Sea Level Predictor - Last Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "32fe00ce",
   "metadata": {},
   "source": [
    "## Sea Level Predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714a8b29",
   "metadata": {},
   "source": [
    "You will analyze a dataset of the global average sea level change since 1880. You will use the data to predict the sea level change through year 2050.\n",
    "\n",
    "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": "markdown",
   "id": "70229ad0",
   "metadata": {},
   "source": [
    "#### Use Pandas to import the data from epa-sea-level.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9c3ae55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from datetime import datetime as dt\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1256384b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\loren\\Desktop\\Data Analysis\\Sea Level Predictor\\epa-sea-level.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b296aa18",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'CSIRO Adjusted Sea Level': 'csiro_level', 'Lower Error Bound': 'lower_error', 'Upper Error Bound': 'upper_error', 'NOAA Adjusted Sea Level':'noaa_level'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b9151a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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_level</th>\n",
       "      <th>lower_error</th>\n",
       "      <th>upper_error</th>\n",
       "      <th>noaa_level</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_level  lower_error  upper_error  noaa_level\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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a2b52a",
   "metadata": {},
   "source": [
    "#### 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b84337f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CSIRO Sea Level')"
      ]
     },
     "execution_count": 39,
     "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": [
    "plt.scatter(df.Year, df.csiro_level, alpha=0.5)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CSIRO Sea Level\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fb793a",
   "metadata": {},
   "source": [
    "#### 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8bbd3323",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_year = pd.Series(range(2014,2051))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b1cc4568",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_year = pd.DataFrame(new_year, columns=['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8b177d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_to_predict = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e00a92ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_to_predict = pd.concat([df_to_predict, df_year],\n",
    "                  ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3db9452b",
   "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>csiro_level</th>\n",
       "      <th>lower_error</th>\n",
       "      <th>upper_error</th>\n",
       "      <th>noaa_level</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>166</th>\n",
       "      <td>2046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>2049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2050</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>171 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  csiro_level  lower_error  upper_error  noaa_level\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",
       "166  2046          NaN          NaN          NaN         NaN\n",
       "167  2047          NaN          NaN          NaN         NaN\n",
       "168  2048          NaN          NaN          NaN         NaN\n",
       "169  2049          NaN          NaN          NaN         NaN\n",
       "170  2050          NaN          NaN          NaN         NaN\n",
       "\n",
       "[171 rows x 5 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_to_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "830e868b",
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intercept, r_value, p_value, std_err = stats.linregress(df.Year, df.csiro_level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e87f478d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CSIRO Sea Level')"
      ]
     },
     "execution_count": 55,
     "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": [
    "plt.plot(intercept + slope*df_to_predict.Year, label='fitted line')\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CSIRO Sea Level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "85b7462d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sea Level (inches)')"
      ]
     },
     "execution_count": 56,
     "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": [
    "plt.scatter(df_to_predict.Year, df_to_predict.csiro_level, alpha=0.5)\n",
    "plt.plot(df_to_predict.Year,intercept + slope*df_to_predict.Year)    \n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Sea Level (inches)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a3a341",
   "metadata": {},
   "source": [
    "#### 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1217b562",
