import pandas as pd23 Métricas
f = "../data/Ti_blanco.csv"
blanco = pd.read_csv(f,index_col=0,parse_dates=True)
blanco.info()
f = "../data/Ti_negro.csv"
negro = pd.read_csv(f,index_col=0,parse_dates=True)
negro.info()<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 52560 entries, 2006-01-01 00:10:00 to 2007-01-01 00:00:00
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Ti_C 52560 non-null float64
1 Ti_R1 52560 non-null float64
2 Ti_R2 52560 non-null float64
3 Ti_S 52560 non-null float64
dtypes: float64(4)
memory usage: 2.0 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 52560 entries, 2006-01-01 00:10:00 to 2007-01-01 00:00:00
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Ti_C 52560 non-null float64
1 Ti_R1 52560 non-null float64
2 Ti_R2 52560 non-null float64
3 Ti_S 52560 non-null float64
dtypes: float64(4)
memory usage: 2.0 MBblanco| Ti_C | Ti_R1 | Ti_R2 | Ti_S | |
|---|---|---|---|---|
| date | ||||
| 2006-01-01 00:10:00 | 19.014610 | 19.024472 | 19.065057 | 19.106503 |
| 2006-01-01 00:20:00 | 19.012693 | 19.022505 | 19.062782 | 19.104997 |
| 2006-01-01 00:30:00 | 19.011030 | 19.020820 | 19.060846 | 19.103773 |
| 2006-01-01 00:40:00 | 19.009526 | 19.019312 | 19.059129 | 19.102714 |
| 2006-01-01 00:50:00 | 19.008070 | 19.017864 | 19.057494 | 19.101684 |
| ... | ... | ... | ... | ... |
| 2006-12-31 23:20:00 | 19.589577 | 19.517255 | 19.780842 | 19.902851 |
| 2006-12-31 23:30:00 | 19.572531 | 19.500154 | 19.757842 | 19.882044 |
| 2006-12-31 23:40:00 | 19.555469 | 19.482987 | 19.734803 | 19.861207 |
| 2006-12-31 23:50:00 | 19.538364 | 19.465734 | 19.711720 | 19.840302 |
| 2007-01-01 00:00:00 | 19.521151 | 19.448329 | 19.688533 | 19.819254 |
52560 rows × 4 columns
negro| Ti_C | Ti_R1 | Ti_R2 | Ti_S | |
|---|---|---|---|---|
| date | ||||
| 2006-01-01 00:10:00 | 19.038413 | 19.047696 | 19.087096 | 19.129994 |
| 2006-01-01 00:20:00 | 19.035998 | 19.045313 | 19.084383 | 19.128110 |
| 2006-01-01 00:30:00 | 19.033880 | 19.043235 | 19.082035 | 19.126533 |
| 2006-01-01 00:40:00 | 19.031973 | 19.041355 | 19.079928 | 19.125138 |
| 2006-01-01 00:50:00 | 19.030158 | 19.039552 | 19.077921 | 19.123787 |
| ... | ... | ... | ... | ... |
| 2006-12-31 23:20:00 | 24.137526 | 23.391216 | 23.667201 | 24.646797 |
| 2006-12-31 23:30:00 | 24.064361 | 23.323456 | 23.589420 | 24.567420 |
| 2006-12-31 23:40:00 | 23.990232 | 23.257064 | 23.512465 | 24.488597 |
| 2006-12-31 23:50:00 | 23.917165 | 23.191858 | 23.436305 | 24.410237 |
| 2007-01-01 00:00:00 | 23.846178 | 23.127226 | 23.360837 | 24.332185 |
52560 rows × 4 columns
diff = blanco - negro(blanco.Ti_C - negro.Ti_C ).mean()np.float64(-5.308517736666285)(blanco.Ti_C - negro.Ti_C ).std()np.float64(2.11990130443126)diff.mean()Ti_C -5.308518
Ti_R1 -5.111807
Ti_R2 -5.039647
Ti_S -5.076488
dtype: float64diff.median()Ti_C -5.132381
Ti_R1 -4.885755
Ti_R2 -4.733711
Ti_S -4.845651
dtype: float64diff.resample("M")C:\Users\gbv\AppData\Local\Temp\ipykernel_57928\2919733548.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
diff.resample("M")<pandas.core.resample.DatetimeIndexResampler object at 0x00000202A8F7D640>diff.resample("3H").mean()C:\Users\gbv\AppData\Local\Temp\ipykernel_57928\3329997795.py:1: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.
