The Python SDK

How to integrate journy.io using our Python SDK.

Updated over a week ago

⚠️ This article relates to both journy.io's SDKs and Twilio Segment SDKs.

journy.io’s Python library lets you record data from your platform, from your Python code.

All of journy.io’s server-side libraries are built for high-performance, so you can use them in your web server controller code. This library uses an internal queue to make identify and track calls non-blocking and fast. It also batches messages and flushes asynchronously to journy.io’s servers.

Getting Started

Make sure you’re using a version of Python that’s 14 or higher.

  1. Run the relevant command to add journy.io’s Python library module to your package.json.

    pip install segment-analytics-python

  2. Initialize the Analytics constructor the module exposes with your journy.io SDK connector Write Key, like so:

    import segment.analytics as analytics 

    analytics.write_key = "YOUR_WRITE_KEY"
    analytics.host = "https://analyze.journy.io/backend"

Be sure to replace YOUR_WRITE_KEY with your actual Write Key which you can find in journy.io by navigating to: Connections > API/SDK Connector and selecting the source tab and going to the Python tab.

This creates an instance of Analytics that you can use to send data to journy.io for your project. The default initialization settings are production-ready and queue 20 messages before sending any requests.

There is an option in journy.io to set a proxy ("Use proxy domain") so traffic flows through your own domain. In that case, host will have another value, pointing to your own domain.

Basic tracking methods

The basic tracking methods below serve as the building blocks of your journy.io tracking. They include Identify, Group, Track, and Page.

These methods correspond with those used in the journy.io Spec. The documentation on this page explains how to use these methods in Python.

Identify

For any of the different methods described on this page, you can replace the properties and traits in the code samples with variables that represent the data collected.

identify lets you tie a user to their actions and record traits about them. It includes a unique User ID and/or anonymous ID, and any optional traits you know about them.

journy.io recommends that you make an identify call:

  • After a user first registers

  • After a user logs in

  • When a user updates their info (for example, they change or add a new address)

Example of an identify call for an identified user Elon Musk:

analytics.identify( 
user_id="abc123", # Elon Musk — unique Id from database
traits={
"firstname": "Elon",
"lastname": "Musk",
"email": "[email protected]",
"friends": 24 }
)

The call above identifies Elon by his unique User ID (the one you know him by in your database), and labels him with the firstname, lastname, email, and friends traits.

The identify call has the following fields:

FIELD

DESCRIPTION

user_id string or int

The ID for this user in your database.

traits dict, optional

A dict of traits you know about the user. Things like: email, name or friends.

context dict, optional

A dict containing any context about the request. To see the full reference of supported keys, check them out in the context reference

timestamp datetime, optional

A datetime object representing when the identify took place. This is most useful if you import historical data. If the identify call just happened, leave it blank and Segment uses the server’s time.

anonymous_id string or int, optional

An anonymous session ID for this user.

integrations dict, optional

A dictionary of destinations to enable or disable

Identifying users happen incremental: You can identify users with a subset of traits; and later make another identify call with another subset of traits. The result of both identify calls will be the union of all traits.

To delete a traits, you have to add them to the identify call with value null:

analytics.identify( 
user_id="abc123", # Elon Musk — unique Id from database
traits={
"firstname": "Elon",
"lastname": "Musk",
"email": None,
"friends": 24 }
)

Find details on the identify method payload in journy.io’s Identify Spec.

Group

group lets you associate an identified user with a group. A group could be a company, organization, account, project or team! It also lets you record custom traits about the group, like industry or number of employees.

journy.io recommends that you make a group call:

  • After a user first registers, entering its company details.

  • After a user logs in. Make a group call for each account the user is part of.

  • When a user updates their info, make a group call for each account the user is part of.

Example group call, adding user Elon Musk to account Tesla, as an 'admin':

analytics.group( 
user_id="abc123", # Elon Musk — unique Id from database
group_id="xyz789", # Tesla — unique Id from database
traits={
"name": "Tesla Inc",
"industry": "Automotive" },
context={
"relationship": {
"role": "admin" } }
)

The group call has the following fields:

FIELD

DESCRIPTION

user_id string or number

The ID for the user that is a part of the group.

group_id string or number

The ID of the group.

traits dict, optional

A dict of traits you know about the group. For a company, they might be things like name, address, or phone.

context dict, optional

A dict containing any context about the request. To see the full reference of supported keys, check them out in the context reference

timestamp datetime, optional

A datetime object representing when the group took place. This is most useful if you’re importing historical data. If the group just happened, leave it blank to use the server’s time.

anonymous_id string or int, optional

An anonymous session ID for this user.

integrations dict, optional

A dictionary of destinations to enable or disable

⚠️ To identify a group, without adding a user, you can use anonymousId with the same value of the groupId. It goes like this:

Analytics.group({ 
anonymous_id: "xyz789", # Tesla — unique Id from database
group_id: "xyz789", # Tesla — same unique Id from database
traits: {
"name": "Tesla Inc",
"industry": "Automotive" }
})

Group-calling accounts happen incremental: You can identify users with a subset of traits; and later make another identify call with another subset of traits. The result of both identify calls will be the union of all traits.

