Stats

stat_pointdensity

class plotnine_extra.stat_pointdensity(mapping=None, data=None, **kwargs)[source]

Bases: stat

Compute density estimation for each point

Usage

stat_pointdensity(

mapping=None, data=None, *, geom=”point”, position=”identity”, na_rm=False, package=”statsmodels”, kde_params=None, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– —————————- x y color `after_stat(‘density’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "density"   # Computed density at a point `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="point") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • package (Literal["statsmodels", "scipy", "sklearn"], default="statsmodels") – Package whose kernel density estimation to use.

  • kde_params (dict, default=None) – Keyword arguments to pass on to the kde class.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_point

The default geom for this stat.

statsmodels.nonparametric.kde.KDEMultivariate, scipy.stats.gaussian_kde, sklearn.neighbors.KernelDensity

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_AES = {'color': after_stat('density')}

Default aesthetics for the stat

DEFAULT_PARAMS = {'geom': 'point', 'kde_params': None, 'na_rm': False, 'package': 'statsmodels', 'position': 'identity'}

Required parameters for the stat

CREATES = {'density'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

setup_params(data)[source]

Override this to verify and/or adjust parameters

Parameters:

data – Data

Returns:

Parameters used by the stats.

Return type:

out

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_mean

class plotnine_extra.stat_mean(mapping=None, data=None, **kwargs)[source]

Bases: stat

Compute group mean points

Calculates the mean x and y coordinates for each group, producing a single centroid point per group.

Usage

stat_mean(

mapping=None, data=None, *, geom=”point”, position=”identity”, na_rm=False, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————— x y

    The bold aesthetics are required.

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="point") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_point

The default geom for this stat.

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_PARAMS = {'geom': 'point', 'na_rm': False, 'position': 'identity'}

Required parameters for the stat

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_chull

class plotnine_extra.stat_chull(mapping=None, data=None, **kwargs)[source]

Bases: stat

Compute the convex hull of a set of points

Calculates the convex hull for each group and returns the subset of points that form the hull boundary.

Usage

stat_chull(

mapping=None, data=None, *, geom=”path”, position=”identity”, na_rm=False, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————— x y

    The bold aesthetics are required.

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="path") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_path

The default geom for this stat.

plotnine.geom_polygon

Alternative geom for filled hulls.

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_PARAMS = {'geom': 'path', 'na_rm': False, 'position': 'identity'}

Required parameters for the stat

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_stars

class plotnine_extra.stat_stars(mapping=None, data=None, **kwargs)[source]

Bases: stat

Draw star segments from centroid to each point

Computes line segments from the group centroid (mean x, y) to each individual data point, creating a star-shaped pattern.

Usage

stat_stars(

mapping=None, data=None, *, geom=”segment”, position=”identity”, na_rm=False, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————— x y

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "xend"  # x-coordinate of segment endpoint "yend"  # y-coordinate of segment endpoint `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="segment") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_segment

The default geom for this stat.

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_PARAMS = {'geom': 'segment', 'na_rm': False, 'position': 'identity'}

Required parameters for the stat

CREATES = {'xend', 'yend'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_central_tendency

class plotnine_extra.stat_central_tendency(mapping=None, data=None, **kwargs)[source]

Bases: stat

Add central tendency measure to a density plot

Computes the mean, median, or mode of the data and returns coordinates for a vertical line at that position.

Usage

stat_central_tendency(

mapping=None, data=None, *, geom=”line”, position=”identity”, na_rm=False, type=”mean”, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————— x

    The bold aesthetics are required.

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="line") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • type (str, default="mean") – Type of central tendency measure. One of "mean", "median", or "mode".

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_line

The default geom for this stat.

REQUIRED_AES = {'x'}

Required aesthetics for the stat

DEFAULT_PARAMS = {'geom': 'line', 'na_rm': False, 'position': 'identity', 'type': 'mean'}

Required parameters for the stat

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_conf_ellipse

class plotnine_extra.stat_conf_ellipse(mapping=None, data=None, **kwargs)[source]

Bases: stat

Compute confidence ellipses around group barycenters

Draws a confidence ellipse based on the covariance structure of the bivariate data within each group.

