<jupyter_start><jupyter_text>-------------TEST BELOW----------------<jupyter_code>column1 = []
column2 = []

for column in my_data:
    column1.append([column[0]])
    column2.append([column[1]])
print('-----------')


print(column1)
print(column2)

print(my_data[5][0])
print(len(my_data[0]))


print(my_data)
i = sy.Symbol('i')
con = np.concatenate((column1, column2), axis=1)
# https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
print(con)<jupyter_output>[[   nan    nan]
 [0.0616 0.1208]
 [0.0615 0.128 ]
 [0.0636 0.1176]
 [0.0613 0.1235]
 [0.0612 0.126 ]]
{'fraction_non_alphanumeric': 0.24784853700516352, 'fraction_numerical': 0.10671256454388985, 'mean_word_length': 3.409090909090909, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 1, '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': '389820', 'license': 'MIT', 'n_tokens_mistral': 271, 'n_tokens_neox': 243, 'n_words': 35, 'path': 'more/summation_physic.ipynb', 'repo_name': 'ARRETdaniel/Python_exerc-cios', 'size': '581'}