Data processors user guide

Creating an eventstream

Throughout this guide we use our demonstration simple_shop dataset. It has already been converted to Eventstream and assigned to stream variable. If you want to use your own dataset, upload it following this instruction.

import pandas as pd
from retentioneering import datasets

stream = datasets.load_simple_shop()

What is a data processor?

Data processor is a class that can modify eventstream data in some special and narrow way. For example, data processors can filter events, add some artificial but useful events, truncate user paths according to custom logic, and much more. Data processors are designed to be nodes of a Preprocessing graph, which allows us creating complex data processing pipelines.

An outline of a preprocessing graph

Helpers and chaining usage

A helper is an Eventstream method that applies a single data processor to the eventstream data and returns a new modified eventstream. Essentially, it is a shortcut for the cases when you want to avoid creating a preprocessing graph.

Since an outcome of a helper method is a new eventstream, it is convenient to use method chaining code style as it is present in other python libraries, such as Pandas, PySpark, etc. For example, here is how to apply AddStartEndEvents and SplitSessions sequentially:

res = stream\
    .add_start_end_events()\
    .split_sessions(timeout=(10, 'm'))\
    .to_dataframe()
res[res['user_id'] == 219483890]

	event_type	event_index	event	timestamp	user_id	session_id
0	path_start	0	path_start	2019-11-01 17:59:13	219483890	219483890_1
2	session_start	2	session_start	2019-11-01 17:59:13	219483890	219483890_1
3	raw	3	catalog	2019-11-01 17:59:13	219483890	219483890_1
...	...	...	...	...	...	...
11	session_end	11	session_end	2019-11-01 17:59:32	219483890	219483890_1
6256	session_start	6256	session_start	2019-12-06 16:22:57	219483890	219483890_2
...	...	...	...	...	...	...
23997	session_end	23997	session_end	2020-02-14 21:04:52	219483890	219483890_4
23998	path_end	23998	path_end	2020-02-14 21:04:52	219483890	219483890_4

Hereafter we will use helpers instead of original data processor classes due to simplicity reasons. See some more complex examples of preprocessing here and here.

Data processors library

The table below summarizes all the data processors implemented in retentioneering library.

Data processors overview

Data processor Helper	What it does
AddStartEndEvents add_start_end_events	Adds two synthetic events in each user’s path: path_start and path_end.
SplitSessions split_sessions	Cuts user path into sessions and adds synthetic events session_start, session_end.
LabelNewUsers label_new_users	Adds synthetic event new_user in the beginning of a user’s path if the user is considered as new. Otherwise adds existing_user.
LabelLostUsers label_lost_users	Adds synthetic event lost_user in the end of user’s path if the user never comes back to the product. Otherwise adds absent_user event.
AddPositiveEvents add_positive_events	Adds synthetic event positive_target for all events which are considered as positive.
AddNegativeEvents add_negative_events	Adds synthetic event negative_target for all events which are considered as negative.
LabelCroppedPaths label_cropped_paths	Adds synthetic events cropped_left and/or cropped_right for those user paths which are considered as truncated by the edges of the whole dataset.
FilterEvents filter_events	Removes events from an eventstream.
DropPaths drop_paths	Removes a too short user paths (in terms of number of events or time duration).
TruncatePaths truncate_paths	Leaves a part of an eventstream between a couple of selected events.
GroupEvents group_events	Groups given events into a single synthetic event.
CollapseLoops collapse_loops	Groups sequences of repetitive events with new synthetic events. E.g. A, A, A → A.

Data processors can be partitioned into three groups:

• Adding: processors that add events to an eventstream;

• Removing: processors that remove events from an eventstream;

• Editing: processors that modify existing events in an eventstream (including grouping operations).

In the next sections we organise our narrative according to these partitions.

Adding processors

The processors of that type add some artificial (we also call them synthetic) events to an eventstream. Let us go through each of them.

