This package allows you to connect to your cumulocity platform and query historical data from the REST API. Currently it only supports devices, measurements and events endpoints. The returned json data is converted to a pandas dataframe.

Getting started

pip install cumulocitypython

Import it

from cumulocitypython import CumulocityConnection

Enter your credentials and create the connection

username = "YOUR_USERNAME"
password = "YOUR_PASSWORD"
tenant_url = "YOUR_TENANT_URL"

connection = CumulocityConnection(tenant_url, username, password)

This creates basic auth headers with your credentials. Make sure you don't include the http/https prefix when entering your tenant url.

Query your data

All the argument values should be available in your cumulocity tenant.

Measurements

measurement_data = connection.get_measurements(
            value_fragment_type="c8y_YourMetric",
            date_from="07-11-19 03:00:00",
            date_to="07-13-19 04:00:00",
        )

Arguments

These are the arguments that the get_measurements function can take. All of them are optional. The arguments are used to filter the data returned to you by Cumulocity.

Arg	Default value	Accepted values	Description
date_from	None	any widely used date format as a string	starting date from which you want to request data from. Uses the dateutil parser for formatting.
date_to	None	any widely used date format as a string	end date from which you want to request data to. Uses the dateutil parser for formatting.
device_id	None	any device id as a int or string	id of the device from which you want to request data from.
measurement_id	None	any measurement id as a int or string	id of the measurement you want to request.
measurement_type	None	any measurement type as a string	type of the measurement you want to request.
value_fragment_type	None	any value fragment type as a string	value fragment type of the measurement you want to request.
value_fragment_series	None	any value fragment series as a strinh	value fragment series of the measurement you want to request.
timedelta	None	timedelta object	speeds up big queries. supports minutes, hours, days, weeks.
page_size	2000	integer from 1 to 2000	determines the amount of rows sent by each request.

Returns

A pandas dataframe object

Events

event_data = connection.get_events(
            device_id="194442191",
            date_from="07-30-19 12:00:00",
            date_to="07-30-19 13:00:00",
            timedelta=timedelta(hours=3)
        )

Arguments

These are the arguments that the get_events function takes

Arg	Default value	Accepted values	Description
date_from	None	any widely used date format as a string	starting date from which you want to request data from. Uses the dateutil parser for formatting.
date_to	None	any widely used date format as a string	end date from which you want to request data to. Uses the dateutil parser for formatting.
device_id	None	any device id as a int or string	id of the device from which you want to request data from.
event_type	None	any event type as a string	type of the event you want to request.
fragment_type	None	any fragment type as a string	fragment type of the event you want to request
timedelta	None	timedelta object	speeds up big queries. supports minutes, hours, days, weeks.
page_size	2000	integer from 1 to 2000	determines the amount of rows sent by each request.

Returns

A pandas dataframe object

Devices

ids=[117925736,117925737,117925738,117925739]
device_data = connection.get_devices(ids=ids, page_size=1000)

Arguments

These are the arguments that the get_devices function takes

Arg	Default value	Accepted values	Description
device_type	None	any device type as a string	type of the device you want to request data from.
fragment_type	None	any device fragment type as a string	fragment type of the device you want to request data to.
ids	None	list of int or string id values	list of ids of the devices you want to request data from.
text	None	text as a string	attached text of the devices you want to request.
query	None	query as a string	query using Cumulocity query language
page_size	2000	integer from 1 to 2000	determines the amount of rows sent by each request.

Returns

A pandas dataframe object

Export data to a CSV

You might want to save the requested data to a csv so there is no need to request it a second time. Since all functions return a Pandas dataframe, you can easily use the built in pandas dataframe function: to_csv

Example:

event_data = connection.get_events(
            device_id="194442191",
            date_from="07-30-19 12:00:00",
            date_to="08-30-19 13:00:00",
            timedelta=timedelta(minutes=30)
        )

event_data.to_csv("YOUR_CSV_NAME.csv", index=False, encoding="utf8")