在Pandas中为DataFrame添加头部并删除索引

Question

我有一个数据表（DataFrame），它被重新采样成一个更小的数据表，并保留了日期时间索引。然后我把这个数据表进行了转置，现在想把日期索引去掉，换成一些字符串（标签），然后导出成.csv格式，以便在JavaScript中使用（所有的数据处理都是在Python中完成的）。

我尝试过直接把它写成没有表头的.csv文件（去掉日期），然后再读回来加上标签，但这样似乎效率不高。

这是.csv文件的链接： https://www.dropbox.com/s/qy72yht2m7lk2pg/17_predicted.csv

Python/pandas代码：

import pandas as pd
import numpy as np
from dateutil.parser import parse
from datetime import datetime
from pandas import *

# Load csv into pandas DataFrame
df = pd.read_csv("17_predicted_dummydata.csv", parse_dates=True, dayfirst=False, keep_date_col=True, index_col=0)

#Creating a date range
df.index = pd.to_datetime(pd.date_range(start='1/1/2000 00:30:00', end='1/1/2000 05:00:00', freq='30T'))

#Rename index
df.index.name = 'date'

df_year = df.resample('D', how='sum')
df_year = np.round(df_year, 0)

df_year.index.name = 'label'
df_year.column = ['value']

df_year = df_year.T

print df_year.head()
print df_year.index.name

df_year.to_csv("17_dummy.csv") #drop index through header=False

CSV输入：

    Date/Time,InteriorEquipment:Electricity:Zone:4419 [J](TimeStep),InteriorEquipment:Electricity:Zone:3967 [J](TimeStep),InteriorEquipment:Electricity:Zone:3993 [J](TimeStep)
 01/01  00:30:00,0.583979872,0.428071889,0.044676234
 01/01  01:00:00,0.583979872,0.428071889,0.044676234
 01/01  01:30:00,0.583979872,0.428071889,0.044676234
 01/01  02:00:00,0.583979872,0.428071889,0.044676234
 01/01  02:30:00,0.583979872,0.428071889,0.044676234
 01/01  03:00:00,0.583979872,0.428071889,0.044676234
 01/01  03:30:00,0.583979872,0.428071889,0.044676234
 01/01  04:00:00,0.583979872,0.428071889,0.044676234
 01/01  04:30:00,0.583979872,0.428071889,0.044676234
 01/01  05:00:00,0.583979872,0.428071889,0.044676234

建议的csv输出：

    label,value
InteriorEquipment:Electricity:Zone:4419 [J](TimeStep),6.0
InteriorEquipment:Electricity:Zone:3967 [J](TimeStep),4.0
InteriorEquipment:Electricity:Zone:3993 [J](TimeStep),0.0

我尝试过按照这个（向pandas数据表插入一行）的方法，但没能成功。

任何帮助都非常感谢！

Answer 1

你可以直接给数据表的索引名称和列名赋值，这样就能让它按照你想要的方式输出。

In [288]: df_year.index.name = 'label'

In [289]: df_year.columns = ['value']

In [290]: print df_year.to_csv()
label,value
Equipment:Electricity:LGF,79468.0
Equipment:Electricity:GF,66724.0
Equipment:Electricity:1st,30700.0
Equipment:Electricity:2nd,24126.0
Lights:Electricity:LGF,30596.0
Lights:Electricity:GF,30596.0
Lights:Electricity:1st,14078.0
Lights:Electricity:2nd,11063.0
General:Equipment:Electricity,201018.0
General:Lights:Electricity,86334.0
Electricity:Facility,314318.0
Electricity:Building,287352.0
Electricity:Plant,6329.0
Gas:Facility,279252.0
Electricity:HVAC,20637.0
General:Fans:Electricity,3554.0
Cooling:Electricity,17083.0
Pumps:Electricity,3708.0
WaterSystems:Electricity,2621.0