Usage

stat_conf_ellipse(

mapping=None, data=None, *, geom=”path”, position=”identity”, na_rm=False, level=0.95, npoint=100, bary=True, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————— x y

    The bold aesthetics are required.

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="path") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • level (float, default=0.95) – Confidence level for the ellipse.

  • npoint (int, default=100) – Number of points used to draw the ellipse.

  • bary (bool, default=True) – If True, compute the ellipse around the barycenter (mean). If False, compute the ellipse around the data cloud.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_path

The default geom for this stat.

plotnine.geom_polygon

Alternative geom for filled ellipses.

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_PARAMS = {'bary': True, 'geom': 'path', 'level': 0.95, 'na_rm': False, 'npoint': 100, 'position': 'identity'}

Required parameters for the stat

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_cor

class plotnine_extra.stat_cor(mapping=None, data=None, **kwargs)[source]

Bases: stat

Add correlation coefficients with p-values to a scatter plot

Computes correlation coefficients (Pearson, Spearman, or Kendall) and formats them as text labels including p-values.

Usage

stat_cor(

mapping=None, data=None, *, geom=”text”, position=”identity”, na_rm=False, method=”pearson”, alternative=”two-sided”, label_x_npc=”left”, label_y_npc=”top”, r_accuracy=0.01, p_accuracy=0.001, label_sep=”, “, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————————– x y label `after_stat(‘label’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "label"    # Formatted correlation label "r"        # Correlation coefficient "rr"       # R-squared "p"        # P-value `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="text") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • method (str, default="pearson") – Correlation method. One of "pearson", "spearman", or "kendall".

  • alternative (str, default="two-sided") – Alternative hypothesis. One of "two-sided", "greater", or "less".

  • label_x_npc (float or str, default="left") – Normalized x position for the label. Float in [0, 1] or one of "left", "center", "right".

  • label_y_npc (float or str, default="top") – Normalized y position for the label. Float in [0, 1] or one of "top", "center", "bottom".

  • r_accuracy (float, default=0.01) – Decimal accuracy for the correlation coefficient.

  • p_accuracy (float, default=0.001) – Decimal accuracy for the p-value.

  • label_sep (str, default=", ") – Separator between the correlation and p-value labels.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_text

The default geom for this stat.

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_AES = {'label': after_stat('label')}

Default aesthetics for the stat

DEFAULT_PARAMS = {'alternative': 'two-sided', 'geom': 'text', 'label_sep': ', ', 'label_x_npc': 'left', 'label_y_npc': 'top', 'method': 'pearson', 'na_rm': False, 'p_accuracy': 0.001, 'position': 'identity', 'r_accuracy': 0.01}

Required parameters for the stat

CREATES = {'label', 'p', 'r', 'rr'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_regline_equation

class plotnine_extra.stat_regline_equation(mapping=None, data=None, **kwargs)[source]

Bases: stat

Add regression line equation and R-squared to a plot

Fits a polynomial regression and formats the equation and goodness-of-fit statistics as a text label.

Usage

stat_regline_equation(

mapping=None, data=None, *, geom=”text”, position=”identity”, na_rm=False, formula=”y ~ x”, label_x_npc=”left”, label_y_npc=”top”, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————————– x y label `after_stat(‘label’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "label"    # Formatted equation label "eq"       # Equation string "rr"       # R-squared "adj_rr"   # Adjusted R-squared "aic"      # Akaike information criterion "bic"      # Bayesian information criterion `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="text") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • formula (str, default="y ~ x") –

    Regression formula. Supported forms:

    • "y ~ x" — simple linear regression

    • "y ~ poly(x, n)" — polynomial of degree n

  • label_x_npc (float or str, default="left") – Normalized x position for the label.

  • label_y_npc (float or str, default="top") – Normalized y position for the label.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_text

The default geom for this stat.

plotnine.stat_smooth

For the regression line itself.

