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Improve the home page (#876)
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Co-authored-by: Maxime Liquet <35924738+maximlt@users.noreply.github.com>
Co-authored-by: Marc Skov Madsen <masma@orsted.com>
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@MarcSkovMadsen @maximlt
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Expand Up @@ -21,65 +21,65 @@ width: 70%
---
```

---
**`.hvplot()` is a powerful and interactive Pandas-like `.plot()` API**

**`.hvplot()` for a more versatile and powerful `.plot()` API**
---

By replacing `.plot()` by `.hvplot()` you get an interactive [Bokeh](https://bokeh.org/) plot.
By replacing `.plot()` with `.hvplot()` you get an interactive plot.

```{code-cell} ipython3
import hvplot.pandas # noqa
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()` supports many data structures of the PyData ecosystem on top of [Pandas](https://pandas.pydata.org/):

::::{tab-set}
---

:::{tab-item} Xarray
```python
import hvplot.xarray # noqa
import xarray as xr
`.hvplot()` can generate plots from [Pandas](https://pandas.pydata.org/) DataFrames and many other data structures of the PyData ecosystem:

xr_ds = xr.tutorial.open_dataset('air_temperature').load().sel(time='2013-06-01 12:00')
xr_ds.hvplot()
```
```{image} ./_static/home/xarray.gif
---
alt: xarray support
align: center
---
```
:::
::::{tab-set}

:::{tab-item} Dask
```python
import dask
import hvplot.dask # noqa
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')
```
```{image} ./_static/home/dask.gif
---
alt: dask support
alt: Works with Dask
align: center
---
:::
:::{tab-item} GeoPandas
```python
import geopandas as gpd
import hvplot.pandas # noqa
import hvplot.pandas
gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
gdf.hvplot(global_extent=True, tiles=True)
```
```{image} ./_static/home/geopandas.gif
---
alt: geopandas support
alt: Works with GeoPandas
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')
```
```{image} ./_static/home/intake.gif
---
alt: Works with Intake
align: center
---
:::
Expand All @@ -94,50 +94,67 @@ hvnx.draw(G, with_labels=True)
```
```{image} ./_static/home/networkx.gif
---
alt: networkx support
alt: Works with Networkx
align: center
---
:::
:::{tab-item} Intake
:::{tab-item} Pandas
```python
import hvplot.intake # noqa
from hvplot.sample_data import catalogue as cat
import hvplot.pandas
from bokeh.sampledata.autompg import autompg_clean as df
cat.us_crime.hvplot.line(x='Year', y='Violent Crime rate')
table = df.groupby(['origin', 'mfr'])['mpg'].mean().sort_values().tail(5)
table.hvplot.barh('mfr', 'mpg', by='origin', stacked=True)
```
```{image} ./_static/home/intake.gif
```{image} ./_static/home/pandas.gif
---
alt: intake support
alt: Works with Pandas
align: center
---
```
:::

:::{tab-item} Streamz
```python
import hvplot.streamz # noqa
import hvplot.streamz
from streamz.dataframe import Random

df_streamz = Random(interval='200ms', freq='50ms')
df_streamz.hvplot()
```
```{image} ./assets/streamz_demo.gif
---
alt: streamz support
alt: Works with Streamz
align: center
---
:::
::::
:::{tab-item} Xarray
```python
import hvplot.xarray
import xarray as xr
`.hvplot()` uses Bokeh as the backend by default. It can also generate plots with [Matplotlib](https://matplotlib.org/) or [Plotly](https://plotly.com/).
xr_ds = xr.tutorial.open_dataset('air_temperature').load().sel(time='2013-06-01 12:00')
xr_ds.hvplot()
```
```{image} ./_static/home/xarray.gif
---
alt: Works with XArray
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
```{code-cell} ipython3
import hvplot.pandas # noqa
import hvplot.pandas
from bokeh.sampledata.penguins import data as df
df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
Expand All @@ -156,7 +173,7 @@ df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
```
```{image} ./_static/home/matplotlib.png
---
alt: matplotlib as a plotting backend
alt: Works with Matplotlib
align: center
---
```
Expand All @@ -174,7 +191,7 @@ df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
```
```{image} ./_static/home/plotly.gif
---
alt: plotly as a plotting backend
alt: Works with Plotly
align: center
---
:::
Expand All @@ -187,7 +204,7 @@ align: center
:::{tab-item} Layout
```python
import hvplot.pandas # noqa
import hvplot.pandas
from hvplot.sample_data import us_crime as df
plot1 = df.hvplot(x='Year', y='Violent Crime rate', width=400)
Expand All @@ -204,7 +221,7 @@ align: center

:::{tab-item} Overlay
```python
import hvplot.pandas # noqa
import hvplot.pandas
import pandas
from bokeh.sampledata.penguins import data

Expand All @@ -221,7 +238,7 @@ align: center

:::{tab-item} Widgets
```python
import hvplot.pandas # noqa
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')
Expand All @@ -236,7 +253,7 @@ align: center

:::{tab-item} Large Data
```python
import hvplot.pandas # noqa
import hvplot.pandas
from hvplot.sample_data import catalogue as cat

df = cat.airline_flights.read()
Expand All @@ -252,7 +269,7 @@ align: center

:::{tab-item} Geographic plots
```python
import hvplot.xarray # noqa
import hvplot.xarray
import xarray as xr, cartopy.crs as crs

air_ds = xr.tutorial.open_dataset('air_temperature').load()
Expand All @@ -275,13 +292,13 @@ 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.
By starting a data pipeline with [`.interactive()`](https://pyviz-dev.github.io/hvplot/getting_started/interactive.html) 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
```python
import hvplot.pandas # noqa
import hvplot.pandas
import panel as pn
from bokeh.sampledata.penguins import data as df

Expand All @@ -301,7 +318,7 @@ align: center

:::{tab-item} Xarray
```python
import hvplot.xarray # noqa
import hvplot.xarray
import panel as pn
import xarray as xr

Expand All @@ -320,10 +337,10 @@ align: center

::::

`.interactive()` supports displaying the pipeline output with `.hvplot()`.
`.interactive()` supports displaying the pipeline output with `.hvplot()`. You can even output to any other output that [Panel](https://panel.holoviz.org/reference/index.html) supports using `.pipe(...)`.

```python
import hvplot.xarray # noqa
import hvplot.xarray
import panel as pn
import xarray as xr

Expand All @@ -336,16 +353,13 @@ da.interactive(loc='left') \
.quantile(q=w_quantile, dim='lon') \
.hvplot(ylabel='Air Temperature [K]', width=500)
```

```{image} ./_static/home/interactive_hvplot.gif
---
alt: interactive pipeline with an hvplot output
align: center
---
```

---

**`explorer()` to explore data in a web application**

The *Explorer* is a [Panel](https://panel.holoviz.org) web application that can be displayed in a Jupyter notebook and that can be used to quickly create customized plots.
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