51  Ejercicio Final - Parte 3: Preparación y visualización de datos

Bienvenidos a este video dedicado a simplificar el proceso de visualización de datos. En esta sesión, nos centraremos únicamente en cargar los datos, prepararlos para la graficación y finalmente, graficarlos. Este enfoque directo nos permitirá obtener resultados rápidamente y sin complicaciones. Sin más preámbulos, ¡empecemos con el proceso de carga y graficación de datos!

import pandas as pd
import matplotlib.pyplot as plt
from dateutil.parser import parse
f = '../data/termopares/Tcafe-12a17h.csv'
cafe = pd.read_csv(f,index_col=0,parse_dates=True)
f = '../data/termopares/Text-12a17h.csv'
ext = pd.read_csv(f,index_col=0,parse_dates=True)
Ti = pd.concat([cafe,ext],axis=1,keys=['cafe','ext'])
Ti
cafe ... ext
T1 T10 T11 T12 T2 T3 T4 T5 T6 T7 ... T10 T11 T12 T2 T3 T4 T6 T7 T8 T9
ts
2023-04-03 12:00:00 29.6670 30.0330 29.7210 29.3180 29.5245 29.3590 29.3670 29.1675 29.1310 -8.96 ... 30.0330 29.7210 29.3180 29.5245 29.3590 29.3670 29.1310 NaN 29.2860 30.5740
2023-04-03 12:10:00 29.6360 29.9245 29.6590 29.3025 29.4470 29.3435 29.3360 29.0125 28.9915 -8.96 ... 29.9245 29.6590 29.3025 29.4470 29.3435 29.3360 28.9915 NaN 29.1000 30.5120
2023-04-03 12:20:00 30.5040 30.9165 30.6820 30.2175 30.3770 30.1885 30.2350 29.9965 29.9625 -8.96 ... 30.9165 30.6820 30.2175 30.3770 30.1885 30.2350 29.9625 NaN 30.1230 31.4815
2023-04-03 12:30:00 30.5970 30.9320 30.6820 30.2325 30.5010 30.2990 30.2970 30.0750 30.0710 -8.96 ... 30.9320 30.6820 30.2325 30.5010 30.2990 30.2970 30.0710 NaN 30.1850 31.5605
2023-04-03 12:40:00 31.3605 31.6795 31.4620 30.9710 31.1250 30.9655 30.9635 30.7895 30.8025 -8.96 ... 31.6795 31.4620 30.9710 31.1250 30.9655 30.9635 30.8025 NaN 30.9755 32.2890
2023-04-03 12:50:00 30.9240 31.2890 31.0870 30.6105 30.8280 30.6400 30.6690 30.3090 30.3655 -8.96 ... 31.2890 31.0870 30.6105 30.8280 30.6400 30.6690 30.3655 NaN 30.5105 31.8705
2023-04-03 13:00:00 30.8145 31.1495 30.9155 30.5160 30.6720 30.4850 30.5450 30.2935 30.3345 -8.96 ... 31.1495 30.9155 30.5160 30.6720 30.4850 30.5450 30.3345 NaN 30.4640 31.7930
2023-04-03 13:10:00 32.0325 32.5535 32.3195 31.8590 31.9060 31.7250 31.7860 31.5510 31.5210 -8.96 ... 32.5535 32.3195 31.8590 31.9060 31.7250 31.7860 31.5210 NaN 31.7195 33.0795
2023-04-03 13:20:00 32.2035 32.6310 32.3350 31.9210 32.0765 31.9110 31.8345 31.7565 31.7065 -8.96 ... 32.6310 32.3350 31.9210 32.0765 31.9110 31.8345 31.7065 NaN 31.7970 33.2035
2023-04-03 13:30:00 32.2810 32.7400 32.4280 32.0295 32.1385 31.9420 31.9890 31.7085 31.7705 -8.96 ... 32.7400 32.4280 32.0295 32.1385 31.9420 31.9890 31.7705 NaN 31.9365 33.2965
2023-04-03 13:40:00 32.0795 32.5230 32.2885 31.9660 32.0455 31.8335 31.8160 31.5040 31.5070 -8.96 ... 32.5230 32.2885 31.9660 32.0455 31.8335 31.8160 31.5070 NaN 31.7040 33.0950
2023-04-03 13:50:00 32.8235 33.3135 33.0170 32.5770 32.7585 32.5775 32.5850 32.3030 32.2820 -8.96 ... 33.3135 33.0170 32.5770 32.7585 32.5775 32.5850 32.2820 NaN 32.5100 33.8700
2023-04-03 14:00:00 33.2590 33.6855 33.5595 33.0745 33.1460 32.9665 33.0350 32.6750 32.6890 -8.96 ... 33.6855 33.5595 33.0745 33.1460 32.9665 33.0350 32.6890 NaN 32.8200 34.3395
