sealevel_scrub.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sea Level Data Scrub and Linear Regression\n",
    "* This Takes the original Source data from NASA and creates a text file that can be used by the other members on the team for correlation analysis. \n",
    "* Visualizations show the initial scrub of the full data and the scrub of the grouped data (when looking at the full data the sea level is rising grouped and only limited to 2015 the picture looks like sea level is dropping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import requests \n",
    "from io import StringIO\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import numpy.linalg as lin\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_fraction</th>\n",
       "      <th>gmsl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1993.011526</td>\n",
       "      <td>92.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1993.038692</td>\n",
       "      <td>95.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1993.065858</td>\n",
       "      <td>92.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1993.093025</td>\n",
       "      <td>96.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1993.120191</td>\n",
       "      <td>94.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_fraction   gmsl\n",
       "0    1993.011526  92.66\n",
       "1    1993.038692  95.39\n",
       "2    1993.065858  92.29\n",
       "3    1993.093025  96.20\n",
       "4    1993.120191  94.64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data = pd.read_csv('data/GMSL_merged_nasa_1993_2020.csv')\n",
    "# sea_data \n",
    "sea_data.head(5)\n",
    "\n",
    "# sea_level = sea_data['GMSL'].values\n",
    "# sea_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Dependancies for datetime\n",
    "from datetime import timedelta, datetime\n",
    "\n",
    "#conversion formula for Converting year fractions aka year decimals into datetimes\n",
    "def convert_partial_year(number):\n",
    "    year = int(number)\n",
    "    d = timedelta(days=(number - year)*365.25)\n",
    "    day_one = datetime(year,1,1)\n",
    "    date = d + day_one\n",
    "    return date\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gmsl</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>92.66</td>\n",
       "      <td>1993-01-05 05:02:12.897607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>95.39</td>\n",
       "      <td>1993-01-15 03:10:26.659203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>92.29</td>\n",
       "      <td>1993-01-25 01:18:40.420798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>96.20</td>\n",
       "      <td>1993-02-03 23:27:25.739997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>94.64</td>\n",
       "      <td>1993-02-13 21:35:39.501599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>991</td>\n",
       "      <td>105.40</td>\n",
       "      <td>2019-12-11 18:19:25.773595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>992</td>\n",
       "      <td>101.96</td>\n",
       "      <td>2019-12-21 16:27:39.535198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>993</td>\n",
       "      <td>100.10</td>\n",
