<jupyter_start><jupyter_text>**REFACTORING_MINER**This notebook the selection of the rellevant attributes of the table `REFACTORING_MINER`.First, we import the libraries we need and, then, we read the corresponding csv.<jupyter_code>import pandas as pd
refactoringMiner = pd.read_csv("../../../data/raw/REFACTORING_MINER.csv")
print(refactoringMiner.shape)
list(refactoringMiner)<jupyter_output>(57530, 4)<jupyter_text>We select the desired attributes of the table.<jupyter_code>attributes = ['projectID', 'commitHash', 'refactoringType']
refactoringMiner = refactoringMiner[attributes]
print(refactoringMiner.shape)
refactoringMiner.head()<jupyter_output>(57530, 3)<jupyter_text>We save this new table into a csv.<jupyter_code>refactoringMiner.to_csv('../../../data/interim/DataPreparation/SelectData/REFACTORING_MINER_select.csv', header=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12, 'fraction_numerical': 0.013714285714285714, 'mean_word_length': 8.319148936170214, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '629985', 'license': 'MIT', 'n_tokens_mistral': 313, 'n_tokens_neox': 297, 'n_words': 56, 'path': 'notebooks/2-DataPreparation/1-SelectData/7-DB-REFACTORING-MINER.ipynb', 'repo_name': 'chus-chus/softwareDevTypes', 'size': '875'}