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Download data from the Food and Agriculture Organisation (FAO)

Project Description

A simple python interface to download data from the Food and Agriculture Organisation (FAO).

What is faodata?

  • faodata is a simple python interface to find and request data from the Food and Agriculture organization of the United Nations (FAO).
  • The package uses the FAO API.
  • Country boundaries that are used to plot data are from Natural Earth (1:110m resolution)


pip install faodata or download the source code and python install

Basic use

To download data, the id of the database, dataset and fields are required:

  • To get the list of FAO databases:

    from faodata import faodownload
    databases = faodownload.get_databases()
  • To get the list of FAO datasets in a given database (e.g. faostat):

    database_id = 'faostat'
    datasets = faodownload.get_datasets(database_id)
  • To get the list of FAO fields in a given dataset (e.g. live-prod):

    database_id = 'faostat'
    dataset_id = 'live-prod'
    fields = faodownload.get_fields(database_id, dataset_id)

When all the previous elements are known, the download procedure is

database_id = 'faostat'
dataset_id = 'live-prod'
field_id = 'm5111'

# Define the year (if None, all years are retrieved)
year = 2010

# Define country (if None, all countries are retrieved)
# The country id is the ISO3 code
# see
country_id = None

# Get data
data = faodownload.get_data(database_id, dataset_id, field_id, country=country_id, year=year)

When data is downloaded, it can be displayed on a world map

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import basemap
from faodata import faodownload, faomap

# Download data
database_id = 'faostat'
dataset_id = 'live-prod'
field_id = 'm5111'
year = 2013
data = faodownload.get_data(database_id, dataset_id, field_id, year=year)

# Select data
item = 'Cattle'
idx = data['Item'] == item
data = data.loc[idx, ['country', 'value']]

# Instantiate matplotlib and basemap objects
fig, ax = plt.subplots()
map = basemap.Basemap(projection='robin', \
        lon_0=10, lat_0=50, ax = ax)


# Categorize data according to percentiles
cat = [np.percentile(data['value'], pp) \
        for pp in range(10, 100, 10)]

# Draw plot
faomap.plot(map, data, cat, ndigits=0)
ax.set_title('%s population, %d' % (item, year),

# Add a footer to the figure to
# indicate data source
faomap.mapfooter(fig, database_id, dataset_id, field_id)

More examples in the example folder directory.

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