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A familiar and high-level API for data exploration and visualization
---
alt: hvPlot diagram
align: center
width: 70%
---
.hvplot() is a powerful and interactive Pandas-like .plot() API
By replacing .plot() with .hvplot() you get an interactive figure. Try it out below!
import hvplot.pandas
from bokeh.sampledata.penguins import data as df
df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
.hvplot() can generate plots from Pandas DataFrames and many other data structures of the PyData ecosystem:
::::{tab-set}
:::{tab-item} Xarray
import hvplot.xarray
import xarray as xr
xr_ds = xr.tutorial.open_dataset('air_temperature').load().sel(time='2013-06-01 12:00')
xr_ds.hvplot()---
alt: Works with XArray
align: center
---
:::
:::{tab-item} Pandas
import hvplot.pandas
from bokeh.sampledata.autompg import autompg_clean as df
table = df.groupby(['origin', 'mfr'])['mpg'].mean().sort_values().tail(5)
table.hvplot.barh('mfr', 'mpg', by='origin', stacked=True)---
alt: Works with Pandas
align: center
---
:::
:::{tab-item} Dask
import dask
import hvplot.dask
df_dask = dask.dataframe.from_pandas(df, npartitions=2)
df_dask.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')---
alt: Works with Dask
align: center
---
:::
:::{tab-item} GeoPandas
```python
import geopandas as gpd
import hvplot.pandas
gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
gdf.hvplot(global_extent=True, tiles=True)
---
alt: Works with GeoPandas
align: center
---
:::
:::{tab-item} Polars
```python
import polars
import hvplot.polars
df_polars = polars.from_pandas(df)
df_polars.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
---
alt: Works with Polars
align: center
---
:::
:::{tab-item} Intake
```python
import hvplot.intake
from hvplot.sample_data import catalogue as cat
cat.us_crime.hvplot.line(x='Year', y='Violent Crime rate')
---
alt: Works with Intake
align: center
---
:::
:::{tab-item} NetworkX
```python
import hvplot.networkx as hvnx
import networkx as nx
G = nx.petersen_graph()
hvnx.draw(G, with_labels=True)
---
alt: Works with Networkx
align: center
---
:::
:::{tab-item} Streamz
```python
import hvplot.streamz
from streamz.dataframe import Random
df_streamz = Random(interval='200ms', freq='50ms')
df_streamz.hvplot()
---
alt: Works with Streamz
align: center
---
:::
::::
`.hvplot()` can generate plots with [Bokeh](https://bokeh.org/) (default), [Matplotlib](https://matplotlib.org/) or [Plotly](https://plotly.com/).
::::{tab-set}
:::{tab-item} Bokeh
```python
import hvplot.pandas
from bokeh.sampledata.penguins import data as df
df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
---
alt: Works with Bokeh (default)
align: center
---
:::
:::{tab-item} Matplotlib
import hvplot.pandas
from bokeh.sampledata.penguins import data as df
hvplot.extension('matplotlib')
df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')---
alt: Works with Matplotlib
align: center
---
:::
:::{tab-item} Plotly
import hvplot.pandas
from bokeh.sampledata.penguins import data as df
hvplot.extension('plotly')
df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')---
alt: Works with Plotly
align: center
---
:::
::::
`.hvplot()` sources its power in the [HoloViz](https://holoviz.org/) ecosystem. With [HoloViews](https://holoviews.org/) you get the ability to easily layout and overlay plots, with [Panel](https://panel.holoviz.org) you can get more interactive control of your plots with widgets, with [DataShader](https://datashader.org/) you can visualize and interactively explore very large data, and with [GeoViews](https://geoviews.org/) you can create geographic plots.
::::{tab-set}
:::{tab-item} Layout
```python
import hvplot.pandas
from hvplot.sample_data import us_crime as df
plot1 = df.hvplot(x='Year', y='Violent Crime rate', width=400)
plot2 = df.hvplot(x='Year', y='Burglary rate', width=400)
plot1 + plot2
---
alt: laying out plots
align: center
---
:::
:::{tab-item} Overlay
import hvplot.pandas
import pandas
from bokeh.sampledata.penguins import data
df = data.groupby('species')['bill_length_mm'].describe().sort_values('mean')
df.hvplot.scatter(y='mean') * dff.hvplot.errorbars(y='mean', yerr1='std')---
alt: overlaying plots
align: center
---
:::
:::{tab-item} Widgets
import hvplot.pandas
from bokeh.sampledata.penguins import data as df
df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', groupby='island', widget_location='top')---
alt: more control with widgets
align: center
---
:::
:::{tab-item} Large Data
import hvplot.pandas
from hvplot.sample_data import catalogue as cat
df = cat.airline_flights.read()
df.hvplot.scatter(x='distance', y='airtime', rasterize=True, cnorm='eq_hist', width=500)---
alt: visualize and explore large data
align: center
---
:::
:::{tab-item} Geographic plots
import hvplot.xarray
import xarray as xr, cartopy.crs as crs
air_ds = xr.tutorial.open_dataset('air_temperature').load()
air_ds.air.sel(time='2013-06-01 12:00').hvplot.quadmesh(
'lon', 'lat', projection=crs.Orthographic(-90, 30), project=True,
global_extent=True, cmap='viridis', coastline=True
)---
alt: geographic plots
align: center
---
:::
::::
.interactive() to turn data pipelines into widget-based interactive applications
By starting a data pipeline with .interactive() you can then inject widgets into an extract and transform data pipeline. The pipeline output dynamically updates with widget changes, making data exploration in Jupyter notebooks in particular a lot more efficient.
::::{tab-set}
:::{tab-item} Pandas
import hvplot.pandas
import panel as pn
from bokeh.sampledata.penguins import data as df
w_sex = pn.widgets.MultiSelect(name='Sex', value=['MALE'], options=['MALE', 'FEMALE'])
w_body_mass = pn.widgets.FloatSlider(name='Min body mass', start=2700, end=6300, step=50)
dfi = df.interactive(loc='left')
dfi.loc[(dfi['sex'].isin(w_sex)) & (dfi['body_mass_g'] > w_body_mass)]['bill_length_mm'].describe()---
alt: interactive app from pandas
align: center
---
:::
:::{tab-item} Xarray
import hvplot.xarray
import panel as pn
import xarray as xr
w_time = pn.widgets.IntSlider(name='time', start=0, end=10)
da = xr.tutorial.open_dataset('air_temperature').air
da.interactive.isel(time=w_time).mean().item() - da.mean().item()---
alt: interactive app from xarray
align: center
---
:::
::::
.interactive() supports displaying the pipeline output with .hvplot(). You can even output to any other output that Panel supports using .pipe(...).
import hvplot.xarray
import panel as pn
import xarray as xr
da = xr.tutorial.open_dataset('air_temperature').air
w_quantile = pn.widgets.FloatSlider(name='quantile', start=0, end=1)
w_time = pn.widgets.IntSlider(name='time', start=0, end=10)
da.interactive(loc='left') \
.isel(time=w_time) \
.quantile(q=w_quantile, dim='lon') \
.hvplot(ylabel='Air Temperature [K]', width=500)---
alt: interactive pipeline with an hvplot output
align: center
---
.hvplot.explorer() to explore data in a web application
The Explorer is a Panel web application that can be displayed in a Jupyter notebook and that can be used to quickly create customized plots.
import hvplot.pandas
from bokeh.sampledata.penguins import data as df
hvexplorer = df.hvplot.explorer()
hvexplorer---
alt: explore data with the hvplot explorer
align: center
---
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