def make_null(r, locs, meta):
"""iterate through each value and see if any values are Nones, set to None (Null in db)"""
for col in meta.columns:
if col.description != 'pk' and etl.xstr(r[locs[col.description] - 1].value) == 'None':
r[locs[col.description] - 1].value = None
return r
def check_zero_entries(r, locs, meta):
"""iterate through each value and see if any ints are Nones, set to 0"""
for col in meta.columns:
if (isinstance(col.type, Integer) or isinstance(col.type, Float)) and col.description != 'pk' \
and etl.xstr(r[locs[col.description] - 1].value) == 'None':
r[locs[col.description] - 1].value = '0'
return r
def test_xstr_null(self):
self.assertEqual(etl.xstr('None', setnull=True), None)
def test_xstr_no_null(self):
self.assertEqual(etl.xstr('string!'), 'string!')
def gen_pk(r, locs):
return etl.xstr(r[locs["imp_agency"]-1].value)+etl.xstr(r[locs["local_partner"]-1].value)+etl.xstr(r[locs["district"]-1].value)+etl.xstr(r[locs["vdc"]-1].value)+etl.xstr(r[locs["ward"]-1].value)+etl.xstr(r[locs["act_type"]-1].value)+etl.xstr(r[locs["act_desc"]-1].value)+etl.xstr(r[locs["quantity"]-1].value)+etl.xstr(r[locs["total_hh"]-1].value)
def prep_row(r, locs):
#check to see if numeric rows are None - if so, make 0
r = etl.get_values(r, setnull=True)
return Distributions(
priority=etl.xstr(r[locs["priority"]-1], setnull=True),
access_method=etl.xstr(r[locs["access_method"]-1], setnull=True),
hub=etl.xstr(r[locs["hub"]-1], setnull=True),
as_of=etl.xstr(r[locs["as_of"]-1], setnull=True),
dist_code=etl.xstr(r[locs["dist_code"]-1], setnull=True),
vdc_code=etl.xstr(r[locs["vdc_code"]-1], setnull=True),
act_cat=etl.xstr(r[locs["act_cat"]-1], setnull=True),
imp_agency=etl.xstr(r[locs["imp_agency"]-1], setnull=True),
source_agency=etl.xstr(r[locs["source_agency"]-1], setnull=True),
local_partner=etl.xstr(r[locs["local_partner"]-1], setnull=True),
contact_name=etl.xstr(r[locs["contact_name"]-1], setnull=True),
contact_email=etl.xstr(r[locs["contact_email"]-1], setnull=True),
contact_phone=etl.xstr(r[locs["contact_phone"]-1], setnull=True),
district=etl.xstr(r[locs["district"]-1], setnull=True),
vdc=etl.xstr(r[locs["vdc"]-1], setnull=True),
ward=etl.xstr(r[locs["ward"]-1], setnull=True),
act_type=etl.xstr(r[locs["act_type"]-1], setnull=True),
act_desc=etl.xstr(r[locs["act_desc"]-1], setnull=True),
targeting=etl.xstr(r[locs["targeting"]-1], setnull=True),
quantity=etl.xstr(r[locs["quantity"]-1], setnull=True),
total_hh=etl.xstr(r[locs["total_hh"]-1], setnull=True),
avg_hh_cost=etl.xstr(r[locs["avg_hh_cost"]-1], setnull=True),
fem_hh=etl.xstr(r[locs["fem_hh"]-1], setnull=True),
vuln_hh=etl.xstr(r[locs["vuln_hh"]-1], setnull=True),
act_status=etl.xstr(r[locs["act_status"]-1], setnull=True),
#start_dt=etl.xstr(r[locs["start_dt"]-1], setnull=True),
start_day=etl.xstr(r[locs["start_day"]-1], setnull=True),
start_month=etl.xstr(r[locs["start_month"]-1], setnull=True),
start_year=etl.xstr(r[locs["start_year"]-1], setnull=True),
#comp_dt=etl.xstr(r[locs["comp_dt"]-1], setnull=True),
comp_day=etl.xstr(r[locs["comp_day"]-1], setnull=True),
comp_month=etl.xstr(r[locs["comp_month"]-1], setnull=True),
comp_year=etl.xstr(r[locs["comp_year"]-1], setnull=True),
comments=etl.xstr(r[locs["comments"]-1], setnull=True))
def test_xstr_null(self):
self.assertEqual(etl.xstr('None', setnull=True), None)
def test_xstr_no_null(self):
self.assertEqual(etl.xstr('string!'), 'string!')
