Rainfall_rescue_data_1840s_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d25d16b9-8b21-4c97-be02-4557e9ba4a06",
   "metadata": {},
   "source": [
    "<h1> Precipitation data  - Location not truncated</h1>\n",
    "\n",
    "<p>Example Script to extract raw rainfall data (in inches per month) from Ed Hawkins Rainfall Rescue Github. 1840s decade folder.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d65044-f5e7-48d3-8112-1ad3534275e0",
   "metadata": {},
   "source": [
    "<h2>Load modules</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf40562b-1980-45b2-b40a-49a45e2aeed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Need a conda environment with the below packaged available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40d53098-27e9-4d62-a494-9b625e90555a",
   "metadata": {},
   "outputs": [],
   "source": [
    "##I had to directly install beautfil soup as this wasnt in my environment\n",
    "#!conda install conda-forge::beautifulsoup4 -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a361e94-804a-4385-b02c-0db0b35bc139",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported Xarray\n",
      "Imported numpy\n",
      "Imported scipy.stats\n",
      "Imported matplotlib\n",
      "Imported cftime\n",
      "Imported math\n",
      "Imported pandas\n",
      "Imported BeautifulSoup\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "print('Imported Xarray')\n",
    "import numpy as np\n",
    "print('Imported numpy')\n",
    "import scipy.stats as stats\n",
    "print('Imported scipy.stats')\n",
    "import matplotlib.pyplot as plt\n",
    "print('Imported matplotlib')\n",
    "import cftime\n",
    "print('Imported cftime')\n",
    "import math\n",
    "print('Imported math')\n",
    "import pandas as pd\n",
    "print('Imported pandas')\n",
    "from bs4 import BeautifulSoup\n",
    "print('Imported BeautifulSoup')\n",
    "import requests\n",
    "import time\n",
    "import re\n",
    "from requests.auth import HTTPBasicAuth\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766a8ce5-89fa-4b31-8829-ad664b8e0e48",
   "metadata": {},
   "source": [
    "<h2>Open Monthly Precipitation Data</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365d22ed-8290-4566-a04e-fc3fc3b3cb1c",
   "metadata": {},
   "source": [
    "<p>Data is all on GitHub - each pdf page as a as single .csv file. Data file layout and encoding is explained on the <a href='https://github.com/ed-hawkins/rainfall-rescue/blob/master/schema.md'>schema page</a>.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a9656ec-27f4-4f86-9a35-c2c739b54f33",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Isolate all urls containing .csv files on monthly rainfall data\n",
    "\n",
    "url = 'https://github.com/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS'\n",
    "stringmatch = 'ALLSHEETS/1'\n",
    "all_data_parent_urls=[]\n",
    "\n",
    "response = requests.get(url)\n",
    "time.sleep(2)\n",
    "soup = BeautifulSoup(response.content, 'html.parser')\n",
    "for link in soup.findAll('a', href=True):\n",
    "    if stringmatch in link.get('href'):\n",
    "        #print(link.get('href'))\n",
    "        all_data_parent_urls.append(link.get('href')) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a425e72f-0f81-48f4-b5f9-45a54d5e9a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##should have 15 unique parent urls\n",
    "all_data_parent_urls=list(set(all_data_parent_urls))\n",
    "len(all_data_parent_urls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa5cd8ac-1240-439c-9c56-09499ef024b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1930s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1830s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1950s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1677s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1870s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1920s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1910s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1860s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1940s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1840s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1820s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1900s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1850s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1890s',\n",
       " '/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1880s']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_parent_urls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc118fc-c13b-4c10-87c0-3c0e4dc8b0ce",
   "metadata": {},
   "source": [
    "<h2>Create text files containing all the possible file names</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac5f0aa-909f-4208-9067-b6ef5f675206",
   "metadata": {},
   "source": [
    "<h3>1840s</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab53ba81-f19a-4625-8c39-2bf8cd355d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "DECADE=str('1840s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f545a2a2-9443-4156-b030-8bb86d1c7178",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manually Check for truncating https://github.com/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1840s\n"
     ]
    }
   ],
   "source": [
    "##there is probably a better way to index the needed url, I just counted and manually entered the index. \n",
    "url='https://github.com'+str(all_data_parent_urls[9])\n",
    "print('Manually Check for truncating '+str(url))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09168ac1-0e88-4b05-939d-45993ed34154",
   "metadata": {},
   "source": [
    "<p> Not truncated </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6d59601e-7fce-4c54-a508-d8650be9f4f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sourcing from: https://github.com/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1840s\n"
     ]
    }
   ],
   "source": [
    "print('Sourcing from: '+str(url))\n",
    "##Get all parent file names\n",
    "stringmatch='TYRain_'\n",
    "parent_files=[]\n",
    "response = requests.get(url)\n",
    "soup = BeautifulSoup(response.content, 'html.parser')\n",
    "for link in soup.findAll('a', href=True):\n",
    "    if stringmatch in link.get_text():\n",
    "        #print(link.get_text())\n",
    "        parent_files.append(link.get_text()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd44b74-b2ce-4d1d-8d31-3771a8cfbb05",
   "metadata": {},
   "source": [
    "<h4>Create list of raw data locations</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0a4e87f-c330-4b68-8778-f5d2d272a60a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(parent_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c8b78a34-f360-4c8e-9e46-25adab1b7281",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_parent_string='https://raw.githubusercontent.com/ed-hawkins/rainfall-rescue/master/ALLSHEETS/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "600c28dc-3734-4f85-a3d8-956e24d2cefc",
   "metadata": {},
   "outputs": [],
   "source": [
    "##All possible filenames\n",
    "raw_urls_1840s_all=[str(raw_parent_string)+str(DECADE)+'/'+str(i) for i in parent_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dcaaa93e-a855-4969-acea-9a0770fc2180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://raw.githubusercontent.com/ed-hawkins/rainfall-rescue/master/ALLSHEETS/1840s/TYRain_1840-1849_D_pt2-158.csv'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_urls_1840s_all[130]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "69b1bb9a-7a6e-49e4-9406-c31070b916db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_urls_1840s_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89f3c66-5b06-4f45-937e-b5e82b11bf79",
   "metadata": {},
   "source": [
    "<h4>Remove raw data locations that dont exist</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "728b68bd-6794-4ee2-b1ef-f3e5bb785008",
   "metadata": {},
   "outputs": [],
   "source": [
    "##remove filenames that dont actually exist\n",
    "##Due to large volume of requests being sent use auth and build in sleep if/when rate limiting is hit\n",
    "errors=[]\n",
    "for i in range(len(raw_urls_1840s_all)):\n",
    "    try:\n",
    "        response=requests.get(raw_urls_1840s_all[i],auth=HTTPBasicAuth('YOURUSERNAME', 'YOURAUTHCODE'))\n",
    "        response.raise_for_status()\n",
    "    except Exception as e:\n",
    "        ##if rate limited wait an hour\n",
    "        if response.status_code == 429:\n",
    "            print('5000 requests hit, sleep for an hour')\n",
    "            time.sleep(60)\n",
    "        #if doesnt exist then collect the filename\n",
    "        if response.status_code == 404:\n",
    "            errors.append(raw_urls_1840s_all[i])\n",
    "        #if something weird has happened then let me know\n",
    "        else:\n",
    "            print('Error unaccounted for: code '+str(response.status_code)+' . Url: '+str(raw_urls_1840s_all[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aec6bb3a-0059-445a-925d-75c4c8426ccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ##should be about 0 blank pages\n",
    "len(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "47171d0e-aa69-4ab7-9019-8768a1ff3f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "##collate raw locations that exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6e612229-acc5-434c-bad3-8afc51aff1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create clean raw list - no empty hits contained\n",
    "raw_urls_1840s=[i for i in raw_urls_1840s_all if i not in errors]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d88cd09e-9d35-41ef-a757-c1ebe2faf0b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_urls_1840s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62681bb5-b912-42a7-bf93-9039f2a8614b",
   "metadata": {},
   "source": [
    "<h4>Save this</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a31b4bdc-3431-47ac-8265-7dc463bac190",
   "metadata": {},
   "outputs": [],
   "source": [
    "##save this as a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "390ec23d-6509-48e7-a6bf-18b02c2c32cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('raw_urls_1840s.csv',raw_urls_1840s,delimiter =',',fmt ='% s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8159e271-1f46-4141-91e4-fe5429bbc7d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #tidy up large unneeded lists\n",
