# CS 150 class example

# Create histograms using dictionaries from any iterable data structure
# (e.g., lists or strings)

def get_counts(data):
    """
    Count how many times each item occurs in data 

    Args:
        data: Iterable collection of comparable items
        
    Returns:
        Dictionary of item : count
    """
    counts = {}  # Empty dictionary

    for item in data:
        counts[item] = counts.get(item,0) + 1
        """
        if item in counts:
            counts[item] += 1  # Increment count if present
        else:
            counts[item] = 1
        """
        
    return counts


def print_counts(counts):
    """
    Print out the counts from a dictionary
    
    Args:
        counts: Dictionary of item : count
    """
    # Equivalent to print("Item\tCount")
    print("Item", "Count", sep="\t")    
    for key, count in counts.items():
        # Arguments to print are automatically converted to strings, the following is
        # equivalent to print(str(key) + "\t" + str(counts[key]))
        print(key, counts[key], sep="\t")


def print_counts_sorted(counts):
    """
    Print out the counts from a dictionary with keys in sorted order
    
    Args:
        counts: Dictionary of item : count
    """
    
    # Need to convert set-like keys to list    
    key_list = list(counts.keys())  
    key_list.sort()
    
    print("Item", "Count", sep="\t") 
    for key in key_list:
        print(key, counts[key], sep="\t")

#print_counts(get_counts("this is a string and strings are iterable"))
#print_counts_sorted(get_counts("this is a string and strings are iterable"))
#print_counts(get_counts("this is a list of strings and a list is iterable".split()))