Setting the file. One moment.
Chapter 47 · Instrument Integration
Subchapter 47.56
references/python.mdMarkdown30 KBView on GitHub
The Python SDK makes it easy to capture events, evaluate feature flags, track errors, and more in your Python apps.
Python 3.9 and lower
Python 3.9 is no longer supported for PostHog Python SDK versions 7.x.x and higher.
Terminal
PostHog AI
pip install posthogUpgrading to v6
Version 6.x of the PostHog Python SDK introduces a new contexts (opens in a new tab) API and breaking changes. If you’re upgrading from 5.x to 6.x, read the migration guide (opens in a new tab) first to learn more.
In your app, import the posthog library and set your project token and host before making any calls.
Python
PostHog AI
from posthog import Posthog
posthog = Posthog('<ph_project_token>', host='https://us.i.posthog.com')Note: As a rule of thumb, we do not recommend having API keys or tokens in plaintext. Setting it as an environment variable is best.
You can find your project token and instance address in the project settings (opens in a new tab) page in PostHog.
Identifying users is required. Backend events need a
distinct_idthat matches the ID your frontend uses when callingposthog.identify(). Without this, backend events are orphaned — they can’t be linked to frontend event captures, session replays (opens in a new tab), LLM traces (opens in a new tab), or error tracking (opens in a new tab).See our guide on identifying users (opens in a new tab) for how to set this up.
You can send custom events using capture:
Python
PostHog AI
# Events captured with no context or explicit distinct_id are marked as personless and have an auto-generated distinct_id:
posthog.capture('some-anon-event')
from posthog import identify_context, new_context
# Use contexts to manage user identification across multiple capture calls
with new_context():
identify_context('distinct_id_of_the_user')
posthog.capture('user_signed_up')
posthog.capture('user_logged_in')
# You can also capture events with a specific distinct_id
posthog.capture('some-custom-action', distinct_id='distinct_id_of_the_user')Tip: We recommend using a
[object] [verb]format for your event names, where[object]is the entity that the behavior relates to, and[verb]is the behavior itself. For example,project created,user signed up, orinvite sent.
Tip: You can define event schemas with typed properties and generate type-safe code using schema management (opens in a new tab).
Optionally, you can include additional information with the event by including a properties (opens in a new tab) object:
Python
PostHog AI
posthog.capture(
"user_signed_up",
distinct_id="distinct_id_of_the_user",
properties={
"login_type": "email",
"is_free_trial": "true"
}
)If you’re aiming for a backend-only implementation of PostHog and won’t be capturing events from your frontend, you can send pageviews from your backend like so:
Python
PostHog AI
posthog.capture('$pageview', distinct_id="distinct_id_of_the_user", properties={'$current_url': 'https://example.com'})The Python SDK captures identified events if the current context is identified or if you pass a distinct ID explicitly. These create person profiles (opens in a new tab). To set person properties (opens in a new tab) in these profiles, include them when capturing an event:
Python
PostHog AI
# Passing a distinct id explicitly
posthog.capture(
'event_name',
distinct_id='user-distinct-id',
properties={
'$set': {'name': 'Max Hedgehog'},
'$set_once': {'initial_url': '/blog'}
}
)
# Using contexts
from posthog import new_context, identify_context
with new_context():
identify_context('user-distinct-id')
posthog.capture('event_name')For more details on the difference between $set and $set_once, see our person properties docs (opens in a new tab).
To capture anonymous events (opens in a new tab) without person profiles, set the event’s $process_person_profile property to False. Events captured with no context or explicit distinct_id are marked as personless, and will have an auto-generated distinct_id:
Python
PostHog AI
posthog.capture(
event='event_name',
properties={
'$process_person_profile': False
}
)Sometimes, you want to assign multiple distinct IDs to a single user. This is helpful when your primary distinct ID is inaccessible. For example, if a distinct ID used on the frontend is not available in your backend.
In this case, you can use alias to assign another distinct ID to the same user.
Python
PostHog AI
posthog.alias(previous_id='distinct_id', distinct_id='alias_id')We strongly recommend reading our docs on alias (opens in a new tab) to best understand how to correctly use this method.
The Python SDK uses nested contexts for managing state that’s shared across events. Contexts are the recommended way to manage things like “which user is taking this action” (through identify_context), rather than manually passing user state through your apps stack.
When events (including exceptions) are captured in a context, the event uses the user distinct ID (opens in a new tab), session ID (opens in a new tab), and tags that are (optionally) set in the context. This is useful for adding properties to multiple events during a single user’s interaction with your product.
