bs_seaborn module

Some Seaborn plotting utilities:

• bs_sns_get_legend: get the Legend object of a Seaborn plot
• bs_sns_bar_x_byf: make a bar plot of x by f
• bs_sns_bar_x_byfg: make a bar plot of x by f and g
• bs_sns_plot_density: basic density plot
• bs_sns_density_estimates: plots the densities of estimates of several coefficients with several methods, superposed by methods and faceted by coefficients.

bs_regplot(data, x, y, title=None, line_color='red', save=None)

Draw a seaborn regplot with title and legend.

Parameters:

Name	Type	Description	Default
data	DataFrame	dataframe, should contain columns x and y	required
x	str	column name of x	required
y	str	column name of y	required
title	str | None	title of plot	None
line_color	str	color of the regression line. Red by default	'red'
save	str | None	where to save it, if requested	None

Returns:

Type	Description
Axes	the plot

Source code in bs_python_utils/bs_seaborn.py

def bs_regplot(
    data: pd.DataFrame,
    x: str,
    y: str,
    title: str | None = None,
    line_color: str = "red",
    save: str | None = None,
) -> Axes:
    """Draw a seaborn regplot with title and legend.

    Args:
        data: dataframe, should contain columns `x` and `y`
        x: column name of x
        y: column name of y
        title: title of plot
        line_color: color of the regression line. Red by default
        save: where to save it, if requested

    Returns:
        the plot
    """
    sns.set_style("whitegrid")
    g = sns.regplot(
        data=data,
        x=x,
        y=y,
        label="Data",
        line_kws={"label": "Linear Fit (95% CI)", "color": line_color},
    )
    if title:
        plt.title(title)
    plt.legend(loc="best")
    if save:
        plt.savefig(f"{save}.png", dpi=400)
    return cast(Axes, g)

bs_sns_bar_x_byf(df, xstr, fstr, statistic=np.mean, label_x=None, label_f=None, title=None)

Make a bar plot of x by f.

Parameters:

Name	Type	Description	Default
df	DataFrame	dataframe, should contain columns xstr and fstr	required
xstr	str	column name of x	required
fstr	str	column name of f	required
statistic	Callable	statistic to plot (by default, the mean)	mean
label_x	str | None	label of x	None
label_f	str | None	label of f	None
title	str | None	title of plot	None

Returns:

Type	Description
Axes	the plot.

Source code in bs_python_utils/bs_seaborn.py

def bs_sns_bar_x_byf(
    df: pd.DataFrame,
    xstr: str,
    fstr: str,
    statistic: Callable = np.mean,
    label_x: str | None = None,
    label_f: str | None = None,
    title: str | None = None,
) -> Axes:
    """Make a bar plot of `x` by `f`.

    Args:
        df: dataframe, should contain columns `xstr` and `fstr`
        xstr: column name of x
        fstr: column name of f
        statistic: statistic to plot (by default, the mean)
        label_x: label of x
        label_f: label of f
        title: title of plot

    Returns:
        the plot.
    """
    fig, ax = plt.subplots()
    gbar = sns.barplot(
        x=fstr,
        y=xstr,
        data=df,
        estimator=statistic,
        errcolor="r",
        errwidth=0.75,
        capsize=0.2,
        ax=ax,
    )
    xlab = fstr if label_f is None else label_f
    ylab = xstr if label_x is None else label_x
    ax.set_xlabel(xlab)
    ax.set_ylabel(ylab)
    if title is not None:
        ax.set_title(title)
    return cast(Axes, gbar)

bs_sns_bar_x_byfg(df, xstr, fstr, gstr, statistic=np.mean, label_x=None, label_f=None, label_g=None, title=None)

Make a bar plot of x by f and g

Parameters:

Name	Type	Description	Default
df	DataFrame	dataframe, should contain columns xstr, fstr, and gstr	required
xstr	str	column name of x	required
fstr	str	column name of f	required
gstr	str	column name of g	required
statistic	Callable	statistic to plot (by default, the mean)	mean
label_x	str | None	label of x	None
label_f	str | None	label of f	None
label_g	str | None	label of g in legend	None
title	str | None	title of plot	None

Returns:

Type	Description
Axes	the plot.

