<jupyter_start><jupyter_text>Add the two numbers `num1` and `num2`<jupyter_code>num1 = 5
num2 = 7
num1+num2<jupyter_output><empty_output><jupyter_text>Add the two lists `hard_skills` and `soft_skills`<jupyter_code>hard_skills = ["Java", "Python"]
soft_skills = ["Presentation", "Leadership"]
hard_skills + soft_skills<jupyter_output><empty_output><jupyter_text>Can you add `num1` to `hard_skills`?<jupyter_code>num1 + hard_skills<jupyter_output><empty_output><jupyter_text>How do we add a number to a list?<jupyter_code>hard_skills + [num1]
hard_skills.append(num1)
hard_skills<jupyter_output><empty_output><jupyter_text>Can you multiply `num1` by `hard_skills`?<jupyter_code>num1 * hard_skills<jupyter_output><empty_output><jupyter_text>Add the two words `word1` and `word2`<jupyter_code>word1 = "Java"
word2 = "Python"
word1 + word2<jupyter_output><empty_output><jupyter_text>Can you add/concatenate `num1` to `word1`?<jupyter_code>word1 + str(num1)<jupyter_output><empty_output><jupyter_text>Multiply `num1` by `word1`. What do you get?<jupyter_code>num1 * word1<jupyter_output><empty_output><jupyter_text>Compare `num1` to `num2`, using `==` and `>=`<jupyter_code>num1 == num2
num1 >= num2<jupyter_output><empty_output><jupyter_text>Compare `word1` to `word2`, using `==` and `>=`<jupyter_code>word1 == word2
word1 >= word2<jupyter_output><empty_output><jupyter_text>Compare `hard_skills` to `soft_skills`, using `==` and `>=`<jupyter_code>hard_skills == soft_skills
hard_skills >= soft_skills<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14079895219384414, 'fraction_numerical': 0.02619515389652914, 'mean_word_length': 11.32258064516129, 'pattern_counts': {'":': 0, '<': 45, '<?xml version=': 0, '>': 51, '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': '11', 'dir': 'jupyter-structured-clean-dedup', 'id': '340689', 'license': 'MIT', 'n_tokens_mistral': 648, 'n_tokens_neox': 639, 'n_words': 99, 'path': '04-warmup-solution_operators.ipynb', 'repo_name': 'hanisaf/advanced-data-management-and-analytics', 'size': '1527'}