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calculate_scores.py
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calculate_scores.py
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import numpy as np
import pandas as pd
import copy
from pathlib import Path
root = Path(__file__).parent.parent
def calculate_scores(input_csv_path):
expanded_grid_df = pd.read_csv(input_csv_path, dtype = str, index_col='index')
service_list = ['amazon','apple','gaana','hungama',
'jiosaavn','spotify','wynk','ytmusic']
column_name_list = ['genre','category','total',] + service_list
new_df = pd.DataFrame(columns = column_name_list)
np_nan_dict = {}
for service in service_list:
np_nan_dict[service] = np.nan
for genre in expanded_grid_df['genre'].unique():
if genre != 'All':
for category in expanded_grid_df['canon_contemp'].unique():
genre_category_total = expanded_grid_df[(expanded_grid_df['genre']== genre) &\
(expanded_grid_df['canon_contemp']== category)].shape[0]
new_row = {'genre': genre, 'category': category, 'total': genre_category_total}
new_row.update(np_nan_dict)
new_df = new_df.append(new_row, ignore_index=True)
else:
for list_name in ['Rolling_Stone_500','NME_500']:
new_row = {'genre': list_name, 'category': 'canon', 'total': 500}
new_row.update(np_nan_dict)
new_df = new_df.append(new_row, ignore_index=True)
for genre in expanded_grid_df['genre'].unique():
for category in expanded_grid_df['canon_contemp'].unique():
for service in service_list:
relevant_column_name = 'matched_url_' + service
if genre != 'All':
service_genre_category_count = expanded_grid_df[(expanded_grid_df['genre']== genre) &\
(expanded_grid_df['canon_contemp']== category) & (expanded_grid_df[relevant_column_name].notnull())].shape[0]
new_df.loc[((new_df['genre'] == genre) & (new_df['category'] == category)), service] = service_genre_category_count
else:
for list_name in ['Rolling_Stone_500','NME_500']:
if category == 'contemporary': continue
service_genre_category_count = expanded_grid_df[(expanded_grid_df['genre']== 'All') &\
(expanded_grid_df['canon_contemp']== 'canon') & (expanded_grid_df['list_name'].str.contains(list_name)) &\
(expanded_grid_df[relevant_column_name].notnull())].shape[0]
new_df.loc[((new_df['genre'] == list_name) & (new_df['category'] == category)), service] = service_genre_category_count
new_df.to_csv(root / 'data/analysis/intermediate_file_01.csv', encoding='utf-8', index=False)
########################################################################
new_df = pd.read_csv(root / 'data/analysis/intermediate_file_01.csv')
service_list = ['amazon','apple','gaana','hungama',
'jiosaavn','spotify','wynk','ytmusic']
for service in service_list:
new_column_name = service + '_percent'
new_df[new_column_name] = round(100 * (new_df[service]/new_df['total']), 3)
columns_to_drop = service_list + ['total']
new_df.drop(columns=columns_to_drop, axis=1, inplace=True)
new_df.to_csv(root / 'data/analysis/intermediate_file_02.csv', encoding='utf-8', index=False)
#######################################################################
old_df = pd.read_csv(root / 'data/analysis/intermediate_file_02.csv')
service_list = ['amazon','apple','gaana','hungama',
'jiosaavn','spotify','wynk','ytmusic']
new_df_raw = old_df[['genre']]
new_df = copy.deepcopy(new_df_raw)
new_df.drop_duplicates(keep = 'first', inplace = True)
for index, row in new_df.iterrows():
genre = row['genre']
for service in service_list:
old_column_name = service + '_percent'
new_column_name = service + '_combined_percent'
if genre in ['Rolling_Stone_500','NME_500']:
canon_percent = 2* old_df.loc[((old_df['genre'] == genre) & (old_df['category'] == 'canon')), old_column_name].values[0]
contemp_percent = 0
else:
canon_percent = old_df.loc[((old_df['genre'] == genre) & (old_df['category'] == 'canon')), old_column_name].values[0]
contemp_percent = old_df.loc[((old_df['genre'] == genre) & (old_df['category'] == 'contemporary')), old_column_name].values[0]
combined_percent = canon_percent + contemp_percent
new_df.loc[index, new_column_name] = combined_percent
new_df.to_csv(root / 'data/analysis/intermediate_file_03.csv', encoding='utf-8', index=False)
########################################################################
old_df = pd.read_csv(root / 'data/analysis/intermediate_file_03.csv')
service_list = ['amazon','apple','gaana','hungama',
'jiosaavn','spotify','wynk','ytmusic']
columns_to_drop = []
for service in service_list:
old_column_name = service + '_combined_percent'
columns_to_drop.append(old_column_name)
new_column_name = service
old_df[new_column_name] = round(old_df[old_column_name]/20,1)
old_df.drop(columns=columns_to_drop, axis=1, inplace=True)
# old_df.set_index('genre', inplace=True)
# new_df = old_df.transpose().rename_axis('service')
new_df = old_df.set_index('genre').T.rename_axis('service').rename_axis(None, axis=1).reset_index()
