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```def calculate_mean(grouped_data):
total_frequency = sum(item[1] for item in grouped_data)
mean = sum(item[0] * item[1] for item in grouped_data) / total_frequency
return mean

def calculate_median(grouped_data):
total_frequency = sum(item[1] for item in grouped_data)
cumulative_frequency = [0]
cumulative_frequency.extend(sum(item[1] for item in grouped_data[:i+1]) for i in range(len(grouped_data)))

for i in range(len(cumulative_frequency)-1):
if cumulative_frequency[i] < total_frequency/2 and cumulative_frequency[i+1] >= total_frequency/2:
lower_limit = grouped_data[i][0]
upper_limit = grouped_data[i+1][0]
median = lower_limit + (upper_limit - lower_limit) * (total_frequency/2 - cumulative_frequency[i]) / (cumulative_frequency[i+1] - cumulative_frequency[i])
return median

def calculate_mode(grouped_data):
mode = max(grouped_data, key=lambda x: x[1])[0]
return mode

grouped_data = [(10, 12), (20, 15), (30, 20), (40, 18), (50, 10)]

mean = calculate_mean(grouped_data)
median = calculate_median(grouped_data)
mode = calculate_mode(grouped_data)

print("Mean:", mean)
print("Median:", median)
print("Mode:", mode)

def calculate_mean(data):

return sum(data) / len(data)

def calculate_median(data):

data.sort()
n = len(data)
if n % 2 == 0:

median = (data[n//2 - 1] + data[n//2]) / 2
else:

median = data[n//2]
return median

def calculate_mode(data):

from collections import Counter
count = Counter(data)
mode = max(count, key=count.get)
return mode

data = [3, 5, 6, 6, 8, 9]
mean = calculate_mean(data)
median = calculate_median(data)
mode = calculate_mode(data)
print("Mean:", mean)
print("Median:", median)
print("Mode:", mode)```