Yes, you can utilize the xarray library to iterate over all the files and subsequently select the desired location, appending it at each time step.

Python can be used to extract time series data for specific (x, y) coordinates from a batch of NetCDF files using The Xarray library as follows

import xarray as xr

import numpy as np

import os

# Directory of your NetCDF files

data_dir = 'path_netcdf_files'

# Target coordinates of your interest

target_x = 120.0 # Replace according to your coordinate

target_y = 100.0

# Initialize a list for saving the extracteds data

time_series_data = []

# Loop for all NetCDF file in the directory

for filename in os.listdir(data_dir):

if filename.endswith('.nc'):

file_path = os.path.join(data_dir, filename)

ds = xr.open_dataset(file_path)

# Find the nearest indices of coordinates

x_idx = np.abs(ds['x'] - target_x).argmin().item()

y_idx = np.abs(ds['y'] - target_y).argmin().item()

# Extract the time series

time_series = ds['variable_name'].isel(x=x_idx, y=y_idx) time_series_data.append(time_series)

# Time series data into a single xarray

combined_time_series = xr.concat(time_series_data, dim='time')

#Atlast

print(combined_time_series)

Thank you Bipul Kumar and Badarvada Yadidya

It took a little tweaking but did exactly what I needed it to do.