    "# del raw_urls_1840s_all, errors, filenames_1840s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7de01127-2f1c-45ff-b5ac-5bb70e131303",
   "metadata": {},
   "outputs": [],
   "source": [
    "# raw_urls_1840s[-100:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbea6541-a4e8-41d7-8b13-c4828195f6fb",
   "metadata": {},
   "source": [
    "<h4>Open the raw data and collate it into a single dataframe for the entire decade</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "91eb71a3-389b-4a60-a926-6ff1184af64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Open the raw urls and convert to list\n",
    "raw_urls_1840s=pd.read_csv('raw_urls_1840s.csv',header=None,delimiter =',')\n",
    "raw_urls_1840s=[i for i in raw_urls_1840s[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b5d9fae2-edb1-438c-ac47-420b005822cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_urls_1840s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bbd79f09-dcde-48ad-8e77-a3d880b49074",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Collect up all raw data for this decade\n",
    "##**this cell takes half an hour to run**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c4ed6863-0ea6-458a-a4da-47031716d072",
   "metadata": {},
   "outputs": [],
   "source": [
    "##create a dataframe with the rainfall monthly csv data (not total or diffs)\n",
    "all_raw_data_1840s=[]\n",
    "\n",
    "for url in raw_urls_1840s:\n",
    "    df = pd.read_csv(url,skiprows=3,nrows=12,header=1,index_col=0,delimiter=',',usecols=[0,1,2,3,4,5,6,7,8,9,10])\n",
    "    all_raw_data_1840s.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "564d45ae-0b0e-4e8f-8d9e-14ae74d7c9fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_raw_data_1840s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eb94306a-0968-4d58-a45b-f89d6ee742f4",
   "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>1840</th>\n",
       "      <th>1841</th>\n",
       "      <th>1842</th>\n",
       "      <th>1843</th>\n",
       "      <th>1844</th>\n",
       "      <th>1845</th>\n",
       "      <th>1846</th>\n",
       "      <th>1847</th>\n",
       "      <th>1848</th>\n",
       "      <th>1849</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1.44</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>0.70</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>0.69</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.27</td>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>3.31</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>1.19</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.72</td>\n",
       "      <td>0.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>3.14</td>\n",
       "      <td>3.66</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>0.75</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1.33</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1.95</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0.54</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>2.91</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>0.98</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.36</td>\n",
       "      <td>2.20</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": [
       "           1840  1841  1842  1843  1844  1845  1846  1847  1848  1849\n",
       "January    1.54  0.54  1.37  0.00  1.43  0.89  1.99   NaN   NaN   NaN\n",
       "February   1.44  1.06  0.91  1.67  1.80  0.10  0.24   NaN   NaN   NaN\n",
       "March      0.70  2.49  2.24  0.50  0.98  1.80  0.99   NaN   NaN   NaN\n",
       "April      0.69  1.59  1.82  1.92  0.25  1.27  2.30   NaN   NaN   NaN\n",
       "May        3.31  2.03  2.99  3.67  0.20  1.91  0.65   NaN   NaN   NaN\n",
       "June       1.19  2.18  1.23  2.66  0.74  2.72  0.91   NaN   NaN   NaN\n",
       "July       3.14  3.66  1.74  1.53  2.87  3.00  2.54   NaN   NaN   NaN\n",
       "August     0.75  1.76  1.05  1.33  1.95  2.74  2.45   NaN   NaN   NaN\n",
       "September  1.33  3.97  3.55  0.22  0.96  1.69  0.13   NaN   NaN   NaN\n",
       "October    1.95  4.42  0.54  3.30  1.54  1.83  2.11   NaN   NaN   NaN\n",
       "November   2.91  2.47  2.89  2.10  0.52  0.55  0.56   NaN   NaN   NaN\n",
       "December   0.98  2.24  0.28  0.30  0.36  2.20   NaN   NaN   NaN   NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_raw_data_1840s[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d3b99394-c5a1-4e01-9536-dcf936522faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_raw_data_1840s[2400:2401]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "efaaca50-0197-46ba-9030-fd84d4df290d",
   "metadata": {},
   "outputs": [],
   "source": [
    "##collect up all the years\n",
    "grouped_by_yr_1840s=pd.concat(all_raw_data_1840s, axis=0, ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a8db5010-f54a-49d6-aa68-902dcdb9ac5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#put the month as a new index\n",
    "grouped_by_yr_1840s.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e5bb6814-e0ca-4d3e-b457-f1f18dbbfe09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MONTH</th>\n",
       "      <th>1840</th>\n",
       "      <th>1841</th>\n",
       "      <th>1842</th>\n",
       "      <th>1843</th>\n",
       "      <th>1844</th>\n",
       "      <th>1845</th>\n",
       "      <th>1846</th>\n",
       "      <th>1847</th>\n",
       "      <th>1848</th>\n",
       "      <th>1849</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>February</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>March</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>April</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>August</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>September</td>\n",
       "      <td>1.33</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>October</td>\n",
       "      <td>1.95</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0.54</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>November</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2399</th>\n",
       "      <td>December</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.36</td>\n",
       "      <td>2.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2400 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          MONTH  1840  1841  1842  1843  1844  1845  1846  1847  1848  1849\n",
       "0       January   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  1.60\n",
       "1      February   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  2.06\n",
       "2         March   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  0.78\n",
       "3         April   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.13\n",
       "4           May   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.39\n",
       "...         ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...\n",
       "2395     August  0.75  1.76  1.05  1.33  1.95  2.74  2.45   NaN   NaN   NaN\n",
       "2396  September  1.33  3.97  3.55  0.22  0.96  1.69  0.13   NaN   NaN   NaN\n",
       "2397    October  1.95  4.42  0.54  3.30  1.54  1.83  2.11   NaN   NaN   NaN\n",
       "2398   November  2.91  2.47  2.89  2.10  0.52  0.55  0.56   NaN   NaN   NaN\n",
       "2399   December  0.98  2.24  0.28  0.30  0.36  2.20   NaN   NaN   NaN   NaN\n",
       "\n",
       "[2400 rows x 11 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_yr_1840s=grouped_by_yr_1840s.rename(columns={'index': 'MONTH'})\n",
    "grouped_by_yr_1840s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d061ac05-31bf-48d2-a632-d757519a0e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1840</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>[nan, nan, nan, nan, 0.18, 0.18, nan, nan, 0.0...</td>\n",
       "      <td>[nan, nan, 1.79, 1.79, nan, nan, 1.42, 1.42, 1...</td>\n",
       "      <td>[nan, nan, 1.83, 1.83, nan, nan, 0.47, 0.47, 0...</td>\n",
       "      <td>[nan, nan, 1.85, 1.85, nan, nan, 1.65, 1.65, 2...</td>\n",
       "      <td>[nan, nan, 0.38, 0.38, nan, nan, 0.53, 0.53, 0...</td>\n",
       "      <td>[nan, nan, 0.78, 0.78, nan, nan, 1.5, 1.5, 1.2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.31, 3.31, 2.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.91, 0.91, 1.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.42, 4.42, 2.7...</td>\n",
       "      <td>[3.13, 3.13, nan, nan, nan, nan, 2.45, 2.45, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>[nan, nan, nan, nan, 1.33, 1.33, nan, nan, 1.3...</td>\n",
       "      <td>[nan, nan, 2.25, 2.25, nan, nan, 2.84, 2.84, 3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.44, 1.44, 2.1...</td>\n",
       "      <td>[nan, nan, 3.15, 3.15, nan, nan, 5.02, 5.02, 2...</td>\n",
       "      <td>[nan, nan, 1.59, 1.59, nan, nan, 2.49, 2.49, 2...</td>\n",
       "      <td>[nan, nan, 2.36, 2.36, nan, nan, 2.53, 2.53, 2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.86, 1.86, 3.6...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.53, 2.53, 1.4...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.74, 4.74, 3.1...</td>\n",
       "      <td>[0.93, 0.93, nan, nan, nan, nan, 0.77, 0.77, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>[nan, nan, nan, nan, 0.52, 0.52, nan, nan, 0.5...</td>\n",
       "      <td>[nan, nan, 1.87, 1.87, nan, nan, 2.27, 2.27, 3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.79, 0.79, 1.1...</td>\n",
       "      <td>[nan, nan, 0.44, 0.44, nan, nan, 0.67, 0.67, 0...</td>\n",
       "      <td>[nan, nan, 0.34, 0.34, nan, nan, 1.06, 1.06, 0...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.34, 2.34, 2.8...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.98, 1.98, 1.0...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.73, 1.73, 2.5...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.33, 2.33, 2.3...</td>\n",
       "      <td>[1.93, 1.93, nan, nan, nan, nan, 3.2, 3.2, 2.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>[nan, nan, nan, nan, 1.41, 1.41, nan, nan, 1.2...</td>\n",
       "      <td>[nan, nan, 0.78, 0.78, nan, nan, 1.16, 1.16, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.0, 1.0, 1.64,...</td>\n",
       "      <td>[nan, nan, 2.23, 2.23, nan, nan, 3.14, 3.14, 1...</td>\n",
       "      <td>[nan, nan, 2.03, 2.03, nan, nan, 2.34, 2.34, 3...</td>\n",
