uso_datetime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eea/anaconda3/lib/python3.7/site-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<urllib3.response.HTTPResponse at 0x7f61367fd9b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import necessary packages\n",
    "#import numpy as np\n",
    "import os\n",
    "#import urllib.request\n",
    "import urllib3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import earthpy as et\n",
    "\n",
    "# make figures plot inline\n",
    "plt.ion()\n",
    "\n",
    "# set working directory\n",
    "os.chdir(os.path.join(et.io.HOME, 'earth-analytics'))\n",
    "\n",
    "# set standard plot parameters for uniform plotting\n",
    "plt.rcParams['figure.figsize'] = (10, 6)\n",
    "# prettier plotting with seaborn\n",
    "import seaborn as sns; \n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"whitegrid\")\n",
    "#file_path = \"https://www.esrl.noaa.gov/psd/boulder/data/boulderdaily.complete\"\n",
    "#http = urllib3.PoolManager()\n",
    "#r = http.request('GET', file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>year</th>\n",
       "      <th>mon</th>\n",
       "      <th>day</th>\n",
       "      <th>tmax</th>\n",
       "      <th>tmin</th>\n",
       "      <th>precip</th>\n",
       "      <th>snow</th>\n",
       "      <th>snowcover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1897</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1897</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1897</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1897</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1897</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>-998</td>\n",
       "      <td>-998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  mon  day  tmax  tmin  precip  snow  snowcover\n",
       "0  1897    1    1  -998  -998  -998.0  -998       -998\n",
       "1  1897    1    2  -998  -998  -998.0  -998       -998\n",
       "2  1897    1    3  -998  -998  -998.0  -998       -998\n",
       "3  1897    1    4  -998  -998  -998.0  -998       -998\n",
       "4  1897    1    5  -998  -998  -998.0  -998       -998"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import file into pandas dataframe\n",
    "boulder_july = pd.read_csv(\"/home/eea/earth-analytics/data.txt\", sep=\" \")\n",
    "boulder_july.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f613508c3c8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1  = boulder_july\n",
    "\n",