You can enter a context using the with statement:
Python
PostHog AI
from posthog import new_context, tag, set_context_session, identify_context
with new_context():
tag("transaction_id", "abc123")
tag("some_arbitrary_value", {"tags": "can be dicts"})
# Sessions are UUIDv7 values and used to track a sequence of events that occur within a single user session
# See https://posthog.com/docs/data/sessions
set_context_session(session_id)
# Setting the context-level distinct ID. See below for more details.
identify_context(user_id)
# This event is captured with the distinct ID, session ID, and tags set above
posthog.capture("order_processed")Contexts are persisted across function calls. If you enter one and then call a function and capture an event in the called function, it uses the context tags and session ID set in the parent context:
Python
PostHog AI
from posthog import new_context, tag
def some_function():
# When called from `outer_function`, this event is captured with the property some-key="value-4"
posthog.capture("order_processed")
def outer_function():
with new_context():
tag("some-key", "value-4")
some_function()Contexts are nested, so tags added to a parent context are inherited by child contexts. If you set the same tag in both a parent and child context, the child context’s value overrides the parent’s at event capture (but the parent context won’t be affected). This nesting also applies to session IDs and distinct IDs.
Python
PostHog AI
from posthog import new_context, tag
with new_context():
tag("some-key", "value-1")
tag("some-other-key", "another-value")
with new_context():
tag("some-key", "value-2")
# This event is captured with some-key="value-2" and some-other-key="another-value"
posthog.capture("order_processed")
# This event is captured with some-key="value-1" and some-other-key="another-value"
posthog.capture("order_processed")You can disable this nesting behavior by passing fresh=True to new_context:
Python
PostHog AI
from posthog import new_context, tag
with new_context(fresh=True):
tag("some-key", "value-2")
# This event only has the property some-key="value-2" from the fresh context
posthog.capture("order_processed")Note: Distinct IDs, session IDs, and properties passed directly to calls to
captureand related functions override context state in the final event captured.
Contexts can be associated with a distinct ID by calling posthog.identify_context:
Python
PostHog AI
from posthog import identify_context
identify_context("distinct-id")Within a context associated with a distinct ID, all events captured are associated with that user. You can override the distinct ID for a specific event by passing a distinct_id argument to capture:
Python
PostHog AI
from posthog import new_context, identify_context
with new_context():
identify_context("distinct-id")
posthog.capture("order_processed") # will be associated with distinct-id
posthog.capture("order_processed", distinct_id="another-distinct-id") # will be associated with another-distinct-idIt’s recommended to pass the currently active distinct ID from the frontend to the backend, using the X-POSTHOG-DISTINCT-ID header. If you’re using our Django middleware, this is extracted and associated with the request handler context automatically.
You can read more about identifying users in the user identification documentation (opens in a new tab).
Contexts can be associated with a session ID by calling posthog.set_context_session. When linking backend events to frontend sessions, use the session ID from the frontend SDK (PostHog session IDs are UUIDv7 strings).
Python
PostHog AI
from posthog import new_context, set_context_session
with new_context():
set_context_session(request.get_header("X-POSTHOG-SESSION-ID"))Using PostHog on your frontend too?
If you’re using the PostHog JavaScript Web SDK on your frontend, it generates a session ID for you. You can pass it to your backend by adding your backend domain to __add_tracing_headers in your config. This will automatically add tracing headers to your requests.
JavaScript
PostHog AI
posthog.init('<ph_project_token>', {
__add_tracing_headers: ['your-backend-domain.com']
});Alternatively, you can retrieve it on the frontend by calling posthog.get_session_id(). You then need to pass that session ID to your backend by setting the X-POSTHOG-SESSION-ID header on each fetch request.
You need to extract the header in your request handler (if you’re using our Django middleware integration, this happens automatically).
If you associate a context with a session, you’ll be able to do things like:
You can read more about sessions in the session tracking (opens in a new tab) documentation.
By default exceptions raised within a context are captured and available in the error tracking (opens in a new tab) dashboard. You can override this behavior by passing capture_exceptions=False to new_context:
Python
PostHog AI
from posthog import new_context, tag
with new_context(capture_exceptions=False):
tag("transaction_id", "abc123")
tag("some_arbitrary_value", {"tags": "can be dicts"})
# This event will be captured with the tags set above
posthog.capture("order_processed")
# This exception will not be captured
raise Exception("Order processing failed")The SDK exposes a function decorator. It takes the same fresh and capture_exceptions arguments as new_context and provides a handy way to mark a whole function as being in a new context. For example:
Python
PostHog AI
from posthog import scoped, identify_context
@scoped(fresh=True)
def process_order(user, order_id):
identify_context(user.distinct_id)
posthog.capture("order_processed") # Associated with the user
raise Exception("Order processing failed") # This exception is also captured and associated with the userGroup analytics allows you to associate an event with a group (e.g. teams, organizations, etc.). Read the Group Analytics (opens in a new tab) guide for more information.
