Skip to main content

Gambling Transaction Analysis in Python

Project description

Welcome to the gamba library!

A Python library for player behaviour tracking research

---------------------------------------------------------------------------

ModuleNotFoundError                       Traceback (most recent call last)

/tmp/ipykernel_5080/442603745.py in <module>
      1 #export
----> 2 from gamba.data import *
      3 from gamba.measures import *
      4 from gamba.statistics import *
      5 from gamba.labelling import *


~/Desktop/gamba/gamba/__init__.py in <module>
      5 # Cell
      6 from .data import *
----> 7 from .measures import *
      8 from .statistics import *
      9 from .labelling import *


~/Desktop/gamba/gamba/measures.py in <module>
    618 
    619 # Cell
--> 620 from tqdm import tqdm
    621 import datetime, pandas as pd
    622 def calculate_labrie_measures(all_player_bets, savedir="", filename="gamba_labrie_measures.csv", loud=False, daily=True,):


ModuleNotFoundError: No module named 'tqdm'

PyPI Commit

The gamba transaction analysis library is a collection of methods for applying analytical methods found in peer-reviewed studies. The library is 100% free and open source, and aims to be used by researchers and analysts with access to transaction data sets.

Features

  • standardise your data across game types and activities
  • compute behavioural measures
  • apply descriptive and comparative statistics
  • run machine learning methods
  • replicate full academic studies on your data in minutes

Getting Started

Getting started using gamba in your research is super easy - the first step is to install the library using one of the commands below. From there, read through one or more of the tutorials, and once you're familiar with the typical structure of an analysis using the gamba library, look through the individual module documentation and extend one of the examples. If you get stuck, please get in touch!

Install

To install gamba use the following pip command;

pip install gamba

Or if you'd like to use the development release, clone the github repository to your machine;

git clone https://github.com/gamba-dev/gamba.git

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gamba-0.1.4.tar.gz (34.4 kB view details)

Uploaded Source

Built Distribution

gamba-0.1.4-py3-none-any.whl (35.1 kB view details)

Uploaded Python 3

File details

Details for the file gamba-0.1.4.tar.gz.

File metadata

  • Download URL: gamba-0.1.4.tar.gz
  • Upload date:
  • Size: 34.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for gamba-0.1.4.tar.gz
Algorithm Hash digest
SHA256 0488f201121759b4c25b17b65a41e4d988f375a1196fdba19054471b6f187314
MD5 de3966c0b56d56e624072167e77944f9
BLAKE2b-256 75ff269a95a5c154e88927a0c92f4af22e4ac0975a6b62bbfbde7d2ad2d4d227

See more details on using hashes here.

File details

Details for the file gamba-0.1.4-py3-none-any.whl.

File metadata

  • Download URL: gamba-0.1.4-py3-none-any.whl
  • Upload date:
  • Size: 35.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for gamba-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 94da544824d17fef3575e95671f4720a48ef8db4070e0275c98e15fffb1aa264
MD5 d2f0f5803e6f6c62332557f7bddf5154
BLAKE2b-256 0ac469ef085c0be7319e30a33f87bc3e38575635cfae8d8d2d32124e9d87f94b

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page