<jupyter_start><jupyter_text>The country with the most movies number<jupyter_code>total_others = total_language[total_language.original_language != 'en']
total_biggest = total_language.loc[0].total
total_left = total_language.total.sum() - total_biggest
df_movies_biggest = pd.DataFrame({'language': 
             [total_language.loc[0].original_language, 'others'], 
             'total': 
             [total_biggest, total_left]})<jupyter_output><empty_output><jupyter_text>Visualization<jupyter_code>sns.barplot(x="language", y="total", data=df_movies_biggest).set_ylabel('Number of movies')
sns.catplot(x="original_language",
            y="total", data=total_others, 
            kind="bar", aspect=2, 
            order=total_others.original_language,
            height=6,
            palette="GnBu_d")

import matplotlib as plt
import numpy as np<jupyter_output><empty_output><jupyter_text>The best movies of 2016<jupyter_code>movies_2016 = movies[(movies["release_date"] >= "2016-01-01") & (movies["release_date"] <= "2016-12-31")]
movies_2016<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13111726685133887, 'fraction_numerical': 0.029547553093259463, 'mean_word_length': 4.956043956043956, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '298323', 'license': 'Apache-2.0', 'n_tokens_mistral': 388, 'n_tokens_neox': 363, 'n_words': 50, 'path': 'TMDB5000.ipynb', 'repo_name': 'kaiocesar/introducao-data-science', 'size': '1083'}