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numbers = [4, 2, 12, 8]
sorted_numbers = sorted(numbers)
print(sorted_numbers)
# Output: [2, 4, 8, 12]
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# List of Integers
numbers = [1, 3, 4, 2]
# Sorting list of Integers
numbers.sort()
print(numbers)
# List of Floating point numbers
decimalnumber = [2.01, 2.00, 3.67, 3.28, 1.68]
# Sorting list of Floating point numbers
decimalnumber.sort()
print(decimalnumber)
# List of strings
words = ["Geeks", "For", "Geeks"]
# Sorting list of strings
words.sort()
print(words)
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cars = ['Ford', 'BMW', 'Volvo']
cars.sort(reverse=True)
# For a dataframe
sorted_df = df.sort_values(by=['cat_col', 'num_col'], ascending=[True, False])
each_group_top = sorted_df.groupby(['cat_col'])['num_col'].head(1) # top row of each category column
# Sorted provides more way of customization... example with networkx
sorted(nx.find_cliques(G), key=lambda x:len(x))[-1]
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#2 ways
nums = [4, 2, 12, 8]
sorted_nums = sorted(nums)
print(sorted_nums) # [2 , 4 , 8 , 12]
nums.sort()
print(nums) # [2 , 4 , 8 , 12]
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prime_numbers = [11, 3, 7, 5, 2]
# sorting the list in ascending order
prime_numbers.sort()
print(prime_numbers)
# Output: [2, 3, 5, 7, 11]
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import array
# Declare a list type object
list_object = [3, 4, 1, 5, 2]
# Declare an integer array object
array_object = array.array('i', [3, 4, 1, 5, 2])
print('Sorted list ->', sorted(list_object))
print('Sorted array ->', sorted(array_object))