   "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>csiro_level</th>\n",
       "      <th>lower_error</th>\n",
       "      <th>upper_error</th>\n",
       "      <th>noaa_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2000</td>\n",
       "      <td>7.062992</td>\n",
       "      <td>6.791339</td>\n",
       "      <td>7.334646</td>\n",
       "      <td>6.920582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2001</td>\n",
       "      <td>7.287402</td>\n",
       "      <td>7.019685</td>\n",
       "      <td>7.555118</td>\n",
       "      <td>7.087460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2002</td>\n",
       "      <td>7.381890</td>\n",
       "      <td>7.110236</td>\n",
       "      <td>7.653543</td>\n",
       "      <td>7.319698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2003</td>\n",
       "      <td>7.759843</td>\n",
       "      <td>7.484252</td>\n",
       "      <td>8.035433</td>\n",
       "      <td>7.488353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2004</td>\n",
       "      <td>7.740157</td>\n",
       "      <td>7.464567</td>\n",
       "      <td>8.015748</td>\n",
       "      <td>7.507933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2005</td>\n",
       "      <td>7.744094</td>\n",
       "      <td>7.472441</td>\n",
       "      <td>8.015748</td>\n",
       "      <td>7.644563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2006</td>\n",
       "      <td>7.917323</td>\n",
       "      <td>7.645669</td>\n",
       "      <td>8.188976</td>\n",
       "      <td>7.697420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2007</td>\n",
       "      <td>7.996063</td>\n",
       "      <td>7.712598</td>\n",
       "      <td>8.279528</td>\n",
       "      <td>7.702147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2008</td>\n",
       "      <td>8.350394</td>\n",
       "      <td>8.078740</td>\n",
       "      <td>8.622047</td>\n",
       "      <td>7.907365</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",
       "</div>"
      ],
      "text/plain": [
       "     Year  csiro_level  lower_error  upper_error  noaa_level\n",
       "120  2000     7.062992     6.791339     7.334646    6.920582\n",
       "121  2001     7.287402     7.019685     7.555118    7.087460\n",
       "122  2002     7.381890     7.110236     7.653543    7.319698\n",
       "123  2003     7.759843     7.484252     8.035433    7.488353\n",
       "124  2004     7.740157     7.464567     8.015748    7.507933\n",
       "125  2005     7.744094     7.472441     8.015748    7.644563\n",
       "126  2006     7.917323     7.645669     8.188976    7.697420\n",
       "127  2007     7.996063     7.712598     8.279528    7.702147\n",
       "128  2008     8.350394     8.078740     8.622047    7.907365\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"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2000 = df[df[\"Year\"] > 1999]\n",
    "df_2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8bb3618c",
   "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>csiro_level</th>\n",
       "      <th>lower_error</th>\n",
       "      <th>upper_error</th>\n",
       "      <th>noaa_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>7.062992</td>\n",
       "      <td>6.791339</td>\n",
       "      <td>7.334646</td>\n",
       "      <td>6.920582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>7.287402</td>\n",
       "      <td>7.019685</td>\n",
       "      <td>7.555118</td>\n",
       "      <td>7.087460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>7.381890</td>\n",
       "      <td>7.110236</td>\n",
       "      <td>7.653543</td>\n",
       "      <td>7.319698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>7.759843</td>\n",
       "      <td>7.484252</td>\n",
       "      <td>8.035433</td>\n",
       "      <td>7.488353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>7.740157</td>\n",
       "      <td>7.464567</td>\n",
       "      <td>8.015748</td>\n",
       "      <td>7.507933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>7.744094</td>\n",
       "      <td>7.472441</td>\n",
       "      <td>8.015748</td>\n",
       "      <td>7.644563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>7.917323</td>\n",
       "      <td>7.645669</td>\n",
       "      <td>8.188976</td>\n",
       "      <td>7.697420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>7.996063</td>\n",
       "      <td>7.712598</td>\n",
       "      <td>8.279528</td>\n",
       "      <td>7.702147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>8.350394</td>\n",
       "      <td>8.078740</td>\n",
       "      <td>8.622047</td>\n",
       "      <td>7.907365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</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>10</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>11</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>12</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>13</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",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2037</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2050</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": [