       "      <td>[nan, nan, 1.12, 1.12, nan, nan, 1.21, 1.21, 0...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.02, 1.02, 1.1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.56, 1.56, 1.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.0, 3.0, 3.39,...</td>\n",
       "      <td>[2.06, 2.06, nan, nan, nan, nan, 2.43, 2.43, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>[nan, nan, nan, nan, 3.12, 3.12, nan, nan, 2.9...</td>\n",
       "      <td>[nan, nan, 2.28, 2.28, nan, nan, 3.0, 3.0, 2.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.46, 1.46, 1.5...</td>\n",
       "      <td>[nan, nan, 1.2, 1.2, nan, nan, 1.48, 1.48, 1.1...</td>\n",
       "      <td>[nan, nan, 2.63, 2.63, nan, nan, 2.35, 2.35, 2...</td>\n",
       "      <td>[nan, nan, 2.78, 2.78, nan, nan, 1.6, 1.6, 2.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.51, 2.51, 3.9...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.54, 1.54, 1.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.55, 1.55, 1.1...</td>\n",
       "      <td>[1.6, 1.6, nan, nan, nan, nan, 1.8, 1.8, 2.2, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>[nan, nan, nan, nan, 1.47, 1.47, nan, nan, 1.2...</td>\n",
       "      <td>[nan, nan, 2.86, 2.86, nan, nan, 3.03, 3.03, 3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.72, 2.72, 3.5...</td>\n",
       "      <td>[nan, nan, 2.48, 2.48, nan, nan, 2.13, 2.13, 3...</td>\n",
       "      <td>[nan, nan, 2.88, 2.88, nan, nan, 2.73, 2.73, 2...</td>\n",
       "      <td>[nan, nan, 2.07, 2.07, nan, nan, 1.81, 1.81, 2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.94, 0.94, 1.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.28, 0.28, 0.9...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.3, 1.3, 2.34,...</td>\n",
       "      <td>[2.83, 2.83, nan, nan, nan, nan, 1.92, 1.92, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>[nan, nan, nan, nan, 2.05, 2.05, nan, nan, 1.3...</td>\n",
       "      <td>[nan, nan, 1.84, 1.84, nan, nan, 2.89, 2.89, 3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.44, 1.44, 2.2...</td>\n",
       "      <td>[nan, nan, 1.07, 1.07, nan, nan, 1.69, 1.69, 1...</td>\n",
       "      <td>[nan, nan, 1.45, 1.45, nan, nan, 2.45, 2.45, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.48, 1.48, 2.5...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.01, 1.01, 0.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.49, 1.49, 2.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.92, 2.92, 3.6...</td>\n",
       "      <td>[0.62, 0.62, nan, nan, nan, nan, 0.7, 0.7, 0.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>[nan, nan, nan, nan, 0.37, 0.37, nan, nan, 0.4...</td>\n",
       "      <td>[nan, nan, 1.22, 1.22, nan, nan, 1.12, 1.12, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.97, 1.97, 2.6...</td>\n",
       "      <td>[nan, nan, 0.32, 0.32, nan, nan, 0.54, 0.54, 0...</td>\n",
       "      <td>[nan, nan, 2.35, 2.35, nan, nan, 2.67, 2.67, 2...</td>\n",
       "      <td>[nan, nan, 1.43, 1.43, nan, nan, 3.02, 3.02, 2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.79, 0.79, 1.2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.97, 0.97, 0.9...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.9, 2.9, 4.14,...</td>\n",
       "      <td>[0.78, 0.78, nan, nan, nan, nan, 0.6, 0.6, 0.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>[nan, nan, nan, nan, 2.5, 2.5, nan, nan, 3.19,...</td>\n",
       "      <td>[nan, nan, 2.77, 2.77, nan, nan, 2.08, 2.08, 1...</td>\n",
       "      <td>[nan, nan, 2.79, 2.79, nan, nan, 1.85, 1.85, 2...</td>\n",
       "      <td>[nan, nan, 5.21, 5.21, nan, nan, 3.32, 3.32, 5...</td>\n",
       "      <td>[nan, nan, 0.3, 0.3, nan, nan, 0.36, 0.36, 0.3...</td>\n",
       "      <td>[nan, nan, 2.08, 2.08, nan, nan, 3.25, 3.25, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.68, 1.68, 1.1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.59, 1.59, 2.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.94, 0.94, 0.2...</td>\n",
       "      <td>[3.39, 3.39, nan, nan, nan, nan, 3.01, 3.01, 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>[nan, nan, nan, nan, 3.31, 3.31, nan, nan, 3.4...</td>\n",
       "      <td>[nan, nan, 2.85, 2.85, nan, nan, 3.17, 3.17, 4...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.23, 4.23, 5.1...</td>\n",
       "      <td>[nan, nan, 2.36, 2.36, nan, nan, 2.14, 2.14, 2...</td>\n",
       "      <td>[nan, nan, 3.54, 3.54, nan, nan, 3.72, 3.72, 3...</td>\n",
       "      <td>[nan, nan, 2.11, 2.11, nan, nan, 1.99, 1.99, 2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.43, 1.43, 1.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.02, 2.02, 1.8...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.2, 1.2, 1.3, ...</td>\n",
       "      <td>[1.08, 1.08, nan, nan, nan, nan, 1.98, 1.98, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>[nan, nan, nan, nan, 1.64, 1.64, nan, nan, 2.0...</td>\n",
       "      <td>[nan, nan, 4.51, 4.51, nan, nan, 6.72, 6.72, 6...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.84, 1.84, 2.0...</td>\n",
       "      <td>[nan, nan, 4.87, 4.87, nan, nan, 4.13, 4.13, 6...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.83, 4.83, 4.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.26, 1.26, 1.5...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.79, 3.79, 5.4...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.15, 2.15, 2.1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.14, 4.14, 3.9...</td>\n",
       "      <td>[2.16, 2.16, nan, nan, nan, nan, 4.28, 4.28, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>[nan, nan, nan, nan, 2.34, 2.34, nan, nan, 3.0...</td>\n",
       "      <td>[nan, nan, 5.85, 5.85, nan, nan, 3.67, 3.67, 4...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.62, 3.62, 3.9...</td>\n",
       "      <td>[nan, nan, 0.56, 0.56, nan, nan, 0.35, 0.35, 0...</td>\n",
       "      <td>[nan, nan, 1.13, 1.13, nan, nan, 1.34, 1.34, 1...</td>\n",
       "      <td>[nan, nan, 1.41, 1.41, nan, nan, 1.48, 1.48, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.25, 2.25, 2.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.79, 1.79, 2.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.08, 3.08, 2.3...</td>\n",
       "      <td>[3.74, 3.74, nan, nan, nan, nan, 5.89, 5.89, 3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        1840  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, nan, nan, 0.18, 0.18, nan, nan, 0.0...   \n",
       "August     [nan, nan, nan, nan, 1.33, 1.33, nan, nan, 1.3...   \n",
       "December   [nan, nan, nan, nan, 0.52, 0.52, nan, nan, 0.5...   \n",
       "February   [nan, nan, nan, nan, 1.41, 1.41, nan, nan, 1.2...   \n",
       "January    [nan, nan, nan, nan, 3.12, 3.12, nan, nan, 2.9...   \n",
       "July       [nan, nan, nan, nan, 1.47, 1.47, nan, nan, 1.2...   \n",
       "June       [nan, nan, nan, nan, 2.05, 2.05, nan, nan, 1.3...   \n",
       "March      [nan, nan, nan, nan, 0.37, 0.37, nan, nan, 0.4...   \n",
       "May        [nan, nan, nan, nan, 2.5, 2.5, nan, nan, 3.19,...   \n",
       "November   [nan, nan, nan, nan, 3.31, 3.31, nan, nan, 3.4...   \n",
       "October    [nan, nan, nan, nan, 1.64, 1.64, nan, nan, 2.0...   \n",
       "September  [nan, nan, nan, nan, 2.34, 2.34, nan, nan, 3.0...   \n",
       "\n",
       "                                                        1841  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 1.79, 1.79, nan, nan, 1.42, 1.42, 1...   \n",
       "August     [nan, nan, 2.25, 2.25, nan, nan, 2.84, 2.84, 3...   \n",
       "December   [nan, nan, 1.87, 1.87, nan, nan, 2.27, 2.27, 3...   \n",
       "February   [nan, nan, 0.78, 0.78, nan, nan, 1.16, 1.16, 1...   \n",
       "January    [nan, nan, 2.28, 2.28, nan, nan, 3.0, 3.0, 2.3...   \n",
       "July       [nan, nan, 2.86, 2.86, nan, nan, 3.03, 3.03, 3...   \n",
       "June       [nan, nan, 1.84, 1.84, nan, nan, 2.89, 2.89, 3...   \n",
       "March      [nan, nan, 1.22, 1.22, nan, nan, 1.12, 1.12, 1...   \n",
       "May        [nan, nan, 2.77, 2.77, nan, nan, 2.08, 2.08, 1...   \n",
       "November   [nan, nan, 2.85, 2.85, nan, nan, 3.17, 3.17, 4...   \n",
       "October    [nan, nan, 4.51, 4.51, nan, nan, 6.72, 6.72, 6...   \n",
       "September  [nan, nan, 5.85, 5.85, nan, nan, 3.67, 3.67, 4...   \n",
       "\n",
       "                                                        1842  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 1.83, 1.83, nan, nan, 0.47, 0.47, 0...   \n",
       "August     [nan, nan, nan, nan, nan, nan, 1.44, 1.44, 2.1...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 0.79, 0.79, 1.1...   \n",
       "February   [nan, nan, nan, nan, nan, nan, 1.0, 1.0, 1.64,...   \n",
       "January    [nan, nan, nan, nan, nan, nan, 1.46, 1.46, 1.5...   \n",
       "July       [nan, nan, nan, nan, nan, nan, 2.72, 2.72, 3.5...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 1.44, 1.44, 2.2...   \n",
       "March      [nan, nan, nan, nan, nan, nan, 1.97, 1.97, 2.6...   \n",
       "May        [nan, nan, 2.79, 2.79, nan, nan, 1.85, 1.85, 2...   \n",
       "November   [nan, nan, nan, nan, nan, nan, 4.23, 4.23, 5.1...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 1.84, 1.84, 2.0...   \n",
       "September  [nan, nan, nan, nan, nan, nan, 3.62, 3.62, 3.9...   \n",
       "\n",
       "                                                        1843  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 1.85, 1.85, nan, nan, 1.65, 1.65, 2...   \n",
       "August     [nan, nan, 3.15, 3.15, nan, nan, 5.02, 5.02, 2...   \n",
       "December   [nan, nan, 0.44, 0.44, nan, nan, 0.67, 0.67, 0...   \n",
       "February   [nan, nan, 2.23, 2.23, nan, nan, 3.14, 3.14, 1...   \n",
       "January    [nan, nan, 1.2, 1.2, nan, nan, 1.48, 1.48, 1.1...   \n",
       "July       [nan, nan, 2.48, 2.48, nan, nan, 2.13, 2.13, 3...   \n",
       "June       [nan, nan, 1.07, 1.07, nan, nan, 1.69, 1.69, 1...   \n",
       "March      [nan, nan, 0.32, 0.32, nan, nan, 0.54, 0.54, 0...   \n",
       "May        [nan, nan, 5.21, 5.21, nan, nan, 3.32, 3.32, 5...   \n",
       "November   [nan, nan, 2.36, 2.36, nan, nan, 2.14, 2.14, 2...   \n",
       "October    [nan, nan, 4.87, 4.87, nan, nan, 4.13, 4.13, 6...   \n",
       "September  [nan, nan, 0.56, 0.56, nan, nan, 0.35, 0.35, 0...   \n",
