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hdx-python-api 2.6.3

HDX Python Library

The HDX Python Library is designed to enable you to easily develop code that interacts with the Humanitarian Data Exchange (HDX) platform. The major goal of the library is to make pushing and pulling data from HDX as simple as possible for the end user. If you have humanitarian-related data, please upload your datasets to HDX.
For more about the purpose and design philosophy, please visit HDX Python Library.


The library has detailed API documentation which can be found here: The code for the library is here:

Getting Started

Obtaining your API Key

If you just want to read data from HDX, then an API key is not necessary and you can ignore the 7 steps below. However, if you want to write data to HDX, then you need to register on the website to obtain an API key. You can supply this key as an argument or create an API key file. If you create an API key file, by default this is assumed to be called .hdxkey and is located in the current user’s home directory ~. Assuming you are using a desktop browser, the API key is obtained by:

  1. Browse to the HDX website
  2. Left click on LOG IN in the top right of the web page if not logged in and log in
  3. Left click on your username in the top right of the web page and select PROFILE from the drop down menu
  4. Scroll down to the bottom of the profile page
  5. Copy the API key which will be of the form: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
  6. To make an API key file, paste the API key into a text file
  7. Save the text file with filename .hdxkey in the current user’s home directory

Installing the Library

To include the HDX Python library in your project, you must pip install or add to your requirements.txt file the following line:

Replace VERSION with the latest tag available from
If you get dependency errors, it is probably the dependencies of the cryptography package that are missing eg. for Ubuntu: python-dev, libffi-dev and libssl-dev. See cryptography dependencies.


The library is also available set up and ready to go in a Docker image:

docker pull mcarans/hdx-python-api
docker run -i -t mcarans/hdx-python-api:latest python3

A Quick Example

Let’s start with a simple example that also ensures that the library is working properly. In this tutorial, we use virtualenv, a sandbox, so that your Python install is not modified.

  1. If you just want to read data from HDX, then an API key is not necessary. However, if you want to write data to HDX, then you need to register on the website to obtain an API key. Please see above about where to find it on the website. Once you have it, then put it into a file in your home directory:

    cd ~
    echo xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx > .hdxkey
  2. If you are using the Docker image, you can jump to step 7, otherwise install virtualenv if not installed:

    pip install virtualenv

On some Linux distributions, you can do the following instead to install from the distribution’s official repository:

sudo apt-get install virtualenv
  1. Create a Python 3 virtualenv and activate it:

On Windows (assuming the Python 3 executable is in your path):

virtualenv test

On other OSs:

virtualenv -p python3 test
source test/bin/activate
  1. Install the HDX Python library:

    pip install hdx-python-api
  2. If you get errors, it is probably the dependencies of the cryptography package

  3. Launch python:

  4. Import required classes:

    from hdx.hdx_configuration import Configuration
    from import Dataset
  5. Use configuration defaults.

    If you only want to read data, then connect to the production HDX server:

    Configuration.create(hdx_site='prod', hdx_read_only=True)

    If you want to write data, then for experimentation, do not use the production HDX server. Instead you can use one of the test servers. Assuming you have an API key stored in a file .hdxkey in the current user’s home directory:

  6. Read this dataset ACLED Conflict Data for Africa (Realtime - 2016) from HDX and view the date of the dataset:

    dataset = Dataset.read_from_hdx('acled-conflict-data-for-africa-realtime-2016')
  7. If you have an API key, as a test, change the dataset date:

    dataset.set_dataset_date('2015-07-26', date_format='%Y-%m-%d')
  8. You can view it on HDX before changing it back (if you have an API key):

    dataset.set_dataset_date('2016-06-25', date_format='%Y-%m-%d')
  9. You can search for datasets on HDX and get their resources:

    datasets = Dataset.search_in_hdx('ACLED', rows=10)
    resources = Dataset.get_all_resources(datasets)
  10. You can download a resource in the dataset:

    url, path = resources[0].download()
    print('Resource URL %s downloaded to %s' % (url, path))
  11. Exit and remove virtualenv:


    On Windows:

    rd /s /q test

    On other OSs:

    rm -rf test

Building a Project

Default Configuration for Facades

The easiest way to get started is to use the facades and configuration defaults. The facades set up both logging and HDX configuration.

The default configuration loads an internal HDX configuration located within the library, and assumes that there is an API key file called .hdxkey in the current user’s home directory ~ and a YAML project configuration located relative to your working directory at config/project_configuration.yml which you must create. The project configuration is used for any configuration specific to your project.

