Climate change impact assessment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2d67447",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "from matplotlib import pyplot as plt\n",
    "from netCDF4 import Dataset, date2index, num2date, date2num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "c1811d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "past = \"F:/CMIP6_Data/Data/Merged/tasmax_day_ACCESS-CM2_historical_r1i1p1f1_gn_1950101-20141231.nc\"\n",
    "future = [\"F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp126_r1i1p1f1_gn_20150101-20641231.nc\", \"F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp126_r1i1p1f1_gn_20650101-21001231.nc\", \"F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp245_r1i1p1f1_gn_20150101-20641231.nc\",\n",
    "         \"F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp245_r1i1p1f1_gn_20650101-21001231.nc\", \"F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp370_r1i1p1f1_gn_20150101-20641231.nc\", \"F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp370_r1i1p1f1_gn_20650101-21001231.nc\",\n",
    "         \"F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp585_r1i1p1f1_gn_20150101-20641231.nc\", \"F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp585_r1i1p1f1_gn_20650101-21001231.nc\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "0f1a5743",
   "metadata": {},
   "outputs": [],
   "source": [
    "nc = Dataset(past)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "88da72fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time', size = 23741,\n",
       " 'bnds': <class 'netCDF4._netCDF4.Dimension'>: name = 'bnds', size = 2,\n",
       " 'lon': <class 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 192,\n",
       " 'lat': <class 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 144}"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc.dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "e2775efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 time(time)\n",
       "     standard_name: time\n",
       "     long_name: time\n",
       "     bounds: time_bnds\n",
       "     units: days since 1850-01-01\n",
       "     calendar: proleptic_gregorian\n",
       "     axis: T\n",
       " unlimited dimensions: time\n",
       " current shape = (23741,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'time_bnds': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 time_bnds(time, bnds)\n",
       " unlimited dimensions: time\n",
       " current shape = (23741, 2)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'lon': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 lon(lon)\n",
       "     standard_name: longitude\n",
       "     long_name: Longitude\n",
       "     units: degrees_east\n",
       "     axis: X\n",
       "     bounds: lon_bnds\n",
       " unlimited dimensions: \n",
       " current shape = (192,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'lon_bnds': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 lon_bnds(lon, bnds)\n",
       " unlimited dimensions: \n",
       " current shape = (192, 2)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'lat': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 lat(lat)\n",
       "     standard_name: latitude\n",
       "     long_name: Latitude\n",
       "     units: degrees_north\n",
       "     axis: Y\n",
       "     bounds: lat_bnds\n",
       " unlimited dimensions: \n",
       " current shape = (144,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'lat_bnds': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 lat_bnds(lat, bnds)\n",
       " unlimited dimensions: \n",
       " current shape = (144, 2)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'height': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 height()\n",
       "     standard_name: height\n",
       "     long_name: height\n",
       "     units: m\n",
       "     positive: up\n",
       "     axis: Z\n",
       " unlimited dimensions: \n",
       " current shape = ()\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'tasmax': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 tasmax(time, lat, lon)\n",
       "     standard_name: air_temperature\n",
       "     long_name: Daily Maximum Near-Surface Air Temperature\n",
       "     units: K\n",
       "     coordinates: height\n",
       "     _FillValue: 1e+20\n",
       "     missing_value: 1e+20\n",
       "     comment: maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')\n",
       "     cell_methods: area: mean time: maximum\n",
       "     cell_measures: area: areacella\n",
       "     history: 2019-11-09T02:14:42Z altered by CMOR: Treated scalar dimension: 'height'. 2019-11-09T02:14:42Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).\n",
       " unlimited dimensions: time\n",
       " current shape = (23741, 144, 192)\n",
       " filling on}"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "84c27160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float64 time(time)\n",
       "    standard_name: time\n",
       "    long_name: time\n",
       "    bounds: time_bnds\n",
       "    units: days since 1850-01-01\n",
       "    calendar: proleptic_gregorian\n",
       "    axis: T\n",
