FCC_Proj_5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4afb92be-0be0-4bbc-806d-8edae56ef728",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "12cd2b83-7759-451d-a729-2c459c4c224a",
   "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 Adjusted Sea Level</th>\n",
       "      <th>Lower Error Bound</th>\n",
       "      <th>Upper Error Bound</th>\n",
       "      <th>NOAA Adjusted Sea 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 Adjusted Sea Level  Lower Error Bound  Upper Error Bound  \\\n",
       "0    1880                  0.000000          -0.952756           0.952756   \n",
       "1    1881                  0.220472          -0.732283           1.173228   \n",
       "2    1882                 -0.440945          -1.346457           0.464567   \n",
       "3    1883                 -0.232283          -1.129921           0.665354   \n",
       "4    1884                  0.590551          -0.283465           1.464567   \n",
       "..    ...                       ...                ...                ...   \n",
       "129  2009                  8.586614           8.311024           8.862205   \n",
       "130  2010                  8.901575           8.618110           9.185039   \n",
       "131  2011                  8.964567           8.661417           9.267717   \n",
       "132  2012                  9.326772           8.992126           9.661417   \n",
       "133  2013                  8.980315           8.622047           9.338583   \n",
       "\n",
       "     NOAA Adjusted Sea Level  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "..                       ...  \n",
       "129                 8.046354  \n",
       "130                 8.122973  \n",
       "131                 8.053065  \n",
       "132                 8.457058  \n",
       "133                 8.546648  \n",
       "\n",
       "[134 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data from file\n",
    "df = pd.read_csv('epa-sea-level.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bce369b1-de9b-4df1-9606-c2a3b940b10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create scatter plot\n",
    "plt.scatter(x=df['Year'], y=df['CSIRO Adjusted Sea Level'])\n",
    "# Create first line of best fit\n",
    "# Calculate the line of best fit\n",
    "slope, intercept, _, _, _ = linregress(df['Year'], df['CSIRO Adjusted Sea Level'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f482431b-6294-4ece-87e4-2d06e33ff85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extend the line to 'Year' = 2050\n",
    "target_x = 2050\n",
    "target_y = slope * target_x + intercept\n",
    "\n",
    "plot_original = plt.scatter(x=df['Year'], y=df['CSIRO Adjusted Sea Level'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d113762-5131-4a35-ad9a-a95f8aa3463d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x211ba3af790>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the line of best fit\n",
    "plt.plot([min(df['Year']), max(df['Year'])],\n",
    "            [slope * min(df['Year']) + intercept, slope * max(df['Year']) + intercept], color='red',\n",
    "            label='Line of Best Fit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ea13408-be7d-45d3-9e92-02b9e95e32cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x211ba44c790>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the extended line\n",
    "plt.plot([max(df['Year']), target_x], [slope * max(df['Year']) + intercept, target_y], linestyle='--',\n",
    "             color='green', label='Extended Line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5cdf96ea-5622-45fe-bb67-032130176b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211ba4db850>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " # Create second line of best fit\n",
    "new_df = df[df['Year'] >= 2000] # copy of dataframe that fulfills condition\n",
    "\n",
    "# Calculate the line of best fit\n",
    "slope, intercept, _, _, _ = linregress(new_df['Year'], new_df['CSIRO Adjusted Sea Level'])\n",
    "\n",
    "# Extend the line to 'Year' = 2050\n",
    "target_x = 2050\n",
    "target_y = slope * target_x + intercept\n",
    "\n",
    "plt.scatter(x=new_df['Year'], y=new_df['CSIRO Adjusted Sea Level'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c6f4304-0cdc-4f90-97f4-048691cae0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the line of best fit\n",
    "plt.plot([min(new_df['Year']), max(new_df['Year'])],\n",
    "             [slope * min(new_df['Year']) + intercept, slope * max(new_df['Year']) + intercept], color='red',\n",
    "             label='Line of Best Fit')\n",
    "\n",
    "# Plot the extended line\n",
    "plt.plot([max(new_df['Year']), target_x], [slope * max(new_df['Year']) + intercept, target_y], linestyle='--',\n",
    "             color='green', label='Extended Line')\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.grid(True)"
   ]
  }
 ],
 "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}