Python Installation

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Python support

The Earth Engine Python client library is compatible with Python versions supported by Google Cloud. Support is updated annually following the Python point release schedule (PEP 602; Status of Python versions). Using unsupported Python versions may cause authentication failures, unexpected behavior, or failure of certain operations.

Install options

If you are using Google Colab, the latest version of the Earth Engine Python client library has already been installed (via pip). Try the following notebook to get started with Earth Engine and Colab:

Run in Google Colab

View source on GitHub

If you don't use Colab, the Earth Engine client library can be manually installed and updated on your system using conda (recommended) or pip:

pip install earthengine-api

pip install earthengine-api --upgrade

Package import

The Python API package is called ee. It must be imported and initialized for each new Python session and script:

import ee

Authentication and Initialization

Prior to using the Earth Engine Python client library, you need to authenticate and use the resultant credentials to initialize the Python client. Run:

ee.Authenticate()

This will select the best authentication mode for your environment, and prompt you to confirm access for your scripts. To initialize, you will need to provide a project that you own, or have permissions to use. This project will be used for running all Earth Engine operations:

ee.Initialize(project='my-project')

See the authentication guide for troubleshooting and to learn more about authentication modes and Cloud projects.

Hello world!

Here is a short script to test that you're all set for working with Earth Engine.

import ee
ee.Authenticate()
ee.Initialize(project='my-project')
print(ee.String('Hello from the Earth Engine servers!').getInfo())

Syntax

Both the Python and JavaScript APIs access the same server-side functionality, but client-side expressions (learn more about client vs. server) can vary because of language syntax differences. The following table includes a list of the common syntax differences you'll encounter when working with the Python API relative to the JavaScript API.

function myFun(arg) {
  return arg;
}

var myFun = function(arg) {
  return arg;
};

def my_fun(arg):
  return arg

var foo = col.map(function(arg) {
  return arg;
});

foo = col.map(lambda arg: arg)

var myVar = 'var';

my_var = 'var'

var match = such.and(that);
var match = such.or(that);
var match = such.not(that);

match = such.And(that)
match = such.Or(that)
match = such.Not(that)

var foo = my.really()
              .reallyLong()
              .methodChain();

foo = (my.really()
       .reallyLong()
       .methodChain())

var dic = {'key': value};
var dic = {key: value};

dic = {'key': value}

var value = dic.key;
var value = dic['key'];

value = dic['key']

// Positional arguments.
var foo = fun(argX, argY, argZ);
// Keyword arguments object.
var foo = fun({y: argY});

# Positional arguments.
foo = fun(arg_x, arg_y, arg_z)
# Keyword arguments dictionary.
foo = fun(**{'y': arg_y})
# Keyword arguments.
foo = fun(x=arg_x, z=arg_z)

var t = true;
var f = false;

t = True
f = False

var na = null;

na = None

//

#

Date objects

Define and manipulate client-side date objects with the datetime module. Include the module in your script:

import datetime

Convert ee.Date to client-side date:

ee_date = ee.Date('2020-01-01')
py_date = datetime.datetime.utcfromtimestamp(ee_date.getInfo()['value']/1000.0)

Convert client-side date to ee.Date:

py_date = datetime.datetime.utcnow()
ee_date = ee.Date(py_date)

Exporting data

Exporting data with the Python API requires the use of the ee.batch module, which provides an interface to the Export functions. Pass parameter arguments as you would with the JavaScript API, minding the differences noted in the syntax table above. Export tasks must be started by calling the start() method on a defined task. Query a task's status by calling the status() method on it. The following example demonstrates exporting an ee.Image object.

Create an export task:

task = ee.batch.Export.image.toDrive(image=my_image,  # an ee.Image object.
                                     region=my_geometry,  # an ee.Geometry object.
                                     description='mock_export',
                                     folder='gdrive_folder',
                                     fileNamePrefix='mock_export',
                                     scale=1000,
                                     crs='EPSG:4326')

Start an export task:

task.start()

Check export task status:

task.status()

The result of task.status() is a dictionary containing information such as the state of the task and its ID.

{
  'state': 'READY',
  'description': 'my_export_task',
  'creation_timestamp_ms': 1647567508236,
  'update_timestamp_ms': 1647567508236,
  'start_timestamp_ms': 0,
  'task_type': 'EXPORT_IMAGE',
  'id': '56TVJIZABUMTD5CJ5YHTMYK4',
  'name': 'projects/earthengine-legacy/operations/56TVJIZABUMTX5CJ5HHTMYK4'
}

You can monitor task progress using the state field. See the Processing Environments page for a list of state values and more information on task lifecycle.

Printing objects

Printing an Earth Engine object in Python prints the serialized request for the object, not the object itself. Refer to the Client vs. server page to understand the reason for this.

Call getInfo() on Earth Engine objects to get the desired object from the server to the client:

# Load a Landsat image.
img = ee.Image('LANDSAT/LT05/C02/T1_L2/LT05_034033_20000913')

# Print image object WITHOUT call to getInfo(); prints serialized request instructions.
print(img)

# Print image object WITH call to getInfo(); prints image metadata.
print(img.getInfo())

UI objects

The Earth Engine ui module is only available through the JavaScript API Code Editor. Use third party libraries for UI elements in Python. Libraries such as geemap, Folium, and ipyleaflet provide interactive map display, while charting can be done with Matplotlib, Altair, or seaborn, to name a few. See examples in the Earth Engine in Colab setup notebook for using geemap and Matplotlib.

Python in the Developer Guide

Python code is included throughout the Earth Engine Developer Guide. Where available, code examples can be viewed by clicking on the "Colab (Python)" tab at the top of code blocks. Guide pages may also include buttons at the top for running the page as a Colab notebook or viewing on GitHub. Python code examples are intended to be run using Google Colab. Interactive map and object exploration are handled by the geemap library. Both the Earth Engine Python client library and geemap are preinstalled in Colab.

Earth Engine setup

Running Python code requires that you import the Earth Engine library, authenticate, and initialize. The following commands are used in examples (see the Authentication and Initialization page for alternatives).

import ee
ee.Authenticate()
ee.Initialize(project='my-project')

Interactive exploration with geemap

The geemap library is used for displaying map tiles and printing rich representations of Earth Engine objects. The library depends respectively on ipyleaflet and eerepr for these features. The geemap library and its dependencies are preinstalled in Google Colab; import it into each session.

import geemap.core as geemap

Geographic Earth Engine data classes, such as ee.Image and ee.FeatureCollection, can be viewed using the geemap.Map object. First, define the map object. Then, add layers to it or alter its viewport.

# Initialize a map object.
m = geemap.Map()

# Define an example image.
img = ee.Image.random()

# Add the image to the map.
m.add_layer(img, None, 'Random image')

# Display the map (you can call the object directly if it is the final line).
display(m)