       "unlimited dimensions: time\n",
       "current shape = (23741,)\n",
       "filling on, default _FillValue of 9.969209968386869e+36 used"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc.variables['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "bca57f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[36524.5, 36525.5, 36526.5, 36527.5, 36528.5, 36529.5,\n",
       "                   36530.5, 36531.5, 36532.5, 36533.5],\n",
       "             mask=False,\n",
       "       fill_value=1e+20)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc_time = nc.variables['time']\n",
    "nc_time[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "a0c53ac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_start = date2index(dt.datetime(1970, 1, 1), nc_time, select=\"nearest\")\n",
    "time_end = date2index(dt.datetime(2000, 12, 31), nc_time, select=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "8fd9c9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cftime.DatetimeProlepticGregorian(2000, 12, 30, 12, 0, 0, 0, has_year_zero=True)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num2date(nc_time[time_end], units=nc_time.units, calendar=nc_time.calendar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "e477e0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bhur station coords\n",
    "target = {\"lat\": 26.9, \"lon\": 90.43}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "42aad382",
   "metadata": {},
   "outputs": [],
   "source": [
    "nc_lat = nc.variables['lat'][:].data\n",
    "nc_lon = nc.variables['lon'][:].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "af2ecb1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_cell = np.argmin(np.abs(nc_lat - target['lat']))\n",
    "lon_cell = np.argmin(np.abs(nc_lon - target['lon']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "0cf10075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[281.46704, 282.52734, 282.80176, 282.66455, 280.49463,\n",
       "                   281.5891 , 281.35132, 284.94458, 285.22876, 283.761  ],\n",
       "             mask=False,\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat = nc.variables['tasmax'][time_start:(time_end + 1), lat_cell, lon_cell]\n",
    "dat[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "28f457e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = np.subtract(dat.data, 273.15) #Convert from Kelvin to Celsius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "ccf8b48c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11323"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_end - time_start + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "8da104e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11323"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "5ffd815b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = num2date(nc_time[time_start:time_end + 1], units=nc_time.units, calendar=nc_time.calendar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "7bd0b7e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Experiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969-12-31 12:00:00</td>\n",
       "      <td>8.317047</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-01-01 12:00:00</td>\n",
       "      <td>9.377350</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-01-02 12:00:00</td>\n",
       "      <td>9.651764</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-01-03 12:00:00</td>\n",
       "      <td>9.514557</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-01-04 12:00:00</td>\n",
       "      <td>7.344635</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date   MaxTemp  Experiment\n",
       "0  1969-12-31 12:00:00  8.317047  historical\n",
       "1  1970-01-01 12:00:00  9.377350  historical\n",
       "2  1970-01-02 12:00:00  9.651764  historical\n",
       "3  1970-01-03 12:00:00  9.514557  historical\n",
       "4  1970-01-04 12:00:00  7.344635  historical"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Date': dates, 'MaxTemp': dat, 'Experiment': 'historical'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "73c664c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = [dt.date(i.year, i.month, i.day) for i in df.Date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "f7c81f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Experiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969-12-31</td>\n",
       "      <td>8.317047</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>9.377350</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-01-02</td>\n",
       "      <td>9.651764</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-01-03</td>\n",
       "      <td>9.514557</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-01-04</td>\n",
       "      <td>7.344635</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   MaxTemp  Experiment\n",
       "0  1969-12-31  8.317047  historical\n",
       "1  1970-01-01  9.377350  historical\n",
       "2  1970-01-02  9.651764  historical\n",
       "3  1970-01-03  9.514557  historical\n",
       "4  1970-01-04  7.344635  historical"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "079a0d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Experiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11318</th>\n",
