index valuve
2017-01-25 01:00:00:00 1
2017-01-25 02:00:00:00 5
2017-01-25 03:00:00:00 7
2017-01-25 07:00:00:00 34
2017-01-25 20:00:00:00 45
2017-01-25 24:00:00:00 45
2017-01-26 1:00:00:00 31
This dataframe is a 24h record of each day, but it misses some record. How can i insert the missing row into the right place and fill 'nan' to the corresponding value?
Here is complicated 24H in datetimes, so necessary replace it to 23H and add one hour. Last use DataFrame.asfreq for add missing values for 24H DatetimeIndex:
mask = df.index.str.contains(' 24:')
idx = df.index.where(~mask, df.index.str.replace(' 24:', ' 23:'))
idx = pd.to_datetime(idx, format='%Y-%m-%d %H:%M:%S:%f')
df.index = idx.where(~mask, idx + pd.Timedelta(1, unit='H'))
df = df.asfreq('H')
print (df)
valuve
index
2017-01-25 01:00:00 1.0
2017-01-25 02:00:00 5.0
2017-01-25 03:00:00 7.0
2017-01-25 04:00:00 NaN
2017-01-25 05:00:00 NaN
2017-01-25 06:00:00 NaN
2017-01-25 07:00:00 34.0
2017-01-25 08:00:00 NaN
2017-01-25 09:00:00 NaN
2017-01-25 10:00:00 NaN
2017-01-25 11:00:00 NaN
2017-01-25 12:00:00 NaN
2017-01-25 13:00:00 NaN
2017-01-25 14:00:00 NaN
2017-01-25 15:00:00 NaN
2017-01-25 16:00:00 NaN
2017-01-25 17:00:00 NaN
2017-01-25 18:00:00 NaN
2017-01-25 19:00:00 NaN
2017-01-25 20:00:00 45.0
2017-01-25 21:00:00 NaN
2017-01-25 22:00:00 NaN
2017-01-25 23:00:00 NaN
2017-01-26 00:00:00 45.0
2017-01-26 01:00:00 31.0
Related
I have a dataframe like the following. I wanted to check the values for each 15minutes. But I see that there is a time at 09:05:51. How can I resample the dataframe for 15minutes?
hour_min value
06:30:00 0.0
06:45:00 0.0
07:00:00 0.0
07:15:00 0.0
07:30:00 102.754717
07:45:00 130.599057
08:00:00 154.117925
08:15:00 189.061321
08:30:00 214.924528
08:45:00 221.382075
09:00:00 190.839623
09:05:51 428.0
09:15:00 170.973995
09:30:00 0.0
09:45:00 0.0
10:00:00 174.448113
10:15:00 174.900943
10:30:00 182.976415
10:45:00 195.783019
11:00:00 200.337292
11:14:00 80.0
11:15:00 206.280952
11:30:00 218.87886
11:45:00 238.251781
12:00:00 115.5
12:15:00 85.5
12:30:00 130.0
12:45:00 141.0
13:00:00 267.353774
13:15:00 257.061321
13:21:00 8.0
13:27:19 80.0
13:30:00 258.761905
13:45:00 254.703088
13:53:52 278.0
14:00:00 254.790476
14:15:00 247.165094
14:30:00 250.061321
14:45:00 264.014151
15:00:00 132.0
15:15:00 108.0
15:30:00 158.5
15:45:00 457.0
16:00:00 273.745283
16:15:00 273.962264
16:30:00 279.089623
16:45:00 280.264151
17:00:00 296.061321
17:15:00 296.481132
17:30:00 282.957547
17:45:00 279.816038
I have tried this line, but i get a typeError.
res = s.resample('15T').sum()
I tried to make the index to date, but it does not work too.
I have a dataframe df which looks like this
restaurant
opentime
closetime
group
ABX
10:00:00
21:00:00
Gold
BWZ
13:00:00
14:00:00
Silver
GTW
10:00:00
11:00:00
Gold
I want to create a time series dataframe df2 based on the start and end date of my choice which shows the restaurants open by group and indexed by all the hours. In this case, I have taken a start date of May 17th 2021 and an end date of May 18th 2021. The final dataframe should look like this
Date
Gold
Silver
2021-05-17 9:00:00
0
0
2021-05-17 10:00:00
2
0
2021-05-17 11:00:00
1
0
2021-05-17 12:00:00
1
0
2021-05-17 13:00:00
1
1
2021-05-17 14:00:00
1
1
2021-05-17 15:00:00
1
0
......................
......
........
......................
......
........