Note: This is a paid feature and is not available on the open-source or free cloud plan. Learn more on our pricing page (opens in a new tab).
To capture an event and associate it with a group:
Python
PostHog AI
posthog.capture('some_event', groups={'company': 'company_id_in_your_db'})To update properties on a group:
Python
PostHog AI
posthog.group_identify('company', 'company_id_in_your_db', {
'name': 'Awesome Inc.',
'employees': 11
})The name is a special property which is used in the PostHog UI for the name of the group. If you don’t specify a name property, the group ID will be used instead.
PostHog’s feature flags (opens in a new tab) enable you to safely deploy and roll back new features as well as target specific users and groups with them.
There are two steps to implement feature flags in Python:
Call posthog.evaluate_flags() once for the user, then read values from the returned snapshot.
Python
PostHog AI
flags = posthog.evaluate_flags("distinct_id_of_your_user")
if flags.is_enabled("flag-key"):
# Do something differently for this user
# Optional: fetch the payload
matched_flag_payload = flags.get_flag_payload("flag-key")Python
PostHog AI
flags = posthog.evaluate_flags("distinct_id_of_your_user")
enabled_variant = flags.get_flag("flag-key")
if enabled_variant == "variant-key": # replace "variant-key" with the key of your variant
# Do something differently for this user
# Optional: fetch the payload
matched_flag_payload = flags.get_flag_payload("flag-key")flags.get_flag() returns the variant string for multivariate flags, True for enabled boolean flags, False for disabled flags, and None when the flag wasn’t returned by the evaluation.
Note:
posthog.feature_enabled(),posthog.get_feature_flag(),posthog.get_feature_flag_payload(), andposthog.capture(send_feature_flags=True)still work during the migration period, but they’re deprecated. Preferposthog.evaluate_flags()for new code.
If you want use your feature flag to breakdown or filter events in your insights (opens in a new tab), you’ll need to include feature flag information in those events. This ensures that the feature flag value is attributed correctly to the event.
Note: This step is only required for events captured using our server-side SDKs or API (opens in a new tab).
There are two methods you can use to include feature flag information in your events:
Pass the same flags object that you used for branching. This attaches the exact flag values from that evaluation and doesn’t make another /flags request.
Python
PostHog AI
flags = posthog.evaluate_flags("distinct_id_of_your_user")
if flags.is_enabled("flag-key"):
# Do something differently for this user
pass
posthog.capture(
"event_name",
distinct_id="distinct_id_of_your_user",
flags=flags,
)By default, this attaches every flag in the snapshot using $feature/<flag-key> properties and $active_feature_flags.
To reduce event property bloat, pass a filtered snapshot:
Python
PostHog AI
# Attach only flags accessed with is_enabled() or get_flag() before this call
posthog.capture(
"event_name",
distinct_id="distinct_id_of_your_user",
flags=flags.only_accessed(),
)
# Attach only specific flags
posthog.capture(
"event_name",
distinct_id="distinct_id_of_your_user",
flags=flags.only(["checkout-flow", "new-dashboard"]),
)only_accessed() is order-dependent. If you call it before accessing any flags with is_enabled() or get_flag(), no feature flag properties are attached.
In the event properties, include $feature/feature_flag_name: variant_key:
Python
PostHog AI
posthog.capture(
"event_name",
distinct_id="distinct_id_of_the_user",
properties={
# Replace feature-flag-key with your flag key and "variant-key" with the key of your variant
"$feature/feature-flag-key": "variant-key",
},
)By default, posthog.evaluate_flags() evaluates every flag for the user. If you only need a few flags, pass flag_keys to request only those flags:
Python
PostHog AI
flags = posthog.evaluate_flags(
"distinct_id_of_your_user",
flag_keys=["checkout-flow", "new-dashboard"],
)Capturing $feature_flag_called events enables PostHog to know when a flag was accessed by a user and provide analytics and insights (opens in a new tab) on the flag. With posthog.evaluate_flags(), the SDK sends this event when you call flags.is_enabled() or flags.get_flag() for a flag.
The SDK deduplicates these events per (distinct_id, flag, value) in a local cache. If you reinitialize the PostHog client, the cache resets and $feature_flag_called events may be sent again. PostHog handles duplicates, so duplicate $feature_flag_called events don’t affect your analytics.
flags.get_flag_payload() doesn’t send $feature_flag_called events and doesn’t count as an access for only_accessed().
