sea-level-predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Read data from file\n",
    "df = pd.read_csv(\"epa-sea-level.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": false
   },
   "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>csiro</th>\n",
       "      <th>lower-bound</th>\n",
       "      <th>upper-bound</th>\n",
       "      <th>noaa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.952756</td>\n",
       "      <td>0.952756</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1881</td>\n",
       "      <td>0.220472</td>\n",
       "      <td>-0.732283</td>\n",
       "      <td>1.173228</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1882</td>\n",
       "      <td>-0.440945</td>\n",
       "      <td>-1.346457</td>\n",
       "      <td>0.464567</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1883</td>\n",
       "      <td>-0.232283</td>\n",
       "      <td>-1.129921</td>\n",
       "      <td>0.665354</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1884</td>\n",
       "      <td>0.590551</td>\n",
       "      <td>-0.283465</td>\n",
       "      <td>1.464567</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>129</th>\n",
       "      <td>2009</td>\n",
       "      <td>8.586614</td>\n",
       "      <td>8.311024</td>\n",
       "      <td>8.862205</td>\n",
       "      <td>8.046354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2010</td>\n",
       "      <td>8.901575</td>\n",
       "      <td>8.618110</td>\n",
       "      <td>9.185039</td>\n",
       "      <td>8.122973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2011</td>\n",
       "      <td>8.964567</td>\n",
       "      <td>8.661417</td>\n",
       "      <td>9.267717</td>\n",
       "      <td>8.053065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.326772</td>\n",
       "      <td>8.992126</td>\n",
       "      <td>9.661417</td>\n",
       "      <td>8.457058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2013</td>\n",
       "      <td>8.980315</td>\n",
       "      <td>8.622047</td>\n",
       "      <td>9.338583</td>\n",
       "      <td>8.546648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year     csiro  lower-bound  upper-bound      noaa\n",
       "0    1880  0.000000    -0.952756     0.952756       NaN\n",
       "1    1881  0.220472    -0.732283     1.173228       NaN\n",
       "2    1882 -0.440945    -1.346457     0.464567       NaN\n",
       "3    1883 -0.232283    -1.129921     0.665354       NaN\n",
       "4    1884  0.590551    -0.283465     1.464567       NaN\n",
       "..    ...       ...          ...          ...       ...\n",
       "129  2009  8.586614     8.311024     8.862205  8.046354\n",
       "130  2010  8.901575     8.618110     9.185039  8.122973\n",
       "131  2011  8.964567     8.661417     9.267717  8.053065\n",
       "132  2012  9.326772     8.992126     9.661417  8.457058\n",
       "133  2013  8.980315     8.622047     9.338583  8.546648\n",
       "\n",
       "[134 rows x 5 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['year','csiro','lower-bound','upper-bound','noaa']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the data to complete the following tasks:\n",
    "\n",
    "- Use Pandas to import the data from epa-sea-level.csv.\n",
    "- Use matplotlib to create a scatter plot using the Year column as the x-axis and the CSIRO - - Adjusted Sea Level column as the y-axis.\n",
    "- Use the linregress function from scipy.stats to get the slope and y-intercept of the line of best fit. Plot the line of best fit over the top of the scatter plot. Make the line go through the year 2050 to predict the sea level rise in 2050.\n",
    "- Plot a new line of best fit just using the data from year 2000 through the most recent year in the dataset. Make the line also go through the year 2050 to predict the sea level rise in 2050 if the rate of rise continues as it has since the year 2000.\n",
    "- The x label should be Year, the y label should be Sea Level (inches), and the title should be Rise in Sea Level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa7382de640>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "df.plot.scatter(x='year',\n",
    "              y='csiro',\n",
    "              color='sandybrown',\n",
    "              label='Original data')\n",
    "\n",
    "# Plot first line of best fit\n",
    "line_a = linregress(df['year'], df['csiro'])\n",
    "x = np.arange(df['year'].min(), 2051)\n",
    "plt.plot(x, (line_a.intercept + line_a.slope * x),\n",
    "       color='red',\n",
    "       label='Line of best fit',\n",
    "       linestyle='--',\n",
    "       linewidth=2)\n",
    "\n",
    "# Plot second line of best fit\n",
    "sub_df = df[df['year'] >= 2000]\n",
    "line_b = linregress(sub_df['year'], sub_df['csiro'])\n",
    "x1 = np.arange(2000, 2051)\n",
    "plt.plot(x1, (line_b.intercept + line_b.slope * x1),\n",
    "       color='blue',\n",
    "       label='Line of best fit from year 2000',\n",
    "       linestyle='dashdot',\n",
    "       linewidth=2)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sea Level (inches)')\n",
    "plt.title('Rise in Sea Level')\n",
    "plt.legend()\n"
   ]
  }
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