<jupyter_start><jupyter_text>Skills Required<jupyter_code>import numpy as np
file['skill_SQL']=np.where(file['description'].str.contains('SQL')==True,1,0)
file['skill_SQL'].value_counts()
file['skill_Python']=np.where(file['description'].str.contains('Python')==True,1,0)
file['skill_Python'].value_counts()
file['skill_Git']=np.where(file['description'].str.contains('Git')==True,1,0)
file['skill_Git'].value_counts()
file['skill_AWS']=np.where(file['description'].str.contains('AWS')==True,1,0)
file['skill_AWS'].value_counts()
file['skill_Tableau']=np.where(file['description'].str.contains('Tableau')==True,1,0)
file['skill_Tableau'].value_counts()
file['skill_Docker']=np.where(file['description'].str.contains('Docker')==True,1,0)
file['skill_Docker'].value_counts()
file['skill_Power BI']=np.where(file['description'].str.contains('Power BI')==True,1,0)
file['skill_Power BI'].value_counts()
file['skill_R']=np.where(file['description'].str.contains('R')==True,1,0)
file['skill_R'].value_counts()
file['skill_Redshift']=np.where(file['description'].str.contains('Redshift')==True,1,0)
file['skill_Redshift'].value_counts()
file['skill_Snowflake']=np.where(file['description'].str.contains('Snowflake')==True,1,0)
file['skill_Snowflake'].value_counts()
file['skill_Kafka']=np.where(file['description'].str.contains('Kafka')==True,1,0)
file['skill_Kafka'].value_counts()
file['skill_Spark']=np.where(file['description'].str.contains('Spark')==True,1,0)
file['skill_Spark'].value_counts()
file['skill_Tensorflow']=np.where(file['description'].str.contains('Tensorflow')==True,1,0)
file['skill_Tensorflow'].value_counts()
file.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.234093637454982, 'fraction_numerical': 0.015606242496998799, 'mean_word_length': 17.943181818181817, 'pattern_counts': {'":': 0, '<': 5, '<?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': '120745', 'license': 'MIT', 'n_tokens_mistral': 623, 'n_tokens_neox': 636, 'n_words': 35, 'path': 'LinkedIn Jobs PyScraper.ipynb', 'repo_name': 'andree-ldn/python', 'size': '1666'}