MySQL 查询”JJA2001”班的选修JAVA课程的学生成绩，按照降序显示。成绩相同，则按照学生编号升序。

你可以使用以下SQL语句来查询"JJA2001"班选修JAVA课程的学生成绩，并按照降序显示。在结果中，如果成绩相同，则按照学生编号升序排列。

SELECT student_id, score
FROM scores
WHERE class = 'JJA2001' AND course = 'JAVA'
ORDER BY score DESC, student_id ASC;

请确保将表名和列名替换为你实际使用的名称。

相关问题

帮我修改代码 f2=xr.open_dataset("D:/dq56/sst.mnmean.v3-2.nc") print(f2) lon=f2['lon'][:] lat=f2['lat'][:] sst=f2['sst'][:] sst1=sst.loc['1961-01-01':'2016-12-01',32:26,106:122]sst11=sst1.where(sst['time.season']=='MAM') sst22=sst1.where(sst['time.season']=='JJA') sst33=sst1.where(sst['time.season']=='SON') sst44=sst1.where(sst['time.season']=='DJF') sst_mean=sst44.groupby('lon').mean(dim=['time']) pre11=pre.loc['1961-01-31':'2016-12-31',26:33:8,106:122:8] pre111=pre11.where(pre['time.season']=='MAM') pre222=pre11.where(pre['time.season']=='JJA') pre333=pre11.where(pre['time.season']=='SON') pre444=pre11.where(pre['time.season']=='DJF') pre_mean=pre222.groupby('lon').mean(dim=['time']) print(np.corrcoef(sst_mean,pre_mean))

Sure, here is the modified code:

import xarray as xr
import numpy as np

# load SST data
f2 = xr.open_dataset("D:/dq56/sst.mnmean.v3-2.nc")
lon = f2['lon'][:]
lat = f2['lat'][:]
sst = f2['sst'][:]
sst1 = sst.loc['1961-01-01':'2016-12-01', 32:26, 106:122]
sst11 = sst1.where(sst1['time.season'] == 'MAM')
sst22 = sst1.where(sst1['time.season'] == 'JJA')
sst33 = sst1.where(sst1['time.season'] == 'SON')
sst44 = sst1.where(sst1['time.season'] == 'DJF')
sst_mean = sst44.groupby('lon').mean(dim=['time'])

# load precipitation data
pre = xr.open_dataset("path/to/precipitation/data.nc")
pre11 = pre.loc['1961-01-31':'2016-12-31', 26:33:8, 106:122:8]
pre111 = pre11.where(pre11['time.season'] == 'MAM')
pre222 = pre11.where(pre11['time.season'] == 'JJA')
pre333 = pre11.where(pre11['time.season'] == 'SON')
pre444 = pre11.where(pre11['time.season'] == 'DJF')
pre_mean = pre222.groupby('lon').mean(dim=['time'])

# compute correlation coefficient
corr = np.corrcoef(sst_mean, pre_mean)
print(corr)

Note: I assumed that you have loaded the precipitation data and it is stored in `pre` variable. Also, I fixed the indexing of `sst1` to be `sst1 = sst.loc['1961-01-01':'2016-12-01', 32:26, 106:122]` instead of `sst1=sst.loc['1961-01-01':'2016-12-01',32:26,106:122]sst11=sst1.where(sst['time.season']=='MAM')`.

years = np.arange(1979, 2020, 1) year_str = [str(i) for i in years] x_min = 70 x_max = 140 y_min = 10 y_max = 55 grid_lon = np.arange(x_min, x_max+1, 1.0) grid_lat = np.arange(y_min, y_max+1, 1.0) nyears = years.shape[0] nlon = grid_lon.shape[0] nlat = grid_lat.shape[0] total_grid_data = np.zeros((nyears, nlat, nlon), dtype=float) i = 0 for year in year_str: f_path = r'E:\gra_thesis\sum_pre_data_new/AMJ_pre_data/'+year+'_AMJ_sumPre.xlsx' df = pd.read_excel(f_path) df = df.dropna(axis=0) lon = df['X'] lat = df['Y'] data= df['总降水量'] Krin = pykrige.OrdinaryKriging(lon, lat, data, variogram_model="gaussian", nlags=6) grid_data, ssl = Krin.execute('grid', grid_lon, grid_lat) grid_data = np.array(grid_data) total_grid_data[i, :, :] = grid_data i = i + 1 # %% # # save data data_nc = xr.Dataset( { "precip":(("time", "lat","lon"), total_grid_data) }, coords={ "time":years, "lat":grid_lat, "lon":grid_lon, } ) data_nc.attrs["long_name"] = "total_grid_precip" data_nc.to_netcdf("E:"+"JJA_pre_total_precip.nc") 解释这段代码i

这段代码是用于读取地理数据，并对其进行插值，得到一个三维数组。years变量存储了年份序列，year_str变量是将年份序列转换为字符串序列。grid_lon变量和grid_lat变量分别是经度和纬度序列。nyears、nlon和nlat是计算数组维度的变量。total_grid_data是三维数组，用于存储地理数据的插值结果。代码使用了pykrige包进行插值，其中OrdinaryKriging()函数是调用普通克里金插值方法进行计算，variogram_model指定了变异函数的类型，nlags是变异函数的参数。for循环逐个读取每个年份的数据，同时将插值结果存储在total_grid_data中。最终代码的结果是得到了一个三维数组，其中每个元素值是地理数据的插值结果。