2023-04-03 14:10:00 33.7600 34.1970 34.0865 33.5270 33.6780 33.4525 33.5000 33.1090 33.1905 -8.96 ... 34.1970 34.0865 33.5270 33.6780 33.4525 33.5000 33.1905 NaN 33.3315 34.8250
2023-04-03 14:20:00 33.5070 33.8405 33.6215 33.2450 33.3480 33.1995 33.2365 32.8145 32.8950 -8.96 ... 33.8405 33.6215 33.2450 33.3480 33.1995 33.2365 32.8950 NaN 32.9905 34.4980
2023-04-03 14:30:00 34.4920 34.9255 34.7065 34.1220 34.4270 34.1840 34.1510 33.9350 33.9520 -8.96 ... 34.9255 34.7065 34.1220 34.4270 34.1840 34.1510 33.9520 NaN 34.1065 35.4610
2023-04-03 14:40:00 34.4145 34.7860 34.5825 34.1530 34.2565 34.0290 34.1045 33.7455 33.8265 -8.96 ... 34.7860 34.5825 34.1530 34.2565 34.0290 34.1045 33.8265 NaN 33.9360 35.4300
2023-04-03 14:50:00 34.4920 35.0650 34.9090 34.4010 34.4580 34.2615 34.2905 33.9040 34.0435 -8.96 ... 35.0650 34.9090 34.4010 34.4580 34.2615 34.2905 34.0435 NaN 34.0910 35.6625
2023-04-03 15:00:00 35.0345 35.5610 35.4265 34.9660 34.9075 34.7575 34.7865 34.3435 34.4540 -8.96 ... 35.5610 35.4265 34.9660 34.9075 34.7575 34.7865 34.4540 NaN 34.6180 36.1430
2023-04-03 15:10:00 35.1430 35.5300 35.2735 34.9360 35.0470 34.8040 34.8950 34.6070 34.6735 -8.96 ... 35.5300 35.2735 34.9360 35.0470 34.8040 34.8950 34.6735 NaN 34.7575 36.2360
2023-04-03 15:20:00 34.9415 35.2975 35.1140 34.6805 34.8455 34.6955 34.6470 34.4055 34.5340 -8.96 ... 35.2975 35.1140 34.6805 34.8455 34.6955 34.6470 34.5340 NaN 34.5560 35.9570
2023-04-03 15:30:00 35.4375 35.7160 35.5380 35.0920 35.3105 35.1295 35.1740 34.8550 34.9215 -8.96 ... 35.7160 35.5380 35.0920 35.3105 35.1295 35.1740 34.9215 NaN 35.0520 36.4065
2023-04-03 15:40:00 35.5775 36.2120 36.0185 35.6355 35.5895 35.3775 35.4840 34.9325 34.9990 -8.96 ... 36.2120 36.0185 35.6355 35.5895 35.3775 35.4840 34.9990 NaN 35.2535 36.8095
2023-04-03 15:50:00 35.9095 36.3515 36.0960 35.7905 35.8375 35.6410 35.6415 35.2580 35.3555 -8.96 ... 36.3515 36.0960 35.7905 35.8375 35.6410 35.6415 35.3555 NaN 35.5015 36.9180
2023-04-03 16:00:00 35.9715 36.2895 36.0805 35.7595 35.8220 35.6410 35.6730 35.3365 35.4655 -8.96 ... 36.2895 36.0805 35.7595 35.8220 35.6410 35.6730 35.4655 NaN 35.5945 36.9180
2023-04-03 16:10:00 36.4240 36.8320 36.6230 36.3375 36.3200 36.0905 36.1745 35.6815 35.8425 -8.96 ... 36.8320 36.6230 36.3375 36.3200 36.0905 36.1745 35.8425 NaN 35.9665 37.3990
2023-04-03 16:20:00 36.3775 36.8165 36.7005 36.3375 36.3665 36.1370 36.2065 35.6320 35.8260 -8.96 ... 36.8165 36.7005 36.3375 36.3665 36.1370 36.2065 35.8260 NaN 35.9820 37.3995
2023-04-03 16:30:00 36.5480 36.9405 36.9330 36.5570 36.4600 36.2765 36.3930 35.7110 35.9515 -8.96 ... 36.9405 36.9330 36.5570 36.4600 36.2765 36.3930 35.9515 NaN 36.0120 37.6500
2023-04-03 16:40:00 36.0210 36.1810 36.1115 35.8370 35.8840 35.6410 35.7180 35.2270 35.4795 -8.96 ... 36.1810 36.1115 35.8370 35.8840 35.6410 35.7180 35.4795 NaN 35.6255 36.9180
2023-04-03 16:50:00 37.1835 37.6845 38.0830 38.3500 37.1945 36.9330 37.0900 36.4765 36.7915 -8.96 ... 37.6845 38.0830 38.3500 37.1945 36.9330 37.0900 36.7915 NaN 36.8110 38.2425
2023-04-03 17:00:00 37.6175 38.4130 38.5170 38.5530 37.5835 37.3865 37.5400 36.6940 36.9470 -8.96 ... 38.4130 38.5170 38.5530 37.5835 37.3865 37.5400 36.9470 NaN 37.1190 38.6640

