我的数据帧约为142264行:
样品:
DateAndTime TAGID TagValue UIB
2017-04-26 00:00:00.000 100 0.9 NaN
2017-04-26 00:00:00.000 101 430.3 NaN
2017-04-26 00:00:00.000 102 112.7 NaN
2017-04-26 00:00:00.000 103 50.0 NaN
2017-04-26 00:00:00.000 104 249.4 NaN
2017-04-26 00:00:00.000 105 109.9 NaN
2017-04-26 00:00:00.000 106 248.4 NaN
2017-04-26 00:00:00.000 107 131.5 NaN
2017-04-26 00:00:00.000 108 247.7 NaN
2017-04-26 00:00:00.000 109 96.8 NaN
2017-04-26 00:00:00.000 113 481.4 NaN
2017-04-26 00:00:00.000 114 243.9 NaN
2017-04-26 00:00:00.000 115 -416.0 NaN
2017-04-26 00:00:00.000 116 -0.5 NaN
2017-04-26 00:00:00.000 117 429.2 NaN
2017-04-26 00:00:00.000 118 646.4 NaN
2017-04-26 00:00:00.000 119 49.5 NaN
2017-04-26 00:00:00.000 120 248.2 NaN
2017-04-26 00:01:00.000 100 0.9 NaN
2017-04-26 00:01:00.000 101 429.7 NaN
2017-04-26 00:01:00.000 102 120.0 NaN
2017-04-26 00:01:00.000 103 49.9 NaN
2017-04-26 00:01:00.000 104 249.2 NaN
2017-04-26 00:01:00.000 105 123.8 NaN
2017-04-26 00:01:00.000 106 248.3 NaN
2017-04-26 00:01:00.000 107 136.3 NaN
2017-04-26 00:01:00.000 108 247.4 NaN
2017-04-26 00:01:00.000 109 99.9 NaN
2017-04-26 00:01:00.000 113 481.4 NaN
2017-04-26 00:01:00.000 114 243.9 NaN
我想在唯一的tagid上过滤数据帧并单独存储新的数据帧。
我试过了:
data = read_json("json_tagid_100_120.json")
tagid101 = data[data["TAGID"] == 101]
print tagid101
通过这样做,我只能存储Tagid 101的数据。
但我希望将各个tagid的数据存储在新的数据框中。
答案 0 :(得分:1)
我认为最好通过将dict
对象转换为DataFrames
然后转换为DataFrameGroupBy
来创建所有tuple
dict
:
dfs = dict(tuple(data.groupby("TAGID")))
print (dfs[101])
DateAndTime TAGID TagValue UIB
1 2017-04-26 00:00:00 101 430.3 NaN
19 2017-04-26 00:01:00 101 429.7 NaN