04USStations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US Linear Relative Sea Level Trends\n",
    "---\n",
    "This notebook takes the US Linear Relative Sea Level Tends from https://tidesandcurrents.noaa.gov/sltrends/mslUSTrendsTable.html \n",
    "The station name column is separated into two parts named station and location.  Focusing on making sure we can analyze based on locations.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import plotly.express as px \n",
    "import seaborn as sns\n",
    "import os\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Station ID    Station Name  First Year  Last Year  Year Range  \\\n",
      "0     1611400  Nawiliwili, HI        1955       2023          69   \n",
      "1     1612340    Honolulu, HI        1905       2023         119   \n",
      "2     1612480    Mokuoloe, HI        1957       2023          67   \n",
      "3     1615680     Kahului, HI        1947       2023          77   \n",
      "4     1617433    Kawaihae, HI        1988       2023          36   \n",
      "\n",
      "   Percent Complete  MSL Trends mm per yr  plus minus CI mm yr   \\\n",
      "0               100                  1.79                  0.39   \n",
      "1               100                  1.54                  0.20   \n",
      "2                84                  1.66                  0.49   \n",
      "3                96                  2.21                  0.37   \n",
      "4                92                  3.55                  1.05   \n",
      "\n",
      "   MSL Trend ft per century  95 CI ft century  Latitude  Longitude  \n",
      "0                      0.59              0.13   21.9544  -159.3561  \n",
      "1                      0.51              0.07   21.3033  -157.8645  \n",
      "2                      0.55              0.16   21.4331  -157.7900  \n",
      "3                      0.73              0.12   20.8949  -156.4690  \n",
      "4                      1.16              0.34   20.0366  -155.8294  \n"
     ]
    }
   ],
   "source": [
    "stations = pd.read_csv('data/USStationsLinearSeaLevelTrends.csv')\n",
    "\n",
    "#Decided not to convert to datetime format\n",
    "# Convert datetime format\n",
    "#stations['First Year'] = pd.to_datetime(stations['First Year'], format='%Y')\n",
    "#stations['Last Year'] = pd.to_datetime(stations['Last Year'], format='%Y')\n",
    "       \n",
    "print(stations.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Station ID', 'Station Name', 'First Year', 'Last Year', 'Year Range',\n",
      "       'Percent Complete', 'MSL Trends mm per yr', 'plus minus CI mm yr ',\n",
      "       'MSL Trend ft per century', '95 CI ft century', 'Latitude',\n",
      "       'Longitude'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(stations.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145 entries, 0 to 144\n",
      "Data columns (total 12 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Station ID                145 non-null    int64  \n",
      " 1   Station Name              145 non-null    object \n",
      " 2   First Year                145 non-null    int64  \n",
      " 3   Last Year                 145 non-null    int64  \n",
      " 4   Year Range                145 non-null    int64  \n",
      " 5   Percent Complete          145 non-null    int64  \n",
      " 6   MSL Trends mm per yr      145 non-null    float64\n",
      " 7   plus minus CI mm yr       145 non-null    float64\n",
      " 8   MSL Trend ft per century  145 non-null    float64\n",
      " 9   95 CI ft century          145 non-null    float64\n",
      " 10  Latitude                  145 non-null    float64\n",
      " 11  Longitude                 145 non-null    float64\n",
      "dtypes: float64(6), int64(5), object(1)\n",
      "memory usage: 13.7+ KB\n"
     ]
    }
   ],
   "source": [
    "stations.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Station ID          Station Name  First Year  Last Year  Year Range  \\\n",
      "0       1611400        Nawiliwili, HI        1955       2023          69   \n",
      "1       1612340          Honolulu, HI        1905       2023         119   \n",
      "2       1612480          Mokuoloe, HI        1957       2023          67   \n",
      "3       1615680           Kahului, HI        1947       2023          77   \n",
      "4       1617433          Kawaihae, HI        1988       2023          36   \n",
      "..          ...                   ...         ...        ...         ...   \n",
      "140     9497645       Prudhoe Bay, AK        1988       2023          36   \n",
      "141     9751401     Lime Tree Bay, VI        1977       2023          47   \n",
      "142     9751639  Charlotte Amalie, VI        1975       2023          49   \n",
      "143     9755371          San Juan, PR        1962       2023          62   \n",
      "144     9759110   Magueyes Island, PR        1955       2023          69   \n",
      "\n",
      "     Percent Complete  MSL Trends mm per yr  plus minus CI mm yr   \\\n",
      "0                 100                  1.79                  0.39   \n",
