pistachioplots 0.1.2

pistachioplots



Plotting utilities for exploratory data analysis and time series, built on

polars, plotnine and pistachiots.



The package installs as 'pistachioplots' and imports as 'PistachioPlots'.





INSTALLATION



    pip install pistachioplots





USAGE



    import PistachioPlots as pst



Every function returns a plotnine plot, or a dictionary of plots when it is

asked for several at once. Variables can be given explicitly or left out, in

which case the function picks the columns whose type fits.





DISTRIBUTIONS



    dist_density(df, var)       histogram with a kernel density overlay, and

                                optional analytic densities on top

    dist_density_cond(df, var, var_cat)

                                the same, conditioned on a categorical

                                variable, overlaid or in facets

    dist_mass(df, var)          bar chart of a categorical variable, with

                                percentage and count labels

    dist_mass_cond(df, var1, var2)

                                the distribution of var1 conditioned on var2,

                                in facets or as side-by-side bars





TIME SERIES



    ts(df, var, response)       one plot per variable, or a single faceted one

    ts_multiple(df, var)        several series in one panel or in facets

    ts_by_keys(df, var)         panel data with one or two keys

    time_breaks(plt, dates)     x-axis breaks and date format



These take a TSPolars object from pistachiots. 'response' gives one variable

the first color and the first place in the legend. 'ts_breaks_params' controls

the breaks: 'jump' (the step between them), 'date_format' and 'plot_minor'.





CORRELATIONS



    corrs(df, response)         Pearson, Spearman and Kendall coefficients

                                against a response, with significance marks

    corrs_matrix(df)            correlation matrix as a heatmap



Lagged variables named L1x, L2x and so on are grouped and sorted by lag number.





SCATTER AND LINES



    scatter(df, var)            scatter matrix across the given variables

    scatter(df, vary, varx)     a response against one or several covariates

    line(df, varx, vary)        a simple line, optionally stepped or filled





THEME AND COLORS



    theme_params_def            the default theme settings; copy it, change

                                what you need and pass it as theme_params

    colors_categ(series)        the palette for a categorical variable

    set_palette(colors)         override the package palette



    tms = pst.theme_params_def.copy()

    tms['x_angle'] = 90

    pst.dist_mass(df, 'region', theme_params = tms)





LICENSE



MIT. See the LICENSE.txt file.