       "\n",
       "                                                        1844  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 0.38, 0.38, nan, nan, 0.53, 0.53, 0...   \n",
       "August     [nan, nan, 1.59, 1.59, nan, nan, 2.49, 2.49, 2...   \n",
       "December   [nan, nan, 0.34, 0.34, nan, nan, 1.06, 1.06, 0...   \n",
       "February   [nan, nan, 2.03, 2.03, nan, nan, 2.34, 2.34, 3...   \n",
       "January    [nan, nan, 2.63, 2.63, nan, nan, 2.35, 2.35, 2...   \n",
       "July       [nan, nan, 2.88, 2.88, nan, nan, 2.73, 2.73, 2...   \n",
       "June       [nan, nan, 1.45, 1.45, nan, nan, 2.45, 2.45, 1...   \n",
       "March      [nan, nan, 2.35, 2.35, nan, nan, 2.67, 2.67, 2...   \n",
       "May        [nan, nan, 0.3, 0.3, nan, nan, 0.36, 0.36, 0.3...   \n",
       "November   [nan, nan, 3.54, 3.54, nan, nan, 3.72, 3.72, 3...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 4.83, 4.83, 4.7...   \n",
       "September  [nan, nan, 1.13, 1.13, nan, nan, 1.34, 1.34, 1...   \n",
       "\n",
       "                                                        1845  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 0.78, 0.78, nan, nan, 1.5, 1.5, 1.2...   \n",
       "August     [nan, nan, 2.36, 2.36, nan, nan, 2.53, 2.53, 2...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 2.34, 2.34, 2.8...   \n",
       "February   [nan, nan, 1.12, 1.12, nan, nan, 1.21, 1.21, 0...   \n",
       "January    [nan, nan, 2.78, 2.78, nan, nan, 1.6, 1.6, 2.7...   \n",
       "July       [nan, nan, 2.07, 2.07, nan, nan, 1.81, 1.81, 2...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 1.48, 1.48, 2.5...   \n",
       "March      [nan, nan, 1.43, 1.43, nan, nan, 3.02, 3.02, 2...   \n",
       "May        [nan, nan, 2.08, 2.08, nan, nan, 3.25, 3.25, 1...   \n",
       "November   [nan, nan, 2.11, 2.11, nan, nan, 1.99, 1.99, 2...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 1.26, 1.26, 1.5...   \n",
       "September  [nan, nan, 1.41, 1.41, nan, nan, 1.48, 1.48, 1...   \n",
       "\n",
       "                                                        1846  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, nan, nan, nan, nan, 3.31, 3.31, 2.7...   \n",
       "August     [nan, nan, nan, nan, nan, nan, 1.86, 1.86, 3.6...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 1.98, 1.98, 1.0...   \n",
       "February   [nan, nan, nan, nan, nan, nan, 1.02, 1.02, 1.1...   \n",
       "January    [nan, nan, nan, nan, nan, nan, 2.51, 2.51, 3.9...   \n",
       "July       [nan, nan, nan, nan, nan, nan, 0.94, 0.94, 1.3...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 1.01, 1.01, 0.3...   \n",
       "March      [nan, nan, nan, nan, nan, nan, 0.79, 0.79, 1.2...   \n",
       "May        [nan, nan, nan, nan, nan, nan, 1.68, 1.68, 1.1...   \n",
       "November   [nan, nan, nan, nan, nan, nan, 1.43, 1.43, 1.3...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 3.79, 3.79, 5.4...   \n",
       "September  [nan, nan, nan, nan, nan, nan, 2.25, 2.25, 2.7...   \n",
       "\n",
       "                                                        1847  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, nan, nan, nan, nan, 0.91, 0.91, 1.3...   \n",
       "August     [nan, nan, nan, nan, nan, nan, 2.53, 2.53, 1.4...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 1.73, 1.73, 2.5...   \n",
       "February   [nan, nan, nan, nan, nan, nan, 1.56, 1.56, 1.3...   \n",
       "January    [nan, nan, nan, nan, nan, nan, 1.54, 1.54, 1.7...   \n",
       "July       [nan, nan, nan, nan, nan, nan, 0.28, 0.28, 0.9...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 1.49, 1.49, 2.3...   \n",
       "March      [nan, nan, nan, nan, nan, nan, 0.97, 0.97, 0.9...   \n",
       "May        [nan, nan, nan, nan, nan, nan, 1.59, 1.59, 2.3...   \n",
       "November   [nan, nan, nan, nan, nan, nan, 2.02, 2.02, 1.8...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 2.15, 2.15, 2.1...   \n",
       "September  [nan, nan, nan, nan, nan, nan, 1.79, 1.79, 2.3...   \n",
       "\n",
       "                                                        1848  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, nan, nan, nan, nan, 4.42, 4.42, 2.7...   \n",
       "August     [nan, nan, nan, nan, nan, nan, 4.74, 4.74, 3.1...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 2.33, 2.33, 2.3...   \n",
       "February   [nan, nan, nan, nan, nan, nan, 3.0, 3.0, 3.39,...   \n",
       "January    [nan, nan, nan, nan, nan, nan, 1.55, 1.55, 1.1...   \n",
       "July       [nan, nan, nan, nan, nan, nan, 1.3, 1.3, 2.34,...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 2.92, 2.92, 3.6...   \n",
       "March      [nan, nan, nan, nan, nan, nan, 2.9, 2.9, 4.14,...   \n",
       "May        [nan, nan, nan, nan, nan, nan, 0.94, 0.94, 0.2...   \n",
       "November   [nan, nan, nan, nan, nan, nan, 1.2, 1.2, 1.3, ...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 4.14, 4.14, 3.9...   \n",
       "September  [nan, nan, nan, nan, nan, nan, 3.08, 3.08, 2.3...   \n",
       "\n",
       "                                                        1849  \n",
       "MONTH                                                         \n",
       "April      [3.13, 3.13, nan, nan, nan, nan, 2.45, 2.45, 2...  \n",
       "August     [0.93, 0.93, nan, nan, nan, nan, 0.77, 0.77, 1...  \n",
       "December   [1.93, 1.93, nan, nan, nan, nan, 3.2, 3.2, 2.7...  \n",
       "February   [2.06, 2.06, nan, nan, nan, nan, 2.43, 2.43, 2...  \n",
       "January    [1.6, 1.6, nan, nan, nan, nan, 1.8, 1.8, 2.2, ...  \n",
       "July       [2.83, 2.83, nan, nan, nan, nan, 1.92, 1.92, 2...  \n",
       "June       [0.62, 0.62, nan, nan, nan, nan, 0.7, 0.7, 0.5...  \n",
       "March      [0.78, 0.78, nan, nan, nan, nan, 0.6, 0.6, 0.8...  \n",
       "May        [3.39, 3.39, nan, nan, nan, nan, 3.01, 3.01, 4...  \n",
       "November   [1.08, 1.08, nan, nan, nan, nan, 1.98, 1.98, 1...  \n",
       "October    [2.16, 2.16, nan, nan, nan, nan, 4.28, 4.28, 2...  \n",
       "September  [3.74, 3.74, nan, nan, nan, nan, 5.89, 5.89, 3...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group data into lists organised by month and year\n",
    "grouped_by_monthyr_1840s=grouped_by_yr_1840s.groupby('MONTH').agg(list)\n",
    "grouped_by_monthyr_1840s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d27d4d12-269d-4d2f-9ac6-f982feb475e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Create a timeaxis for the decade of interest\n",
    "time_ax=pd.date_range('1840-01-01', freq='M', periods=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "16f557bb-6dc2-4d4d-bf71-1e2d86979a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1840-01-31', '1840-02-29', '1840-03-31', '1840-04-30',\n",
       "               '1840-05-31', '1840-06-30', '1840-07-31', '1840-08-31',\n",
       "               '1840-09-30', '1840-10-31',\n",
       "               ...\n",
       "               '1849-03-31', '1849-04-30', '1849-05-31', '1849-06-30',\n",
       "               '1849-07-31', '1849-08-31', '1849-09-30', '1849-10-31',\n",
       "               '1849-11-30', '1849-12-31'],\n",
       "              dtype='datetime64[ns]', length=120, freq='M')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "308a0119-b033-4754-b819-ff5120d68dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Extract the data from the 2D year by month into a 1D timeseries\n",
    "timeseries_1840s=[]\n",
    "for i in range(len(time_ax)):\n",
    "    YR=time_ax[i].year\n",
    "    MNTH=pd.to_datetime(time_ax[i].month, format='%m').month_name()\n",
    "    #print(str(YR)+' '+str(MNTH))\n",
    "    data=grouped_by_monthyr_1840s[str(YR)].loc[[str(MNTH)]].to_list()[0]\n",
    "    timeseries_1840s.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "62f6968b-113c-41fa-8a84-c3a822d9c87a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(timeseries_1840s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e346686d-e5b5-4383-b2d9-9ca66f565638",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dummy coord for data station\n",
    "dummy_loc=np.arange(len(timeseries_1840s[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "24fd1628-4509-46bd-9dd4-f8566cbd4721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1840</th>\n",
       "      <th>1841</th>\n",
       "      <th>1842</th>\n",
       "      <th>1843</th>\n",
       "      <th>1844</th>\n",
       "      <th>1845</th>\n",
       "      <th>1846</th>\n",
       "      <th>1847</th>\n",
       "      <th>1848</th>\n",
       "      <th>1849</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>[nan, nan, nan, nan, 0.18, 0.18, nan, nan, 0.0...</td>\n",
       "      <td>[nan, nan, 1.79, 1.79, nan, nan, 1.42, 1.42, 1...</td>\n",
       "      <td>[nan, nan, 1.83, 1.83, nan, nan, 0.47, 0.47, 0...</td>\n",
       "      <td>[nan, nan, 1.85, 1.85, nan, nan, 1.65, 1.65, 2...</td>\n",
       "      <td>[nan, nan, 0.38, 0.38, nan, nan, 0.53, 0.53, 0...</td>\n",
       "      <td>[nan, nan, 0.78, 0.78, nan, nan, 1.5, 1.5, 1.2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.31, 3.31, 2.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.91, 0.91, 1.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.42, 4.42, 2.7...</td>\n",
       "      <td>[3.13, 3.13, nan, nan, nan, nan, 2.45, 2.45, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>[nan, nan, nan, nan, 1.33, 1.33, nan, nan, 1.3...</td>\n",
       "      <td>[nan, nan, 2.25, 2.25, nan, nan, 2.84, 2.84, 3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.44, 1.44, 2.1...</td>\n",
       "      <td>[nan, nan, 3.15, 3.15, nan, nan, 5.02, 5.02, 2...</td>\n",
       "      <td>[nan, nan, 1.59, 1.59, nan, nan, 2.49, 2.49, 2...</td>\n",
       "      <td>[nan, nan, 2.36, 2.36, nan, nan, 2.53, 2.53, 2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.86, 1.86, 3.6...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.53, 2.53, 1.4...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.74, 4.74, 3.1...</td>\n",
       "      <td>[0.93, 0.93, nan, nan, nan, nan, 0.77, 0.77, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>[nan, nan, nan, nan, 0.52, 0.52, nan, nan, 0.5...</td>\n",
       "      <td>[nan, nan, 1.87, 1.87, nan, nan, 2.27, 2.27, 3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.79, 0.79, 1.1...</td>\n",
       "      <td>[nan, nan, 0.44, 0.44, nan, nan, 0.67, 0.67, 0...</td>\n",