      "1                 100                  1.54                  0.20   \n",
      "2                  84                  1.66                  0.49   \n",
      "3                  96                  2.21                  0.37   \n",
      "4                  92                  3.55                  1.05   \n",
      "..                ...                   ...                   ...   \n",
      "140                97                  3.72                  1.31   \n",
      "141                89                  2.69                  0.56   \n",
      "142                91                  2.16                  0.51   \n",
      "143                93                  2.14                  0.34   \n",
      "144                96                  2.01                  0.28   \n",
      "\n",
      "     MSL Trend ft per century  95 CI ft century  Latitude  Longitude  \n",
      "0                        0.59              0.13   21.9544  -159.3561  \n",
      "1                        0.51              0.07   21.3033  -157.8645  \n",
      "2                        0.55              0.16   21.4331  -157.7900  \n",
      "3                        0.73              0.12   20.8949  -156.4690  \n",
      "4                        1.16              0.34   20.0366  -155.8294  \n",
      "..                        ...               ...       ...        ...  \n",
      "140                      1.22              0.43   70.4114  -148.5317  \n",
      "141                      0.88              0.18   17.6947   -64.7538  \n",
      "142                      0.71              0.17   18.3306   -64.9258  \n",
      "143                      0.70              0.11   18.4589   -66.1164  \n",
      "144                      0.66              0.09   17.9701   -67.0464  \n",
      "\n",
      "[145 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "# Ensuring every row has at least one comma in the 'Station Name' column\n",
    "stations['Station Name'] = stations['Station Name'].apply(lambda x: x if ',' in x else x + ', Unknown')\n",
    "print(stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Station ID          Station Name  First Year  Last Year  Year Range  \\\n",
      "0       1611400        Nawiliwili, HI        1955       2023          69   \n",
      "1       1612340          Honolulu, HI        1905       2023         119   \n",
      "2       1612480          Mokuoloe, HI        1957       2023          67   \n",
      "3       1615680           Kahului, HI        1947       2023          77   \n",
      "4       1617433          Kawaihae, HI        1988       2023          36   \n",
      "..          ...                   ...         ...        ...         ...   \n",
      "140     9497645       Prudhoe Bay, AK        1988       2023          36   \n",
      "141     9751401     Lime Tree Bay, VI        1977       2023          47   \n",
      "142     9751639  Charlotte Amalie, VI        1975       2023          49   \n",
      "143     9755371          San Juan, PR        1962       2023          62   \n",
      "144     9759110   Magueyes Island, PR        1955       2023          69   \n",
      "\n",
      "     Percent Complete  MSL Trends mm per yr  plus minus CI mm yr   \\\n",
      "0                 100                  1.79                  0.39   \n",
      "1                 100                  1.54                  0.20   \n",
      "2                  84                  1.66                  0.49   \n",
      "3                  96                  2.21                  0.37   \n",
      "4                  92                  3.55                  1.05   \n",
      "..                ...                   ...                   ...   \n",
      "140                97                  3.72                  1.31   \n",
      "141                89                  2.69                  0.56   \n",
      "142                91                  2.16                  0.51   \n",
      "143                93                  2.14                  0.34   \n",
      "144                96                  2.01                  0.28   \n",
      "\n",
      "     MSL Trend ft per century  95 CI ft century  Latitude  Longitude  \\\n",
      "0                        0.59              0.13   21.9544  -159.3561   \n",
      "1                        0.51              0.07   21.3033  -157.8645   \n",
      "2                        0.55              0.16   21.4331  -157.7900   \n",
      "3                        0.73              0.12   20.8949  -156.4690   \n",
      "4                        1.16              0.34   20.0366  -155.8294   \n",
      "..                        ...               ...       ...        ...   \n",
      "140                      1.22              0.43   70.4114  -148.5317   \n",
      "141                      0.88              0.18   17.6947   -64.7538   \n",
      "142                      0.71              0.17   18.3306   -64.9258   \n",
      "143                      0.70              0.11   18.4589   -66.1164   \n",