AddStartEndEvents

For each user, AddStartEndEvents generates an event called path_start right before the first user event, and an event path_end right after the last user event.

Applying AddStartEndEvents to mark user trajectory start and finish:

res = stream.add_start_end_events().to_dataframe()
res[res['user_id'] == 219483890]

	event_type	event_index	event	timestamp	user_id
0	path_start	0	path_start	2019-11-01 17:59:13	219483890
1	raw	1	catalog	2019-11-01 17:59:13	219483890
...	...	...	...	...	...
10213	path_end	10213	path_end	2020-02-14 21:04:52	219483890

As the DataFrame above shows, the generated events path_start and path_end have identical timestamps as the corresponding first and last events.

Note

We recommend applying this data processor each time you analyze an eventstream - since it explicitly sets the borders of an eventstream. It can help displaying user paths in TransitionGraph, StepMatrix, and StepSankey tools or calculating user lifetime.

SplitSessions

SplitSessions data processor splits user paths into sessions according to some splitting condition. For each session, it creates a couple of synthetic events session_start and session_end, similar to AddStartEndEvents. Session identifiers are formed according to the template <user_id>_<user_session_number> and can be found in the column defined by session_id parameter. The user_session_number is associated with a session ordinal number within a user path and always starts with 1.

The splitting condition might be 3-fold:

• based either on timeout between two consecutive events (timeout parameter)

• based on a custom delimiting event or a pair of events (delimiter_events parameter)

• based on a column that already contains session identifiers (delimiter_col parameter).

Below is an example of applying SplitSessions with 10 minutes timeout:

res = stream.split_sessions(timeout=(10, 'm')).to_dataframe()
res[res['user_id'] == 219483890]

	event_type	event_index	event	timestamp	user_id	session_id
0	session_start	0	session_start	2019-11-01 17:59:13	219483890	219483890_1
1	raw	1	catalog	2019-11-01 17:59:13	219483890	219483890_1
...	...	...	...	...	...	...
9	session_end	9	session_end	2019-11-01 17:59:32	219483890	219483890_1
5316	session_start	5316	session_start	2019-12-06 16:22:57	219483890	219483890_2
...	...	...	...	...	...	...
21049	session_end	21049	session_end	2020-02-14 21:04:52	219483890	219483890_4

The result for one user is displayed above. We see that the user trajectory is partitioned into three sessions. The time distance between consecutive events within each session is less than 10 minutes.

Note

In order to estimate appropriate value of timeout parameter, Eventstream.timedelta_hist() might be useful.

Suppose you have some events indicating the start and end of a session. You can define them in the delimiter_events parameter, so that SplitSessions can split sessions according to their occurrence. Note that the delimiting events will be replaced with session_start and session_end synthetic events. See an example in the SplitSessions documentation.

Also, sometimes you have pre-defined session splitting, so that a special column contains session identifiers. In this case you can pass this column name to delimiter_col parameter. session_start and session_end events will be added according to your custom session splitting. See an example in the SplitSessions documentation.

LabelNewUsers

Given a list of users (considered “new”), the LabelNewUsers data processor labels those users in an eventstream by adding a synthetic new_user event to each user trajectory start. For all other users, adds an existing_user synthetic event. All users will be labeled as new when passed ‘all’ instead of a list.

new_users = [219483890, 964964743, 965024600]
res = stream.label_new_users(new_users_list=new_users).to_dataframe()
res[res['user_id'] == 219483890].head()

	event_type	event_index	event	timestamp	user_id
0	new_user	0	new_user	2019-11-01 17:59:13	219483890
1	raw	1	catalog	2019-11-01 17:59:13	219483890
2	raw	2	product1	2019-11-01 17:59:28	219483890
3	raw	3	cart	2019-11-01 17:59:29	219483890
4	raw	4	catalog	2019-11-01 17:59:32	219483890

We can see that user 219483890 is marked as a new user.

