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A flexible event tracker for rapid analysis.

Project description

Build Status GitHub license


pawprint allows you to quickly track events occurring in your application, and analyse them using pandas. For the full API, see the documentation. These are a work in progress.

Write data flexibly

tracker.write(event="logged_in", user_id="alice")
tracker.write(event="navigation", user_id="bob", metadata={"to": "dashboard"})
tracker.write(event="invoice", metadata={"details": {"amount": 1214, "from": "Ardbeg"}})
tracker.write(event="invoice", metadata={"details": {"amount": 123, "from": "Lagavulin"}})

Query data intuitively

Read the full dataset.
    id                    timestamp   user_id           event                                            metadata  
0    1   2017-03-31 15:51:50.590018      None   server_booted                                                None
1    2   2017-03-31 15:51:50.599256     alice       logged_in                                                None
2    3   2017-03-31 15:51:50.610069       bob      navigation                                 {'to': 'dashboard'}
3    4   2017-03-31 15:51:50.620759      None         invoice     {'details': {'from': 'Ardbeg', 'amount': 1214}}
4    5   2017-03-31 15:51:50.629837      None         invoice   {'details': {'from': 'Lagavulin', 'amount': 123}}

List only events where the user was Alice."event", user_id="alice")
0   logged_in

Query unstructured data to find out who invoiced you and when."timestamp", "metadata__details__from", event="invoice")
                     timestamp   json_field
0   2017-03-31 15:51:50.620759       Ardbeg
1   2017-03-31 15:51:50.629837    Lagavulin

Perform aggregates over time.

tracker.count("logged_in", resolution="week")
      datetime   count
0   2017-03-27       1

Aggregate JSON subfields.

tracker.sum(event="invoice", field="metadata__details__amount", resolution="year")
      datetime      sum
0   2017-01-01   1337.0


For installation, dependencies, API details, and a quickstart, please RTFM !

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