      "144                      0.66              0.09   17.9701   -67.0464   \n",
      "\n",
      "                 City State  \n",
      "0          Nawiliwili    HI  \n",
      "1            Honolulu    HI  \n",
      "2            Mokuoloe    HI  \n",
      "3             Kahului    HI  \n",
      "4            Kawaihae    HI  \n",
      "..                ...   ...  \n",
      "140       Prudhoe Bay    AK  \n",
      "141     Lime Tree Bay    VI  \n",
      "142  Charlotte Amalie    VI  \n",
      "143          San Juan    PR  \n",
      "144   Magueyes Island    PR  \n",
      "\n",
      "[145 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "split_columns = stations['Station Name'].str.split(',', n=1, expand=True)\n",
    "\n",
    "# Check if the split resulted in one or two columns and handle accordingly\n",
    "if len(split_columns.columns) == 1:\n",
    "    # Only one column means no commas were found; you can choose how to handle this.\n",
    "    # For example, assign the entire value to 'City' and set 'State' as None.\n",
    "    stations['City'] = split_columns[0]\n",
    "    stations['State'] = None\n",
    "else:\n",
    "    # Assign the split columns to 'City' and 'State'\n",
    "    stations['City'], stations['State'] = split_columns[0], split_columns[1]\n",
    "\n",
    "# Optionally, handle missing or cleanup tasks here (e.g., strip whitespace)\n",
    "stations['City'] = stations['City'].str.strip()\n",
    "stations['State'] = stations['State'].str.strip()\n",
    "\n",
    "print(stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145 entries, 0 to 144\n",
      "Data columns (total 14 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Station ID                145 non-null    int64  \n",
      " 1   Station Name              145 non-null    object \n",
      " 2   First Year                145 non-null    int64  \n",
      " 3   Last Year                 145 non-null    int64  \n",
      " 4   Year Range                145 non-null    int64  \n",
      " 5   Percent Complete          145 non-null    int64  \n",
      " 6   MSL Trends mm per yr      145 non-null    float64\n",
      " 7   plus minus CI mm yr       145 non-null    float64\n",
      " 8   MSL Trend ft per century  145 non-null    float64\n",
      " 9   95 CI ft century          145 non-null    float64\n",
      " 10  Latitude                  145 non-null    float64\n",
      " 11  Longitude                 145 non-null    float64\n",
      " 12  City                      145 non-null    object \n",
      " 13  State                     145 non-null    object \n",
      "dtypes: float64(6), int64(5), object(3)\n",
      "memory usage: 16.0+ KB\n"
     ]
    }
   ],
   "source": [
    "stations.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station ID</th>\n",
       "      <th>First Year</th>\n",
       "      <th>Last Year</th>\n",
       "      <th>Year Range</th>\n",
       "      <th>Percent Complete</th>\n",
       "      <th>MSL Trends mm per yr</th>\n",
       "      <th>plus minus CI mm yr</th>\n",
       "      <th>MSL Trend ft per century</th>\n",
       "      <th>95 CI ft century</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.306193e+06</td>\n",
       "      <td>1952.565517</td>\n",
       "      <td>2022.606897</td>\n",
       "      <td>70.917241</td>\n",
       "      <td>89.937931</td>\n",
       "      <td>2.363724</td>\n",
       "      <td>0.542621</td>\n",
       "      <td>0.775655</td>\n",
       "      <td>0.177862</td>\n",
       "      <td>36.943835</td>\n",
       "      <td>-95.736030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.080343e+06</td>\n",
       "      <td>26.358852</td>\n",
       "      <td>1.980097</td>\n",
       "      <td>26.565564</td>\n",
       "      <td>13.516050</td>\n",
       "      <td>4.033597</td>\n",
       "      <td>0.415129</td>\n",
       "      <td>1.323432</td>\n",
       "      <td>0.135985</td>\n",
       "      <td>12.092958</td>\n",
       "      <td>48.340606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.611400e+06</td>\n",
       "      <td>1856.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>-18.040000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>-5.920000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>-14.280000</td>\n",
       "      <td>-177.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.570283e+06</td>\n",
       "      <td>1932.000000</td>\n",
       "      <td>2023.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.830000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>29.357500</td>\n",
       "      <td>-123.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.726724e+06</td>\n",
       "      <td>1957.000000</td>\n",
       "      <td>2023.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>37.506800</td>\n",
       "      <td>-85.878600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.416841e+06</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>2023.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>4.210000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>42.353900</td>\n",
