Scatter stats

fleur.scatterstats.ScatterStats

Statistical correlation and plotting class for numerical variables.

Attributes:

Name	Type	Description
n_obs	int	Total number of observations.
correlation	float	Value of the correlation (Pearson, etc).
alpha	float	Probability of rejecting a true null hypothesis.
dof	int	Degrees of freedom for t-test.
pvalue	float	P-value of the t-test.
intercept	float	The intercept (estimation of beta2) in the model.
slope	float	The slope (estimation of beta1) in the model.
stderr_slope	float	Standard error of the slope.
ci_lower	float	Lower bound of the confidence interval.
ci_upper	float	Upper bound of the confidence interval.
ax	Axes	The main matplotlib axes.
fig	Figure	The matplotlib figure.

__init__(x, y, data=None, alternative='two-sided', effect_size='pearson', ci=95)

Initialize a ScatterStats() instance.

Parameters:

Name	Type	Description	Default
x	str | SeriesT | Iterable	Colname of data or a Series or array-like.	required
y	str | SeriesT | Iterable	Colname of data or a Series or array-like.	required
data	Frame | None	An optional dataframe.	None
alternative	str	Defines the alternative hypothesis. Default is 'two-sided'. Must be one of 'two-sided', 'less' and 'greater'.	'two-sided'
effect_size	str	The correlation measure to use. Default is 'pearson'. Must be one of 'pearson', 'kendall', 'spearman'.	'pearson'
ci	int | float	Confidence level for the label and the regression plot. The default value is 95 (for a 95% confidence level).	95

plot(*, bins=None, hist=True, scatter=True, line=True, area=True, scatter_kws=None, line_kws=None, area_kws=None, hist_kws=None, subplot_mosaic_kwargs=None, show_stats=True)

Plot a scatter plot of two variables, with a linear regression line and annotate it with main statistical results.

Parameters:

Name	Type	Description	Default
bins	int | list[int] | None	Number of bins for the marginal distributions. This can be an integer or a list of two integers (the first for the top distribution and the second for the other).	None
hist	bool	Whether to include histograms of marginal distributions.	True
scatter	bool	Whether to include the scatter plot.	True
line	bool	Whether to include the line of the regression.	True
area	bool	Whether to include the area of the confidence interval.	True
line_kws	dict | None	Additional parameters which will be passed to the plot() function in matplotlib.	None
scatter_kws	dict | None	Additional parameters which will be passed to the scatter() function in matplotlib.	None
area_kws	dict | None	Additional parameters which will be passed to the fill_between() function in matplotlib.	None
hist_kws	dict | None	Additional parameters which will be passed to the hist() function in matplotlib.	None
subplot_mosaic_kwargs	dict | None	Additional keyword arguments to pass to plt.subplot_mosaic(). Default is None.	None
show_stats	bool	If True, display statistics on the plot.	True

Examples

• Minimalist example

# mkdocs: render
from fleur import ScatterStats
from fleur import data

df = data.load_iris()

ScatterStats(x=df["sepal_length"], y=df["sepal_width"]).plot()