2021-05-18 23:00:00
0
0
If the Date part is too difficult to recreate, then just time would also help in such a way it looks like this
Time
Gold
Silver
9:00:00
0
0
10:00:00
2
0
11:00:00
1
0
12:00:00
1
0
13:00:00
1
1
14:00:00
1
1
15:00:00
1
0
......................
......
........
......................
......
........
23:00:00
0
0
Any help will be appreciated.
First part: Time
Create a list that contains all hours between opentime and closetime then explode the list into rows and group by (time, group) and count values for each group.
Second part: Date
Create a datetime index that contains all hours between start_date and end_date. Transform as a series and set time as index.
Last part: Merge Date and Time
Merge dfd (date dataframe) and dft (time dataframe) together to get the groups for each datetime.
start_date = "2021-05-17"
end_date = "2021-05-18"
# compute hours between opentime and closetime
df["time"] = df.apply(lambda x: pd.date_range(x["opentime"],
x["closetime"],
freq="1H").time, axis="columns")
# value count by time and group
dft = df.explode("time").value_counts(["time", "group"]).unstack("group")
# create datetime index between start_date and end_date
dti = pd.date_range(start=pd.to_datetime(start_date),
end=pd.to_datetime(end_date) + pd.DateOffset(days=1),
closed="left", freq="1H", name="datetime")
dfd = dti.to_series(index=dti.time)
# merge date and time dataframes
out = pd.merge(dfd, dft, left_index=True, right_index=True, how="left") \
.set_index("datetime").sort_index().fillna(0)
>>> out
Gold Silver
datetime
2021-05-17 00:00:00 0.0 0.0
2021-05-17 01:00:00 0.0 0.0
2021-05-17 02:00:00 0.0 0.0
2021-05-17 03:00:00 0.0 0.0
2021-05-17 04:00:00 0.0 0.0
2021-05-17 05:00:00 0.0 0.0
2021-05-17 06:00:00 0.0 0.0
2021-05-17 07:00:00 0.0 0.0
2021-05-17 08:00:00 0.0 0.0
2021-05-17 09:00:00 0.0 0.0
2021-05-17 10:00:00 2.0 0.0
2021-05-17 11:00:00 2.0 0.0
2021-05-17 12:00:00 1.0 0.0
2021-05-17 13:00:00 1.0 1.0
2021-05-17 14:00:00 1.0 1.0
2021-05-17 15:00:00 1.0 0.0
2021-05-17 16:00:00 1.0 0.0
2021-05-17 17:00:00 1.0 0.0
2021-05-17 18:00:00 1.0 0.0
2021-05-17 19:00:00 1.0 0.0
2021-05-17 20:00:00 1.0 0.0
2021-05-17 21:00:00 1.0 0.0
2021-05-17 22:00:00 0.0 0.0
2021-05-17 23:00:00 0.0 0.0
2021-05-18 00:00:00 0.0 0.0
2021-05-18 01:00:00 0.0 0.0
2021-05-18 02:00:00 0.0 0.0
2021-05-18 03:00:00 0.0 0.0
2021-05-18 04:00:00 0.0 0.0
2021-05-18 05:00:00 0.0 0.0
2021-05-18 06:00:00 0.0 0.0
2021-05-18 07:00:00 0.0 0.0
2021-05-18 08:00:00 0.0 0.0
2021-05-18 09:00:00 0.0 0.0
2021-05-18 10:00:00 2.0 0.0
2021-05-18 11:00:00 2.0 0.0
2021-05-18 12:00:00 1.0 0.0
2021-05-18 13:00:00 1.0 1.0
2021-05-18 14:00:00 1.0 1.0
2021-05-18 15:00:00 1.0 0.0
2021-05-18 16:00:00 1.0 0.0
2021-05-18 17:00:00 1.0 0.0
2021-05-18 18:00:00 1.0 0.0
2021-05-18 19:00:00 1.0 0.0
2021-05-18 20:00:00 1.0 0.0
2021-05-18 21:00:00 1.0 0.0
2021-05-18 22:00:00 0.0 0.0
>>> dfd.index
Index([00:00:00, 01:00:00, 02:00:00, 03:00:00, 04:00:00, 05:00:00, 06:00:00,
07:00:00, 08:00:00, 09:00:00, 10:00:00, 11:00:00, 12:00:00, 13:00:00,
14:00:00, 15:00:00, 16:00:00, 17:00:00, 18:00:00, 19:00:00, 20:00:00,
21:00:00, 22:00:00, 23:00:00, 00:00:00, 01:00:00, 02:00:00, 03:00:00,
04:00:00, 05:00:00, 06:00:00, 07:00:00, 08:00:00, 09:00:00, 10:00:00,
11:00:00, 12:00:00, 13:00:00, 14:00:00, 15:00:00, 16:00:00, 17:00:00,
18:00:00, 19:00:00, 20:00:00, 21:00:00, 22:00:00, 23:00:00],
dtype='object')
>>> dft.index
Index([10:00:00, 11:00:00, 12:00:00, 13:00:00, 14:00:00, 15:00:00, 16:00:00,
17:00:00, 18:00:00, 19:00:00, 20:00:00, 21:00:00],
dtype='object', name='time')
I have two data frames that stores different types of medical information of patients. The common elements of both the data frames are the encounter ID (hadm_id), the time the information was recorded ((n|c)e_charttime).