    "data1.plot(kind=\"scatter\", x=\"mon\", y=\"precip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6135a22080>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O1NSpUzVy5MhLjlHyjeBy8s8WKSe/SBHBfgqu56cf9uRr+YYc+dkuFPbgbhFq35IzI2u7sp+D6uTpVZtLKfv5H9QlTP/ekucilEjnS2Utfz/D3rd07TRtGKADx8+pWaMARYUHulVfNblNKsoX/31w/W/cSfvhA4O7XbhIeum2LrENtGX3mUt+Dvz9DP3X4KaWbBtvrYm/LT+oE/lc4xPW6xzbQINTIl0+x0pmNYuIiHC5Wpmbe+FizCUrmhXh7p0ckpKkIQM9v+u6NquLd3KQKv7Fp7pdqiZcff7bxLs+jMLVSuOIWy5dO75eX9U1/5qsifLeQ9m2sru8XX0OelbxnuTeXhP9j9TXB6uzPTgz1Fa39G6vpDIrmZWtB0Kmm+Li4vT555+ruLhYNtvFS19kZWVJkuLj46v19WvLrh6gMsp+/t09jOJytePr9eXr85cqdgWKin4OarN7b2ynJWuzHXZhl1yNqTJtZbkzlp+f4bA7NjDgwkErl9rd66qP1W218TWtHr9po2CHExKbNw2x9OQfQqab+vfvryVLlmjt2rXq16+fvX3ZsmVq1apVlc8sB+Ceil4WC7VbXdzmy2YO5uxyj5xdnqVB18dzdnkFcEymm0zT1H333acdO3Zo0qRJio6O1rJly7Rs2TKlpqaqT58+lx2jImcNwjPq6jbwhTNp4Rl1dRtQEyhPXdwGnF1eQwzDUGpqqmbNmqXZs2crLy9PcXFxmjNnToUCJgAAQF1AyKyEkJAQTZkyRVOmTPH0VAAAALwSN20FAACA5QiZAAAAsBwhEwAAAJYjZAIAAMByhEwAAABYjpAJAAAAyxEyAQAAYDlCJgAAACxHyAQAAIDlCJkAAACwHCETAAAAliNkAgAAwHKETAAAAFiOkAkAAADLETIBAABgOUImAAAALEfIBAAAgOUImQAAALAcIRMAAACWI2QCAADAcoRMAAAAWI6QCQAAAMsRMgEAAGA5QiYAAAAsR8gEAACA5QiZAAAAsBwhEwAAAJYjZAIAAMByboXMXbt2XbbPqlWrKj0ZAAAA1A5uhcxhw4bpww8/dPlcYWGhpkyZoscee8ySiQEAAMB3uRUy27VrpylTpujxxx/XqVOn7O3Z2dm64447tHjxYo0cOdLySQIAAMC3+LvTeeHChfr73/+u119/XZmZmZo5c6a2b9+uGTNmqF69epozZ4769etXXXMFAACAj3ArZNpsNk2YMEHdu3fXpEmTdPfdd0uSunbtqpkzZ6pp06bVMkkAAAD4FrdCZomgoCD5+/vLNE1JUvPmzdWwYUNLJwYAAADf5fYljN544w3dc889Ki4u1ptvvql7771XH330kYYNG6bs7OzqmCMAAAB8jFsh88EHH9SsWbPUs2dPLVu2TN27d9df/vIXpaam6siRI7rzzjv1wQcfVNdcAQAA4CPcCpmbNm2yh8rw8HB7e58+fbRixQq1b99eU6dOtXqOAAAA8DFuHZP5wQcfqG3bti6fa9Kkid5++22lpqZaMjEAAAD4LrdWMssLmCUMw9C4ceOqNCEAAAD4Pu5dDgAAAMu5fQmj9PR0vfHGG/r++++Vl5dnv4xRCcMwtG3bNssmCAAAAN/j9ok/9913n77//nt17NhRxcXF6tatm9q3by/TNNW6dWsNHjy4uuYKAAAAH+FWyJw7d66ioqL0r3/9S9OnT5ckjRkzRosXL9b8+fO1f/9+DRs2rFomCgAAAN/hVsjcunWrhg0bpsjISNlsF360ZHf59ddfr8GDB+vVV1+1fpYAAADwKW6FzMLCQjVp0kSSFBgYKEnKz8+3P9+2bVv9+OOPFk4PAAAAvsitkBkVFaXDhw9Lkho0aKCwsDBlZWXZnz98+LD8/St1O3QAAADUIm4lwvbt2+u7776zP+7Ro4feeustNWvWTMXFxVq0aJE6dOhg+SQBAADgW9xayRw2bJgiIiJ09uxZSdIf//hHBQUF6amnntLTTz+tgIAATZo0qVomCgAAAN/h1kpmjx491KNHD/vjmJgYffbZZ0pLS5Ofn5+SkpIUGhpq+SQrY+nSpfrzn//s8rmtW7cqKCjIoW3lypWaN2+efv75ZzVs2FC33XabHn30Uad+x44d08yZM/Xll1+qoKBA7dq10xNPPKEuXbpU23sBAADwNVU+gLJBgwbq27evFXOpFjNmzFDLli0d2kpOWiqxfPlyPfnkkxo5cqSefvpp7dq1Sy+99JIOHDig2bNn2/sVFBRo9OjROn36tCZPnqyIiAi99dZbGj16tN5//321a9euJt4SAACA16v1Z+kkJCRc8p7rRUVFmjlzpvr06aOpU6dKklJSUhQQEKDJkydr9OjR6tixoyRpyZIlys7O1tKlS3XNNddIkpKTk3XjjTdq1qxZmj9/frW/HwAAAF/g9r3LV65cqREjRqh79+5q27at03++tpqXkZGho0ePasiQIQ7tt956qwICAvTZZ5/Z21avXq34+Hh7wJQurIrecsstWr9+vU6dOlVj8wYAAPBmbq1kpqam6u9//7saNWqkzp07Kzw8vLrmZZmHHnpIJ06cUFhYmK677jo9/vjjatGihf357OxsSVLr1q0dfq5+/fqKiYmxP1/St1u3bk6vkZCQoKKiIu3evZuz6wEAAORmyHz33XeVnJys+fPnKyAgoLrmZInGjRtr7Nix6tSpk4KDg7Vt2za9/vrrGj58uJYsWaKYmBhJUk5OjiS5DMzh4eH250v6ltdPkk6ePFkdbwUAAMDnuBUy8/PzdeONN9Z4wNywYYNGjRpVob5paWmKjIxUz5491bNnT3t7cnKyrrvuOg0dOlRz587VtGnTHH7OMAyX45VtL6/f5Z5zJTMz06ktPT3drTFgPbaB51AT3olt4DnUhHdiG1SMWyGzbdu2OnToUHXNpVyxsbGaPn16hfqGhISU+1x8fLwSExOVkZFhb4uIiJB0YZWyYcOGDv1zc3MVHR3t0Lf0ymbpfqXHqqjExESHSySlp6crKSnJrTFgrbq6DQoKClz+Matp1IT3qavbgJpAeeriNqhsPbgVMh9//HE9+uijGjBggMPJL9UtKipKQ4cOtWSs4uJi2WwXz3eKi4uTdOF4y1atWtnbz5w5o3379ql3794OfUvfRrPEjh075Ofnp9jYWEvmCAAA4OvcCpnJycmaNm2a7rrrLnXq1EnNmjVzCGzShV3Gzz//vKWTtEpWVpa2bdum22+/3d7WqVMnRUVFafny5RowYIC9/ZNPPtG5c+cc2vr3769nn31W27dvt18WqbCwUJ9++qm6d+9+yVVUAACAusStkPn999/rz3/+s86fP6/Nmzdr8+bNTn28JWTef//9SklJUVxcnBo0aKDt27dr3rx5Cg0N1dixY+39/P39NXHiRD311FN69tlnNXDgQPvF2AcOHKhOnTrZ+w4bNkyLFi3S+PHjNXHiRIWHh+vtt9/WkSNH9Morr3jibQIAAHglt0LmtGnT5O/vr9TUVHXt2lVhYWHVNa8qa926tVasWKFDhw6poKBAUVFR6tOnj8aNG6errrrKoe+QIUNks9k0f/58LV68WA0bNtSIESP02GOPOfQLCgrSW2+9pRdffFFTp06131ZywYIFSkxMrMm3BwAA4NXcCpk7duzQ+PHj1adPn+qaj2Wefvppt/oPHjxYgwcPvmy/qKgozZw5s7LTAgAAqBPcuuNPo0aNvP76mAAAAPA8t0Lm0KFDtWLFCp0/f7665gMAAIBawK3d5UlJSfryyy81fPhw3X333YqOjpafn59Tv2uvvdayCQIAAMD3uBUy77//fvv//8tf/uJ0hxvTNGUYhrZv327N7AAAAOCT3AqZFb3rDgAAAOo2t0Lmxo0bNWLECHXs2NHl81u3btV7772nIUOGWDI5AAAA+Ca3Tvz5+OOPtXfv3nKf379/v5YtW1blSQEAAMC3uRUyL+f06dPy93drcRQAAAC10GUT4cGDB3XgwAH74927d2vTpk1O/XJzc/Xee++pRYsW1s4QAAAAPueyIXPp0qWaM2eODMOQYRiaO3eu5s6d69TPNE3ZbDavuG85AAAAPOuyIbNfv35q1qyZTNPU008/reHDh6tz584OfQzDUIMGDdS+fXtdeeWV1TZZAAAA+IbLhsw2bdqoTZs2ki7sOh8wYIDi4+OrfWIAAADwXW6dpTN+/PjqmgcAAABqEUvPLgcAAAAkQiYAAACqASETAAAAliNkAgAAwHKETAAAAFiOkAkAAADLETIBAABgOUImAAAALEfIBAAAgOUImQAAALAcIRMAAACWI2QCAADAcoRMAAAAWI6QCQAAAMsRMgEAAGA5QiYAAAAsR8gEAACA5QiZAAAAsBwhEwAAAJYjZAIAAMByhEwAAABYjpAJAAAAyxEyAQAAYDlCJgAAACxHyAQAAIDlCJkAAACwHCETAAAAliNkAgAAwHKETAAAAFiOkAkAAADLETIBAABgOUImAAAALEfIBAAAgOUImQAAALAcIRMAAACW87mQmZ2drWeeeUbDhg1T+/btlZCQoP3795fb/+2339bAgQOVmJiofv36ad68eSouLnbqt3fvXj3yyCNKSkpS586d9fDDD2vnzp1VGhMAAKCu8rmQmZmZqS+++EKNGzdWly5dLtk3NTVV06dP10033aR//OMfGjZsmF555RXNmjXLod/x48d1991368CBA5oxY4ZmzZql3Nxc3XvvvTp8+HClxgQAAKjL/KZOnTrV05NwR0JCgh544AHdcsstys3N1ddff6377rtPYWFhDv1Onjyp8ePHa+TIkXryyScVHR2trl27qrCwUAsWLNAdd9yhkJAQSdKcOXO0adMmffjhh