       "      <td>2019-12-31 14:35:53.296800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>994</td>\n",
       "      <td>98.39</td>\n",
       "      <td>2020-01-10 06:07:49.583997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>995</td>\n",
       "      <td>97.91</td>\n",
       "      <td>2020-01-20 03:37:08.083199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>996 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gmsl                       date\n",
       "0     92.66 1993-01-05 05:02:12.897607\n",
       "1     95.39 1993-01-15 03:10:26.659203\n",
       "2     92.29 1993-01-25 01:18:40.420798\n",
       "3     96.20 1993-02-03 23:27:25.739997\n",
       "4     94.64 1993-02-13 21:35:39.501599\n",
       "..      ...                        ...\n",
       "991  105.40 2019-12-11 18:19:25.773595\n",
       "992  101.96 2019-12-21 16:27:39.535198\n",
       "993  100.10 2019-12-31 14:35:53.296800\n",
       "994   98.39 2020-01-10 06:07:49.583997\n",
       "995   97.91 2020-01-20 03:37:08.083199\n",
       "\n",
       "[996 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get Results\n",
    "sea_data['date'] = sea_data['year_fraction'].apply(convert_partial_year)\n",
    "#sea_data\n",
    "\n",
    "#Drop the unused column\n",
    "sea_data_1 = sea_data.drop(['year_fraction'], axis=1)\n",
    "sea_data_1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>gmsl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>991</td>\n",
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       "      <td>992</td>\n",
       "      <td>2019</td>\n",
       "      <td>101.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>993</td>\n",
       "      <td>2019</td>\n",
       "      <td>100.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>994</td>\n",
       "      <td>2020</td>\n",
       "      <td>98.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>995</td>\n",
       "      <td>2020</td>\n",
       "      <td>97.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     year    gmsl\n",
       "991  2019  105.40\n",
       "992  2019  101.96\n",
       "993  2019  100.10\n",
       "994  2020   98.39\n",
       "995  2020   97.91"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the Year\n",
    "sea_data_1['year'] = sea_data_1['date'].apply(lambda x: x.year)\n",
    "#sea_data_1\n",
    "\n",
    "#Drop unused date\n",
    "sea_data_2 = sea_data_1.drop(['date'], axis=1)\n",
    "sea_data_2\n",
    "\n",
    "# Re-order Columns\n",
    "sea_data_2 = sea_data_2[['year', 'gmsl']]\n",
    "sea_data_2.tail(5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>GMSL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>92.66</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1993</td>\n",
       "      <td>95.39</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1993</td>\n",
       "      <td>92.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1993</td>\n",
       "      <td>96.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1993</td>\n",
       "      <td>94.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>991</td>\n",
       "      <td>2019</td>\n",
       "      <td>105.40</td>\n",
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       "    <tr>\n",
