kepler.gl for Jupyter User Guide

Install
1. Load kepler.gl Map
- KeplerGl()
2. Add Data
- .add_data()
- .data
3. Data Format
- CSV
- GeoJSON
- DataFrame
- GeoDataFrame
- WKT
4. Customize the map
5. Save and load config
- .config
6. Match config with data
7. Save Map
- .save_to_html()
Demo Notebooks

Install

pip install keplergl

1. Load keplergl map

`KeplerGl()`

Input:
- height optional default: 400
  
  Height of the map display
- data dict optional
  
  Datasets as a dictionary, key is the name of the dataset. Read more on Accepted data format
- config dict optional Map config as a dictionary. The dataId in the layer and filter settings should match the name of the dataset they are created under

The following command will load kepler.gl widget below a cell. The map object created here is map_1 it will be used throughout the code example in this doc.

# Load an empty map
from keplergl import KeplerGl
map_1 = KeplerGl()
map_1

You can also create the map and pass in the data or data and config at the same time. Follow the instruction to match config with data

# Load a map with data and config and height
from keplergl import KeplerGl
map_2 = KeplerGl(height=400, data={"data_1": my_df}, config=config)
map_2

2. Add Data

`.add_data()`

Inputs
- data required CSV, GeoJSON or DataFrame. Read more on Accepted data format
- name required Name of the data entry.

name of the dataset will be the saved to the dataId property of each layer, filter and interactionConfig in the config.

kepler.gl expected the data to be CSV, GeoJSON, DataFrame or GeoDataFrame. You can call add_data multiple times to add multiple datasets to kepler.gl

# DataFrame
df = pd.read_csv('hex-data.csv')
map_1.add_data(data=df, name='data_1')

# CSV
with open('csv-data.csv', 'r') as f:
    csvData = f.read()
map_1.add_data(data=csvData, name='data_2')

# GeoJSON as string
with open('sf_zip_geo.json', 'r') as f:
    geojson = f.read()

map_1.add_data(data=geojson, name='geojson')

`.data`

Print the current data added to the map. As a Dict

map_1.data
# {'data_1': 'hex_id,value\n89283082c2fffff,64\n8928308288fffff,73\n89283082c07ffff,65\n89283082817ffff,74\n89283082c3bffff,66\n8...`,
#  'data_3': 'location, lat, lng, name\n..',
#  'data_3': '{"type": "FeatureCollecti...'}

3. Data Format

kepler.gl supports CSV, GeoJSON, Pandas DataFrame or GeoPandas GeoDataFrame.

`CSV`

You can create a CSV string by reading from a CSV file.

with open('csv-data.csv', 'r') as f:
    csvData = f.read()
# csvData = "hex_id,value\n89283082c2fffff,64\n8928308288fffff,73\n89283082c07ffff,65\n89283082817ffff,74\n89283082c3bffff,66\n8..."
map_1.add_data(data=csvData, name='data_2')

`GeoJSON`

According to GeoJSON Specification (RFC 7946): GeoJSON is a format for encoding a variety of geographic data structures. A GeoJSON object may represent a region of space (a Geometry), a spatially bounded entity (a Feature), or a list of Features (a FeatureCollection). GeoJSON supports the following geometry types: Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection. Features in GeoJSON contain a Geometry object and additional properties, and a FeatureCollection contains a list of Features.

kepler.gl supports all the GeoJSON types above excepts GeometryCollection. You can pass in either a single Feature or a FeatureCollection. You can format the GeoJSON either as a string or a dict type

feature = {
    "type": "Feature",
    "properties": {"name": "Coors Field"},
    "geometry": {"type": "Point", "coordinates": [-104.99404, 39.75621]}
}

featureCollection = {
    "type": "FeatureCollection",
    "features": [{
        "type": "Feature",
        "geometry": {"type": "Point", "coordinates": [102.0, 0.5]},
        "properties": {"prop0": "value0"}
    }]
}

map_1.add_data(data=feature, name="feature")
map_1.add_data(data=featureCollection, name="feature_collection")

Geometries (Polygons, LindStrings) can be embedded into CSV or DataFrame with a GeoJSON Json string. Use the geometry property of a Feature, which includes type and coordinates.

