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Toolkit that creates portable objects specialized on analyzing web navigation data.

Project description

Navigation Analytics

Toolkit that creates portable objects specialized on analyzing web navigation data.


This package is available in pypi and can be installed using pip:

pip install navigation_data


The package expects a data model as follows:


It mainly consists of the following tables:

  • duration_table: Provided a page (page_id visited by an user, it stores the approximate duration of the user in that single page (checkin), it also provides the ranking of that page when it was searched (result_position).

  • search_table: Provides the number of results linked to a page_id.

  • page_data: Look up table of the page_id (primary key) used in duration_table and search_table. It also links every page_id with a session_id.

  • session_data: Look up table with all session_ids (primary key). It associates every session with a group.

  • groups: List of groups defined for each session.

This relational model allows to define a data structure that preserves data integrity, and enables to perform A/B testing in a safe fashion. Furthermore this data structure, namely NavigationDataAnalyzer allows to compute the following metrics:

  • Click Through Rate

  • Most Common Result

  • Session Length

  • Zero Result Rate

Further it allows to save the object with their results as a pickle, enabling thus its traceability and storage in a data lake. Results can also be exported in an Excel Spreadsheet.


Before using the Navigation Data Analyzer it is compulsory to define a config file or dictionary with the following information:

  "metadata": {
    "data_types": { -- Provides the data types of the input table containing the data to be analyzed.
      "uuid": "str",
      "timestamp": "float",
      "session_id": "str",
      "group": "str",
      "action": "str",
      "checkin": "float",
      "page_id": "str",
      "n_results": "float",
      "result_position": "float"
    "primary_keys": { -- Provides the names of 3 of the 5 primary keys in data, this is the hierarchy: events - pages - sessions
      "events": "uuid",
      "pages": "page_id",
      "sessions": "session_id"
    "valid_values": { -- Information of column names and valid values in data.
      "groups": { -- Name of the column defining the groups and the correct/valid values of such.
        "group_id": "group",
        "valid": ["a", "b"]
      "actions": { -- All valid actions to be performed during a session and the name of the column with this information.
        "action_id": "action",
        "valid": ["checkin", "searchResultPage", "visitPage"],
        "search_action": "searchResultPage",
        "visit_action": "visitPage"
      "kpis": { -- Name of the columns containing KPIs
        "number_results": "n_results",
        "result_position": "result_position",
        "duration_page": "checkin"
    "na_vector": ["NA"], -- String expressing how NAs values are expressed in data.
    "datetime": "timestamp", -- Name of the column with timestamp
    "date_format": "%Y%m%d%H%M%S" -- Format of the date in the data.


This dictionary is used to perform sanity checks and avoid hardcoded values in the script.


This section provides a series of short demos with hands-on examples of how to use this package.

1. Computing Click Through Rate

data_analyzer = NavigationDataAnalyzer(input_data=input_data,
# General ctr

2. Computing Ranking of results

data_analyzer = NavigationDataAnalyzer(input_data=input_data,

3. Computing Zero Result Rate For Group 'a'

data_analyzer = NavigationDataAnalyzer(input_data=input_data,

4. Computing Median Session duration for Group 'b'

data_analyzer = NavigationDataAnalyzer(input_data=input_data,
session_length_b = data_analyzer.session_analyzer.compute_session_length(group_id='b')

5. Saving an object

data_analyzer = NavigationDataAnalyzer(input_data=input_data,

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