def update_salaries(jobs=[], cities=[], df=[], skiprows=0,
table='salary', verbose_=True):
columns = ['job', 'city', 'state', 'salary']
#columns += ['n_postings', 'state_name']
#columns += ['relative_salary', 'salaries_max', 'salaries_median',
# 'trend_last2first', 'trend_median', 'trend_max']
# get jobs from text file
if not any(jobs):
jobs = pd.read_csv(PATH + 'jobs.txt')[skiprows:]
#jobs.job = jobs.job.str.title()
# get unique cities from postings
if not any(cities):
cities = db.get_cities_from_db()
if not any(df):
df = pd.DataFrame(columns=columns)
for job, location in itertools.product(jobs.job.values, cities.values):
city, state = location
if db.queryNotInDb(job, city, state, table):
df = scrape_indeed(job, city, state, df)
if verbose_:
print df.tail(1)
db.to_sql(df.tail(1), table, 'append', null=0)
return df
def update_postings(jobs=[], cities=[], skiprows=0, force=False,
table='postings'):
# get jobs from text file
if not any(jobs):
jobs = pd.read_csv(PATH + 'jobs.txt')[skiprows:]
#jobs.job = jobs.job.str.title()
# get unique cities from postings
if not any(cities):
cities = db.get_cities_from_db()
for job, location in itertools.product(jobs.job.values, cities.values):
city, state = location
print job, city, state
if db.queryNotInDb(job, city, state, table) or force:
indeed_api(job, city, state, table)