       "      <td>2000-12-26</td>\n",
       "      <td>9.005768</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11319</th>\n",
       "      <td>2000-12-27</td>\n",
       "      <td>10.194733</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11320</th>\n",
       "      <td>2000-12-28</td>\n",
       "      <td>9.781647</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11321</th>\n",
       "      <td>2000-12-29</td>\n",
       "      <td>9.636139</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11322</th>\n",
       "      <td>2000-12-30</td>\n",
       "      <td>10.643707</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "             Date    MaxTemp  Experiment\n",
       "11318  2000-12-26   9.005768  historical\n",
       "11319  2000-12-27  10.194733  historical\n",
       "11320  2000-12-28   9.781647  historical\n",
       "11321  2000-12-29   9.636139  historical\n",
       "11322  2000-12-30  10.643707  historical"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "7533d70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Daily Maximum Temperature (°C)')"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.Date, df.MaxTemp)\n",
    "plt.title(r'ACCESS-CM2 Daily $T_\\mathrm{Maximum}$ in Bhur (2000‒2014)')\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Daily Maximum Temperature (°C)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "8ba6f53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nc.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "8526a2de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp126_r1i1p1f1_gn_20150101-20641231.nc',\n",
       " 'F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp126_r1i1p1f1_gn_20650101-21001231.nc',\n",
       " 'F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp245_r1i1p1f1_gn_20150101-20641231.nc',\n",
       " 'F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp245_r1i1p1f1_gn_20650101-21001231.nc',\n",
       " 'F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp370_r1i1p1f1_gn_20150101-20641231.nc',\n",
       " 'F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp370_r1i1p1f1_gn_20650101-21001231.nc',\n",
       " 'F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp585_r1i1p1f1_gn_20150101-20641231.nc',\n",
       " 'F:/CMIP6_Data/Data/tasmax_day_ACCESS-CM2_ssp585_r1i1p1f1_gn_20650101-21001231.nc']"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "a28b62cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for file in future:\n",
    "    nc = Dataset(file)\n",
    "    nc_time = nc.variables[\"time\"]\n",
    "    time_start = date2index(dt.datetime(2010, 1, 1), nc_time, select=\"nearest\")\n",
    "    time_end = date2index(dt.datetime(2099, 12, 31), nc_time, select=\"nearest\")\n",
    "    lat_cell = np.argmin(np.abs(nc_lat - target['lat']))\n",
    "    lon_cell = np.argmin(np.abs(nc_lon - target['lon']))\n",
    "    dat = nc.variables['tasmax'][time_start:(time_end + 1), lat_cell, lon_cell]\n",
    "    dat = np.subtract(dat.data, 273.15)\n",
    "    dates = num2date(nc_time[time_start:time_end + 1], units=nc_time.units, calendar=nc_time.calendar)\n",
    "    df2 = pd.DataFrame({'Date': dates, 'MaxTemp': dat, 'Experiment': re.findall(r'ssp[012345678]{3}', file)[0]})\n",
    "    df2['Date'] = [dt.date(i.year, i.month, i.day) for i in df2.Date]\n",
    "    df = df.append(df2, ignore_index=True, sort=True)\n",
    "    nc.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "7d49cc61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of               Date  Experiment    MaxTemp\n",
       "0       1969-12-31  historical   8.317047\n",
       "1       1970-01-01  historical   9.377350\n",
       "2       1970-01-02  historical   9.651764\n",
       "3       1970-01-03  historical   9.514557\n",
       "4       1970-01-04  historical   7.344635\n",
       "...            ...         ...        ...\n",
       "261142  2099-12-27      ssp585  18.048737\n",
       "261143  2099-12-28      ssp585  17.401520\n",
       "261144  2099-12-29      ssp585  15.163727\n",
       "261145  2099-12-30      ssp585  15.474518\n",
       "261146  2099-12-31      ssp585  14.898102\n",
       "\n",
       "[261147 rows x 3 columns]>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "6ad60b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "c30b37b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Daily Maximum Temperature (°C)')"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.Date[df.Experiment == \"ssp126\"], df.MaxTemp[df.Experiment == \"ssp126\"], 'y-', alpha=0.7, label = 'ssp 126')\n",
    "plt.plot(df.Date[df.Experiment == \"ssp245\"], df.MaxTemp[df.Experiment == \"ssp245\"], 'y-', alpha=0.7, label = 'ssp 245')\n",
    "plt.plot(df.Date[df.Experiment == \"ssp370\"], df.MaxTemp[df.Experiment == \"ssp370\"], 'y-', alpha=0.7, label = 'ssp 370')\n",
