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A silly utility to explore your Tinder data.

Project description

Tinderdata: Get insight on your Tinder user data.

A very simple package to mine your Tinder user data, and get insight on your time on the service.

Install

This code is compatible with Python 3.6+. If for some reason you have a need for it, you can simply install it in your virtual enrivonment with:

pip install tinderdata

Usage

The script parses arguments from the commandline. The usage goes as:

python -m tinder_data [-h] -p PATH [--show-figures SHOW] [--save-figures SAVE] [-l LOG_LEVEL]

The different options are as follows:

usage: __main__.py [-h] -p PATH [--show-figures SHOW] [--save-figures SAVE]
                   [-l LOG_LEVEL]

Getting insight on your Tinder usage.

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path-to-data PATH
                        Absolute path to the json file of your tinder data.
                        Can be absolute or relative.
  --show-figures SHOW   Whether or not to show figures when plotting insights.
                        Defaults to False.
  --save-figures SAVE   Whether or not to save figures when plotting insights.
                        Defaults to False.
  -l LOG_LEVEL, --logs LOG_LEVEL
                        The base console logging level. Can be 'debug',
                        'info', 'warning' and 'error'. Defaults to 'info'.

An example command is then:

python -m tinderdata -p path_to_tinderdata.json --logs debug --save-figures True

The script print out a number of insight statements, and finally the text you should paste to get a Sankey diagram. It will then create a plots folder and populate it with visuals.

You can otherwise import the high-level object from the package, and use at your convenience:

from tinderdata import TinderData

tinder = TinderData("tinderdata.json")
tinder.output_sankey_string()
tinder.plot_messages_loyalty(showfig=True, savefig=False)

Output example

Here are examples of the script's outputs:

License

Copyright © 2019-2020 Felix Soubelet. MIT License

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