def main():
sc = SparkContext( appName="Transforming Eff Care" )
src_effcare = sc.textFile(utils.data_home + "/effective_care.csv")
src_readmission = sc.textFile(utils.data_home + "/readmissions.csv")
transform1 = partial(transform_row,
orig_headers=eff_care_headers,
new_headers=utils.map_headers(proc_headers, eff_care_headers))
transform2 = partial(transform_row,
orig_headers=readmission_headers,
new_headers=utils.map_headers(proc_headers, readmission_headers))
transformed_eff = src_effcare.map(utils.to_row_sep).map(transform1).map(utils.to_row_string)
transformed_readmission = src_readmission.map(utils.to_row_sep).map(transform2).map(utils.to_row_string)
(transformed_eff + transformed_readmission) \
.saveAsTextFile(utils.data_home + "/procedures_data")
def transform_row(line_parts):
headers = utils.map_headers(new_headers, orig_headers)
new_parts = []
for (key, index), transform in zip(headers.iteritems(), transform_fns):
if index > -1:
value = line_parts[index]
value = transform(value)
else:
value = utils.NULL_FMT
new_parts.append(value)
return new_parts