The default logging configuration reads a configuration file internal to the library that sets up an coloured console handler outputting at DEBUG level and a file handler writing to errors.log at ERROR level.


You will most likely just need the simple facade. If you are in the HDX team, you may need to use the ScraperWiki facade which reports status to that platform (in which case replace simple with scraperwiki in the code below):

from hdx.facades.simple import facade

def main():
    ***YOUR CODE HERE***

if __name__ == '__main__':

Customising the Configuration

It is possible to pass configuration parameters in the facade call eg.

facade(main, hdx_site = HDX_SITE_TO_USE, hdx_read_only = ONLY_READ_NOT_WRITE, hdx_key_file = LOCATION_OF_HDX_KEY_FILE, hdx_config_yaml=PATH_TO_HDX_YAML_CONFIGURATION, project_config_dict = {'MY_PARAMETER', 'MY_VALUE'})

If you do not use the facade, you can use the create method of the Configuration class directly, passing in appropriate keyword arguments ie.

from hdx.hdx_configuration import Configuration
Configuration.create(KEYWORD ARGUMENTS)


Choose Argument Type Value Default
  hdx_site Optional[bool] HDX site to use eg. prod, feature test
One of: hdx_read_only bool Read only or read/write access to HDX False
or hdx_key Optional[str] HDX key  
or hdx_key_file Optional[str] Path to HDX key file. ~/.hdxkey
One of: hdx_config_dict dict HDX configuration dictionary  
or hdx_config_json str Path to JSON HDX configuration  
or hdx_config_yaml str Path to YAML HDX configuration Library’s internal hdx_configuration.yml
One of: project_config_dict dict Project configuration dictionary  
or project_config_json str Path to JSON Project configuration  
or project_config_yaml str Path to YAML Project configuration  

To access the configuration, you use the read method of the Configuration class as follows:

For more advanced users, there are methods to allow you to pass in your own configuration object, remote CKAN object and list of valid locations. See the API documentation for more information.

This global configuration is used by default by the library but can be replaced by Configuration instances passed to the constructors of HDX objects like Dataset eg.

configuration = Configuration(KEYWORD ARGUMENTS)
configuration.setup_remoteckan(REMOTE CKAN OBJECT)
configuration.setup_validlocations(LIST OF VALID LOCATIONS)
dataset = Dataset(configuration=configuration)

Configuring Logging

If you wish to change the logging configuration from the defaults, you will need to call setup_logging with arguments unless you have used the simple or ScraperWiki facades, in which case you must update the hdx.facades module variable logging_kwargs before importing the facade.

If not using facade:

from hdx.utilities.easy_logging import setup_logging
logger = logging.getLogger(__name__)
setup_logging(KEYWORD ARGUMENTS)

If using facade:

from hdx.facades import logging_kwargs

from hdx.facades.simple import facade


Choose Argument Type Value Default
One of: logging_config_dict dict Logging configuration dictionary  
or logging_config_json str Path to JSON Logging configuration  
or logging_config_yaml str Path to YAML Logging configuration Library’s internal logging_configuration.yml
One of: smtp_config_dict dict Email Logging configuration dictionary  
or smtp_config_json str Path to JSON Email Logging configuration  
or smtp_config_yaml str Path to YAML Email Logging configuration  

Do not supply smtp_config_dict, smtp_config_json or smtp_config_yaml unless you are using the default logging configuration!

If you are using the default logging configuration, you have the option to have a default SMTP handler that sends an email in the event of a CRITICAL error by supplying either smtp_config_dict, smtp_config_json or smtp_config_yaml. Here is a template of a YAML file that can be passed as the smtp_config_yaml parameter:

        toaddrs: EMAIL_ADDRESSES
        subject: "RUN FAILED: MY_PROJECT_NAME"

Unless you override it, the mail server mailhost for the default SMTP handler is localhost and the from address fromaddr is noreply@localhost.

