将NETCDF文件变成平均值的数据框
我对NetCDF并不熟悉，但这是我的文件的设置方式。
这是一个在经度（-15至-10）和纬度（10至6.5）上的网格，其中包含常规的小网格盒，大小为0.25度x 0.25度。网格存在一个小时，然后下一个小时是一个相同的网格，并在该小时内具有数据，因此在24小时内将有24个相同的LAT/长网格。
我有多年的数据。我要做的是为每个0.25x0.25框找到每年的总平均值，并将该值添加到数据框架中：
这就是我到目前为止尝试的：
precip_full1 = xr.open_dataset('era_yr1979.nc')
precip_full2 = xr.open_dataset('era_yr1980.nc')
precip_full3 = xr.open_dataset('era_yr1981.nc')
precip_full = xr.concat([precip_full1,precip_full2,precip_full3],dim='time')
output = []
for x in np.arange(6.5,10,0.25):
for y in np.arange(-15,-10,0.25):
precip = precip_full.where((precip_full.latitude==int(x))&(precip_full.longitude==int(y)),drop=True)
roll = precip.rolling(time=1,center=False).sum()
annual = roll.groupby('time.year').max()
tab = annual.to_dataframe().rename(columns={'tp':1})
# Keep track of the output by appending it to the output list
output.append(tab)
output = pd.concat(output,1)
print(output)
mean = output.mean()
data_mean = pd.DataFrame(mean, columns=['mean'])
这是我得到的输出：
mean x y
1 NaN 9.75 -10.25
1 NaN 9.75 -10.25
1 NaN 9.75 -10.25
1 NaN 9.75 -10.25
1 NaN 9.75 -10.25
.. ... ... ...
1 0.015662 9.75 -10.25
1 0.015662 9.75 -10.25
1 0.013323 9.75 -10.25
1 0.013323 9.75 -10.25
1 0.013323 9.75 -10.25
[280 rows x 3 columns]
以前有人遇到过NETCDF的这种问题，并且知道为什么我会得到NAN，还是我写了错误的东西？
I'm not very familiar with NetCDF, but this is how my file is set up.
It's a grid over a longitude (-15 to -10) and latitude (10 to 6.5), containing regular small grid boxes which are 0.25 degrees x 0.25 degrees in size. The grid exists for a single hour, then the next hour is an identical grid with the data for that hour, so in 24 hours there will be 24 identical lat/long grids.
I have numerous years of data. What I'm trying to do is find the overall mean value of every year for each 0.25x0.25 box, and add that value to a data frame:
Here's what I've tried so far:
precip_full1 = xr.open_dataset('era_yr1979.nc')
precip_full2 = xr.open_dataset('era_yr1980.nc')
precip_full3 = xr.open_dataset('era_yr1981.nc')
precip_full = xr.concat([precip_full1,precip_full2,precip_full3],dim='time')
output = []
for x in np.arange(6.5,10,0.25):
for y in np.arange(-15,-10,0.25):
precip = precip_full.where((precip_full.latitude==int(x))&(precip_full.longitude==int(y)),drop=True)
roll = precip.rolling(time=1,center=False).sum()
annual = roll.groupby('time.year').max()
tab = annual.to_dataframe().rename(columns={'tp':1})
# Keep track of the output by appending it to the output list
output.append(tab)
output = pd.concat(output,1)
print(output)
mean = output.mean()
data_mean = pd.DataFrame(mean, columns=['mean'])
Here's the output I get:
mean x y
1 NaN 9.75 -10.25
1 NaN 9.75 -10.25
1 NaN 9.75 -10.25
1 NaN 9.75 -10.25
1 NaN 9.75 -10.25
.. ... ... ...
1 0.015662 9.75 -10.25
1 0.015662 9.75 -10.25
1 0.013323 9.75 -10.25
1 0.013323 9.75 -10.25
1 0.013323 9.75 -10.25
[280 rows x 3 columns]
Has anyone come across this sort of problem with NetCDF before and know why I'm getting NaN, or am I writing something wrong?
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我不太确定您的最终目标是什么，但是假设您的数据具有相同的网格，您可以通过每年的平均值来计算
pandas.dataframe：I'm not quite sure what your end goal is but assuming your data has the same grids, you can calculate for annual mean per gid point by and convert that into a
pandas.DataFrame: