.crayon_palette | repository-level | non_external |
seaborn | 44 | seaborn/palettes.py | def cubehelix_palette(n_colors=6, start=0, rot=.4, gamma=1.0, hue=0.8,
light=.85, dark=.15, reverse=False, as_cmap=False):
"""Make a sequential palette from the cubehelix system.
This produces a colormap with linearly-decreasing (or increasing)
brightness. That means that information ... | /usr/src/app/target_test_cases/failed_tests_palettes.cubehelix_palette.txt | def cubehelix_palette(n_colors=6, start=0, rot=.4, gamma=1.0, hue=0.8,
light=.85, dark=.15, reverse=False, as_cmap=False):
"""Make a sequential palette from the cubehelix system.
This produces a colormap with linearly-decreasing (or increasing)
brightness. That means that information ... | palettes.cubehelix_palette | file-level | external |
seaborn | 45 | seaborn/palettes.py | def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from dark to ``color``.
This kind of palette is good for data that range between relatively
uninteresting low values and interesting high values.
The ``color`` parameter can be spec... | /usr/src/app/target_test_cases/failed_tests_palettes.dark_palette.txt | def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from dark to ``color``.
This kind of palette is good for data that range between relatively
uninteresting low values and interesting high values.
The ``color`` parameter can be spec... | palettes.dark_palette | repository-level | non_external |
seaborn | 46 | seaborn/palettes.py | def diverging_palette(h_neg, h_pos, s=75, l=50, sep=1, n=6, # noqa
center="light", as_cmap=False):
"""Make a diverging palette between two HUSL colors.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_diverging_palette` func... | /usr/src/app/target_test_cases/failed_tests_palettes.diverging_palette.txt | def diverging_palette(h_neg, h_pos, s=75, l=50, sep=1, n=6, # noqa
center="light", as_cmap=False):
"""Make a diverging palette between two HUSL colors.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_diverging_palette` func... | palettes.diverging_palette | file-level | external |
seaborn | 47 | seaborn/palettes.py | def hls_palette(n_colors=6, h=.01, l=.6, s=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HLS system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` val... | /usr/src/app/target_test_cases/failed_tests_palettes.hls_palette.txt | def hls_palette(n_colors=6, h=.01, l=.6, s=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HLS system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` val... | palettes.hls_palette | file-level | external |
seaborn | 48 | seaborn/palettes.py | def husl_palette(n_colors=6, h=.01, s=.9, l=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HUSL system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` v... | /usr/src/app/target_test_cases/failed_tests_palettes.husl_palette.txt | def husl_palette(n_colors=6, h=.01, s=.9, l=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HUSL system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` v... | palettes.husl_palette | file-level | external |
seaborn | 49 | seaborn/palettes.py | def light_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from light to ``color``.
The ``color`` parameter can be specified in a number of ways, including
all options for defining a color in matplotlib and several additional
color spaces t... | /usr/src/app/target_test_cases/failed_tests_palettes.light_palette.txt | def light_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from light to ``color``.
The ``color`` parameter can be specified in a number of ways, including
all options for defining a color in matplotlib and several additional
color spaces t... | palettes.light_palette | repository-level | non_external |
seaborn | 50 | seaborn/palettes.py | def set_color_codes(palette="deep"):
"""Change how matplotlib color shorthands are interpreted.
Calling this will change how shorthand codes like "b" or "g"
are interpreted by matplotlib in subsequent plots.
Parameters
----------
palette : {deep, muted, pastel, dark, bright, colorblind}
... | /usr/src/app/target_test_cases/failed_tests_palettes.set_color_codes.txt | def set_color_codes(palette="deep"):
"""Change how matplotlib color shorthands are interpreted.
Calling this will change how shorthand codes like "b" or "g"
are interpreted by matplotlib in subsequent plots.
Parameters
----------
palette : {deep, muted, pastel, dark, bright, colorblind}
... | palettes.set_color_codes | file-level | external |
seaborn | 51 | seaborn/palettes.py | def xkcd_palette(colors):
"""Make a palette with color names from the xkcd color survey.
