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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert a set of CSV's to a NetCDF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import glob\n",
    "\n",
    "# 1. Get a list of all CSV files in your folder, assuming they all start with 'data_'\n",
    "csv_files = sorted(glob.glob(\"data_*.csv\"))\n",
    "\n",
    "# 2. Read and concatenate all CSVs\n",
    "df_list = [pd.read_csv(f) for f in csv_files]\n",
    "df_all = pd.concat(df_list, ignore_index=True)\n",
    "\n",
    "# 3. Convert to xarray Dataset\n",
    "# Assumes columns representing coordinates are: time, lat, lon\n",
    "# Anything that should be considered a variable (like 'temperature', 'salinit', etc) should not be included here\n",
    "ds = df_all.set_index(['time', 'lat', 'lon']).to_xarray()\n",
    "\n",
    "# 4. Convert time to datetime if needed (you can skip this step or convert to/from whatever datetime formats you want)\n",
    "ds['time'] = pd.to_datetime(ds['time'])\n",
    "\n",
    "# 5. Write to NetCDF\n",
    "ds.to_netcdf(\"combined_data.nc\")"
   ]
  }
 ],
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