带有多索引的熊猫数据帧重新采样时间序列索引

Question

I have a set of data, how can I resample its time stamp to 1 second interval, and fill in the data column (other than 'UUT') with 0.

                        UUT  Sent  Received Latency(ms)  Sum
DateTime                                                    
2018-01-25 15:03:05  uut-1     1         1         427    2
2018-01-25 15:03:05  uut-2     1         1         664    2
2018-01-25 15:03:17  uut-1     1         1         637    2
2018-01-25 15:03:17  uut-2     1         1        1229    2
2018-01-25 15:03:29  uut-1     1         1        1154    2
2018-01-25 15:03:29  uut-2     1         1        1148    2
2018-01-25 15:04:00  uut-1     1         1         279    2

Output something like this:

                        UUT  Sent  Received Latency(ms)  Sum
DateTime                                                    
2018-01-25 15:03:05  uut-1     1         1         427    2
2018-01-25 15:03:05  uut-2     1         1         664    2
2018-01-25 15:03:06  uut-1     0         0           0    0
2018-01-25 15:03:06  uut-2     0         0           0    0
2018-01-25 15:03:07  uut-1     0         0           0    0
2018-01-25 15:03:07  uut-2     0         0           0    0
2018-01-25 15:03:08  uut-1     0         0           0    0
2018-01-25 15:03:08  uut-2     0         0           0    0
....
2018-01-25 15:03:17  uut-1     1         1         637    2
2018-01-25 15:03:17  uut-2     1         1        1229    2
2018-01-25 15:03:18  uut-1     0         0           0    0
2018-01-25 15:03:18  uut-2     0         0           0    0
.....

The ultimate goal is to use groupby('UUT') to plot each UUT's time vs any other remaining columns (eg 'Sent', Received', 'Latency(ms)')

Answer 1

It's not neat but you could be able to do things you wanted with following code.

---

1. Reproduction

idx = ['2018-01-25 15:03:05', '2018-01-25 15:03:05', '2018-01-25 15:03:17', '2018-01-25 15:03:17','2018-01-25 15:03:29', '2018-01-25 15:03:29']
dt = pd.DatetimeIndex(idx)
arrays = [
  dt,
  ['uut1', 'uut2', 'uut1', 'uut2', 'uut1', 'uut2']
]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])

data = pd.DataFrame({
      'a' : range(1, 7),
      'b' : range(1, 7)},
      index=index)

2. Manipulation

data_manipulated = data.reset_index('second')
for second, df_gb in data_manipulated.groupby('second'):
    vars()['df_{}'.format(second)] = df_gb.resample('1s').first().fillna(0)

df_uut1['second'] = 'uut1'
df_uut2['second'] = 'uut2'

df_uut1['first'] = df_uut1.index.values
df_uut1.index = range(len(df_uut1))

df_uut2['first'] = df_uut2.index.values
df_uut2.index = range(len(df_uut2), len(df_uut2)*2)

result = df_uut1.append(df_uut2)
result.index = [result['first'], result['second']]
result = result[['a', 'b']].astype(int)
result.sort_index(ascending=True, inplace=True)

3. Result

---

Is this something you were trying to do? Again, code itself ins't that readable. I guess you can make it better on your own though.

Answer 2

I ended up using re sampling

data2 = data.reset_index(level=[1])
                    second  a  b
first                           
2018-01-25 15:03:05   uut1  1  1
2018-01-25 15:03:05   uut2  2  2
2018-01-25 15:03:17   uut1  3  3
2018-01-25 15:03:17   uut2  4  4
2018-01-25 15:03:29   uut1  5  5
2018-01-25 15:03:29   uut2  6  6

and then groupby

grouped = data2.groupby('second')
<pandas.core.groupby.DataFrameGroupBy object at 0x0000000005AB6E48>

# the groupby dataframe looks something like this:
grouped.get_group('uut1')
               second  a  b
first                           
2018-01-25 15:03:05   uut1  1  1
2018-01-25 15:03:17   uut1  3  3
2018-01-25 15:03:29   uut1  5  5

Now resample each group and fill up the upsample data with 0:

grouped_df = grouped.get_group(key).resample('1S').asfreq(0)

finally, replace all '0' entries in second with 'uut1' grouped_df['second'] = 'uut1'

The final dataframe looks like this:

grouped.get_group('uut1')
                    second  a  b
first                           
2018-01-25 15:03:05   uut1  1  1
2018-01-25 15:03:06   uut1  0  0
2018-01-25 15:03:07   uut1  0  0
2018-01-25 15:03:08   uut1  0  0
...
2018-01-25 15:03:27   uut1  0  0
2018-01-25 15:03:28   uut1  0  0
2018-01-25 15:03:29   uut1  5  5