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I have a 10-year sst monthly data. I want to plot the interannual monthly climatology for the month of October. By using the code I have for python with me I am able to plot the interannual monthly climatology for all the 12 months. If anyone can help me by going over my code and say where I have to put index so that I can have monthly climatology only for the month of October it will be much appreciated. Below is my code:

import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
#time_slice = slice(9,10)
lat_slice = slice(30, 0)
lon_slice = slice(40, 80)
nc = xr.open_dataset('E:/X49.37.14.247.143.9.43.4.nc')
np.disp(nc)
ds = nc.groupby('time.month', squeeze = False).mean('time')
dt = ds.sst.sel(lat = lat_slice,lon =lon_slice)
np.disp(dt)
dt.plot.pcolormesh(x = 'lon', y = 'lat', col = 'month', col_wrap =3) 
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ds = nc.groupby('time.month', squeeze = False).mean('time')

Here, you may put a "filter" either before or after group by.

Either you can do this-

df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
                          'foo', 'bar'],
                   'B' : [1, 2, 3, 4, 5, 6],
                   'C' : [2.0, 5., 8., 1., 2., 9.]})
grouped = df.groupby('A')
grouped.filter(lambda x: x['B'].mean() > 3.)

Source - https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.filter.html

Or something like this-

gdf.apply(lambda g: g[g['team'] == 'A']).reset_index(drop=True).groupby(gdf.grouper.names)

Source- https://stackoverflow.com/questions/27488080/python-pandas-filter-rows-after-groupby

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