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@ -2,6 +2,7 @@ import pandas as pd
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from prophet import Prophet
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import math
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import matplotlib.pyplot as plt
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import os
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pd.set_option('display.width',None)
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def normal(x):
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@ -16,16 +17,15 @@ df.drop(columns=['500kv(含330kv)及以上','220kv','110kv(含66kv)','20kv','pow
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print(df.columns)
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# print(df.head())
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print(dict(zip(df.columns,[(df[x]==0).sum()/len(df) for x in df.columns])))
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df_ct = df[df['org_name']==df['org_name'][0]]
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print(df_ct.head())
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for i in range(30):
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df_ct = df[df['org_name']==df['org_name'].drop_duplicates().values[i]]
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# print(df_ct.head())
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df_ct['1-10kv'] /= 10000
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df_ct['35kv'] /= 10000
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df_ct['0.4kv及以下'] /= 10000
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s1 = df_ct[['日期','1-10kv']]
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s1.dropna(how='any',inplace=True)
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s1 = s1.loc[normal(s1['1-10kv']).index]
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print(s1)
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# plt.plot(range(len(s1)),s1['1-10kv'])
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# plt.show()
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@ -35,8 +35,8 @@ dd = s1.rename(columns={'日期':'ds','1-10kv':'y'})
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dd['ds'] = pd.to_datetime(dd['ds'])
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# 划分数据,划分为训练集和验证集,预测的数据设置为未来一个月
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df_train = dd[:-3]
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df_test = dd[-3:]
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df_train = dd[(dd['ds']>='2019-01-01')&(dd['ds']<='2023-10-31')][:-3]
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df_test = dd[(dd['ds']>='2019-01-01')&(dd['ds']<='2023-10-31')][-3:]
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# 数据的变动会受到季节、周、天的影响,存在一定的规律性,因此我们将这三个参数设置为True
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model = Prophet(yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=True)
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# 采用中国的假期模式,其余参数均保持默认
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@ -58,16 +58,21 @@ df_test = df_test.set_index('ds')
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# 把预测到的数据取出ds列,预测值列yhat,同样把ds列设置为索引列。
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forecast = forecast[['ds','yhat']].set_index('ds')
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print(forecast)
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# join:按照索引进行连接,
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# dropna:能够找到DataFrame类型数据的空值(缺失值),将空值所在的行/列删除后,将新的DataFrame作为返回值返回。
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df_all = forecast.join(df_test).dropna()
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print(df_all)
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df_all.plot()
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# 设置左上角小标
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plt.legend(['true', 'yhat'])
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plt.show()
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df_all = forecast.join(dd.set_index('ds')).dropna()
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print(df_all.loc['2023-10'])
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# # 创建一个ExcelWriter对象
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# with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\output.xlsx',mode='a',if_sheet_exists='replace') as writer:
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# # 将不同的子文件写入同一个Excel文件的不同工作表
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# df_all.to_excel(writer, sheet_name=f'Sheet{i+1}')
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# df_all.plot()
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# # 设置左上角小标
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# plt.legend(['true', 'yhat'])
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# plt.show()
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