To use logging in your files, simply add the line below to the top of each Python file:

logger = logging.getLogger(__name__)

Then use the logger like this:

logger.debug('DEBUG message')'INFORMATION message')
logger.warning('WARNING message')
logger.error('ERROR message')
logger.critical('CRITICAL error message')

Operations on HDX Objects

You can read an existing HDX object with the static read_from_hdx method which takes an identifier parameter and returns the an object of the appropriate HDX object type eg. Dataset or None depending upon whether the object was read eg.

dataset = Dataset.read_from_hdx('DATASET_ID_OR_NAME')

You can search for datasets and resources in HDX using the search_in_hdx method which takes a query parameter and returns the a list of objects of the appropriate HDX object type eg. list[Dataset] eg.

datasets = Dataset.search_in_hdx('QUERY', **kwargs)

The query parameter takes a different format depending upon whether it is for a dataset or a resource. The resource level search is limited to fields in the resource, so in most cases, it is preferable to search for datasets and then get their resources.

Various additional arguments (**kwargs) can be supplied. These are detailed in the API documentation. The rows parameter for datasets (limit for resources) is the maximum number of matches returned and is by default everything.

You can create an HDX Object, such as a dataset, resource, showcase, organization or user by calling the constructor with an optional dictionary containing metadata. For example:

from import Dataset

dataset = Dataset({
    'name': slugified_name,
    'title': title

The dataset name should not contain special characters and hence if there is any chance of that, then it needs to be slugified. Slugifying is way of making a string valid within a URL (eg. ae replaces ä). There are various packages that can do this eg. awesome-slugify.

You can add metadata using the standard Python dictionary square brackets eg.

dataset['name'] = 'My Dataset'

You can also do so by the standard dictionary update method, which takes a dictionary eg.

dataset.update({'name': 'My Dataset'})

Larger amounts of static metadata are best added from files. YAML is very human readable and recommended, while JSON is also accepted eg.



The default path if unspecified is config/hdx_TYPE_static.yml for YAML and config/hdx_TYPE_static.json for JSON where TYPE is an HDX object’s type like dataset or resource eg. config/hdx_showcase_static.json. The YAML file takes the following form:

owner_org: "acled"
maintainer: "acled"
    - name: "conflict"
    - name: "political violence"
      description: "Resource1"
      url: "http://resource1.xlsx"
      format: "xlsx"

Notice how you can define resources (each resource starts with a dash ‘-‘) within the file as shown above.

You can check if all the fields required by HDX are populated by calling check_required_fields. This will throw an exception if any fields are missing. Before the library posts data to HDX, it will call this method automatically. You can provide a list of fields to ignore in the check. An example usage:


Once the HDX object is ready ie. it has all the required metadata, you simply call create_in_hdx eg.


Existing HDX objects can be updated by calling update_in_hdx eg.


You can delete HDX objects using delete_from_hdx and update an object that already exists in HDX with the method update_in_hdx. These do not take any parameters or return anything and throw exceptions for failures like the object to delete or update not existing.

Dataset Specific Operations

A dataset can have resources and can be in a showcase.

If you wish to add resources, you can supply a list and call the add_update_resources* function, for example:

resources = [{
    'name': xlsx_resourcename,
    'format': 'xlsx',
    'url': xlsx_url
 }, {
    'name': csv_resourcename,
    'format': 'zipped csv',
    'url': csv_url
 for resource in resources:
     resource['description'] = resource['url'].rsplit('/', 1)[-1]

Calling add_update_resources creates a list of HDX Resource objects in dataset and operations can be performed on those objects.

To see the list of resources, you use the get_resources* function eg.

resources = dataset.get_resources()

If you wish to add one resource, you can supply an id string, dictionary or Resource object and call the add_update_resource* function, for example:


You can delete a Resource object from the dataset using the delete_resource* function, for example:


You can get all the resources from a list of datasets as follows:

resources = Dataset.get_all_resources(datasets)

To see the list of showcases a dataset is in, you use the get_showcases* function eg.

resources = dataset.get_showcases()

If you wish to add the dataset to a showcase, you must first create the showcase in HDX if it does not already exist:

showcase = Showcase({'name': 'new-showcase-1',
                     'title': 'MyShowcase1',
                     'notes': 'My Showcase',
                     'package_id': '6f36a41c-f126-4b18-aaaf-6c2ddfbc5d4d',
                     'image_display_url': 'http://myvisual/visual.png',
                     'url': 'http://visualisation/url/'})

Then you can supply an id, dictionary or Showcase object and call the add_showcase* function, for example:


You can remove the dataset from a showcase using the remove_showcase* function, for example:


Dataset Date

Dataset date is a mandatory field in HDX. This date is the date of the data in the dataset, not to be confused with when data was last added/changed in the dataset. It can be a single date or a range.