But user 501098384 is marked as an existing user:

res[res['user_id'] == 501098384].head()

	event_type	event_index	event	timestamp	user_id
17387	existing_user	17387	existing_user	2020-04-02 05:36:04	501098384
17388	raw	17388	main	2020-04-02 05:36:04	501098384
17389	raw	17389	catalog	2020-04-02 05:36:05	501098384
17390	raw	17390	main	2020-04-02 05:36:40	501098384
17391	raw	17391	catalog	2020-04-02 05:36:41	501098384

This data processor can be helpful when you have data that chronologically precedes the clickstream you are working with. For instance, your clickstream might cover 1-month of user data, and also you have the user login data for the whole year. In that case, you can use LabelNewUsers to split users into two categories:

• new users,

• users who have appeared this year before.

LabelLostUsers

Given a list of users (considered “lost”), the LabelLostUsers data processor labels those users by adding a synthetic lost_user event to each user trajectory end. For all other users, adds an absent_user synthetic event. When passed a timeout timedelta value, the method labels users based on the following strategy: if the timedelta between the user last event and the eventstream last event exceeds timeout, label as lost_user; otherwise, label as absent_user.

lost_users_list = [219483890, 964964743, 965024600]
res = stream.label_lost_users(lost_users_list=lost_users_list).to_dataframe()
res[res['user_id'] == 219483890].tail()

	event_type	event_index	event	timestamp	user_id
5175	raw	5175	catalog	2020-01-06 22:11:28	219483890
9329	raw	9329	main	2020-02-14 21:04:49	219483890
9330	raw	9330	catalog	2020-02-14 21:04:51	219483890
9332	lost_user	9332	lost_user	2020-02-14 21:04:52	219483890

As opposed to user 219483890, the user 501098384 is labeled as an absent_user.

res[res['user_id'] == 501098384].tail()

	event_type	event_index	event	timestamp	user_id
39127	raw	39127	catalog	2020-04-29 12:48:01	501098384
39128	raw	39128	main	2020-04-29 12:48:01	501098384
39129	raw	39129	catalog	2020-04-29 12:48:06	501098384
39130	absent_user	39130	absent_user	2020-04-29 12:48:06	501098384

The function of this data processor is similar to LabelNewUsers, except that it adds labels to the end of user trajectory.

We can also run LabelLostUsers with timeout passed, to arbitrarily label some users as lost. Assume we consider a user absent if there have been no events for 30 days:

res = stream.label_lost_users(timeout=(30, 'D')).to_dataframe()

Before we inspect the results of applying the data processor, notice that the eventstream ends at 2020-04-29 12:48:07.

res['timestamp'].max()

Timestamp('2020-04-29 12:48:07.595390')

User 495985018 is labeled as lost since her last event occurred on 2019-11-02. It’s more than 30 days before the end of the eventstream.

res[res['user_id'] == 495985018]

	event_type	event_index	event	timestamp	user_id
47	raw	47	catalog	2019-11-02 01:14:08	495985018
48	raw	48	cart	2019-11-02 01:14:37	495985018
49	lost_user	49	lost_user	2019-11-02 01:14:37	495985018

On the other hand, user 819489198 is labeled absent because her last event occurred on 2020-04-15, less than 30 days before 2020-04-29.

res[res['user_id'] == 819489198]

	event_type	event_index	event	timestamp	user_id
26529	raw	26529	main	2020-04-15 21:02:36	819489198
...	...	...	...	...	...
26544	raw	26544	payment_card	2020-04-15 21:03:46	819489198
26545	raw	26545	payment_done	2020-04-15 21:03:47	819489198
26546	absent_user	26546	absent_user	2020-04-15 21:03:47	819489198

AddPositiveEvents

AddPositiveEvents data processor supports two parameters:

• targets - list of “positive” events (for instance, associated with some conversion goal of the user behavior)

• func - this function accepts parent Eventstream as an argument and returns pandas.DataFrame contains only the lines of the events we would like to label as positive.

