Quickstart: Python

This tutorial will help you start sending data from your servers to Segment and any of our destinations, using our Python library. As soon as you’re set up you’ll be able to turn on any new destinations with the flip of a switch!

If you want to dive deeper at any point, check out the Python library reference.

Step 1: Create a Source in the Segment app

Before you begin, you need a Workspace (which is a container that holds all of the sources and destinations which are billed together for an organization). If you already created one, great! If not, you can sign up for a free Segment account and create one.

Next, create a Python source from your Workspace:

  1. Click Add Source.
  2. From the source catalog page, click Python.
  3. Click Add Source again from the informational panel that appears to the right.
  4. Give the source a display name, and enter the URL the source will collect data from.

When you create a Source in the Segment web app, it tells the Segment servers that you’ll be sending data from a specific source type. When you create (or change!) a Source in the Segment app, Segment generates a new Write Key for that source. You use the write key in your code to tell the Segment servers where the data is coming from, so Segment can route it to your destinations and other tools.

Step 2: Install the library

You can install analytics-python using pip:

pip install analytics-python

If you’re using a system for managing dependencies, you’ll want to pin the library to 1.X to avoid breaking changes when the library is updated

Then inside your app, you’ll want to set your write_key before making any analytics calls:

import analytics

analytics.write_key = 'YOUR_WRITE_KEY'

The default initialization settings are production-ready and will queue individual analytics calls. A separate background thread is responsible for making the requests to our API, so any calls to the library won’t block your program’s execution.

Note: If you need to send data to multiple Segment sources, you can initialize a new Client for each write_key!

Once you’ve got that, you’re ready to…

Step 3: Identify Users

Good to know: For any of the different methods described in this quickstart, you can replace the properties and traits in the code samples with variables that represent the data collected.

The identify method is how you tell Segment who the current user is. It includes a unique User ID and any optional traits you know about them. You can read more about it in the identify reference.

Here’s what a basic call to identify might look like:

analytics.identify('f4ca124298', {
    'name': 'Michael Bolton',
    'email': 'mbolton@example.com',
    'created_at': datetime.datetime.now()

That’s identifying Michael by his unique User ID (the one you know him by in your database) and labeling him with name and email traits.

When you’re using our Python library, you don’t need to identify a user on every request they make to your servers. Instead, we recommend calling identify a single time when the user’s account is first created, and only identifying again later when their traits are changed.

Once you’ve added an identify call, you can move on to…

Step 4: Track Actions

The track method is how you tell Segment about which actions your users are performing. Every action triggers what we call an “event”, which can also have associated properties. You can read more about track in the track reference.

Here’s what a call to track might look like when a user signs up:

analytics.track('f4ca124298', 'Signed Up', {
  'plan': 'Enterprise'

That’s just telling us that your user just triggered the Signed Up event and chose your hypothetical 'Enterprise' plan. Properties can be anything you want to record, for example:

analytics.track('f4ca124298', 'Article Bookmarked', {
    'title': 'Snow Fall',
    'subtitle': 'The Avalance at Tunnel Creek',
    'author': 'John Branch'

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!

And voila, you’re done! You’ve just successfully installed analytics tracking on your servers. Now you’re ready to turn on any destination you fancy from our interface, martini in hand.

What’s Next?

We just walked through the quickest way to get started with Segment using Python. You might also want to check out our full Python library reference to see what else is possible, or read about the Tracking API methods to get a sense for the bigger picture.

You might also want to consider installing Analytics.js so that you can use destinations that require being loaded in the browser, like live chat tools or user feedback systems.

This page was last modified: 06 Aug 2020

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