import pandas as pd, numpy as np
idx = pd.date_range('1/1/2000', periods=1000)
df  = pd.DataFrame(np.random.randn(1000, 4), index=idx, columns=list('ABCD')).cumsum()

import hvplot.pandas  # noqa
df.hvplot()

pd.options.plotting.backend = 'holoviews'

df.A.hist()

from hvplot.sample_data import us_crime

columns = ['Burglary rate', 'Larceny-theft rate', 'Robbery rate', 'Violent Crime rate']
us_crime.plot.violin(y=columns, group_label='Type of crime', value_label='Rate per 100k', invert=True, color='Type of crime')

us_crime.plot.bivariate('Burglary rate', 'Property crime rate', legend=False, width=500, height=400) * \
us_crime.plot.scatter(  'Burglary rate', 'Property crime rate', color='black', size=15, legend=False) +\
us_crime.plot.table(['Burglary rate', 'Property crime rate'], width=350, height=350)

import hvplot.streamz  # noqa
from streamz.dataframe import Random

streaming_df = Random(freq='5ms') 

streaming_df.hvplot(backlog=100, height=400, width=500) +\
streaming_df.hvplot.hexbin(x='x', y='z', backlog=2000, height=400, width=500);

import xarray as xr, cartopy.crs as crs
import hvplot.xarray  # noqa

air_ds = xr.tutorial.open_dataset('air_temperature').load()
proj = crs.Orthographic(-90, 30)

air_ds.air.isel(time=slice(0, 9, 3)).hvplot.quadmesh(
    'lon', 'lat', projection=proj, project=True, global_extent=True, 
    cmap='viridis', rasterize=True, dynamic=False, coastline=True, 
    frame_width=500)

import networkx as nx
import hvplot.networkx as hvnx

G = nx.karate_club_graph()

hvnx.draw_spring(G, labels='club', font_size='10pt', node_color='club', cmap='Category10', width=500, height=500)

hvplot.extension('matplotlib')

air_ds.air.isel(time=slice(0, 9, 3)).hvplot.quadmesh(
    'lon', 'lat', projection=proj, project=True, global_extent=True, 
    cmap='viridis', rasterize=True, dynamic=False, coastline=True,
    xaxis=None, yaxis=None, width=500
)