Pandas 数据帧输出到 JSON
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Pandas Dataframe output to JSON
提问by cmiller8
I have a Pandas Dataframe with a DateTimeIndex and columns with hourly objects and I would like to transform and output a single column into a JSON file composed of an array of daily arrays of hourly values.
我有一个带有 DateTimeIndex 的 Pandas Dataframe 和带有每小时对象的列，我想将单个列转换并输出到由每小时值的每日数组数组组成的 JSON 文件。
A simple example:
一个简单的例子：
If I have the Dataframe:
如果我有数据框：
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
I would like this json file:
我想要这个 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]
]
My actual dataframe is many days longer therefore would roughly look like this:
我的实际数据框要长很多天，因此大致如下所示：
[
[value@hour1day1, [email protected]@hour24day1],
[value@hour1day2, [email protected]@hour24day2],
[value@hour1day3, [email protected]@hour24day3],
....
[value@hour1LastDay, [email protected]@hour24LastDay]
]
回答by root
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)
out:
出去：
[
[
-0.6916923670267555,
0.23075256008033393,
1.2390943452146521,
-0.9421708175530891,
-1.4622768586461448,
-0.3973987276444045,
-0.04983495806442656,
-1.9139530636627042,
1.9562147260518052,
-0.8296105620697014,
0.2888681009437529,
-2.3943000262784424
]
]
Or as a full example with multiple days, using groupbyfunctionality:
或者作为多天的完整示例，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)
out:
出去：
[
[
-0.8939584996681688,
...
-1.1332895023662326
],
[
-0.1514553673781838,
...
-1.8380494963443343
],
[
-1.8342085568898159
]
]