new_df.to_csv(root / 'data/analysis/genre_wise_out_of_10.csv', encoding='utf-8', index=False)
#######################################################################
old_df = pd.read_csv(root / 'data/analysis/intermediate_file_03.csv')
index_list = old_df[(old_df['genre'] == 'Rolling_Stone_500') |\
(old_df['genre'] == 'NME_500')].index
old_df.drop(index_list, inplace = True)
old_df.reset_index(inplace = True)
service_list = ['amazon','apple','gaana','hungama',
'jiosaavn','spotify','wynk','ytmusic']
new_df = pd.DataFrame({'service': service_list})
for index, row in new_df.iterrows():
service = row['service']
old_column_name = service + '_combined_percent'
service_sum = old_df[old_column_name].sum()
value_for_insertion = round(10*(service_sum/2000), 1)
new_df.loc[index, 'overall_out_of_10'] = value_for_insertion
new_df.to_csv('data/analysis/overall_out_of_10.csv', encoding='utf-8', index=False)
############################################################
old_df = pd.read_csv(root / 'data/analysis/intermediate_file_03.csv')
old_df.set_index('genre')
old_df['average'] = round(10*(old_df.mean(axis=1)/200),1)
service_list = ['amazon','apple','gaana','hungama',
'jiosaavn','spotify','wynk','ytmusic']
columns_to_drop = []
for service in service_list:
old_column_name = service + '_combined_percent'
columns_to_drop.append(old_column_name)
old_df.drop(columns=columns_to_drop, axis=1, inplace=True)
old_df.to_csv(root / 'data/analysis/genre_wise_average.csv', encoding='utf-8', index=False)
############################################################
service_list = ['amazon','apple','gaana','hungama',
'jiosaavn','spotify','wynk','ytmusic']
expanded_grid_df = pd.read_csv(input_csv_path, dtype = str,
index_col='index')
for listx in ['Rolling_Stone_500','NME_500']:
expanded_grid_df_culled =\
expanded_grid_df[expanded_grid_df['list_name'].str.contains(listx)]
new_df = pd.DataFrame({'service': service_list})
for index, row in new_df.iterrows():
service = row['service']
service_col_name = 'matched_url_' + service
expanded_grid_df_service =\
expanded_grid_df_culled[expanded_grid_df_culled[service_col_name].notnull()]
insertion_value = len(expanded_grid_df_service)
new_df.loc[index, 'out_of_500'] = insertion_value
new_df_sorted = new_df.sort_values(by=['out_of_500'], ascending=False)
save_path = root / ('data/analysis/' + listx + '_available.csv')
new_df_sorted.to_csv(save_path, encoding='utf-8', index=False)
############################################################
genre_ratings_csv_path = root / 'data/analysis/genre_wise_out_of_10.csv'
genre_ratings_df = pd.read_csv(genre_ratings_csv_path)
proper_name_dict = {
'spotify': 'Spotify',
'apple': 'Apple',
'ytmusic': 'YouTube Music',
'amazon': 'Amazon',
'jiosaavn': 'JioSaavn',
'wynk': 'Wynk',
'hungama': 'Hungama',
'gaana': 'Gaana',
}
genre_ratings_df['service'] =\
genre_ratings_df['service'].map(proper_name_dict)
global_list= ['Amazon','Apple','Spotify','YouTube Music']
homegrown_list = ['Gaana','Hungama','JioSaavn','Wynk']
genre_ratings_df.rename(columns =\
{'Western Classical':"West'n Class'l"}, inplace = True)
genre_list = ['Pop', 'EDM', 'Rock', 'Metal', 'Hip-Hop', 'R&B',
'Jazz', 'Blues', "West'n Class'l",'World Music']
new_df = pd.DataFrame(genre_list, columns =['genre'])
new_cols = ['Best global service',
'Best global service rating',
'Best homegrown service',
'Best homegrown service rating'
]
for col in new_cols:
new_df[col] = np.nan
for genre in genre_list:
culled_df_raw = genre_ratings_df[['service', genre]]
culled_df = copy.deepcopy(culled_df_raw)
culled_df_global = culled_df[culled_df['service'].isin(global_list)]
sorted_df_global =\
culled_df_global.sort_values(by=[genre], ascending=False)
# print(sorted_df_global)
top_global_service = sorted_df_global['service'].iloc[0]
top_global_service_rating = sorted_df_global[genre].iloc[0]
new_df.loc[new_df['genre'] == genre, 'Best global service'] =\
top_global_service
new_df.loc[new_df['genre'] == genre, 'Best global service rating'] =\
top_global_service_rating
culled_df_homegrown = culled_df[culled_df['service'].isin(homegrown_list)]
sorted_df_homegrown =\
culled_df_homegrown.sort_values(by=[genre], ascending=False)
# print(sorted_df_homegrown)
top_homegrown_service = sorted_df_homegrown['service'].iloc[0]
top_homegrown_service_rating = sorted_df_homegrown[genre].iloc[0]
new_df.loc[new_df['genre'] == genre, 'Best homegrown service'] =\
top_homegrown_service
new_df.loc[new_df['genre'] == genre, 'Best homegrown service rating'] =\
top_homegrown_service_rating
new_df.to_csv(root / 'data/analysis/top_homegrown_global.csv',
encoding='utf-8', index=False)
########## RUN THE FUNCTION #######################
input_csv_path =\
root/'data/match_grid_expanded_sep_27_after_jiosaavn_rights_holder_final.csv'
calculate_scores(input_csv_path)