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2_Scripts_collection_delays.py
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150 lines (142 loc) · 7.04 KB
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from pyspark.sql.functions import *
import csv
from pyspark.sql.types import *
from pyspark.sql.functions import *
from pyspark import SparkContext
from pyspark.sql import HiveContext
from pyspark.sql.functions import *
from pyspark.sql.functions import udf
from pyspark.sql.types import BooleanType
from pyspark.sql import Row
import csv
from pyspark.sql import SQLContext
def parseCSV1(idx, part):
if idx==0:
part.next()
for p in csv.reader(part):
if p[8] != 'NULL' :
if p[8] == 'CARRIER':
pass
else:
yield Row(
CARRIER = p[8],
AIRLINE_ID = p[7],
DEST_AIRPORT_ID = int(p[20]))
def parseCSV2(idx, part):
if idx==0:
part.next()
#count = 0
for p in csv.reader(part):
if p[8] != 'NULL' :
yield Row(CARRIER=p[8],
DEP_DEL15 = p[33],
DEP_DELAY_NEW = p[32])
def parseCSV3(idx, part):
if idx==0:
part.next()
count = 0
for p in csv.reader(part):
if p[8] != 'NULL' :
if p[8] == 'CARRIER':
pass
else:
yield Row(CARRIER=p[8],
CARRIER_DELAY=p[56],
NAS_DELAY=p[58],
SECURITY_DELAY=p[59],
WEATHER_DELAY = p[57],
LATE_AIRCRAFT_DELAY = p[60],
DEP_DELAY_NEW = p[32])
def parseCSV4(idx, part):
if idx==0:
part.next()
count = 0
for p in csv.reader(part):
if p[8] != 'NULL' :
yield Row(CARRIER=p[8],
CARRIER_DELAY=p[56],
NAS_DELAY=p[58],
SECURITY_DELAY=p[59],
WEATHER_DELAY = p[57],
LATE_AIRCRAFT_DELAY = p[60],
DEP_DELAY_NEW = p[32])
def parseCSV5(idx, part):
if idx==0:
part.next()
for p in csv.reader(part):
if p[8] != 'NULL' :
if p[8]=='CARRIER':
pass
else:
yield Row(
CARRIER = p[8],
AIRLINE_ID = p[7],
DEST_AIRPORT_ID = str(p[20]))
def main(sc):
spark = HiveContext(sc)
sqlContext = HiveContext(sc)
# airline_most_departure_delays
rows = sc.textFile('../lmf445/Flight_Project/Data/864625436_T_ONTIME_2*.csv').mapPartitionsWithIndex(parseCSV1)
df = sqlContext.createDataFrame(rows)
airl_origin= df.select('CARRIER', 'AIRLINE_ID').groupBy('CARRIER').count().withColumnRenamed('count', 'airlineId_count')
airl_origin = airl_origin.withColumnRenamed('CARRIER', 'Airline_origin')
airl_origin.toPandas().to_csv('Output/airline_most_departure_delays_new.csv')
# airline_with_maximum_delay_new
rows = sc.textFile('../lmf445/Flight_Project/Data/864625436_T_ONTIME_2*.csv').mapPartitionsWithIndex(parseCSV2)
da = sqlContext.createDataFrame(rows)
db = da.select('CARRIER', 'DEP_DEL15').where(da.DEP_DEL15 == 1).groupby('CARRIER').count().withColumnRenamed(
'count', 'delay_count')
db = db.na.drop()
dp = da.select('CARRIER', 'DEP_DEL15', 'DEP_DELAY_NEW').where(da.DEP_DEL15 == 0).groupby(
'CARRIER').count().withColumnRenamed('count', 'not_delay_count')
dp = dp.na.drop()
dp.toPandas().to_csv('Output/airline_with_maximum_delay_new.csv')
# most_common_reason_for_delay
rows = sc.textFile('../lmf445/Flight_Project/Data/864625436_T_ONTIME_2*.csv').mapPartitionsWithIndex(parseCSV3)
d = sqlContext.createDataFrame(rows)
d_c = d.select('CARRIER', 'CARRIER_DELAY').filter(d.CARRIER_DELAY > 0).groupby('CARRIER').count().withColumnRenamed(
