gne.py

#!/usr/bin/env python3
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os

# MODE_KAGGLE => specific basedir / filein = *.in / fileout = submission.csv
# ELSE => filein = *.txt / fileout = submission_samefilein


def main():
    contributors = create_df_contrib_sample()
    contributors['dispo'] = True
    contributors['skill_assigned_to'] = ""

    data_projects = [{'name': 'Logging', 'duration': 5, 'award':10, 'best_time':5, 'skills_nbr':1},
        {'name': 'WebServer', 'duration': 7, 'award':10, 'best_time':7, 'skills_nbr':2},
        {'name': 'WebChat', 'duration': 10, 'award':20, 'best_time':20, 'skills_nbr':2}
    ]
    projects = pd.DataFrame(data_projects)
    projects = projects.sort_values(by=['award'], ascending=False)

    data_project_skills = [{'name': 'Logging', 'skill_name': 'C++', 'skill_lvl':3, 'skill_pos':0},
        {'name': 'WebServer', 'skill_name': 'HTML', 'skill_lvl':3, 'skill_pos':0},
        {'name': 'WebServer', 'skill_name': 'C++', 'skill_lvl':2, 'skill_pos':1},
        {'name': 'WebChat', 'skill_name': 'Python', 'skill_lvl':3, 'skill_pos':0},
        {'name': 'WebChat', 'skill_name': 'HTML', 'skill_lvl':3, 'skill_pos':1}
        ]
    project_skills = pd.DataFrame(data_project_skills)
    # project_skill = {'name': 'Logging', 'skill_name': 'C++', 'skill_lvl':5, 'skill_pos':0}

    for inc in range(projects.index.size):
        proj_name = projects.loc[inc]['name']
        if is_faisable(proj_name):
            project_skills_list = project_skills[(project_skills['name'] == proj_name)]
            assign_worker_to_role(contributors, project_skills_list, organization, proj_name)

def create_df_contrib_sample():
    data = [{'name': 'Anna', 'skill_name': 'C++', 'skill_lvl':2},
        {'name': 'Bob', 'skill_name': 'HTML', 'skill_lvl':5},
        {'name': 'Bob', 'skill_name': 'CSS', 'skill_lvl':5},
        {'name': 'Maria', 'skill_name': 'Python', 'skill_lvl':3},
        {'name': 'Jean', 'skill_name': 'C++', 'skill_lvl':8},
        ]
    df = pd.DataFrame(data)
    return(df)

def is_faisable(ff):
    return True

def add_to_organization(organization, contributor, project_name):
    row = organization.loc(organization['name'] == project_name)
    #  if row:
    #  else:
    last_pos = organization['project_pos'].max()
    new_row = {'name': project_name, 'contributors': contributor['name'], 'project_pos': last_pos + 1}
    print(new_row)
    organization.append(new_row)
    return organization

def assign_worker_to_role(contributors, project_skills, organization, project_name):
    like_df = contributors['name'].str.split(expand=True).stack().value_counts().reset_index()
    like_df.columns = ['name', 'id']
    nb_worker = like_df['name'].drop_duplicates().size

    for n in project_skills.index:
        project_skill = project_skills.loc[n]
        good_contributor = contributors[(contributors['skill_name'] == project_skill['skill_name']) &
                                        (contributors['skill_lvl'] >= project_skill['skill_lvl'])   &
                                        (contributors['dispo'] == True)].head(1)

        if good_contributor.index.size >= 1:
            good_contributor.loc[good_contributor.index, ['dispo']] = False
            good_contributor.loc[good_contributor.index, ['skill_assigned_to']] = project_skill['skill_name']
        add_to_organization(organization, good_contributor, project_name)

if __name__ == "__main__":
    main()