Great_Lakes_and_Grand_River.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Lakes and the Grand River\n",
    "\\* Based off a CMSE 201 assignment\n",
    "\n",
    "Were going to use Pandas or Numpy and Matplotlib to interact with some data that pertains to the water levels of the [Great Lakes](https://en.wikipedia.org/wiki/Great_Lakes) and the [discharge](https://en.wikipedia.org/wiki/Discharge_(hydrology)) of the [Grand River](https://en.wikipedia.org/wiki/Grand_River_(Michigan)).\n",
    "![picture](https://upload.wikimedia.org/wikipedia/commons/5/57/Great_Lakes_from_space_crop_labeled.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Great Lakes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bring in NumPy, Pandas, and Matplotlib, should be all you need to plot. \n",
    "# IF you need more make sure to notify your group.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use this notebook for your in-class assignment, you will need these files, which are available as part of the GitHub repository. They live in the \"data\" folder:\n",
    "* `mhu.csv` -- [Lake Michigan](https://en.wikipedia.org/wiki/Lake_Michigan) and [Lake Huron](https://en.wikipedia.org/wiki/Lake_Huron)\n",
    "* `sup.csv` -- [Lake Superior](https://en.wikipedia.org/wiki/Lake_Superior)\n",
    "* `eri.csv` -- [Lake Erie](https://en.wikipedia.org/wiki/Lake_Erie)\n",
    "* `ont.csv` -- [Lake Ontario](https://en.wikipedia.org/wiki/Lake_Ontario)\n",
    "\n",
    "This notebook **might look very familiar!** It is based off a CMSE 201 assignment that focused on exploring some [Great Lakes](https://en.wikipedia.org/wiki/Great_Lakes) water level data. We will be doing similar calculations with the same data to get some practice with version contol while your remind yourself of your Python skills. Make sure you **try to complete this activity without looking at the old notebook**. The old notebook came up in 201 right after you first learned how to use Matplotlib and as you began to learn about NumPy. This is good moment to reflect on how far you have come.\n",
    "\n",
    "**As instructed in the in-class activity notebook for today, you are only expected to complete one \"PART\" below. Do not worry if your group is not big enough to finish all parts below, but if you have extra time, you're welcome to do so.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 1\n",
    "\n",
    "**Using the Michigan/Huron Dataset, plot the Water Level, the second column, as a function of time [years]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Insert Code Here\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv(\"data/mhu.csv\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df['Michigan-Huron annual averages'], df['AnnAvg'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Water Level')\n",
    "plt.title('Michigan-Huron Water Level over Time')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!git checkout part-1\n",
    "!git add .\n",
    "!git commit -m \"part-1\"\n",
    "!push push origin part-1\n",
    "!git checkout main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 2\n",
    "\n",
    "**Using the Superior Dataset, plot the Water Level, the second column, as a function of time [years]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Lake Superior annual averages</th>\n",
       "      <th>AnnAvg</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1860</td>\n",
       "      <td>183.397928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1860-1917 Marquette levels translated to Pt Ir...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1861</td>\n",
       "      <td>183.410761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PI = -.00108382*year+2.153 + Marquette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1862</td>\n",
       "      <td>183.324594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1918-2010 lakewide averages from Corps website</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1863</td>\n",
       "      <td>183.208593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1864</td>\n",
       "      <td>183.149176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2011</td>\n",
       "      <td>183.093417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2012</td>\n",
       "      <td>183.100833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2013</td>\n",
       "      <td>183.220000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2014</td>\n",
       "      <td>183.510000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2015</td>\n",
       "      <td>183.570000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Lake Superior annual averages      AnnAvg  Unnamed: 2  \\\n",
       "0                             1860  183.397928         NaN   \n",
       "1                             1861  183.410761         NaN   \n",
       "2                             1862  183.324594         NaN   \n",
       "3                             1863  183.208593         NaN   \n",
       "4                             1864  183.149176         NaN   \n",
       "..                             ...         ...         ...   \n",
       "151                           2011  183.093417         NaN   \n",
       "152                           2012  183.100833         NaN   \n",
       "153                           2013  183.220000         NaN   \n",
       "154                           2014  183.510000         NaN   \n",
       "155                           2015  183.570000         NaN   \n",
       "\n",
