pandas resample starting before the dataset first entry
Dear experienced community,
I can't find an elegant solution to my problem.
I have a subsample of my dataset which I want to resample weekly, but starting some weeks before the first entry in my data frame (so a few weeks with 0 counts)
A sample of the data:
In:
print(df_pec.head())
Out:
Count Image_Sequence_DateTime
18 1 2015-11-06 03:22:19
21 1 2015-11-11 01:48:51
22 1 2015-11-11 07:30:47
37 1 2015-11-25 09:42:23
48 1 2015-12-05 12:12:34
With the earliest image sequence at:
In:
df_pec.Image_Sequence_DateTime.min()
Out:
2015-09-30 15:16:38
I have another function that gives me the starting point of the first week and the last point of the last week ever measured in that experiment, which are:
In:
print(s_startend)
Out:
Start 2015-09-28
End 2017-12-25
dtype: datetime64[ns]
My problem is that I want to resample df_pec weekly, but starting on the very first second of the very first day of the very first week of the experimental deployment.(using s_startend as reference)
I try:
df_pec=df_pec.resample('1W', on='Image_Sequence_DateTime').sum()
print(df_pec.head(),'n',df_pec.tail())
Out:
Count
Image_Sequence_DateTime
2015-10-04 26.0
2015-10-11 92.0
2015-10-18 204.0
2015-10-25 193.0
2015-11-01 187.0
Count
Image_Sequence_DateTime
2017-11-19 20.0
2017-11-26 34.0
2017-12-03 16.0
2017-12-10 11.0
2017-12-17 3.0
This is pretty weird because it is even skipping the first days of data in df_pec.(starting 2015-09-30 15:16:38)
And even if it worked, I have no way of indicating the resampling to start and end in specified values (s_startend from my example), even if there are no records in the earliest and latest weeks in my subsample df_pec.
I thought about artificially adding two entries to df_pec with the real start and real end, but I think it is not so elegant and I don't want to be adding meaningless keys to my df.
Thank you very much for your wisdom!
python python-3.x pandas resampling
asked Nov 26 '18 at 12:04
Ferran FFerran F
304
add a comment |
Dear experienced community,
I can't find an elegant solution to my problem.
I have a subsample of my dataset which I want to resample weekly, but starting some weeks before the first entry in my data frame (so a few weeks with 0 counts)
A sample of the data:
In:
print(df_pec.head())
Out:
Count Image_Sequence_DateTime
18 1 2015-11-06 03:22:19
21 1 2015-11-11 01:48:51
22 1 2015-11-11 07:30:47
37 1 2015-11-25 09:42:23
48 1 2015-12-05 12:12:34
With the earliest image sequence at:
In:
df_pec.Image_Sequence_DateTime.min()
Out:
2015-09-30 15:16:38
I have another function that gives me the starting point of the first week and the last point of the last week ever measured in that experiment, which are:
In:
print(s_startend)
Out:
Start 2015-09-28
End 2017-12-25
dtype: datetime64[ns]
My problem is that I want to resample df_pec weekly, but starting on the very first second of the very first day of the very first week of the experimental deployment.(using s_startend as reference)
I try:
df_pec=df_pec.resample('1W', on='Image_Sequence_DateTime').sum()
print(df_pec.head(),'n',df_pec.tail())
Out:
Count
Image_Sequence_DateTime
2015-10-04 26.0
2015-10-11 92.0
2015-10-18 204.0
2015-10-25 193.0
2015-11-01 187.0
Count
Image_Sequence_DateTime
2017-11-19 20.0
2017-11-26 34.0
2017-12-03 16.0
2017-12-10 11.0
2017-12-17 3.0
This is pretty weird because it is even skipping the first days of data in df_pec.(starting 2015-09-30 15:16:38)
And even if it worked, I have no way of indicating the resampling to start and end in specified values (s_startend from my example), even if there are no records in the earliest and latest weeks in my subsample df_pec.
I thought about artificially adding two entries to df_pec with the real start and real end, but I think it is not so elegant and I don't want to be adding meaningless keys to my df.
Thank you very much for your wisdom!
python python-3.x pandas resampling
asked Nov 26 '18 at 12:04
Ferran FFerran F
304
add a comment |
Dear experienced community,
I can't find an elegant solution to my problem.
