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the 10th chapter
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‎chapter10/.ipynb_checkpoints/1_1dataGuiyue-checkpoint.ipynb‎

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‎chapter10/.ipynb_checkpoints/2_1dataExchange_divideEvent-checkpoint.ipynb‎

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‎chapter10/.ipynb_checkpoints/2_2dataExchange_thresholdOptimization-checkpoint.ipynb‎

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‎chapter10/.ipynb_checkpoints/2_3dataExchange_attributeConstruction-checkpoint.ipynb‎

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‎chapter10/.ipynb_checkpoints/3_1modelBuild-checkpoint.ipynb‎

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‎chapter10/1TimeWaterDivide.xlsx‎

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‎chapter10/1_1dataGuiyue.ipynb‎

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‎chapter10/1_1dataGuiyue.py‎

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# coding: utf-8
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# In[1]:
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# -*- utf-8 -*-
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# 1 数据抽取
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# 2 数据探索分析
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# 通过频率分布直方图分析用户用水停顿时间间隔的规律性--->探究划分一次完整用水事件的时间间隔阈值
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#
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# 3 数据预处理
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# （1）数据规约 data_guiyue.py
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# -*- utf-8 -*-
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# 规约掉"热水器编号"、"有无水流"、"节能模式"三个属性
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# 注意：
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#书中提到：规约掉热水器"开关机状态"=="关"且”水流量”==0的数据，说明热水器不处于工作状态，数据记录可以规约掉。但由后文知，此条件不能进行规约
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# 因为，"开关机状态"=="关"且”水流量”==0可能是一次用水中的停顿部分，删掉后则无法准确计算关于停顿的数据
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# In[2]:
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import pandas as pd
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import numpy as np
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from pandas import DataFrame
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or_data = pd.read_excel('original_data.xls',encoding='gbk')
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or_data.head()
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# In[3]:
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data = or_data.drop(or_data.columns[[0,5,9]],axis=1) # 删掉不相关属性
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data.head()
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# In[4]:
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data.info()
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# In[5]:
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data[u'发生时间'] = pd.to_datetime(data[u'发生时间'], format = '%Y%m%d%H%M%S')#将时间列转成日期格式（***）
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print len(data)
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# 由后文知，此条件无用
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# data1 = data[(data[u'开关机状态']==u'开')|(data[u'水流量']!=0)]
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# data1.head()
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data.head(10)
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data.to_excel('data_guiyue.xlsx')
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