    "plt.plot(df.Date[df.Experiment == \"ssp585\"], df.MaxTemp[df.Experiment == \"ssp585\"], 'y-', alpha=0.7, label = 'ssp 585')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title(r'ACCESS-CM2 Daily $T_\\mathrm{Maximum}$ in Bhur (2010‒2099)')\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Daily Maximum Temperature (°C)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "49c2138a",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12952/1801012997.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Date\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"MaxTemp\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Experiment\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Maximum Temperature (°C)\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mr'ACCESS-CM2 Daily $T_\\mathrm{Maximum}$ in Bhur (2000‒2100)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[1;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[0;32m    708\u001b[0m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_attach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    709\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 710\u001b[1;33m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    711\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    712\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m    497\u001b[0m                     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"estimator must be None when specifying units\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 499\u001b[1;33m                 \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_ci\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    500\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m                 \u001b[0my_ci\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36maggregate\u001b[1;34m(self, vals, grouper, units)\u001b[0m\n\u001b[0;32m    412\u001b[0m                                columns=[\"low\", \"high\"]).stack()\n\u001b[0;32m    413\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 414\u001b[1;33m             \u001b[0mcis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbootstrapped_cis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    416\u001b[0m         \u001b[1;31m# Unpack the CIs into \"wide\" format for plotting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m    221\u001b[0m     )\n\u001b[0;32m    222\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_agg_template\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexamples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_agg_examples_doc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Series\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1270\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0moption_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"mode.chained_assignment\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1271\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1272\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_apply_general\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_selected_obj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1273\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1274\u001b[0m                 \u001b[1;31m# gh-20949\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36m_python_apply_general\u001b[1;34m(self, f, data)\u001b[0m\n\u001b[0;32m   1304\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mapplying\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1305\u001b[0m         \"\"\"\n\u001b[1;32m-> 1306\u001b[1;33m         \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmutated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1308\u001b[0m         return self._wrap_applied_output(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m    818\u001b[0m             \u001b[1;31m# group might be modified\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    819\u001b[0m             \u001b[0mgroup_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 820\u001b[1;33m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    821\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_is_indexed_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroup_axes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    822\u001b[0m                 \u001b[0mmutated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mbootstrapped_cis\u001b[1;34m(vals)\u001b[0m\n\u001b[0;32m    393\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mnull_ci\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 395\u001b[1;33m             \u001b[0mboots\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbootstrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    396\u001b[0m             \u001b[0mcis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mci_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mboots\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    397\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"low\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"high\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\algorithms.py\u001b[0m in \u001b[0;36mbootstrap\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m     \u001b[0mboot_dist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m         \u001b[0mresampler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mintegers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# intp is indexing dtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m         \u001b[0msample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresampler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[0mboot_dist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.lineplot(data=df, x=\"Date\", y=\"MaxTemp\", hue=\"Experiment\").set(ylabel=\"Maximum Temperature (°C)\", title=r'ACCESS-CM2 Daily $T_\\mathrm{Maximum}$ in Bhur (2000‒2100)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "f3eae637",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Index contains duplicate entries, cannot reshape",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12952/911080228.