<jupyter_start><jupyter_text>Entendendo os dados<jupyter_code>df_vgs.head()
df_vgs.describe()
df_vgs.isnull().sum()
df_vgs.dtypes<jupyter_output><empty_output><jupyter_text>Tratamentos<jupyter_code>df_vgs.set_index('Rank', inplace = True)
df_vgs['Year'] = df_vgs['Year'].fillna(df_vgs['Year'].mean())
# Removed null values in 'Publisher'
df_vgs.dropna(axis=0, inplace=True)
df_vgs.isnull().sum()
df_vgs['Platform'].unique()
df_vgs['Genre'].unique()
all_publisher = df_vgs['Publisher'].value_counts()
df_vgs['Publisher'] = df_vgs['Publisher'].apply(lambda x: 'Others' \
                                                if all_publisher[x] < 50 else x) 
df_vgs['Publisher'].value_counts()<jupyter_output><empty_output><jupyter_text>Análises<jupyter_code>df_vgs.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16498740554156172, 'fraction_numerical': 0.003778337531486146, 'mean_word_length': 6.5, 'pattern_counts': {'":': 0, '<': 14, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '512438', 'license': 'MIT', 'n_tokens_mistral': 333, 'n_tokens_neox': 318, 'n_words': 32, 'path': 'MachineLearning/VideoGameSales/DataAnalysisML.ipynb', 'repo_name': 'Renanrbsc/DataScience', 'size': '794'}