<jupyter_start><jupyter_text>Song recommender<jupyter_code>import pandas as pd
df = pd.read_csv('./song_data.csv')
df.head()
artists = df.groupby('artist')
artists.nunique().loc[['<NAME>', '<NAME>', '<NAME>', '<NAME>']]
artists.sum().loc[['<NAME>', 'Coldplay', '<NAME>', '<NAME>']]
artists.sum().loc[['<NAME>', 'Velvet Underground & Nico', '<NAME>', 'The Cool Kids']]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2619647355163728, 'fraction_numerical': 0.0, 'mean_word_length': 8.707317073170731, 'pattern_counts': {'":': 0, '<': 14, '<?xml version=': 0, '>': 14, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '27883', 'license': 'MIT', 'n_tokens_mistral': 156, 'n_tokens_neox': 157, 'n_words': 26, 'path': 'Machine_Learning_WashingTon/Machine Learning Foundations (A Case Study Approach)/week5_recommending_products/Song Recommender.ipynb', 'repo_name': 'PerpetualSmile/-Coursera', 'size': '445'}