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Lists and For Loops-312.py
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## 1. Lists ##
row_2 = ['Instagram',0.0,'USD', 2161558, 4.5]
row_3 = ['Clash of Clans', 0.0, 'USD', 2130805, 4.5]
## 2. Indexing ##
row_1 = ['Facebook', 0.0, 'USD', 2974676, 3.5]
row_2 = ['Instagram', 0.0, 'USD', 2161558, 4.5]
row_3 = ['Clash of Clans', 0.0, 'USD', 2130805, 4.5]
ratings_1 = row_1[3]
ratings_2 = row_2[3]
ratings_3 = row_3[3]
total = ratings_1 + ratings_2 + ratings_3
average = total / 3
print(average)
## 3. Negative Indexing ##
row_1 = ['Facebook', 0.0, 'USD', 2974676, 3.5]
row_2 = ['Instagram', 0.0, 'USD', 2161558, 4.5]
row_3 = ['Clash of Clans', 0.0, 'USD', 2130805, 4.5]
rating_1 = row_1[-1]
rating_2 = row_2[-1]
rating_3 = row_3[-1]
total_rating = rating_1 + rating_2 + rating_3
average_rating = total_rating / 3
print(average_rating)
## 4. Retrieving Multiple List Elements ##
row_1 = ['Facebook', 0.0, 'USD', 2974676, 3.5]
row_2 = ['Instagram', 0.0, 'USD', 2161558, 4.5]
row_3 = ['Clash of Clans', 0.0, 'USD', 2130805, 4.5]
row_4 = ['Temple Run', 0.0, 'USD', 1724546, 4.5]
row_5 = ['Pandora - Music & Radio', 0.0, 'USD', 1126879, 4.0]
fb_rating_data = [row_1[0], row_1[3], row_1[-1]]
insta_rating_data = [row_2[0], row_2[3], row_2[-1]]
pandora_rating_data = [row_5[0], row_5[3], row_5[-1]]
avg_rating =(fb_rating_data[2]+insta_rating_data[2]+pandora_rating_data[2])/3
## 5. List Slicing ##
row_1 = ['Facebook', 0.0, 'USD', 2974676, 3.5]
row_2 = ['Instagram', 0.0, 'USD', 2161558, 4.5]
row_3 = ['Clash of Clans', 0.0, 'USD', 2130805, 4.5]
row_4 = ['Temple Run', 0.0, 'USD', 1724546, 4.5]
row_5 = ['Pandora - Music & Radio', 0.0, 'USD', 1126879, 4.0]
first_4_fb = row_1[0:4]
last_3_fb = row_1[-3:]
pandora_3_4 = row_5[2:4]
print(first_4_fb)
print(last_3_fb)
print(pandora_3_4)
## 6. List of Lists ##
row_1 = ['Facebook', 0.0, 'USD', 2974676, 3.5]
row_2 = ['Instagram', 0.0, 'USD', 2161558, 4.5]
row_3 = ['Clash of Clans', 0.0, 'USD', 2130805, 4.5]
row_4 = ['Temple Run', 0.0, 'USD', 1724546, 4.5]
row_5 = ['Pandora - Music & Radio', 0.0, 'USD', 1126879, 4.0]
app_data_set = [row_1, row_2, row_3, row_4, row_5]
avg_rating = (app_data_set[0][-1]+app_data_set[1][-1]+app_data_set[2][-1]+app_data_set[3][-1]+app_data_set[4][-1])/5
print(avg_rating)
## 7. Opening a File ##
from csv import reader
opened_file = open('AppleStore.csv')
read_file = reader(opened_file)
apps_data = list(read_file)
print(len(apps_data))
print(apps_data[0])
print(apps_data[1:2])
## 8. Repetitive Processes ##
row_1 = ['Facebook', 0.0, 'USD', 2974676, 3.5]
row_2 = ['Instagram', 0.0, 'USD', 2161558, 4.5]
row_3 = ['Clash of Clans', 0.0, 'USD', 2130805, 4.5]
row_4 = ['Temple Run', 0.0, 'USD', 1724546, 4.5]
row_5 = ['Pandora - Music & Radio', 0.0, 'USD', 1126879, 4.0]
app_data_set = [row_1, row_2, row_3, row_4, row_5]
for each_list in app_data_set:
print(each_list)
## 9. For Loops ##
row_1 = ['Facebook', 0.0, 'USD', 2974676, 3.5]
row_2 = ['Instagram', 0.0, 'USD', 2161558, 4.5]
row_3 = ['Clash of Clans', 0.0, 'USD', 2130805, 4.5]
row_4 = ['Temple Run', 0.0, 'USD', 1724546, 4.5]
row_5 = ['Pandora - Music & Radio', 0.0, 'USD', 1126879, 4.0]
app_data_set = [row_1, row_2, row_3, row_4, row_5]
rating_sum = 0
for each_row in app_data_set:
rating = each_row[-1]
rating_sum = rating + rating_sum
avg_rating = rating_sum / 5
print(avg_rating)
## 10. The Average App Rating ##
opened_file = open('AppleStore.csv')
from csv import reader
read_file = reader(opened_file)
apps_data = list(read_file)
header = apps_data[0]
apps_data = apps_data[1:]
#print(len(apps_data))
rating_sum = 0
for row in apps_data:
rating = float(row[7])
rating_sum = rating + rating_sum
avg_rating = rating_sum / 7197
print(avg_rating)
## 11. Alternative Method to Compute an Average ##
opened_file = open('AppleStore.csv')
from csv import reader
read_file = reader(opened_file)
apps_data = list(read_file)
header = apps_data[0]
apps_data = apps_data[1:]
all_ratings = []
for row in apps_data:
rating = float(row[7])
all_ratings.append(rating)
print(all_ratings)
avg_rating = sum(all_ratings) / len(all_ratings)
print (avg_rating)