'count', 'CARRIER_DELAY_count')
d_n = d.select('CARRIER', 'NAS_DELAY').filter(d.NAS_DELAY > 0).groupby('CARRIER').count().withColumnRenamed('count',
'NAS_DELAY_count')
d_s = d.select('CARRIER', 'SECURITY_DELAY').filter(d.SECURITY_DELAY > 0).groupby('CARRIER').count().withColumnRenamed(
'count', 'SECURITY_DELAY_count')
d_w = d.select('CARRIER', 'WEATHER_DELAY').filter(d.WEATHER_DELAY > 0).groupby('CARRIER').count().withColumnRenamed(
'count', 'WEATHER_DELAY_count')
d_l = d.select('CARRIER', 'LATE_AIRCRAFT_DELAY').filter(d.LATE_AIRCRAFT_DELAY > 0).groupby(
'CARRIER').count().withColumnRenamed('count', 'LATE_AIRCRAFT_DELAY_count')
delay_rn_count = d_n.join(d_c, ["CARRIER"])
delay_rn_count = delay_rn_count.join(d_s, ["CARRIER"], how='outer')
delay_rn_count = delay_rn_count.join(d_w, ["CARRIER"], how='outer')
delay_rn_count = delay_rn_count.join(d_l, ["CARRIER"], how='outer')
delay_rn_count.toPandas().to_csv('Output/most_common_reason_for_delay_new.csv')
# total_min_of_each_delay
rows = sc.textFile('../lmf445/Flight_Project/Data/864625436_T_ONTIME_2*.csv').mapPartitionsWithIndex(parseCSV4)
da = sqlContext.createDataFrame(rows)
da = da.withColumn('CARRIER_DELAY', da['CARRIER_DELAY'].cast('int'))
da = da.withColumn('NAS_DELAY', da['NAS_DELAY'].cast('int'))
da = da.withColumn('SECURITY_DELAY', da['SECURITY_DELAY'].cast('int'))
da = da.withColumn('WEATHER_DELAY', da['WEATHER_DELAY'].cast('int'))
da = da.withColumn('LATE_AIRCRAFT_DELAY', da['LATE_AIRCRAFT_DELAY'].cast('int'))
d_c_s = da.select('CARRIER', 'CARRIER_DELAY').filter(da.CARRIER_DELAY > 0).groupby('CARRIER').sum().withColumnRenamed(
'sum(CARRIER_DELAY)', 'CARRIER_DELAY_sum')
d_n_s = da.select('CARRIER', 'NAS_DELAY').filter(da.NAS_DELAY > 0).groupby('CARRIER').sum().withColumnRenamed(
'sum(NAS_DELAY)', 'NAS_DELAY_sum')
d_s_s = da.select('CARRIER', 'SECURITY_DELAY').filter(da.SECURITY_DELAY > 0).groupby('CARRIER').sum().withColumnRenamed(
'sum(SECURITY_DELAY)', 'SECURITY_DELAY_sum')
d_w_s = da.select('CARRIER', 'WEATHER_DELAY').filter(da.WEATHER_DELAY > 0).groupby('CARRIER').sum().withColumnRenamed(
'sum(WEATHER_DELAY)', 'WEATHER_DELAY_sum')
d_l_s = da.select('CARRIER', 'LATE_AIRCRAFT_DELAY').filter(da.LATE_AIRCRAFT_DELAY > 0).groupby(
'CARRIER').sum().withColumnRenamed('sum(LATE_AIRCRAFT_DELAY)', 'LATE_AIRCRAFT_DELAY_sum')
delay_rn_sum = d_n_s.join(d_c_s, ["CARRIER"], how='outer')
delay_rn_sum = delay_rn_sum.join(d_s_s, ["CARRIER"], how='outer')
delay_rn_sum = delay_rn_sum.join(d_w_s, ["CARRIER"], how='outer')
delay_rn_sum = delay_rn_sum.join(d_l_s, ["CARRIER"], how='outer')
delay_rn_sum.toPandas().to_csv('Output/total_min_of_each_delay_new.csv')
rows = sc.textFile('../lmf445/Flight_Project/Data/864625436_T_ONTIME_2*.csv').mapPartitionsWithIndex(parseCSV5)
df1 = sqlContext.createDataFrame(rows)
airl_origin= df1.select('CARRIER', 'AIRLINE_ID').groupBy('CARRIER').count().withColumnRenamed('count', 'airlineId_count')
airl_origin = airl_origin.withColumnRenamed('CARRIER', 'Airline_origin')
airl_origin.toPandas().to_csv('Output/number_of_flights_new.csv')
if __name__ == "__main__":
sc = SparkContext()
main(sc)