Python Installation

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:

Colab logo Run in Google Colab GitHub logo 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:


Install the API to an arbitrary Python environment using pip. From a terminal or command prompt:

pip install earthengine-api

Once installed, you can import, authenticate and initialize the Earth Engine API as described here.

Update the 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

Authenticate to the Earth Engine servers:

ee.Authenticate()

Initialize the API:

ee.Initialize()

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.

Common syntax differences between JavaScript and Python
Property JavaScript Python
Function definition 
function myFun(arg) {
  return arg;
}

var myFun = function(arg) {
  return arg;
};
 
def my_fun(arg):
  return arg
Anonymous function mapping 
var foo = col.map(function(arg) {
  return arg;
});
 
foo = col.map(lambda arg: arg)
Variable definition 
var myVar = 'var';
 
my_var = 'var'
Logical operators 
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)
Multi-line method chain 
var foo = my.really()
              .reallyLong()
              .methodChain();
 
foo = (my.really()
       .reallyLong()
       .methodChain())
Dictionary keys 
var dic = {'key': value};
var dic = {key: value};
 
dic = {'key': value}
Dictionary object access 
var value = dic.key;
var value = dic['key'];
 
value = dic['key']
Function argument definition 
// 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)
Boolean 
var t = true;
var f = false;
 
t = True
f = False
Null values 
var na = null;
 
na = None
comment 
//
 
#

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()

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/C01/T1_SR/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())
Note that getInfo() is a synchronous operation, meaning execution of expressions following the getInfo() call are blocked until the result is returned to the client. Additionally, requests for a lot of data or expensive computations can return an error and/or hang. In general, the best practice is to export your results, and once complete, import them into a new script for further analysis.

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. Folium and ipyleaflet provide interactive map handling, 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 Folium and Matplotlib.

Python in the Developer Guide

Python code is included for some examples in the Earth Engine Developer Guide (stay tuned for more!). On these particular pages, you'll find buttons at the top of the page to run it as a Colab Jupyter notebook or view it on GitHub. You'll also find tabbed code snippet widgets that allow you to alternate between JavaScript Code Editor code and the equivalent Python Colab code. The Colab notebooks include everything to get the examples running, but if you are copying and pasting code to run in your own environment, you'll need to do a little setup first.

Earth Engine setup

Nearly every example uses the Earth Engine API so you'll need to import the API, authenticate, and initialize.

import ee
ee.Authenticate()
ee.Initialize()

Folium interactive map display

The Folium library can be used to display ee.Image objects in an interactive map. Folium maps are used throughout the Earth Engine Developer Guide pages when Python examples display map tiles. Before you can run these examples, you need to import Folium into your Python session and add a method to the folium.Map object for handling Earth Engine tiles. Here's how:

import folium


def add_ee_layer(self, ee_image_object, vis_params, name):
  map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)
  folium.raster_layers.TileLayer(
      tiles=map_id_dict['tile_fetcher'].url_format,
      attr='Map Data &copy; <a href="https://earthengine.google.com/">Google Earth Engine</a>',
      name=name,
      overlay=True,
      control=True
  ).add_to(self)

folium.Map.add_ee_layer = add_ee_layer

Pretty-print

Some printed Earth Engine objects are in a nested JSON format that is quite long and/or complex. To improve readability in these cases, the pprint.pprint function is used; import pprint is included directly in examples as needed.