Counting most common value in Arbitrary number of dictionaries

You can iterate over the list to group each city with all its related weather values and then use collections.Counter:

from collections import Counter
d = [{'New York': 'cloudy', 'Chicago': 'snowy', 'Seattle': 'rainy'}, {'New York': 'cloudy', 'Chicago': 'hailing', 'Seattle': 'sunny'}, {'New York': 'sunny', 'Chicago': 'snowy', 'Seattle': 'rainy'}, {'New York': 'hailing', 'Chicago': 'snowy', 'Seattle': 'snowy'}]
weather = {i:Counter([c[i] for c in d]).most_common(1)[0][0] for b in d for i in b}

Output:

{'New York': 'cloudy', 'Chicago': 'snowy', 'Seattle': 'rainy'}

Edit: assuming all dictionaries in d contain the same keys, only the latter attributes need to be iterated over from the very first dictionary in the list:

weather = {i:Counter([c[i] for c in d]).most_common(1)[0][0] for i in d[0]}

Convert your list of dictionaries into a pandas DataFrame, count values for each city, find the index of the maximal value:

import pandas as pd
typical = pd.DataFrame(your_list_of_dicts).apply(pd.value_counts).idxmax()
#Chicago      snowy
#New York    cloudy
#Seattle      rainy

Make it a dictionary if needed:

typical.to_dict()
#{'Chicago': 'snowy', 'New York': 'cloudy', 'Seattle': 'rainy'}

Why not a one-liner dictionary comprehension then?, like:

print({k:max([i[k] for i in weather],key=[i[k] for i in weather].count) for k in list(weather[0].keys())})

Now you get the desired output as:

{'New York': 'cloudy', 'Chicago': 'snowy', 'Seattle': 'rainy'}