See xkcd for the full list of colors: https://xkcd.com/color/rgb/
This is just a simple wrapper around the `seaborn.xkcd_rgb` dictionary.
Parameters
----------
colors : list of strings
List of key... | /usr/src/app/target_test_cases/failed_tests_palettes.xkcd_palette.txt | def xkcd_palette(colors):
"""Make a palette with color names from the xkcd color survey.
See xkcd for the full list of colors: https://xkcd.com/color/rgb/
This is just a simple wrapper around the `seaborn.xkcd_rgb` dictionary.
Parameters
----------
colors : list of strings
List of key... | palettes.xkcd_palette | repository-level | non_external |
seaborn | 52 | seaborn/_core/plot.py | def label(
self, *,
title: str | None = None,
legend: str | None = None,
**variables: str | Callable[[str], str]
) -> Plot:
"""
Control the labels and titles for axes, legends, and subplots.
Additional keywords correspond to variables defined in the plot.... | /usr/src/app/target_test_cases/failed_tests_plot.Plot.label.txt | def label(
self, *,
title: str | None = None,
legend: str | None = None,
**variables: str | Callable[[str], str]
) -> Plot:
"""
Control the labels and titles for axes, legends, and subplots.
Additional keywords correspond to variables defined in the plot.... | plot.Plot.label | file-level | external |
seaborn | 53 | seaborn/_core/plot.py | def layout(
self,
*,
size: tuple[float, float] | Default = default,
engine: str | None | Default = default,
extent: tuple[float, float, float, float] | Default = default,
) -> Plot:
"""
Control the figure size and layout.
.. note::
De... | /usr/src/app/target_test_cases/failed_tests_plot.Plot.layout.txt | def layout(
self,
*,
size: tuple[float, float] | Default = default,
engine: str | None | Default = default,
extent: tuple[float, float, float, float] | Default = default,
) -> Plot:
"""
Control the figure size and layout.
.. note::
De... | plot.Plot.layout | repository-level | non_external |
seaborn | 54 | seaborn/_core/plot.py | def limit(self, **limits: tuple[Any, Any]) -> Plot:
"""
Control the range of visible data.
Keywords correspond to variables defined in the plot, and values are a
`(min, max)` tuple (where either can be `None` to leave unset).
Limits apply only to the axis; data outside the ... | /usr/src/app/target_test_cases/failed_tests_plot.Plot.limit.txt | def limit(self, **limits: tuple[Any, Any]) -> Plot:
"""
Control the range of visible data.
Keywords correspond to variables defined in the plot, and values are a
`(min, max)` tuple (where either can be `None` to leave unset).
Limits apply only to the axis; data outside the ... | plot.Plot.limit | file-level | external |
seaborn | 55 | seaborn/_core/plot.py | def save(self, loc, **kwargs) -> Plot:
"""
Compile the plot and write it to a buffer or file on disk.
Parameters
----------
loc : str, path, or buffer
Location on disk to save the figure, or a buffer to write into.
kwargs
Other keyword argumen... | /usr/src/app/target_test_cases/failed_tests_plot.Plot.save.txt | def save(self, loc, **kwargs) -> Plot:
"""
Compile the plot and write it to a buffer or file on disk.
Parameters
----------
loc : str, path, or buffer
Location on disk to save the figure, or a buffer to write into.
kwargs
Other keyword argumen... | plot.Plot.save | file-level | non_external |
seaborn | 56 | seaborn/_core/plot.py | def scale(self, **scales: Scale) -> Plot:
"""
Specify mappings from data units to visual properties.
Keywords correspond to variables defined in the plot, including coordinate
variables (`x`, `y`) and semantic variables (`color`, `pointsize`, etc.).
A number of "magic" argu... | /usr/src/app/target_test_cases/failed_tests_plot.Plot.scale.txt | def scale(self, **scales: Scale) -> Plot:
"""
Specify mappings from data units to visual properties.