To determine if a dataset date is a single date or range you can call:


It returns ‘date’ for a single date or ‘range’ for a date range.

To get the dataset start date of a range or single date as a string, you can do as shown below. You can supply a date format. If you don’t, the output format will be an ISO 8601 date eg. 2007-01-25.

dataset_date = dataset.get_dataset_date('FORMAT')

To get the dataset end date of a range, you call:

dataset_date = dataset.get_dataset_end_date('FORMAT')

To set the dataset date, you pass a start date and end date for a range or just a start date for a single date. If you do not supply any dates format, the method will try to guess, which for unambiguous formats should be fine.

dataset.set_dataset_date('START DATE', 'END DATE', 'FORMAT')

To retrieve the dataset date or range as a datetime.datetime object, you can do:

dataset_date = dataset.get_dataset_date_as_datetime()
dataset_date = dataset.get_dataset_end_date_as_datetime()

The method below allows you to set the dataset’s date using a datetime.datetime object:


Expected Update Frequency

HDX datasets have a mandatory field, the expected update frequency. This is your best guess of how often the dataset will be updated.

The HDX web interface uses set frequencies:

Every day
Every week
Every two weeks
Every month
Every three months
Every six months
Every year

Although the API allows much greater granularity (a number of days), you are encouraged to use the options above (avoiding using Never if possible). To assist with this, you can use methods that allow this.

The following method will return a textual expected update frequency corresponding to what would be shown in the HDX web interface.

update_frequency = dataset.get_expected_update_frequency()

The method below allows you to set the dataset’s expected update frequency using one of the set frequencies above. (It also allows you to pass a number of days cast to a string, but this is discouraged.)


Transforming backwards and forwards between representations can be achieved with this function:

update_frequency = Dataset.transform_update_frequency('UPDATE_FREQUENCY')


Each HDX dataset must have at least one location associated with it.

If you wish to get the current location (ISO 3 country codes), you can call the method below:

locations = dataset.get_location()

If you want to add a country, you do as shown below. If you don’t provide an ISO 3 country code, the text you give will be parsed and converted to an ISO 3 code if it is a valid country name.

dataset.add_country_location('ISO 3 COUNTRY CODE')

If you want to add a list of countries, the following method enables you to do it. If you don’t provide ISO 3 country codes, conversion will take place where valid country names are found.

dataset.add_country_locations(['ISO 3','ISO 3','ISO 3'...])

If you want to add a region, you do it as follows. If you don’t provide a three digit UNStats M49 region code, then parsing and conversion will occur if a valid region name is supplied.

dataset.add_region_location('M49 REGION CODE')

add_region_location accepts regions, intermediate regions or subregions as specified on the UNStats M49 website.

If you want to add any other kind of location (which must be in this list of valid locations), you do as shown below.



HDX datasets can have tags which help people to find them eg. “COD”, “PROTESTS”.

If you wish to get the current tags, you can use this method:

tags = dataset.get_tags()

If you want to add a tag, you do it like this:


If you want to add a list of tags, you do it as follows:



HDX datasets must have a maintainer.

If you wish to get the current maintainer, you can do this:

maintainer = dataset.get_maintainer()

If you want to set the maintainer, you do it like this:


USER is either a string id, dictionary or a User object.


HDX datasets must be part of an organization.

If you wish to get the current organization, you can do this:

organization = dataset.get_organization()

If you want to set the organization, you do it like this:


ORGANIZATION is either a string id, dictionary or an Organization object.

Resource Specific Operations

You can download a resource using the download function eg.


If you do not supply FOLDER_TO_DOWNLOAD_TO, then a temporary folder is used.

Before creating or updating a resource, it is possible to specify the path to a local file to upload to the HDX filestore if that is preferred over hosting the file externally to HDX. Rather than the url of the resource pointing to your server or api, in this case the url will point to a location in the HDX filestore containing a copy of your file.


There is a getter to read the value back:

file_to_upload = resource.get_file_to_upload()

If you wish to set up the data preview feature in HDX and your file (HDX or externally hosted) is a csv, then you can call the create_datastore or update_datastore methods. If you do not pass any parameters, all fields in the csv will be assumed to be text.


