Question

# columns are [0]title [1]year [2]rating [3]length(min) [4]genre [5]budget($mil) [6]box_office_gross($mil)
oscar_data = [
["The Shape of Water", 2017, 6.914, 123, ['sci-fi', 'drama'], 19.4, 195.243464],
["Moonlight", 2016, 6.151, 110, ['drama'], 1.5, 65.046687],
["Spotlight", 2015, 7.489, 129, ['drama', 'crime', 'history'], 20.0, 88.346473],
["Birdman", 2014, 7.604, 119, ['drama', 'comedy'], 18.0, 103.215094],
["12 Years a Slave", 2013, 7.71, 133, ['drama', 'biography', 'history'], 20.0, 178.371993],
["Argo", 2012, 7.517, 120, ['thriller', 'drama', 'biography'], 44.5, 232.324128],
["The Artist", 2011, 7.942, 96, ['drama', 'melodrama', 'comedy'], 15.0, 133.432856],
["The King\'s Speech", 2010, 7.977, 118, ['drama', 'biography', 'history'], 15.0, 414.211549],
["The Hurt Locker", 2008, 7.298, 126, ['thriller', 'drama', 'war', 'history'], 15.0, 49.230772],
["Slumdog Millionaire", 2008, 7.724, 120, ['drama', 'melodrama'], 15.0, 377.910544],
["No Country for Old Men", 2007, 7.726, 122, ['thriller', 'drama', 'crime'], 25.0, 171.627166],
["The Departed", 2006, 8.456, 151, ['thriller', 'drama', 'crime'], 90.0, 289.847354],
["Crash", 2004, 7.896, 108, ['thriller', 'drama', 'crime'], 6.5, 98.410061],
["Million Dollar Baby", 2004, 8.075, 132, ['drama', 'sport'], 30.0, 216.763646],
["The Lord of the Rings: Return of the King", 2003, 8.617, 201, ['fantasy', 'drama', 'adventure'], 94.0, 1119.110941],
["Chicago", 2002, 7.669, 113, ['musical', 'comedy', 'crime'], 45.0, 306.776732],
['A Beautiful Mind', 2001, 8.557, 135, ['drama', 'biography', 'melodrama'], 58.0, 313.542341],
["Gladiator", 2000, 8.585, 155, ['action', 'drama', 'adventure'], 103.0, 457.640427],
["American Beauty", 1999, 7.965, 122, ['drama'], 15.0, 356.296601],
["Shakespeare in Love", 1998, 7.452, 123, ['drama', 'melodrama', 'comedy', 'history'], 25.0, 289.317794],
["Titanic", 1997, 8.369, 194, ['drama', 'melodrama'], 200.0, 2185.372302],
["The English Patient", 1996, 7.849, 155, ['drama', 'melodrama', 'war'], 27.0, 231.976425],
["Braveheart", 1995, 8.283, 178, ['drama', 'war', 'biography', 'history'], 72.0, 210.409945],
["Forrest Gump", 1994, 8.915, 142, ['drama', 'melodrama'], 55.0, 677.386686],
["Schindler\'s List", 1993, 8.819, 195, ['drama', 'biography', 'history'], 22.0, 321.265768],
["Unforgiven", 1992, 7.858, 131, ['drama', 'western'], 14.4, 159.157447],
["Silence of the Lambs", 1990, 8.335, 114, ['thriller', 'crime', 'mystery', 'drama', 'horror'], 19.0, 272.742922],
["Dances with Wolves", 1990, 8.112, 181, ['drama', 'adventure', 'western'], 22.0, 424.208848],
["Driving Miss Daisy", 1989, 7.645, 99, ['drama'], 7.5, 145.793296],
["Rain Man", 1988, 8.25, 133, ['drama'], 25.0, 354.825435],
]

def column_sum(data, column):
result = 0
for row in data:
result += row[column]
return result

def column_mean(data, column):
total = column_sum(oscar_data, 6)
mean = total / len(data)
return mean

# < write code here >
  

mean_score = column_mean(oscar_data, 2)
print('Average rating: {:.2f}'.format(mean_score))

mean_length = column_mean(oscar_data, 3)
print('Average length: {:.2f} min.'.format(mean_length))

mean_budget = column_mean(oscar_data, 5)
print('Average budget: ${:.2f} mil.'.format(mean_budget))

mean_gross = column_mean(oscar_data, 6)
print('Average revenue: ${:.2f} mil.'.format(mean_gross))

Answer 1

ANSWER:

As nothing is described in question about your query, I assume you are worried about your output which is same for every statement i.e. function call.

I have provided the properly commented and indented code so you can easily copy the code as well as check for correct indentation.

I have provided the output image of the code so you can easily cross-check for the correct output of the code.

Have a nice and healthy day!!

