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
Related
I have a Dataframe like the following:
df = pd.DataFrame()
df['datetime'] = pd.date_range(start='2023-1-2', end='2023-1-29', freq='15min')
df['week'] = df['datetime'].apply(lambda x: int(x.isocalendar()[1]))
df['day_of_week'] = df['datetime'].dt.weekday
df['hour'] = df['datetime'].dt.hour
df['minutes'] = pd.DatetimeIndex(df['datetime']).minute
df['value'] = range(len(df))
df.set_index('datetime',inplace=True)
df = week day_of_week hour minutes value
datetime
2023-01-02 00:00:00 1 0 0 0 0
2023-01-02 00:15:00 1 0 0 15 1
2023-01-02 00:30:00 1 0 0 30 2
2023-01-02 00:45:00 1 0 0 45 3
2023-01-02 01:00:00 1 0 1 0 4
... ... ... ... ... ...
2023-01-08 23:00:00 1 6 23 0 668
2023-01-08 23:15:00 1 6 23 15 669
2023-01-08 23:30:00 1 6 23 30 670
2023-01-08 23:45:00 1 6 23 45 671
2023-01-09 00:00:00 2 0 0 0 672
And I want to calculate the average of the column "value" for the same hour/minute/day, every two consecutive weeks.
What I would like to get is the following:
df=
value
day_of_week hour minutes datetime
0 0 0 2023-01-02 00:00:00 NaN
2023-01-09 00:00:00 NaN
2023-01-16 00:00:00 336
2023-01-23 00:00:00 1008
15 2023-01-02 00:15:00 NaN
2023-01-09 00:15:00 NaN
2023-01-16 00:15:00 337
2023-01-23 00:15:00 1009
So the first two weeks should have NaN values and week-3 should be the average of week-1 and week-2 and then week-4 the average of week-2 and week-3 and so on.
I tried the following code but it does not seem to do what I expect:
df = pd.DataFrame(df.groupby(['day_of_week','hour','minutes'])['value'].rolling(window='14D', min_periods=1).mean())
As what I am getting is:
value
day_of_week hour minutes. datetime
0 0 0 2023-01-02 00:00:00 0
2023-01-09 00:00:00 336
2023-01-16 00:00:00 1008
2023-01-23 00:00:00 1680
15 2023-01-02 00:15:00 1
2023-01-09 00:15:00 337
2023-01-16 00:15:00 1009
2023-01-23 00:15:00 1681
I think you want to shift within each group. Then you need another groupby:
(df.groupby(['day_of_week','hour','minutes'])['value']
.rolling(window='14D', min_periods=2).mean() # `min_periods` is different
.groupby(['day_of_week','hour','minutes']).shift() # shift within each group
.to_frame()
)
Output:
value
day_of_week hour minutes datetime
0 0 0 2023-01-02 00:00:00 NaN
2023-01-09 00:00:00 NaN
2023-01-16 00:00:00 336.0
2023-01-23 00:00:00 1008.0
15 2023-01-02 00:15:00 NaN
... ...
6 23 30 2023-01-15 23:30:00 NaN
2023-01-22 23:30:00 1006.0
45 2023-01-08 23:45:00 NaN
2023-01-15 23:45:00 NaN
2023-01-22 23:45:00 1007.0
My data has the following structure:
np.random.seed(25)
tdf = pd.DataFrame({'person_id' :[1,1,1,1,
2,2,
3,3,3,3,3,
4,4,4,
5,5,5,5,5,5,5,
6,
7,7,
8,8,8,8,8,8,8,
9,9,
10,10
],
'Date': ['2021-01-02','2021-01-05','2021-01-07','2021-01-09',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11',
'2021-01-02','2021-01-05','2021-01-07',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11','2021-01-13','2021-01-15',
'2021-01-02',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11','2021-01-13','2021-01-15',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05'
],
'Quantity': np.floor(np.random.random(size=35)*100)
})
And I want to calculate moving average (2 periods) over Date. So, the final output looks like the following. For first MA, we are taking 2021-01-02 & 2021-01-05 across all observations & calculate the MA (50). Similarly for other dates. The output need not be in the structure I'm showing the report. I just need date & MA column in the final data.
Thanks!
IIUC, you can aggregate the similar dates first, getting the sum and count.
Then take the sum per rolling 2 dates (here it doesn't look like you want to take care of a defined period but rather raw successive values, so I am assuming here prior sorting).