+rQoYNatWqlfv366c0331ReXp569+7t9piXUlRUpCNHjuiKK66Qv7+/vf3QoUO66qqr7I83bjusJWuyZbMZahYVUqU2K8eqza+58utfFBwS7JPvadvPx/XZt7/I39+mqIYNJEn7fv1Nm7b9qgB/m8JDgtz+TNYUasJ7x6cmqAlfGb+mXtPTNVGVsSpbE5WtB8M0TbPCvb3MP//5T02fPl1r1qxRdHS0w3MrVqzQpEmTtHTpUl1zzTX29j179mjgwIGaMmWK7rnnHknSgAEDFBsbq7lz5zqMMXHiRKWlpWn9+vVuj3kpBQUFyszMVGJiooKCLm7Q9PR0JSUlSZLGvbhGe389ZX+uedMQyVSl2vwMqajUVq7KWHX5Nb35PUWG1lNG9jF7W6f4xmrWOESfrt9jb7u5R0uNHdpRrpT3mawp1IT3j+9rr0lNuNfG59M3XrMq41elJipbDzX/9ayGZGdnyzAMtW7d2qG9ZcuWqlevnrKzsyVJZ8+e1d69ezVo0CCnMRISEvTJJ5/o+PHjatSoUYXHrKqN2w47fDgkae/hU079KtpW+gNZ1bHq8mt683sq256RdUwZWccc2j79Zo9u7hGrmCahTmN4O2rC8+P72mtSE+618fn0jdesyvieqAmfOyazonJyclS/fn0FBgY6PRcWFqacnBxJUm5urkzTVHh4uFO/iIgI+1jujFlVaVsPWTIOUNaOX056egqVQk2gulATgCMra8KjK5kbNmzQqFGjKtQ3LS1NkZGRlr22YRiXfGzFmJeTmZnp1Jaenq6o+qerPBfAlfOnDig9/ainp1EuagI1jZoAHFlZEx4NmbGxsZo+fXqF+lbkhJrSIiIidObMGRUWFjqtPObl5dlXLsPDw2UYhstVyJK2khXNio5ZUeUda5OUJH2TtcZhabt50wvvvzJtfjapqNQVlqoyVl1+TW9+T5Fh9Rx2e3SKb6xmUSH69Js99rabe7TUoD6XPv7M06gJ7x3f116TmnCvjc+nb7xmVcavSk1Uth48GjKjoqI0dOjQahk7Li5OpmkqOzvb4SSdX375RWfPnrUfV1mvXj3FxMQoKyvLaYysrCxFRkaqUaNGbo1phf+Z1Fcbtx1W2tZD6t7hSiW3aypJlW6zcqza/JqffPGDbund3iff07afj2vLT0fUpc0Vatfqwmf25h6x2vHLSSW0aOiTx52VRk14Znxqwnv5Wk1482fFl2qiKmPVeE2YPuzNN9804+PjzX379jk9d+LECTMxMdGcNm2aQ/vs2bPNdu3amQe30CyBAAAfTklEQVQPHrS3vfDCC2b79u3NI0eO2NtOnjxpdunSxZw8eXKlxryUs2fPmps3bzbPnj3r0L558+YK/TyqT13dBuV9Jj39+nV1e3iTuroNqAmUpy5ug8rWg8+dXX7mzBmtW7dOkrRjxw5J0ldffaXIyEhFRkYqOTlZktSwYUONGTNGqampCg0NVbdu3ZSRkaH58+dr1KhRuvLKK+1jPvjgg1qxYoV+//vfa9y4cfL399drr70mf39/jR071t7PnTEBAADqMp8LmcePH9eECRMc2v76179KkpKTk7Vw4UJ7+7hx4xQSEqJ3331Xr7/+uq644go9+uijevjhhx1+vnHjxlq0aJFmzJihJ598UqZpKikpSe+8847DRW/dGRMAAKAu87mQGR0dbV/BvBzDMDR69GiNHj36sn1btmyp1157zdIxAQAA6iqfC5m1gfmfmywVFhY6PVdQUFDT00EZdXEblHwWTQ/dAIya8G51cRtQE7iUurYNKlsPPn1bSV/122+/uTybHfC0+Ph4hYbW/Bm31AS8FTUBXORuPRAyPaC4uFj5+fkKCAiw5CLwQFWZpqlz584pODhYNlvN3wiMmoC3oSaAiypbD4RMAAAAWK7W3rscAAAAnkPIBAAAgOUImQAAALAcIRMAAACWI2QCAADAcoRMAAAAWI6QCQAAAMsRMgEAAGA5QiYAAAAsR8gEAACA5QiZAAAAsBwhEwAAAJYjZAIAAMByhEwAAABYjpAJAAAAyxEyAQAAYDlCJgAAACxHyAQAAIDlCJkAAACwHCETAAAAliNkAgAAwHKETAAAAFiOkAkAAADLETIBAABgOUImAAAALEfIBAAAgOX8PT2Buqi4uFj5+fkKCAiQYRieng4g0zR17tw5BQcHy2ar+e+e1AS8DTUBXFTZeiBkekB+fr6ysrI8PQ3ASXx8vEJDQ2v8dakJeCtqArjI3XogZHpAQECApAsbKzAw0N6emZmpxMRET00LqrvboLCwUFlZWfbPZk2jJrxXXd0G1ATKUxe3QWXrgZDpASW7PgIDAxUUFOTwXNnHqHl1eRt4arccNeHd6vI2oCbgSl3dBu7WAyf+AAAAwHKETAAAAFiOkAkAAADLETIBAABgOUImAAAALEfIBAAAgOUImQAAALAcIRMAAACWI2QCAADAcoTMSsjPz9dzzz2n66+/Xh06dNDQoUO1Zs0aT08LAADAaxAyK2H