       "                                            Unnamed: 3  \\\n",
       "0    1860-1917 Marquette levels translated to Pt Ir...   \n",
       "1                                                  NaN   \n",
       "2       1918-2010 lakewide averages from Corps website   \n",
       "3                                                  NaN   \n",
       "4                                                  NaN   \n",
       "..                                                 ...   \n",
       "151                                                NaN   \n",
       "152                                                NaN   \n",
       "153                                                NaN   \n",
       "154                                                NaN   \n",
       "155                                                NaN   \n",
       "\n",
       "                                 Unnamed: 4  \n",
       "0                                       NaN  \n",
       "1    PI = -.00108382*year+2.153 + Marquette  \n",
       "2                                       NaN  \n",
       "3                                       NaN  \n",
       "4                                       NaN  \n",
       "..                                      ...  \n",
       "151                                     NaN  \n",
       "152                                     NaN  \n",
       "153                                     NaN  \n",
       "154                                     NaN  \n",
       "155                                     NaN  \n",
       "\n",
       "[156 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lake Superior water level')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Insert Code Here\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df_superior=pd.read_csv(\"./data/sup.csv\")\n",
    "display(df_superior)\n",
    "\n",
    "plt.plot(df_superior['Lake Superior annual averages'],df_superior['AnnAvg'])\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Water Level\")\n",
    "plt.title(\"Lake Superior water level\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 3\n",
    "\n",
    "**Using the Erie Dataset, plot the Water Level, the second column, as a function of time [years]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 4\n",
    "\n",
    "**Using the Ontario Dataset, plot the Water Level, the second column, as a function of time [years]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 5\n",
    "\n",
    "**Using the Michigan/Huron and Superior Datasets, plot the Michigan/Hurion Water Level vs Superior Water Level to see if there is any correlation between the water levels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 6\n",
    "\n",
    "**Using the Michigan/Hurion and Erie Datasets, plot the Michigan/Huron Water Level vs Erie Water Level to see if there is any correlation between the water levels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code Here\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "mhu = pd.read_csv(\"data/mhu.csv\")\n",
    "erie = pd.read_csv(\"data/eri.csv\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df['Michigan-Huron annual averages'], df['AnnAvg'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Water Level')\n",
    "plt.title('Michigan-Huron Water Level over Time')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Lake Erie annual averages</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1860</td>\n",
       "      <td>174.688483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1860-1917, Cleveland levels translated to Buff...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1861</td>\n",
       "      <td>174.712317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Buff = -.001008382*year+2.153 + Cleveland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1862</td>\n",
       "      <td>174.744983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1918-present, lakewide averages</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1863</td>\n",
       "      <td>174.655733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1864</td>\n",
       "      <td>174.471567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2011</td>\n",
       "      <td>174.240667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2012</td>\n",
       "      <td>174.128333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2013</td>\n",
       "      <td>174.060000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2014</td>\n",
       "      <td>174.210000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2015</td>\n",
       "      <td>174.320000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Lake Erie annual averages  Unnamed: 1  Unnamed: 2  \\\n",
       "0                         1860  174.688483         NaN   \n",
       "1                         1861  174.712317         NaN   \n",
       "2                         1862  174.744983         NaN   \n",
       "3                         1863  174.655733         NaN   \n",
       "4                         1864  174.471567         NaN   \n",
       "..                         ...         ...         ...   \n",
       "151                       2011  174.240667         NaN   \n",
       "152                       2012  174.128333         NaN   \n",
       "153                       2013  174.060000         NaN   \n",
       "154                       2014  174.210000         NaN   \n",
       "155                       2015  174.320000         NaN   \n",
       "\n",
