data_cleaning.py

import pandas as pd

df = pd.read_csv('glassdoor_jobs.csv')

# salary parsing

df['hourly'] = df['Salary Estimate'].apply(lambda x: 1 if 'per hour' in x.lower() else  0)
df['employer_provided'] = df['Salary Estimate'].apply(lambda x: 1 if 'employer provided salary' in x.lower() else  0)

df = df[df['Salary Estimate'] != '-1']
salary = df['Salary Estimate'].apply(lambda x: x.split('(')[0])
minus_kd = salary.apply(lambda x: x.replace('K','').replace('$',''))

min_hr = minus_kd.apply(lambda x: x.lower().replace('per hour', '').replace('employer provided salary:', ''))

df['min_salary'] = min_hr.apply(lambda x: int(x.split('-')[0]))
df['max_salary'] = min_hr.apply(lambda x: int(x.split('-')[1]))
df['avg_salaty'] = (df.min_salary+df.max_salary)/2

# company name text only

df['company_txt'] = df.apply(lambda x: x['Company Name'] if x['Rating'] < 0 else x['Company Name'][:-3],  axis = 1)

# state field

df['job_state'] = df['Location'].apply(lambda x: x.split(',')[1])
df.job_state.value_counts()

# headquarter check

df['same_state'] = df.apply(lambda x: 1 if x.Location == x.Headquarters else 0, axis = 1)

# age of company

df['company_age'] = df.Founded.apply(lambda x: x if x < 1 else 2020 - x)

# parsing job descriptions

#python
df['python'] = df['Job Description'].apply(lambda x: 1 if 'python' in x.lower() else 0)
#r studio
df['r_studio'] = df['Job Description'].apply(lambda x: 1 if 'r studio' in x.lower() else 0)
#spark
df['spark'] = df['Job Description'].apply(lambda x: 1 if 'spark' in x.lower() else 0)
#aws
df['aws'] = df['Job Description'].apply(lambda x: 1 if 'aws' in x.lower() else 0)
#excel
df['excel'] = df['Job Description'].apply(lambda x: 1 if 'excel' in x.lower() else 0)
#sql
df['sql'] = df['Job Description'].apply(lambda x: 1 if 'sql' in x.lower() else 0)

df.columns

df_out = df.drop(['Unnamed: 0'], axis = 1)

df_out.to_csv('salary_data_cleaned.csv',index= False)
pd.read_csv('salary_data_cleaned.csv')