Interactive plotting with HoloViews#

Authors & Contributors#



  • Alejandro Coca-Castro, The Alan Turing Institute (United Kingdom), @acocac

  • Anne Fouilloux, University of Oslo (Norway), @annefou


  • How to clip Xarray DataSet with a shapefile to plot an area of interest?
  • How to generate interactive maps with Holoviews?
  • How to browse multiple times with Holoviews interactive maps?
  • How to generate an interactive 1D plot with Holoviews?
  • Learn about Holoviews and Xarray for creating interactive plots
  • Learn to create interactive maps with Holoviews
  • Learn to create multiple interactive plots with holoviews
  • Learn to create a 1D line plot with holoviews


We will be using HoloViews, a tool part of the HoloViz ecosystem, with Xarray to visualize the Vegetation Condition Index (VCI) [Kog95], a well-established indicator to estimate droughts from remote sensing data.


We will use data that have been generated in the previous episode.

If the dataset is not present in the same folder as this Jupyter notebook, it will be downloaded from zenodo using pooch, a very handy python-based library to download and cache your data files locally (see further info here).

import pooch

cgls_file = pooch.retrieve(


This episode uses the following main Python packages:

Please install these packages if not already available in your Python environment.


In this episode, Python packages are imported when we start to use them. However, for best software practices, we recommend you to install and import all the necessary libraries at the top of your Jupyter notebook.

import xarray as xr

Open local dataset#

cgls_ds = xr.open_dataset(cgls_file)


If you get an error with the previous command, check the previous episode where the input file some_hash-C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.netcdf is downloaded locally and it is in the same directory as your Jupyter Notebook.

Dimensions:  (time: 20, lon: 984, lat: 612)
  * time     (time) datetime64[ns] 2022-01-01 2022-01-11 ... 2022-07-11
  * lon      (lon) float64 8.502 8.505 8.508 8.511 ... 11.42 11.42 11.42 11.43
  * lat      (lat) float64 46.5 46.5 46.49 46.49 ... 44.69 44.69 44.68 44.68
Data variables:
    NDVI     (time, lat, lon) float64 ...

Clipping data according to a polygon#

One of the basic concepts in GIS is to clip data using a vector geometry. Xarray is not directly capable of dealing with vectors but thanks to Rioxarray that can be easily achieved. Rioxarray extends Xarray with most of the features that Rasterio (GDAL) brings.

Read a shapefile with the Area Of Interest (AOI)#

import geopandas as gpd

We define the area of interest using the Global Administrative Unit Layers GAUL G2015_2014 provided by FAO-UN (see Documentation). GeoPandas, a python-based library extending the capabilities of Pandas to deal with geometry and spatial operations, will help to manage geodata.

The official data distribution from FAO is through the WFS service (see below how to retrieve data):

GAUL = gpd.read_file(''

Unfortunately it seems that the service is pretty slow. As an alternative to this approach the JRC MARS unit is distributing the original dataset that was in shapefile format. To accelerate the fetch we highly recommend to follow this approach.

For the training course, we also created a tiny file containing information about Italy only.

    GAUL = gpd.read_file('Italy.geojson')
    GAUL = gpd.read_file('zip+') 

Data are organized in a tabular structure. For each element an index, data (made of columns) and a geometry are defined.

Geometries are defined through shapely geometry objects with three different basic classes:

  • Points and Multi-Points

  • Lines and Multi-Lines

  • Polygons and Multi-Polygons

asap1_id name1 name1_shr name0 asap0_id name0_shr km2_tot km2_crop km2_range an_crop an_range water_lim geometry
0 1172 Friuli-venezia Giulia Friuli Italy 172 Italy 7722 2995 1111 1 1 0 POLYGON ((12.79517 46.64600, 12.80945 46.63482...
1 1202 Calabria Calabria Italy 172 Italy 15203 7152 2127 1 1 1 POLYGON ((16.42261 40.14416, 16.43624 40.12897...
2 1204 Campania Campania Italy 172 Italy 13618 7376 2378 1 1 1 POLYGON ((14.16099 41.48058, 14.16653 41.48039...
3 1207 Abruzzi Abruzzi Italy 172 Italy 10797 4699 3300 1 1 1 POLYGON ((13.91523 42.89441, 13.92206 42.85505...
4 1211 Emilia-romagna Emilia-romagna Italy 172 Italy 22204 14958 771 1 1 1 POLYGON ((12.49151 43.91780, 12.48442 43.90761...

In the cell below, we subset the polygon geometry in which the name1 field equals to Lombardia.

AOI_name = 'Lombardia'
AOI = GAUL[GAUL.name1 == AOI_name]
AOI_poly = AOI.geometry
14    POLYGON ((10.23973 46.62177, 10.25084 46.61110...
Name: geometry, dtype: geometry

In a second step we set a EPSG:4326 Geodetic coordinate reference system to the polygon geometry. To achieve this we need to rely on rioxarray that extends xarray with the rasterio capabilities. The rio accessor is activated through importing rioxarray as has been done at the top., inplace=True)
Dimensions:      (time: 20, lon: 984, lat: 612)
  * time         (time) datetime64[ns] 2022-01-01 2022-01-11 ... 2022-07-11
  * lon          (lon) float64 8.502 8.505 8.508 8.511 ... 11.42 11.42 11.43
  * lat          (lat) float64 46.5 46.5 46.49 46.49 ... 44.69 44.69 44.68 44.68
    spatial_ref  int64 0
Data variables:
    NDVI         (time, lat, lon) float64 ...

Once this has been done we can clip the data with the polygon that has been obtained through geopandas at the beginning of the notebook.

NDVI_AOI =, crs=4326)

Visualize with matplotlib#

import matplotlib.pyplot as plt
import as ccrs
fig = plt.figure(1, figsize=[20, 10])

# We're using cartopy and are plotting in PlateCarree projection 
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())
#ax.set_extent([15.5, 27.5, 36, 41], crs=ccrs.PlateCarree()) # lon1 lon2 lat1 lat2

NDVI_AOI.sel(time='2022-06-01').plot(ax=ax, transform=ccrs.PlateCarree(), cmap="RdYlGn")

# One way to customize your title
plt.title("S3 NDVI over Lombardia", fontsize=18)
Text(0.5, 1.0, 'S3 NDVI over Lombardia')
/usr/share/miniconda/envs/foss4g/lib/python3.10/site-packages/cartopy/io/ DownloadWarning: Downloading:
  warnings.warn(f'Downloading: {url}', DownloadWarning)

Visualization with HoloViews#

import holoviews as hv
import hvplot.xarray
import hvplot.pandas