熊猫数据帧输出到JSON

Question

我有一个带有DateTimeIndex和每小时对象的列的Pandas Dataframe，我想将单个列转换并输出到由每小时值数组组成的JSON文件中。

一个简单的例子：

如果我有数据帧：

In [106]: 
rng = pd.date_range('1/1/2011 01:00:00', periods=12, freq='H') 
df = pd.DataFrame(randn(12, 1), index=rng, columns=['A']) 

In [107]: 
df 

Out[107]: 
        A 
2011-01-01 01:00:00 -0.067214 
2011-01-01 02:00:00 0.820595 
2011-01-01 03:00:00 0.442557 
2011-01-01 04:00:00 -1.000434 
2011-01-01 05:00:00 -0.760783 
2011-01-01 06:00:00 -0.106619 
2011-01-01 07:00:00 0.786618 
2011-01-01 08:00:00 0.144663 
2011-01-01 09:00:00 -1.455017 
2011-01-01 10:00:00 0.865593 
2011-01-01 11:00:00 1.289754 
2011-01-01 12:00:00 0.601067 

我想这个JSON文件：

[  
[-0.0672138259,0.8205950583,0.4425568167,-1.0004337373,-0.7607833867,-0.1066187698,0.7866183048,0.1446634381,-1.4550165851,0.8655931982,1.2897541164,0.6010672247] 
] 

我的实际数据帧是多天的时间，因此将大致看像这样：

[ 
[value@hour1day1, value@hour2day1.....value@hour24day1], 
[value@hour1day2, value@hour2day2.....value@hour24day2], 
[value@hour1day3, value@hour2day3.....value@hour24day3], 
.... 
[value@hour1LastDay, value@hour2LastDay.....value@hour24LastDay] 
] 

Answer 1

import json 
import pandas as pd 
import numpy as np 

rng = pd.date_range('1/1/2011 01:00:00', periods=12, freq='H') 
df = pd.DataFrame(np.random.randn(12, 1), index=rng, columns=['A']) 

print json.dumps(df.T.as_matrix().tolist(),indent=4) 

出来：

[ 
    [ 
     -0.6916923670267555, 
     0.23075256008033393, 
     1.2390943452146521, 
     -0.9421708175530891, 
     -1.4622768586461448, 
     -0.3973987276444045, 
     -0.04983495806442656, 
     -1.9139530636627042, 
     1.9562147260518052, 
     -0.8296105620697014, 
     0.2888681009437529, 
     -2.3943000262784424 
    ] 
] 

或具有多天的一个完整的例子，使用groupby功能：

rng = pd.date_range('1/1/2011 01:00:00', periods=48, freq='H') 
df = pd.DataFrame(np.random.randn(48, 1), index=rng, columns=['A']) 

grouped = df.groupby(lambda x: x.day) 
data = [group['A'].values.tolist() for day, group in grouped] 
print json.dumps(data, indent=4) 

出来：

[ 
    [ 
     -0.8939584996681688, 
     ... 
     -1.1332895023662326 
    ], 
    [ 
     -0.1514553673781838, 
     ... 
     -1.8380494963443343 
    ], 
    [ 
     -1.8342085568898159 
    ] 
]