asin_find_st.py
3.7 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
import os
import sys
import pandas as pd
from pyspark.sql.window import Window
sys.path.append(os.path.dirname(sys.path[0])) # 上级目录
from pyspark.storagelevel import StorageLevel
from utils.templates import Templates
# from ..utils.templates import Templates
from pyspark.sql import functions as F
from pyspark.sql.types import StructType,StructField, StringType, IntegerType
class DwdStMeasure(Templates):
def __init__(self, site_name='us', date_type="month", date_info='2022-01'):
super().__init__()
self.site_name = site_name
self.date_type = date_type
self.date_info = date_info
self.db_save = f'dwd_st_rank'
self.spark = self.create_spark_object(
app_name=f"{self.db_save}: {self.site_name}, {self.date_type}, {self.date_info}")
# self.df_date = self.get_year_week_tuple() # pandas的df对象
self.df_st_month = self.spark.sql(f"select 1+1;")
self.df_st_week = self.spark.sql(f"select 1+1;")
def read_data(self):
sql_month = f"select search_term , asin, st_asin_zr_page, st_asin_zr_page_row , st_ao_val from dwt_st_asin_reverse where site_name ='us' and date_type='month' and date_info='2023-10';"
# and st_asin_zr_page=1 and st_asin_zr_page_row<=3
print("sql:", sql_month)
self.df_st_month = self.spark.sql(sql_month).cache()
self.df_st_month.show(20, truncate=False)
print("month:", self.df_st_month.count())
pdf = pd.read_excel(f"/root/asin.xlsx")
schema = StructType([StructField("asin", StringType(), True)])
self.df_st_week = self.spark.createDataFrame(pdf, schema=schema).cache()
# sql_week = f"SELECT distinct(trim(asin_brand_name)) as asin_brand_name from dim_asin_detail WHERE site_name ='us' and date_type ='week' and date_info BETWEEN '2023-14' and '2023-39';"
# self.df_st_week = self.spark.sql(sql_week).cache()
self.df_st_week.show(20, truncate=False)
print("week:", self.df_st_week.count())
def handle_data(self):
# # 合并两个DataFrame
# df_combined = self.df_st_month.union(self.df_st_week)
#
# # 只选择 row_number 为 1 的行
# df_unique = df_combined.drop_duplicates(['asin_brand_name'])
#
# self.df_combined_unique = df_unique
# self.df_combined_unique.show(20, truncate=False)
self.df_combined_unique = self.df_st_month.join(self.df_st_week, on='asin', how='inner')
# , 'search_term'
window = Window.partitionBy(['asin']).orderBy(
self.df_combined_unique.st_asin_zr_page_row.asc(),
)
self.df_combined_unique = self.df_combined_unique. \
withColumn("page_rank_top", F.row_number().over(window=window))
# print("self.df_st_asin_info, 开窗去重前:", self.df_st_asin_info.count())
self.df_combined_unique = self.df_combined_unique.filter("page_rank_top<=3")
print("combined:", self.df_combined_unique.count())
def save_data(self):
# 转换为 Pandas DataFrame
pdf = self.df_combined_unique.toPandas()
# 根据需求将每100万行数据保存为一个CSV文件
num_rows_per_file = 1000000
num_files = (len(pdf) // num_rows_per_file) + (1 if len(pdf) % num_rows_per_file != 0 else 0)
for i in range(num_files):
start_idx = i * num_rows_per_file
end_idx = start_idx + num_rows_per_file
output_path = os.path.join("/root", f"asin_find_st_{i + 1}.csv")
# 将子集保存为CSV
pdf.iloc[start_idx:end_idx].to_csv(output_path, index=False)
print(f"Data saved into {num_files} CSV files.")
if __name__ == '__main__':
handle_obj = DwdStMeasure()
handle_obj.run()