By default, for each user trajectory, an event from the specified list (and minimum timestamp) is taken and cloned with positive_target_<EVENTNAME> as the event and positive_target type.

positive_events = ['cart', 'payment_done']
res = stream.add_positive_events(
    targets=positive_events
    ).to_dataframe()

Consider user 219483890, whose cart event appeared in her trajectory with event_index=2. A synthetic event positive_target_cart is added right after it.

res[res['user_id'] == 219483890]

	event_type	event_index	event	timestamp	user_id
0	raw	0	catalog	2019-11-01 17:59:13	219483890
1	raw	1	product1	2019-11-01 17:59:28	219483890
2	raw	2	cart	2019-11-01 17:59:29	219483890
3	positive_target	3	positive_target_cart	2019-11-01 17:59:29	219483890
...	...	...	...	...	...
5116	raw	5116	cart	2020-01-06 22:10:42	219483890
5117	raw	5117	catalog	2020-01-06 22:10:52	219483890
...	...	...	...	...	...
9187	raw	9187	catalog	2020-02-14 21:04:51	219483890

In opposite to this user, user 24427596 has no positive events, so her path remains unchanged:

res[res['user_id'] == 24427596]

	event_type	event_index	event	timestamp	user_id
68	raw	68	main	2019-11-02 07:28:07	24427596
69	raw	69	catalog	2019-11-02 07:28:14	24427596
...	...	...	...	...	...
71	raw	71	catalog	2019-11-02 07:29:42	24427596

This data processor can make it easier to label events that we would like to consider as positive. It might be helpful for further analysis with tools like TransitionGraph, StepMatrix, and SankeyStep - as it will help to highlight the positive events.

Another way to set positive events is to pass a custom function in func. For example, assume we need to label each target in a trajectory, not just the first one:

def custom_func(eventstream, targets) -> pd.DataFrame:

    event_col = eventstream.schema.event_name
    df = eventstream.to_dataframe()

    return df[df[event_col].isin(targets)]

res = stream.add_positive_events(
          targets=positive_events,
          func=custom_func
          ).to_dataframe()

res[res['user_id'] == 219483890]

	event_type	event_index	event	timestamp	user_id
0	raw	0	catalog	2019-11-01 17:59:13	219483890
1	raw	1	product1	2019-11-01 17:59:28	219483890
2	raw	2	cart	2019-11-01 17:59:29	219483890
3	positive_target	3	positive_target_cart	2019-11-01 17:59:29	219483890
...	...	...	...	...	...
5116	raw	5116	cart	2020-01-06 22:10:42	219483890
5117	positive_target	5117	positive_target_cart	2020-01-06 22:10:42	219483890
5118	raw	5118	catalog	2020-01-06 22:10:52	219483890
...	...	...	...	...	...
9188	raw	9188	catalog	2020-02-14 21:04:51	219483890

AddNegativeEvents

The idea of AddNegativeEvents data processor is the same as AddPositiveEvents, but applied to negative labels instead of positive ones.

• targets - list of “positive” events

  (for instance, associated with some negative result of the user behavior)

• func - this function accepts parent Eventstream as an argument and returns pandas.DataFrame, which contains only the lines of the events we would like to label as negative.

negative_events = ['delivery_courier']

res = stream.add_negative_events(
          targets=negative_events
          ).to_dataframe()

Works similarly to the AddPositiveEvents data processor - in this case, it will add negative event next to the delivery_courier event:

res[res['user_id'] == 629881394]

	event_type	event_index	event	timestamp	user_id
7	raw	7	main	2019-11-01 22:28:54	629881394
...	...	...	...	...	...
39	raw	39	delivery_courier	2019-11-01 22:36:02	629881394
41	negative_target	41	negative_target_delivery_courier	2019-11-01 22:36:02	629881394
44	raw	44	payment_choice	2019-11-01 22:36:02	629881394
...	...	..	...	...	...
13724	raw	13724	catalog	2020-03-30 03:19:59	629881394

LabelCroppedPaths

LabelCroppedPaths addresses a common practical problem, when some trajectories are truncated due to the dataset’s natural boundaries.