Keywords correspond to variables defined in the plot, including coordinate
variables (`x`, `y`) and semantic variables (`color`, `pointsize`, etc.).
A number of "magic" argu... | plot.Plot.scale | repository-level | non_external |
seaborn | 57 | seaborn/_core/plot.py | def share(self, **shares: bool | str) -> Plot:
"""
Control sharing of axis limits and ticks across subplots.
Keywords correspond to variables defined in the plot, and values can be
boolean (to share across all subplots), or one of "row" or "col" (to share
more selectively ac... | /usr/src/app/target_test_cases/failed_tests_plot.Plot.share.txt | def share(self, **shares: bool | str) -> Plot:
"""
Control sharing of axis limits and ticks across subplots.
Keywords correspond to variables defined in the plot, and values can be
boolean (to share across all subplots), or one of "row" or "col" (to share
more selectively ac... | plot.Plot.share | file-level | non_external |
seaborn | 58 | seaborn/_core/plot.py | def theme(self, config: Mapping[str, Any], /) -> Plot:
"""
Control the appearance of elements in the plot.
.. note::
The API for customizing plot appearance is not yet finalized.
Currently, the only valid argument is a dict of matplotlib rc parameters.
(... | /usr/src/app/target_test_cases/failed_tests_plot.Plot.theme.txt | def theme(self, config: Mapping[str, Any], /) -> Plot:
"""
Control the appearance of elements in the plot.
.. note::
The API for customizing plot appearance is not yet finalized.
Currently, the only valid argument is a dict of matplotlib rc parameters.
(... | plot.Plot.theme | file-level | external |
seaborn | 59 | seaborn/_core/properties.py | def _default_values(self, n: int) -> list[DashPatternWithOffset]:
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings o... | /usr/src/app/target_test_cases/failed_tests_properties.LineStyle._default_values.txt | def _default_values(self, n: int) -> list[DashPatternWithOffset]:
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings o... | properties.LineStyle._default_values | file-level | non_external |
seaborn | 60 | seaborn/_core/properties.py | def _default_values(self, n: int) -> list[MarkerStyle]:
"""Build an arbitrarily long list of unique marker styles.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
... | /usr/src/app/target_test_cases/failed_tests_properties.Marker._default_values.txt | def _default_values(self, n: int) -> list[MarkerStyle]:
"""Build an arbitrarily long list of unique marker styles.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
... | properties.Marker._default_values | self-contained | external |
seaborn | 61 | seaborn/rcmod.py | def set_context(context=None, font_scale=1, rc=None):
"""
Set the parameters that control the scaling of plot elements.
These parameters correspond to label size, line thickness, etc.
Calling this function modifies the global matplotlib `rcParams`. For more
information, see the :doc:`aesthetics tut... | /usr/src/app/target_test_cases/failed_tests_rcmod.set_context.txt | def set_context(context=None, font_scale=1, rc=None):
"""
Set the parameters that control the scaling of plot elements.
These parameters correspond to label size, line thickness, etc.
Calling this function modifies the global matplotlib `rcParams`. For more
information, see the :doc:`aesthetics tut... | rcmod.set_context | file-level | external |
seaborn | 62 | seaborn/rcmod.py | def set_palette(palette, n_colors=None, desat=None, color_codes=False):
"""Set the matplotlib color cycle using a seaborn palette.
Parameters
----------
palette : seaborn color palette | matplotlib colormap | hls | husl
Palette definition. Should be something :func:`color_palette` can process.
... | /usr/src/app/target_test_cases/failed_tests_rcmod.set_palette.txt | def set_palette(palette, n_colors=None, desat=None, color_codes=False):
"""Set the matplotlib color cycle using a seaborn palette.
Parameters
----------
palette : seaborn color palette | matplotlib colormap | hls | husl
Palette definition. Should be something :func:`color_palette` can process.
... | rcmod.set_palette | repository-level | external |
seaborn | 63 | seaborn/rcmod.py | def set_style(style=None, rc=None):
"""
Set the parameters that control the general style of the plots.
