## Calculating monthly mean from daily netcdf file in python

Hello I have a netcdf file with daily data. Shape of the file is (5844, 89, 89) i.e 16 years data. I tried to get monthly average from daily data. I am looking for simillar to `resample`

function in pandas dataframe. Is there anyways to do that in python.
As I know it is very easy to calculate by using cdo and nco but I am looking in python.

Sample code that I used to read netcdf file is:

import netCDF4 from netCDF4 import Dataset fh = Dataset(ncfile, mode='r') time = fh.variables['time'][:] lon = fh.variables['longitude'][:] lat = fh.variables['latitude'][:] data = fh.variables['t2m'][:] data.shape

@ jhamman Thank you for suggesting `xarray.resample`

. It is simpler than I thought and the answer to my question is:

import xarray as xr ds = xr.open_dataset(nc_file) monthly_data = ds.resample(freq = 'm', dim = 'time', how = 'mean')

**Calculating Seasonal Averages from Timeseries of Monthly Means ,** Suppose we have a netCDF or xray Dataset of monthly mean data and we want to RACM R1002RBRxaaa01a output_frequency: daily output_mode: averaged calculate the month lengths for each monthly data record - calculate weights� Suppose we have a netCDF or xarray.Dataset of monthly mean data and we want to calculate the seasonal average. To do this properly, we need to calculate the weighted average considering that each month has a different number of days. : %matplotlib inline import numpy as np import pandas as pd import xarray as xr import matplotlib.pyplot as plt

by new versions of xarray, the usage comes like this

monthly_data=ds.resample(time='m').mean()

**Making monthly mean data from daily data,** Ncview is one of the simplest visual browser for netCDF format files. First I wanted to concatenate the history files into one, since CDO has an operator for calculating monthly means. You can use Python for plotting and visualising data. Example: Calculating the NINO 3.4 index The NINO 3.4 index is defined as the anomaly over the region (5N - 5S, 170W - 120W) compared to the historical average between 1961 and 1990 Notebook

If you are working in Linux this can be easily done using nctoolkit, which uses CDO as a backend. (Installation instructions here).

If you want to get the monthly mean, you just need the following:

import nctoolkit as nc data = nc.open_data(ncfile) data.monthly_mean()

This can be plotted:

data.plot()

If you wanted to convert it to a pandas dataframe:

df = data.to_dataframe()

**Calculating Seasonal Averages from Timeseries of Monthly ,** The data used for this example can be found in the xarray-data repository. You may need to change the path to rasm.nc below. Suppose we have a netCDF or xarray.Dataset of monthly mean data and we want to calculate the seasonal average. source : RACM R1002RBRxaaa01a; output_frequency : daily; output_mode :� I have a text file with four columns: year, month, day and snow depth. This is daily data for a 30-year period, 1979-2009. I would like to calculate 360 (30yrs X 12 months) individual monthly averages using pandas techniques (i.e. isolating all the values for Jan-1979, Feb-1979, Dec-2009 and averaging each).

**Any example code that does monthly averaging using netcdf4 ,** What other netCDF related Python libraries should I know about? Suppose we have a netCDF or xray Dataset of monthly mean data and we want to source: RACM R1002RBRxaaa01a output_frequency: daily output_mode: averaged calculate the month lengths for each monthly data record - calculate weights using� ncra -F -d time,1,,1 input_file output_file will calculate the time mean of a file. To sum over a dimension, for example to sum a file of daily precipitation to obtain an annual total ncra -h -O -y ttl in.nc out.nc will sum in time. I am not sure if this is because time is the record dimension in my file. Return

Someone must have a python code that uses netcdf4-python to do monthly averaging of daily or hourly data, right? It would be great to add this� import numpy as np import pandas as pd import xray def calc_month_start (dates): return pd.Index(np.asarray(dates)).to_period(' M ').to_timestamp() # "my_file.nc" is any netcdf with time encoded according to CF conventions data = xray.open_dataset(' my_file.nc ') data[' month_start '] = (' time ', calc_month_start(data[' time '])) tmin_monthly = data[' tmin '].groupby(' month_start ').mean()

I was able to open a netcdf file in R using the package "ncdf4" and extract rainfall (using ncvar_get). However I can't find information on how to calculate monthly values from daily values. Is this possible while staying in the netcdf format or should I convert it to something else? - but what format would keep the lat, long and time

##### Comments

- check out the resample functionality in xarray. It is built on top of Pandas' resample tools: xarray.pydata.org/en/stable/…