       "      <td>[nan, nan, 0.34, 0.34, nan, nan, 1.06, 1.06, 0...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.34, 2.34, 2.8...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.98, 1.98, 1.0...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.73, 1.73, 2.5...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.33, 2.33, 2.3...</td>\n",
       "      <td>[1.93, 1.93, nan, nan, nan, nan, 3.2, 3.2, 2.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>[nan, nan, nan, nan, 1.41, 1.41, nan, nan, 1.2...</td>\n",
       "      <td>[nan, nan, 0.78, 0.78, nan, nan, 1.16, 1.16, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.0, 1.0, 1.64,...</td>\n",
       "      <td>[nan, nan, 2.23, 2.23, nan, nan, 3.14, 3.14, 1...</td>\n",
       "      <td>[nan, nan, 2.03, 2.03, nan, nan, 2.34, 2.34, 3...</td>\n",
       "      <td>[nan, nan, 1.12, 1.12, nan, nan, 1.21, 1.21, 0...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.02, 1.02, 1.1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.56, 1.56, 1.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.0, 3.0, 3.39,...</td>\n",
       "      <td>[2.06, 2.06, nan, nan, nan, nan, 2.43, 2.43, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>[nan, nan, nan, nan, 3.12, 3.12, nan, nan, 2.9...</td>\n",
       "      <td>[nan, nan, 2.28, 2.28, nan, nan, 3.0, 3.0, 2.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.46, 1.46, 1.5...</td>\n",
       "      <td>[nan, nan, 1.2, 1.2, nan, nan, 1.48, 1.48, 1.1...</td>\n",
       "      <td>[nan, nan, 2.63, 2.63, nan, nan, 2.35, 2.35, 2...</td>\n",
       "      <td>[nan, nan, 2.78, 2.78, nan, nan, 1.6, 1.6, 2.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.51, 2.51, 3.9...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.54, 1.54, 1.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.55, 1.55, 1.1...</td>\n",
       "      <td>[1.6, 1.6, nan, nan, nan, nan, 1.8, 1.8, 2.2, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>[nan, nan, nan, nan, 1.47, 1.47, nan, nan, 1.2...</td>\n",
       "      <td>[nan, nan, 2.86, 2.86, nan, nan, 3.03, 3.03, 3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.72, 2.72, 3.5...</td>\n",
       "      <td>[nan, nan, 2.48, 2.48, nan, nan, 2.13, 2.13, 3...</td>\n",
       "      <td>[nan, nan, 2.88, 2.88, nan, nan, 2.73, 2.73, 2...</td>\n",
       "      <td>[nan, nan, 2.07, 2.07, nan, nan, 1.81, 1.81, 2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.94, 0.94, 1.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.28, 0.28, 0.9...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.3, 1.3, 2.34,...</td>\n",
       "      <td>[2.83, 2.83, nan, nan, nan, nan, 1.92, 1.92, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>[nan, nan, nan, nan, 2.05, 2.05, nan, nan, 1.3...</td>\n",
       "      <td>[nan, nan, 1.84, 1.84, nan, nan, 2.89, 2.89, 3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.44, 1.44, 2.2...</td>\n",
       "      <td>[nan, nan, 1.07, 1.07, nan, nan, 1.69, 1.69, 1...</td>\n",
       "      <td>[nan, nan, 1.45, 1.45, nan, nan, 2.45, 2.45, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.48, 1.48, 2.5...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.01, 1.01, 0.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.49, 1.49, 2.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.92, 2.92, 3.6...</td>\n",
       "      <td>[0.62, 0.62, nan, nan, nan, nan, 0.7, 0.7, 0.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>[nan, nan, nan, nan, 0.37, 0.37, nan, nan, 0.4...</td>\n",
       "      <td>[nan, nan, 1.22, 1.22, nan, nan, 1.12, 1.12, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.97, 1.97, 2.6...</td>\n",
       "      <td>[nan, nan, 0.32, 0.32, nan, nan, 0.54, 0.54, 0...</td>\n",
       "      <td>[nan, nan, 2.35, 2.35, nan, nan, 2.67, 2.67, 2...</td>\n",
       "      <td>[nan, nan, 1.43, 1.43, nan, nan, 3.02, 3.02, 2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.79, 0.79, 1.2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.97, 0.97, 0.9...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.9, 2.9, 4.14,...</td>\n",
       "      <td>[0.78, 0.78, nan, nan, nan, nan, 0.6, 0.6, 0.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>[nan, nan, nan, nan, 2.5, 2.5, nan, nan, 3.19,...</td>\n",
       "      <td>[nan, nan, 2.77, 2.77, nan, nan, 2.08, 2.08, 1...</td>\n",
       "      <td>[nan, nan, 2.79, 2.79, nan, nan, 1.85, 1.85, 2...</td>\n",
       "      <td>[nan, nan, 5.21, 5.21, nan, nan, 3.32, 3.32, 5...</td>\n",
       "      <td>[nan, nan, 0.3, 0.3, nan, nan, 0.36, 0.36, 0.3...</td>\n",
       "      <td>[nan, nan, 2.08, 2.08, nan, nan, 3.25, 3.25, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.68, 1.68, 1.1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.59, 1.59, 2.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 0.94, 0.94, 0.2...</td>\n",
       "      <td>[3.39, 3.39, nan, nan, nan, nan, 3.01, 3.01, 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>[nan, nan, nan, nan, 3.31, 3.31, nan, nan, 3.4...</td>\n",
       "      <td>[nan, nan, 2.85, 2.85, nan, nan, 3.17, 3.17, 4...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.23, 4.23, 5.1...</td>\n",
       "      <td>[nan, nan, 2.36, 2.36, nan, nan, 2.14, 2.14, 2...</td>\n",
       "      <td>[nan, nan, 3.54, 3.54, nan, nan, 3.72, 3.72, 3...</td>\n",
       "      <td>[nan, nan, 2.11, 2.11, nan, nan, 1.99, 1.99, 2...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.43, 1.43, 1.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.02, 2.02, 1.8...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.2, 1.2, 1.3, ...</td>\n",
       "      <td>[1.08, 1.08, nan, nan, nan, nan, 1.98, 1.98, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>[nan, nan, nan, nan, 1.64, 1.64, nan, nan, 2.0...</td>\n",
       "      <td>[nan, nan, 4.51, 4.51, nan, nan, 6.72, 6.72, 6...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.84, 1.84, 2.0...</td>\n",
       "      <td>[nan, nan, 4.87, 4.87, nan, nan, 4.13, 4.13, 6...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.83, 4.83, 4.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.26, 1.26, 1.5...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.79, 3.79, 5.4...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.15, 2.15, 2.1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 4.14, 4.14, 3.9...</td>\n",
       "      <td>[2.16, 2.16, nan, nan, nan, nan, 4.28, 4.28, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>[nan, nan, nan, nan, 2.34, 2.34, nan, nan, 3.0...</td>\n",
       "      <td>[nan, nan, 5.85, 5.85, nan, nan, 3.67, 3.67, 4...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.62, 3.62, 3.9...</td>\n",
       "      <td>[nan, nan, 0.56, 0.56, nan, nan, 0.35, 0.35, 0...</td>\n",
       "      <td>[nan, nan, 1.13, 1.13, nan, nan, 1.34, 1.34, 1...</td>\n",
       "      <td>[nan, nan, 1.41, 1.41, nan, nan, 1.48, 1.48, 1...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 2.25, 2.25, 2.7...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 1.79, 1.79, 2.3...</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, 3.08, 3.08, 2.3...</td>\n",
       "      <td>[3.74, 3.74, nan, nan, nan, nan, 5.89, 5.89, 3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        1840  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, nan, nan, 0.18, 0.18, nan, nan, 0.0...   \n",
       "August     [nan, nan, nan, nan, 1.33, 1.33, nan, nan, 1.3...   \n",
       "December   [nan, nan, nan, nan, 0.52, 0.52, nan, nan, 0.5...   \n",
       "February   [nan, nan, nan, nan, 1.41, 1.41, nan, nan, 1.2...   \n",
       "January    [nan, nan, nan, nan, 3.12, 3.12, nan, nan, 2.9...   \n",
       "July       [nan, nan, nan, nan, 1.47, 1.47, nan, nan, 1.2...   \n",
       "June       [nan, nan, nan, nan, 2.05, 2.05, nan, nan, 1.3...   \n",
       "March      [nan, nan, nan, nan, 0.37, 0.37, nan, nan, 0.4...   \n",
       "May        [nan, nan, nan, nan, 2.5, 2.5, nan, nan, 3.19,...   \n",
       "November   [nan, nan, nan, nan, 3.31, 3.31, nan, nan, 3.4...   \n",
       "October    [nan, nan, nan, nan, 1.64, 1.64, nan, nan, 2.0...   \n",
       "September  [nan, nan, nan, nan, 2.34, 2.34, nan, nan, 3.0...   \n",
       "\n",
       "                                                        1841  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 1.79, 1.79, nan, nan, 1.42, 1.42, 1...   \n",
       "August     [nan, nan, 2.25, 2.25, nan, nan, 2.84, 2.84, 3...   \n",
       "December   [nan, nan, 1.87, 1.87, nan, nan, 2.27, 2.27, 3...   \n",
       "February   [nan, nan, 0.78, 0.78, nan, nan, 1.16, 1.16, 1...   \n",
       "January    [nan, nan, 2.28, 2.28, nan, nan, 3.0, 3.0, 2.3...   \n",
       "July       [nan, nan, 2.86, 2.86, nan, nan, 3.03, 3.03, 3...   \n",
       "June       [nan, nan, 1.84, 1.84, nan, nan, 2.89, 2.89, 3...   \n",
       "March      [nan, nan, 1.22, 1.22, nan, nan, 1.12, 1.12, 1...   \n",
       "May        [nan, nan, 2.77, 2.77, nan, nan, 2.08, 2.08, 1...   \n",
       "November   [nan, nan, 2.85, 2.85, nan, nan, 3.17, 3.17, 4...   \n",
       "October    [nan, nan, 4.51, 4.51, nan, nan, 6.72, 6.72, 6...   \n",
       "September  [nan, nan, 5.85, 5.85, nan, nan, 3.67, 3.67, 4...   \n",
       "\n",
       "                                                        1842  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 1.83, 1.83, nan, nan, 0.47, 0.47, 0...   \n",
       "August     [nan, nan, nan, nan, nan, nan, 1.44, 1.44, 2.1...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 0.79, 0.79, 1.1...   \n",
       "February   [nan, nan, nan, nan, nan, nan, 1.0, 1.0, 1.64,...   \n",
       "January    [nan, nan, nan, nan, nan, nan, 1.46, 1.46, 1.5...   \n",
       "July       [nan, nan, nan, nan, nan, nan, 2.72, 2.72, 3.5...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 1.44, 1.44, 2.2...   \n",
       "March      [nan, nan, nan, nan, nan, nan, 1.97, 1.97, 2.6...   \n",
       "May        [nan, nan, 2.79, 2.79, nan, nan, 1.85, 1.85, 2...   \n",
       "November   [nan, nan, nan, nan, nan, nan, 4.23, 4.23, 5.1...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 1.84, 1.84, 2.0...   \n",
       "September  [nan, nan, nan, nan, nan, nan, 3.62, 3.62, 3.9...   \n",
       "\n",
       "                                                        1843  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 1.85, 1.85, nan, nan, 1.65, 1.65, 2...   \n",