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_wide\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Experiment'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'MaxTemp'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf_wide\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mpivot\u001b[1;34m(self, index, columns, values)\u001b[0m\n\u001b[0;32m   7791\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpivot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7792\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7793\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   7794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7795\u001b[0m     _shared_docs[\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\pivot.py\u001b[0m in \u001b[0;36mpivot\u001b[1;34m(data, index, columns, values)\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m             \u001b[0mindexed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmultiindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mindexed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns_listlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36munstack\u001b[1;34m(self, level, fill_value)\u001b[0m\n\u001b[0;32m   8323\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0munstack\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   8324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 8325\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   8326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   8327\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unstack\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36munstack\u001b[1;34m(obj, level, fill_value)\u001b[0m\n\u001b[0;32m    445\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0m_unstack_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    448\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36m_unstack_frame\u001b[1;34m(obj, level, fill_value)\u001b[0m\n\u001b[0;32m    472\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    473\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m         \u001b[0munstacker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_Unstacker\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    475\u001b[0m         return unstacker.get_result(\n\u001b[0;32m    476\u001b[0m             \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue_columns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, index, level, constructor)\u001b[0m\n\u001b[0;32m    131\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Unstacked DataFrame is too big, causing int32 overflow\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 133\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_selectors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    134\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36m_make_selectors\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    183\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 185\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Index contains duplicate entries, cannot reshape\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomp_index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Index contains duplicate entries, cannot reshape"
     ]
    }
   ],
   "source": [
    "df_wide = df.pivot(index='Date', columns='Experiment')['MaxTemp']\n",
    "df_wide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "8400fa67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>MaxTemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969-12-31</td>\n",
       "      <td>historical</td>\n",
       "      <td>8.317047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>historical</td>\n",
       "      <td>9.377350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-01-02</td>\n",
       "      <td>historical</td>\n",
       "      <td>9.651764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-01-03</td>\n",
       "      <td>historical</td>\n",
       "      <td>9.514557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-01-04</td>\n",
       "      <td>historical</td>\n",
       "      <td>7.344635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261142</th>\n",
       "      <td>2099-12-27</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>18.048737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261143</th>\n",
       "      <td>2099-12-28</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>17.401520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261144</th>\n",
       "      <td>2099-12-29</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>15.163727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261145</th>\n",
       "      <td>2099-12-30</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>15.474518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261146</th>\n",
