Mean of value by days
1
I have the follow dataset
dVal eVal
0 2015-01-01 00:00:00.000 3.622833
1 2015-01-01 01:00:00.000 3.501333
2 2015-01-01 02:00:00.000 3.469167
3 2015-01-01 03:00:00.000 3.436333
4 2015-01-01 04:00:00.000 3.428000
5 2015-01-01 05:00:00.000 3.400667
6 2015-01-01 06:00:00.000 3.405667
7 2015-01-01 07:00:00.000 3.401500
8 2015-01-01 08:00:00.000 3.404333
9 2015-01-01 09:00:00.000 3.424833
10 2015-01-01 10:00:00.000 3.489500
11 2015-01-01 11:00:00.000 3.521000
12 2015-01-01 12:00:00.000 3.527833
13 2015-01-01 13:00:00.000 3.523500
14 2015-01-01 14:00:00.000 3.511667
15 2015-01-01 15:00:00.000 3.602500
16 2015-01-01 16:00:00.000 3.657667
17 2015-01-01 17:00:00.000 3.616667
18 2015-01-01 18:00:00.000 3.534500
19 2015-01-01 19:00:00.000 3.529167
20 2015-01-01 20:00:00.000 3.548167
21 2015-01-01 21:00:00.000 3.565500
22 2015-01-01 22:00:00.000 3.539833
23 2015-01-01 23:00:00.000 3.485667
24 2015-01-02 00:00:00.000 3.493167
.........
.........
I want do a mean, by day, of the column eVal.
First step if transform the dVal column to datetime.
time['dVal'] = pd.to_datetime(time['dVal'])
Next I set the datetime column as the index
time.index = time['dVal']
Finally I count mean for each day
me = time.resample('D').mean()
The mean calculated it's wrong.
dVal eVal
2015-01-01 4.014973 --> The correct mean of the first day is 3.5
2015-01-02 4.006548
2015-01-03 4.010406
2015-01-04 4.034531
python-3.x time pandas-groupby
asked Nov 21 at 9:04
jjgasse
848
add a comment |
1
I have the follow dataset
dVal eVal
0 2015-01-01 00:00:00.000 3.622833
1 2015-01-01 01:00:00.000 3.501333
2 2015-01-01 02:00:00.000 3.469167
3 2015-01-01 03:00:00.000 3.436333
4 2015-01-01 04:00:00.000 3.428000
5 2015-01-01 05:00:00.000 3.400667
6 2015-01-01 06:00:00.000 3.405667
7 2015-01-01 07:00:00.000 3.401500
8 2015-01-01 08:00:00.000 3.404333
9 2015-01-01 09:00:00.000 3.424833
10 2015-01-01 10:00:00.000 3.489500
11 2015-01-01 11:00:00.000 3.521000
12 2015-01-01 12:00:00.000 3.527833
13 2015-01-01 13:00:00.000 3.523500
14 2015-01-01 14:00:00.000 3.511667
15 2015-01-01 15:00:00.000 3.602500
16 2015-01-01 16:00:00.000 3.657667
17 2015-01-01 17:00:00.000 3.616667
18 2015-01-01 18:00:00.000 3.534500
19 2015-01-01 19:00:00.000 3.529167
20 2015-01-01 20:00:00.000 3.548167
21 2015-01-01 21:00:00.000 3.565500
22 2015-01-01 22:00:00.000 3.539833
23 2015-01-01 23:00:00.000 3.485667
24 2015-01-02 00:00:00.000 3.493167
.........
.........
I want do a mean, by day, of the column eVal.
First step if transform the dVal column to datetime.
time['dVal'] = pd.to_datetime(time['dVal'])
Next I set the datetime column as the index
time.index = time['dVal']
Finally I count mean for each day
me = time.resample('D').mean()
The mean calculated it's wrong.
dVal eVal
2015-01-01 4.014973 --> The correct mean of the first day is 3.5
2015-01-02 4.006548
2015-01-03 4.010406
2015-01-04 4.034531
python-3.x time pandas-groupby
asked Nov 21 at 9:04
jjgasse
848
works fine for meeVal dVal 2015-01-01 3.506160 2015-01-02 3.493167
– RomanPerekhrest
Nov 21 at 9:52
You follow my step? Your eVal column is a float like mine? If you try min() or max() function, give you the correct resoult? Bacause I have a problem with this two others functions
– jjgasse
Nov 21 at 9:57
dVal datetime64[ns] eVal float64 dtype: object
– RomanPerekhrest
Nov 21 at 10:00
I try to use different function but i don't arrive to the correct result. I try to use: time.set_index('dVal').groupby(pd.TimeGrouper('D')).mean().dropna() but it give me the same result above. The dtype is the same yours
– jjgasse
Nov 22 at 9:16
If I use onlyme = time.resample('D')and next I do a describe function for understand the situation in generalme.describe(), all the value are wrog ( like max, min,mean).