       "      <td>-75.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.759110e+06</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2023.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.930000</td>\n",
       "      <td>3.140000</td>\n",
       "      <td>4.240000</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>70.411400</td>\n",
       "      <td>167.736100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Station ID   First Year    Last Year  Year Range  Percent Complete  \\\n",
       "count  1.450000e+02   145.000000   145.000000  145.000000        145.000000   \n",
       "mean   8.306193e+06  1952.565517  2022.606897   70.917241         89.937931   \n",
       "std    2.080343e+06    26.358852     1.980097   26.565564         13.516050   \n",
       "min    1.611400e+06  1856.000000  2009.000000   30.000000         42.000000   \n",
       "25%    8.570283e+06  1932.000000  2023.000000   51.000000         88.000000   \n",
       "50%    8.726724e+06  1957.000000  2023.000000   66.000000         96.000000   \n",
       "75%    9.416841e+06  1973.000000  2023.000000   91.000000         99.000000   \n",
       "max    9.759110e+06  1994.000000  2023.000000  168.000000        100.000000   \n",
       "\n",
       "       MSL Trends mm per yr  plus minus CI mm yr   MSL Trend ft per century  \\\n",
       "count            145.000000            145.000000                145.000000   \n",
       "mean               2.363724              0.542621                  0.775655   \n",
       "std                4.033597              0.415129                  1.323432   \n",
       "min              -18.040000              0.090000                 -5.920000   \n",
       "25%                1.830000              0.240000                  0.600000   \n",
       "50%                2.940000              0.450000                  0.960000   \n",
       "75%                4.210000              0.690000                  1.380000   \n",
       "max               12.930000              3.140000                  4.240000   \n",
       "\n",
       "       95 CI ft century    Latitude   Longitude  \n",
       "count        145.000000  145.000000  145.000000  \n",
       "mean           0.177862   36.943835  -95.736030  \n",
       "std            0.135985   12.092958   48.340606  \n",
       "min            0.030000  -14.280000 -177.360000  \n",
       "25%            0.080000   29.357500 -123.440000  \n",
       "50%            0.150000   37.506800  -85.878600  \n",
       "75%            0.230000   42.353900  -75.810000  \n",
       "max            1.030000   70.411400  167.736100  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               State  MSL Trends mm per yr\n",
      "0                 AK             -4.844444\n",
      "1                 AL              4.630000\n",
      "2     American Samoa              2.480000\n",
      "3            Bermuda              2.215000\n",
      "4                 CA              1.726667\n",
      "5                 CT              3.100000\n",
      "6                 DC              3.490000\n",
      "7                 DE              3.800000\n",
      "8                 FL              3.768333\n",
      "9                 GA              3.610000\n",
      "10              Guam              4.530000\n",
      "11                HI              2.310000\n",
      "12                LA              7.660000\n",
      "13                MA              3.340000\n",
      "14                MD              4.097143\n",
      "15                ME              2.228000\n",
      "16                MS              4.700000\n",
      "17  Marshall Islands              2.060000\n",
      "18                NC              4.180000\n",
      "19                NH              2.040000\n",
      "20                NJ              4.476667\n",
      "21                NY              3.432500\n",
      "22                OR              1.040000\n",
      "23                PA              3.110000\n",
      "24                PR              2.075000\n",
      "25     Pacific Ocean              2.070000\n",
      "26                RI              2.740000\n",
      "27                SC              3.385000\n",
      "28                TX              5.841538\n",
      "29           Unknown              1.560000\n",
      "30                VA              5.504444\n",
      "31                VI              2.425000\n",
      "32                WA              0.605714\n"
     ]
    }
   ],
   "source": [
    "avg_by_state = stations.groupby('State')['MSL Trends mm per yr'].mean()\n",
    "avg_by_state = avg_by_state.reset_index()\n",
    "print(avg_by_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Station ID          Station Name  First Year  Last Year  Year Range  \\\n",
      "0       1611400        Nawiliwili, HI        1955       2023          69   \n",
      "1       1612340          Honolulu, HI        1905       2023         119   \n",
      "2       1612480          Mokuoloe, HI        1957       2023          67   \n",
      "3       1615680           Kahului, HI        1947       2023          77   \n",