One data frame (df_str) contains structured information such as vital signs and lab test values and values derived from these (such as change statistics over 24 hours). The other data frame (df_notes) contains a column with a clinical note recorded at a specified time for an encounter. Both these data frames contain multiple encounters, but the common element is the encounter ID (hadm_id).
Here are examples of the data frames for ONE encounter ID (hadm_id) with a subset of variables:
df_str
hadm_id ce_charttime hr resp magnesium hr_24hr_mean
0 196673 2108-03-05 15:34:00 95.0 12.0 NaN 95.000000
1 196673 2108-03-05 16:00:00 85.0 11.0 NaN 90.000000
2 196673 2108-03-05 16:16:00 85.0 11.0 1.8 88.333333
3 196673 2108-03-05 17:00:00 109.0 12.0 1.8 93.500000
4 196673 2108-03-05 18:00:00 97.0 12.0 1.8 94.200000
5 196673 2108-03-05 19:00:00 99.0 16.0 1.8 95.000000
6 196673 2108-03-05 20:00:00 98.0 13.0 1.8 95.428571
7 196673 2108-03-05 21:00:00 97.0 14.0 1.8 95.625000
8 196673 2108-03-05 22:00:00 101.0 12.0 1.8 96.222222
9 196673 2108-03-05 23:00:00 97.0 13.0 1.8 96.300000
10 196673 2108-03-06 00:00:00 93.0 13.0 1.8 96.000000
11 196673 2108-03-06 01:00:00 89.0 12.0 1.8 95.416667
12 196673 2108-03-06 02:00:00 88.0 10.0 1.8 94.846154
13 196673 2108-03-06 03:00:00 87.0 12.0 1.8 94.285714
14 196673 2108-03-06 04:00:00 97.0 19.0 1.8 94.466667
15 196673 2108-03-06 05:00:00 95.0 11.0 1.8 94.500000
16 196673 2108-03-06 05:43:00 95.0 11.0 2.0 94.529412
17 196673 2108-03-06 06:00:00 103.0 17.0 2.0 95.000000
18 196673 2108-03-06 07:00:00 101.0 12.0 2.0 95.315789
19 196673 2108-03-06 08:00:00 103.0 20.0 2.0 95.700000
20 196673 2108-03-06 09:00:00 84.0 11.0 2.0 95.142857
21 196673 2108-03-06 10:00:00 89.0 11.0 2.0 94.863636
22 196673 2108-03-06 11:00:00 91.0 14.0 2.0 94.695652
23 196673 2108-03-06 12:00:00 85.0 10.0 2.0 94.291667
24 196673 2108-03-06 13:00:00 98.0 14.0 2.0 94.440000
25 196673 2108-03-06 14:00:00 100.0 18.0 2.0 94.653846
26 196673 2108-03-06 15:00:00 95.0 12.0 2.0 94.666667
27 196673 2108-03-06 16:00:00 96.0 20.0 2.0 95.076923
28 196673 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
df_notes
hadm_id ne_charttime note
0 196673 2108-03-05 16:54:00 Nursing\nNursing Progress Note\nPt is a 43 yo ...
1 196673 2108-03-05 17:54:00 Physician \nPhysician Resident Admission Note\...
2 196673 2108-03-05 18:09:00 Physician \nPhysician Resident Admission Note\...
3 196673 2108-03-06 06:11:00 Nursing\nNursing Progress Note\nPain control (...
4 196673 2108-03-06 08:06:00 Physician \nPhysician Resident Progress Note\n...
5 196673 2108-03-06 12:40:00 Nursing\nNursing Progress Note\nChief Complain...
6 196673 2108-03-06 13:01:00 Nursing\nNursing Progress Note\nPain control (...
7 196673 2108-03-06 17:09:00 Nursing\nNursing Transfer Note\nChief Complain...
8 196673 2108-03-06 17:12:00 Nursing\nNursing Transfer Note\nPain control (...
9 196673 2108-03-07 15:25:00 Radiology\nCHEST (PA & LAT)\n[**2108-3-7**] 3:...