Sometimes, you may want to evaluate feature flags using person properties (opens in a new tab), groups (opens in a new tab), or group properties that haven’t been ingested yet, or were set incorrectly earlier.
You can provide properties to evaluate the flag with by using the person properties, groups, and group properties arguments. PostHog will then use these values to evaluate the flag, instead of any properties currently stored on your PostHog server.
For example:
Python
PostHog AI
flags = posthog.evaluate_flags(
"distinct_id_of_the_user",
person_properties={"property_name": "value"},
groups={
"your_group_type": "your_group_id",
"another_group_type": "your_group_id",
},
group_properties={
"your_group_type": {"group_property_name": "value"},
"another_group_type": {"group_property_name": "value"},
},
)
if flags.is_enabled("flag-key"):
# Do something differently for this userBy default, a user’s GeoIP properties are set using the IP address they use to capture events on the frontend. You may want to override the these properties when evaluating feature flags. A common reason to do this is when you’re not using PostHog on your frontend, so the user has no GeoIP properties.
You can override GeoIP properties by including them in the person_properties parameter when evaluating feature flags. This is useful when you’re evaluating flags on your backend and want to use the client’s location instead of your server’s location.
The following GeoIP properties can be overridden:
$geoip_country_code$geoip_country_name$geoip_city_name$geoip_city_confidence$geoip_continent_code$geoip_continent_name$geoip_latitude$geoip_longitude$geoip_postal_code$geoip_subdivision_1_code$geoip_subdivision_1_name$geoip_subdivision_2_code$geoip_subdivision_2_name$geoip_subdivision_3_code$geoip_subdivision_3_name$geoip_time_zoneSimply include any of these properties in the person_properties parameter alongside your other person properties when calling feature flags.
You can configure the feature_flags_request_timeout_seconds parameter when initializing your PostHog client to set a flag request timeout. This helps prevent your code from being blocked if PostHog’s servers are too slow to respond. By default, this is set to 3 seconds.
Python
PostHog AI
posthog = Posthog(
"<ph_project_token>",
host="https://us.i.posthog.com",
feature_flags_request_timeout_seconds=3, # Time in seconds. Defaults to 3.
)Evaluating feature flags requires making a request to PostHog for each flag. However, you can improve performance by evaluating flags locally. Instead of making a request for each flag, PostHog will periodically request and store feature flag definitions locally, enabling you to evaluate flags without making additional requests.
It is best practice to use local evaluation flags when possible, since this enables you to resolve flags faster and with fewer API calls.
For details on how to implement local evaluation, see our local evaluation guide (opens in a new tab).
In multi-worker or edge environments, you can implement custom caching for flag definitions using Redis, Cloudflare KV, or other storage backends. This enables sharing definitions across workers and coordinating fetches. See our guide for local evaluation in distributed environments (opens in a new tab) for details.
Since experiments (opens in a new tab) use feature flags, the code for running an experiment is very similar to the feature flags code:
Python
PostHog AI
flags = posthog.evaluate_flags("user_distinct_id")
variant = flags.get_flag("experiment-feature-flag-key")
if variant == "variant-name":
# Do somethingIt’s also possible to run experiments without using feature flags (opens in a new tab).
Our Python SDK includes a built-in AI Observability feature. It enables you to capture LLM usage, performance, and more. Check out our analytics docs (opens in a new tab) for more details on setting it up.
You can autocapture exceptions (opens in a new tab) by setting the enable_exception_autocapture argument to True when initializing the PostHog client.
Python
PostHog AI
from posthog import Posthog
posthog = Posthog("<ph_project_token>", enable_exception_autocapture=True, ...)You can also manually capture exceptions using the capture_exception method:
Python
PostHog AI
posthog.capture_exception(e, distinct_id='user_distinct_id', properties=additional_properties)Contexts automatically capture exceptions thrown inside them, unless disable it by passing capture_exceptions=False to new_context().
The Python SDK can automatically capture the state of local variables when an exception occurs. This gives you a debugger-like view of your application state at the time of the error:
Python
PostHog AI
posthog = Posthog(
"<ph_project_token>",
enable_exception_autocapture=True,
capture_exception_code_variables=True,
)You can configure which variables are captured, masked, or ignored. See the code variables documentation (opens in a new tab) for detailed configuration options.
Before posthog-python v3.0, we added GeoIP properties to all incoming events by default. We also used these properties for feature flag evaluation, based on the IP address of the request. This isn’t ideal since they are created based on your server IP address, rather than the user’s, leading to incorrect location resolution.