# GeoJson Feature geometry
geometryString = {
    'type': 'Polygon',
    'coordinates': [[[-74.158491,40.835947],[-74.148473,40.834522],[-74.142598,40.833128],[-74.151923,40.832074],[-74.158491,40.835947]]]
}

# create json string
json_str = json.dumps(geometryString)

# create data frame
df_with_geometry = pd.DataFrame({
    'id': [1],
    'geometry_string': [json_str]
})

# add to map
map_1.add_data(df_with_geometry, "df_with_geometry")

`DataFrame`

kepler.gl accepts pandas.DataFrame

df = pd.DataFrame(
    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})

w1.add_data(data=df, name='cities')

`GeoDataFrame`

kepler.gl accepts geopandas.GeoDataFrame, it automatically converts the current geometry column from shapely to wkt string.

url = 'http://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_500k.json'
country_gdf = geopandas.read_file(url)
w1.add_data(data=country_gdf, name="state")

`WKT`

You can embed geometries (Polygon, LineStrings etc) into CSV or DataFrame using WKT

# WKT
wkt_str = 'POLYGON ((-74.158491 40.835947, -74.130031 40.819962, -74.148818 40.830916, -74.151923 40.832074, -74.158491 40.835947))'

df_w_wkt = pd.DataFrame({
    'id': [1],
    'wkt_string': [wkt_str]
})

map_1.add_data(df_w_wkt, "df_w_wkt")

4. Customize the map

Interact with kepler.gl and customize layers and filters. Map data and config will be stored locally to the widget state. To make sure the map state is saved, select Widgets > Save Notebook Widget State, before shutting down the kernel.

5. Save and load config

`.config`

you can print your current map configuration at any time.

map_1.config
## {u'config': {u'mapState': {u'bearing': 2.6192893401015205,
#  u'dragRotate': True,
#   u'isSplit': False,
#   u'latitude': 37.76209132041332,
#   u'longitude': -122.42590232651203,

When the map is final, you can copy this config and load it later to reproduce the same map. Follow the instruction to match config with data. There are 2 ways to apply config to a map:

Directly apply config to the map.

config = {
    'version': 'v1',
    'config': {
        'mapState': {
            'latitude': 37.76209132041332,
            'longitude': -122.42590232651203,
            'zoom': 12.32053899007826
        }
        ...
    }
},
map_1.add_data(data=df, name='data_1')
map_1.config = config

Load it when creating the map

map_1 = KeplerGl(height=400, data={'data_1': my_df}, config=config)

If want to load the map next time with this saved config, the easiest way to do is to save the it to a file and use the magic command %run to load it w/o cluttering up your notebook.

# Save map_1 config to a file
with open('hex_config.py', 'w') as f:
   f.write('config = {}'.format(map_1.config))

# load the config
%run hex_config.py

6. Match config with data

All layers, filters and tooltips are associated with a specific dataset. Therefore the data and config in the map has to be able to match each other. The name of the dataset is assigned to:

dataId of layer.config,
dataId of filter
key in interactionConfig.tooltip.fieldToShow.

You can use the same config on another dataset with the same name and schema.

7. Save Map

When you click in the map and change settings, config is saved to widget state. Closing the notebook and reopen it will reload current map. However, you need to manually select Widget > Save Notebook Widget State before shut downing the kernel to make sure it will be reloaded.

`.save_to_html()`

input
- data: optional A data dictionary {"name": data}, if not provided, will use current map data
- config: optional map config dictionary, if not provided, will use current map config
- file_name: optional the html file name, default is keplergl_map.html
- read_only: optional if read_only is True, hide side panel to disable map customization

You can export your current map as an interactive html file.

# this will save current map
map_1.save_to_html(file_name='first_map.html')

# this will save map with provided data and config
map_1.save_to_html(data={'data_1': df}, config=config, file_name='first_map.html')

# this will save map with the interaction panel disabled
map_1.save_to_html(file_name='first_map.html' read_only=True)

Demo Notebooks

Load kepler.gl: Load kepler.gl widget, add data and config
Geometry as String: Embed Polygon geometries as GeoJson and WKT inside a CSV
GeoJSON: Load GeoJSON to kepler.gl
DataFrame: Load DataFrame to kepler.gl
GeoDataFrame: Load GeoDataFrame to kepler.gl

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user-guide.md

user-guide.md

kepler.gl for Jupyter User Guide

Install

1. Load keplergl map

`KeplerGl()`

2. Add Data

`.add_data()`

`.data`

3. Data Format

`CSV`

`GeoJSON`

`DataFrame`

`GeoDataFrame`

`WKT`

4. Customize the map

5. Save and load config

`.config`

6. Match config with data

7. Save Map

`.save_to_html()`

Demo Notebooks

Files

user-guide.md

Latest commit

History

user-guide.md

File metadata and controls

kepler.gl for Jupyter User Guide

Install

1. Load keplergl map

KeplerGl()

2. Add Data

.add_data()

.data

3. Data Format

CSV

GeoJSON

DataFrame

GeoDataFrame

WKT

4. Customize the map

5. Save and load config

.config

6. Match config with data

7. Save Map

.save_to_html()

Demo Notebooks

`KeplerGl()`

`.add_data()`

`.data`

`CSV`

`GeoJSON`

`DataFrame`

`GeoDataFrame`

`WKT`

`.config`

`.save_to_html()`