The diagram above illustrates this problem. Consider two user paths – blue and orange. In reality, the blue path started before the beginning of the eventstream. But we cannot observe that - since we haven’t access to the events to the left from the beginning of the eventstream. So, instead of the actual start of the user path, we observe a “false” beginning, and the observed trajectory is truncated.

A similar situation occurs with the orange user path. Instead of the actual trajectory end, we only observe the “false” trajectory end.

One possible way to mark truncated paths is to detect trajectories that are “too short” for a typical trajectory, and whose shortness can be attributed to being truncated.

LabelCroppedPaths data processor uses passed left_cutoff and right_cutoff timedeltas and labels user trajectories as cropped_left or cropped_right based on the following policy:

• if the last event of a user trajectory is distanced from the first event of the whole eventstream by less than left_cutoff, consider the user trajectory truncated from the left, and create cropped_left synthetic event at the trajectory start;

• if the first event of a user trajectory is distanced from the last event of the whole eventstream by less than right_cutoff, consider the user trajectory truncated from the right, and create cropped_right synthetic event at the trajectory end.

Sometimes, it can be a good practice to use different cutoff values and compare them in some fashion to select the best.

It can be helpful to use TimedeltaHist method with specified event_pair=('eventstream_start', 'path_end') for choosing left_cutoff value and event_pair=('path_start', 'eventstream_end') for choosing right_cutoff.

See more about eventstream descriptive methods.

params = {
    'left_cutoff': (4, 'D'),
    'right_cutoff': (3, 'D')
}

res = stream.label_cropped_paths(**params).to_dataframe()

Displaying the eventstream start and end timestamps:

print('Eventstream start: {}'.format(res.timestamp.min()))
print('Eventstream end: {}'.format(res.timestamp.max()))

Eventstream start: 2019-11-01 17:59:13.273932
Eventstream end: 2020-04-29 12:48:07.595390

The trajectory of the following user ends at 2019-11-02 01:14:38 - which is too close to the eventstream start(for the given left_cutoff value), so the LabelCroppedPaths data processor labels it as truncated from the left:

res[res['user_id'] == 495985018]

	event_type	event_index	event	timestamp	user_id
47	cropped_left	47	cropped_left	2019-11-02 01:14:08	495985018
48	raw	48	catalog	2019-11-02 01:14:08	495985018
49	raw	49	cart	2019-11-02 01:14:37	495985018

The trajectory of the following user starts at 2020-04-29 12:24:21 - which is too close to the eventstream end(for the given right_cutoff value), so the LabelCroppedPaths data processor labels it as truncated from the right:

res[res['user_id'] == 831491833]

	event_type	event_index	event	timestamp	user_id
35627	raw	35627	catalog	2020-04-29 12:24:21	831491833
35628	raw	35628	catalog	2020-04-29 12:24:33	831491833
35629	raw	35629	product2	2020-04-29 12:24:39	831491833
35630	raw	35630	cart	2020-04-29 12:24:59	831491833
35631	raw	35631	catalog	2020-04-29 12:25:06	831491833
35632	cropped_right	35632	cropped_right	2020-04-29 12:25:06	831491833

Removing processors

FilterEvents

FilterEvents keeps events based on the masking function func. The function should return a boolean mask for the input dataframe(a series of boolean True or False variables that filter the DataFrame underlying the eventstream).

Let us say we are interested only in specific events - for example, only in events of users that appear in some pre-defined list of users. FilterEvents allows us to access only those events:

def save_specific_users(df, schema):
    users_to_save = [219483890, 964964743, 965024600]
    return df[schema.user_id].isin(users_to_save)

res = stream.filter_events(func=save_specific_users).to_dataframe()

The resulting eventstream includes these three users only:

res['user_id'].unique().astype(int)

array([219483890, 964964743, 965024600])

Note that the masking function accepts not just pandas.DataFrame associated with the eventstream, but schema parameter as well. Having this parameter, you can access any eventstream column, defined in its EventstreamSchema.

