From e723fa0d417bbd36ec058ed05916e523977ab6d8 Mon Sep 17 00:00:00 2001
From: get <2316994765@qq.com>
Date: Wed, 18 Oct 2023 14:49:17 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
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---
 .../杭州日电量数据预处理.py         | 79 +++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 杭州日电量/杭州日电量数据预处理.py

diff --git a/杭州日电量/杭州日电量数据预处理.py b/杭州日电量/杭州日电量数据预处理.py
new file mode 100644
index 0000000..aaa6555
--- /dev/null
+++ b/杭州日电量/杭州日电量数据预处理.py
@@ -0,0 +1,79 @@
+import pandas as pd
+import datetime
+from sklearn.preprocessing import LabelEncoder
+import os
+import math
+import chinese_calendar as cc
+is_holiday = cc.is_workday(datetime.date(2023, 10, 7))
+print(is_holiday)
+
+def holiday_work(x):
+    if cc.is_workday(x):
+        return 0
+    if cc.is_holiday(x):
+        return 1
+
+tq_df = pd.read_excel(r'C:\Users\鸽子\Desktop\杭州\杭州气象数据.xlsx',sheet_name=0)
+pd.set_option('display.width',None)
+tq_df.columns = tq_df.columns.map(lambda x:x.strip())
+tq_df = tq_df[['city_name','dtdate','tem_max','tem_min','rh','rh_max','rh_min','pre','prs','prs_max','prs_min','win_s_max','win_s_min']]
+tq_df.drop_duplicates(subset='dtdate',inplace=True)
+tq_df = tq_df.sort_values(by='dtdate',ascending=True)
+tq_df.reset_index(inplace=True,drop=True)
+
+tq_df['dtdate'] = pd.to_datetime(tq_df['dtdate'],format='%Y%m%d')
+tq_df['holiday'] = tq_df['dtdate'].apply(holiday_work)
+print(tq_df.columns)
+print(tq_df.head())
+
+
+
+print(tq_df.info())
+def jq(y,x):
+    a=365.242 * (y - 1900) + 6.2 + 15.22 * x - 1.9 * math.sin(0.262 * x)
+    return datetime.date(1899,12,31)+datetime.timedelta(days=int(a))
+# print(jq(2020,0))
+jq_list=['小寒', '大寒', '立春', '雨水', '惊蛰', '春分', '清明', '谷雨', '立夏', '小满', '芒种', '夏至', '小暑', '大暑', '立秋', '处暑', '白露', '秋分', '寒露', '霜降', '立冬', '小雪', '大雪','冬至']
+jq_dict={}
+for j in range(2019,2024):
+    for i in range(24):
+        jq_dict[jq(j,i).strftime('%Y-%m-%d')]=jq_list[i]
+# print(jq_dict)
+
+tq_df['24ST']=tq_df.dtdate
+tq_df['24ST']=tq_df['24ST'].astype('string').map(jq_dict)
+tq_df['24ST'].fillna(method='ffill',inplace=True)
+tq_df['24ST'].fillna('冬至',inplace=True)
+
+
+# data为数据集  product_tags为需要编码的特征列(假设为第一列)
+le = LabelEncoder()
+tq_df['24ST'] = le.fit_transform(tq_df['24ST'])
+print(tq_df)
+
+file2=os.getcwd()+'/气象数据.csv'
+try:
+    tq_df.to_csv(file2,encoding='gbk')
+except:
+    tq_df.to_csv(file2,encoding='utf-8')
+
+elec_df = pd.read_excel(r'C:\Users\鸽子\Desktop\杭州\杭州日电量.xlsx')
+elec_df['售电量'] = (elec_df['售电量']/10000).map(lambda x:round(x,2))
+
+print(elec_df.columns)
+elec_df.sort_values(by=' 天 ',ascending=True,inplace=True)
+elec_df[' 天 '] = pd.to_datetime(elec_df[' 天 '],format='%Y%m%d')
+print(elec_df.info())
+print(elec_df)
+
+df = pd.merge(tq_df,elec_df,left_on='dtdate',right_on=' 天 ')
+df.drop(columns=' 天 ',inplace=True)
+
+df.set_index('dtdate',inplace=True,drop=True)
+print(df)
+df.to_csv(os.getcwd()+'\入模数据.csv',encoding='gbk')
+
+
+
+
+