The style parameters control properties like the color of the background and
whether a grid is enabled by default. This is accomplished using the
matplotlib rcParams system.
The options are ... | /usr/src/app/target_test_cases/failed_tests_rcmod.set_style.txt | def set_style(style=None, rc=None):
"""
Set the parameters that control the general style of the plots.
The style parameters control properties like the color of the background and
whether a grid is enabled by default. This is accomplished using the
matplotlib rcParams system.
The options are ... | rcmod.set_style | file-level | external |
seaborn | 64 | seaborn/rcmod.py | def set_theme(context="notebook", style="darkgrid", palette="deep",
font="sans-serif", font_scale=1, color_codes=True, rc=None):
"""
Set aspects of the visual theme for all matplotlib and seaborn plots.
This function changes the global defaults for all plots using the
matplotlib rcParams ... | /usr/src/app/target_test_cases/failed_tests_rcmod.set_theme.txt | def set_theme(context="notebook", style="darkgrid", palette="deep",
font="sans-serif", font_scale=1, color_codes=True, rc=None):
"""
Set aspects of the visual theme for all matplotlib and seaborn plots.
This function changes the global defaults for all plots using the
matplotlib rcParams ... | rcmod.set_theme | file-level | external |
seaborn | 65 | seaborn/regression.py | def residplot(
data=None, *, x=None, y=None,
x_partial=None, y_partial=None, lowess=False,
order=1, robust=False, dropna=True, label=None, color=None,
scatter_kws=None, line_kws=None, ax=None
):
"""Plot the residuals of a linear regression.
This function will regress y on x (possibly as a robus... | /usr/src/app/target_test_cases/failed_tests_regression.residplot.txt | def residplot(
data=None, *, x=None, y=None,
x_partial=None, y_partial=None, lowess=False,
order=1, robust=False, dropna=True, label=None, color=None,
scatter_kws=None, line_kws=None, ax=None
):
"""Plot the residuals of a linear regression.
This function will regress y on x (possibly as a robus... | regression.residplot | file-level | external |
seaborn | 66 | seaborn/_core/rules.py | def variable_type(
vector: Series,
boolean_type: Literal["numeric", "categorical", "boolean"] = "numeric",
strict_boolean: bool = False,
) -> VarType:
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in a few ways:
... | /usr/src/app/target_test_cases/failed_tests_rules.variable_type.txt | def variable_type(
vector: Series,
boolean_type: Literal["numeric", "categorical", "boolean"] = "numeric",
strict_boolean: bool = False,
) -> VarType:
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in a few ways:
... | rules.variable_type | file-level | external |
seaborn | 67 | seaborn/_core/scales.py | def tick(
self,
locator: Locator | None = None, *,
at: Sequence[float] | None = None,
upto: int | None = None,
count: int | None = None,
every: float | None = None,
between: tuple[float, float] | None = None,
minor: int | None = None,
) -> Continuo... | /usr/src/app/target_test_cases/failed_tests_scales.Continuous.tick.txt | def tick(
self,
locator: Locator | None = None, *,
at: Sequence[float] | None = None,
upto: int | None = None,
count: int | None = None,
every: float | None = None,
between: tuple[float, float] | None = None,
minor: int | None = None,
) -> Continuo... | scales.Continuous.tick | file-level | external |
seaborn | 68 | seaborn/_core/scales.py | def label(
self,
formatter: Formatter | None = None, *,
concise: bool = False,
) -> Temporal:
"""
Configure the appearance of tick labels for the scale's axis or legend.
.. note::
This API is under construction and will be enhanced over time.
... | /usr/src/app/target_test_cases/failed_tests_scales.Temporal.label.txt | def label(
self,
formatter: Formatter | None = None, *,
concise: bool = False,
) -> Temporal:
"""
Configure the appearance of tick labels for the scale's axis or legend.
.. note::
This API is under construction and will be enhanced over time.
