<jupyter_start><jupyter_text>Learning Cell Headers<jupyter_code>24 * 7
hours_in_a_week = 24 * 7
hours_in_a_week
hours_in_a_week * 100
60 * 60 * 24 * 365
seconds_per_year = 60 * 60 * 24 * 365
seconds_per_year
#built in functions
abs(-8)
abs(3-8)
max(3,4)
y = max(3,4)
y<jupyter_output><empty_output><jupyter_text>Tables!<jupyter_code>Table.read_table('flowers.csv')
flowers = Table.read_table('flowers.csv')
flowers
flowers.select('Petals')
flowers
flowers.select('Petals','Name')
flowers.drop('Color')
flowers
movies = Table.read_table('top_movies_by_title.csv')
movies
movies.sort('Year', descending = True)
movies.show(3)
movies.sort('Gross')
sorted_by_gross = movies.sort('Gross', descending=True)
sorted_by_gross
sorted_by_gross = movies.where('Studio','RKO')
sorted_by_gross
sorted_by_gross = movies.sort('Gross', descending=True)
sorted_by_gross
sorted_by_gross.sort('Studio')
sorted_by_gross.sort('Studio',distinct=True)
top_per_studio = sorted_by_gross.sort('Studio')
top_per_studio.barh('Studio', 'Gross')
top_studios = top_per_studio.sort('Gross', descending=True)
top_studios.barh('Studio','Gross')
revenues = top_studios.select('Studio','Gross','Gross (Adjusted)')
revenues
revenues.barh('Studio')
nba = Table.read_table('nba_salaries.csv')
nba
nba.sort('SALARY ($M)')
nba.where('SALARY ($M)', are.above(10))
nba.where('SALARY ($M)', are.above(10)).sort('SALARY ($M)')
nba.where('SALARY ($M)', are.between(10,14))<jupyter_output><empty_output>
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