– jjgasse
Nov 27 at 9:49
add a comment |
1
1
1
I have the follow dataset
dVal eVal
0 2015-01-01 00:00:00.000 3.622833
1 2015-01-01 01:00:00.000 3.501333
2 2015-01-01 02:00:00.000 3.469167
3 2015-01-01 03:00:00.000 3.436333
4 2015-01-01 04:00:00.000 3.428000
5 2015-01-01 05:00:00.000 3.400667
6 2015-01-01 06:00:00.000 3.405667
7 2015-01-01 07:00:00.000 3.401500
8 2015-01-01 08:00:00.000 3.404333
9 2015-01-01 09:00:00.000 3.424833
10 2015-01-01 10:00:00.000 3.489500
11 2015-01-01 11:00:00.000 3.521000
12 2015-01-01 12:00:00.000 3.527833
13 2015-01-01 13:00:00.000 3.523500
14 2015-01-01 14:00:00.000 3.511667
15 2015-01-01 15:00:00.000 3.602500
16 2015-01-01 16:00:00.000 3.657667
17 2015-01-01 17:00:00.000 3.616667
18 2015-01-01 18:00:00.000 3.534500
19 2015-01-01 19:00:00.000 3.529167
20 2015-01-01 20:00:00.000 3.548167
21 2015-01-01 21:00:00.000 3.565500
22 2015-01-01 22:00:00.000 3.539833
23 2015-01-01 23:00:00.000 3.485667
24 2015-01-02 00:00:00.000 3.493167
.........
.........
I want do a mean, by day, of the column eVal.
First step if transform the dVal column to datetime.
time['dVal'] = pd.to_datetime(time['dVal'])
Next I set the datetime column as the index
time.index = time['dVal']
Finally I count mean for each day
me = time.resample('D').mean()
The mean calculated it's wrong.
dVal eVal
2015-01-01 4.014973 --> The correct mean of the first day is 3.5
2015-01-02 4.006548
2015-01-03 4.010406
2015-01-04 4.034531
python-3.x time pandas-groupby
asked Nov 21 at 9:04
jjgasse
848
I have the follow dataset
dVal eVal
0 2015-01-01 00:00:00.000 3.622833
1 2015-01-01 01:00:00.000 3.501333
2 2015-01-01 02:00:00.000 3.469167
3 2015-01-01 03:00:00.000 3.436333
4 2015-01-01 04:00:00.000 3.428000
5 2015-01-01 05:00:00.000 3.400667
6 2015-01-01 06:00:00.000 3.405667
7 2015-01-01 07:00:00.000 3.401500
8 2015-01-01 08:00:00.000 3.404333
9 2015-01-01 09:00:00.000 3.424833
10 2015-01-01 10:00:00.000 3.489500
11 2015-01-01 11:00:00.000 3.521000
12 2015-01-01 12:00:00.000 3.527833
13 2015-01-01 13:00:00.000 3.523500
14 2015-01-01 14:00:00.000 3.511667
15 2015-01-01 15:00:00.000 3.602500
16 2015-01-01 16:00:00.000 3.657667
17 2015-01-01 17:00:00.000 3.616667
18 2015-01-01 18:00:00.000 3.534500
19 2015-01-01 19:00:00.000 3.529167
20 2015-01-01 20:00:00.000 3.548167
21 2015-01-01 21:00:00.000 3.565500
22 2015-01-01 22:00:00.000 3.539833
23 2015-01-01 23:00:00.000 3.485667
24 2015-01-02 00:00:00.000 3.493167
.........
.........
I want do a mean, by day, of the column eVal.
