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xxx.py
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xxx.py
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from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, IntegerType
spark = SparkSession.builder.config("spark.sql.session.charset", "UTF-8").appName("data_analysis").master("local").getOrCreate()
sc = spark.sparkContext
df = spark.read.format("jdbc"). \
option("url", "jdbc:mysql://192.168.101.20:3306/spark?useSSL=false&Unicode=true&characterEncoding=UTF-8"). \
option("dbtable", 'base_data'). \
option("user", "spark"). \
option("password", "12345678"). \
load()
csv_list = ['lianjia-双流.csv', 'lianjia-大邑.csv', 'lianjia-崇州.csv', 'lianjia-彭州.csv', 'lianjia-成华.csv',
'lianjia-新津.csv', 'lianjia-新都.csv', 'lianjia-武侯.csv', 'lianjia-浦江.csv', 'lianjia-温江.csv',
'lianjia-简阳.csv', 'lianjia-郫都.csv', 'lianjia-都江堰.csv', 'lianjia-金堂.csv', 'lianjia-金牛.csv',
'lianjia-锦江.csv', 'lianjia-青白江.csv', 'lianjia-青羊.csv', 'lianjia-龙泉驿.csv']
schema = StructType([
StructField("name", StringType(), False), # 名称
StructField("address", StringType(), False), # 地址
StructField("type", StringType(), False), # 房型
StructField("room", DoubleType(), False), # 面积
StructField("orient", StringType(), False), # 朝向
StructField("what_fix", StringType(), False), # 装修
StructField("level", StringType(), False), # 楼层
StructField("built_time", IntegerType(), False), # 建成时间
StructField("level_structure", StringType(), False), # 楼层结构
StructField("total_price", DoubleType(), False), # 总价格
StructField("room_price", DoubleType(), False), # 平方价格
StructField("concern_rate", IntegerType(), False), # 关注度
StructField("publish_time", StringType(), False) # 发布时间
])
def function1():
begin = 1979
end = 2024
bili = list()
df.createOrReplaceTempView("df_view")
for year in range(begin, end, 5):
sql_log = "select * from df_view where `built_time` < "+ str(year+5) + " and " + " `built_time` " + " >= " + str(year)
#print('总：', df.count())
#print('year 分', spark.sql(sql_log).count())
r = (float(spark.sql(sql_log).count()) / float(df.count()))*100
bili.append([str(year)+'-'+str(year+5),spark.sql(sql_log).count(),r])
print(bili)
rdd = sc.parallelize(bili)
df_to_mysql = rdd.toDF( StructType([StructField("built_time_partition", StringType(), False),
StructField("partition_count", IntegerType(), False),
StructField("rate", DoubleType(), False)])
)
print(df_to_mysql.show())
df_to_mysql.write.mode("overwrite"). \
format("jdbc"). \
option("url", "jdbc:mysql://192.168.101.20:3306/spark?useSSL=false&Unicode=true"). \
option("dbtable", "built_time_analysis"). \
option("user", "spark"). \
option("password", "12345678"). \
save()
def function2():
from pyspark.sql import functions as F
df.createOrReplaceTempView("df_view")
sql_log = "SELECT what_fix, count(*) as count, count(*) as rate FROM `df_view` GROUP BY what_fix"
df_ans = spark.sql(sql_log)
df_ans = df_ans.withColumn('rate', (F.col('rate').cast('float') / F.lit(df.count())) * 100)
print(df_ans.show())
df_ans.write.mode("overwrite"). \
format("jdbc"). \
option("url", "jdbc:mysql://192.168.101.20:3306/spark?useSSL=false&Unicode=true"). \
option("dbtable", "mod_analysis"). \
option("user", "spark"). \
option("password", "12345678"). \
save()
def function3():
from pyspark.sql import functions as F
df_new = df.withColumn('level', F.col('level').cast('string').substr(0, 3))
df_new.createOrReplaceTempView('df_new_view')
sql_log = "SELECT level, count(*) as count, count(*) as rate FROM `df_new_view` GROUP BY level"
df_new = spark.sql(sql_log)
df_new = df_new.withColumn('rate', (F.col('rate').cast('float') / F.lit(df.count())) * 100 )
print(df_new.show())
df_new.write.mode("overwrite"). \
format("jdbc"). \
option("url", "jdbc:mysql://192.168.101.20:3306/spark?useSSL=false&Unicode=true"). \
option("dbtable", "level_analysis"). \
option("user", "spark"). \
option("password", "12345678"). \
save()
def function4():
df.createOrReplaceTempView('df_view')
sql_log = "SELECT type ,count(*) as count, SUM(concern_rate) as concern_rate_sum, SUM(concern_rate)/ count(*) as factor " \
"from df_view GROUP BY type"
df_new = spark.sql(sql_log)
print(df_new.show())
df_new.write.mode("overwrite"). \
format("jdbc"). \
option("url", "jdbc:mysql://192.168.101.20:3306/spark?useSSL=false&Unicode=true"). \
option("dbtable", "type_and_concern_analysis"). \
option("user", "spark"). \
