hurex.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Tracking and Anlayzing Hurricane Data Using PANDAS </h1>\n",
    "<h3> Ian Russell | December 2018  </h3>\n",
    "<h3> Introduction </h3>\n",
    "<p> \n",
    "    The goal of this analysis will be to model hurricane data in order to track hurricanes throughout US history. Exploratory analysis of hurricane data will be an important intial step in studying hurricane patterns. Once the data is effectively cleaned and explored it may prove useful in generating predictive models for future hurricanes.\n",
    "<p>\n",
    "    \n",
    "<h3>Procedure</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import altair as alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>record_id</th>\n",
       "      <th>status</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>max_wind</th>\n",
       "      <th>min_pressure</th>\n",
       "      <th>ne34ktr</th>\n",
       "      <th>se34ktr</th>\n",
       "      <th>sw34ktr</th>\n",
       "      <th>nw34ktr</th>\n",
       "      <th>ne50ktr</th>\n",
       "      <th>se50ktr</th>\n",
       "      <th>sw50ktr</th>\n",
       "      <th>nw50ktr</th>\n",
       "      <th>ne64ktr</th>\n",
       "      <th>se64ktr</th>\n",
       "      <th>sw64ktr</th>\n",
       "      <th>nw64ktr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>UNNAMED</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18510625</td>\n",
       "      <td>0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0N</td>\n",
       "      <td>94.8W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18510625</td>\n",
       "      <td>0600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0N</td>\n",
       "      <td>95.4W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18510625</td>\n",
       "      <td>1200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0N</td>\n",
       "      <td>96.0W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18510625</td>\n",
       "      <td>1800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.1N</td>\n",
       "      <td>96.5W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date     time record_id status latitude longitude  max_wind  \\\n",
       "0  AL011851  UNNAMED        14    NaN      NaN       NaN       NaN   \n",
       "1  18510625     0000       NaN     HU    28.0N     94.8W      80.0   \n",
       "2  18510625     0600       NaN     HU    28.0N     95.4W      80.0   \n",
       "3  18510625     1200       NaN     HU    28.0N     96.0W      80.0   \n",
       "4  18510625     1800       NaN     HU    28.1N     96.5W      80.0   \n",
       "\n",
       "   min_pressure  ne34ktr  se34ktr  sw34ktr  nw34ktr  ne50ktr  se50ktr  \\\n",
       "0           NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1        -999.0   -999.0   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "2        -999.0   -999.0   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "3        -999.0   -999.0   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "4        -999.0   -999.0   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "\n",
       "   sw50ktr  nw50ktr  ne64ktr  se64ktr  sw64ktr  nw64ktr  \n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "1   -999.0   -999.0   -999.0   -999.0   -999.0   -999.0  \n",
       "2   -999.0   -999.0   -999.0   -999.0   -999.0   -999.0  \n",
       "3   -999.0   -999.0   -999.0   -999.0   -999.0   -999.0  \n",
       "4   -999.0   -999.0   -999.0   -999.0   -999.0   -999.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the data\n",
    "\n",
    "# list of column names to use\n",
    "names = [\"date\",\"time\",\"record_id\",\"status\",\"latitude\", \"longitude\", \"max_wind\", \"min_pressure\" , \"ne34ktr\", \"se34ktr\", \"sw34ktr\", \"nw34ktr\" , \"ne50ktr\", \"se50ktr\", \"sw50ktr\", \"nw50ktr\", \"ne64ktr\", \"se64ktr\", \"sw64ktr\", \"nw64ktr\"]\n",
    "\n",
    "# read in data\n",
    "data = pd.read_csv(\"hurdat.txt\",\n",
    "                   header = None,\n",
    "                   names = names,\n",
    "                   skipinitialspace=True)\n",
    "\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Cleaning: Extracting and Filling Identifiers</h3>\n",
    "<p>\n",
    "After importing the data there now remains an issue: each hurrican identifier entry is mixed in with all of the rows that contain the respective hurricane's tracking information. It will be neccesary to tack this single identifier at the top of each tracking set of points to each row of tracking points; then remove the original identifier row. Accomplishing this task will require four steps. \n",
    "</p>\n",
    "<ul>\n",
    "    <li>\n",
    "        Step 1: Extract out those rows with the identifier, name, and number of points, and put           them in new columns named named identifier, name, and num_pts.\n",
    "    </li>\n",
    "    <li>\n",
    "        Step 2: Fill in this information for the tracking points.\n",