       "      <td>2099-12-31</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>14.898102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261147 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date  Experiment    MaxTemp\n",
       "0       1969-12-31  historical   8.317047\n",
       "1       1970-01-01  historical   9.377350\n",
       "2       1970-01-02  historical   9.651764\n",
       "3       1970-01-03  historical   9.514557\n",
       "4       1970-01-04  historical   7.344635\n",
       "...            ...         ...        ...\n",
       "261142  2099-12-27      ssp585  18.048737\n",
       "261143  2099-12-28      ssp585  17.401520\n",
       "261144  2099-12-29      ssp585  15.163727\n",
       "261145  2099-12-30      ssp585  15.474518\n",
       "261146  2099-12-31      ssp585  14.898102\n",
       "\n",
       "[261147 rows x 3 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8a84f4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Experiment</th>\n",
       "      <th>historical</th>\n",
       "      <th>ssp126</th>\n",
       "      <th>ssp245</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2100-12-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.359039</td>\n",
       "      <td>16.023590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-12-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.847076</td>\n",
       "      <td>13.919830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-12-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.317535</td>\n",
       "      <td>18.381500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-12-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.486725</td>\n",
       "      <td>17.382721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-12-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.409821</td>\n",
       "      <td>14.300934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Experiment  historical     ssp126     ssp245\n",
       "Date                                        \n",
       "2100-12-26         NaN  16.359039  16.023590\n",
       "2100-12-27         NaN  13.847076  13.919830\n",
       "2100-12-28         NaN  13.317535  18.381500\n",
       "2100-12-29         NaN  14.486725  17.382721\n",
       "2100-12-30         NaN  13.409821  14.300934"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wide.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5d9b335e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Maximum Temperature (°C)')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_wide.plot(title = r'ACCESS-CM2 Daily $T_\\mathrm{Maximum}$ in Bhur (2000‒2100)')\n",
    "plt.ylabel(\"Maximum Temperature (°C)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "fc5ad615",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base = df[(df.Date >= dt.date(1970, 1, 1)) & (df.Date <= dt.date(2000, 12, 31))] #reference period\n",
    "df_2020s = df[(df.Date >= dt.date(2010, 1, 1)) & (df.Date <= dt.date(2039, 12, 31))] #2020s\n",
    "df_2030s = df[(df.Date >= dt.date(2020, 1, 1)) & (df.Date <= dt.date(2049, 12, 31))] #2030s\n",
    "df_2040s = df[(df.Date >= dt.date(2030, 1, 1)) & (df.Date <= dt.date(2059, 12, 31))] #2040s\n",
    "df_2050s = df[(df.Date >= dt.date(2040, 1, 1)) & (df.Date <= dt.date(2069, 12, 31))] #2050s\n",
    "df_2060s = df[(df.Date >= dt.date(2050, 1, 1)) & (df.Date <= dt.date(2079, 12, 31))] #2060s\n",
    "df_2070s = df[(df.Date >= dt.date(2060, 1, 1)) & (df.Date <= dt.date(2089, 12, 31))] #2070s\n",
    "df_2080s = df[(df.Date >= dt.date(2070, 1, 1)) & (df.Date <= dt.date(2099, 12, 31))] #2080s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "63922ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "bsln126 = df_base[df_base.Experiment.isin([\"historical\", \"ssp126\"])].MaxTemp.mean()\n",
    "bsln245 = df_base[df_base.Experiment.isin([\"historical\", \"ssp245\"])].MaxTemp.mean()\n",
    "bsln370 = df_base[df_base.Experiment.isin([\"historical\", \"ssp370\"])].MaxTemp.mean()\n",
    "bsln545 = df_base[df_base.Experiment.isin([\"historical\", \"ssp545\"])].MaxTemp.mean()\n",
    "bsln = np.mean([bsln126, bsln245, bsln370, bsln545])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "74f30b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "cf2020s_ssp126 = df_2020s[df_2020s.Experiment == \"ssp126\"].MaxTemp.mean() - bsln\n",
    "cf2020s_ssp245 = df_2020s[df_2020s.Experiment == \"ssp245\"].MaxTemp.mean() - bsln\n",
    "cf2020s_ssp370 = df_2020s[df_2020s.Experiment == \"ssp370\"].MaxTemp.mean() - bsln\n",
    "cf2020s_ssp545 = df_2020s[df_2020s.Experiment == \"ssp545\"].MaxTemp.mean() - bsln\n",
    "cf2030s_ssp126 = df_2030s[df_2030s.Experiment == \"ssp126\"].MaxTemp.mean() - bsln\n",
    "cf2030s_ssp245 = df_2030s[df_2030s.Experiment == \"ssp245\"].MaxTemp.mean() - bsln\n",
    "cf2030s_ssp370 = df_2030s[df_2030s.Experiment == \"ssp370\"].MaxTemp.mean() - bsln\n",
    "cf2030s_ssp545 = df_2030s[df_2030s.Experiment == \"ssp545\"].MaxTemp.mean() - bsln\n",
    "cf2040s_ssp126 = df_2040s[df_2040s.Experiment == \"ssp126\"].MaxTemp.mean() - bsln\n",
    "cf2040s_ssp245 = df_2040s[df_2040s.Experiment == \"ssp245\"].MaxTemp.mean() - bsln\n",