option("password", "12345678"). \
save()
def format_func(modified_list):
for i in range(1, len(modified_list), 1):
for j in range(13):
# format room from string to float
if j == 3:
modified_list[i][j] = float(str(modified_list[i][j]))
# format built_date from string to int
if j == 7:
modified_list[i][j] = int(modified_list[i][j])
# format total_price from string to float
if j == 9:
modified_list[i][j] = float(modified_list[i][j])
# format room_price from string to float (元/平)
if j == 10:
modified_list[i][j] = float(modified_list[i][j])
# format Concern_rate from string to int
if j == 11:
modified_list[i][j] = int(modified_list[i][j])
return modified_list
def function5():
from pyspark.sql import functions as F
df_list = list()
for each_csv in csv_list:
file_rdd = sc.textFile('hdfs://node-1:9000/user/root/spark/' + each_csv)
base_list = file_rdd.map(lambda x: x).collect()
modified_list = list()
label_list = ['名称', '地址', '房型', '面积', '朝向', '装修', '楼层', '建成时间', '楼层结构', '总价格',
'平方价格', '关注度', '发布时间']
modified_list.append(label_list)
for count_1 in range(1, len(base_list), 1):
each_item_List = str(base_list[count_1]).split(',')
# print(each_item_List)
# 处理每行的每列
write_info = True
for count_2 in range(len(each_item_List)):
# print(each_item_List[count_2], end=' ')
if count_2 == 0:
temp = str(each_item_List[count_2]).split(' ')[0]
each_item_List[count_2] = temp
# print(each_item_List[count_2])
if count_2 == 3:
temp = str(each_item_List[count_2]).split('平')[0]
each_item_List[count_2] = temp
# print(each_item_List[count_2])
if count_2 == 4:
temp = str(each_item_List[count_2]).split(' ')[0]
each_item_List[count_2] = temp
# print(each_item_List[count_2])
if count_2 == 6:
if '楼层' not in each_item_List[count_2]:
write_info = False
# print(each_item_List[count_2])
if count_2 == 7:
if not any(char.isdigit() for char in each_item_List[count_2]):
write_info = False
continue
temp = str(each_item_List[count_2]).split('年')[0]
each_item_List[count_2] = int(temp)
# print(each_item_List[count_2])
if count_2 == 9:
temp = float(str(each_item_List[count_2]).split('万')[0]) * 10000
each_item_List[count_2] = str(int(temp))
# print(each_item_List[count_2])
if count_2 == 10:
temp = each_item_List[count_2].split('"')[1] + \
each_item_List[count_2 + 1].split("元")[0]
each_item_List[count_2] = temp
each_item_List.pop(11)
# print(each_item_List[count_2])
if count_2 == 11:
temp = each_item_List[count_2].split('人')[0]
each_item_List[count_2] = temp
# print(each_item_List[count_2])
if count_2 == 12:
temp = each_item_List[count_2]
has_digit = any(char.isdigit() for char in temp)
if not has_digit:
write_info = False
if write_info:
modified_list.append(each_item_List)
# print(type(modified_list[1][3]))
modified_list = format_func(modified_list)
# print(type(modified_list[1][3]))
modified_list = modified_list[1: len(modified_list)]
rdd = sc.parallelize(modified_list)
df_new = rdd.toDF(schema)
print(each_csv + '去重前', df_new.count())
df_new = df_new.dropDuplicates()
print('去重后', df_new.count())
df_list.append(df_new)
index = 0
for each_df in df_list:
each_df_select = each_df.select('what_fix', 'room_price')
each_df_select_mean = each_df_select.groupBy('what_fix').avg().withColumnRenamed('avg(room_price)', 'mean_room_price')
each_df_select_max = each_df_select.groupBy('what_fix').max().withColumnRenamed('max(room_price)', 'max_room_price')
each_df_select_min = each_df_select.groupBy('what_fix').min().withColumnRenamed('min(room_price)', 'min_room_price')
each_df_select_mean = each_df_select_mean.withColumn('mean_room_price', F.round(F.col('mean_room_price'), 2))
each_df_select_max = each_df_select_max.withColumn('max_room_price', F.round(F.col('max_room_price'), 2))
each_df_select_min = each_df_select_min.withColumn('min_room_price', F.round(F.col('min_room_price'), 2))
each_df_select = each_df_select_max.join(each_df_select_mean, ['what_fix'] ,'inner')
each_df_select = each_df_select.join(each_df_select_min, ['what_fix'], 'inner')
df_list[index] = each_df_select
index += 1
print(each_df_select.show())
index = 0
for df_ in df_list:
# print(df_.show())
df_.write.mode("overwrite"). \
format("jdbc"). \
option("url", "jdbc:mysql://192.168.101.20:3306/spark?useSSL=false&Unicode=true"). \
option("dbtable", csv_list[index].split('-')[1].split('.')[0] + 'mod_A_room_pirce_analysis_db'). \
option("user", "spark"). \
option("password", "12345678"). \
save()
index += 1
function5()