       "      <td>992</td>\n",
       "      <td>2019</td>\n",
       "      <td>101.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>993</td>\n",
       "      <td>2019</td>\n",
       "      <td>100.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>994</td>\n",
       "      <td>2020</td>\n",
       "      <td>98.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>995</td>\n",
       "      <td>2020</td>\n",
       "      <td>97.91</td>\n",
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       "<p>996 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     Year    GMSL\n",
       "0    1993   92.66\n",
       "1    1993   95.39\n",
       "2    1993   92.29\n",
       "3    1993   96.20\n",
       "4    1993   94.64\n",
       "..    ...     ...\n",
       "991  2019  105.40\n",
       "992  2019  101.96\n",
       "993  2019  100.10\n",
       "994  2020   98.39\n",
       "995  2020   97.91\n",
       "\n",
       "[996 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename Columns \n",
    "sea_data_2.columns = ['Year', 'GMSL']\n",
    "sea_data_2\n",
    "\n",
    "#Dropping off outliers index row 9 for 1993 , 2018 945-953 and for 2019 remove 980-993\n",
    "# sea_data_drop = sea_data_2.drop(sea_data_2.index[9])\n",
    "# sea_data_drop\n",
    "\n",
    "# #Drop the 2018 945-953\n",
    "# sea_data_3 = sea_data_drop.drop(sea_data_drop.index[944:953])\n",
    "# sea_data_3.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To Do: Group by Year and use Average gmsl \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group by and reset index \n",
    "# Drop everything after 2015 \n",
    "#Find average gmsl after grouping \n",
    "\n",
    "sea_data_3 = sea_data_2.groupby('Year')['GMSL'].mean()\n",
    "sea_data_3.reset_index()\n",
    "\n",
    "#Drop everything after 2015\n",
    "sea_data_4 = sea_data_3.drop(sea_data_3.index[23:28])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import dependancies \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  # To visualize\n",
    "import pandas as pd  # To read data\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data \n",
    "data = sea_data_3.reset_index() # load data set  \n",
    "X = data.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = data.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, Y)  # perform linear regression\n",
    "Y_pred = linear_regressor.predict(X)  # make predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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QKijcJ+lSSecAewM3A0gaMZBCI+KqiDgoIo4EngYeBJYA10XyB1LQ2XEg5ZiZWWVKBYUzgadI4wqTImJNdnw/4NJqCy0MImeL6Z0MzAKuJ7VGkLQvMCwr28zM6qTU2kddwCsGlCPiTuDOAZR7raQdgHXAWRGxUtK3gG9JWkialXR6RDhBzsysjkpNSb2vv+cj4oBqCo2II3o5thZ4fzXnMzOz2iiVgPYyabD5B8DPSRvsmJlZmyqV0Xwg8I/AVqTA8EVgf2BpRDyaf/XMzKyeSi6IFxF/jojzI+IgUmvhO8A5udfMzKxFXT9/KYfPuJU9pt/I4TNu5fr5SxtdpbKVXL9I0mjgPaSs45WkgFDVEhdmZu3u+vlLOfe6BXStWw/A0lVdnHvdAoCW2Pyq1B7Nt5NaB0OBD5Cymm8EhknaPvfamZm1mJmdi7sDQkHXuvXM7FzcoBpVplRLYXfSQPM/AVOLjis7vmdO9TIza0nLVvU+H6ev482mVJ7C2DrVw8ysLYwaMZylvQSAUSNaYwuaUt1HQyRtVfR4oqQjs9vW+VfPzKy1TJs8juFDh2x0bPjQIUybPK5BNapMqe6jS4DlQGHDm1mkVU43B+4GPptf1czMWk9hMHlm52KWrepi1IjhTJs8riUGmaF0UDgWKF7CelVEnJCtXnpHftUyM2tdUyaMbpkg0FOpPIVNso1wCj4LkK1JtFXvbzEzs1ZVKigMKx47iIjC0tnbkrqQzMysjZQKClcCP8qWuAZA0u6ksYUr86yYmZnVX6kpqV+RtAb4raQtSbkJq0l7Kf93PSpoZmb1U3KZi4j4BvCNbGqqIuK5/KtlZmaNUCpP4f2SNgGIiOd7BgRJe0l6Y54VNDOz+inVUtgBmC9pHjAPWEEaYN4bOIq0Xeb0SguV9HHSVp8CroyIy4qe+zQwE9gpIrwdp5lZHZUaU/iqpMtJeycfDhxA2mhnEXBqRDxWaYGSxpMCwqGkbTdvknRjRDwoaTfgzUDF5zUzs4ErZ0xhPTA7u9XCa4C7ImINdK/E+nZS1vR/AJ8BbqhRWWZmVoGSm+zkYCFwpKQdJG0BHA/sJulE0o5u9/b3ZklTJc2VNHfFihX1qK+Z2aBRsqVQaxGxSNIlpJbH88C9wEvAecCkMt5/BXAFQEdHR+RYVTOzQacRLQUi4qqIOCgijgSeBh4B9gDulfQIsCtwt6SdG1E/M7PBqt+WgqRP9vd8RHylmkIljYyI5Vmm9MnAYRHx1aLnHwE6PPvIzKy+SnUf5bVnwrWSdgDWAWdFxMqcyjEzswqUmpJ6QR6FRsQRJZ4fm0e5ZmbWv7LGFCTtK+lXkhZmjw+Q9G/5Vs3MzDayZg3cdBOccw788Y+5FFHu7KMrgWnANwEi4j5JPwC+kEutzMwMImDBAujshJtvhjvugBdfhM02g/Hj4ZBDSp+jQuUGhS0i4g9pw7VuL/X1YjMzq9Ly5XDLLRsCwd/+lo7vtx/8y7/A5MlwxBGwxRa5FF9uUHhK0l6kpbOR9A7giVxqZGY2mKxdC3femQJAZyfcfXc6vv328OY3w6RJ6bbrrnWpTrlB4SxSwtirJS0FHgbel1utzMzaVQQ89NCGIHDbbfD88zBkCBx2GFx0UQoCBx+cjtVZuUHh0Yg4LttoZxPvqWBmVoFnnoFbb90QCB5+OB3fYw94//tTl9DRR8O22za2npQfFB6WdBPwI+DWHOtjZtb61q+HefM2jAv8/vfp2FZbwTHHwKc+lQLB3ns3uqavUG5QGAecQOpGukrS/wE/jIjf5lYzM7NWsmTJhpbALbfA00+DBAcdBJ/9bAoCEyfCsGGNrmm/ygoKEdEFXANcI2k74KvA7UD9O7zMzJpBVxfcfvuGQPCnP6Xju+wCJ5yQgsBxx8FOOzW2nhUqe5VUSUcB7wbeCvwReFdelTIzazoRsHDhhi6h3/xmQ87AkUfCGWekAeLx41MLoUWVFRQkPQzcQ2otTIuI1bnWysysGTz1FMyeDZ2ddN14E8OfehKAh0fuzvp3nMbep56Sa85AI5TbUnhdRDyba03MzBpt7do0KFycMxDB2m1HcNvoA7jtkHdzx9iD+Ns2OzJ86BAuHjmeKW0UEKD8oLCzpJ8Cr4qI8ZIOAE6MCC9zYWatKwL+8pcUAHrLGbjwQpg0iWNveYbHn1270Vu71q1nZudipkwY3aDK58NrH5nZ4FJFzsCS627s9VTLVnXVo8Z15bWPzKy91SBnYNSI4SztJQCMGjE8z5o3hNc+MrP201fOwMEHV5UzMG3yOM69bgFd69Z3Hxs+dAjTJo/L6woapiFrH0n6OHAmIODKiLhM0kxSgtxa4C/AByNiVbVlmNkgsmZNmiLaW87AiSemqaIDyBkojBvM7FzMslVdjBoxnGmTx7XdeAKAIqL8FxetfSTpExFxWcUFSuOBHwKHkgLATcA/A3sAt0bES5IuAYiIz/Z3ro6Ojpg7d26lVTCzVlecM9DZufE+A0cemYLA5MktnzOQF0nzIqKjt+fKTl4D6JGf8Emg4qAAvAa4KyLWZJW7HXh7RHy56DV3Ae+o4txm1q5WrEg5AzffnG5PZD3Y+++/YZ+BI4+E4e3Xz19PFQWFHqoNvwuBL0raAegCjgd6ft0/g7T43isLlaYCUwHGjBlTZRXMrOkVcgYKA8RZzkD3PgOTJ6efddpnYLAYSFAov9+p+E0Ri7LuodnA88C9FM1kknRe9vj7fbz/CtL4Bh0dHVXVwcyaUF/7DGy66YacgcmT0wJzDdhnYLDoNyhIeo7e//gLqLqNFhFXAVdlZXwJWJLdPx34B+DYqGSww8xaU185A3vuCaeemsYGjjkGttmmsfUcRPoNChGxdR6FShoZEcsljQFOBg6T9Bbgs8BRhfEGM2szxTkDnZ1w110b5wx8+tOpNbDXXo2u6aA1kO6jgbg2G1NYB5wVESslXQ5sBszOkuTuioiPNKh+ZlYr5ewzcNhhMHRoo2tqNCgoRMQRvRxrvi2IzKxyhZyBwgBxIWdg1KiUM1DYZ2DHHRtbT+tVo1oKZtYu+ssZOOqotM/A5Mlp6qhzBpqeg4KZVa6vnIH99nPOQIsrd5OdicDXSYlnw0jbcK6OCE8JMBsMnDMwaJTbUrgceA/wY6ADOA3wGIBZu3LOwKBVdvdRRDwkaUhErAe+LenOHOtlZvXmnAGj/KCwRtIw4B5JXyYtm71lftUys9ytXw9z524IAs4ZMMoPCqcCmwBnA+cAuwGn5FUpM8vJkiUbxgX62mfAOQODWllBISIelTQc2CUiLsi5TmZWK8U5A52dsGhROu6cAetDubOPTgAuJc082kPSgcCFEXFinpUzswpFwIIFG1oDhZyBzTdPU0Q/9CHnDFi/yu0++jxpU5xfA0TEPZLG5lIjM6vM8uWpK6gQCP72t3R8//3hrLNSEDjiCOcMWFnKDQovRcQzaoNvFtfPXzoottSzNrZ2Ldx558Y5AwA77JByBSZNSrfR/r22ypUbFBZKei8wRNI+wMeAlpuSev38pRttvr10VRfnXrcAwIHBmlchZ6AQBHrmDHzhCykIOGfAaqDcoPBR4DzgRWAW0AlclFel8jKzc3F3QCjoWreemZ2LHRSsuRRyBgqBoGfOwOTJcPTRzhmwmit39tEaUlA4T9J2wKpW3ARn2aquio6b1Y1zBqxJlNp57XPANRHxZ0mbAb8EXgesl/TeiLilHpWslVEjhrO0lwAwaoQH4KwBnDNgTahUS+HdbOgmOp2UwDYS2Be4GmipoDBt8riNxhQAhg8dwrTJ4xpYKxs0vM+AtYBSQWFtUTfRZGBWtvbRIklVL7st6ePAmaS9nq+MiMskbQ/8CBgLPAK8KyJWVltGbwrjBp59ZHVRTs7ApEnOGbCmUuoP+4uSxgNPAkcDny56botqCszOdyYp72EtcJOkG7Njv4qIGZKmA9NJezbX1JQJox0ELD995QyMH59yBiZN8j4D1tRKBYWPAz8BdgL+IyIeBpB0PDC/yjJfQ9p/eU12rtuBtwMnAW/KXnM1KVGu5kHBrKYKOQOFAWLnDFiLU70nEUl6DXADcBjQBfwKmAucGhEjil63MiK26+X9U4GpAGPGjDn40UcfrUu9zYDSOQOTJztnwJqepHkR0dHbc3XfjjMiFkm6BJgNPA/cC7xUwfuvAK4A6OjoaLlpsdaCinMGOjvhkUfS8b32cs6AtZ2G7NEcEVcBVwFI+hKwBHhS0i4R8YSkXYDljaibWZ85A1tvnXIGPvOZ1BpwzoC1oYYEBUkjI2K5pDHAyaSupD1I015nZD9vaETdevJaSYPE449vCAK33AIrV27IGZg+PbUGJk50zoC1vbKDgqQ3kKaLdr8nIr5TZbnXStoBWAecFRErJc0ArpH0IeAx4J1VnrtmvFZSG1uzBm6/fUMgKN5n4KSTnDNgg1a5+yl8F9gLuAcoZH4FUFVQiIgjejn2d+DYas6XF6+V1EaKcwY6O1POwNq1G3IGPvxh5wyYUX5LoQPYrxXXOxoIr5XU4vrLGTj7bO8zYNaLspfOBnYGnsixLk3HayW1mP72GTjuuA3TRZ0zYNancoPCjsCfJP2BtHw2AO2+HafXSmpyxTkDnZ0pZ2D1au8zYDYAlWzHOeh4raQm1F/OwGmnOWeghjzzbnCqe0ZzLXV0dMTcuXMbXQ3LU385A0cfnYKA9xmouZ4z7yC1ki8++bUODG1gwBnNkiYCXyetWzQMGAKsjgh/HbPa62+fgenTU5eQ9xnIlWfeDV7ldh9dDrwH+DFpJtJpwD55VcoGGe8z0HQ8827wKjt5LSIekjQk20/h25LuzLFe1s7622fgiCPgjDNSIHDOQMN45t3gVW5QWCNpGHCPpC+TpqZumV+1rO2U2mfAOQNNxTPvBq9yg8KppK04zwbOAXYDTsmrUtYG+ssZ8D4DTc8z7wavsmcfSRoOjImIxflWqXyefdRESuUMFGYJTZjgnAGriKfG1l4tZh+dAFxKmnm0h6QDgQvbPXnNSnjmGfjVrzZMFy3OGTj99NQScM6ADYAXpay/SpLXDiVtkUlE3CNpbC41suZVyBkotAbmzPE+A5YrT42tv3KDwksR8Yw8E2TwefzxjXMGCvsMdHR4nwHLnafG1l/ZC+JJei8wRNI+wMcAT0ltR4V9BgqBoHifgSlTUkvAOQNWJ54aW3/lBoWPAueRFsObBXQCF+VVKaujCLjvvg3jAr3tMzB5Muy3n3MGrO48Nbb+ygoKEbGGFBTOq0Whks4BPkzaqGcB8EHgcGAmaerr88AHIuKhWpRnPSxfDrNnp0BQnDOw//7eZ8CaiqfG1l+/U1Il/ay/N1cz+0jSaOC3pE17uiRdA/wC+FfgpIhYJOlfgEMj4gP9nctTUsu0di387ncbWgPz56fj22+fcgYmT04/d921sfU0s7oYyJTUw4DHSV1Gc4Ba9R9sCgyXtA7YAlhGajUU5i5umx2zakTAgw9uGBfwPgOW8Zx/K6VUUNgZeDPwj8B7gRuBWRFxf7UFRsRSSZcCjwFdwM0RcbOkDwO/kNQFPAtM7O39kqYCUwHGjBlTbTXaTyFnoBAIyswZ8B+JwaOZ5vz79655VZLRvBkpOMwkJa59vaoCpe2Aa4F3A6tIK6/+BDgZuCQi5kiaBoyLiA/3d65B3X1UKmegsPVkPzkDXjN/cDl8xq29zuQZPWI4v5t+TN3q4d+7xhtQRnMWDN5GCghjga8B1w2gPscBD0fEiuz815EGmV8XEXOy1/wIuGkAZbSnxx/fMC5Qg5wBJwYNLs0y59+/d82t36Ag6WpgPPBL4IKIWFiDMh8DJkragtR9dCwwF3inpH0j4gFSl9WiGpTV2nLOGWiWPxJWH3nO+a+kO8i/d82tVEvhVGA1sC/wsaKMZgFRzc5rWffQT4C7gZeA+cAVwBLgWkkvAyuBMyo9d8urc86AE4MGl7zm/Fc6VuHfu+bWb1CIiE3yKDQizgfO73H4p9mtZVU1eNZXzsD48bnnDDgxaHDJa85/pd1B/r1rbmXvvGb9K/vbUvE+A8U5Aw3YZ8CJQYPPlAmja/75Vtod5N+75uagUCN9flu66c9M2XL1hi6hnjkDF12UWgMNyhnI449E3jydsblU04KL/54AAA2LSURBVB3Uir93g4WDQo0Ufyva+sXVvOHRezny4bs58uH58K9Ppif22gtOOy0FAe8zUJVmmmtvibuD2ouDQi2sX89xzz7Cqxf8niMfns+EZX9m03iZ54YNZ/7eE9jtS59LgcD7DAyYpzM2H3cHtZdSU1KfIy0/8YqnqHL2UdvokTNw5cqVvIxYsPPefGPiO/jNHgexaPf9ueidE8D/OWrG0xmbk7uD2kep2Udb16siTa+QM1AIBL3kDNw0cn+++IcV3d+WLvK3pZobDNMZPWZijVRR95GkkcDmhccR8VjNa9QsImDBgg2JY3fcAS++uHHOwKRJabnpLGfgeOD4+q0WMCi1e/+1x0ys0coKCpJOBP4dGAUsB3YnZRzvn1/VGmD58rR8RCEQFO8zcNZZg36fgWb4Btvu/dceM7FGK7elcBFp1dJbImKCpKNJayG1tkLOQKFL6O670/EG5Aw0u2b6BtvO/dceM7FGKzcorIuIv0vaRNImEXGbpEtyrVmeOjvh8su9z0AF/A22PgbDmIk1t3KDwipJWwF3AN+XtJy0blFrWrIkDRQ7Z6Bs/gZbH+0+ZmLNr9ygcBLwAvAJ4H2kndEuzKtSufvgB+FDH2p0LVqKv8HWR7uPmVjzKysoRMRqSTsDhwJPA50R8fdca5anTXJZ56+tteo32GYYHK9UO4+ZWPMr669jtlXmH0i7o70DuEvS4FvaehCbMmE0F5/8WkaPGI5Iu3U1+05ZhcHxpau6CDYMjl8/f2mjq2bWtMrajlPSYuANhdaBpB2AOyOioV8TB/V2nFZS3ttPtmIrxAz6346z3H6UJcBzRY+fAx4fQIXOkXS/pIWSZknaXMkXJT0gaZGkj1V7fjPId3DcrRBrV6XWPvpkdncpMEfSDaS1kE4idSdVTNJo4GPAfhHRJeka4D2k9ZR2A14dES9n2dNmVctzcNxTdK1dlRpoLqx99JfsVnBDDcodLmkdsAWwDPgC8N6IeBkgIpYPsAxrIXl0xeQ5OO4putauSi2Id0HxY0lbp8PxfLUFRsRSSZcCjwFdwM0RcbOkWcC7Jb0dWAF8LCIerLYcax15ZUvnOb3TU3StXZW79tF44LvA9tnjp4DTIuL+SguUtB2p+2kPYBXwY0nvBzYDXoiIDkknA98Cjujl/VOBqQBjxoyptHhrQnl2xeQ1vbNVp+ialVJu8toVwCcj4jYASW8CrgTeUEWZxwEPR8SK7FzXZedZAlybveanwLd7e3NEXJHVh46OjtJTp6zptWJXTDMlmXkWlNVSuUFhy0JAAIiIX0vassoyHwMmStqC1H10LDAXeBY4htRCOAp4oMrzW4tp1a6YZkgya6aFCq09lDsl9a+S/p+ksdnt34CHqykwIuYAPwHuBhZkdbgCmAGcImkBcDHw4WrO3yqun7+Uw2fcyh7Tb+TwGbcO6qmM0yaPY/jQjRcgdFdMefrrejOrRrkthTOAC4DrSFNHfwN8sNpCI+J84Pweh18E3lbtOVuJv91tzF0x1WvFrjdrbuWufbSSlFtgNeA57q/krpjqtGrXmzWvUslrPyclq/UqIk6seY0GAX+7a055Buu8WiCeBWW1VqqlcGldajHI+Ntdc8orWOfZAmmmrjdrD6WS126vV0UGE3+7a055Beu8uwuboevN2ke/s48knSTprKLHcyT9Nbu9I//qtadWXIZ6MMhrFpS7C62VlOo++gxpsbqCzYBDgC1JyWU/yalebc/f7ppPXl0x7i60VlIqKAyLiOIlsn+b7anw9wEkr5k1rTyCtbsLrZWUCgrbFT+IiLOLHu5U++qYtR8PBlsrKRUU5kg6MyKuLD4o6Z+ocj8FGxxaLQksb+4utFZRKiicA1wv6b2kZSkADiaNLUzJs2LWuloxCczMkn5nH0XE8oh4A3AR8Eh2uzAiDouIJ/OvnrUir8dj1rrKXebiVuDWnOtibcJTMM1aV7mrpJqVra+plp6Cadb8HBSs5rwUtlnrKnfpbLOyeQqmWetyULBceAqmWWtqSPeRpHMk3S9poaRZkjYveu7rkp5vRL3MzAa7ugcFSaNJG/Z0RMR4YAjZ+kqSOoAR9a6TmZkljRpo3hQYLmlTYAtgmaQhwEzSInxmZtYAdQ8KEbGUtHnPY8ATwDMRcTNwNvCziHiiv/dLmipprqS5K1asyL/CZmaDSCO6j7YDTgL2AEYBW0o6DXgn8PVS74+IKyKiIyI6dtrJa/KZmdVSI2YfHQc8HBErACRdB1wADAcekgSwhaSHImLv/k40b968pyQ9mneFq7Aj8FSjK5Gjdr8+aP9r9PW1voFc4+59PdGIoPAYMFHSFkAXcCzwlYjobiVIer5UQACIiKZsKkiaGxEdja5HXtr9+qD9r9HX1/ryusZGjCnMIe3YdjewIKvDFfWuh5mZvVJDktci4nzg/H6e36qO1TEzs4zXPspHu7d82v36oP2v0dfX+nK5RkVEHuc1M7MW5JaCmZl1c1AwM7NuDgplkPQtScslLSw69jpJv5e0QNLPJW2THR8m6dvZ8XslvanoPb+WtFjSPdltZAMu5xUk7SbpNkmLsoUKP54d317SbEkPZj+3y45L0tckPSTpPkkHFZ3r9Oz1D0o6vVHX1FONr3F90Wf4s0ZdU7Eqru/V2e/vi5I+3eNcb8l+Tx+SNL0R19NTja/vkez/5z2S5jbienpTxTW+L/vdvE/SnZJeV3Su6j/DiPCtxA04EjgIWFh07I/AUdn9M4CLsvtnAd/O7o8E5gGbZI9/TVoIsOHX1OP6dgEOyu5vDTwA7Ad8GZieHZ8OXJLdPx74JSBgIjAnO7498Nfs53bZ/e0afX21vMbsuecbfT01uL6RwCHAF4FPF51nCPAXYE9gGHAvsF+7XF/23CPAjo2+phpc4xsK/7+Atxb9PxzQZ+iWQhki4jfA0z0OjwN+k92fDZyS3d8P+FX2vuXAKqCpk2gi4omIuDu7/xywCBhNWo7k6uxlVwNTsvsnAd+J5C5ghKRdgMnA7Ih4OiJWkv5d3lLHS+lTDa+xKVV6fRGxPCL+CKzrcapDgYci4q8RsRb4YXaOhqrh9TWtKq7xzuz/GcBdwK7Z/QF9hg4K1VsInJjdfyewW3b/XuAkSZtK2gM4uOg5gG9nzdb/J6U1PZqJpLHABGAO8KrIFijMfha6u0YDjxe9bUl2rK/jTWWA1wiwudKijHdJmkKTKfP6+tL0n+EArw8ggJslzZM0Na96DkQV1/ghUssWBvgZeue16p0BfE3S54CfAWuz498CXgPMBR4F7gReyp57X0QslbQ1cC1wKvCduta6H5K2ItXrExHxbD8xq7cnop/jTaMG1wgwJiKWSdoTuFXSgoj4Sw7VrVgF19fnKXo51jSfYQ2uD+Dw7PMbCcyW9OesN6ApVHqNko4mBYU3Fg718rKyP0O3FKoUEX+OiEkRcTAwi9SHR0S8FBHnRMSBEXESadOgB7PnlmY/nwN+QGrmNQVJQ0m/iN+PiOuyw08Wukyyn8uz40vYuPWzK7Csn+NNoUbXSEQUfv6VNE40IffKl6HC6+tL036GNbq+4s9vOfBTWvf/IZIOAP4HOCki/p4dHtBn6KBQpexbBpI2Af4N+Eb2eAtJW2b33wy8FBF/yrqTdsyODwX+gdQF1XBZN9ZVwKKI+ErRUz8DCjOITgduKDp+WjZDZyJpT4wngE5gkqTtshkSk7JjDVera8yubbPsnDsChwN/qstF9KOK6+vLH4F9JO0haRhpV8SGz7Cq1fVJ2jJrqZP9P51Ei/4/lDQGuA44NSIeKHr9wD7Deo2st/KN1BJ4gjRotYTUVPs4aXbAA8AMNmSHjwUWkwaJbgF2z45vSZqJdB9wP/BVYEijry2r2xtJzcv7gHuy2/HADqRB8wezn9tnrxfwn6TW0QKKZlSRutUeym4fbPS11foaSTM+FpDGjhYAH2r0tVV5fTtnv8vPkiZDLAG2yZ47Pvu9/gtwXqOvrZbXR5qRc292u79Zrq/Ka/wfYGXRa+cWnavqz9DLXJiZWTd3H5mZWTcHBTMz6+agYGZm3RwUzMysm4OCmZl1c1AwKyHLVfitpLcWHXuXpJsaWS+zPHhKqlkZJI0HfkzKXh5Cmhf+lqhieQtJQyJifY2raFYTDgpmZZL0ZWA1KRFxNbA78FrSGmKfj4gbsoXMvpu9BuDsiLhTaV+N80lJkAeSlnW+hrQEwRDS0us/qtvFmPXBQcGsTNmyCHeTFj/8P+D+iPiepBHAH0itiABejogXJO0DzIqIjiwo3AiMj4iHJZ1CammcmZ1724h4pgGXZbYRBwWzCki6EHgeeBewORtWwN2etJ/EMuByUmtgPbBvRGxRaClExNHZefYlrQt1DfB/EXFHPa/DrC9eOtusMi9nNwGnRMTi4iclfR54EngdaSLHC0VPry7ciYgHJB1MWqPmYkk3R8SFOdfdrCTPPjKrTifw0cJGSZIKy2dvCzwRES+T9ssY0tubJY0C1kTE94BLSdu9mjWcWwpm1bkIuAy4LwsMj5CWQ/8v4FpJ7wRuo6h10MNrgZmSXiatvvvPudfYrAweUzAzs27uPjIzs24OCmZm1s1BwczMujkomJlZNwcFMzPr5qBgZmbdHBTMzKzb/wf/EoTxABcA8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot \n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, Y_pred, color='red')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Global Mean Sea Level (GMSL) in (mm)')\n",
    "plt.title('Sea Level over Time (in mm)')\n",
    "#Save it\n",
    "plt.savefig('images/sealevel_scrub_fig1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset Index \n",
    "sea_level_data = sea_data_4.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>GMSL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>90.471622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>89.516216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1995</td>\n",
       "      <td>87.334722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1996</td>\n",
       "      <td>85.473243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1997</td>\n",
       "      <td>99.442432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1998</td>\n",
       "      <td>96.977297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1999</td>\n",
       "      <td>87.769459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2000</td>\n",
       "      <td>86.657297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2001</td>\n",
       "      <td>84.688378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2002</td>\n",
       "      <td>85.895278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>87.178919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2004</td>\n",
       "      <td>86.263784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2005</td>\n",
       "      <td>85.427838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2006</td>\n",
       "      <td>86.161389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2007</td>\n",
       "      <td>85.265946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2008</td>\n",
       "      <td>86.838889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2009</td>\n",
       "      <td>85.634054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2010</td>\n",
       "      <td>87.830811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2011</td>\n",
       "      <td>89.016757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2012</td>\n",
       "      <td>87.514324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2013</td>\n",
       "      <td>87.777297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2014</td>\n",
       "      <td>88.426667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>2015</td>\n",
       "      <td>100.599189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year        GMSL\n",
       "0   1993   90.471622\n",
       "1   1994   89.516216\n",
       "2   1995   87.334722\n",
       "3   1996   85.473243\n",
       "4   1997   99.442432\n",
       "5   1998   96.977297\n",
       "6   1999   87.769459\n",