    "cf2040s_ssp370 = df_2040s[df_2040s.Experiment == \"ssp370\"].MaxTemp.mean() - bsln\n",
    "cf2040s_ssp545 = df_2040s[df_2040s.Experiment == \"ssp545\"].MaxTemp.mean() - bsln\n",
    "cf2050s_ssp126 = df_2050s[df_2050s.Experiment == \"ssp126\"].MaxTemp.mean() - bsln\n",
    "cf2050s_ssp245 = df_2050s[df_2050s.Experiment == \"ssp245\"].MaxTemp.mean() - bsln\n",
    "cf2050s_ssp370 = df_2050s[df_2050s.Experiment == \"ssp370\"].MaxTemp.mean() - bsln\n",
    "cf2050s_ssp545 = df_2050s[df_2050s.Experiment == \"ssp545\"].MaxTemp.mean() - bsln\n",
    "cf2060s_ssp126 = df_2060s[df_2060s.Experiment == \"ssp126\"].MaxTemp.mean() - bsln\n",
    "cf2060s_ssp245 = df_2060s[df_2060s.Experiment == \"ssp245\"].MaxTemp.mean() - bsln\n",
    "cf2060s_ssp370 = df_2060s[df_2060s.Experiment == \"ssp370\"].MaxTemp.mean() - bsln\n",
    "cf2060s_ssp545 = df_2060s[df_2060s.Experiment == \"ssp545\"].MaxTemp.mean() - bsln\n",
    "cf2070s_ssp126 = df_2070s[df_2070s.Experiment == \"ssp126\"].MaxTemp.mean() - bsln\n",
    "cf2070s_ssp245 = df_2070s[df_2070s.Experiment == \"ssp245\"].MaxTemp.mean() - bsln\n",
    "cf2070s_ssp370 = df_2070s[df_2070s.Experiment == \"ssp370\"].MaxTemp.mean() - bsln\n",
    "cf2070s_ssp545 = df_2070s[df_2070s.Experiment == \"ssp545\"].MaxTemp.mean() - bsln\n",
    "cf2080s_ssp126 = df_2080s[df_2080s.Experiment == \"ssp126\"].MaxTemp.mean() - bsln\n",
    "cf2080s_ssp245 = df_2080s[df_2080s.Experiment == \"ssp245\"].MaxTemp.mean() - bsln\n",
    "cf2080s_ssp370 = df_2080s[df_2080s.Experiment == \"ssp370\"].MaxTemp.mean() - bsln\n",
    "cf2080s_ssp545 = df_2080s[df_2080s.Experiment == \"ssp545\"].MaxTemp.mean() - bsln"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "caa0b5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Ensemble</th>\n",
       "      <th>Scenario</th>\n",
       "      <th>Baseline (°C)</th>\n",
       "      <th>2020s</th>\n",
       "      <th>2030s</th>\n",
       "      <th>2040s</th>\n",
       "      <th>2050s</th>\n",
       "      <th>2060s</th>\n",
       "      <th>2070s</th>\n",
       "      <th>2080s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>r1i1p1</td>\n",
       "      <td>ssp126</td>\n",
       "      <td>17.766876</td>\n",
       "      <td>1.188120</td>\n",
       "      <td>1.486721</td>\n",
       "      <td>1.816484</td>\n",
       "      <td>2.099861</td>\n",
       "      <td>2.186138</td>\n",
       "      <td>2.245155</td>\n",
       "      <td>2.256041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>r1i1p1</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>17.766876</td>\n",
       "      <td>1.091707</td>\n",
       "      <td>1.408997</td>\n",
       "      <td>1.837358</td>\n",
       "      <td>2.272375</td>\n",
       "      <td>2.613728</td>\n",
       "      <td>2.901901</td>\n",
       "      <td>3.121983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>r1i1p1</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>17.766876</td>\n",
       "      <td>1.222782</td>\n",
       "      <td>1.548956</td>\n",
       "      <td>2.022907</td>\n",
       "      <td>2.461065</td>\n",
       "      <td>2.981953</td>\n",
       "      <td>3.512262</td>\n",
       "      <td>4.089325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>r1i1p1</td>\n",
       "      <td>ssp545</td>\n",
       "      <td>17.766876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Model Ensemble Scenario  Baseline (°C)     2020s     2030s     2040s  \\\n",
       "0  ACCESS-CM2   r1i1p1   ssp126      17.766876  1.188120  1.486721  1.816484   \n",
       "1  ACCESS-CM2   r1i1p1   ssp245      17.766876  1.091707  1.408997  1.837358   \n",
       "2  ACCESS-CM2   r1i1p1   ssp370      17.766876  1.222782  1.548956  2.022907   \n",
       "3  ACCESS-CM2   r1i1p1   ssp545      17.766876       NaN       NaN       NaN   \n",
       "\n",
       "      2050s     2060s     2070s     2080s  \n",
       "0  2.099861  2.186138  2.245155  2.256041  \n",
       "1  2.272375  2.613728  2.901901  3.121983  \n",
       "2  2.461065  2.981953  3.512262  4.089325  \n",
       "3       NaN       NaN       NaN       NaN  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Model\": np.repeat(\"ACCESS-CM2\", 4),\n",
    "              \"Ensemble\": np.repeat(\"r1i1p1\", 4),\n",
    "              \"Scenario\": [\"ssp126\", \"ssp245\", \"ssp370\", \"ssp545\"],\n",
    "              \"Baseline (°C)\": np.repeat([bsln], 4),\n",
    "              \"2020s\": [cf2020s_ssp126, cf2020s_ssp245, cf2020s_ssp370, cf2020s_ssp545],\n",
    "              \"2030s\": [cf2030s_ssp126, cf2030s_ssp245, cf2030s_ssp370, cf2030s_ssp545],\n",
    "              \"2040s\": [cf2040s_ssp126, cf2040s_ssp245, cf2040s_ssp370, cf2040s_ssp545],\n",
    "              \"2050s\": [cf2050s_ssp126, cf2050s_ssp245, cf2050s_ssp370, cf2050s_ssp545],\n",
    "              \"2060s\": [cf2060s_ssp126, cf2060s_ssp245, cf2060s_ssp370, cf2060s_ssp545],\n",
    "              \"2070s\": [cf2070s_ssp126, cf2070s_ssp245, cf2070s_ssp370, cf2070s_ssp545],\n",
    "              \"2080s\": [cf2080s_ssp126, cf2080s_ssp245, cf2080s_ssp370, cf2080s_ssp545]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc5e5091",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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