Get started with Earth Engine for Python

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This quickstart will give you an interactive introduction to visualizing and analyzing geospatial data with the Earth Engine Python interface.

Before you begin

Register or create a Google Cloud Project; you'll be prompted to complete the following steps. If you already have a project registered for Earth Engine access, skip to the next section.

  • Select the project's purpose: commercial or noncommercial.
  • If the purpose is noncommercial, select a project type.
  • Create a new Google Cloud project or select an existing project.
  • If the purpose is commercial, verify or set up billing for your project.
  • Confirm your project information.

    Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources owned by the project.

Notebook setup

Jupyter notebooks allow you to use Earth Engine and explore results interactively. The quickest way to get started is with a notebook in Google Colab notebook. You can either open a new notebook and copy the following code chunks into individual cells or use the prefilled Earth Engine Python Quickstart notebook.
  1. Import the Earth Engine and geemap libraries. 
    import ee
import geemap.core as geemap

  1. Authenticate and initialize the Earth Engine service. Follow the resulting prompts to complete authentication. Be sure to replace PROJECT_ID with the name of the project you set up for this quickstart. 
    ee.Authenticate()
ee.Initialize(project='PROJECT_ID')

Add raster data to a map

  1. Load climate data for a given period and display its metadata. 
    jan_2023_climate = (
    ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_AGGR')
    .filterDate('2023-01', '2023-02')
    .first()
)
jan_2023_climate

  1. Instantiate a map object and add the temperature band as a layer with specific visualization properties. Display the map. 
    m = geemap.Map(center=[30, 0], zoom=2)

vis_params = {
    'bands': ['temperature_2m'],
    'min': 229,
    'max': 304,
    'palette': 'inferno',
}
m.add_layer(jan_2023_climate, vis_params, 'Temperature (K)')
m

Add vector data to a map

  1. Create a vector data object with points for three cities. 
    cities = ee.FeatureCollection([
    ee.Feature(ee.Geometry.Point(10.75, 59.91), {'city': 'Oslo'}),
    ee.Feature(ee.Geometry.Point(-118.24, 34.05), {'city': 'Los Angeles'}),
    ee.Feature(ee.Geometry.Point(103.83, 1.33), {'city': 'Singapore'}),
])
cities
  1. Add the city locations to the map and redisplay it. 
    m.add_layer(cities, name='Cities')
m

Extract and chart data

  1. Import the Altair charting library. 
    %pip install -q --upgrade altair
import altair as alt

  1. Extract the climate data for the three cities as a pandas DataFrame. 
    city_climates = jan_2023_climate.reduceRegions(cities, ee.Reducer.first())

city_climates_dataframe = ee.data.computeFeatures(
    {'expression': city_climates, 'fileFormat': 'PANDAS_DATAFRAME'}
)
city_climates_dataframe
  1. Plot the temperature for the cities as a bar chart. 
    alt.Chart(city_climates_dataframe).mark_bar(size=100).encode(
    alt.X('city:N', sort='y', axis=alt.Axis(labelAngle=0), title='City'),
    alt.Y('temperature_2m:Q', title='Temperature (K)'),
    tooltip=[
        alt.Tooltip('city:N', title='City'),
        alt.Tooltip('temperature_2m:Q', title='Temperature (K)'),
    ],
).properties(title='January 2023 temperature for selected cities', width=500)

What's next