def prep_row(r, locs):
# check to see if numeric rows are None - if so, make 0
r = etl.get_values(r, setnull=True)
return Trainings(
priority=etl.xstr(r[locs["priority"]-1], setnull=True),
access_method=etl.xstr(r[locs["access_method"]-1], setnull=True),
hub=etl.xstr(r[locs["hub"]-1], setnull=True),
as_of=etl.xstr(r[locs["as_of"]-1], setnull=True),
dist_code=etl.xstr(r[locs["dist_code"]-1], setnull=True),
vdc_code=etl.xstr(r[locs["vdc_code"]-1], setnull=True),
uid=etl.xstr(r[locs["uid"]-1], setnull=True),
imp_agency=etl.xstr(r[locs["imp_agency"]-1], setnull=True),
source_agency=etl.xstr(r[locs["source_agency"]-1], setnull=True),
local_partner=etl.xstr(r[locs["local_partner"]-1], setnull=True),
contact_name=etl.xstr(r[locs["contact_name"]-1], setnull=True),
contact_email=etl.xstr(r[locs["contact_email"]-1], setnull=True),
contact_phone=etl.xstr(r[locs["contact_phone"]-1], setnull=True),
district=etl.xstr(r[locs["district"]-1], setnull=True),
vdc=etl.xstr(r[locs["vdc"]-1], setnull=True),
ward=etl.xstr(r[locs["ward"]-1], setnull=True),
train_sub=etl.xstr(r[locs["train_sub"]-1], setnull=True),
audience=etl.xstr(r[locs["audience"]-1], setnull=True),
train_title=etl.xstr(r[locs["train_title"]-1], setnull=True),
demo_inc=etl.xstr(r[locs["demo_inc"]-1], setnull=True),
iec_dist=etl.xstr(r[locs["iec_dist"]-1], setnull=True),
dur_session=etl.xstr(r[locs["dur_session"]-1], setnull=True),
amt_parti=etl.xstr(r[locs["amt_parti"]-1], setnull=True),
total_cost=etl.xstr(r[locs["total_cost"]-1], setnull=True),
total_parti=etl.xstr(r[locs["total_parti"]-1], setnull=True),
males=etl.xstr(r[locs["males"]-1], setnull=True),
females=etl.xstr(r[locs["females"]-1], setnull=True),
third_gen=etl.xstr(r[locs["third_gen"]-1], setnull=True),
elderly=etl.xstr(r[locs["elderly"]-1], setnull=True),
children=etl.xstr(r[locs["children"]-1], setnull=True),
person_dis=etl.xstr(r[locs["person_dis"]-1], setnull=True),
fem_hh=etl.xstr(r[locs["fem_hh"]-1], setnull=True),
vuln_hh=etl.xstr(r[locs["vuln_hh"]-1], setnull=True),
act_status=etl.xstr(r[locs["act_status"]-1], setnull=True),
#start_dt=etl.xstr(r[locs["start_dt"]-1], setnull=True),
start_day=etl.xstr(r[locs["start_day"]-1], setnull=True),
start_month=etl.xstr(r[locs["start_month"]-1], setnull=True),
start_year=etl.xstr(r[locs["start_year"]-1], setnull=True),
#comp_dt=etl.xstr(r[locs["comp_dt"]-1], setnull=True),
comp_day=etl.xstr(r[locs["comp_day"]-1], setnull=True),
comp_month=etl.xstr(r[locs["comp_month"]-1], setnull=True),
comp_year=etl.xstr(r[locs["comp_year"]-1], setnull=True),
comments=etl.xstr(r[locs["comments"]-1], setnull=True))
