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CheezItMan
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May 6, 2021
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Nice work Christina. You hit the main learning goals here. I had a few comments on big O regarding the 2nd method, but otherwise this is well done. You even got the Sudoku problem. Well done.
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| # Time Complexity: O(n * m * log m) | ||
| # where n is the number of strings in the array to iterate over, | ||
| # and m is the number of characters to sort in a string (m log m), | ||
| # Space Complexity: O(n * m) from sorting characters (m) and returning a new array (n) | ||
| # where n is the number of strings in the array | ||
| # and m is the number of characters in a string | ||
| def grouped_anagrams(strings) |
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| # Time Complexity: O(n + m log m) where n is the number of elements in the list and | ||
| # and m is the number of unique elements to sort (m log m) | ||
| # Space Complexity: O(m * k) where m is the number of unique elements to put in hash | ||
| # and k is the number of elements to return | ||
| def top_k_frequent_elements(list, k) |
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👍 I would say this is O(n log n) since k < n, and you're potentially sorting a hash of n elements.
For space complexity since k < n, and you make a hash of potentially n elements this is O(n).
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| # Time Complexity: O(1) constant time to iterate over table that is always 9x9 | ||
| # Space Complexity: O(1) | ||
| def valid_sudoku(table) |
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Hash Table Practice
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Comprehension Questions