climate-avg.ipynb

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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import sys\n",
    "import netCDF4\n",
    "from netCDF4 import Dataset\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "np.set_printoptions(threshold=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "places_df = pd.read_json('./files/places2.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in places_df.iterrows():\n",
    "\n",
    "    filtered_df = pd.DataFrame()\n",
    "\n",
    "    lat = row['lat']\n",
    "    lon = row['lon']\n",
    "\n",
    "    if(lat != \"\" and lon != \"\"):\n",
    "\n",
    "        for metric in ['tmax', 'tmin', 'tavg']:\n",
    "\n",
    "            if(metric == 'tmax'):\n",
    "                df = pd.read_csv('./files/Berkeley/TMAX-temperature.csv')\n",
    "            elif(metric == 'tmin'):\n",
    "                df = pd.read_csv('./files/Berkeley/TMIN-temperature.csv')\n",
    "            else:\n",
    "                df = pd.read_csv('./files/Berkeley/TAVG-temperature.csv')\n",
    "\n",
    "            for index2, year in enumerate([2017, 2012, 2007, 2002, 1997, 1993]):\n",
    "\n",
    "                filtered_df = df[df['time'] >= year]\n",
    "\n",
    "                filtered_df = filtered_df[(filtered_df['latitude'] >= lat - 0.5) & (filtered_df['latitude'] <= lat + 0.5) & (filtered_df['longitude'] >= lon - 0.5) & (filtered_df['longitude'] <= lon + 0.5)]\n",
    "\n",
    "                avg_temp = filtered_df['temperature'].mean()\n",
    "\n",
    "                avg_temp = round(avg_temp, 2)\n",
    "\n",
    "                if(avg_temp == avg_temp):\n",
    "                    if(metric == 'tmax'):\n",
    "                        places_df.at[index, 'avg_tmax_' + str(((index2 + 1) * 5))] = avg_temp\n",
    "                    elif(metric == 'tmin'):\n",
    "                        places_df.at[index, 'avg_tmin_' + str(((index2 + 1) * 5))] = avg_temp\n",
    "                    else:\n",
    "                        places_df.at[index, 'avg_avg_' + str(((index2 + 1) * 5))] = avg_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# places_df to csv\n",
    "places_df.to_csv('./files/AVG_Temperature_Anomaly.csv', index=False)"
   ]
  }
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