block.py

#import pandas as pd
#from constants import *
#from src.commons import utils
#import matplotlib.pyplot as plt
#import numpy as np

#       Read the list of all paintings (scraped data)
data = pd.read_csv('{}/data.csv'.format(DATA_FOLDER))
for year in data['year'].unique():
    full_data = pd.DataFrame()
    for idx, row in data[data['year'] == year].iterrows():
        #       Load images results
        tmp_data = pd.read_csv('{}/images_schemes/{}_data.csv'.format(OUTPUT_FOLDER, row.title))
        full_data = pd.concat([full_data, tmp_data], axis=0)
    full_data = full_data.groupby(['points']).sum().reset_index()
    full_data['perc'] = full_data['count'] / full_data['count'].sum()

    #   Merge colors
    color_scheme = pd.read_csv('{}/color_scheme.csv'.format(OUTPUT_FOLDER))
    full_data = pd.merge(full_data, color_scheme, how='inner', left_on='points'
                            , right_on=color_scheme.columns[0])

    full_data['R'] = full_data['R'].apply(np.int)
    full_data['G'] = full_data['G'].apply(np.int)
    full_data['B'] = full_data['B'].apply(np.int)

    full_data.to_csv('{}/years_proportions/{}.csv'.format(OUTPUT_FOLDER, year),
                     columns=['perc', 'R', 'G', 'B', 'H', 'S', 'V'],
                     index=False)