Python 熊猫-从时间序列数据中以10毫秒为单位重新聚集柱_Python_Pandas_Time Series

Python 熊猫-从时间序列数据中以10毫秒为单位重新聚集柱

python pandas

Python 熊猫-从时间序列数据中以10毫秒为单位重新聚集柱,python,pandas,time-series,Python,Pandas,Time Series,对于如下所示的时间序列csv数据，需要每隔40ms生成Col2到Col13列的聚合平均值 Time,Col2,Col3,Col4,Col5,Col6,Col7,Col8,Col9,Col10,Col11,Col12,Col13 05:17:55.703,,,,,,21,,3, 89,891,11, 05:17:55.703,,,,,,21,,3, 217,891,12, 05:17:55.703,,,,,,21,,3, 217,891,13, 05:17:55.703,,,,,,2

对于如下所示的时间序列csv数据，需要每隔40ms生成Col2到Col13列的聚合平均值

Time,Col2,Col3,Col4,Col5,Col6,Col7,Col8,Col9,Col10,Col11,Col12,Col13
05:17:55.703,,,,,,21,,3,    89,891,11,
05:17:55.703,,,,,,21,,3,   217,891,12,
05:17:55.703,,,,,,21,,3,   217,891,13,
05:17:55.703,,,,,,21,,3,   217,891,15,
05:17:55.703,,,,,,21,,3,   217,891,16,
05:17:55.703,,,,,,21,,3,   217,891,17,
05:17:55.703,,,,,,21,,3,   217,891,18,
05:17:55.707,,,,,,18,,3,   185,892,0,
05:17:55.707,,,,,,21,,3,   185,892,1,
05:17:55.707,,,,,,17,,3,    73,892,5,
05:17:55.707,,,,,,17,,3,   185,892,6,
05:17:55.707,,,,,,21,,3,    73,892,7,
05:17:55.708,268,4,28,-67.60,13,,2,,,,,2
05:17:55.711,,,,,,18,,3,    57,892,10,
05:17:55.711,,,,,,21,,3,   201,892,11,
05:17:55.711,,,,,,21,,3,    25,892,12,
05:17:55.723,,,,,,21,,3,   217,893,11,
05:17:55.723,,,,,,21,,3,   217,893,15,
05:17:55.723,,,,,,21,,3,   217,893,16,
05:17:55.726,268,4,,-67.80,,,,,,,,
05:17:55.728,,,28,,12,31,2,3,   185,894,0,1
05:17:55.728,,,,,,31,,3,   185,894,1,
05:17:55.731,,,,,,31,,3,   217,894,10,
05:17:55.731,,,,,,20,,3,   217,894,11,
05:17:55.731,,,,,,20,,3,   217,894,12,
05:17:55.731,,,,,,20,,3,   217,894,13,
05:17:55.743,,,,,,20,,3,   217,895,11,
05:17:55.743,,,,,,20,,3,   217,895,15,
05:17:55.743,,,,,,20,,3,   217,895,16,
05:17:55.746,268,4,,-67.82,,,,,,,,
05:17:55.747,,,28,,13,20,2,3,   185,896,1,2
05:17:55.747,,,,,,20,,3,   185,896,2,
05:17:55.747,,,,,,30,,3,   217,896,5,
05:17:55.751,,,,,,18,,3,   217,896,10,
05:17:55.751,,,,,,21,,3,   217,896,11,
05:17:55.751,,,,,,21,,3,   217,896,12,
05:17:55.751,,,,,,21,,3,   217,896,13,
05:17:55.763,,,,,,31,,3,   217,897,11,
05:17:55.763,,,,,,30,,3,   217,897,15,
05:17:55.763,,,,,,20,,3,   217,897,16,
05:17:55.763,,,,,,20,,3,   217,897,17,
05:17:55.766,268,4,,-67.13,,,,,,,,
05:17:55.768,,,28,,12,20,2,3,   185,898,3,2
05:17:55.768,,,,,,16,,3,   217,898,6,
05:17:55.771,,,,,,18,,3,   217,898,10,
05:17:55.771,,,,,,20,,3,   217,898,11,
05:17:55.771,,,,,,20,,3,   217,898,12,
05:17:55.784,,,,,,20,,3,   217,899,11,
05:17:55.784,,,,,,20,,3,    41,899,12,
05:17:55.784,,,,,,20,,3,    25,899,13,
05:17:55.784,,,,,,20,,3,   217,899,15,
05:17:55.784,,,,,,20,,3,   217,899,16,
05:17:55.784,,,,,,20,,3,   217,899,17,
05:17:55.784,,,,,,20,,3,   217,899,18,
05:17:55.786,268,4,,-67.66,,,,,,,,
05:17:55.788,,,28,,13,18,2,3,   185,900,0,2
05:17:55.788,,,,,,20,,3,   185,900,1,
05:17:55.788,,,,,,20,,3,   185,900,2,
05:17:55.788,,,,,,16,,3,    41,900,5,
05:17:55.788,,,,,,17,,3,   185,900,6,
05:17:55.791,,,,,,20,,3,   105,900,7,
05:17:55.791,,,,,,20,,3,    89,900,8,
05:17:55.791,,,,,,18,,3,   217,900,10,
05:17:55.791,,,,,,20,,3,   217,900,11,
05:17:55.791,,,,,,20,,3,    25,900,12,
05:17:55.806,268,4,,-67.50,,,,,,,,
05:17:55.808,,,28,,12,31,2,3,   185,902,0,1
05:17:55.808,,,,,,31,,3,   185,902,1,
05:17:55.808,,,,,,20,,3,    25,902,2,
05:17:55.808,,,,,,20,,3,    25,902,3,
05:17:55.808,,,,,,16,,3,   217,902,5,
05:17:55.808,,,,,,16,,3,   217,902,6,
05:17:55.811,,,,,,20,,3,    89,902,7,
05:17:55.811,,,,,,20,,3,   121,902,8,
05:17:55.811,,,,,,18,,3,   217,902,10,
05:17:55.811,,,,,,20,,3,   217,902,11,
05:17:55.811,,,,,,20,,3,    73,902,12,
05:17:55.811,,,,,,20,,3,     9,902,15,
05:17:55.815,,,,,,20,,3,   217,902,16,
05:17:55.815,,,,,,20,,3,    25,902,17,
05:17:55.815,,,,,,20,,3,   217,902,18,
05:17:55.815,,,,,,18,,3,   217,903,0,
05:17:55.815,,,,,,21,,3,   217,903,1,
05:17:55.815,,,,,,19,,3,   105,903,2,
05:17:55.815,,,,,,21,,3,    41,903,3,
05:17:55.823,,,,,,21,,3,   217,903,11,
05:17:55.823,,,,,,21,,3,     9,903,12,
05:17:55.823,,,,,,21,,3,   105,903,13,
05:17:55.823,,,,,,21,,3,   217,903,15,
05:17:55.823,,,,,,21,,3,   217,903,16,
05:17:55.823,,,,,,21,,3,   121,903,17,
05:17:55.823,,,,,,21,,3,    89,903,18,
05:17:55.826,268,4,,-67.51,,,,,,,,
05:17:55.828,,,28,,12,18,2,3,   185,904,0,1
05:17:55.828,,,,,,21,,3,   185,904,1,
05:17:55.828,,,,,,21,,3,   185,904,2,
05:17:55.828,,,,,,21,,3,   185,904,3,
05:17:55.828,,,,,,17,,3,   217,904,5,
05:17:55.828,,,,,,17,,3,   217,904,6,
05:17:55.831,,,,,,21,,3,   217,904,7,
05:17:55.831,,,,,,20,,3,   169,904,11,
05:17:55.831,,,,,,20,,3,   217,904,12,
05:17:55.831,,,,,,20,,3,   217,904,13,
05:17:55.846,268,4,,-67.01,,,,,,,,
05:17:55.848,,,28,,13,19,2,3,    57,906,1,2
05:17:55.848,,,,,,19,,3,    41,906,2,
05:17:55.848,,,,,,19,,3,    73,906,3,
05:17:55.848,,,,,,16,,3,   217,906,5,
05:17:55.848,,,,,,16,,3,   217,906,6,
05:17:55.848,,,,,,19,,3,     9,906,7,
05:17:55.851,,,,,,20,,3,   121,906,11,
05:17:55.851,,,,,,20,,3,    57,906,12,
05:17:55.851,,,,,,20,,3,   105,906,13,
05:17:55.855,,,,,,20,,3,   217,906,15,
05:17:55.855,,,,,,20,,3,   217,906,16,
05:17:55.855,,,,,,20,,3,   105,906,17,
05:17:55.855,,,,,,17,,3,   185,907,0,
05:17:55.855,,,,,,20,,3,   217,907,1,
05:17:55.855,,,,,,20,,3,     9,907,2,