       "August     [nan, nan, 3.15, 3.15, nan, nan, 5.02, 5.02, 2...   \n",
       "December   [nan, nan, 0.44, 0.44, nan, nan, 0.67, 0.67, 0...   \n",
       "February   [nan, nan, 2.23, 2.23, nan, nan, 3.14, 3.14, 1...   \n",
       "January    [nan, nan, 1.2, 1.2, nan, nan, 1.48, 1.48, 1.1...   \n",
       "July       [nan, nan, 2.48, 2.48, nan, nan, 2.13, 2.13, 3...   \n",
       "June       [nan, nan, 1.07, 1.07, nan, nan, 1.69, 1.69, 1...   \n",
       "March      [nan, nan, 0.32, 0.32, nan, nan, 0.54, 0.54, 0...   \n",
       "May        [nan, nan, 5.21, 5.21, nan, nan, 3.32, 3.32, 5...   \n",
       "November   [nan, nan, 2.36, 2.36, nan, nan, 2.14, 2.14, 2...   \n",
       "October    [nan, nan, 4.87, 4.87, nan, nan, 4.13, 4.13, 6...   \n",
       "September  [nan, nan, 0.56, 0.56, nan, nan, 0.35, 0.35, 0...   \n",
       "\n",
       "                                                        1844  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 0.38, 0.38, nan, nan, 0.53, 0.53, 0...   \n",
       "August     [nan, nan, 1.59, 1.59, nan, nan, 2.49, 2.49, 2...   \n",
       "December   [nan, nan, 0.34, 0.34, nan, nan, 1.06, 1.06, 0...   \n",
       "February   [nan, nan, 2.03, 2.03, nan, nan, 2.34, 2.34, 3...   \n",
       "January    [nan, nan, 2.63, 2.63, nan, nan, 2.35, 2.35, 2...   \n",
       "July       [nan, nan, 2.88, 2.88, nan, nan, 2.73, 2.73, 2...   \n",
       "June       [nan, nan, 1.45, 1.45, nan, nan, 2.45, 2.45, 1...   \n",
       "March      [nan, nan, 2.35, 2.35, nan, nan, 2.67, 2.67, 2...   \n",
       "May        [nan, nan, 0.3, 0.3, nan, nan, 0.36, 0.36, 0.3...   \n",
       "November   [nan, nan, 3.54, 3.54, nan, nan, 3.72, 3.72, 3...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 4.83, 4.83, 4.7...   \n",
       "September  [nan, nan, 1.13, 1.13, nan, nan, 1.34, 1.34, 1...   \n",
       "\n",
       "                                                        1845  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, 0.78, 0.78, nan, nan, 1.5, 1.5, 1.2...   \n",
       "August     [nan, nan, 2.36, 2.36, nan, nan, 2.53, 2.53, 2...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 2.34, 2.34, 2.8...   \n",
       "February   [nan, nan, 1.12, 1.12, nan, nan, 1.21, 1.21, 0...   \n",
       "January    [nan, nan, 2.78, 2.78, nan, nan, 1.6, 1.6, 2.7...   \n",
       "July       [nan, nan, 2.07, 2.07, nan, nan, 1.81, 1.81, 2...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 1.48, 1.48, 2.5...   \n",
       "March      [nan, nan, 1.43, 1.43, nan, nan, 3.02, 3.02, 2...   \n",
       "May        [nan, nan, 2.08, 2.08, nan, nan, 3.25, 3.25, 1...   \n",
       "November   [nan, nan, 2.11, 2.11, nan, nan, 1.99, 1.99, 2...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 1.26, 1.26, 1.5...   \n",
       "September  [nan, nan, 1.41, 1.41, nan, nan, 1.48, 1.48, 1...   \n",
       "\n",
       "                                                        1846  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, nan, nan, nan, nan, 3.31, 3.31, 2.7...   \n",
       "August     [nan, nan, nan, nan, nan, nan, 1.86, 1.86, 3.6...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 1.98, 1.98, 1.0...   \n",
       "February   [nan, nan, nan, nan, nan, nan, 1.02, 1.02, 1.1...   \n",
       "January    [nan, nan, nan, nan, nan, nan, 2.51, 2.51, 3.9...   \n",
       "July       [nan, nan, nan, nan, nan, nan, 0.94, 0.94, 1.3...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 1.01, 1.01, 0.3...   \n",
       "March      [nan, nan, nan, nan, nan, nan, 0.79, 0.79, 1.2...   \n",
       "May        [nan, nan, nan, nan, nan, nan, 1.68, 1.68, 1.1...   \n",
       "November   [nan, nan, nan, nan, nan, nan, 1.43, 1.43, 1.3...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 3.79, 3.79, 5.4...   \n",
       "September  [nan, nan, nan, nan, nan, nan, 2.25, 2.25, 2.7...   \n",
       "\n",
       "                                                        1847  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, nan, nan, nan, nan, 0.91, 0.91, 1.3...   \n",
       "August     [nan, nan, nan, nan, nan, nan, 2.53, 2.53, 1.4...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 1.73, 1.73, 2.5...   \n",
       "February   [nan, nan, nan, nan, nan, nan, 1.56, 1.56, 1.3...   \n",
       "January    [nan, nan, nan, nan, nan, nan, 1.54, 1.54, 1.7...   \n",
       "July       [nan, nan, nan, nan, nan, nan, 0.28, 0.28, 0.9...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 1.49, 1.49, 2.3...   \n",
       "March      [nan, nan, nan, nan, nan, nan, 0.97, 0.97, 0.9...   \n",
       "May        [nan, nan, nan, nan, nan, nan, 1.59, 1.59, 2.3...   \n",
       "November   [nan, nan, nan, nan, nan, nan, 2.02, 2.02, 1.8...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 2.15, 2.15, 2.1...   \n",
       "September  [nan, nan, nan, nan, nan, nan, 1.79, 1.79, 2.3...   \n",
       "\n",
       "                                                        1848  \\\n",
       "MONTH                                                          \n",
       "April      [nan, nan, nan, nan, nan, nan, 4.42, 4.42, 2.7...   \n",
       "August     [nan, nan, nan, nan, nan, nan, 4.74, 4.74, 3.1...   \n",
       "December   [nan, nan, nan, nan, nan, nan, 2.33, 2.33, 2.3...   \n",
       "February   [nan, nan, nan, nan, nan, nan, 3.0, 3.0, 3.39,...   \n",
       "January    [nan, nan, nan, nan, nan, nan, 1.55, 1.55, 1.1...   \n",
       "July       [nan, nan, nan, nan, nan, nan, 1.3, 1.3, 2.34,...   \n",
       "June       [nan, nan, nan, nan, nan, nan, 2.92, 2.92, 3.6...   \n",
       "March      [nan, nan, nan, nan, nan, nan, 2.9, 2.9, 4.14,...   \n",
       "May        [nan, nan, nan, nan, nan, nan, 0.94, 0.94, 0.2...   \n",
       "November   [nan, nan, nan, nan, nan, nan, 1.2, 1.2, 1.3, ...   \n",
       "October    [nan, nan, nan, nan, nan, nan, 4.14, 4.14, 3.9...   \n",
       "September  [nan, nan, nan, nan, nan, nan, 3.08, 3.08, 2.3...   \n",
       "\n",
       "                                                        1849  \n",
       "MONTH                                                         \n",
       "April      [3.13, 3.13, nan, nan, nan, nan, 2.45, 2.45, 2...  \n",
       "August     [0.93, 0.93, nan, nan, nan, nan, 0.77, 0.77, 1...  \n",
       "December   [1.93, 1.93, nan, nan, nan, nan, 3.2, 3.2, 2.7...  \n",
       "February   [2.06, 2.06, nan, nan, nan, nan, 2.43, 2.43, 2...  \n",
       "January    [1.6, 1.6, nan, nan, nan, nan, 1.8, 1.8, 2.2, ...  \n",
       "July       [2.83, 2.83, nan, nan, nan, nan, 1.92, 1.92, 2...  \n",
       "June       [0.62, 0.62, nan, nan, nan, nan, 0.7, 0.7, 0.5...  \n",
       "March      [0.78, 0.78, nan, nan, nan, nan, 0.6, 0.6, 0.8...  \n",
       "May        [3.39, 3.39, nan, nan, nan, nan, 3.01, 3.01, 4...  \n",
       "November   [1.08, 1.08, nan, nan, nan, nan, 1.98, 1.98, 1...  \n",
       "October    [2.16, 2.16, nan, nan, nan, nan, 4.28, 4.28, 2...  \n",
       "September  [3.74, 3.74, nan, nan, nan, nan, 5.89, 5.89, 3...  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_monthyr_1840s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "65e794d3-9d98-48e9-ad05-c1260af101ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Sanity Check\n",
    "grouped_by_monthyr_1840s['1840'].loc[['April']].to_list()[0][7]-timeseries_1840s[3][7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "136e4f5b-f2e3-449f-87b1-e612723b9864",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Create a dataframe with time_ax as coord and 'empty' as data. \n",
    "precip_1840s=xr.DataArray(data=np.asarray(timeseries_1840s),coords={'time':time_ax,'site_index':dummy_loc},dims=['time','site_index'],name='monthly_precip',attrs={'DataSource':'Ed Hawkins Rainfall Rescue. GitHub directory for 1840s data. https://github.com/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1840s','Site_index':'Corresponds to row number of raw url from where data is sourced (raw_urls_1840s.csv).','Units':'inches/month'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "32c1a666-5565-43af-99e7-02b3dfdad7df",
   "metadata": {},
   "outputs": [
    {
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       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;monthly_precip&#x27; (time: 120, site_index: 200)&gt;\n",
       "array([[      nan,       nan,       nan, ...,  2.64e+00,  1.54e+00,\n",
       "         1.54e+00],\n",
       "       [      nan,       nan,       nan, ...,  1.40e+00,  1.44e+00,\n",
       "         1.44e+00],\n",
       "       [      nan,       nan,       nan, ..., -9.99e+02,  7.00e-01,\n",
       "         7.00e-01],\n",
       "       ...,\n",
       "       [ 2.16e+00,  2.16e+00,       nan, ...,  1.58e+00,       nan,\n",
       "              nan],\n",
       "       [ 1.08e+00,  1.08e+00,       nan, ...,  9.40e-01,       nan,\n",
       "              nan],\n",
       "       [ 1.93e+00,  1.93e+00,       nan, ...,  2.45e+00,       nan,\n",
       "              nan]])\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 1840-01-31 1840-02-29 ... 1849-12-31\n",
       "  * site_index  (site_index) int64 0 1 2 3 4 5 6 ... 193 194 195 196 197 198 199\n",
       "Attributes:\n",
       "    DataSource:  Ed Hawkins Rainfall Rescue. GitHub directory for 1840s data....\n",
       "    Site_index:  Corresponds to row number of raw url from where data is sour...\n",
       "    Units:       inches/month</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'monthly_precip'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 120</li><li><span class='xr-has-index'>site_index</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-714d6542-8293-47be-a63f-9eb0690c5b52' class='xr-array-in' type='checkbox' checked><label for='section-714d6542-8293-47be-a63f-9eb0690c5b52' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 3.12 3.12 nan nan ... 3.75 3.17 3.17 2.45 2.45 nan nan</span></div><div class='xr-array-data'><pre>array([[      nan,       nan,       nan, ...,  2.64e+00,  1.54e+00,\n",
       "         1.54e+00],\n",
       "       [      nan,       nan,       nan, ...,  1.40e+00,  1.44e+00,\n",
       "         1.44e+00],\n",
       "       [      nan,       nan,       nan, ..., -9.99e+02,  7.00e-01,\n",
       "         7.00e-01],\n",
       "       ...,\n",
       "       [ 2.16e+00,  2.16e+00,       nan, ...,  1.58e+00,       nan,\n",
       "              nan],\n",