10 196673 2108-03-07 18:34:00 Radiology\nCTA CHEST W&W/O C&RECONS, NON-CORON...
11 196673 2108-03-09 09:10:00 Radiology\nABDOMEN (SUPINE & ERECT)\n[**2108-3...
12 196673 2108-03-09 12:22:00 Radiology\nCT ABDOMEN W/CONTRAST\n[**2108-3-9*...
13 196673 2108-03-10 05:26:00 Radiology\nABDOMEN (SUPINE & ERECT)\n[**2108-3...
14 196673 2108-03-10 05:27:00 Radiology\nCHEST (PA & LAT)\n[**2108-3-10**] 5...
What I want to do is to combine both the data frames based on the time when that information was recorded. More specifically, for each row in df_notes, I want a corresponding row from df_str with ce_charttime <= ne_charttime.
As an example, the first row in df_notes has ne_charttime = 2108-03-05 16:54:00. There are three rows in df_str with record times less than this time: ce_charttime = 2108-03-05 15:34:00, ce_charttime = 2108-03-05 16:00:00, ce_charttime = 2108-03-05 16:16:00. The most recent of these is the row with ce_charttime = 2108-03-05 16:16:00. So in my resulting data frame, for ne_charttime = 2108-03-05 16:54:00, I will have hr = 85.0, resp = 11.0, magnesium = 1.8, hr_24hr_mean = 88.33.
Essentially, in this example the resulting data frame will look like this:
hadm_id ne_charttime note hr resp magnesium hr_24hr_mean
0 196673 2108-03-05 16:54:00 Nursing\nNursing Progress Note\nPt is a 43 yo ... 85.0 11.0 1.8 88.333333
1 196673 2108-03-05 17:54:00 Physician \nPhysician Resident Admission Note\... 109.0 12.0 1.8 93.500000
2 196673 2108-03-05 18:09:00 Physician \nPhysician Resident Admission Note\... 97.0 12.0 1.8 94.200000
3 196673 2108-03-06 06:11:00 Nursing\nNursing Progress Note\nPain control (... 103.0 17.0 2.0 95.000000
4 196673 2108-03-06 08:06:00 Physician \nPhysician Resident Progress Note\n... 103.0 20.0 2.0 95.700000
5 196673 2108-03-06 12:40:00 Nursing\nNursing Progress Note\nChief Complain... 85.0 10.0 2.0 94.291667
6 196673 2108-03-06 13:01:00 Nursing\nNursing Progress Note\nPain control (... 98.0 14.0 2.0 94.440000
7 196673 2108-03-06 17:09:00 Nursing\nNursing Transfer Note\nChief Complain... 106.0 21.0 2.0 95.360000
8 196673 2108-03-06 17:12:00 Nursing\nNursing Transfer Note\nPain control (... NaN NaN NaN NaN
9 196673 2108-03-07 15:25:00 Radiology\nCHEST (PA & LAT)\n[**2108-3-7**] 3:... NaN NaN NaN NaN
10 196673 2108-03-07 18:34:00 Radiology\nCTA CHEST W&W/O C&RECONS, NON-CORON... NaN NaN NaN NaN
11 196673 2108-03-09 09:10:00 Radiology\nABDOMEN (SUPINE & ERECT)\n[**2108-3... NaN NaN NaN NaN
12 196673 2108-03-09 12:22:00 Radiology\nCT ABDOMEN W/CONTRAST\n[**2108-3-9*... NaN NaN NaN NaN
13 196673 2108-03-10 05:26:00 Radiology\nABDOMEN (SUPINE & ERECT)\n[**2108-3... NaN NaN NaN NaN
14 196673 2108-03-10 05:27:00 Radiology\nCHEST (PA & LAT)\n[**2108-3-10**] 5... NaN NaN NaN NaN
The resulting data frame will be of the same length as df_notes. I have been able to come with a very inefficient piece of code using for loops and explicit indexing to get this result:
cols = list(df_str.columns[2:])
final_df = df_notes.copy()
for col in cols:
final_df[col] = np.nan
idx = 0
for i, note_row in final_df.iterrows():
ne = note_row['ne_charttime']
for j, str_row in df_str.iterrows():
ce = str_row['ce_charttime']
if ne < ce:
idx += 1
for col in cols:
final_df.iloc[i, final_df.columns.get_loc(col)] = df_str.iloc[j-1][col]
break
for col in cols:
final_df.iloc[idx, final_df.columns.get_loc(col)] = df_str.iloc[-1][col]
This piece of code is bad because it is very inefficient and while it may work for this example, in my example dataset, I have over 30 different columns of structured variables, and over 10,000 encounters.