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_AES = {'label': after_stat('label')}

Default aesthetics for the stat

DEFAULT_PARAMS = {'formula': 'y ~ x', 'geom': 'text', 'label_x_npc': 'left', 'label_y_npc': 'top', 'na_rm': False, 'position': 'identity'}

Required parameters for the stat

CREATES = {'adj_rr', 'aic', 'bic', 'eq', 'label', 'rr'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_overlay_normal_density

class plotnine_extra.stat_overlay_normal_density(mapping=None, data=None, **kwargs)[source]

Bases: stat

Overlay a normal density curve on a histogram or density plot

Computes a theoretical normal distribution with the same mean and standard deviation as the data, useful for checking normality assumptions.

Usage

stat_overlay_normal_density(

mapping=None, data=None, *, geom=”line”, position=”identity”, na_rm=False, n=512, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————— x

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "density"  # Normal density at each point `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="line") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • n (int, default=512) – Number of points at which to evaluate the density.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_line

The default geom for this stat.

plotnine.stat_density

For kernel density estimation.

REQUIRED_AES = {'x'}

Required aesthetics for the stat

DEFAULT_PARAMS = {'geom': 'line', 'n': 512, 'na_rm': False, 'position': 'identity'}

Required parameters for the stat

CREATES = {'density'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

compute_group(data, scales)[source]

Calculate statistics for the group

All stats should implement this method

Parameters:

  • data – Data for a group

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – Parameters

Return type:

DataFrame

stat_compare_means

class plotnine_extra.stat_compare_means(mapping=None, data=None, **kwargs)[source]

Bases: stat

Add mean comparison p-values to a plot

Performs statistical tests comparing groups and displays the results as text annotations. Supports t-test, Wilcoxon, ANOVA, and Kruskal-Wallis tests.

Usage

stat_compare_means(

mapping=None, data=None, *, geom=”text”, position=”identity”, na_rm=False, method=”wilcox.test”, paired=False, comparisons=None, ref_group=None, hide_ns=False, label=”p.format”, label_x_npc=”center”, label_y_npc=”top”, p_digits=3, step_increase=0.1, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————————– x y label `after_stat(‘label’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "label"    # Formatted test result label "p"        # P-value "p_signif" # Significance symbol "method"   # Name of the test `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="text") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • method (str, default="wilcox.test") – Statistical test method. One of "t.test", "wilcox.test", "anova", "kruskal.test".

  • paired (bool, default=False) – Whether to perform a paired test.

  • comparisons (list of tuple, default=None) – List of group pairs to compare, e.g. [("A", "B"), ("A", "C")]. If None, performs a global test across all groups.

  • ref_group (str, default=None) – Reference group for pairwise comparisons. Each group is compared against this reference.

  • hide_ns (bool, default=False) – If True, hide non-significant results.

  • label (str, default="p.format") – Label format. One of "p.format", "p.signif", "p.format.signif".

  • label_x_npc (float or str, default="center") – Normalized x position for global test label.

  • label_y_npc (float or str, default="top") – Normalized y position for global test label.

  • p_digits (int, default=3) – Number of digits for p-value formatting.

  • step_increase (float, default=0.1) – Fraction of y-range to step between comparison brackets.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_text

The default geom for this stat.

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_AES = {'label': after_stat('label')}

Default aesthetics for the stat

DEFAULT_PARAMS = {'comparisons': None, 'geom': 'text', 'hide_ns': False, 'label': 'p.format', 'label_x_npc': 'center', 'label_y_npc': 'top', 'method': 'wilcox.test', 'na_rm': False, 'p_digits': 3, 'paired': False, 'position': 'identity', 'ref_group': None, 'step_increase': 0.1}

Required parameters for the stat

CREATES = {'label', 'method', 'p', 'p_signif'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

compute_panel(data, scales)[source]

Calculate the statistics for all the groups

Return the results in a single dataframe.

This is a default function that can be overridden by individual stats

Parameters:

  • data – data for the computing

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – The parameters for the stat. It includes default values if user did not set a particular parameter.

Return type:

DataFrame

stat_anova_test

class plotnine_extra.stat_anova_test(mapping=None, data=None, **kwargs)[source]

Bases: _base_stat_test

Add ANOVA test p-values to a plot

Performs one-way ANOVA and displays the result as a text annotation including F-statistic, degrees of freedom, and p-value.