      "4       1617433          Kawaihae, HI        1988       2023          36   \n",
      "..          ...                   ...         ...        ...         ...   \n",
      "140     9497645       Prudhoe Bay, AK        1988       2023          36   \n",
      "141     9751401     Lime Tree Bay, VI        1977       2023          47   \n",
      "142     9751639  Charlotte Amalie, VI        1975       2023          49   \n",
      "143     9755371          San Juan, PR        1962       2023          62   \n",
      "144     9759110   Magueyes Island, PR        1955       2023          69   \n",
      "\n",
      "     Percent Complete  MSL Trends mm per yr  plus minus CI mm yr   \\\n",
      "0                 100                  1.79                  0.39   \n",
      "1                 100                  1.54                  0.20   \n",
      "2                  84                  1.66                  0.49   \n",
      "3                  96                  2.21                  0.37   \n",
      "4                  92                  3.55                  1.05   \n",
      "..                ...                   ...                   ...   \n",
      "140                97                  3.72                  1.31   \n",
      "141                89                  2.69                  0.56   \n",
      "142                91                  2.16                  0.51   \n",
      "143                93                  2.14                  0.34   \n",
      "144                96                  2.01                  0.28   \n",
      "\n",
      "     MSL Trend ft per century  95 CI ft century  Latitude  Longitude  \\\n",
      "0                        0.59              0.13   21.9544  -159.3561   \n",
      "1                        0.51              0.07   21.3033  -157.8645   \n",
      "2                        0.55              0.16   21.4331  -157.7900   \n",
      "3                        0.73              0.12   20.8949  -156.4690   \n",
      "4                        1.16              0.34   20.0366  -155.8294   \n",
      "..                        ...               ...       ...        ...   \n",
      "140                      1.22              0.43   70.4114  -148.5317   \n",
      "141                      0.88              0.18   17.6947   -64.7538   \n",
      "142                      0.71              0.17   18.3306   -64.9258   \n",
      "143                      0.70              0.11   18.4589   -66.1164   \n",
      "144                      0.66              0.09   17.9701   -67.0464   \n",
      "\n",
      "                 City State      Region  \n",
      "0          Nawiliwili    HI      Island  \n",
      "1            Honolulu    HI      Island  \n",
      "2            Mokuoloe    HI      Island  \n",
      "3             Kahului    HI      Island  \n",
      "4            Kawaihae    HI      Island  \n",
      "..                ...   ...         ...  \n",
      "140       Prudhoe Bay    AK  West Coast  \n",
      "141     Lime Tree Bay    VI       Other  \n",
      "142  Charlotte Amalie    VI       Other  \n",
      "143          San Juan    PR      Island  \n",
      "144   Magueyes Island    PR      Island  \n",
      "\n",
      "[145 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "from categorizeregion import add_region_column\n",
    "\n",
    "stations = add_region_column(stations)\n",
    "print(stations)\n",
    "\n",
    "stations.to_excel('data/usstations.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart\n",
    "plt.figure(figsize=(10, 10))  \n",
    "plt.bar(avg_by_state['State'], avg_by_state['MSL Trends mm per yr'], color='skyblue')\n",
    "plt.xlabel('State')  \n",
    "plt.ylabel('Mean Sea Level Trend (mm per year)') \n",
    "plt.title('Mean Sea Level Trends by State')  \n",
    "plt.xticks(rotation=45)  # Rotate state names for better visibility\n",
    "plt.tight_layout()  # Adjust the layout to make sure everything fits without overlapping\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('images/sea_level_trends_by_state.png', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the box plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='Region', y='MSL Trends mm per yr', data=stations)\n",
    "\n",
    "plt.title('Sea Level Trends by Region')\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('MSL Trends (mm per year)')\n",
    "\n",
    "plt.savefig('images/sea_level_trends_gulf.png', dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Station ID          Station Name  First Year  Last Year  Year Range  \\\n",
      "0       1611400        Nawiliwili, HI        1955       2023          69   \n",
      "1       1612340          Honolulu, HI        1905       2023         119   \n",
      "2       1612480          Mokuoloe, HI        1957       2023          67   \n",
      "3       1615680           Kahului, HI        1947       2023          77   \n",
      "4       1617433          Kawaihae, HI        1988       2023          36   \n",