EDIT-2:
#Stef has provided an excellent answer which seems to work and replace my elaborate loopy code with a single line (amazing). However, while that works for this particular example, I am running into problems when I apply it to a bigger subset which includes multiple encounters. For example, consider the following example:
df_str.shape, df_notes.shape
((217, 386), (35, 4))
df_notes[['hadm_id', 'ne_charttime']]
hadm_id ne_charttime
0 100104 2201-06-21 20:00:00
1 100104 2201-06-21 22:51:00
2 100104 2201-06-22 05:00:00
3 100104 2201-06-23 04:33:00
4 100104 2201-06-23 12:59:00
5 100104 2201-06-24 05:15:00
6 100372 2115-12-20 02:29:00
7 100372 2115-12-21 10:15:00
8 100372 2115-12-22 13:05:00
9 100372 2115-12-25 17:16:00
10 100372 2115-12-30 10:58:00
11 100372 2115-12-30 13:07:00
12 100372 2115-12-30 14:16:00
13 100372 2115-12-30 22:34:00
14 100372 2116-01-03 09:10:00
15 100372 2116-01-07 11:08:00
16 100975 2126-03-02 06:06:00
17 100975 2126-03-02 17:44:00
18 100975 2126-03-03 05:36:00
19 100975 2126-03-03 18:27:00
20 100975 2126-03-04 05:29:00
21 100975 2126-03-04 10:48:00
22 100975 2126-03-04 16:42:00
23 100975 2126-03-05 22:12:00
24 100975 2126-03-05 23:01:00
25 100975 2126-03-06 11:02:00
26 100975 2126-03-06 13:38:00
27 100975 2126-03-08 13:39:00
28 100975 2126-03-11 10:41:00
29 101511 2199-04-30 09:29:00
30 101511 2199-04-30 09:53:00
31 101511 2199-04-30 18:06:00
32 101511 2199-05-01 08:28:00
33 111073 2195-05-01 01:56:00
34 111073 2195-05-01 21:49:00
This example has 5 encounters. The dataframe is sorted by hadm_id and within each hadm_id, ne_charttime is sorted. However, the column ne_charttime by itself is NOT sorted as seen from row 0 ce_charttime=2201-06-21 20:00:00 and row 6 ne_charttime=2115-12-20 02:29:00. When I try to do a merge_asof, I get the following error:
ValueError: left keys must be sorted. Is this because of the fact that ne_charttime column is not sorted? If so, how do I rectify this while maintaining the integrity of the encounter ID group?
EDIT-1:
I was able to loop over the encounters as well:
cols = list(dev_str.columns[1:]) # get the cols to merge (everything except hadm_id)
final_dfs = []
grouped = dev_notes.groupby('hadm_id') # get groups of encounter ids
for name, group in grouped:
final_df = group.copy().reset_index(drop=True) # make a copy of notes for that encounter
for col in cols:
final_df[col] = np.nan # set the values to nan
idx = 0 # index to track the final row in the given encounter
for i, note_row in final_df.iterrows():
ne = note_row['ne_charttime']
sub = dev_str.loc[(dev_str['hadm_id'] == name)].reset_index(drop=True) # get the df corresponding to the ecounter
for j, str_row in sub.iterrows():
ce = str_row['ce_charttime']
if ne < ce: # if the variable charttime < note charttime
idx += 1
# grab the previous values for the variables and break
for col in cols:
final_df.iloc[i, final_df.columns.get_loc(col)] = sub.iloc[j-1][col]
break
# get the last value in the df for the variables
for col in cols:
final_df.iloc[idx, final_df.columns.get_loc(col)] = sub.iloc[-1][col]
final_dfs.append(final_df) # append the df to the list
# cat the list to get final df and reset index
final_df = pd.concat(final_dfs)
final_df.reset_index(inplace=True, drop=True)
Again this very inefficient but does the job.
Is there a better way to achieve what I want? Any help is appreciated.
Thanks.