More fine grained control is possible by passing certain parameters and using other related methods eg.

resource.create_datastore(schema={'id': 'FIELD', 'type': 'TYPE'}, primary_key='PRIMARY_KEY_OF_SCHEMA', delete_first=0 (No) / 1 (Yes) / 2 (If no primary key), path='LOCAL_PATH_OF_UPLOADED_FILE') -> None:
resource.create_datastore_from_yaml_schema(yaml_path='PATH_TO_YAML_SCHEMA', delete_first=0 (No) / 1 (Yes) / 2 (If no primary key), path='LOCAL_PATH_OF_UPLOADED_FILE')
resource.update_datastore(schema={'id': 'FIELD', 'type': 'TYPE'}, primary_key='PRIMARY_KEY_OF_SCHEMA', path='LOCAL_PATH_OF_UPLOADED_FILE') -> None:
resource.update_datastore_from_json_schema(json_path='PATH_TO_JSON_SCHEMA', path='LOCAL_PATH_OF_UPLOADED_FILE')

User Management

The User class enables you to manage users, creating, deleting and updating (as for other HDX objects) according to your permissions.

You can email a user. First you need to set up an email server using a dictionary or file:

email_config_dict = {'connection_type': 'TYPE', 'host': 'HOST',
                     'port': PORT, 'username': USERNAME,
                     'password': PASSWORD}

Then you can email a user like this:'SUBJECT', 'BODY', sender='SENDER EMAIL')

You can email multiple users like this:

User.email_users(LIST_OF_USERS, 'SUBJECT', 'BODY', sender='SENDER EMAIL')

Organization Management

The Organization class enables you to manage organizations, creating, deleting and updating (as for other HDX objects) according to your permissions.

You can get the datasets in an organization as follows:

datasets = organization.get_datasets(**kwargs)

Various additional arguments (**kwargs) can be supplied. These are detailed in the API documentation.

You can get the users in an organization like this:

users = organization.get_users('OPTIONAL FILTER')

OPTIONAL FILTER can be member, editor, admin.

You can add or update a user in an organization as shown below:


You need to include a capacity field in the USER where capacity is member, editor, admin.

You can add or update multiple users in an organization as follows:

organization.add_update_users([LIST OF USERS])

You can delete a user from an organization:

organization.delete_user('USER ID')

Working Example

Here we will create a working example from scratch.

First, pip install the library or alternatively add it to a requirements.txt file if you are comfortable with doing so as described above.

Next create a file called and copy into it the code below.

# -*- coding: utf-8 -*-
Calls a function that generates a dataset and creates it in HDX.

import logging
from hdx.facades.scraperwiki import facade
from .my_code import generate_dataset

logger = logging.getLogger(__name__)

def main():
    '''Generate dataset and create it in HDX'''

    dataset = generate_dataset()

if __name__ == '__main__':
    facade(main, hdx_site='test')

The above file will create in HDX a dataset generated by a function called generate_dataset that can be found in the file which we will now write.

Create a file and copy into it the code below:

# -*- coding: utf-8 -*-
Generate a dataset

import logging
from import Dataset

logger = logging.getLogger(__name__)

def generate_dataset():
    '''Create a dataset
    logger.debug('Generating dataset!')

You can then fill out the function generate_dataset as required.

ACLED Example

A complete example can be found here:

In particular, take a look at the files, and the config folder. If you run it unchanged, it will conflict with the existing dataset in the ACLED organisation! Therefore, you will need to modify the dataset name in and change the organisation information to your organisation. Also update metadata in config/hdx_dataset_static.yml appropriately.

The ACLED scraper creates a dataset in HDX for ACLED realtime data if it doesn’t already exist, populating all the required metadata. It then creates resources that point to urls of Excel and csv files for Realtime 2017 All Africa data (or updates the links and metadata if the resources already exist). Finally it creates a showcase that points to these dynamic maps and graphs.

The first iteration of the ACLED scraper was written without the HDX Python library and it became clear looking at this and previous work by others that there are operations that are frequently required and which add unnecessary complexity to the task of coding against HDX. Simplifying the interface to HDX drove the development of the Python library and the second iteration of the scraper was built using it. With the interface using HDX terminology and mapping directly on to datasets, resources and showcases, the ACLED scraper was faster to develop and is much easier to understand for someone inexperienced in how it works and what it is doing. The challenge with ACLED is that sometimes the urls that the resources point to have not been updated and hence do not work. In this situation, the extensive logging and transparent communication of errors is invaluable and enables action to be taken to resolve the issue as quickly as possible. The static metadata for ACLED is held in human readable files so if it needs to be modified, it is straightforward. This is another feature of the HDX Python library that makes putting data programmatically into HDX a breeze.

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