... | scales.Temporal.label | file-level | external |
seaborn | 69 | seaborn/_core/scales.py | def tick(
self, locator: Locator | None = None, *,
upto: int | None = None,
) -> Temporal:
"""
Configure the selection of ticks for the scale's axis or legend.
.. note::
This API is under construction and will be enhanced over time.
Parameters
... | /usr/src/app/target_test_cases/failed_tests_scales.Temporal.tick.txt | def tick(
self, locator: Locator | None = None, *,
upto: int | None = None,
) -> Temporal:
"""
Configure the selection of ticks for the scale's axis or legend.
.. note::
This API is under construction and will be enhanced over time.
Parameters
... | scales.Temporal.tick | file-level | external |
seaborn | 70 | seaborn/utils.py | def ci_to_errsize(cis, heights):
"""Convert intervals to error arguments relative to plot heights.
Parameters
----------
cis : 2 x n sequence
sequence of confidence interval limits
heights : n sequence
sequence of plot heights
Returns
-------
errsize : 2 x n array
... | /usr/src/app/target_test_cases/failed_tests_utils.ci_to_errsize.txt | def ci_to_errsize(cis, heights):
"""Convert intervals to error arguments relative to plot heights.
Parameters
----------
cis : 2 x n sequence
sequence of confidence interval limits
heights : n sequence
sequence of plot heights
Returns
-------
errsize : 2 x n array
... | utils.ci_to_errsize | self-contained | external |
seaborn | 71 | seaborn/utils.py | def despine(fig=None, ax=None, top=True, right=True, left=False,
bottom=False, offset=None, trim=False):
"""Remove the top and right spines from plot(s).
fig : matplotlib figure, optional
Figure to despine all axes of, defaults to the current figure.
ax : matplotlib axes, optional
... | /usr/src/app/target_test_cases/failed_tests_utils.despine.txt | def despine(fig=None, ax=None, top=True, right=True, left=False,
bottom=False, offset=None, trim=False):
"""Remove the top and right spines from plot(s).
fig : matplotlib figure, optional
Figure to despine all axes of, defaults to the current figure.
ax : matplotlib axes, optional
... | utils.despine | file-level | external |
seaborn | 72 | seaborn/utils.py | def get_color_cycle():
"""Return the list of colors in the current matplotlib color cycle
Parameters
----------
None
Returns
-------
colors : list
List of matplotlib colors in the current cycle, or dark gray if
the current color cycle is empty.
"""
| /usr/src/app/target_test_cases/failed_tests_utils.get_color_cycle.txt | def get_color_cycle():
"""Return the list of colors in the current matplotlib color cycle
Parameters
----------
None
Returns
-------
colors : list
List of matplotlib colors in the current cycle, or dark gray if
the current color cycle is empty.
"""
cycler = mpl.rcPa... | utils.get_color_cycle | self-contained | external |
seaborn | 73 | seaborn/utils.py | def move_legend(obj, loc, **kwargs):
"""
Recreate a plot's legend at a new location.
The name is a slight misnomer. Matplotlib legends do not expose public
control over their position parameters. So this function creates a new legend,
copying over the data from the original object, which is then re... | /usr/src/app/target_test_cases/failed_tests_utils.move_legend.txt | def move_legend(obj, loc, **kwargs):
"""
Recreate a plot's legend at a new location.
The name is a slight misnomer. Matplotlib legends do not expose public
control over their position parameters. So this function creates a new legend,
copying over the data from the original object, which is then re... | utils.move_legend | repository-level | external |
seaborn | 74 | seaborn/utils.py | def remove_na(vector):
"""Helper method for removing null values from data vectors.
Parameters
----------
vector : vector object
Must implement boolean masking with [] subscript syntax.
Returns
-------
clean_clean : same type as ``vector``
Vector of data with null values re... | /usr/src/app/target_test_cases/failed_tests_utils.remove_na.txt | def remove_na(vector):
"""Helper method for removing null values from data vectors.