预期的输出是一个数据帧，每40毫秒聚合一次平均值（忽略空值），如下所示

+--------------+------+------+------+---------+------+------+------+------+--------+-------+-------+-------+
|     Time     | Col2 | Col3 | Col4 |  Col5   | Col6 | Col7 | Col8 | Col9 | Col10  | Col11 | Col12 | Col13 |
+--------------+------+------+------+---------+------+------+------+------+--------+-------+-------+-------+
| 05:17:55.740 |  268 |    4 |   28 |   -67.7 | 12.5 |   21 |    2 |    3 | 177.67 |   894 |    13 |   1.5 |
| 05:17:55.780 |  268 |    4 |   28 | -67.475 | 12.5 |   20 |    2 |    3 |  212.2 |   898 |    12 |     2 |
| 05:17:55.820 |  268 |    4 |   28 |  -67.58 | 12.5 |   20 |    2 |    3 | 144.56 |   903 |    11 |   1.5 |
| 05:17:55.860 |  268 |    4 |   28 |  -67.26 | 12.5 |   20 |    2 |    3 | 155.06 |   907 |     2 |   1.5 |
+--------------+------+------+------+---------+------+------+------+------+--------+-------+-------+-------+

如何将

pandas

中的记录分组以实现此目的？也可以对不同的列应用不同的聚合函数，如

Col5

的平均值、

Col2

的模式、

Col7

的中位数等？

以下是可能的用途：

或使用

groupby

，并在字典中使用聚合函数指定列：

d = {'Col2':'mean', 'Col3':'max', 'Col5':'median'}
df2 = df.groupby(pd.Grouper(freq='40L', key='Time')).agg(d)
print (df2)
                  Col2  Col3    Col5
Time                                
05:17:55.703000  268.0   4.0 -67.700
05:17:55.743000  268.0   4.0 -67.475
05:17:55.783000  268.0   4.0 -67.580
05:17:55.823000  268.0   4.0 -67.260
05:17:55.863000    NaN   NaN     NaN

d = {'Col2':'mean', 'Col3':'max', 'Col5':'median'}
df2 = df.groupby(pd.Grouper(freq='40L', key='Time')).agg(d)
print (df2)
                  Col2  Col3    Col5
Time                                
05:17:55.703000  268.0   4.0 -67.700
05:17:55.743000  268.0   4.0 -67.475
05:17:55.783000  268.0   4.0 -67.580
05:17:55.823000  268.0   4.0 -67.260
05:17:55.863000    NaN   NaN     NaN