First step if transform the dVal column to datetime.
time['dVal'] = pd.to_datetime(time['dVal'])
Next I set the datetime column as the index
time.index = time['dVal']
Finally I count mean for each day
me = time.resample('D').mean()
The mean calculated it's wrong.
dVal eVal
2015-01-01 4.014973 --> The correct mean of the first day is 3.5
2015-01-02 4.006548
2015-01-03 4.010406
2015-01-04 4.034531
python-3.x time pandas-groupby
python-3.x time pandas-groupby
asked Nov 21 at 9:04
jjgasse
848
asked Nov 21 at 9:04
jjgasse
848
asked Nov 21 at 9:04
jjgasse
848
asked Nov 21 at 9:04
jjgasse
848
asked Nov 21 at 9:04
jjgasse
848
848
works fine for meeVal dVal 2015-01-01 3.506160 2015-01-02 3.493167
– RomanPerekhrest
Nov 21 at 9:52
You follow my step? Your eVal column is a float like mine? If you try min() or max() function, give you the correct resoult? Bacause I have a problem with this two others functions
– jjgasse
Nov 21 at 9:57
dVal datetime64[ns] eVal float64 dtype: object
– RomanPerekhrest
Nov 21 at 10:00
I try to use different function but i don't arrive to the correct result. I try to use: time.set_index('dVal').groupby(pd.TimeGrouper('D')).mean().dropna() but it give me the same result above. The dtype is the same yours
– jjgasse
Nov 22 at 9:16
If I use onlyme = time.resample('D')and next I do a describe function for understand the situation in generalme.describe(), all the value are wrog ( like max, min,mean).
– jjgasse
Nov 27 at 9:49
add a comment |
works fine for meeVal dVal 2015-01-01 3.506160 2015-01-02 3.493167
– RomanPerekhrest
Nov 21 at 9:52
You follow my step? Your eVal column is a float like mine? If you try min() or max() function, give you the correct resoult? Bacause I have a problem with this two others functions
– jjgasse
Nov 21 at 9:57
dVal datetime64[ns] eVal float64 dtype: object
– RomanPerekhrest
Nov 21 at 10:00
I try to use different function but i don't arrive to the correct result. I try to use: time.set_index('dVal').groupby(pd.TimeGrouper('D')).mean().dropna() but it give me the same result above. The dtype is the same yours
– jjgasse
Nov 22 at 9:16
If I use onlyme = time.resample('D')and next I do a describe function for understand the situation in generalme.describe(), all the value are wrog ( like max, min,mean).
– jjgasse
Nov 27 at 9:49
works fine for me
eVal dVal 2015-01-01 3.506160 2015-01-02 3.493167 – RomanPerekhrest
Nov 21 at 9:52
works fine for me
eVal dVal 2015-01-01 3.506160 2015-01-02 3.493167 – RomanPerekhrest
Nov 21 at 9:52
You follow my step? Your eVal column is a float like mine? If you try min() or max() function, give you the correct resoult? Bacause I have a problem with this two others functions
– jjgasse
Nov 21 at 9:57
You follow my step? Your eVal column is a float like mine? If you try min() or max() function, give you the correct resoult? Bacause I have a problem with this two others functions
– jjgasse
Nov 21 at 9:57
dVal datetime64[ns] eVal float64 dtype: object– RomanPerekhrest
Nov 21 at 10:00
dVal datetime64[ns] eVal float64 dtype: object– RomanPerekhrest
Nov 21 at 10:00
I try to use different function but i don't arrive to the correct result. I try to use: time.set_index('dVal').groupby(pd.TimeGrouper('D')).mean().dropna() but it give me the same result above. The dtype is the same yours
– jjgasse
Nov 22 at 9:16
I try to use different function but i don't arrive to the correct result. I try to use: time.set_index('dVal').groupby(pd.TimeGrouper('D')).mean().dropna() but it give me the same result above. The dtype is the same yours
– jjgasse
Nov 22 at 9:16
If I use only
me = time.resample('D') and next I do a describe function for understand the situation in general me.describe(), all the value are wrog ( like max, min,mean).– jjgasse
Nov 27 at 9:49
If I use only
me = time.resample('D') and next I do a describe function for understand the situation in general me.describe(), all the value are wrog ( like max, min,mean).– jjgasse
Nov 27 at 9:49
add a comment |
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works fine for me
eVal dVal 2015-01-01 3.506160 2015-01-02 3.493167– RomanPerekhrest
Nov 21 at 9:52
You follow my step? Your eVal column is a float like mine? If you try min() or max() function, give you the correct resoult? Bacause I have a problem with this two others functions
– jjgasse
Nov 21 at 9:57
dVal datetime64[ns] eVal float64 dtype: object– RomanPerekhrest
Nov 21 at 10:00
I try to use different function but i don't arrive to the correct result. I try to use: time.set_index('dVal').groupby(pd.TimeGrouper('D')).mean().dropna() but it give me the same result above. The dtype is the same yours
– jjgasse
Nov 22 at 9:16
If I use only
me = time.resample('D')and next I do a describe function for understand the situation in generalme.describe(), all the value are wrog ( like max, min,mean).– jjgasse
Nov 27 at 9:49