https://anaconda.org/pyviz/project/webinar/revisions
from hvplot.sample_data import airline_flights
flights = airline_flights.to_dask().persist()
pd.set_option("plotting.backend", "hvplot")
or
df.hvplot(...)
import hvplot.pandas
df.hvplot.scatter(x="x", y="y", hover_cols=["col1", "col2"])
df.hvplot.scatter(x="x", y="y", hover_cols="all")
df_line_space.hvplot.line(
x="x",
y="y",
xlabel="xlabel",
ylabel="ylabel",
) * df.hvplot.scatter(
x="x2", y="y2", c="value"
)
(df.hvplot.line(label="df") * df2.hvplot.line(label="df2")).opts(legend_position='top_left')
df.hvplot.line(y='COL', datashade=True, aggregator='any')
import hvplot.pandas
gdf.hvplot(c="COL")
gdf.hvplot(c="COL", geo=True, tiles="OSM", frame_width=600, frame_height=600, title="title")
gdf.hvplot(geo=True, tiles="OSM", frame_width=600, frame_height=600, color="red", hover_cols=["timestamp"])
gdf.hvplot.points("lon", "lat", geo=True, tiles="OSM")
gdf.hvplot(
c="red", geo=True, tiles="OSM", frame_width=600, frame_height=600, title="title"
) * gdf2.hvplot(
c="blue", geo=True)
import hvplot.xarray
import matplotlib.pyplot as plt
ds.hvplot(cmap=plt.cm.viridis.name)
da.hvplot.quadmesh(x='longitude', y='latitude', rasterize=True, geo=True, tiles='OSM', cmap='turbo')
da.hvplot.image()
from holoviews.operation.datashader import rasterize
import holoviews as hv
hv.element.tiles.EsriImagery()
hv.element.tiles.EsriImagery().opts(width=600, height=550)
hv_pts = hv.Points(
gdf[["latitude", "longitude"]], kdims=["longitude", "latitude"], vdims=[]
)
hv.Image(arr, kdims=["dim_1", "dim_0"])
import geoviews as gv
import geoviews.feature as gf
gv.extension("bokeh")
Background/tiles
tiles = gv.tile_sources.OSM
tiles = gv.tile_sources.Wikipedia
tiles = gv.tile_sources.CartoMidnight
tiles = gv.tile_sources.EsriImagery
tiles = gv.tile_sources.EsriNatGeo # good for ocean as shows bathymetry
gv.Points(gdf, vdims=[])
gv.Polygons(gdf)
gds = gv.Dataset(ds[VAR])
gds.to(gv.Image).opts(tools=["hover"])
gv_img = gds.to(gv.Image, kdims=["lon", "lat"])
gv_raster = rasterize(gv_img)
gv_pts = gv.Points(gdf)
gf.coastline()
gf.borders()
gf.states(color=None)
gv_raster.opts(
tools=["hover"],
width=1000,
height=600,
cmap=plt.cm.viridis.name,
colorbar=True,
colorbar_position="right",
)
gv_pts.opts(
tools=["hover"], color="black", size=2
)
Interactive plotting
import hvplot.xarray
import panel.widgets as pnw
ds["VAR"].interactive.sel(time=pnw.DiscreteSlider).plot()
import panel as pn
import panel.widgets as pnw
from matplotlib.figure import Figure
pn.extension(sizing_mode="stretch_width")
pn.extension(sizing_mode="scale_both")
pn.panel("# Title").servable()
pn.panel("## subtitle").servable()
value = pn.widgets.Select(name="value:", options=["a", "b"]).servable()
@pn.depends(value)
def plot_value(value: str) -> pn.Column:
fig0 = Figure()
ax0 = fig0.subplots()
df[df["COL"] == value].plot(ax=ax0)
return fig0
pn.panel(fig0).servable()
plot = gdf.hvplot(...)
pn.panel(plot).servable()
pn.Column(
"# hello",
"## yo",
pn.Row(fig0, fig1),
).servable()
pn.pane.HTML(html_report).servable()
file_input = pn.widgets.FileInput(accept=".json")
df = pn.widgets.DataFrame()
def load_json(data):
if data is not None:
df.value = pd.read_json(io.BytesIO(data), orient="index")
return df.value
f = pn.bind(load_json, file_input.param.value)
pn.Column(file_input, f).servable()
file_download = pn.widgets.FileDownload(file='FileDownload.ipynb', filename='custom_filename.ipynb')
def _callback():
if file_input.value is not None:
df = pd.read_csv(io.BytesIO(file_input.value))
return io.BytesIO(df.iloc[0].to_csv(index=False).encode())
else:
return io.BytesIO()
def _disable_download(event):
if file_input.value is None:
file_download.disabled = True
else:
file_download.disabled = False
file_input = pn.widgets.FileInput(accept=".csv", mime_type="text/csv")
file_download = pn.widgets.FileDownload(
filename="out.csv",
callback=_callback,
disabled=True,
)
pn.bind(_disable_download, file_input.param.value, watch=True)
pn.Column(file_input, file_download).servable()
$ panel serve app.py --show --autoreload --port 5007
import datashader as ds
cvs = ds.Canvas(plot_width=850, plot_height=500)
agg = cvs.points(df, 'Longitude', 'Latitude')
img = ds.tf.shade(agg, cmap=colorcet.fire, how='log')