8+PFauXKlJkyYoNdff11xcXEaP3681q1b5+mpAQAAeAXuXe6mdevWaf369ZozZ4769+8vSUpJSdG+ffv0wgsvqFevXh6eIQAAgOexkummVatWKTQ0VH379rW3GYahIUOGaPfu3dq5c6cHZwcAAOAdCJluys7OVlxcnGw2x19dQkKCJCkrK8sT0wIAAPAqhEw35eTkKDw83Km9pC0nJ6empwQAAOB1OCazEgzDqNRzZWVmZjq1paenV2pOsA7bwHOoCe/ENvAcasI7sQ0qhpDppoiICJerlbm5uZLkcpWzPImJiQoKCrI/Tk9PV1JSUtUniUqrq9ugoKDA5R+zmkZNeJ+6ug2oCZSnLm6DytYDu8vdFBcXp127dqm4uNihveRYzPj4eE9MCwAAwKsQMt3Uv39/5eXlae3atQ7ty5YtU6tWrRQXF+ehmQEAAHgPdpe7qVevXurWrZv++7//Wzk5OYqOjtayZcuUnp6u1NRUT08PAADAKxAy3WQYhlJTUzVr1izNnj1beXl5iouL05w5c9SnTx9PTw8AAMArEDIrISQkRFOmTNGUKVM8PRUAAACvxDGZAAAAsBwhEwAAAJYjZAIAAMByhEwAAABYjpAJAAAAyxEyAQAAYDlCJgAAACxHyAQAAIDlCJkAAACwHCETAAAAliNkAgAAwHKETAAAAFiOkAkAAADLETIBAABgOUImAAAALEfIBAAAgOUImQAAALAcIRMAAACWI2QCAADAcoRMAAAAWI6QCQAAAMsRMgEAAGA5QiYAAAAsR8gEAACA5QiZAAAAsBwhEwAAAJYjZAIAAMByhEwAAABYzq2Q+eyzz2rVqlXKycmprvkAAACgFvB3p/MHH3yg9957T4ZhKCEhQSkpKUpJSVHXrl0VHBxcXXMEAACAj3ErZG7cuFGbNm3St99+q40bN+qtt97Sm2++KX9/fyUmJqpbt25KSUlR9+7dq2u+AAAA8AFuhczg4GDdcMMNuuGGGyRJeXl52rBhgzZs2KCvvvpKb7zxhubNm6dt27ZVx1wBAADgIyp94k9RUZF27dql7Oxs7dixQ4cOHZJpmoqKirJyfgAAAPBBbq1k/vTTT/r222+VlpamzZs3Kz8/XxEREUpOTtaf//xnpaSkKDY2trrmCgAAAB/hVsi8/fbb5efnpx49emj8+PFKSUlR27Ztq2tuAAAA8FFuhcz69evrzJkzyszMVHBwsP2/5s2bV9f8AAAA4IPcCpmbNm3S999/r2+//VbffvutnnvuOZ07d05NmzZVSkqKunXrpu7du6tJkybVNV8AAAD4ALdCpr+/v5KSkpSUlKRx48apoKBAW7ZsUVpamlatWqVly5bJMAzOLgcAAKjj3AqZpZ05c0abN2+2nwj0yy+/yDRN2WzcqRIAAKCuc3t3eVpamr799lv98MMPOn/+vEzTVOvWrXX33Xfbd5kDAACgbnMrZP7ud7+TJDVv3lxDhgyx31YyMjKyWiYHAAAA3+RWyJw+fbq6d++upk2bVtd8AAAAUAu4FTKHDBlSXfMAAABALVKpE3++/fZbrV69Wvv27ZMkxcTEqH///hyPCQAAAEluhszi4mL96U9/0ieffOJwJnlxcbEWLVqkW2+9VTNmzJBhGNUyWQAAAPgGt0LmggULtHLlSg0aNEhjx47V1VdfLUnatWuX3njjDa1cuVJt2rTRAw88UC2TBQAAgG9w66KWH3/8sXr06KFXXnlFbdq0UUBAgAICAtSmTRvNmjVL1113nT766KPqmisAAAB8hFshc9++ferTp0+5z/fp08d+nCYAAADqLrdCZv369XXs2LFynz969Kjq169f5UlZYenSpUpISHD5X0FBgVP/lStX6rbbblP79u3Vs2dPvfTSSy77HTt2TH/605/UrVs3derUSXfffbe2bNlSE28JAADAZ7h1TGbXrl21aNEi3XTTTWrdurXDczt37tS7776r5ORkSydYVTNmzFDLli0d2gIDAx0eL1++XE8++aRGjhypp59+Wrt27dJLL72kAwcOaPbs2fZ+BQUFGj16tE6fPq3JkycrIiJCb731lkaPHq33339f7dq1q4m3BAAA4PXcCpmPPfaY7rrrLg0ZMkR9+vRRXFycpAsBc+3atQoICNCjjz5aLROtrISEBLVt27bc54uKijRz5kz16dNHU6dOlSSlpKQoICBAkydP1ujRo9WxY0dJ0pIlS5Sdna2lS5fqmmuukSQlJyfrxhtv1KxZszR//vxqfz8AAAC+wK3d5QkJCVq4cKESExP1+eefKzU1Vampqfr888/Vvn17LVy4UAkJCdU112qRkZGho0ePOl1o/tZbb1VAQIA+++wze9vq1asVHx9vD5jShVXRW265RevXr9epU6dqbN4AAADezO2Lsbdv317vv/++Tpw4of3798s0TcXExHjt/csfeughnThxQmFhYbruuuv0+OOPq0WLFvbns7OzJclp93/9+vUVExNjf76kr6sLzickJKioqEi7d+9Whw4dqumdAAAA+I5K3fFHkiIjI702WEpS48aNNXbsWHXq1EnBwcHatm2bXn/9dQ0fPlxLlixRTEyMJCknJ0eSFB4e7jRGeHi4/fmSvuX1k6STJ09Wx1sBAADwOZUKmWfOnNGBAweUk5Mj0zSdnr/22murPLHSNmzYoFGjRlWob1pamiIjI9WzZ0/17NnT3p6cnKzrrrtOQ4cO1dy5czVt2jSHnyvvLkVl2y91NyN373SUmZnp1Jaenu7WGLAe28BzqAnvxDbwHGrCO7ENKsatkHnmzBlNnz5dS5cuVVFRkdPzpmnKMAxt377dsglKUmxsrKZPn16hviEhIeU+Fx8fr8TERGVkZNjbIiIiJF1YpWzYsKFD/9zcXEVHRzv0Lb2yWbpf6bEqKjExUUFBQfbH6enpSkpKcmsMWKuuboOCggKXf8xqGjXhferqNqAmUJ66uA0qWw9uhcxp06ZpyZIl6tWrl1JSUtwOVZUVFRWloUOHWjJWcXGx/Z7rkuxnyGdnZ6tVq1b29jNnzmjfvn3q3bu3Q9+srCynMXfs2CE/Pz/FxsZaMkcAAABf51bIXL16tW6++Wa9/PLL1TWfapWVlaVt27bp9ttvt7d16tRJUVFRWr58uQYMGGBv/+STT3Tu3DmHtv79++vZZ5/V9u3b7ZdFKiws1Keffqru3btfchUVAACgLnErZBYUFLg8u9ob3X///UpJSVFcXJwaNGig7du3a968eQoNDdXYsWPt/fz9/TVx4kQ99dRTevbZZzVw4ED7xdgHDhyoTp062fsOGzZMixYt0vjx4zVx4kSFh4fr7bff1pEjR/TKK6944m0CAAB4JbdCZmJiovbs2VNNU7FW69attWLFCh06dEgFBQWKiopSnz59NG7cOF111VUOfYcMGSKbzab58+dr8eLFatiwoUaMGKHHHnvMoV9QUJDeeustvfjii5o6daoKCgrUrl07LViwQImJiTX59gAAALyaWyFz4sSJGjt2rAYNGuT114N8+umn3eo/ePBgDR48+LL9oqKiNHPmzMpOCwAAoE5wK2R+8MEHatq0qUaMGKFOnTopJibG4SQa6cJlfJ5//nlLJwkAAADf4lbI/Pjjj+3/f8uWLdqyZYtTH0ImAAAA3AqZP/30U3XNAwAAALVIpW8rWeL8+fNas2aN8vLy1Lt3bzVu3NiKeQEAAMCHuRUyX3zxRW3YsEEfffSRpAt3+Bk9erTS09NlmqYiIiK0ePFiNW/evFomCwAAAN9gu3yXi/7v//5PXbt2tT9eu3atNm/erAcffNB+gfY33njD2hkCAADA57i1knn48GG1aNHC/viLL75QdHS0nnjiCUkXbs24cuVKa2cIAAAAn+PWSua5c+fk5+dnf7xhwwZdd9119scxMTE6evSodbMDAACAT3IrZDZt2lQZGRmSLqxa7tu3T9dee639+ePHj6tBgwbWzhAAAAA+x63d5TfffLNSU1N14sQJZWdnKyQkRL169bI/v337dk76AQAAgHsrmWPGjNGQIUOUkZEhwzA0Y8YMhYWFSZJ+++03rV27Vt27d6+WiQIAAMB3uLWSGRgYWO7dfIKDg/X111+rXr16lkwMAAAAvqvKF2MvYbPZFBoaatVwAAAA8GFu7S4HAAAAKoKQCQAAAMsRMgEAAGA5QiYAAAAsR8gEAACA5QiZAAAAsBwhEwAAAJYjZAIAAMByhEwAAABYjpAJAAAAyxEyAQAAYDlCJgAAACxHyAQAAIDlCJkAAACwHCETAAAAliNkAgAAwHKETAAAAFiOkAkAAADLETIBAABgOUImAAAALEfIBAAAgOUImQAAALAcIRMAAACWI2QCAADAcoRMAAAAWI6QCQAAAMsRMgEAAGA5QiYAAAAsR8gEAACA5QiZAAAAsBwhEwAAAJYjZAIAAMByhEwAAABYjpAJAAAAyxEyAQAAYDmfC5nZ2dl65plnNGzYMLVv314JCQnav39/uf3ffvttDRw4UImJierXr5/mzZun4uJip3579+7VI488oqSkJHXu3FkPP/ywdu7cWaUxAQAA6iqfC5mZmZn64osv1LhxY3Xp0uWSfVNTUzV9+nTddNNN+sc//qFhw4bplVde0axZsxz6HT9+XHfffbcOHDigGTNmaNasWcrNzdW9996rw4cPV2pMAACAusxv6tSpUz09CXckJCTogQce0C233KLc3Fx9/fXXuu+++xQWFubQ7+TJkxo/frxGjhypJ598UtHR0eratasKCwu1YMEC3XHHHQoJCZEkzZkzR5s2bdKHH36oDh06qFWrVurXr5/efPNN5eXlqXfv3m6PeSlFRUU6cuSIrrjiCvn7+9vbDx06pKuuusr+eMZbG/TqBxn6+cBJXd8pukptVRlr9rub9er732nfr3nq3v6qCrdV9uc8+ZrvrT2gA0dP+eR7+p8Pv9PfPsjQoWOnlHzNlZKkT7/ZrfnLM1V4vkjxzRu6/ZmsKdSEd3z+qYnLfyZrSkVrAjWvLm6DytaDYZqmWY3zqlb//Oc/NX36dK1Zs0bR0dEOz61YsUKTJk3S0qVLdc0119jb9+zZo4EDB2rKlCm65557JEkDBgxQbGys5s6d6zDGxIkTlZaWpvXr17s95qUUFBQoMzNTiYmJCgoKsrenp6crKSlJknTrxOVu/jYAR8H1/JR/tuji4/p+ev+5W1z2Le8zWVOoCdSE2lYT8Iy6uA0qWw8+t7u8orKzs2UYhlq3bu3Q3rJlS9WrV0/Z2dmSpLNnz2rv3r2Kj493GiMhIUHHjx/X8ePH3Rqzqma8tcGScVC3lf5jKkn5Z4r06Te7PTSbqqEmYIXaVBOAL6i1ITMnJ0f169dXYGCg03NhYWHKycmRJOXm5so0TYWHhzv1i4iIsI/lzphVtfmno5aMA5S1bssBT0+hUqgJVBdfrQnAF9T8gSalbNiwQaNGjapQ37S0NEVGRlr22oZhXPKxFWNeTmZmplNbenq6Wl3hr+37i1z8BFA1rRqdV3p6uqenUS5qAjXNV2sCnsU2qBiPhszY2FhNnz69Qn0rckJNaRERETpz5owKCwudVh7z8vLsK5fh4eEyDMPlKmRJW8mKZkXHrKjyjrVJSuL4M1RdcH0/5Z9xPP7sD3f3dtm35HgbT6MmUJ1qU03Ac+riNqhsPXg0ZEZFRWno0KHVMnZcXJxM01R2drbDSTq//PKLzp49az+usl69eoqJiVFWVpbTGFlZWYqMjFSjRo3cGtMKK18erBlvbdDmn46qa5so/em+bpJU6baqjDX73c3a8OOv6nZNE/3X3V0r3FbZn/Pka67//oCu69jMJ9/T/3z4ndJ+OKzu7Ztq3J2dJV04k3bdlgPq1aWZbu4RW8lPo3egJjwzPjUBoNJMH/bmm2+a8fHx5r59+5yeO3HihJmYmGhOmzbNoX327Nlmu3btzIMHD9rbXnjhBbN9+/bmkSNH7G0nT540u3TpYk6ePLlSY17K2bNnzc2bN5tnz551aN+8eXOFfh7Vp65ug/I+k55+/bq6PbxJXd0G1ATKUxe3QWXrwaMrmZVx5swZrVu3TpK0Y8cOSdJXX32lyMhIRUZGKjk5WZLUsGFDjRkzRqmpqQoNDVW3bt2UkZGh+fPna9SoUbryyivtYz744INasWKFfv/732vcuHHy9/fXa6+9Jn9/f40dO9bez50xAQAA6jKfC5nHjx/XhAkTHNr++te/SpKSk5O1cOFCe/u4ceMUEhKid999V6+//rquuOIKPfroo3r44Ycdfr5x48ZatGiRZsyYoSeffFKmaSopKUnvvPOO0wVXKzomAABAXeZzITM6Otq+gnk5hmFo9OjRGj169GX7tmzZUq+99pqlYwIAANRVPhcyawPzPzdZKiwsdHquoKCgpqeDMuriNij5LJoeugEYNeHd6uI2oCZwKXVtG1S2Hnz6tpK+6rfffnN5NjvgafHx8QoNDa3x16Um4K2oCeAid+uBkOkBxcXFys/PV0BAgCUXgQeqyjRNnTt3TsHBwbLZav5GYNQEvA01AVxU2XogZAIAAMBytfbe5QAAAPAcQiYAAAAsR8gEAACA5QiZAAAAsBwhEwAAAJYjZAIAAMByhEwAAABYjttKelh+fr5mz56tf//738rLy1NcXJzGjRunvn37enpqtU5aWpqWL1+u7777TocPH1Z4eLg6dOigRx99VAkJCQ59v/nmG7366qv66aefFBwcrP79++uJJ55QWFiYh2Zfd1ATNYN68B3URM2gJqzHxdg97P7779e2bdv0xBNPKDo6Wh9//LFWrlypuXPnqlevXp6eXq3y2GOPKScnRzfeeKOuvvpqHTt2TPPnz1d2drYWLlyoTp06SZI2bNigBx54QH379tWIESN05MgRvfTSS4qOjta7777rkbt/1CXURM2gHnwHNVEzqIlqYMJjvvzySzM+Pt78/PPP7W3FxcXmiBEjzEGDBnlwZrXTsWPHnNpyc3PNrl27muPHj7e33XHHHebgwYPNoqIie9vXX39txsfHm59++mmNzLWuoiZqDvXgG6iJmkNNWI+47UGrVq1SaGiowy4PwzA0ZMgQ7d69Wzt37vTg7GqfRo0aObWFhYWpRYsWOnz4sCTp119/1Q8//KDBgwc7fBvt0aOHmjRpos8++6zG5lsXURM1h3rwDdREzaEmrEfI9KDs7GzFxcU5La2XHPuRlZXliWnVKSdOnFB2drZat24t6eLvvORxafHx8crOzq7R+dU11IRnUQ/eh5rwLGqiagiZHpSTk6Pw8HCn9pK2nJycmp5SnWKapiZPnqzi4mI9+OCDki7+zsvbLmyT6kVNeA714J2oCc+hJqqOs8s9zDCMSj2HqnvxxRe1evVqTZ8+XVdffbXDc+X97tkm1Y+a8AzqwXtRE55BTVQdK5keFBER4fJbT25uriTX35RgjdmzZ2vBggX67//+bw0dOtTeHhERIcn16kBubi7bpJpRE55BPXgvasIzqAlrEDI9KC4uTrt27VJxcbFDe8kxH/Hx8Z6YVq336quvau7cuZo0aZJGjRrl8FzJcTaujqvJyspyeRwOrENN1DzqwbtREzWPmrAOIdOD+vfvr7y8PK1du9ahfdmyZWrVqpXi4uI8NLPaa86cOUpNTdWECRP00EMPOT3ftGlTJSYmauXKlQ7/qKelpenXX3/VgAEDanK6dQ41UbOoB+9HTdQsasJaflOnTp3q6UnUVS1atNCmTZu0ePFiNWzYUHl5eZozZ46++OILPf/882rVqpWnp1irLFiwQLNmzVLv3r01ZMgQHT582P7fiRMnFBUVJUlq3ry5FixYoJ07dyo8PFzp6en661//qtatW+upp57iQrvViJqoOdSDb6Amag41YT3u+ONhp06d0qxZs/TZZ5853C6sX79+np5arfO73/1OGzdudPlcs2bNHFYKvvrqK/3973+33zKsX79+mjRpEsfb1ABqomZQD76DmqgZ1IT1CJkAAACwHGu6AAAAsBwhEwAAAJYjZAIAAMByhEwAAABYjpAJAAAAyxEyAQAAYDlCJrxanz599Lvf/c7T0wC8BjUBwFcQMgEAAGA5QiYAAAAsR8gEAACA5QiZ8AqHDh3ShAkTlJSUpC5dumjs2LHau3evy77/+te/NHbsWN1www1KTExUt27d9Mgjj+inn35y6HfbbbfphhtuUHFxscsxEhIStGzZsmp5P0BVURMAfB0hEx6Xl5ene+65R6tWrdLgwYM1ceJE1atXT6NGjdLp06ed+r/zzjsyDEPDhw/XM888o+HDhys9PV0jR47Unj177P2GDx+uQ4cO6ZtvvnEa46OPPlJoaKgGDRpUnW8NqBRqAkCtYAIe9vLLL5vx8fHmkiVLHNqfe+45Mz4+3rz33nsd2vPz853G2Llzp3nNNdeYzzzzjL0tLy/P7Nixo/nYY4859D148KDZpk0bh76AN6EmANQGrGTC41avXq3GjRvr9ttvd2h/+OGHXfZv0KCBJMk0TZ06dUonTpxQw4YN1apVK23dutXer2RVZs2aNTpx4oS9/aOPPlJxcbGGDRtWDe8GqDpqAkBtQMiEx+3bt08tWrSQn5+fQ/sVV1yhsLAwp/7btm3TmDFj1KVLFyUlJal79+7q3r27srKylJub69B3+PDhOnfunFasWCHpwh/hpUuXqm3btkpMTKy+NwVUATUBoDbw9/QEAEkyDMNlu2maDo8PHjyoe+65RyEhIfrDH/6g2NhY1a9fX4Zh6Pnnn3c6Xq1Lly6Kj4/XRx99pNGjRystLU0HDhzQgw8+WG3vBbACNQHA1xEy4XExMTHas2ePioqKHFZujhw5ot9++82h76pVq3T69Gm99tprSklJcXguJydHgYGBTuPfeeedmjZtmrZu3aolS5YoKChIt956a/W8GcAC1ASA2oDd5fC4vn376tixY06XTpk3b55T35I/uGVXcxYvXqyjR4+6HH/w4MEKCgrS/PnztWrVKg0YMMDlLkfAW1ATAGoDVjLhcQ899JA++eQTTZ48WT/++KPi4uK0ceNGZWRkqGHDhg59e/bsqfr16+vJJ5/Uvffeq7CwMG3ZskVfffWVmjdvrqKiIqfxw8PDNXDgQPsxaHfeeWeNvC+gsqgJALUBK5nwuPDwcC1atEj9+vXTsmXLNHPmTJ09e1Zvv/22/azZEs2bN9e8efMUHR2tuXPn6uWXX1Zubq4WLlyopk2blvsad911lySpRYsWSk5Ortb3A1QVNQGgNjDMsvtYgFpo69atuvPOO/XHP/5RY8aM8fR0AI+jJgBUN1YyUSe88847CggI0NChQz09FcArUBMAqhvHZKLWOn36tL744gtlZ2drxYoVGj58uKKiojw9LcBjqAkANYnd5ai19u/fr759+6pBgwbq2bOnpk2bppCQEE9PC/AYagJATSJkAgAAwHIckwkAAADLETIBAABgOUImAAAALEfIBAAAgOUImQAAALAcIRMAAACW+383fxSfB5AHDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "figure, axs = plt.subplots(2,3, sharey=True, sharex=True)\n",
    "data1.plot(kind=\"scatter\", x=\"day\", y=\"tmax\", ax=axs[0][0])\n",
    "data1.plot(kind=\"scatter\", x=\"day\", y=\"tmin\", ax=axs[0][1])\n",
    "data1.plot(kind=\"scatter\", x=\"day\", y=\"precip\", ax=axs[0][2])\n",
    "data1.plot(kind=\"scatter\", x=\"day\", y=\"snow\", ax=axs[1][0])\n",
    "data1.plot(kind=\"scatter\", x=\"day\", y=\"snowcover\",ax=axs[1][1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}