Finally, perform the ratio of sum and count to get the mean:
g = tdf.groupby('Date')['Quantity']
out = g.sum().rolling(2).sum()/g.count().rolling(2).sum()
output:
Date
2021-01-02 NaN
2021-01-05 50.210526
2021-01-07 45.071429
2021-01-09 41.000000
2021-01-11 44.571429
2021-01-13 48.800000
2021-01-15 50.500000
Name: Quantity, dtype: float64
joining the original data:
g = tdf.groupby('Date')['Quantity']
s = g.sum().rolling(2).sum()/g.count().rolling(2).sum()
tdf.merge(s.rename('Quantity_MA(2)'), left_on='Date', right_index=True)
output:
person_id Date Quantity Quantity_MA(2)
0 1 2021-01-02 87.0 NaN
4 2 2021-01-02 41.0 NaN
6 3 2021-01-02 68.0 NaN
11 4 2021-01-02 11.0 NaN
14 5 2021-01-02 16.0 NaN
21 6 2021-01-02 51.0 NaN
22 7 2021-01-02 38.0 NaN
24 8 2021-01-02 51.0 NaN
31 9 2021-01-02 90.0 NaN
33 10 2021-01-02 45.0 NaN
1 1 2021-01-05 58.0 50.210526
5 2 2021-01-05 11.0 50.210526
7 3 2021-01-05 43.0 50.210526
12 4 2021-01-05 44.0 50.210526
15 5 2021-01-05 52.0 50.210526
23 7 2021-01-05 99.0 50.210526
25 8 2021-01-05 55.0 50.210526
32 9 2021-01-05 66.0 50.210526
34 10 2021-01-05 28.0 50.210526
2 1 2021-01-07 27.0 45.071429
8 3 2021-01-07 55.0 45.071429
13 4 2021-01-07 58.0 45.071429
16 5 2021-01-07 32.0 45.071429
26 8 2021-01-07 3.0 45.071429
3 1 2021-01-09 18.0 41.000000
9 3 2021-01-09 36.0 41.000000
17 5 2021-01-09 69.0 41.000000
27 8 2021-01-09 71.0 41.000000
10 3 2021-01-11 40.0 44.571429
18 5 2021-01-11 36.0 44.571429
28 8 2021-01-11 42.0 44.571429
19 5 2021-01-13 83.0 48.800000
29 8 2021-01-13 43.0 48.800000
20 5 2021-01-15 48.0 50.500000
30 8 2021-01-15 28.0 50.500000
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')
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
please find the input and output below. Input in the code and output in the image.
input:
time value index_no date block_out no_load
0 2018-07-16 00:30:00 1 2.0 2018-07-16
1 2018-07-16 01:00:00 -1 3.0 2018-07-16 3.0
2 2018-07-16 01:30:00 -1 4.0 2018-07-16 4.0
3 2018-07-16 02:00:00 -1 5.0 2018-07-16 5.0
4 2018-07-16 02:30:00 1 6.0 2018-07-16
5 2018-07-16 03:00:00 1 7.0 2018-07-16
6 2018-07-16 03:30:00 0 8.0 2018-07-16 8.0
7 2018-07-16 04:00:00 1 9.0 2018-07-16
8 2018-07-16 04:30:00 -1 10.0 2018-07-16 10.0
9 2018-07-16 05:00:00 2 11.0 2018-07-16
10 2018-07-16 05:30:00 3 12.0 2018-07-16
11 2018-07-16 06:00:00 2 13.0 2018-07-16
12 2018-07-16 06:30:00 2 14.0 2018-07-16
13 2018-07-16 07:00:00 -1 15.0 2018-07-16 15.0
14 2018-07-16 07:30:00 1 16.0 2018-07-16
15 2018-07-16 08:00:00 -1 17.0 2018-07-16 17.0
16 2018-07-16 08:30:00 2 18.0 2018-07-16
17 2018-07-16 09:00:00 2 19.0 2018-07-16
18 2018-07-16 09:30:00 3 20.0 2018-07-16
19 2018-07-16 10:00:00 -1 21.0 2018-07-16 21.0
This is how you group by column in pandas
import pandas as pd
import numpy as np
# Generating random data
data = np.random.randint(0, 4, 15).reshape(5, 3)
# Wrapping it with pandas DataFrame
df = pd.DataFrame(data, columns=['A', 'B', 'C'])
# Group by column 'A'
groupbys = df.groupby('A')
In your specific problem you want to split your 'time' column to 'date', 'time' and then group by 'date' but it probably won't get you anywhere, because you didn't define what kind of aggregation you want to do.
If you don't know how to split your "time" column you can use a map function and convert it to datetime object as so:
def to_datetime(str):
return datetime.strptime('2018-07-16 00:30:00', "%Y-%m-%d %H:%M:%S")
and than create a new column like so:
df['date'] = list(map(to_datetime, df['time']))