Parameters
----------
vector : vector object
Must implement boolean masking with [] subscript syntax.
Returns
-------
clean_clean : same type as ``vector``
Vector of data with null values re... | utils.remove_na | self-contained | external |
seaborn | 75 | seaborn/utils.py | def saturate(color):
"""Return a fully saturated color with the same hue.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
Returns
-------
new_color : rgb tuple
saturated color code in RGB tuple representation
"""
| /usr/src/app/target_test_cases/failed_tests_utils.saturate.txt | def saturate(color):
"""Return a fully saturated color with the same hue.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
Returns
-------
new_color : rgb tuple
saturated color code in RGB tuple representation
"""
return set_hls_val... | utils.saturate | file-level | non_external |
seaborn | 76 | seaborn/utils.py | def set_hls_values(color, h=None, l=None, s=None): # noqa
"""Independently manipulate the h, l, or s channels of a color.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
h, l, s : floats between 0 and 1, or None
new values for each channel in hls s... | /usr/src/app/target_test_cases/failed_tests_utils.set_hls_values.txt | def set_hls_values(color, h=None, l=None, s=None): # noqa
"""Independently manipulate the h, l, or s channels of a color.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
h, l, s : floats between 0 and 1, or None
new values for each channel in hls s... | utils.set_hls_values | self-contained | external |
seaborn | 77 | seaborn/utils.py | def to_utf8(obj):
"""Return a string representing a Python object.
Strings (i.e. type ``str``) are returned unchanged.
Byte strings (i.e. type ``bytes``) are returned as UTF-8-decoded strings.
For other objects, the method ``__str__()`` is called, and the result is
returned as a string.
Para... | /usr/src/app/target_test_cases/failed_tests_utils.to_utf8.txt | def to_utf8(obj):
"""Return a string representing a Python object.
Strings (i.e. type ``str``) are returned unchanged.
Byte strings (i.e. type ``bytes``) are returned as UTF-8-decoded strings.
For other objects, the method ``__str__()`` is called, and the result is
returned as a string.
Para... | utils.to_utf8 | self-contained | non_external |
scikit-learn | 0 | sklearn/linear_model/_bayes.py | def fit(self, X, y):
"""Fit the model according to the given training data and parameters.
Iterative procedure to maximize the evidence
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samp... | /usr/src/app/target_test_cases/failed_tests_ARDRegression.fit.txt | def fit(self, X, y):
"""Fit the model according to the given training data and parameters.
Iterative procedure to maximize the evidence
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samp... | ARDRegression.fit | repository-level | external |
scikit-learn | 1 | sklearn/linear_model/_bayes.py | def predict(self, X, return_std=False):
"""Predict using the linear model.
In addition to the mean of the predictive distribution, also its
standard deviation can be returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
... | /usr/src/app/target_test_cases/failed_tests_ARDRegression.predict.txt | def predict(self, X, return_std=False):
"""Predict using the linear model.
In addition to the mean of the predictive distribution, also its
standard deviation can be returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
... | ARDRegression.predict | repository-level | external |
scikit-learn | 2 | sklearn/ensemble/_weight_boosting.py | def decision_function(self, X):
"""Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL ar... | /usr/src/app/target_test_cases/failed_tests_AdaBoostClassifier.decision_function.txt | def decision_function(self, X):
"""Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL ar... | AdaBoostClassifier.decision_function | repository-level | external |
scikit-learn | 3 | sklearn/kernel_approximation.py | def fit(self, X, y=None):
"""Only validates estimator's parameters.
This method allows to: (i) validate the estimator's parameters and
(ii) be consistent with the scikit-learn transformer API.
Parameters
----------
X : array-like, shape (n_samples, n_features)
... | /usr/src/app/target_test_cases/failed_tests_AdditiveChi2Sampler.fit.txt | def fit(self, X, y=None):
"""Only validates estimator's parameters.
This method allows to: (i) validate the estimator's parameters and
(ii) be consistent with the scikit-learn transformer API.
