only_codes_read_NetCDF_Stat.py

#!/usr/bin/env python
# coding: utf-8

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import xarray as xr
import numpy as np 
import pandas as pd


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#load water eden water depth data 2021 q3
ds = xr.open_dataset ("C:/Users/ihasan2020/Desktop/2011_q3_depth_v3.nc")
da =ds ["depth"]
da


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#variables are in our dataset
ds.data_vars


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#monthly_data = ds.resample (freq = 'm', dim = 'time', how = 'mean')


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#look at the NetCDF representation 
ds.info()


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#slelect one variable and pick the first entry along the first axis (time)
ds.depth [0]


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#plot depth of one date 
ds.depth [0].plot()


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#dataset dimensions
ds.dims


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#dataset coordinates
ds.coords


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#The actual (numpy) arrray
ds.depth.data


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#Looking at the attribute 
ds.attrs


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#demonstrating customised time-seris data  
ds.sel(time= slice("2021-07-01","2021-08-30"))
       


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#performing arithmetic operation on the data set (+)
da + 1.5


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da_mean =da.mean(dim="time")
da_mean


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#Calculating standard deviation of water depth and ploting across time 

da.std (dim =["y","x"]).plot()


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#compute monthly mean depth
m_mean = ds.groupby("time.month").mean()
m_mean


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#Calculating median vlaue of water depth and ploting across time 
da.median (dim =["y","x"]).plot()


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#Resample to 5days mean (experimental)
ds.depth.resample (time ="5D").mean()


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#resample to tri_monthly mean uisng data form 2021-07-01 to 2021-09-30
F_mean = ds.depth.resample (time ="3MS").mean().plot()
F_mean


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#for exportable file 
F_mean = ds.depth.resample (time ="3MS").mean()
F_mean


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#Exporting final mean of water deapth data as.nc format to import in ArcMap
F_mean.to_netcdf("C:/Users/ihasan2020/Desktop/watrDpt_mean.nc")


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