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Playbooks for data. Open, process and save table based data.

Project description

Data Playbook

:book: Playbooks for data. Open, process and save table based data. CircleCI codecov

Automate repetitive tasks on table based data. Include various input and output tasks. Can be extended with custom modules.

Install: pip install dataplaybook

Use: dataplaybook playbook.yaml

Playbook structure

The playbook.yaml file allows you to load additional modules (containing tasks) and specify the tasks to execute in sequence, with all their parameters.

The tasks to perform typically follow the the structure of read, process, write.

Example yaml: (please note yaml is case sensitive)

modules: [list, of, modules]

  - task_name:  # See a list of tasks below
      task_setting_1: 1
    tables: # The INPUT. One of more tables used by this task
    target: # The OUTPUT. Target table name of this function
    debug: True/False # Print extra debug message, default: False


Jinja2 and JMESPath expressions can be used to create parameters for subsequent tasks. Jinja2 simly use the "{{ var[res1] }}" bracket syntax and jmespath expressions should start with the word jmespath followed by a space.

Both the vars and template tasks achieve a similar result: (this will search a table matching string "2" on the key column and return the value in the value column)

  - vars:
      res1: jmespath test[?key=='2'].value | [0]
  # is equal to
  - template:
      jmespath: "test[?key=='2'].value | [0]"
    target: res1

  # ... then use it with `{{ var.res1 }}`

The JMESpath task template task has an advantage that you can create new variables or tables.

If you have a lookup you use regularly you can do the following:

 - build_lookup_var:
     key: key
     columns: [value]
   target: lookup1
  # and then use it as follows to get a similar results to the previous example
  - vars:
      res1: "{{ var['lookup1']['2'].value }}"

When searching through a table with Jinja, a similar one-liner, using selectattr, seems much more complex:

  - vars:
      res1: "{{ test | selectattr('key', 'equalto', '2') | map(attribute='value') | first }}"


Tasks are implemented as simple Python functions and the modules can be found in the dataplaybook/tasks folder.

Default tasks dataplaybook.tasks (loaded by default)

  • build_lookup
  • build_lookup_var
  • combine
  • drop
  • extend
  • filter
  • fuzzy_match (pip install fuzzywuzzy)
  • print
  • replace
  • unique
  • vlookup

Templates (loaded by default)

  • template - can be used to
  • vars - set variables using

Other modules

These modules can be loaded if required

  • IETF dataplaybook.tasks.ietf - IETF/RFC/draft related tasks
  • GIS dataplaybook.tasks.gis - tasks for QGIS data
  • FNB dataplaybook.tasks.fnb

IO Tasks

Excel files dataplaybook.tasks.io_xlsx (loaded by default)

  • read_excel
  • write_excel

Miscellaneous I/O dataplaybook.tasks.io_misc (loaded by default)

  • read_csv
  • read_tab_delim
  • read_text_regex
  • wget
  • write_csv

Mongo DB dataplaybook.tasks.io_mongo

Requires pymongo

  • read_mongo
  • write_mongo
  • columns_to_list
  • list_to_columns

Module io_pdf

Requires pdftotext executable file on your path

  • read_pdf_pages
  • read_pdf_files

Module io_xml

  • read_xml

Special yaml functions

  • !re <expression> Regular expression
  • !es <search string> Search a file using Everything by Voidtools

Install the development version

  1. Clone the repo
  2. pip install <path> -e

Data Playbook origins

Data playbooks was created to replace various snippets of code I had lying around. They were all created to ensure repeatability of some menial task, and generally followed a similar structure of load something, process it and save it. (Process network data into GIS tools, network audits & reporting on router & NMS output, Extract IETF standards to complete SOCs, read my bank statements into my Excel budgeting tool, etc.)

For many of these tasks I have specific processing code (, loaded with modules: [tasks_x] in the playbook), but in almost all cases input & output tasks (and configuring these names etc) are common. The idea of the modular tasks originally came from Home Assistant, where I started learning Python and the idea of "custom components" to add your own integrations, although one could argue this also has similarities to Ansible playbooks.

In many cases I have a 'loose' coupling to actual file names, using Everything search (!es search_pattern in the playbook) to resolve a search pattern to the correct file used for input.

It has some parts in common with Ansible Playbooks, especially the name was chosen after I was introduced to Ansible Playbooks. The task structure has been updated in 2019 to match the Ansible Playbooks 2.0/2.5+ format and allow names. This format will also be easier to introduce loop mechanisms etc.

Comparison to Ansible Playbooks

Data playbooks is intended to create and modify variables in the environment (similar to inventory). Data playbooks starts with an empty environment (although you can read the environment from various sources inside the play). Although new variables can be created using register: in Ansible, data playbook functions requires the output to be captured through target:.

Data playbook tasks are different form Ansible's actions:

  • They are mostly not idempotent, since the intention is to modify tables as we go along,
  • they can return lists containing rows or be Python iterators (that yield rows of a table)
  • if they dont return any tabular data (a list), the return value will be added to the var table in the environment
  • Each have a strict voluptuous schema, evaluated when loading and during runtime (e.g. to expand templates) to allow quick troubleshooting

You could argue I can do this with Ansible, but it won't be as elegant with single item hosts files, gather_facts: no and delegate_to: localhost throughout the playbooks. It will likely only be half as much fun trying to force it into my way of thinking.

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