31 rows × 23 columns

Ti['cafe'].mean()
T1     33.775290
T10    34.193855
T11    34.018613
T12    33.641129
T2     33.673581
T3     33.479774
T4     33.521403
T5     33.155145
T6     33.234935
T7     -8.960000
T8     33.368887
T9     34.790387
dtype: float64
Ti['cafe'].mean(axis=1)
ts
2023-04-03 12:00:00    26.349000
2023-04-03 12:10:00    26.275375
2023-04-03 12:20:00    27.143667
2023-04-03 12:30:00    27.206000
2023-04-03 12:40:00    27.868625
2023-04-03 12:50:00    27.511917
2023-04-03 13:00:00    27.418542
2023-04-03 13:10:00    28.591042
2023-04-03 13:20:00    28.701333
2023-04-03 13:30:00    28.775000
2023-04-03 13:40:00    28.616833
2023-04-03 13:50:00    29.304750
2023-04-03 14:00:00    29.690792
2023-04-03 14:10:00    30.141417
2023-04-03 14:20:00    29.853000
2023-04-03 14:30:00    30.791875
2023-04-03 14:40:00    30.692000
2023-04-03 14:50:00    30.884833
2023-04-03 15:00:00    31.336500
2023-04-03 15:10:00    31.411875
2023-04-03 15:20:00    31.226167
2023-04-03 15:30:00    31.639375
2023-04-03 15:40:00    31.910750
2023-04-03 15:50:00    32.111708
2023-04-03 16:00:00    32.132625
2023-04-03 16:10:00    32.560917
2023-04-03 16:20:00    32.568458
2023-04-03 16:30:00    32.706042
2023-04-03 16:40:00    32.140292
2023-04-03 16:50:00    33.490000
2023-04-03 17:00:00    33.839542
dtype: float64
fig, ax = plt.subplots(figsize=(12,4))

ax.plot(Ti['cafe'].mean(axis=1),'r.',label='cafe')
ax.plot( Ti['ext'].mean(axis=1),'k.',label='ext')

ax.set_ylim(20,40)
ax.set_xlabel('Fecha')
ax.set_ylabel('Temperatura [oC]')
ax.legend()