CODE

# columns are [0]title [1]year [2]rating [3]length(min) [4]genre [5]budget($mil) [6]box_office_gross($mil)
oscar_data = [
["The Shape of Water", 2017, 6.914, 123, ['sci-fi', 'drama'], 19.4, 195.243464],
["Moonlight", 2016, 6.151, 110, ['drama'], 1.5, 65.046687],
["Spotlight", 2015, 7.489, 129, ['drama', 'crime', 'history'], 20.0, 88.346473],
["Birdman", 2014, 7.604, 119, ['drama', 'comedy'], 18.0, 103.215094],
["12 Years a Slave", 2013, 7.71, 133, ['drama', 'biography', 'history'], 20.0, 178.371993],
["Argo", 2012, 7.517, 120, ['thriller', 'drama', 'biography'], 44.5, 232.324128],
["The Artist", 2011, 7.942, 96, ['drama', 'melodrama', 'comedy'], 15.0, 133.432856],
["The King\'s Speech", 2010, 7.977, 118, ['drama', 'biography', 'history'], 15.0, 414.211549],
["The Hurt Locker", 2008, 7.298, 126, ['thriller', 'drama', 'war', 'history'], 15.0, 49.230772],
["Slumdog Millionaire", 2008, 7.724, 120, ['drama', 'melodrama'], 15.0, 377.910544],
["No Country for Old Men", 2007, 7.726, 122, ['thriller', 'drama', 'crime'], 25.0, 171.627166],
["The Departed", 2006, 8.456, 151, ['thriller', 'drama', 'crime'], 90.0, 289.847354],
["Crash", 2004, 7.896, 108, ['thriller', 'drama', 'crime'], 6.5, 98.410061],
["Million Dollar Baby", 2004, 8.075, 132, ['drama', 'sport'], 30.0, 216.763646],
["The Lord of the Rings: Return of the King", 2003, 8.617, 201, ['fantasy', 'drama', 'adventure'], 94.0, 1119.110941],
["Chicago", 2002, 7.669, 113, ['musical', 'comedy', 'crime'], 45.0, 306.776732],
['A Beautiful Mind', 2001, 8.557, 135, ['drama', 'biography', 'melodrama'], 58.0, 313.542341],
["Gladiator", 2000, 8.585, 155, ['action', 'drama', 'adventure'], 103.0, 457.640427],
["American Beauty", 1999, 7.965, 122, ['drama'], 15.0, 356.296601],
["Shakespeare in Love", 1998, 7.452, 123, ['drama', 'melodrama', 'comedy', 'history'], 25.0, 289.317794],
["Titanic", 1997, 8.369, 194, ['drama', 'melodrama'], 200.0, 2185.372302],
["The English Patient", 1996, 7.849, 155, ['drama', 'melodrama', 'war'], 27.0, 231.976425],
["Braveheart", 1995, 8.283, 178, ['drama', 'war', 'biography', 'history'], 72.0, 210.409945],
["Forrest Gump", 1994, 8.915, 142, ['drama', 'melodrama'], 55.0, 677.386686],
["Schindler\'s List", 1993, 8.819, 195, ['drama', 'biography', 'history'], 22.0, 321.265768],
["Unforgiven", 1992, 7.858, 131, ['drama', 'western'], 14.4, 159.157447],
["Silence of the Lambs", 1990, 8.335, 114, ['thriller', 'crime', 'mystery', 'drama', 'horror'], 19.0, 272.742922],
["Dances with Wolves", 1990, 8.112, 181, ['drama', 'adventure', 'western'], 22.0, 424.208848],
["Driving Miss Daisy", 1989, 7.645, 99, ['drama'], 7.5, 145.793296],
["Rain Man", 1988, 8.25, 133, ['drama'], 25.0, 354.825435],
]

# function to sum column wise in given data
def column_sum(data, column):
    # defining result counter, to add sum of each element of column
    result = 0
    # looping through row
    for row in data:
        # fetching data at column and adding to result
        result += row[column]
    # returning result
    return result

# function column_mean, return mean of data according to column
def column_mean(data, column):
    # calculating sum of specified column using function column_sum
    total = column_sum(data, column)
    # dividing total by total rows
    mean = total / len(data)
    # returning mean
    return mean


# < write code here >
mean_score = column_mean(oscar_data, 2)
print('Average rating: {:.2f}'.format(mean_score))

mean_length = column_mean(oscar_data, 3)
print('Average length: {:.2f} min.'.format(mean_length))

mean_budget = column_mean(oscar_data, 5)
print('Average budget: ${:.2f} mil.'.format(mean_budget))

mean_gross = column_mean(oscar_data, 6)
print('Average revenue: ${:.2f} mil.'.format(mean_gross))

OUTPUT IMAGE