from collections import Counter # Sample list data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4] # Creating a Counter counter = Counter(data) print(counter)
Counter({4: 4, 3: 3, 2: 2, 1: 1})
from collections import Counter # Sample string text = "data science bootcamp" # Creating a Counter counter = Counter(text) print(counter)
Counter({'a': 3, 'c': 3, 't': 2, ' ': 2, 'e': 2, 'o': 2, 'd': 1, 's': 1, 'i': 1, 'n': 1, 'b': 1, 'm': 1, 'p': 1})
from collections import Counter # Sample data data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4] # Creating a Counter counter = Counter(data) # Get the 2 most common elements most_common_elements = counter.most_common(2) print(most_common_elements)
[(4, 4), (3, 3)]
from collections import Counter # Initial data data = [1, 2, 2, 3, 3, 3] # Creating a Counter counter = Counter(data) # Data to update with update_data = [2, 3, 4, 4] # Updating the counter counter.update(update_data) print(counter)
Counter({3: 4, 2: 3, 4: 2, 1: 1})
from collections import Counter # Initial data data = [1, 2, 2, 3, 3, 3] # Creating a Counter counter = Counter(data) # Data to subtract subtract_data = [2, 3, 4] # Subtracting from the counter counter.subtract(subtract_data) print(counter)
Counter({3: 2, 1: 1, 2: 1, 4: -1})
from collections import Counter # Sample data data = [1, 2, 2, 3, 3, 3] # Creating a Counter counter = Counter(data) # Getting elements elements = list(counter.elements()) print(elements)
[1, 2, 2, 3, 3, 3]
from collections import Counter # Sample data counter1 = Counter([1, 2, 2, 3]) counter2 = Counter([2, 3, 3, 4]) # Addition print(counter1 + counter2) # Subtraction print(counter1 - counter2) # Intersection (minimum of corresponding counts) print(counter1 & counter2) # Union (maximum of corresponding counts) print(counter1 | counter2)
Counter({2: 3, 3: 3, 1: 1, 4: 1})
Counter({1: 1, 2: 1})
Counter({2: 1, 3: 1})
Counter({2: 2, 3: 2, 1: 1, 4: 1})
from collections import Counter import re # Sample text text = "Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data." # Clean and split the text into words words = re.findall(r'\w+', text.lower()) # Creating a Counter counter = Counter(words) # Most common words print(counter.most_common(5))
[('and', 3), ('data', 2), ('science', 1), ('is', 1), ('an', 1)]
from collections import Counter
# Sample dataset: list of tuples (ID, category)
dataset = [
(1, 'A'),
(2, 'B'),
(3, 'A'),
(4, 'A'),
(5, 'B'),
(6, 'C')
]
# Extract categories
categories = [category for _, category in dataset]
# Creating a Counter
category_counter = Counter(categories)
print(category_counter)Counter({'A': 3, 'B': 2, 'C': 1})