You can use merge_asof (both dataframes must be sorted by the columns you're merging them on, which is already the case in your example):
final_df = pd.merge_asof(df_notes, df_str, left_on='ne_charttime', right_on='ce_charttime', by='hadm_id')
Result:
hadm_id ne_charttime note ce_charttime hr resp magnesium hr_24hr_mean
0 196673 2108-03-05 16:54:00 Nursing\nNursing Progress Note\nPt is a 43 yo ... 2108-03-05 16:16:00 85.0 11.0 1.8 88.333333
1 196673 2108-03-05 17:54:00 Physician \nPhysician Resident Admission Note\... 2108-03-05 17:00:00 109.0 12.0 1.8 93.500000
2 196673 2108-03-05 18:09:00 Physician \nPhysician Resident Admission Note\... 2108-03-05 18:00:00 97.0 12.0 1.8 94.200000
3 196673 2108-03-06 06:11:00 Nursing\nNursing Progress Note\nPain control (... 2108-03-06 06:00:00 103.0 17.0 2.0 95.000000
4 196673 2108-03-06 08:06:00 Physician \nPhysician Resident Progress Note\n... 2108-03-06 08:00:00 103.0 20.0 2.0 95.700000
5 196673 2108-03-06 12:40:00 Nursing\nNursing Progress Note\nChief Complain... 2108-03-06 12:00:00 85.0 10.0 2.0 94.291667
6 196673 2108-03-06 13:01:00 Nursing\nNursing Progress Note\nPain control (... 2108-03-06 13:00:00 98.0 14.0 2.0 94.440000
7 196673 2108-03-06 17:09:00 Nursing\nNursing Transfer Note\nChief Complain... 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
8 196673 2108-03-06 17:12:00 Nursing\nNursing Transfer Note\nPain control (... 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
9 196673 2108-03-07 15:25:00 Radiology\nCHEST (PA & LAT)\n[**2108-3-7**] 3:... 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
10 196673 2108-03-07 18:34:00 Radiology\nCTA CHEST W&W/O C&RECONS, NON-CORON... 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
11 196673 2108-03-09 09:10:00 Radiology\nABDOMEN (SUPINE & ERECT)\n[**2108-3... 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
12 196673 2108-03-09 12:22:00 Radiology\nCT ABDOMEN W/CONTRAST\n[**2108-3-9*... 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
13 196673 2108-03-10 05:26:00 Radiology\nABDOMEN (SUPINE & ERECT)\n[**2108-3... 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
14 196673 2108-03-10 05:27:00 Radiology\nCHEST (PA & LAT)\n[**2108-3-10**] 5... 2108-03-06 17:00:00 106.0 21.0 2.0 95.360000
PS: This gives you the correct result for all rows. There's a logical flaw in your code: you look for the first time ce_charttime > ne_charttime and then take the previous row. If there's no such time, you'll never have the chance to take the previous row, hence the NaNs in your result table starting from row 8.
PPS: This includes ce_charttime in the final dataframe. You can replace it by a column of how old the information is and/or remove it:
final_df['info_age'] = final_df.ne_charttime - final_df.ce_charttime
final_df = final_df.drop(columns='ce_charttime')
UPDATE for EDIT-2: As I wrote at the very beginning, repeated in the comments and as the docs clearly states: both ce_charttime and ne_charttime must be sorted (hadm_id need not be sorted). If this condition is not met, you'll have to (temporarily) sort your dataframes as required. See the following example:
import pandas as pd, string
df_str = pd.DataFrame( {'hadm_id': pd.np.tile([111111, 222222],10), 'ce_charttime': pd.date_range('2019-10-01 00:30', periods=20, freq='30T'), 'hr': pd.np.random.randint(80,120,20)})
df_notes = pd.DataFrame( {'hadm_id': pd.np.tile([111111, 222222],3), 'ne_charttime': pd.date_range('2019-10-01 00:45', periods=6, freq='40T'), 'note': [''.join(pd.np.random.choice(list(string.ascii_letters), 10)) for _ in range(6)]}).sort_values('hadm_id')
final_df = pd.merge_asof(df_notes.sort_values('ne_charttime'), df_str, left_on='ne_charttime', right_on='ce_charttime', by='hadm_id').sort_values(['hadm_id', 'ne_charttime'])
print(df_str); print(df_notes); print(final_df)
Output:
hadm_id ce_charttime hr
0 111111 2019-10-01 00:30:00 118
1 222222 2019-10-01 01:00:00 93
2 111111 2019-10-01 01:30:00 92
3 222222 2019-10-01 02:00:00 86
4 111111 2019-10-01 02:30:00 88
5 222222 2019-10-01 03:00:00 86
6 111111 2019-10-01 03:30:00 106
7 222222 2019-10-01 04:00:00 91
