My program works with a CSV file with three columns: names, cities, and heights. I need to create a table that determines the difference in height from the average city size. This allows me to decide which two people have the minimum difference from the average. These difference tables must be in Python lists for each city. They must not use tuples, any dictionaries, named functions, or the key or lambda function. To start with, could we try finding the average height in each city? import csv csv_data = [] with open("C:\\Users\\lucas\\Downloads\\sample-data.csv") as f: reader = csv.reader(f) for row in csv.reader(f): csv_data .append(list(row)) f.close() csv_data.pop(0) difference_list = [] def Sorting_cities(data): cities = [] for i in range(len(data)): cities.append(data[i][1]) cities.sort() return cities city_list = Sorting_cities(csv_data) print(city_list) def Listing_cities(cities): single_cities = [] for city in cities: if city not in single_cities: single_cities.append(city) return single_cities unique_cities = Listing_cities(city_list) print(unique_cities) def Counting_cities_and_people(single_cities, cities): people_count = [] for city in single_cities: count = cities.count(city) people_count.append(count) for k in range(len(single_cities)): print(f'City: {single_cities[k]}, Population: {people_count[k]}') return people_count population = Counting_cities_and_people(unique_cities, city_list) print(population) def Find_heights(data): heights = [] for city in unique_cities: for d in data: if city == d[1]: heights.append(int(d[2])) return heights height_list = Find_heights(csv_data) print(height_list) def Find_names(data): names = [] for city in unique_cities: for d in data: if city == d[1]: names.append(d[0]) return names name_list = Find_names(csv_data) def Find_indexes(data): index_list = [] index = 0 for height in data: index_list.append(height) index += 1 return index_list indexes = Find_indexes(csv_data) print(indexes) def Find_average(height_list): local_averages = [] for i in height_list: heights = indexes[i][2] average = sum(heights) / len(heights) if heights == indexes[i][2]: local_averages.append(average) return average avg = Find_average(height_list) print(avg) def Find_difference(): for height in height_list: diff = abs(height - avg) difference_list.append(diff) index = 0 closest_to_average_height = 1000000000 closest_to_average_name = " " second_closest_height = 1000000000 second_closest_name = "placeholder" difference_list_two = difference_list.copy() closest_to_average_diff = 1000000000 second_closest_to_average_diff = closest_to_average_diff difference_list.sort() for index in range(len(height_list)): current_diff = difference_list_two[index] if current_diff <= closest_to_average_diff: second_closest_height = (height_list[index]) r = 0 for index in difference_list_two: if index == difference_list[0]: closest_to_average_diff = index closest_to_average_height = height_list[r] closest_to_average_name = name_list[r] r += 1 newR = 0 for index in difference_list_two: if index == difference_list[1] and name_list[newR] != closest_to_average_name: second_closest_to_average_diff = index second_closest_name = name_list[newR] second_closest_height = height_list[newR] newR += 1 City = "City" most_average = "Most_Average" Height = "Height" next_most = "Second_Most_Average" Next_height = "Height" print(f"{City:^15} {most_average:^25} {Height:^6} {next_most:^25} {Next_height:^6}") print(f'{city:^15} {closest_to_average_name:^25} {closest_to_average_height:^6} ' f'{second_closest_name:^25} {second_closest_height:^6}')
My program works with a CSV file with three columns: names, cities, and heights. I need to create a table that determines the difference in height from the average city size. This allows me to decide which two people have the minimum difference from the average. These difference tables must be in Python lists for each city. They must not use tuples, any dictionaries, named functions, or the key or lambda function.
To start with, could we try finding the average height in each city?