      "..          ...                   ...         ...        ...         ...   \n",
      "140     9497645       Prudhoe Bay, AK        1988       2023          36   \n",
      "141     9751401     Lime Tree Bay, VI        1977       2023          47   \n",
      "142     9751639  Charlotte Amalie, VI        1975       2023          49   \n",
      "143     9755371          San Juan, PR        1962       2023          62   \n",
      "144     9759110   Magueyes Island, PR        1955       2023          69   \n",
      "\n",
      "     Percent Complete  MSL Trends mm per yr  plus minus CI mm yr   \\\n",
      "0                 100                  1.79                  0.39   \n",
      "1                 100                  1.54                  0.20   \n",
      "2                  84                  1.66                  0.49   \n",
      "3                  96                  2.21                  0.37   \n",
      "4                  92                  3.55                  1.05   \n",
      "..                ...                   ...                   ...   \n",
      "140                97                  3.72                  1.31   \n",
      "141                89                  2.69                  0.56   \n",
      "142                91                  2.16                  0.51   \n",
      "143                93                  2.14                  0.34   \n",
      "144                96                  2.01                  0.28   \n",
      "\n",
      "     MSL Trend ft per century  95 CI ft century  Latitude  Longitude  \\\n",
      "0                        0.59              0.13   21.9544  -159.3561   \n",
      "1                        0.51              0.07   21.3033  -157.8645   \n",
      "2                        0.55              0.16   21.4331  -157.7900   \n",
      "3                        0.73              0.12   20.8949  -156.4690   \n",
      "4                        1.16              0.34   20.0366  -155.8294   \n",
      "..                        ...               ...       ...        ...   \n",
      "140                      1.22              0.43   70.4114  -148.5317   \n",
      "141                      0.88              0.18   17.6947   -64.7538   \n",
      "142                      0.71              0.17   18.3306   -64.9258   \n",
      "143                      0.70              0.11   18.4589   -66.1164   \n",
      "144                      0.66              0.09   17.9701   -67.0464   \n",
      "\n",
      "                 City State      Region  Overall Change mm  \n",
      "0          Nawiliwili    HI      Island             123.51  \n",
      "1            Honolulu    HI      Island             183.26  \n",
      "2            Mokuoloe    HI      Island             111.22  \n",
      "3             Kahului    HI      Island             170.17  \n",
      "4            Kawaihae    HI      Island             127.80  \n",
      "..                ...   ...         ...                ...  \n",
      "140       Prudhoe Bay    AK  West Coast             133.92  \n",
      "141     Lime Tree Bay    VI       Other             126.43  \n",
      "142  Charlotte Amalie    VI       Other             105.84  \n",
      "143          San Juan    PR      Island             132.68  \n",
      "144   Magueyes Island    PR      Island             138.69  \n",
      "\n",
      "[145 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "def calculate_overall_change(row):\n",
    "   overall_change = row['Year Range'] * row['MSL Trends mm per yr']\n",
    "   return overall_change\n",
    "\n",
    "# Apply the function to each row of your DataFrame\n",
    "stations['Overall Change mm'] = stations.apply(calculate_overall_change, axis=1)\n",
    "print(stations)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Region  Overall Change mm\n",
      "0  East Coast         287.681064\n",
      "1  Gulf Coast         293.447222\n",
      "2      Island         167.369091\n",
      "3       Other         134.543333\n",
      "4  West Coast         -72.384889\n"
     ]
    }
   ],
   "source": [
    "avg_change_by_region = stations.groupby('Region')['Overall Change mm'].mean().reset_index()\n",
    "\n",
    "print(avg_change_by_region)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
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    "fig = px.bar(avg_change_by_region, x='Region', y='Overall Change mm',\n",
    "             title='Average Overall Change by Region',\n",
    "             labels={'Overall Change mm': 'Average Overall Change (mm)', 'Region': 'Region'},\n",
    "             color='Overall Change mm',  # Optionally use color to represent magnitude\n",
    "             barmode='group')\n",
    "\n",
    "fig.update_layout(xaxis_title='Region',\n",
    "                  yaxis_title='Average Overall Change (mm)',\n",
    "                  xaxis={'categoryorder':'total descending'})  # Optional: sort regions by descending order of change\n",
    "\n",
    "fig.show()\n"
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   "source": []
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