8 111111 2019-10-01 04:30:00 109
9 222222 2019-10-01 05:00:00 95
10 111111 2019-10-01 05:30:00 113
11 222222 2019-10-01 06:00:00 92
12 111111 2019-10-01 06:30:00 104
13 222222 2019-10-01 07:00:00 83
14 111111 2019-10-01 07:30:00 114
15 222222 2019-10-01 08:00:00 98
16 111111 2019-10-01 08:30:00 110
17 222222 2019-10-01 09:00:00 89
18 111111 2019-10-01 09:30:00 98
19 222222 2019-10-01 10:00:00 109
hadm_id ne_charttime note
0 111111 2019-10-01 00:45:00 jOcRWVdPDF
2 111111 2019-10-01 02:05:00 mvScJNrwra
4 111111 2019-10-01 03:25:00 FBAFbJYflE
1 222222 2019-10-01 01:25:00 ilNuInOsYZ
3 222222 2019-10-01 02:45:00 ysyolaNmkV
5 222222 2019-10-01 04:05:00 wvowGGETaP
hadm_id ne_charttime note ce_charttime hr
0 111111 2019-10-01 00:45:00 jOcRWVdPDF 2019-10-01 00:30:00 118
2 111111 2019-10-01 02:05:00 mvScJNrwra 2019-10-01 01:30:00 92
4 111111 2019-10-01 03:25:00 FBAFbJYflE 2019-10-01 02:30:00 88
1 222222 2019-10-01 01:25:00 ilNuInOsYZ 2019-10-01 01:00:00 93
3 222222 2019-10-01 02:45:00 ysyolaNmkV 2019-10-01 02:00:00 86
5 222222 2019-10-01 04:05:00 wvowGGETaP 2019-10-01 04:00:00 91
You can do full merge and then filter with query:
df_notes.merge(df_str, on=hadm_id).query('ce_charttime <= ne_charttime')
I have a data which contains Id, DateTime and Value Column. Data are supposed to be collected in every 10 mins. However, there are some data which has gaps of over 10 mins ( for example gaps of over 20 mins, 1hr, 2 hr). Data are collected for one full month. I want to use re sampling and use linear interpolation on my Value Column so that every Value columns contain data for fixed interval of time ( let say for every 1 hr and (weekly based)).
This is my sample data
Id DateTime Value
20 2018-04-08 00:28:52 10
20 2018-04-08 00:38:34 11
20 2018-04-08 00:48:57 9
20 2018-04-08 01:18:22 7
............................
205 2018-04-08 01:08:28 11
205 2018-04-08 01:18:33 13
205 2018-04-08 01:27:22 8
205 2018-04-08 01:37:02 7
205 2018-04-08 01:56:44 6
205 2018-04-08 02:16:14 10
.....
2053 2018-04-08 02:06:03 11
2053 2018-04-08 02:17:10 12
2053 2018-04-08 02:26:33 8
2053 2018-04-08 02:36:53 9
2053 2018-04-08 03:26:33 13
Any suggestions ?
Thanks
I believe need:
print (df)
Id DateTime Value
0 20 2018-04-08 00:28:52 10
1 20 2018-04-08 00:38:34 11
2 20 2018-04-08 00:48:57 9
3 20 2018-04-08 01:18:22 7
4 205 2018-04-08 01:08:28 11
5 205 2018-04-08 01:18:33 13
6 205 2018-04-08 01:27:22 8
7 205 2018-04-08 01:37:02 7
8 205 2018-04-08 01:56:44 6
9 205 2018-04-08 02:16:14 10
10 2053 2018-04-08 10:06:03 11
11 2053 2018-04-08 10:17:10 12
12 2053 2018-04-08 10:26:33 8
13 2053 2018-04-08 10:36:53 9
14 2053 2018-04-08 10:26:33 13
df = df.set_index('DateTime')['Value'].resample('1H').mean().interpolate()
print (df)
DateTime
2018-04-08 00:00:00 10.000000
2018-04-08 01:00:00 8.666667
2018-04-08 02:00:00 10.000000
2018-04-08 03:00:00 10.075000
2018-04-08 04:00:00 10.150000
2018-04-08 05:00:00 10.225000
2018-04-08 06:00:00 10.300000
2018-04-08 07:00:00 10.375000
2018-04-08 08:00:00 10.450000
2018-04-08 09:00:00 10.525000
2018-04-08 10:00:00 10.600000
Freq: H, Name: Value, dtype: float64
EDIT:
If need resample per groups also add groupby with reindex for same DatetimeIndex per each unique Ids:
df = df.set_index('DateTime').groupby('Id')['Value'].resample('1H').mean()
mux = pd.MultiIndex.from_product([df.index.levels[0], pd.date_range(df.index.levels[1].min(), df.index.levels[1].max(), freq='h')])
df = df.reindex(mux)
df = df.groupby(level=0).apply(lambda x: x.interpolate())
print (df)
20 2018-04-08 00:00:00 10.0
2018-04-08 01:00:00 7.0
2018-04-08 02:00:00 7.0
2018-04-08 03:00:00 7.0
2018-04-08 04:00:00 7.0
2018-04-08 05:00:00 7.0
2018-04-08 06:00:00 7.0
2018-04-08 07:00:00 7.0
2018-04-08 08:00:00 7.0
2018-04-08 09:00:00 7.0
2018-04-08 10:00:00 7.0
205 2018-04-08 00:00:00 NaN
2018-04-08 01:00:00 9.0
2018-04-08 02:00:00 10.0
2018-04-08 03:00:00 10.0
2018-04-08 04:00:00 10.0
2018-04-08 05:00:00 10.0
2018-04-08 06:00:00 10.0
2018-04-08 07:00:00 10.0
2018-04-08 08:00:00 10.0
2018-04-08 09:00:00 10.0
2018-04-08 10:00:00 10.0
2053 2018-04-08 00:00:00 NaN
2018-04-08 01:00:00 NaN
2018-04-08 02:00:00 NaN
...
