从2列创建分类数据-Python Pandas

Question

I have a problem with creating a dataframe which holds a time interval where a measurement of temperature is in. As for now the dataframe has its index as time and another column as the measurements and i would like to have the time converted to an interval of 12 hours and the measurement to be the mean of the values in that timelapse.

                         measurement
time
2016-11-04 08:49:25    17.730000
2016-11-04 10:23:52    18.059999
2016-11-04 11:02:09    18.370001
2016-11-04 12:04:20    18.090000
2016-11-04 14:26:43    18.320000

so instead of having each time related to the measurement i want the mean of the value of let's say 12 hours like this:

                                              measurement
time
2016-11-04 00:00:00 - 2016-11-04 12:00:00     17.730000
2016-11-04 12:00:00 - 2016-11-05 00:00:00     18.059999
2016-11-05 00:00:00 - 2016-11-05 12:00:00     18.370001
2016-11-05 12:00:00 - 2016-11-06 00:00:00     18.090000
2016-11-06 00:00:00 - 2016-11-06 12:00:00     18.320000

is there an easy way to do this with pandas?

Later i would like to convert the measurements into intervals as well so that the data becomes boolean like this:

                                              17.0-18.0   18.0-19.0  19.0-20
time
2016-11-04 00:00:00 - 2016-11-04 12:00:00         1           0         0
2016-11-04 12:00:00 - 2016-11-05 00:00:00         0           1         0
2016-11-05 00:00:00 - 2016-11-05 12:00:00         0           1         0
2016-11-05 12:00:00 - 2016-11-06 00:00:00         0           1         0
2016-11-06 00:00:00 - 2016-11-06 12:00:00         0           1         0

EDIT: I used a solution first posted by Coldspeed

df = pd.DataFrame({'timestamp':time.values, 'readings':readings.values})
df = df.groupby(pd.Grouper(key='timestamp', freq='12H'))['readings'].mean()
v = pd.cut(df, bins=[17,18,19,20,21,22,23,24,25,26,27,28], labels=['17-18','18-19','19-20','20-21','21-22','22-23','23-24','24-25','25-26','26-27','27-28'])

I know that the bins and labels could have been done but a for loop but this is just a quick fix. the groupby function which groups the value of 'timestamp' in the frequency of 12 hours and gets the readings mean value in the timelapse.

Then the cut function is used to categorize the means into their categories.

result:

                     17-18  18-19  19-20  20-21  21-22  22-23  23-24  24-25  \
timestamp
2016-11-04 00:00:00      0      1      0      0      0      0      0      0
2016-11-04 12:00:00      0      1      0      0      0      0      0      0
2016-11-05 00:00:00      0      0      0      0      0      0      0      0
2016-11-05 12:00:00      1      0      0      0      0      0      0      0
2016-11-06 00:00:00      1      0      0      0      0      0      0      0
2016-11-06 12:00:00      0      0      0      0      0      0      0      0
2016-11-07 00:00:00      0      1      0      0      0      0      0      0
2016-11-07 12:00:00      1      0      0      0      0      0      0      0
2016-11-08 00:00:00      0      0      0      0      0      0      0      0
2016-11-08 12:00:00      0      0      0      0      0      0      0      0
2016-11-09 00:00:00      1      0      0      0      0      0      0      0
2016-11-09 12:00:00      1      0      0      0      0      0      0      0
2016-11-10 00:00:00      0      1      0      0      0      0      0      0
2016-11-10 12:00:00      0      0      0      0      0      0      0      0
2016-11-11 00:00:00      0      0      0      0      0      0      0      0
2016-11-11 12:00:00      0      0      0      0      0      0      0      0
2016-11-12 00:00:00      0      0      0      0      0      0      0      0
2016-11-12 12:00:00      0      0      0      0      0      0      0      0
2016-11-13 00:00:00      0      0      0      0      0      0      0      0
2016-11-13 12:00:00      0      0      0      0      0      0      0      0
2016-11-14 00:00:00      0      0      0      0      0      0      0      0
2016-11-14 12:00:00      0      1      0      0      0      0      0      0
2016-11-15 00:00:00      0      0      0      1      0      0      0      0
2016-11-15 12:00:00      0      0      0      0      0      1      0      0
2016-11-16 00:00:00      0      0      0      0      0      0      1      0
2016-11-16 12:00:00      0      0      0      0      0      0      0      0
2016-11-17 00:00:00      0      0      0      0      0      0      0      0

Answer 1

Use pd.cut + pd.get_dummies :

v = pd.cut(df.measurement, bins=[17, 18, 19, 20], labels=['17-18', '18-19', '19-20'])
pd.get_dummies(v)

   17-18  18-19  19-20
0      1      0      0
1      0      1      0
2      0      1      0
3      0      1      0
4      0      1      0

Answer 2

IIUC you want to resample by 12 hour chunks, then create dummies.
pd.cut is a perfectly acceptable way to cut the resultant data into bins.
However, I use np.searchsorted to accomplish the task.

bins = np.array([17, 18, 19, 20])
labels = np.array(['<17', '17-18', '18-19', '19-20', '>20'])
resampled = df.resample('12H').measurement.mean()
pd.get_dummies(pd.Series(labels[bins.searchsorted(resampled.values)], resampled.index))

                     17-18  18-19  19-20  >20
2018-03-20 00:00:00      0      1      0    0
2018-03-20 12:00:00      1      0      0    0
2018-03-21 00:00:00      0      1      0    0
2018-03-21 12:00:00      0      0      0    1
2018-03-22 00:00:00      0      0      1    0
2018-03-22 12:00:00      0      0      0    1

---

Setup

np.random.seed(int(np.pi * 1E6))

tidx = pd.date_range(pd.Timestamp('now'), freq='3H', periods=20)
df = pd.DataFrame(dict(measurement=np.random.rand(len(tidx)) * 6 + 17), tidx)

df

                            measurement
2018-03-20 06:58:30.484383    17.960744
2018-03-20 09:58:30.484383    18.572100
2018-03-20 12:58:30.484383    17.646766
2018-03-20 15:58:30.484383    19.025463
2018-03-20 18:58:30.484383    17.521399
2018-03-20 21:58:30.484383    17.318663
2018-03-21 00:58:30.484383    19.388553
2018-03-21 03:58:30.484383    19.520969
2018-03-21 06:58:30.484383    19.060640
2018-03-21 09:58:30.484383    17.106034
2018-03-21 12:58:30.484383    22.887546
2018-03-21 15:58:30.484383    18.437271
2018-03-21 18:58:30.484383    18.426362
2018-03-21 21:58:30.484383    20.558928
2018-03-22 00:58:30.484383    22.555121
2018-03-22 03:58:30.484383    17.139489
2018-03-22 06:58:30.484383    17.209499
2018-03-22 09:58:30.484383    19.466367
2018-03-22 12:58:30.484383    21.765692
2018-03-22 15:58:30.484383    19.680785

Answer 3

You can use pd.cut() + pd.get_dummies() :

df["measurement"] = pd.cut(df["measurement"], bins=[17.0,18.0,19.0,20.0])
dummies = pd.get_dummies(df["measurement"])

Answer 4

对于第一个问题：您可以使用pandas.TimeGrouper每12小时（或任何其他频率）进行分组，然后取各组的平均值。

df.groupby([pd.TimeGrouper(freq='12H')]).mean()