Parameters
----------
X : array-like, shape (n_samples, n_features)
... | AdditiveChi2Sampler.fit | repository-level | non_external |
scikit-learn | 4 | sklearn/kernel_approximation.py | def transform(self, X):
"""Apply approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
... | /usr/src/app/target_test_cases/failed_tests_AdditiveChi2Sampler.transform.txt | def transform(self, X):
"""Apply approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
... | AdditiveChi2Sampler.transform | repository-level | external |
scikit-learn | 5 | sklearn/cluster/_affinity_propagation.py | def fit(self, X, y=None):
"""Fit the clustering from features, or affinity matrix.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
array-like of shape (n_samples, n_samples)
Training instances to cluster, or si... | /usr/src/app/target_test_cases/failed_tests_AffinityPropagation.fit.txt | def fit(self, X, y=None):
"""Fit the clustering from features, or affinity matrix.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
array-like of shape (n_samples, n_samples)
Training instances to cluster, or si... | AffinityPropagation.fit | repository-level | external |
scikit-learn | 6 | sklearn/cluster/_affinity_propagation.py | def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict. If a sparse matrix is provided, it will be
converted into a sparse ``... | /usr/src/app/target_test_cases/failed_tests_AffinityPropagation.predict.txt | def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict. If a sparse matrix is provided, it will be
converted into a sparse ``... | AffinityPropagation.predict | repository-level | external |
scikit-learn | 7 | sklearn/ensemble/_bagging.py | def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the base
estimators in the ensemble.
Parameters
----------
... | /usr/src/app/target_test_cases/failed_tests_BaggingClassifier.predict_log_proba.txt | def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the base
estimators in the ensemble.
Parameters
----------
... | BaggingClassifier.predict_log_proba | repository-level | external |
scikit-learn | 8 | sklearn/ensemble/_bagging.py | def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method... | /usr/src/app/target_test_cases/failed_tests_BaggingClassifier.predict_proba.txt | def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method... | BaggingClassifier.predict_proba | repository-level | non_external |
scikit-learn | 9 | sklearn/linear_model/_bayes.py | def fit(self, X, y, sample_weight=None):
"""Fit the model.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.
y : ndarray of shape (n_samples,)
Target values. Will be cast to X's dtype if necessary.
sample_weigh... | /usr/src/app/target_test_cases/failed_tests_BayesianRidge.fit.txt | def fit(self, X, y, sample_weight=None):
"""Fit the model.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.
y : ndarray of shape (n_samples,)
Target values. Will be cast to X's dtype if necessary.
sample_weigh... | BayesianRidge.fit | repository-level | external |
scikit-learn | 10 | sklearn/linear_model/_bayes.py | def predict(self, X, return_std=False):
"""Predict using the linear model.
In addition to the mean of the predictive distribution, also its
standard deviation can be returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
... | /usr/src/app/target_test_cases/failed_tests_BayesianRidge.predict.txt | def predict(self, X, return_std=False):
"""Predict using the linear model.
In addition to the mean of the predictive distribution, also its
standard deviation can be returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
... | BayesianRidge.predict | file-level | external |
scikit-learn | 11 | sklearn/neural_network/_rbm.py | def fit(self, X, y=None):
"""Fit the model to the data X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values... | /usr/src/app/target_test_cases/failed_tests_BernoulliRBM.fit.txt | def fit(self, X, y=None):
"""Fit the model to the data X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values... | BernoulliRBM.fit | repository-level | external |
scikit-learn | 12 | sklearn/neural_network/_rbm.py | def partial_fit(self, X, y=None):
"""Fit the model to the partial segment of the data X.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Ta... | /usr/src/app/target_test_cases/failed_tests_BernoulliRBM.partial_fit.txt | def partial_fit(self, X, y=None):
"""Fit the model to the partial segment of the data X.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Ta... | BernoulliRBM.partial_fit | repository-level | external |
scikit-learn | 13 | sklearn/neural_network/_rbm.py | def score_samples(self, X):
"""Compute the pseudo-likelihood of X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Values of the visible layer. Must be all-boolean (not checked).