I have a subsample of my dataset which I want to resample weekly, but starting some weeks before the first entry in my data frame (so a few weeks with 0 counts)
A sample of the data:
In:
print(df_pec.head())
Out:
Count Image_Sequence_DateTime
18 1 2015-11-06 03:22:19
21 1 2015-11-11 01:48:51
22 1 2015-11-11 07:30:47
37 1 2015-11-25 09:42:23
48 1 2015-12-05 12:12:34
With the earliest image sequence at:
In:
df_pec.Image_Sequence_DateTime.min()
Out:
2015-09-30 15:16:38
I have another function that gives me the starting point of the first week and the last point of the last week ever measured in that experiment, which are:
In:
print(s_startend)
Out:
Start 2015-09-28
End 2017-12-25
dtype: datetime64[ns]
My problem is that I want to resample df_pec weekly, but starting on the very first second of the very first day of the very first week of the experimental deployment.(using s_startend as reference)
I try:
df_pec=df_pec.resample('1W', on='Image_Sequence_DateTime').sum()
print(df_pec.head(),'n',df_pec.tail())
Out:
Count
Image_Sequence_DateTime
2015-10-04 26.0
2015-10-11 92.0
2015-10-18 204.0
2015-10-25 193.0
2015-11-01 187.0
Count
Image_Sequence_DateTime
2017-11-19 20.0
2017-11-26 34.0
2017-12-03 16.0
2017-12-10 11.0
2017-12-17 3.0
This is pretty weird because it is even skipping the first days of data in df_pec.(starting 2015-09-30 15:16:38)
And even if it worked, I have no way of indicating the resampling to start and end in specified values (s_startend from my example), even if there are no records in the earliest and latest weeks in my subsample df_pec.
I thought about artificially adding two entries to df_pec with the real start and real end, but I think it is not so elegant and I don't want to be adding meaningless keys to my df.
Thank you very much for your wisdom!
python python-3.x pandas resampling
asked Nov 26 '18 at 12:04
Ferran FFerran F
304
Dear experienced community,
I can't find an elegant solution to my problem.
I have a subsample of my dataset which I want to resample weekly, but starting some weeks before the first entry in my data frame (so a few weeks with 0 counts)
A sample of the data:
In:
print(df_pec.head())
Out:
Count Image_Sequence_DateTime
18 1 2015-11-06 03:22:19
21 1 2015-11-11 01:48:51
22 1 2015-11-11 07:30:47
37 1 2015-11-25 09:42:23
48 1 2015-12-05 12:12:34
With the earliest image sequence at:
In:
df_pec.Image_Sequence_DateTime.min()
Out:
2015-09-30 15:16:38
I have another function that gives me the starting point of the first week and the last point of the last week ever measured in that experiment, which are:
In:
print(s_startend)
Out:
Start 2015-09-28
End 2017-12-25
dtype: datetime64[ns]
My problem is that I want to resample df_pec weekly, but starting on the very first second of the very first day of the very first week of the experimental deployment.(using s_startend as reference)
I try:
df_pec=df_pec.resample('1W', on='Image_Sequence_DateTime').sum()
print(df_pec.head(),'n',df_pec.tail())
Out:
Count
Image_Sequence_DateTime
2015-10-04 26.0
2015-10-11 92.0
2015-10-18 204.0
2015-10-25 193.0
2015-11-01 187.0
Count
Image_Sequence_DateTime
2017-11-19 20.0
2017-11-26 34.0
2017-12-03 16.0
2017-12-10 11.0
2017-12-17 3.0
This is pretty weird because it is even skipping the first days of data in df_pec.(starting 2015-09-30 15:16:38)
And even if it worked, I have no way of indicating the resampling to start and end in specified values (s_startend from my example), even if there are no records in the earliest and latest weeks in my subsample df_pec.
I thought about artificially adding two entries to df_pec with the real start and real end, but I think it is not so elegant and I don't want to be adding meaningless keys to my df.
Thank you very much for your wisdom!
python python-3.x pandas resampling
python python-3.x pandas resampling
asked Nov 26 '18 at 12:04
Ferran FFerran F
304
asked Nov 26 '18 at 12:04
Ferran FFerran F
304
edited Dec 3 '18 at 10:50
Ferran F
asked Nov 26 '18 at 12:04
Ferran FFerran F
304
asked Nov 26 '18 at 12:04
Ferran FFerran F
304
asked Nov 26 '18 at 12:04
Ferran FFerran F
304
304
add a comment |
add a comment |
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