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This article mainly introduces "how to use python to merge multiple regularly named nc files". In daily operation, I believe many people have doubts about how to use python to merge multiple regularly named nc files. Xiaobian consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful for you to answer the doubts of "how to use python to merge multiple regularly named nc files". Next, please follow the editor to study!

There are many nc files with the same naming except for different years. You want to merge multiple nc files into one.

Each nc file contains the following contents, including 12-month average data

So, we can know that if we merge two nc files, time should be ='24 files, merge three, time='36'. And so on, merge x years, time=x*12.

The merge function is mainly realized through xarray, and the merged nc file is written:

# first of all, use a loop to pick out the years that need to be merged, the library import xarray as xrimport os# used to import. I choose the data file_xadv= for 1982-2012 [] for year in range (1982 Magazine 2013): fn=os.path.join (path2,'xadv'+'.0f'%year+'.mon.161x720.nc') file_xadv.append (fn)

After running, you can get a list of all the files in the full path:

Here is the merger:

# merge the nc file, open the nc file through xarray, and get the xarray.DataArray of the required variables, and then use xarray.concat () to realize the merging function xadv_new= [] for i in range (len (file_xadv)): xadv=xr.open_dataset (file_ Xadvi) ['xadv'] xadv_new.append ((xadv)) da=xr.concat (hadv_new,dim='time')

Run and find that the merge is done:

The official introduction of xarray.concat () is as follows:

Xarray.concat

As an exception, the official website also gives other ways to merge, and you can ask for as much as you need:

1. For information about combining datasets or data arrays along a single dimension, use concatenate.

2. Use merge for merging datasets with different variables.

3. Use combine for information about merging datasets or data arrays with different indexes or missing values.

4. For information about combining datasets or data arrays along multiple dimensions, use combining.multi.

Combining data

Attach the complete implementation code:

Import xarray as xrimport ospath2='E:\\ BaiduNetdiskDownload\\ MSE_budget\\ xadv\\ file_xadv= [] for year in range (1982 Magi 2013): fn=os.path.join (path2,'xadv'+'.0f'%year+'.mon.161x720.nc') file_xadv.append (fn) hadv_new= [] for i in range (len (file_xadv)): xadv=xr.open_dataset (file_ xadvi) ['xadv'] hadv_new.append ((xadv)) da=xr.concat (hadv_new) Dim='time') da.to_netcdf ('D:\\ desktopppp\\ sst_olr\\ interp_nan\\ xadv_interp.nc') # output the merged nc file here The study on "how to use python to merge multiple regularly named nc files" is over. I hope I can solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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