<jupyter_start><jupyter_text>38. Count and SayThe count-and-say sequence is the sequence of integers with the first five terms as following:1.     12.     113.     214.     12115.     1112211 is read off as "one 1" or 11.11 is read off as "two 1s" or 21.21 is read off as "one 2, then one 1" or 1211.Given an integer n where 1 ≤ n ≤ 30, generate the nth term of the count-and-say sequence.Note: Each term of the sequence of integers will be represented as a string. Example 1:Input: 1Output: "1"Example 2:Input: 4Output: "1211"<jupyter_code>def count_and_say(n):
    """
    :type n: int
    :rtype: str
    """
    if n < 1:
        return ""

    term = [1]
    for _ in range(1,n):
        next_term = []
        i=0
        while i < len(term):
            val = term[i]
            count=1
            i += 1 
            while i < len(term) and term[i] == val:
                count+=1
                i+=1

            next_term.append(count)
            next_term.append(val)

        term = next_term

    return "".join(str(num) for num in term)
[count_and_say(n) for n in range(1,30)]
[len(count_and_say(n)) for n in range(1,30)]
import numpy as np
[np.log2(len(count_and_say(n))) for n in range(1,30)]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11182622687047465, 'fraction_numerical': 0.05551086082059534, 'mean_word_length': 2.248041775456919, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '441234', 'license': 'MIT', 'n_tokens_mistral': 475, 'n_tokens_neox': 422, 'n_words': 149, 'path': 'LeetCode/0038_count_and_say/test_count_and_say.ipynb', 'repo_name': 'NathanielBlairStahn/python-practice', 'size': '1243'}