Usage

stat_anova_test(

mapping=None, data=None, *, geom=”text”, position=”identity”, na_rm=False, method=”one_way”, effect_size=”ges”, label_x_npc=”center”, label_y_npc=”top”, p_digits=3, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————————– x y label `after_stat(‘label’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "label"        # Formatted test result label "p"            # P-value "p_signif"     # Significance symbol "f"            # F-statistic "df"           # Numerator degrees of freedom "df_residual"  # Denominator degrees of freedom "effect_size"  # Effect size (eta-squared) "method"       # Name of the test `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="text") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • method (str, default="one_way") – ANOVA method. Currently supports "one_way".

  • effect_size (str, default="ges") – Type of effect size. One of "ges" (generalized eta-squared) or "pes" (partial eta-squared).

  • label_x_npc (float or str, default="center") – Normalized x position for the label.

  • label_y_npc (float or str, default="top") – Normalized y position for the label.

  • p_digits (int, default=3) – Number of digits for p-value formatting.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_text

The default geom for this stat.

DEFAULT_PARAMS = {'effect_size': 'ges', 'geom': 'text', 'label_x_npc': 'center', 'label_y_npc': 'top', 'method': 'one_way', 'na_rm': False, 'p_digits': 3, 'position': 'identity'}

Required parameters for the stat

CREATES = {'df', 'df_residual', 'effect_size', 'f', 'label', 'method', 'p', 'p_signif'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

stat_kruskal_test

class plotnine_extra.stat_kruskal_test(mapping=None, data=None, **kwargs)[source]

Bases: _base_stat_test

Add Kruskal-Wallis test p-values to a plot

Performs a Kruskal-Wallis rank sum test and displays the result as a text annotation. This is a non-parametric alternative to one-way ANOVA.

Usage

stat_kruskal_test(

mapping=None, data=None, *, geom=”text”, position=”identity”, na_rm=False, label_x_npc=”center”, label_y_npc=”top”, p_digits=3, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————————– x y label `after_stat(‘label’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "label"      # Formatted test result label "p"          # P-value "p_signif"   # Significance symbol "statistic"  # Test statistic (H) "df"         # Degrees of freedom "method"     # Name of the test `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="text") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • label_x_npc (float or str, default="center") – Normalized x position for the label.

  • label_y_npc (float or str, default="top") – Normalized y position for the label.

  • p_digits (int, default=3) – Number of digits for p-value formatting.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_text

The default geom for this stat.

DEFAULT_PARAMS = {'geom': 'text', 'label_x_npc': 'center', 'label_y_npc': 'top', 'na_rm': False, 'p_digits': 3, 'position': 'identity'}

Required parameters for the stat

CREATES = {'df', 'label', 'method', 'p', 'p_signif', 'statistic'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

stat_welch_anova_test

class plotnine_extra.stat_welch_anova_test(mapping=None, data=None, **kwargs)[source]

Bases: _base_stat_test

Add Welch’s ANOVA test p-values to a plot

Performs Welch’s one-way ANOVA, which does not assume equal variances across groups, and displays the result as a text annotation.

Usage

stat_welch_anova_test(

mapping=None, data=None, *, geom=”text”, position=”identity”, na_rm=False, label_x_npc=”center”, label_y_npc=”top”, p_digits=3, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————————– x y label `after_stat(‘label’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "label"    # Formatted test result label "p"        # P-value "p_signif" # Significance symbol "f"        # F-statistic "df1"      # Numerator degrees of freedom "df2"      # Denominator degrees of freedom "method"   # Name of the test `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="text") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • label_x_npc (float or str, default="center") – Normalized x position for the label.

  • label_y_npc (float or str, default="top") – Normalized y position for the label.

  • p_digits (int, default=3) – Number of digits for p-value formatting.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_text

The default geom for this stat.

DEFAULT_PARAMS = {'geom': 'text', 'label_x_npc': 'center', 'label_y_npc': 'top', 'na_rm': False, 'p_digits': 3, 'position': 'identity'}

Required parameters for the stat

CREATES = {'df1', 'df2', 'f', 'label', 'method', 'p', 'p_signif'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

stat_friedman_test

class plotnine_extra.stat_friedman_test(mapping=None, data=None, **kwargs)[source]

Bases: _base_stat_test

Add Friedman test p-values to a plot

Performs the Friedman test, a non-parametric test for repeated measures (alternative to repeated-measures ANOVA), and displays the result as a text annotation.