       "    Year  csiro_level  lower_error  upper_error  noaa_level\n",
       "0   2000     7.062992     6.791339     7.334646    6.920582\n",
       "1   2001     7.287402     7.019685     7.555118    7.087460\n",
       "2   2002     7.381890     7.110236     7.653543    7.319698\n",
       "3   2003     7.759843     7.484252     8.035433    7.488353\n",
       "4   2004     7.740157     7.464567     8.015748    7.507933\n",
       "5   2005     7.744094     7.472441     8.015748    7.644563\n",
       "6   2006     7.917323     7.645669     8.188976    7.697420\n",
       "7   2007     7.996063     7.712598     8.279528    7.702147\n",
       "8   2008     8.350394     8.078740     8.622047    7.907365\n",
       "9   2009     8.586614     8.311024     8.862205    8.046354\n",
       "10  2010     8.901575     8.618110     9.185039    8.122973\n",
       "11  2011     8.964567     8.661417     9.267717    8.053065\n",
       "12  2012     9.326772     8.992126     9.661417    8.457058\n",
       "13  2013     8.980315     8.622047     9.338583    8.546648\n",
       "14  2014          NaN          NaN          NaN         NaN\n",
       "15  2015          NaN          NaN          NaN         NaN\n",
       "16  2016          NaN          NaN          NaN         NaN\n",
       "17  2017          NaN          NaN          NaN         NaN\n",
       "18  2018          NaN          NaN          NaN         NaN\n",
       "19  2019          NaN          NaN          NaN         NaN\n",
       "20  2020          NaN          NaN          NaN         NaN\n",
       "21  2021          NaN          NaN          NaN         NaN\n",
       "22  2022          NaN          NaN          NaN         NaN\n",
       "23  2023          NaN          NaN          NaN         NaN\n",
       "24  2024          NaN          NaN          NaN         NaN\n",
       "25  2025          NaN          NaN          NaN         NaN\n",
       "26  2026          NaN          NaN          NaN         NaN\n",
       "27  2027          NaN          NaN          NaN         NaN\n",
       "28  2028          NaN          NaN          NaN         NaN\n",
       "29  2029          NaN          NaN          NaN         NaN\n",
       "30  2030          NaN          NaN          NaN         NaN\n",
       "31  2031          NaN          NaN          NaN         NaN\n",
       "32  2032          NaN          NaN          NaN         NaN\n",
       "33  2033          NaN          NaN          NaN         NaN\n",
       "34  2034          NaN          NaN          NaN         NaN\n",
       "35  2035          NaN          NaN          NaN         NaN\n",
       "36  2036          NaN          NaN          NaN         NaN\n",
       "37  2037          NaN          NaN          NaN         NaN\n",
       "38  2038          NaN          NaN          NaN         NaN\n",
       "39  2039          NaN          NaN          NaN         NaN\n",
       "40  2040          NaN          NaN          NaN         NaN\n",
       "41  2041          NaN          NaN          NaN         NaN\n",
       "42  2042          NaN          NaN          NaN         NaN\n",
       "43  2043          NaN          NaN          NaN         NaN\n",
       "44  2044          NaN          NaN          NaN         NaN\n",
       "45  2045          NaN          NaN          NaN         NaN\n",
       "46  2046          NaN          NaN          NaN         NaN\n",
       "47  2047          NaN          NaN          NaN         NaN\n",
       "48  2048          NaN          NaN          NaN         NaN\n",
       "49  2049          NaN          NaN          NaN         NaN\n",
       "50  2050          NaN          NaN          NaN         NaN"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2000_to_predict = df_2000.copy()\n",
    "df2000_to_predict = pd.concat([df2000_to_predict, df_year],\n",
    "                  ignore_index = True)\n",
    "\n",
    "df2000_to_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8a19dded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CSIRO Sea Level')"
      ]
     },
     "execution_count": 75,
     "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": [
    "slope_2000, intercept_2000, r_value_2000, p_value_2000, std_err_2000 = stats.linregress(df_2000.Year, df_2000.csiro_level)\n",
    "plt.plot(intercept_2000 + slope_2000*df_2000.Year, label='fitted line')\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CSIRO Sea Level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9cbfd417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sea Level (inches)')"
      ]
     },
     "execution_count": 77,
     "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": [
    "plt.scatter(df_to_predict.Year, df_to_predict.csiro_level, alpha=0.5)\n",
    "plt.plot(df_to_predict.Year,intercept + slope*df_to_predict.Year)  \n",
    "plt.plot(df2000_to_predict.Year,intercept_2000 + slope_2000*df2000_to_predict.Year)    \n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Sea Level (inches)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89965f8b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}