This makes such masking functions reusable regardless of eventstream column titles.

Using FilterEvents data processor, we can also remove specific events from the eventstream. Let us remove all catalog and main events, assuming they are non-informative for us:

stream.to_dataframe()\
    ['event']\
    .value_counts()\
    [lambda s: s.index.isin(['catalog', 'main'])]

catalog    14518
main        5635
Name: event, dtype: int64

def exclude_events(df, schema):
    events_to_exclude = ['catalog', 'main']
    return ~df[schema.event_name].isin(events_to_exclude)

res = stream.filter_events(func=exclude_events).to_dataframe()

We can see that res DataFrame does not have “useless” events anymore.

res['event']\
    .value_counts()\
    [lambda s: s.index.isin(['catalog', 'main'])]

Series([], Name: event, dtype: int64)

DropPaths

DropPaths removes the paths which we consider “too short”. We might be interested in excluding such paths - in case they are too short to be informative for our task.

Path length can be specified in the following ways:

• setting the number of events comprising a path,

• setting the time distance between the beginning and the end of the path.

The former is associated with min_steps parameter, the latter – with min_time parameter. Thus, DropPaths removes all the paths of length less than min_steps or min_time.

Diagram for specified min_steps:

Diagram for specified min_time:

Let us showcase both variants of the DropPaths data processor:

A minimum number of events specified:

res = stream.drop_paths(min_steps=25).to_dataframe()

Any remaining user has at least 25 events. For example, user 629881394 has 48 events.

len(res[res['user_id'] == 629881394])

48

A minimum path length (user lifetime) is specified:

res = stream.drop_paths(min_time=(1, 'M')).to_dataframe()

Any remaining user has been “alive” for at least a month. For example, user 964964743 started her trajectory on 2019-11-01 and ended on 2019-12-09.

res[res['user_id'] == 964964743].iloc[[0, -1]]

	event_type	event_index	event	timestamp	user_id
4	raw	4	catalog	2019-11-01 21:38:19	964964743
3457	raw	3457	delivery_pickup	2019-12-09 01:43:57	964964743

TruncatePaths

For each user trajectory, TruncatePaths drops all events before or after a particular event. The following parameters specify the behavior:

• drop_before: event name before which part of the user’s path is dropped. The specified event remains in the eventstream.

• drop_after: event name after which part of the user’s path is dropped. The specified event remains in the eventstream.

• occurrence_before: if set to first (by default), all events before the first occurrence of the drop_before event are dropped. If set to last, all events before the last occurrence of the drop_before event are dropped.

• occurrence_after: the same behavior as in the occurrence_before, but for right (after the event) path truncation.

• shift_before: sets the number of steps by which the truncate point is shifted from the selected event. If the value is negative, the offset occurs to the left along the timeline; if positive, then the offset occurs to the right.

• shift_after: the same behavior as in the shift_before, but for right (after the event) path truncation.

The path remains unchanged if the specified event is not present in a user path.

Suppose we want to see what happens to the user after she jumps to a cart event and also to find out which events preceded the cart event. To do this, we can use TruncatePaths with specified drop_before='cart' and shift_before=-2:

res = stream.truncate_paths(
          drop_before='cart',
          shift_before=-2
          ).to_dataframe()

Now some users have their trajectories truncated, because they had at least one cart in their path:

res[res['user_id'] == 219483890]

	event_type	event_index	event	timestamp	user_id
0	raw	0	catalog	2019-11-01 17:59:13	219483890
1	raw	1	product1	2019-11-01 17:59:28	219483890
2	raw	2	cart	2019-11-01 17:59:29	219483890
3	raw	3	catalog	2019-11-01 17:59:32	219483890
...	...	...	...	...	...
10317	raw	10317	catalog	2020-02-14 21:04:51	219483890

As we can see, this path now starts with the two events preceding the cart (event_index=0,1) and the cart event right after them (event_index=2). Another cart event occurred here (event_index=5827), but since the default occurrence_before='first' was triggered, the data processor ignored this second cart.