Usage

stat_friedman_test(

mapping=None, data=None, *, geom=”text”, position=”identity”, na_rm=False, wid=None, label_x_npc=”center”, label_y_npc=”top”, p_digits=3, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————————– x y label `after_stat(‘label’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "label"      # Formatted test result label "p"          # P-value "p_signif"   # Significance symbol "statistic"  # Test statistic (chi-squared) "df"         # Degrees of freedom "method"     # Name of the test `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="text") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • wid (str, default=None) – Column name identifying subjects/individuals. Required for reshaping the data into the wide format needed by the Friedman test.

  • label_x_npc (float or str, default="center") – Normalized x position for the label.

  • label_y_npc (float or str, default="top") – Normalized y position for the label.

  • p_digits (int, default=3) – Number of digits for p-value formatting.

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine.geom_text

The default geom for this stat.

DEFAULT_PARAMS = {'geom': 'text', 'label_x_npc': 'center', 'label_y_npc': 'top', 'na_rm': False, 'p_digits': 3, 'position': 'identity', 'wid': None}

Required parameters for the stat

CREATES = {'df', 'label', 'method', 'p', 'p_signif', 'statistic'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

stat_pvalue_manual

class plotnine_extra.stat_pvalue_manual(data, label=None, y_position=None, xmin='group1', xmax='group2', tip_length=0.02, step_increase=0.05, step_group_by=None, hide_ns=False, remove_bracket=False, bracket_nudge_y=0, label_size=8, vjust=-0.5, color='black', **kwargs)[source]

Bases:

Add manually specified p-values to a plot with brackets.

Parameters:

  • data (DataFrame) – Data frame containing at minimum columns for group positions and p-values. Expected columns include group1, group2, and p or p.adj.

  • label (str, optional) – Column name to use for the label, or "p.signif" to auto-convert p-values to significance symbols. If None, uses "p" column formatted.

  • y_position (str or float, optional) – Column name for y-coordinates of brackets, or a single float value. If None, uses "y.position" or "y_position" column.

  • xmin (str, default="group1") – Column name for the left x-coordinate of brackets.

  • xmax (str, default="group2") – Column name for the right x-coordinate of brackets.

  • tip_length (float, default=0.02) – Length of bracket tips as fraction of y range.

  • step_increase (float, default=0.05) – Step increase between brackets as fraction of y range.

  • step_group_by (str, optional) – Column to group comparisons for stacking.

  • hide_ns (bool, default=False) – If True, hide non-significant results.

  • remove_bracket (bool, default=False) – If True, show only labels without brackets.

  • bracket_nudge_y (float, default=0) – Vertical nudge for brackets.

  • label_size (float, default=8) – Font size for labels.

  • vjust (float, default=-0.5) – Vertical justification of labels.

  • color (str, default="black") – Color for brackets and labels.

  • **kwargs – Additional aesthetic parameters.

Returns:

List of plotnine layers (geom_segment + geom_text) that can be added to a ggplot.

Return type:

list

stat_pwc

class plotnine_extra.stat_pwc(mapping=None, data=None, **kwargs)[source]

Bases: stat

Add pairwise comparison p-values to a plot

Performs pairwise statistical tests between groups and displays the results as bracket annotations. Supports t-test and Wilcoxon (Mann-Whitney U) tests. The default geom is geom_bracket.

Usage

stat_pwc(

mapping=None, data=None, *, geom=”bracket”, position=”identity”, na_rm=False, method=”wilcox.test”, paired=False, ref_group=None, comparisons=None, p_adjust_method=”holm”, label=”p.format”, hide_ns=False, p_digits=3, step_increase=0.12, bracket_nudge_y=0.05, tip_length=0.03, bracket_shorten=0, remove_bracket=False, **kwargs

)

Parameters:

  • mapping (plotnine.aes, default=None) –

    Aesthetic mappings created with [aes](plotnine.aes). If specified and `inherit_aes=True`{.py}, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

    Aesthetic Default value

    ———– ————————– x y label `after_stat(‘label’)`{.py}

    The bold aesthetics are required.