As of posthog-python v3.0, the default now is to disregard the server IP, not add the GeoIP properties, and not use the values for feature flag evaluations.
You can go back to previous behavior by doing setting the disable_geoip argument in your initialization to False:
Python
PostHog AI
posthog = Posthog('api_key', disable_geoip=False)The list of properties that this overrides:
$geoip_city_name$geoip_country_name$geoip_country_code$geoip_continent_name$geoip_continent_code$geoip_postal_code$geoip_time_zoneYou can also explicitly chose to enable or disable GeoIP for a single capture request like so:
Python
PostHog AI
posthog.capture('test_event', disable_geoip=True|False)If you’re not seeing the expected events being captured, the feature flags being evaluated, or the surveys being shown, you can enable debug mode to see what’s happening.
You can enable debug mode by setting the debug option to True in the PostHog object. This will enable verbose logs about the inner workings of the SDK.
Python
PostHog AI
posthog.debug = TrueYou can disable requests during tests by setting the disabled option to True in the PostHog object. This means no events will be captured or no requests will be sent to PostHog.
Python
PostHog AI
if settings.TEST:
posthog.disabled = TrueThe SDK uses HTTP connection pooling internally for better performance. These settings typically need not be changed, but in some environments, such as when running behind NAT gateways, pooled connections may be terminated non-gracefully, causing request failures.
You can configure connection behavior in several ways. The following settings should be called during initialization, before any API requests are made.
TCP keepalive probes help prevent idle connections from being dropped by network infrastructure. This is the recommended approach for most cases where idle connections are terminated.
Python
PostHog AI
import posthog
posthog.enable_keep_alive()This enables TCP keepalive with sensible defaults (60 second idle time, 60 second probe interval, 3 probes before timeout).
If you need each request to use a fresh connection, you can disable connection reuse entirely. This will incur additional overhead per request but may be desirable in some circumstances.
Python
PostHog AI
import posthog
posthog.disable_connection_reuse()For advanced use cases, you can configure arbitrary socket options on the underlying HTTP connection.
Python
PostHog AI
import socket
import posthog
posthog.set_socket_options([
(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),
# Add additional socket options as needed
])Pass None to set_socket_options() to reset to default behavior.
You can use the Python or Node SDK to run historical migrations (opens in a new tab) of data into PostHog. To do so, set the historical_migration option to true when initializing the client.
PostHog AI
from posthog import Posthog
from datetime import datetime
posthog = Posthog(
'<ph_project_token>',
host='https://us.i.posthog.com',
debug=True,
historical_migration=True
)
events = [
{
"event": "batched_event_name",
"properties": {
"distinct_id": "user_id",
"timestamp": datetime.fromisoformat("2024-04-02T12:00:00")
}
},
{
"event": "batched_event_name",
"properties": {
"distinct_id": "used_id",
"timestamp": datetime.fromisoformat("2024-04-02T12:00:00")
}
}
]
for event in events:
posthog.capture(
distinct_id=event["properties"]["distinct_id"],
event=event["event"],
properties=event["properties"],
timestamp=event["properties"]["timestamp"],
)import { PostHog } from 'posthog-node'
const client = new PostHog(
'<ph_project_token>',
{
host: 'https://us.i.posthog.com',
historicalMigration: true
}
)
client.debug()
client.capture({
event: "batched_event_name",
distinctId: "user_id",
properties: {},
timestamp: "2024-04-03T12:00:00Z"
})
client.capture({
event: "batched_event_name",
distinctId: "user_id",
properties: {},
timestamp: "2024-04-03T13:00:00Z"
})
await client.shutdown()By default, the library buffers events before sending them to the capture endpoint, for better performance. This can lead to lost events in serverless environments, if the Python process is terminated by the platform before the buffer is fully flushed. To avoid this, you can either:
posthog.shutdown() is called after processing every request by adding a middleware to your server. This allows posthog.capture() to remain asynchronous for better performance. posthog.shutdown() is blocking.sync_mode option when initializing the client, so that all calls to posthog.capture() become synchronous.See our Django docs (opens in a new tab) for how to set up PostHog in Django. Our library includes a contexts middleware (opens in a new tab) that can automatically capture distinct IDs, session IDs, and other properties you can set up with tags.
As our open source project PostHog (opens in a new tab) shares the same module name, we created a special posthoganalytics package, mostly for internal use to avoid module collision. It is the exact same.
This library is largely based on the analytics-python package.
Ask a question
HelpfulCould be better
This file