<jupyter_start><jupyter_text>Games Analysis<jupyter_code>import pandas as pd
df = pd.read_csv('data/games/games_beated.csv')
df_to_play = pd.read_csv('data/games/games_to_play_order_by_priority.csv')
df_games_only_played = pd.read_csv('data/games/games_only_played.csv')
print('Beated games: ', len(df))
print('Games to play: ', len(df_to_play))
print('Games only played: ', len(df_games_only_played))
df.head()
df
df_games_only_played
games_four_score = df_games_only_played[df_games_only_played['score'] == 4]
games_four_score
games_four_score = df[df['score'] == 4]
games_four_score
games_top_score = df[df['score'] == 5]
games_top_score
len(games_top_score)
df.groupby('year_I_played')['title'].count().plot(kind='bar')
df_to_play.groupby(by=["where_to_play"]).count()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15211970074812967, 'fraction_numerical': 0.003740648379052369, 'mean_word_length': 10.471428571428572, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '622108', 'license': 'MIT', 'n_tokens_mistral': 345, 'n_tokens_neox': 315, 'n_words': 40, 'path': 'games_analysis.ipynb', 'repo_name': 'andredarcie/my-tracker', 'size': '802'}