    Options for computed aesthetics

    `python "label"      # Formatted test result label "xmin"       # Left x-coordinate of bracket "xmax"       # Right x-coordinate of bracket "y"          # Y-coordinate of bracket "p"          # Raw p-value "p_adj"      # Adjusted p-value "p_signif"   # Significance symbol (raw p) "p_adj_signif" # Significance symbol (adjusted p) "method"     # Name of the test "group1"     # First group in comparison "group2"     # Second group in comparison `

  • data (DataFrame, default=None) – The data to be displayed in this layer. If `None`{.py}, the data from from the `ggplot()`{.py} call is used. If specified, it overrides the data from the `ggplot()`{.py} call.

  • geom (str | geom, default="bracket") – The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

  • position (str | position, default="identity") – Position adjustment. If it is a string, it must be registered and known to Plotnine.

  • na_rm (bool, default=False) – If `False`{.py}, removes missing values with a warning. If `True`{.py} silently removes missing values.

  • method (str, default="wilcox.test") – Statistical test method for pairwise comparisons. One of "t.test" or "wilcox.test".

  • paired (bool, default=False) – Whether to perform a paired test.

  • ref_group (str or int, default=None) – Reference group for pairwise comparisons. Each group is compared against this reference. If None, all pairwise combinations are tested.

  • comparisons (list of tuple, default=None) – Explicit list of group pairs to compare, e.g. [("A", "B"), ("A", "C")]. Overrides ref_group when specified.

  • p_adjust_method (str, default="holm") – Method for adjusting p-values for multiple comparisons. One of "bonferroni", "holm", "hochberg", "hommel", "BH", "BY", "fdr", "none".

  • label (str, default="p.format") – Label format. One of "p.format", "p.signif", "p.adj.format", "p.adj.signif", "p.format.signif", "p.adj.format.signif".

  • hide_ns (bool, default=False) – If True, hide non-significant comparisons.

  • p_digits (int, default=3) – Number of digits for p-value formatting.

  • step_increase (float, default=0.12) – Fraction of y-range to step between comparison brackets to minimize overlap.

  • bracket_nudge_y (float, default=0.05) – Vertical offset for brackets as a fraction of the y-range.

  • tip_length (float, default=0.03) – Length of bracket tips as a fraction of y-range, passed to geom_bracket.

  • bracket_shorten (float, default=0) – Amount to shorten brackets from each end.

  • remove_bracket (bool, default=False) – If True, remove brackets and only show labels (uses geom_text instead of geom_bracket).

  • **kwargs (Any) – Aesthetics or parameters used by the geom.

See also

plotnine_extra.geom_bracket

The default geom for this stat.

plotnine_extra.stat_compare_means

Mean comparison with support for global tests.

REQUIRED_AES = {'x', 'y'}

Required aesthetics for the stat

DEFAULT_AES = {'label': after_stat('label')}

Default aesthetics for the stat

DEFAULT_PARAMS = {'bracket_nudge_y': 0.05, 'bracket_shorten': 0, 'comparisons': None, 'geom': 'bracket', 'hide_ns': False, 'label': 'p.format', 'method': 'wilcox.test', 'na_rm': False, 'p_adjust_method': 'holm', 'p_digits': 3, 'paired': False, 'position': 'identity', 'ref_group': None, 'remove_bracket': False, 'step_increase': 0.12, 'tip_length': 0.03}

Required parameters for the stat

CREATES = {'group1', 'group2', 'label', 'method', 'p', 'p_adj', 'p_adj_signif', 'p_signif', 'xmax', 'xmin', 'y'}

Stats may modify existing columns or create extra columns.

Any extra columns that may be created by the stat should be specified in this set see: stat_bin

Documentation for the aesthetics. It ie added under the documentation for mapping parameter. Use {aesthetics_table} placeholder to insert a table for all the aesthetics and their default values.

compute_panel(data, scales)[source]

Calculate the statistics for all the groups

Return the results in a single dataframe.

This is a default function that can be overridden by individual stats

Parameters:

  • data – data for the computing

  • scales –

    x (scales.x) and y (scales.y) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g.

    `python range_x = scales.x.dimension() `

  • params – The parameters for the stat. It includes default values if user did not set a particular parameter.