       "       [ 1.08e+00,  1.08e+00,       nan, ...,  9.40e-01,       nan,\n",
       "              nan],\n",
       "       [ 1.93e+00,  1.93e+00,       nan, ...,  2.45e+00,       nan,\n",
       "              nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-f6e33c6f-9ebc-42a3-905d-f1e763836634' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f6e33c6f-9ebc-42a3-905d-f1e763836634' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1840-01-31 ... 1849-12-31</div><input id='attrs-87cc1990-82ab-4560-b213-4f4c0dc7a3f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87cc1990-82ab-4560-b213-4f4c0dc7a3f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bd424c4-fea3-4db6-a4ab-586603d26923' class='xr-var-data-in' type='checkbox'><label for='data-8bd424c4-fea3-4db6-a4ab-586603d26923' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1840-01-31T00:00:00.000000000&#x27;, &#x27;1840-02-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;1840-03-31T00:00:00.000000000&#x27;, &#x27;1840-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1840-05-31T00:00:00.000000000&#x27;, &#x27;1840-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1840-07-31T00:00:00.000000000&#x27;, &#x27;1840-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1840-09-30T00:00:00.000000000&#x27;, &#x27;1840-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1840-11-30T00:00:00.000000000&#x27;, &#x27;1840-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1841-01-31T00:00:00.000000000&#x27;, &#x27;1841-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1841-03-31T00:00:00.000000000&#x27;, &#x27;1841-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1841-05-31T00:00:00.000000000&#x27;, &#x27;1841-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1841-07-31T00:00:00.000000000&#x27;, &#x27;1841-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1841-09-30T00:00:00.000000000&#x27;, &#x27;1841-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1841-11-30T00:00:00.000000000&#x27;, &#x27;1841-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1842-01-31T00:00:00.000000000&#x27;, &#x27;1842-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1842-03-31T00:00:00.000000000&#x27;, &#x27;1842-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1842-05-31T00:00:00.000000000&#x27;, &#x27;1842-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1842-07-31T00:00:00.000000000&#x27;, &#x27;1842-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1842-09-30T00:00:00.000000000&#x27;, &#x27;1842-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1842-11-30T00:00:00.000000000&#x27;, &#x27;1842-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1843-01-31T00:00:00.000000000&#x27;, &#x27;1843-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1843-03-31T00:00:00.000000000&#x27;, &#x27;1843-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1843-05-31T00:00:00.000000000&#x27;, &#x27;1843-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1843-07-31T00:00:00.000000000&#x27;, &#x27;1843-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1843-09-30T00:00:00.000000000&#x27;, &#x27;1843-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1843-11-30T00:00:00.000000000&#x27;, &#x27;1843-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1844-01-31T00:00:00.000000000&#x27;, &#x27;1844-02-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;1844-03-31T00:00:00.000000000&#x27;, &#x27;1844-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1844-05-31T00:00:00.000000000&#x27;, &#x27;1844-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1844-07-31T00:00:00.000000000&#x27;, &#x27;1844-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1844-09-30T00:00:00.000000000&#x27;, &#x27;1844-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1844-11-30T00:00:00.000000000&#x27;, &#x27;1844-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1845-01-31T00:00:00.000000000&#x27;, &#x27;1845-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1845-03-31T00:00:00.000000000&#x27;, &#x27;1845-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1845-05-31T00:00:00.000000000&#x27;, &#x27;1845-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1845-07-31T00:00:00.000000000&#x27;, &#x27;1845-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1845-09-30T00:00:00.000000000&#x27;, &#x27;1845-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1845-11-30T00:00:00.000000000&#x27;, &#x27;1845-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1846-01-31T00:00:00.000000000&#x27;, &#x27;1846-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1846-03-31T00:00:00.000000000&#x27;, &#x27;1846-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1846-05-31T00:00:00.000000000&#x27;, &#x27;1846-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1846-07-31T00:00:00.000000000&#x27;, &#x27;1846-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1846-09-30T00:00:00.000000000&#x27;, &#x27;1846-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1846-11-30T00:00:00.000000000&#x27;, &#x27;1846-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1847-01-31T00:00:00.000000000&#x27;, &#x27;1847-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1847-03-31T00:00:00.000000000&#x27;, &#x27;1847-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1847-05-31T00:00:00.000000000&#x27;, &#x27;1847-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1847-07-31T00:00:00.000000000&#x27;, &#x27;1847-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1847-09-30T00:00:00.000000000&#x27;, &#x27;1847-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1847-11-30T00:00:00.000000000&#x27;, &#x27;1847-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1848-01-31T00:00:00.000000000&#x27;, &#x27;1848-02-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;1848-03-31T00:00:00.000000000&#x27;, &#x27;1848-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1848-05-31T00:00:00.000000000&#x27;, &#x27;1848-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1848-07-31T00:00:00.000000000&#x27;, &#x27;1848-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1848-09-30T00:00:00.000000000&#x27;, &#x27;1848-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1848-11-30T00:00:00.000000000&#x27;, &#x27;1848-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1849-01-31T00:00:00.000000000&#x27;, &#x27;1849-02-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1849-03-31T00:00:00.000000000&#x27;, &#x27;1849-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1849-05-31T00:00:00.000000000&#x27;, &#x27;1849-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1849-07-31T00:00:00.000000000&#x27;, &#x27;1849-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1849-09-30T00:00:00.000000000&#x27;, &#x27;1849-10-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1849-11-30T00:00:00.000000000&#x27;, &#x27;1849-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>site_index</span></div><div class='xr-var-dims'>(site_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-d3efcfb0-e8ab-4dd7-bc77-a1f20afef6b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3efcfb0-e8ab-4dd7-bc77-a1f20afef6b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f228c85-0f68-4c52-bb36-e6437feb447b' class='xr-var-data-in' type='checkbox'><label for='data-4f228c85-0f68-4c52-bb36-e6437feb447b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b789cbd-560a-43ec-995e-0bccac7fb29f' class='xr-section-summary-in' type='checkbox'  ><label for='section-3b789cbd-560a-43ec-995e-0bccac7fb29f' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d35357e-44fe-4b3e-9bf0-acc2ed010767' class='xr-index-data-in' type='checkbox'/><label for='index-3d35357e-44fe-4b3e-9bf0-acc2ed010767' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1840-01-31&#x27;, &#x27;1840-02-29&#x27;, &#x27;1840-03-31&#x27;, &#x27;1840-04-30&#x27;,\n",
       "               &#x27;1840-05-31&#x27;, &#x27;1840-06-30&#x27;, &#x27;1840-07-31&#x27;, &#x27;1840-08-31&#x27;,\n",
       "               &#x27;1840-09-30&#x27;, &#x27;1840-10-31&#x27;,\n",
       "               ...\n",
       "               &#x27;1849-03-31&#x27;, &#x27;1849-04-30&#x27;, &#x27;1849-05-31&#x27;, &#x27;1849-06-30&#x27;,\n",
       "               &#x27;1849-07-31&#x27;, &#x27;1849-08-31&#x27;, &#x27;1849-09-30&#x27;, &#x27;1849-10-31&#x27;,\n",
       "               &#x27;1849-11-30&#x27;, &#x27;1849-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=120, freq=&#x27;M&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>site_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e437bdd8-cb68-4a1e-9cad-2c2d82aec7de' class='xr-index-data-in' type='checkbox'/><label for='index-e437bdd8-cb68-4a1e-9cad-2c2d82aec7de' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            190, 191, 192, 193, 194, 195, 196, 197, 198, 199],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;site_index&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48b2e5d0-1556-4737-b45c-462620d9f9de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48b2e5d0-1556-4737-b45c-462620d9f9de' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>DataSource :</span></dt><dd>Ed Hawkins Rainfall Rescue. GitHub directory for 1840s data. https://github.com/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1840s</dd><dt><span>Site_index :</span></dt><dd>Corresponds to row number of raw url from where data is sourced (raw_urls_1840s.csv).</dd><dt><span>Units :</span></dt><dd>inches/month</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'monthly_precip' (time: 120, site_index: 200)>\n",
       "array([[      nan,       nan,       nan, ...,  2.64e+00,  1.54e+00,\n",
       "         1.54e+00],\n",
       "       [      nan,       nan,       nan, ...,  1.40e+00,  1.44e+00,\n",
       "         1.44e+00],\n",
       "       [      nan,       nan,       nan, ..., -9.99e+02,  7.00e-01,\n",
       "         7.00e-01],\n",
       "       ...,\n",
       "       [ 2.16e+00,  2.16e+00,       nan, ...,  1.58e+00,       nan,\n",
       "              nan],\n",
       "       [ 1.08e+00,  1.08e+00,       nan, ...,  9.40e-01,       nan,\n",
       "              nan],\n",
       "       [ 1.93e+00,  1.93e+00,       nan, ...,  2.45e+00,       nan,\n",
       "              nan]])\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 1840-01-31 1840-02-29 ... 1849-12-31\n",
       "  * site_index  (site_index) int64 0 1 2 3 4 5 6 ... 193 194 195 196 197 198 199\n",
