""" An example demonstrating how to put together a crossfilter app based on the Auto MPG dataset. Demonstrates how to dynamically generate bokeh plots using the HoloViews API and replacing the bokeh plot based on the current widget selections. """ import holoviews as hv from bokeh.layouts import row, widgetbox from bokeh.models import Select from bokeh.plotting import curdoc from bokeh.sampledata.autompg import autompg df = autompg.copy() SIZES = list(range(6, 22, 3)) ORIGINS = ['North America', 'Europe', 'Asia'] # data cleanup df.cyl = [str(x) for x in df.cyl] df.origin = [ORIGINS[x-1] for x in df.origin] df['year'] = [str(x) for x in df.yr] del df['yr'] df['mfr'] = [x.split()[0] for x in df.name] df.loc[df.mfr=='chevy', 'mfr'] = 'chevrolet' df.loc[df.mfr=='chevroelt', 'mfr'] = 'chevrolet' df.loc[df.mfr=='maxda', 'mfr'] = 'mazda' df.loc[df.mfr=='mercedes-benz', 'mfr'] = 'mercedes' df.loc[df.mfr=='toyouta', 'mfr'] = 'toyota'
""" An example demonstrating how to put together a cross-selector app based on the Auto MPG dataset. """ import holoviews as hv import panel as pn import panel.widgets as pnw from bokeh.sampledata.autompg import autompg df = autompg.copy() ORIGINS = ['North America', 'Europe', 'Asia'] # data cleanup df.origin = [ORIGINS[x - 1] for x in df.origin] df['mfr'] = [x.split()[0] for x in df.name] df.loc[df.mfr == 'chevy', 'mfr'] = 'chevrolet' df.loc[df.mfr == 'chevroelt', 'mfr'] = 'chevrolet' df.loc[df.mfr == 'maxda', 'mfr'] = 'mazda' df.loc[df.mfr == 'mercedes-benz', 'mfr'] = 'mercedes' df.loc[df.mfr == 'toyouta', 'mfr'] = 'toyota' df.loc[df.mfr == 'vokswagen', 'mfr'] = 'volkswagen' df.loc[df.mfr == 'vw', 'mfr'] = 'volkswagen' del df['name'] columns = sorted(df.columns) discrete = [x for x in columns if df[x].dtype == object] continuous = [x for x in columns if x not in discrete] quantileable = [x for x in continuous if len(df[x].unique()) > 20]