Some users do not have any cart events - and their trajectories have not been changed:

res[res['user_id'] == 24427596]

	event_type	event_index	event	timestamp	user_id
89	raw	89	main	2019-11-02 07:28:07	24427596
90	raw	90	catalog	2019-11-02 07:28:14	24427596
91	raw	91	catalog	2019-11-02 07:29:08	24427596
92	raw	92	catalog	2019-11-02 07:29:41	24427596

We can also perform truncation from the right, or specify for the truncation point to be not the first but the last occurrence of the cart. To demonstrate both, let us set drop_after="cart" and occurrence_after="last":

res = stream.truncate_paths(
          drop_after='cart',
          occurrence_after="last"
          ).to_dataframe()

Now, any trajectory which includes a cart is truncated to the end with the last cart:

res[res['user_id'] == 219483890]

	event_type	event_index	event	timestamp	user_id
0	raw	0	catalog	2019-11-01 17:59:13	219483890
1	raw	1	product1	2019-11-01 17:59:28	219483890
2	raw	2	cart	2019-11-01 17:59:29	219483890
...	...	...	...	...	...
5639	raw	5639	catalog	2020-01-06 22:10:15	219483890
5640	raw	5640	cart	2020-01-06 22:10:42	219483890

Editing processors

GroupEvents

Given a masking function passed as a func, GroupEvents replaces all the events marked by func with newly created synthetic events of event_name name and event_type type (group_alias by default). The timestamps of these synthetic events are the same as their parents’. func can be any function that returns a series of boolean (True/False) variables that can be used as a filter for the DataFrame underlying the eventstream.

With GroupEvents, we can group events based on the event name. Suppose we need to assign a common name product to events product1 and product2:

def group_events(df, schema):
    events_to_group = ['product1', 'product2']
    return df[schema.event_name].isin(events_to_group)

params = {
    'event_name': 'product',
    'func': group_events
}

res = stream.group_events(**params).to_dataframe()

As we can see, user 456870964 now has two product events (event_index=160, 164) with event_type=‘group_alias’).

res[res['user_id'] == 456870964]

	event_type	event_index	event	timestamp	user_id
157	raw	157	catalog	2019-11-03 11:46:55	456870964
158	raw	158	catalog	2019-11-03 11:47:46	456870964
159	raw	159	catalog	2019-11-03 11:47:58	456870964
160	group_alias	160	product	2019-11-03 11:48:43	456870964
162	raw	162	cart	2019-11-03 11:49:17	456870964
163	raw	163	catalog	2019-11-03 11:49:17	456870964
164	group_alias	164	product	2019-11-03 11:49:28	456870964
166	raw	166	catalog	2019-11-03 11:49:30	456870964

Previously, both events were named product1 and product2 and had raw event types:

stream.to_dataframe().query('user_id == 456870964')

	event_type	event_index	event	timestamp	user_id
140	raw	140	catalog	2019-11-03 11:46:55	456870964
141	raw	141	catalog	2019-11-03 11:47:46	456870964
142	raw	142	catalog	2019-11-03 11:47:58	456870964
143	raw	143	product1	2019-11-03 11:48:43	456870964
144	raw	144	cart	2019-11-03 11:49:17	456870964
145	raw	145	catalog	2019-11-03 11:49:17	456870964
146	raw	146	product2	2019-11-03 11:49:28	456870964
147	raw	147	catalog	2019-11-03 11:49:30	456870964

You can also notice that the newly created product events have event_id that differs from their parents’ event_ids.

