From 53037416246d572869bb2b9c7391026d23f893ae Mon Sep 17 00:00:00 2001
From: xtao <xtao@xtaolink.cn>
Date: Sat, 1 Jun 2024 12:39:58 +0800
Subject: [PATCH] format

---
 spark/aqi_now_analyse.py  |  2 +-
 spark/client.py           |  1 +
 spark/rain_24h_analyse.py |  6 +++---
 spark/tem_2024_analyse.py | 18 +++++++++---------
 4 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/spark/aqi_now_analyse.py b/spark/aqi_now_analyse.py
index a89d8df..9898fce 100644
--- a/spark/aqi_now_analyse.py
+++ b/spark/aqi_now_analyse.py
@@ -1,4 +1,4 @@
-from pyspark.sql.types import IntegerType, FloatType
+from pyspark.sql.types import FloatType
 
 from client import spark_app
 from data.data import data_now_hdfs, mysql_jdbc_uri, mysql_jdbc_prop
diff --git a/spark/client.py b/spark/client.py
index d12d670..ce6900a 100644
--- a/spark/client.py
+++ b/spark/client.py
@@ -1,4 +1,5 @@
 import os
+
 os.environ["JAVA_HOME"] = "/opt/modules/jdk1.8.0_212"
 os.environ["YARN_CONF_DIR"] = "/usr/hdp/2.6.3.0-235/hadoop-yarn/etc/hadoop/"
 
diff --git a/spark/rain_24h_analyse.py b/spark/rain_24h_analyse.py
index 901242b..0712108 100644
--- a/spark/rain_24h_analyse.py
+++ b/spark/rain_24h_analyse.py
@@ -1,5 +1,5 @@
-from pyspark.sql.types import DecimalType
 from pyspark.sql import functions
+from pyspark.sql.types import DecimalType
 
 from client import spark_app
 from data.data import data_hour_hdfs, mysql_jdbc_uri, mysql_jdbc_prop
@@ -17,8 +17,8 @@ def rain_24h_analyse():
     ).filter(df['rain1h'] < 9999)
     # 分组、聚合、排序
     df_rain_sum_data = (df_rain_data.groupBy("city_index", "city_name")
-        .agg(functions.sum("rain1h").alias("rain24h"))
-        .sort(functions.desc("rain24h")))
+                        .agg(functions.sum("rain1h").alias("rain24h"))
+                        .sort(functions.desc("rain24h")))
     df_rain_sum_data.cache()
     print("处理完成，保存数据到数据库")
     df_rain_sum_data.coalesce(1).write.jdbc(mysql_jdbc_uri, "rain_24h_analyse", "ignore", mysql_jdbc_prop)
diff --git a/spark/tem_2024_analyse.py b/spark/tem_2024_analyse.py
index 4ae80ca..22f292e 100644
--- a/spark/tem_2024_analyse.py
+++ b/spark/tem_2024_analyse.py
@@ -1,12 +1,12 @@
-from pyspark.sql.types import IntegerType
 from pyspark.sql import functions
+from pyspark.sql.types import IntegerType
 
 from client import spark_app
 from data.data import data_day_hdfs, mysql_jdbc_uri, mysql_jdbc_prop
 
 
 def tem_2024_analyse():
-    print("计算重庆市各个区县 2024 年 每天的 最高气温，最低气温，平均气温")
+    print("计算重庆市各个区县 2024 年 每月的 最高气温，最低气温，平均气温")
     app = spark_app("tem_2024_analyse")
     print("创建应用完成，开始读取数据")
     df = app.read.csv(data_day_hdfs, header=True)
@@ -20,19 +20,19 @@ def tem_2024_analyse():
     # 转换 'date' 列的日期格式
     df_rain_data = df_rain_data.withColumn(
         'date',
-         functions.date_format(functions.from_unixtime(
-             functions.unix_timestamp('date', 'yyyyMMdd')), 'yyyyMM').cast(IntegerType())
+        functions.date_format(functions.from_unixtime(
+            functions.unix_timestamp('date', 'yyyyMMdd')), 'yyyyMM').cast(IntegerType())
     )
     # 分组、聚合
     df_rain_data = (df_rain_data.groupBy("city", "date")
-        .agg(functions.max("hmax").alias("hmax"),
-             functions.min("hmin").alias("hmin"),
-             functions.round(functions.avg("havg"), 2).alias("havg"))
-        .sort("city", df_rain_data.date.desc()))
+                    .agg(functions.max("hmax").alias("hmax"),
+                         functions.min("hmin").alias("hmin"),
+                         functions.round(functions.avg("havg"), 2).alias("havg"))
+                    .sort("city", df_rain_data.date.desc()))
     df_rain_data.cache()
     print("处理完成，保存数据到数据库")
     df_rain_data.coalesce(1).write.jdbc(mysql_jdbc_uri, "tem_2024_analyse", "ignore", mysql_jdbc_prop)
-    print("各个区县 2024 年 每天的 最高气温，最低气温，平均气温计算完毕")
+    print("各个区县 2024 年 每月的 最高气温，最低气温，平均气温计算完毕")
     return df_rain_data.head(20)