       "7   2000   86.657297\n",
       "8   2001   84.688378\n",
       "9   2002   85.895278\n",
       "10  2003   87.178919\n",
       "11  2004   86.263784\n",
       "12  2005   85.427838\n",
       "13  2006   86.161389\n",
       "14  2007   85.265946\n",
       "15  2008   86.838889\n",
       "16  2009   85.634054\n",
       "17  2010   87.830811\n",
       "18  2011   89.016757\n",
       "19  2012   87.514324\n",
       "20  2013   87.777297\n",
       "21  2014   88.426667\n",
       "22  2015  100.599189"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_level_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import dependancies \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  # To visualize\n",
    "import pandas as pd  # To read data\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data \n",
    "data = sea_level_data # load data set  \n",
    "X = data.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = data.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, Y)  # perform linear regression\n",
    "Y_pred = linear_regressor.predict(X)  # make predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Plot \n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, Y_pred, color='red')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Global Mean Sea Level (GMSL) in (mm)')\n",
    "plt.title('Sea Level over Time (in mm)')\n",
    "\n",
    "\n",
    "#Save it\n",
    "plt.savefig('images/sealevel_scrub_fig2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sea level from 1993 - 2015 has some outliers and I decided to keep the outliers pending furthe research.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GMSL is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2 and Jason-3. It starts in September 1992 to present, with a lag of up to 4 months. All biases and cross-calibrations have been applied to the data so SSHA are consistent between satellites. Data are reported as changes relative to January 1, 1993 and are 2-month averages. Glacial Isostatic Adjustment (GIA) has been applied. These data are available in ASCII format. Reference: Beckley et al., 2017,\n",
    "\n",
    "This is the “uncertainty margin,” or the range from the mean (average) within which there is a high probability that the true number resides. For example, when we say that global mean sea level for a particular month is 50mm +/- 4 mm, the true value for that month has a high probability of being between 46 and 54 mm. The uncertainty margin exists both because of sea level variability (sea level above the first point in the time series) in the ocean and the accuracy with which we measure the individual values. https://sealevel.nasa.gov/understanding-sea-level/key-indicators/global-mean-sea-level/  Source: https://podaac-tools.jpl.nasa.gov/drive/files/allData/merged_alt/L2/TP_J1_OSTM/global_mean_sea_level/GMSL_TPJAOS_4.2_199209_202001.txt\n",
    "\n",
    "- Melissa Wright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To make a sea level data that can be used for the rest of the team in consolidating the data. \n",
    "sea_level_data.to_csv(\"data/sealevel_data.csv\", index = False, header = True)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}