如何通过 hargreaves-samani 方程和使用 python 计算每日蒸发量？

Question

I have a ten-year weather data including maximum temperature ( Tmax ), minimum temperature ( Tmin ), rainfall and solar radiation ( Ra ) for each day.

At first, I would like to calculate evapotranspiration (ETo) for each day using the following equation:

ETo=0.0023*(((Tmax+Tmin)/2)+17.8)*sqrt(Tmax-Tmin)*Ra

Then, calculation of the monthly and yearly average of all parameters (Tmax,Tmin, Rainfall, Ra and ETo) and print them in Excel format.

I have written some parts. could you possibly help me with completing it? I think it may need a loop .

import numpy as np
import pandas as pd
import math as mh
# load the weather data file
data_file = pd.read_excel(r'weather data.xlsx', sheet_name='city_1')
# defining time
year = data_file['Year']
month = data_file['month']
day = data_file['day']
# defining weather parameters
Tmax = data_file.loc[:,'Tmax']
Tmin = data_file.loc[:,'Tmin']
Rainfall = data_file.loc[:,'Rainfall']
Ra = data_file.loc[:,'Ra']
# adjusting time to start at zero
year = year-year[0]
month=month-month[0]
day=day-day[0]
#calculation process for estimation of evapotranspiration
ET0=(0.0023*(((Tmax+Tmin)/2)+17.8)*(mh.sqrt(Tmax-Tmin))*Ra

Answer 1

Looks like you've got one data row (record) per day.

Since you already have Tmax, Tmin, Rainfall, and Sunhours in the row, you could add a net ET0 row with the calculation like this:

    data_file['ET0'] = data_file.apply(lambda x: 0.0023*(((x.Tmax+x.Tmin)/2)+17.8)*(mh.sqrt(x.Tmax-x.Tmin))*x.Ra, axis=0)