<jupyter_start><jupyter_text>What are the ten most common movie names of all time?<jupyter_code>titles.title.value_counts().head(10)<jupyter_output><empty_output><jupyter_text>Which three years of the 1930s saw the most films released?<jupyter_code>t = titles
t = t[t.year // 10 == 193]
t.year.value_counts().head(3)<jupyter_output><empty_output><jupyter_text>Plot the number of films that have been released each decade over the history of cinema.<jupyter_code>t = titles
(t.year // 10 * 10).value_counts().sort_index().plot(kind='bar')<jupyter_output><empty_output><jupyter_text>Plot the number of "Hamlet" films made each decade.<jupyter_code>t = titles
t = t[t.title == 'Hamlet']
(t.year // 10 * 10).value_counts().sort_index().plot(kind = 'bar')<jupyter_output><empty_output><jupyter_text>Plot the number of "Rustler" characters in each decade of the history of film.<jupyter_code>c = cast
c = c[c.character == 'Rustler']
(c.year // 10 * 10).value_counts().sort_index().plot(kind = 'bar')<jupyter_output><empty_output><jupyter_text>Plot the number of "Hamlet" characters each decade.<jupyter_code>c = cast
c = c[c.character == 'Hamlet']
(c.year // 10 * 10).value_counts().sort_index().plot(kind = 'bar')<jupyter_output><empty_output><jupyter_text>What are the 11 most common character names in movie history?<jupyter_code>cast.character.value_counts().head(11)<jupyter_output><empty_output><jupyter_text>Who are the 10 people most often credited as "Herself" in film history?<jupyter_code>c = cast
c[c.character == 'Herself'].name.value_counts().head(10)<jupyter_output><empty_output><jupyter_text>Who are the 10 people most often credited as "Himself" in film history?<jupyter_code>c = cast
c[c.character == 'Himself'].name.value_counts().head(10)<jupyter_output><empty_output><jupyter_text>Which actors or actresses appeared in the most movies in the year 1945?<jupyter_code>cast[cast.year ==1945].name.value_counts().head(10)<jupyter_output><empty_output><jupyter_text>Which actors or actresses appeared in the most movies in the year 1985?<jupyter_code>cast[cast.year ==1985].name.value_counts().head(10)<jupyter_output><empty_output><jupyter_text>Plot how many roles Mammootty has played in each year of his career.<jupyter_code>c[cast.name == 'Mammootty'].year.value_counts().sort_index().plot()<jupyter_output><empty_output><jupyter_text>What are the 10 most frequent roles that start with the phrase "Patron in"?<jupyter_code>c = cast
c[c.character.str.startswith('Patron in')].character.value_counts().head(10)<jupyter_output><empty_output><jupyter_text>What are the 10 most frequent roles that start with the word "Science"?<jupyter_code>c = cast
c[c.character.str.startswith('Science')].character.value_counts().head(10)<jupyter_output><empty_output><jupyter_text>Plot the n-values of the roles that <NAME> has played over her career.<jupyter_code>c = cast
c = c[c.name == '<NAME>'].sort_values(by=('year'))
c = c[c.n.notnull()]
c.plot(x = 'year', y = 'n', kind = 'scatter')<jupyter_output><empty_output><jupyter_text>Plot the n-values of <NAME>'s roles through his career.<jupyter_code>c = cast
c = c[c.name == '<NAME>'].sort_values(by=('year'))
c = c[c.n.notnull()]
c.plot(x = 'year', y = 'n', kind = 'scatter')<jupyter_output><empty_output><jupyter_text>Plot the n-value of the roles that <NAME> has acted over the years.<jupyter_code>c = cast
c = c[c.name == '<NAME>'].sort_values(by=('year'))
c = c[c.n.notnull()]
c.plot(x = 'year', y = 'n', kind = 'scatter')<jupyter_output><empty_output><jupyter_text>How many leading (n=1) roles were available to actors, and how many to actresses, in the 1950s?<jupyter_code>c = cast
c = c[c.year // 10 ==195]
c = c[c.n ==1]
c.type.value_counts()<jupyter_output><empty_output><jupyter_text>How many supporting (n=2) roles were available to actors, and how many to actresses, in the 1950s?<jupyter_code>c = cast
c = c[c.year // 10 ==195]
c = c[c.n ==2]
c.type.value_counts()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15147689977278464, 'fraction_numerical': 0.022721534965917698, 'mean_word_length': 6.987903225806452, 'pattern_counts': {'":': 0, '<': 83, '<?xml version=': 0, '>': 83, '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': '19', 'dir': 'jupyter-structured-clean-dedup', 'id': '570853', 'license': 'MIT', 'n_tokens_mistral': 1558, 'n_tokens_neox': 1456, 'n_words': 325, 'path': 'Exercises-2.ipynb', 'repo_name': 'jlvengo/pandas-deep-dive', 'size': '3993'}