2018-04-08 07:00:00 NaN
2018-04-08 08:00:00 NaN
2018-04-08 09:00:00 NaN
2018-04-08 10:00:00 10.6
Name: Value, dtype: float64
Another solution if need interpolate each group separately:
df = (df.set_index('DateTime')
.groupby('Id')['Value']
.resample('1H')
.mean()
.groupby(level=0)
.apply(lambda x: x.interpolate()))
print (df)
Id DateTime
20 2018-04-08 00:00:00 10.0
2018-04-08 01:00:00 7.0
205 2018-04-08 01:00:00 9.0
2018-04-08 02:00:00 10.0
2053 2018-04-08 10:00:00 10.6
Name: Value, dtype: float64
i have below dataframe. date/time is multi-indexed indexes.
when i doing this code,
<code>
idx = pd.IndexSlice
print(df_per_wday_temp.loc[idx[:,datetime.time(4, 0, 0): datetime.time(7, 0, 0)]])"
but i got error 'MultiIndex Slicing requires the index to be fully lexsorted tuple len (2), lexsort depth (1)'. this may be error in
index slicing but i don't know why this happened. anybody can solve it ?
a b
date time
2018-01-26 19:00:00 25.08 -7.85
19:15:00 24.86 -7.81
19:30:00 24.67 -8.24
19:45:00 NaN -9.32
20:00:00 NaN -8.29
20:15:00 NaN -8.58
20:30:00 NaN -9.48
20:45:00 NaN -8.73
21:00:00 NaN -8.60
21:15:00 NaN -8.70
21:30:00 NaN -8.53
21:45:00 NaN -8.90
22:00:00 NaN -8.55
22:15:00 NaN -8.48
22:30:00 NaN -9.90
22:45:00 NaN -9.70
23:00:00 NaN -8.98
23:15:00 NaN -9.17
23:30:00 NaN -9.07
23:45:00 NaN -9.45
00:00:00 NaN -9.64
00:15:00 NaN -10.08
00:30:00 NaN -8.87
00:45:00 NaN -9.91
01:00:00 NaN -9.91
01:15:00 NaN -9.93
01:30:00 NaN -9.55
01:45:00 NaN -9.51
02:00:00 NaN -9.75
02:15:00 NaN -9.44
... ... ...
03:45:00 NaN -9.28
04:00:00 NaN -9.96
04:15:00 NaN -10.19
04:30:00 NaN -10.20
04:45:00 NaN -9.85
05:00:00 NaN -10.33
05:15:00 NaN -10.18
05:30:00 NaN -10.81
05:45:00 NaN -10.51
06:00:00 NaN -10.41
06:15:00 NaN -10.49
06:30:00 NaN -10.13
06:45:00 NaN -10.36
07:00:00 NaN -10.71
07:15:00 NaN -12.11
07:30:00 NaN -10.76
07:45:00 NaN -10.76
08:00:00 NaN -11.63
08:15:00 NaN -11.18
08:30:00 NaN -10.49
08:45:00 NaN -11.18
09:00:00 NaN -10.67
09:15:00 NaN -10.60
09:30:00 NaN -10.36
09:45:00 NaN -9.39
10:00:00 NaN -9.77
10:15:00 NaN -9.54
10:30:00 NaN -8.99
10:45:00 NaN -9.01
11:00:00 NaN -10.01
thanks in advance
If is not possible sorting index, is necessary create boolean mask and filter by boolean indexing:
from datetime import time
mask = df1.index.get_level_values(1).to_series().between(time(4, 0, 0), time(7, 0, 0)).values
df = df1[mask]
print (df)
a b
date time
2018-01-26 04:00:00 NaN -9.96
04:15:00 NaN -10.19
04:30:00 NaN -10.20
04:45:00 NaN -9.85
05:00:00 NaN -10.33
05:15:00 NaN -10.18
05:30:00 NaN -10.81