Plot ee.FeatureCollection objects using matplotlib

The geetools extension contains a set of functions for rendering charts from ee.FeatureCollection objects. The choice of function determines the arrangement of data in the chart, i.e., what defines x- and y-axis values and what defines the series. Use the following function descriptions and examples to determine the best function and chart type for your purpose.

Set up environment

Install all the required libs if necessary and perform the import statements upstream.

# uncomment if installation of libs is necessary
# !pip install earthengine-api geetools

from matplotlib import pyplot as plt

import ee
import geetools #noqa: F401

# uncomment if authetication to GEE is needed
# ee.Authenticate()

# uncomment if initialization is required
# ee.Initialize()

Example data

The following examples rely on a FeatureCollection composed of three ecoregion features with properties that describe climate normals.

# Import the example feature collection.
ecoregions = ee.FeatureCollection('projects/google/charts_feature_example')

ee.FeatureCollection.geetools.plot_by_features

Column chart

Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by adjacent columns defined by a list of property names whose values are plotted along the y-axis.

fig, ax = plt.subplots()

# initialize the plot with the ecoregions data
ecoregions.geetools.plot_by_features(
    type = "bar",
    featureId = "label",
    properties = ['01_tmean', '02_tmean', '03_tmean', '04_tmean', '05_tmean', '06_tmean', '07_tmean', '08_tmean', '09_tmean', '10_tmean', '11_tmean', '12_tmean'],
    labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"],
    colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],
    ax = ax
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("Average Monthly Temperature by Ecoregion")
ax.set_xlabel("Ecoregion")
ax.set_ylabel("Temperature (°C)")
plt.show()

Stacked column chart

Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by stacked columns defined by a list of property names whose values are plotted along the y-axis as the cumulative series sum.

fig, ax = plt.subplots()

# initialize theplot with the ecoregions data
ecoregions.geetools.plot_by_features(
    type = "stacked",
    featureId = "label",
    properties = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],
    labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"],
    colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],
    ax = ax
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("Average Monthly Precipitation by Ecoregion")
ax.set_xlabel("Ecoregion")
ax.set_ylabel("Precipitation (mm)")
plt.show()

Scatter chart

Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by points defined by a list of property names whose values are plotted along the y-axis.

fig, ax = plt.subplots()

# initialize theplot with the ecoregions data
ecoregions.geetools.plot_by_features(
    type = "scatter",
    featureId = "label",
    properties = ['01_ppt', '06_ppt', '09_ppt'],
    labels = ["jan", "jun", "sep"],
    ax = ax
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("Average Monthly Precipitation by Ecoregion")
ax.set_xlabel("Ecoregion")
ax.set_ylabel("Precipitation (mm)")
plt.show()

Pie chart

The pie is a property, each slice is the share from each feature whose value is cast as a percentage of the sum of all values of features composing the pie.

fig, ax = plt.subplots()

# initialize theplot with the ecoregions data
ecoregions.geetools.plot_by_features(
    type = "pie",
    featureId = "label",
    properties = ['06_ppt'],
    colors = ["#f0af07", "#0f8755", "#76b349"],
    ax = ax
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("Share of precipitation in June by Ecoregion")
plt.show()

Donut chart

The donut is a property, each slice is the share from each feature whose value is cast as a percentage of the sum of all values of features composing the donut.

fig, ax = plt.subplots()

# initialize theplot with the ecoregions data
ecoregions.geetools.plot_by_features(
    type = "donut",
    featureId = "label",
    properties = ['07_ppt'],
    colors = ["#f0af07", "#0f8755", "#76b349"],
    ax = ax
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("Share of precipitation in July by Ecoregion")
plt.show()

ee.FEatureCollection.geetools.plot_by_properties

Column chart

Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by columns, labeled by values of a selected property.

fig, ax = plt.subplots()


# initialize theplot with the ecoregions data
ax = ecoregions.geetools.plot_by_properties(
    type = "bar",
    properties = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],
    labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"],
    featureId = "label",
    colors = ["#f0af07", "#0f8755", "#76b349"],
    ax = ax
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("Average Monthly Precipitation by Ecoregion")
ax.set_xlabel("Month")
ax.set_ylabel("Precipitation (mm)")
plt.show()

Line chart

Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by columns, labeled by values of a selected property.

fig, ax = plt.subplots()

# initialize theplot with the ecoregions data
ax = ecoregions.geetools.plot_by_properties(
    type = "plot",
    properties = ["01_ppt", "02_ppt", "03_ppt", "04_ppt", "05_ppt", "06_ppt", "07_ppt", "08_ppt", "09_ppt", "10_ppt", "11_ppt", "12_ppt"],
    featureId = "label",
    labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"],
    colors = ["#f0af07", "#0f8755", "#76b349"],
    ax = ax
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("Average Monthly Precipitation by Ecoregion")
ax.set_xlabel("Month")
ax.set_ylabel("Precipitation (mm)")
plt.show()

Area chart

Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by lines and shaded areas, labeled by values of a selected property.

fig, ax = plt.subplots()

# initialize the plot with the ecoregions data
ax = ecoregions.geetools.plot_by_properties(
    type = "fill_between",
    properties = ["01_ppt", "02_ppt", "03_ppt", "04_ppt", "05_ppt", "06_ppt", "07_ppt", "08_ppt", "09_ppt", "10_ppt", "11_ppt", "12_ppt"],
    labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"],
    featureId = "label",
    colors = ["#f0af07", "#0f8755", "#76b349"],
    ax = ax
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("Average Monthly Precipitation by Ecoregion")
ax.set_xlabel("Month")
ax.set_ylabel("Precipitation (mm)")
plt.show()

ee.FEatureCollection.geetools.plot_hist

The x-axis is defined by value bins for the range of values of a selected property; the y-axis is the number of elements in the given bin.

fig, ax = plt.subplots()

# load some data
normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm81m').toBands()

# Make a point sample of climate variables for a region in western USA.
region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14)
climSamp = normClim.sample(region, 5000)


# initialize the plot with the ecoregions data
ax = climSamp.geetools.plot_hist(
    property = "07_ppt",
    label = "July Precipitation (mm)",
    color = '#1d6b99',
    ax = ax,
    bins = 30
)

# once created the axes can be modified as needed using pure matplotlib functions
ax.set_title("July Precipitation Distribution for NW USA")
plt.show()