To delete a traits, you have to add them to the group call with a null reference:

Analytics.group({ 
anonymous_id: "xyz789", # Tesla — unique Id from database
group_id: "xyz789", # Tesla — same unique Id from database
traits: {
"name": "Tesla Inc",
"industry": None }
})

Find more details about group, including the group payload, in the journy.io Spec.

Track

track lets you record the actions your users perform, optionally within the context of an account. Every action triggers what we call an “event”, which can also have associated event metadata.

You’ll want to track events that are indicators of success for your site, like Signed Up, Item Purchased or Article Bookmarked.

To get started, we recommend tracking just a few important events. You can always add more later!

Example identified track call by a user Elon Musk:

analytics.track( 
user_id="abc123", # Elon Musk — same Id from identify call
event="Car Sold",
properties={
"total amount": 39999.99,
"currency": "usd",
"shippingMethod": "200-day" }
)

B2B example identified track call by a user Elon Musk in the context of account Tesla, back on Dec 12th 2015:

analytics.track( 
user_id="abc123", # Elon Musk — same Id from identify call
event="Car Sold",
properties={
"total amount": 39999.99,
"currency": "usd",
"shippingMethod": "200-day" },
timestamp="2015-12-12T19:11:01.249Z", #optional, in the past
context={
"group_id": "xyz789" } # Tesla — same Id from group call
)

This example track call tells us that Elon Musk triggered the Car Sold event with a revenue of $39999.99 and chose your hypothetical ‘200-day’ shipping, back on Dec 12th 2015.

track event metadata ( properties ) can be anything you want to record. In this case, revenue and shipping method.

The track call has the following fields:

FIELD

DESCRIPTION

user_id string

The ID for this user in your database.

event string

The name of the event you’re tracking. Use human-readable names like Song Played or Status Updated.

properties dict, optional

A dictionary of properties for the event. If the event was Product Added, it might have properties like price or product.

context dict, optional

A dict containing any context about the request. To see the full reference of supported keys, check them out in the context reference

timestamp datetime, optional

A datetime object representing when the track took place. This is most useful if you’re importing historical data. If the track just happened, leave it blank to use the server’s time.

anonymous_id string or int, optional

An anonymous session ID for this user.

integrations dict, optional

A dictionary of destinations to enable or disable

Find details on best practices in event naming as well as the track method payload in the journy.io Spec.

Page

The page method lets you record page views on your website, along with optional extra information about the page being viewed. It is also user to record screen views in your app/on your platform.

⚠️ Important Note:

  • When a name is provided in the page call, journy.io will collect those calls and stores them as (app) screen objects.

  • Without name, journy.io regards those calls as (website) page objects.

If you’re using our client-side set up in combination with the Python library, page calls are already tracked for you by default. However, if you want to record your own page views manually and aren’t using our client-side library, read on!

Example page call, where a user Elon Musk visits the pricing page:

analytics.page({ 
user_id="abc123", # Elon Musk — same Id from identify call
category="Web",
name="pricing page",
properties={
"url": "https://www.example.com/pricing" }
})

B2B example page call, where a user Elon Musk visits the pricing page, when being in account Tesla, back on Dec 12th 2015:

analytics.page(
user_id="abc123", # Elon Musk — same Id from identify call
category="Web",
name="pricing page",
properties={
"url": "https://www.example.com/pricing" },
timestamp="2015-12-12T19:11:01.249Z", # optional, in the past
context={
"group_id": "xyz789" } # Tesla — same Id from group call
)

The page call has the following fields:

FIELD

DESCRIPTION

user_id _string

The ID for the user that is a part of the group.

category string, optional

The category of the page. Useful for things like ecommerce where many pages often live under a larger category.

name string, optional

The name of the page, for example Signup or Home.

properties dict, optional

The page properties. To see a reference of reserved page properties, see the spec here.

context dict, optional

A dict containing any context about the request. To see the full reference of supported keys, check them out in the context reference

timestamp datetime, optional

A datetime object representing when the page took place. This is most useful if you’re importing historical data. If the page just happened, leave it blank to use the server’s time.

anonymous_id string or int, optional

An anonymous session ID for this user.

integrations dict, optional

A dictionary of destinations to enable or disable

Find details on the page payload in the journy.io Spec.

Additional Configuration in Development Mode

In development, Segment recommends that you enable the following settings to help spot problems:

  • analytics.debug to log debugging information to the Python logger

  • an on_error handler to print the response you receive from Segment’s API.

def on_error(error, items): print("An error occurred:", error) analytics.debug = True 
analytics.on_error = on_error

If you don’t want to send information to Segment during testing, add the following code to your test:

analytics.send = False

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