<jupyter_start><jupyter_text>Analysis<jupyter_code>info_column = ['title', 'authors', 'publishedDate', 'ISBN_10', 'ISBN_13']
google_columns = ['google_avg_rating', 'google_rating_count']
amazon_columns = ['amazon_rating', 'amazon_n_rated', 'is_on_audible']
overdrive_columns = list(df.columns[df.columns.str.contains("overdrive")])
shelf_columns = ['shelf_Wish List', 'shelf_Favorites']

key_columns = info_column + google_columns + amazon_columns + overdrive_columns + shelf_columns<jupyter_output><empty_output><jupyter_text>Wish list items by rating<jupyter_code>wish_list_books = df.loc[df['shelf_Wish List']==True]
wish_list_books = wish_list_books[['title', 'authors'] + google_columns + amazon_columns + overdrive_columns]
wish_list_books.sort_values('amazon_rating', ascending=False).head(45)<jupyter_output><empty_output><jupyter_text>Availabilty of wish list items<jupyter_code>columns = ['title', 'authors', 'is_on_audible']
for col_prefix in ('available_on_overdrive', 'overdrive_ebook', 'overdrive_audiobook'):
    columns += list(df.columns[df.columns.str.startswith(col_prefix)])

wish_list_books[columns]<jupyter_output><empty_output><jupyter_text>All items sorted by ratings<jupyter_code>sorted_by_ratings = df.loc[df['amazon_n_rated']>10].sort_values('amazon_rating', ascending=False)[key_columns]
sorted_by_ratings.head(10)
sorted_by_ratings.tail(10)<jupyter_output><empty_output><jupyter_text>Most reviewed book<jupyter_code>df.loc[df.google_rating_count == df.google_rating_count.max()]<jupyter_output><empty_output><jupyter_text>Highest rated book<jupyter_code>df.loc[df.amazon_n_rated == df.amazon_n_rated.max()]<jupyter_output><empty_output><jupyter_text>2021 Analysis vs 2022 goal<jupyter_code># pages_2021 = df.loc[df.shelf_2021].pageCount.sum()
# pages_2022 = df.loc[df['shelf_2022 Goal Books']].pageCount.sum()
# total = df.loc[df['shelf_Have read']].pageCount.sum()
# print(f"2021: {pages_2021}, % of total: {pages_2021*100/total}%, 2022: {pages_2022}, % increase = {(pages_2022-pages_2021) * 100 / pages_2021}%")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14478764478764478, 'fraction_numerical': 0.03571428571428571, 'mean_word_length': 11.487951807228916, 'pattern_counts': {'":': 0, '<': 29, '<?xml version=': 0, '>': 30, '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': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '401502', 'license': 'MIT', 'n_tokens_mistral': 837, 'n_tokens_neox': 775, 'n_words': 93, 'path': 'Book Analyis.ipynb', 'repo_name': 'smonaco47/book-finder', 'size': '2072'}