import csv
csv_data = []
with open("C:\\Users\\lucas\\Downloads\\sample-data.csv") as f:
reader = csv.reader(f)
for row in csv.reader(f):
csv_data .append(list(row))
f.close()
csv_data.pop(0)
difference_list = []
def Sorting_cities(data):
cities = []
for i in range(len(data)):
cities.append(data[i][1])
cities.sort()
return cities
city_list = Sorting_cities(csv_data)
print(city_list)
def Listing_cities(cities):
single_cities = []
for city in cities:
if city not in single_cities:
single_cities.append(city)
return single_cities
unique_cities = Listing_cities(city_list)
print(unique_cities)
def Counting_cities_and_people(single_cities, cities):
people_count = []
for city in single_cities:
count = cities.count(city)
people_count.append(count)
for k in range(len(single_cities)):
print(f'City: {single_cities[k]}, Population: {people_count[k]}')
return people_count
population = Counting_cities_and_people(unique_cities, city_list)
print(population)
def Find_heights(data):
heights = []
for city in unique_cities:
for d in data:
if city == d[1]:
heights.append(int(d[2]))
return heights
height_list = Find_heights(csv_data)
print(height_list)
def Find_names(data):
names = []
for city in unique_cities:
for d in data:
if city == d[1]:
names.append(d[0])
return names
name_list = Find_names(csv_data)
def Find_indexes(data):
index_list = []
index = 0
for height in data:
index_list.append(height)
index += 1
return index_list
indexes = Find_indexes(csv_data)
print(indexes)
def Find_average(height_list):
local_averages = []
for i in height_list:
heights = indexes[i][2]
average = sum(heights) / len(heights)
if heights == indexes[i][2]:
local_averages.append(average)
return average
avg = Find_average(height_list)
print(avg)
def Find_difference():
for height in height_list:
diff = abs(height - avg)
difference_list.append(diff)
index = 0
closest_to_average_height = 1000000000
closest_to_average_name = " "
second_closest_height = 1000000000
second_closest_name = "placeholder"
difference_list_two = difference_list.copy()
closest_to_average_diff = 1000000000
second_closest_to_average_diff = closest_to_average_diff
difference_list.sort()
for index in range(len(height_list)):
current_diff = difference_list_two[index]
if current_diff <= closest_to_average_diff:
second_closest_height = (height_list[index])
r = 0
for index in difference_list_two:
if index == difference_list[0]:
closest_to_average_diff = index
closest_to_average_height = height_list[r]
closest_to_average_name = name_list[r]
r += 1
newR = 0
for index in difference_list_two:
if index == difference_list[1] and name_list[newR] != closest_to_average_name:
second_closest_to_average_diff = index
second_closest_name = name_list[newR]
second_closest_height = height_list[newR]
newR += 1
City = "City"
most_average = "Most_Average"
Height = "Height"
next_most = "Second_Most_Average"
Next_height = "Height"
print(f"{City:^15} {most_average:^25} {Height:^6} {next_most:^25} {Next_height:^6}")
print(f'{city:^15} {closest_to_average_name:^25} {closest_to_average_height:^6} '
f'{second_closest_name:^25} {second_closest_height:^6}')
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What if this code could be done with user-defined functions? All other previous rules would continue to apply.
import csv
csv_data = []
with open("sample-data.csv") as f:
reader = csv.reader(f)
for row in reader:
csv_data.append(row)
f.close()
csv_data.pop(0)
# Find unique cities
cities = set()
for row in csv_data:
cities.add(row[1])
# Calculate average height for each city
city_heights = []
for city in cities:
heights_sum = 0
heights_count = 0
for row in csv_data:
if row[1] == city:
heights_sum += float(row[2])
heights_count += 1
avg_height = heights_sum / heights_count
city_heights.append((city, avg_height))
# Calculate difference from average height for each person
person_diffs = []
for row in csv_data:
name = row[0]
city = row[1]
height = float(row[2])
city_avg_height = None
for c, h in city_heights:
if c == city:
city_avg_height = h
break
diff = abs(height - city_avg_height)
person_diffs.append((name, city, diff))
# Find the two people with the minimum difference from the average for each city
for city, avg_height in city_heights:
city_person_diffs = []
for pd in person_diffs:
if pd[1] == city:
city_person_diffs.append((pd[0], pd[2])) # add name and height difference to list
n = len(city_person_diffs)
for i in range(n - 1):
min_idx = i
for j in range(i + 1, n):
if city_person_diffs[j][1] < city_person_diffs[min_idx][1]:
min_idx = j
city_person_diffs[i], city_person_diffs[min_idx] = city_person_diffs[min_idx], city_person_diffs[i] # swap the elements
print("\n" + city + ":")
print("Average height: {:.2f}".format(avg_height))
print("{:<20} {:<20}".format("Name", "Difference"))
for i in range(min(2, n)):
name = city_person_diffs[i][0]
diff = city_person_diffs[i][1]
print("{:<20} {:<20.2f}".format(name, diff))
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