Returns
-------
pseudo_likel... | /usr/src/app/target_test_cases/failed_tests_BernoulliRBM.score_samples.txt | def score_samples(self, X):
"""Compute the pseudo-likelihood of X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Values of the visible layer. Must be all-boolean (not checked).
Returns
-------
pseudo_likel... | BernoulliRBM.score_samples | repository-level | external |
scikit-learn | 14 | sklearn/cluster/_bisect_k_means.py | def fit(self, X, y=None, sample_weight=None):
"""Compute bisecting k-means clustering.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster.
.. note:: The data will be converted to C ordering,
... | /usr/src/app/target_test_cases/failed_tests_BisectingKMeans.fit.txt | def fit(self, X, y=None, sample_weight=None):
"""Compute bisecting k-means clustering.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster.
.. note:: The data will be converted to C ordering,
... | BisectingKMeans.fit | repository-level | external |
scikit-learn | 15 | sklearn/calibration.py | def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit the calibrated model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,)
Target values.
sample_weight : arra... | /usr/src/app/target_test_cases/failed_tests_CalibratedClassifierCV.fit.txt | def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit the calibrated model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,)
Target values.
sample_weight : arra... | CalibratedClassifierCV.fit | repository-level | external |
scikit-learn | 16 | sklearn/calibration.py | def predict_proba(self, X):
"""Calibrated probabilities of classification.
This function returns calibrated probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
... | /usr/src/app/target_test_cases/failed_tests_CalibratedClassifierCV.predict_proba.txt | def predict_proba(self, X):
"""Calibrated probabilities of classification.
This function returns calibrated probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
... | CalibratedClassifierCV.predict_proba | repository-level | external |
scikit-learn | 17 | sklearn/calibration.py | def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
"""Plot visualization.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Parameters
----------
ax : Matplotlib Axes, default=None
Axes object to plot on. If `None`, a... | /usr/src/app/target_test_cases/failed_tests_CalibrationDisplay.plot.txt | def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
"""Plot visualization.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Parameters
----------
ax : Matplotlib Axes, default=None
Axes object to plot on. If `None`, a... | CalibrationDisplay.plot | file-level | non_external |
scikit-learn | 18 | sklearn/utils/_mocking.py | def decision_function(self, X):
"""Confidence score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
decision : ndarray of shape (n_samples,) if n_classes == 2\
else (n_samples... | /usr/src/app/target_test_cases/failed_tests_CheckingClassifier.decision_function.txt | def decision_function(self, X):
"""Confidence score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
decision : ndarray of shape (n_samples,) if n_classes == 2\
else (n_samples... | CheckingClassifier.decision_function | repository-level | non_external |
scikit-learn | 19 | sklearn/utils/_mocking.py | def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y ... | /usr/src/app/target_test_cases/failed_tests_CheckingClassifier.fit.txt | def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y ... | CheckingClassifier.fit | repository-level | external |
scikit-learn | 20 | sklearn/utils/_mocking.py | def predict_proba(self, X):
"""Predict probabilities for each class.
Here, the dummy classifier will provide a probability of 1 for the
first class of `classes_` and 0 otherwise.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The i... | /usr/src/app/target_test_cases/failed_tests_CheckingClassifier.predict_proba.txt | def predict_proba(self, X):
"""Predict probabilities for each class.
Here, the dummy classifier will provide a probability of 1 for the
first class of `classes_` and 0 otherwise.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The i... | CheckingClassifier.predict_proba | repository-level | external |
scikit-learn | 21 | sklearn/utils/_mocking.py | def score(self, X=None, Y=None):
"""Fake score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.
Y : array-like of shape (n_sample... | /usr/src/app/target_test_cases/failed_tests_CheckingClassifier.score.txt | def score(self, X=None, Y=None):
"""Fake score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.
Y : array-like of shape (n_sample... | CheckingClassifier.score | file-level | non_external |
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