We’d like to introduce you to an awesome spatial (geographic) data visualization tool: keplergl.

Keplergl is completely open source by Uber and is the default tool for spatial data visualization within Uber.

Through its open interface package keplergl for Python, we can pass in a variety of formats of data by writing Python code in jupyter notebook, and use its built-in rich spatial data visualization functions in its interactive window embedded in notebook. Here are 3 main addresses for learning.

1. the official website address: https://kepler.gl/

2. jupyter notebook manual address: https://github.com/keplergl/kepler.gl/tree/master/docs/keplergl-jupyter#geojson

3. Case study address: https://github.com/keplergl/kepler.gl/tree/master/bindings/kepler.gl-jupyter/notebooks

Installation

The installation of keplergl is very simple.

pip install keplergl

Amazing graphics

A wave of stunning graphics are coming.

Getting Started

import pandas as pd
import geopandas as gpd

from keplergl import KeplerGl

# Create an object
kep1 = KeplerGl(height=600)
# Activate the object and load it into jupyter notebook
kep1

As you can see, after running the basic code in Jupyter directly generated the built-in graphics, the graphics themselves are also dynamic; dark black background is also my favorite:

Adding data

By default, keplergl can add 3 types of data:

1. csv
2. GeoJSON
3. DataFrame

csv format

There is a csv data in the local directory: china.csv, which records the latitude and longitude of each province in China.

with open("china.csv", "r") as f:
    csv_data = f.read()
    
# add_data add data
kep1.add_data(data=csv_data, name="csv_kep")
kep1

DataFrame format

china = pd.read_csv("china.csv")
kep1.add_data(data=china, name="dataframe_kep")
kep1

GeoJson format

url = 'http://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_500k.json'
country_gdf = gpd.read_file(url) # geopandas read json file

kep1.add_data(data=country_gdf, name="state")
kep1

Custom graphics

Keplergl’s customization method: the criticality button. Once inside, you can customize the operation

Saving and reusing configurations

The configuration of the instantiated kep can be saved and reused in the following instance objects.

1. Save.

# Save as a file
with open('config1.py','w') as f:
    f.write('config={}'.format(kep1.config))
    
# Run: magic command %run
%run config1.py

2. Reuse

kep2 = KeplerGl(height=400,
                data={"layer1":df},
                config=kep1.config # configuration of kep1
               )
kep2

Save graphics

1. minimalist version, mainly the file name

kep1.save_to_html(file_name="first_kep.html")

2. full version: file name, configuration, data, readability

# 4 parameters
kep1.save_to_html(file_name="first_kep.html",
                  data={'data_1':china},
                  config=config,
                  read_only=True
                 )

Web app

The operations shown above are all done in the notebook, we can also do them directly online: https://kepler.gl/demo

We will share more articles after we have studied this tool seriously, this library is worth studying

Source