diff.resample("3H").mean()| Ti_C | Ti_R1 | Ti_R2 | Ti_S | |
|---|---|---|---|---|
| date | ||||
| 2006-01-01 00:00:00 | -0.021167 | -0.020358 | -0.019159 | -0.020962 |
| 2006-01-01 03:00:00 | -0.017316 | -0.015935 | -0.014671 | -0.016956 |
| 2006-01-01 06:00:00 | -0.015232 | -0.013807 | -0.012120 | -0.014461 |
| 2006-01-01 09:00:00 | -0.023016 | -0.022017 | -0.021280 | -0.022006 |
| 2006-01-01 12:00:00 | -0.031571 | -0.031032 | -0.031827 | -0.031410 |
| ... | ... | ... | ... | ... |
| 2006-12-31 12:00:00 | -5.025595 | -4.172613 | -4.128075 | -4.715904 |
| 2006-12-31 15:00:00 | -6.143345 | -5.158807 | -5.343249 | -6.114931 |
| 2006-12-31 18:00:00 | -5.896546 | -5.067058 | -5.307056 | -6.196140 |
| 2006-12-31 21:00:00 | -4.859050 | -4.156382 | -4.210848 | -5.082399 |
| 2007-01-01 00:00:00 | -4.325027 | -3.678896 | -3.672305 | -4.512931 |
2921 rows × 4 columns
diff.resample("3M").mean()C:\Users\gbv\AppData\Local\Temp\ipykernel_57928\1973734845.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
diff.resample("3M").mean()| Ti_C | Ti_R1 | Ti_R2 | Ti_S | |
|---|---|---|---|---|
| date | ||||
| 2006-01-31 | -4.314191 | -3.491715 | -3.456784 | -4.163554 |
| 2006-04-30 | -5.205744 | -4.988880 | -4.892002 | -5.013828 |
| 2006-07-31 | -5.998308 | -6.372570 | -6.308787 | -5.721852 |
| 2006-10-31 | -5.717239 | -5.572823 | -5.484666 | -5.413686 |
| 2007-01-31 | -4.307009 | -3.517697 | -3.474178 | -4.149963 |
diff.resample("D").max()| Ti_C | Ti_R1 | Ti_R2 | Ti_S | |
|---|---|---|---|---|
| date | ||||
| 2006-01-01 | -0.013989 | -0.012218 | -0.010572 | -0.013044 |
| 2006-01-02 | -0.009047 | -0.008165 | -0.005351 | -0.008393 |
| 2006-01-03 | -0.519950 | -0.480064 | -0.422005 | -0.484904 |
| 2006-01-04 | -0.630201 | -0.561868 | -0.494864 | -0.577895 |
| 2006-01-05 | -2.829595 | -2.438844 | -2.349026 | -2.968532 |
| ... | ... | ... | ... | ... |
| 2006-12-28 | -2.573831 | -2.098926 | -1.923344 | -2.577069 |
| 2006-12-29 | -2.717307 | -2.238275 | -1.970042 | -2.660889 |
| 2006-12-30 | -3.269922 | -2.662732 | -2.256935 | -3.187538 |
| 2006-12-31 | -3.297358 | -2.686110 | -2.239008 | -3.165496 |
| 2007-01-01 | -4.325027 | -3.678896 | -3.672305 | -4.512931 |
366 rows × 4 columns
blanco.resample("YE").max()| Ti_C | Ti_R1 | Ti_R2 | Ti_S | |
|---|---|---|---|---|
| date | ||||