CollapseLoops

CollapseLoops replaces all uninterrupted series of repetitive user events (loops) with one new loop - like event. The suffix parameter defines the name of the new event:

• given suffix=None, names new event with the old event_name, i.e. passes along the name of the repeating event;

• given suffix="loop", names new event event_name_loop;

• given suffix="count", names new event event_name_loop_{number of event repetitions}.

The time_agg value determines the new event timestamp:

• given time_agg="max" (the default option), passes the timestamp of the last event from the loop;

• given time_agg="min", passes the timestamp of the first event from the loop;

• given time_agg="mean", passes the average loop timestamp.

res = stream.collapse_loops(suffix='loop', time_agg='max').to_dataframe()

Consider for example user 2112338. In the original eventstream she had three consecutive catalog events.

stream.to_dataframe().query('user_id == 2112338')

	event_type	event_index	event	timestamp	user_id
3327	raw	3327	main	2019-12-24 12:58:04	2112338
3328	raw	3328	catalog	2019-12-24 12:58:08	2112338
3329	raw	3329	catalog	2019-12-24 12:58:16	2112338
3330	raw	3330	catalog	2019-12-24 12:58:44	2112338
3331	raw	3331	main	2019-12-24 12:58:52	2112338

In the resulting DataFrame, the repeating “catalog” events have been collapsed to a single catalog_loop event. The timestamp of this synthetic event is the same as the timestamp of the last looping event: 2019-12-24 12:58:44.

res[res['user_id'] == 2112338]

	event_type	event_index	event	timestamp	user_id
5061	raw	5061	main	2019-12-24 12:58:04	2112338
5066	group_alias	5066	catalog_loop	2019-12-24 12:58:44	2112338
5069	raw	5069	main	2019-12-24 12:58:52	2112338

We can set the suffix to see the length of the loops we removed. Also, let us see how time_agg works if we set it to mean.

params = {
    'suffix': 'count',
    'time_agg': 'mean'
}

res = stream.collapse_loops(**params).to_dataframe()
res[res['user_id'] == 2112338]

	event_type	event_index	event	timestamp	user_id
5071	raw	5071	main	2019-12-24 12:58:04	2112338
5076	group_alias	5076	catalog_loop_3	2019-12-24 12:58:23	2112338
5079	raw	5079	main	2019-12-24 12:58:52	2112338

Now, the synthetic catalog_loop_3 event has 12:58:23 time - the average of 12:58:08, 12:58:16 and 12:58:44.

The CollapseLoops data processor can be useful for compressing the data:

• by packing loop information into single events,

• removing looping events, in case they are not desirable (which can be a common case in clickstream visualization).

Synthetic events order

Let us summarize the information about event type and event order in the eventstream. As we have already discussed in the eventstream guide: event_type column and reindex method.

All events came from a sourcing DataFrame are of raw event type. When we apply adding or editing data processors new synthetic events are created. General idea is that each synthetic event has a “parent” or “parents” that defines its timestamp.

When you apply multiple data processors, timestamp collisions might occur, so it is unclear how the events should be ordered. For colliding events, the following sorting order is applied, based on event types (earlier event types are added earlier), also you can see which data processor for which event_type is responsible:

Mapping of event_types and data processors.

Order	event_type	helper
1	profile
2	path_start	add_start_end_events
3	new_user	label_new_users
4	existing_user	label_new_users
5	cropped_left	label_cropped_paths
6	session_start	split_sessions
7	session_start_cropped	split_sessions
8	group_alias	group_events
9	raw
10	raw_sleep
11	None
12	synthetic
13	synthetic_sleep
14	add_positive_events	add_positive_events
15	add_negative_events	add_negative_events
16	session_end_cropped	split_sessions
17	session_end	split_sessions
18	session_sleep
19	cropped_right	label_cropped_paths
20	absent_user	label_lost_users
21	lost_user	label_lost_users
22	path_end	add_start_end_events