       "                                            Unnamed: 3  \\\n",
       "0    1860-1917, Cleveland levels translated to Buff...   \n",
       "1                                                  NaN   \n",
       "2                      1918-present, lakewide averages   \n",
       "3                                                  NaN   \n",
       "4                                                  NaN   \n",
       "..                                                 ...   \n",
       "151                                                NaN   \n",
       "152                                                NaN   \n",
       "153                                                NaN   \n",
       "154                                                NaN   \n",
       "155                                                NaN   \n",
       "\n",
       "                                    Unnamed: 4  \n",
       "0                                          NaN  \n",
       "1    Buff = -.001008382*year+2.153 + Cleveland  \n",
       "2                                          NaN  \n",
       "3                                          NaN  \n",
       "4                                          NaN  \n",
       "..                                         ...  \n",
       "151                                        NaN  \n",
       "152                                        NaN  \n",
       "153                                        NaN  \n",
       "154                                        NaN  \n",
       "155                                        NaN  \n",
       "\n",
       "[156 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erie = pd.read_csv(\"data/eri.csv\")\n",
    "erie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 7\n",
    "\n",
    "**Using the Superior and Ontario Datasets, plot the Superior Water Level vs Ontario Water Level to see if there is any correlation between the water levels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 8\n",
    "\n",
    "**Using the Erie and Ontario Datasets, plot the Erie Water Level vs Ontario Water Level to see if there is any correlation between the water levels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### &#128721; STOP \n",
    "The next section is for the **Exploring the version history and moving between versions** section of today's in-class activity.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grand River\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Grand_River_%28Michigan%29_map.svg/800px-Grand_River_%28Michigan%29_map.svg.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Grand River](https://en.wikipedia.org/wiki/Grand_River_(Michigan)) is an important landmark in [Southern Michigan](https://en.wikipedia.org/wiki/Southern_Michigan). It is the longest river in Michigan at 252 miles (406 km) and it runs through many of the noteable cities in [Southern Michigan](https://en.wikipedia.org/wiki/Southern_Michigan), including [Lansing](https://en.wikipedia.org/wiki/Lansing,_Michigan). \"Grand River\" is also the name of a major road way, also known as [U.S. Route 16](https://en.wikipedia.org/wiki/U.S._Route_16_in_Michigan), that stretches from the rivers end point at [Lake Michigan](https://en.wikipedia.org/wiki/Lake_Michigan) in [Grand Haven](https://en.wikipedia.org/wiki/Grand_Haven,_Michigan) to [Detroit](https://en.wikipedia.org/wiki/Detroit). The river is one of the three major [tributaries](https://en.wikipedia.org/wiki/Tributary) of Lake Michigan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using data on the [discharge](https://en.wikipedia.org/wiki/Discharge_(hydrology)) of the [Grand River](https://en.wikipedia.org/wiki/Grand_River_(Michigan)) and water level of [Lake Michigan](https://en.wikipedia.org/wiki/Lake_Michigan)/[Lake Huron](https://en.wikipedia.org/wiki/Lake_Huron), we will see if there is any correlation between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Seaborn for regplot\n",
    "import seaborn as sns\n",
    "\n",
    "#loading in Data for Grand River from riverdata.com\n",
    "import urllib.request\n",
    "print('Beginning file download with urllib2...')\n",
    "url = 'http://riverdata.com/grand.csv'\n",
    "urllib.request.urlretrieve(url, './data/grand.csv')\n",
    "grand = pd.read_csv(\"./data/grand.csv\")\n",
    "\n",
    "#Loading in Data for mhu.csv\n",
    "mhu = pd.read_csv(\"./data/mhu.csv\")\n",
    "mhu = mhu[mhu[\"Michigan-Huron annual averages\"] >= 1935]\n",
    "grand = grand[grand[\"Calendar Year\"] <= 2015]\n",
    "grand = grand[grand[\"Calendar Year\"] >= 1935]\n",
    "\n",
    "# Setting Size of Figures\n",
    "plt.figure(figsize=(7,10))\n",
    "\n",
    "# Plotting Discharge versus time\n",
    "plt.subplot(2,1,1)\n",
    "plt.scatter(grand[\"Calendar Year\"],grand[\"Discharge (ft^3/s)\"])\n",
    "plt.xlabel(\"Calendar Years\")\n",
    "plt.ylabel(\"Discharge (ft^3/s)\")\n",
    "plt.title(\"Grand River Discharge over Time\")\n",
    "\n",
    "#Plotting Grand River Discharge versus Lake Michigan/Huron Water Level (Normalized)\n",
    "ax2 = plt.subplot(2,1,2)\n",
    "sns.regplot(grand[\"Discharge (ft^3/s)\"],mhu[\"AnnAvg\"],ax=ax2)\n",
    "plt.xlabel(\"Grand River Discharge (ft^3/s)\")\n",
    "plt.ylabel(\"Lake Michigan/Huron Water Level\")\n",
    "plt.title(\"Grand River Discharge vs Lake Michigan/Huron Water Level\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#169; Copyright 2020,  Michigan State University Board of Trustees."
   ]
  }
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