| 2006-12-31 | 27.291816 | 27.796187 | 28.240923 | 28.290655 |
| 2007-12-31 | 19.521151 | 19.448329 | 19.688533 | 19.819254 |
- diff = mean(Ti_b - T_n)
- Diferencia promedio por mes
- Desviación estándard
- Moda
- mean absolute error mean(sum| Ti_b - Ti_n | )
blanco| Ti_C | Ti_R1 | Ti_R2 | Ti_S | |
|---|---|---|---|---|
| date | ||||
| 2006-01-01 00:10:00 | 19.014610 | 19.024472 | 19.065057 | 19.106503 |
| 2006-01-01 00:20:00 | 19.012693 | 19.022505 | 19.062782 | 19.104997 |
| 2006-01-01 00:30:00 | 19.011030 | 19.020820 | 19.060846 | 19.103773 |
| 2006-01-01 00:40:00 | 19.009526 | 19.019312 | 19.059129 | 19.102714 |
| 2006-01-01 00:50:00 | 19.008070 | 19.017864 | 19.057494 | 19.101684 |
| ... | ... | ... | ... | ... |
| 2006-12-31 23:20:00 | 19.589577 | 19.517255 | 19.780842 | 19.902851 |
| 2006-12-31 23:30:00 | 19.572531 | 19.500154 | 19.757842 | 19.882044 |
| 2006-12-31 23:40:00 | 19.555469 | 19.482987 | 19.734803 | 19.861207 |
| 2006-12-31 23:50:00 | 19.538364 | 19.465734 | 19.711720 | 19.840302 |
| 2007-01-01 00:00:00 | 19.521151 | 19.448329 | 19.688533 | 19.819254 |
52560 rows × 4 columns
negro| Ti_C | Ti_R1 | Ti_R2 | Ti_S | |
|---|---|---|---|---|
| date | ||||
| 2006-01-01 00:10:00 | 19.038413 | 19.047696 | 19.087096 | 19.129994 |
| 2006-01-01 00:20:00 | 19.035998 | 19.045313 | 19.084383 | 19.128110 |
| 2006-01-01 00:30:00 | 19.033880 | 19.043235 | 19.082035 | 19.126533 |
| 2006-01-01 00:40:00 | 19.031973 | 19.041355 | 19.079928 | 19.125138 |
| 2006-01-01 00:50:00 | 19.030158 | 19.039552 | 19.077921 | 19.123787 |
| ... | ... | ... | ... | ... |
| 2006-12-31 23:20:00 | 24.137526 | 23.391216 | 23.667201 | 24.646797 |
| 2006-12-31 23:30:00 | 24.064361 | 23.323456 | 23.589420 | 24.567420 |
| 2006-12-31 23:40:00 | 23.990232 | 23.257064 | 23.512465 | 24.488597 |
| 2006-12-31 23:50:00 | 23.917165 | 23.191858 | 23.436305 | 24.410237 |
| 2007-01-01 00:00:00 | 23.846178 | 23.127226 | 23.360837 | 24.332185 |
52560 rows × 4 columns
blanco.Ti_C * negro.Ti_Cdate
2006-01-01 00:10:00 362.007996
2006-01-01 00:20:00 361.925572
2006-01-01 00:30:00 361.853663
2006-01-01 00:40:00 361.788785
2006-01-01 00:50:00 361.726578
...
2006-12-31 23:20:00 472.843913
2006-12-31 23:30:00 471.000451
2006-12-31 23:40:00 469.140241
2006-12-31 23:50:00 467.302274
2007-01-01 00:00:00 465.504851
Name: Ti_C, Length: 52560, dtype: float64