       "Attributes:\n",
       "    DataSource:  Ed Hawkins Rainfall Rescue. GitHub directory for 1840s data....\n",
       "    Site_index:  Corresponds to row number of raw url from where data is sour...\n",
       "    Units:       inches/month"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precip_1840s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d0fed209-51d6-4532-a1c6-814aa2ed0457",
   "metadata": {},
   "outputs": [],
   "source": [
    "####some nan data is encoded as -999, so set these to np.nans\n",
    "precip_1840s=(precip_1840s.where(precip_1840s>=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "39646b24-9fd3-42a4-88f6-c2ed4530b71a",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;monthly_precip&#x27; (time: 120, site_index: 200)&gt;\n",
       "array([[ nan,  nan,  nan, ..., 2.64, 1.54, 1.54],\n",
       "       [ nan,  nan,  nan, ..., 1.4 , 1.44, 1.44],\n",
       "       [ nan,  nan,  nan, ...,  nan, 0.7 , 0.7 ],\n",
       "       ...,\n",
       "       [2.16, 2.16,  nan, ..., 1.58,  nan,  nan],\n",
       "       [1.08, 1.08,  nan, ..., 0.94,  nan,  nan],\n",
       "       [1.93, 1.93,  nan, ..., 2.45,  nan,  nan]])\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 1840-01-31 1840-02-29 ... 1849-12-31\n",
       "  * site_index  (site_index) int64 0 1 2 3 4 5 6 ... 193 194 195 196 197 198 199\n",
       "Attributes:\n",
       "    DataSource:  Ed Hawkins Rainfall Rescue. GitHub directory for 1840s data....\n",
       "    Site_index:  Corresponds to row number of raw url from where data is sour...\n",
       "    Units:       inches/month</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'monthly_precip'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 120</li><li><span class='xr-has-index'>site_index</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-287df366-080a-4ea2-914c-3703a8f4152c' class='xr-array-in' type='checkbox' checked><label for='section-287df366-080a-4ea2-914c-3703a8f4152c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan 3.12 3.12 nan nan ... 3.75 3.17 3.17 2.45 2.45 nan nan</span></div><div class='xr-array-data'><pre>array([[ nan,  nan,  nan, ..., 2.64, 1.54, 1.54],\n",
       "       [ nan,  nan,  nan, ..., 1.4 , 1.44, 1.44],\n",
       "       [ nan,  nan,  nan, ...,  nan, 0.7 , 0.7 ],\n",
       "       ...,\n",
       "       [2.16, 2.16,  nan, ..., 1.58,  nan,  nan],\n",
       "       [1.08, 1.08,  nan, ..., 0.94,  nan,  nan],\n",
       "       [1.93, 1.93,  nan, ..., 2.45,  nan,  nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-b3d038aa-9e1a-4eb5-a859-890c8d243416' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3d038aa-9e1a-4eb5-a859-890c8d243416' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1840-01-31 ... 1849-12-31</div><input id='attrs-8979ff54-a2bf-44de-84cf-8e374ff780fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8979ff54-a2bf-44de-84cf-8e374ff780fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0978b64-24e2-4655-8606-09ba8485135f' class='xr-var-data-in' type='checkbox'><label for='data-b0978b64-24e2-4655-8606-09ba8485135f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1840-01-31T00:00:00.000000000&#x27;, &#x27;1840-02-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;1840-03-31T00:00:00.000000000&#x27;, &#x27;1840-04-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1840-05-31T00:00:00.000000000&#x27;, &#x27;1840-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;1840-07-31T00:00:00.000000000&#x27;, &#x27;1840-08-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1849-03-31T00:00:00.000000000&#x27;, &#x27;1849-04-30T00:00:00.000000000&#x27;,\n",
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       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>site_index</span></div><div class='xr-var-dims'>(site_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-1bec36ad-cf1b-402a-98b6-c946a7471d7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bec36ad-cf1b-402a-98b6-c946a7471d7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9aa0d94-fac5-42bf-8322-268efedc4ebd' class='xr-var-data-in' type='checkbox'><label for='data-f9aa0d94-fac5-42bf-8322-268efedc4ebd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1307a63f-8a2a-42e2-ad53-2699b09ab8d2' class='xr-section-summary-in' type='checkbox'  ><label for='section-1307a63f-8a2a-42e2-ad53-2699b09ab8d2' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3cc189af-8f7a-4e56-bfad-4bd784d0d76f' class='xr-index-data-in' type='checkbox'/><label for='index-3cc189af-8f7a-4e56-bfad-4bd784d0d76f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1840-01-31&#x27;, &#x27;1840-02-29&#x27;, &#x27;1840-03-31&#x27;, &#x27;1840-04-30&#x27;,\n",
       "               &#x27;1840-05-31&#x27;, &#x27;1840-06-30&#x27;, &#x27;1840-07-31&#x27;, &#x27;1840-08-31&#x27;,\n",
       "               &#x27;1840-09-30&#x27;, &#x27;1840-10-31&#x27;,\n",
       "               ...\n",
       "               &#x27;1849-03-31&#x27;, &#x27;1849-04-30&#x27;, &#x27;1849-05-31&#x27;, &#x27;1849-06-30&#x27;,\n",
       "               &#x27;1849-07-31&#x27;, &#x27;1849-08-31&#x27;, &#x27;1849-09-30&#x27;, &#x27;1849-10-31&#x27;,\n",
       "               &#x27;1849-11-30&#x27;, &#x27;1849-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=120, freq=&#x27;M&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>site_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-40d00225-1671-41c6-b6f1-626a4414e358' class='xr-index-data-in' type='checkbox'/><label for='index-40d00225-1671-41c6-b6f1-626a4414e358' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            190, 191, 192, 193, 194, 195, 196, 197, 198, 199],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;site_index&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-08f15d7d-9172-410b-88fc-caf8d758439e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08f15d7d-9172-410b-88fc-caf8d758439e' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>DataSource :</span></dt><dd>Ed Hawkins Rainfall Rescue. GitHub directory for 1840s data. https://github.com/ed-hawkins/rainfall-rescue/tree/master/ALLSHEETS/1840s</dd><dt><span>Site_index :</span></dt><dd>Corresponds to row number of raw url from where data is sourced (raw_urls_1840s.csv).</dd><dt><span>Units :</span></dt><dd>inches/month</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'monthly_precip' (time: 120, site_index: 200)>\n",
       "array([[ nan,  nan,  nan, ..., 2.64, 1.54, 1.54],\n",
       "       [ nan,  nan,  nan, ..., 1.4 , 1.44, 1.44],\n",
       "       [ nan,  nan,  nan, ...,  nan, 0.7 , 0.7 ],\n",
       "       ...,\n",
       "       [2.16, 2.16,  nan, ..., 1.58,  nan,  nan],\n",
       "       [1.08, 1.08,  nan, ..., 0.94,  nan,  nan],\n",
       "       [1.93, 1.93,  nan, ..., 2.45,  nan,  nan]])\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 1840-01-31 1840-02-29 ... 1849-12-31\n",
       "  * site_index  (site_index) int64 0 1 2 3 4 5 6 ... 193 194 195 196 197 198 199\n",
       "Attributes:\n",
       "    DataSource:  Ed Hawkins Rainfall Rescue. GitHub directory for 1840s data....\n",
       "    Site_index:  Corresponds to row number of raw url from where data is sour...\n",
       "    Units:       inches/month"
      ]
     },
     "execution_count": 52,
     "metadata": {},
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   ],
   "source": [
    "precip_1840s"
   ]
  },
  {
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   "execution_count": 53,
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       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;monthly_precip&#x27; ()&gt;\n",
       "array(0)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'monthly_precip'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4a5bc49a-2d0f-420d-af51-f7dcd29da229' class='xr-array-in' type='checkbox' checked><label for='section-4a5bc49a-2d0f-420d-af51-f7dcd29da229' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-f322e7e9-d91e-41d7-8eb7-2cd680f42734' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f322e7e9-d91e-41d7-8eb7-2cd680f42734' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-fae99e91-9823-47ef-9d12-2910bc7deec5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fae99e91-9823-47ef-9d12-2910bc7deec5' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-32ab87fc-455c-41ce-b702-ffe5af43b052' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-32ab87fc-455c-41ce-b702-ffe5af43b052' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'monthly_precip' ()>\n",
       "array(0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##double check no -999 values left\n",
    "np.sum(precip_1840s<0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c9963c2b-a483-4246-9b3c-e732be07e107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 10)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_monthyr_1840s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b32de154-63f6-4976-81a7-5105777b8010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 200)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precip_1840s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8de8327c-7821-4b04-9260-9d2142abe52c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ##Save this\n",
    "precip_1840s.to_netcdf(path='RainfallRescue_1840s.cdf', mode='w',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b3ee3a4b-f95f-4703-9107-6badc234b067",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Open Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "648e9896-e422-4e7b-a008-620915ce1193",
   "metadata": {},
   "outputs": [],
   "source": [
    "precip_1840s=xr.open_dataset('RainfallRescue_1840s.cdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "be3a139f-b302-4891-b6a9-e397856416ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ad7ff4ced60>]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(precip_1840s.mean('site_index').resample(time='M').mean().monthly_precip*25.4).plot()   #converted from inches to mm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363995f1-f648-4856-8985-8f29e8f2749f",
   "metadata": {},
   "source": [
    "<p>This can be visually compared to <a href='https://github.com/ed-hawkins/rainfall-rescue/blob/master/REGIONAL-TIMESERIES/uk_rainfall.png'>UK monthly mean graphic on Github.</a></p>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}