fakeme 0.2.2
Relative Data Generator: generate relative tables data, data generator for multi tables that depend on each other
License: MIT
Requires Python: >=3.6.2,<4.0


Fakeme
^^^^^^

Data Generator for Chained and Relative Data


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Documentation in process: https://fakeme.readthedocs.io/en/latest/ 

How to use
^^^^^^^^^^

.. code-block:: bash


       pip install fakeme

Check examples: https://github.com/xnuinside/fakeme/tree/master/examples

What is Fakeme?
^^^^^^^^^^^^^^^

Fakeme is a tools that try to understand your data based on schemas & fields name and generate data relative to expected.

It create dependencies graph and generate relative data.

**Fakeme** oriented on generation data that depend on values in another tables/datasets.
Data, that knitted together as real.

**Fakeme** can help you if you want to generate several tables, that must contains in columns values, 
that you will use like key for join.

For example, *user_data* table has field *user_id* and *users* table contains list of users with column id. 
You want join it on user_id = id.

**Fakeme** will generate for you 2 tables with same values in those 2 columns. 

It does not matter to have columns with same name you can define dependencies between tables with alias names. 

TODO in next releases:
----------------------


#. Add integration with simple-ddl-parser to generated data from different SQL dialects
#. Add integration with py-models-parser to generated data from different Python models
#. Fix cases in todo folder
#. Improve test coverage 

What you can to do
^^^^^^^^^^^^^^^^^^


#. 
   Define that fields in your datasets must contain similar values

#. 
   You can set up how much values must intersect, for example, you want to emulate data for email validation pipeline -  you have one dataset with *incoming* messages  and you need to find all emails that was not added previously in your *contacts* table.

So you will have incoming messages table, that contains, for example only 70% of emails that exist in contacts table. 


#. 
   You can use multiply columns as a key (dependency) in another column, for example, 
   *player_final_report* must contains for each player same values as in other tables, for example, you have *player* table
   with players details and *in_game_player_activity* with all player activities for some test reasons it's critical
   to you generate *player_final_report* with 1-to-1 data from other 2 tables.

#. 
   Union tables. You can generate tables that contains all rows from another tables. 

#. 
   You can define your own generator for fields on Python.

#. 
   You can define your own output format

Examples
^^^^^^^^

You can find usage examples in 'fakeme/examples/' folder.

Example from fakeme/examples/generate_data_related_to_existed_files:

.. code-block:: python


       from fakeme import Fakeme

       # to use some existed data you should provide with_data argument -
       # and put inside list with the paths to the file with data

       # data file must be in same format as .json or csv output of fakeme.
       # so it must be [{"column_name": value, "column_name2": value2 ..},
       #   {"column_name" : value, "column_name2": value2 ..}..]
       # Please check example in countries.json

       cities_schema = [{"name": "name"},
                        {"name": "country_id"},
                        {"name": "id"}]

       # all fields are strings - so I don't define type, because String will be used as default type for the column

       tables_list = [('cities', cities_schema)]

       Fakeme(
           tables=tables_list,
           with_data=['countries.json'],
           rls={'cities': {  # this mean: for table 'cities'
               'country_id': {  # and column 'country_id' in table 'cities'
                   'table': 'countries.json',   # please take data from data  in countries.json
                   'alias': 'id',  # with alias name 'id'
                   'matches': 1  # and I want all values in country_id must be from countries.id column, all.
               }
           }},
           settings={'row_numbers': 1300}  # we want to have several cities for each country,
                                           # so we need to have more rows,
       ).run()

       # run and you will see a list of cities, that generated with same ids as in countries.json

Docs: https://fakeme.readthedocs.io/en/latest/

Changelog
---------

**v0.2.2**

Fixes:
^^^^^^


#. generate_data_related_to_existed_files example now works well (generation data based on already existing files).
#. Added integration tests to run examples
#. Examples are cleaned up, unworking samples moved to 'todo'

**v0.2.1**


#. Now you can define tables as Table class object if it will be more easily for you.

.. code-block:: python

       from fakeme import Table

       Table(name='table_name_example', schema='path/to/schema.json')

       # or 
       user_schema = [{'name': 'id'},
               {'name': 'title'},
               {'name': 'rights', 'type': 'list', 'alias': 'right_id'},
               {'name': 'description'}]
       Table(name='table_name_example', schema=user_schema)

samples it tests: tests/unittests/test_core.py


#. Relationships between tables was corrected 

**v0.1.0**


#. Added code changes to support Python 3.8 and upper (relative to changes in python multiprocessing module)
#. Added tests runner on GitHub
#. Autoaliasing was fixed
#. Added some unit tests