Source code in bs_python_utils/bs_seaborn.py

def bs_sns_bar_x_byfg(
    df: pd.DataFrame,
    xstr: str,
    fstr: str,
    gstr: str,
    statistic: Callable = np.mean,
    label_x: str | None = None,
    label_f: str | None = None,
    label_g: str | None = None,
    title: str | None = None,
) -> Axes:
    """Make a bar plot of x by f and g

    Args:
        df: dataframe, should contain columns  `xstr`, `fstr`,  and `gstr`
        xstr: column name of x
        fstr: column name of f
        gstr: column name of g
        statistic: statistic to plot (by default, the mean)
        label_x: label of x
        label_f: label of f
        label_g: label of g in legend
        title: title of plot

    Returns:
        the plot.
    """
    _, ax = plt.subplots()
    gbar = sns.barplot(
        x=fstr,
        y=xstr,
        data=df,
        hue=gstr,
        estimator=statistic,
        errcolor="r",
        errwidth=0.75,
        capsize=0.2,
        ax=ax,
    )
    xlab = fstr if label_f is None else label_f
    ylab = xstr if label_x is None else label_x
    ax.set_xlabel(xlab)
    ax.set_ylabel(ylab)
    if label_g is not None:
        plt.gca().legend(title=label_g)
    if title is not None:
        ax.set_title(title)
    return cast(Axes, gbar)

bs_sns_density_estimates(df, true_values, method_string='Estimator', coeff_string='Parameter', estimate_string='Estimate', max_cols=3)

Plots the densities of estimates of several coefficients with several methods, superposed by methods and faceted by coefficients.

Parameters:

Name	Type	Description	Default
df	DataFrame	contains columns method_string, coeff_name, estimate_value	required
true_values	ndarray	the true values of the coefficients	required
method_string	str | None	the name of the column that indicates the method	'Estimator'
coeff_string	str | None	the name of the column that indicates the coefficient	'Parameter'
estimate_string	str | None	the name of the column that gives the value of the estimate	'Estimate'
max_cols	int	we wrap after that	3

Returns:

Type	Description
FacetGrid	the FacetGrid plot.

Source code in bs_python_utils/bs_seaborn.py

def bs_sns_density_estimates(
    df: pd.DataFrame,
    true_values: np.ndarray,
    method_string: str | None = "Estimator",
    coeff_string: str | None = "Parameter",
    estimate_string: str | None = "Estimate",
    max_cols: int = 3,
) -> sns.FacetGrid:
    """
    Plots the densities of estimates of several coefficients with several methods,
    superposed by methods and faceted by coefficients.

    Args:
        df: contains columns `method_string`, `coeff_name`, `estimate_value`
        true_values: the true values of the coefficients
        method_string: the name of the column that indicates the method
        coeff_string: the name of the column that indicates the coefficient
        estimate_string: the name of the column that gives the value of the estimate
        max_cols: we wrap after that

    Returns:
        the `FacetGrid` plot.

    """
    g = sns.FacetGrid(
        data=df,
        sharex=False,
        sharey=False,
        hue=method_string,
        col=coeff_string,
        col_wrap=max_cols,
    )
    g.map(sns.kdeplot, estimate_string)
    g.set_titles("{col_name}")
    for true_val, ax in zip(true_values, g.axes.ravel(), strict=True):
        ax.vlines(true_val, *ax.get_ylim(), color="k", linestyles="dashed")
    g.add_legend()

    return g

bs_sns_plot_density(df, var_name, save_to=None)

plots the density of a variable

Parameters:

Name	Type	Description	Default
df	DataFrame	dataframe, should contain column var_name	required
var_name	str	the name of a continuous variable	required
save_to	str | None	(maybe) where we save the plot, with .png extension.	None

Source code in bs_python_utils/bs_seaborn.py

def bs_sns_plot_density(
    df: pd.DataFrame, var_name: str, save_to: str | None = None
) -> None:
    """plots the density of a variable

    Args:
        df: dataframe, should contain column `var_name`
        var_name:  the name of a continuous variable
        save_to: (maybe) where we save the plot, with `.png` extension.
    """
    var_y = df[var_name].values
    var_fig = sns.kdeplot(var_y)
    var_fig.axvline(x=0, c="k", ls="dashed")
    var_fig.set_title(f"Density of the {var_name}")
    var_fig.set_xlabel(f"Value of {var_name}")
    var_fig.set_ylabel("Value of the density")
    if save_to:
        plt.savefig(f"{save_to}.png")