    "    </li>\n",
    "      <li>\n",
    "        Step 3: Remove the rows that just contain identifier information\n",
    "    </li>\n",
    "     <li>\n",
    "        Step 4: Move the new columns to the front of the data frame.\n",
    "    </li>\n",
    " \n",
    " \n",
    "    \n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>identifier</th>\n",
       "      <th>name</th>\n",
       "      <th>num_pts</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>record_id</th>\n",
       "      <th>status</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>max_wind</th>\n",
       "      <th>...</th>\n",
       "      <th>sw34ktr</th>\n",
       "      <th>nw34ktr</th>\n",
       "      <th>ne50ktr</th>\n",
       "      <th>se50ktr</th>\n",
       "      <th>sw50ktr</th>\n",
       "      <th>nw50ktr</th>\n",
       "      <th>ne64ktr</th>\n",
       "      <th>se64ktr</th>\n",
       "      <th>sw64ktr</th>\n",
       "      <th>nw64ktr</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>UNNAMED</td>\n",
       "      <td>14</td>\n",
       "      <td>18510625</td>\n",
       "      <td>0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0N</td>\n",
       "      <td>94.8W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>UNNAMED</td>\n",
       "      <td>14</td>\n",
       "      <td>18510625</td>\n",
       "      <td>0600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0N</td>\n",
       "      <td>95.4W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>UNNAMED</td>\n",
       "      <td>14</td>\n",
       "      <td>18510625</td>\n",
       "      <td>1200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0N</td>\n",
       "      <td>96.0W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>UNNAMED</td>\n",
       "      <td>14</td>\n",
       "      <td>18510625</td>\n",
       "      <td>1800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.1N</td>\n",
       "      <td>96.5W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>UNNAMED</td>\n",
       "      <td>14</td>\n",
       "      <td>18510625</td>\n",
       "      <td>2100</td>\n",
       "      <td>L</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.2N</td>\n",
       "      <td>96.8W</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  identifier     name num_pts      date  time record_id status latitude  \\\n",
       "1   AL011851  UNNAMED      14  18510625  0000       NaN     HU    28.0N   \n",
       "2   AL011851  UNNAMED      14  18510625  0600       NaN     HU    28.0N   \n",
       "3   AL011851  UNNAMED      14  18510625  1200       NaN     HU    28.0N   \n",
       "4   AL011851  UNNAMED      14  18510625  1800       NaN     HU    28.1N   \n",
       "5   AL011851  UNNAMED      14  18510625  2100         L     HU    28.2N   \n",
       "\n",
       "  longitude  max_wind  ...  sw34ktr  nw34ktr  ne50ktr  se50ktr  sw50ktr  \\\n",
       "1     94.8W      80.0  ...   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "2     95.4W      80.0  ...   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "3     96.0W      80.0  ...   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "4     96.5W      80.0  ...   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "5     96.8W      80.0  ...   -999.0   -999.0   -999.0   -999.0   -999.0   \n",
       "\n",
       "   nw50ktr  ne64ktr  se64ktr  sw64ktr  nw64ktr  \n",
       "1   -999.0   -999.0   -999.0   -999.0   -999.0  \n",
       "2   -999.0   -999.0   -999.0   -999.0   -999.0  \n",
       "3   -999.0   -999.0   -999.0   -999.0   -999.0  \n",
       "4   -999.0   -999.0   -999.0   -999.0   -999.0  \n",
       "5   -999.0   -999.0   -999.0   -999.0   -999.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraction and filling\n",
    "\n",
    "\n",
    "# extract identifiers\n",
    "identities = data[[\"status\"]].isnull()\n",
    "\n",
    "# find locations of each identifier in OG dataset\n",
    "nameRowLocations = identities.index[identities['status'] == True].tolist()\n",
    "\n",
    "# create new dataframe with appropriate columns, 'setup row'\n",
    "newdf = data.iloc[[0]]\n",
    "\n",
    "# append each unique identifier\n",
    "for i in nameRowLocations:\n",
    "\n",
    "    newdf = newdf.append(data.iloc[[i]])\n",
    "    \n",
    "# ids is identifiers dataframe\n",
    "ids = newdf.iloc[1:]  # Delete first 'setup row'\n",
    "\n",
    "# trim off all other columns\n",
    "ids = ids.dropna(axis='columns')\n",
    "\n",
    "# reindex to match dimensions\n",
    "idx= pd.Series(list(range(0,52150)))\n",
    "ids = ids.reindex(idx)\n",
    "\n",
    "# fill in NaN's\n",
    "ids = ids.fillna(method = 'ffill')\n",
    "\n",
    "\n",
    "# rename approriately\n",
    "ids.columns = ['identifier', 'name', 'record_id']\n",
    "\n",
    "# concatenate columns together\n",
    "df = pd.concat([ids,data],axis=1)\n",
    "\n",
    "# remove all rows that would be where the unique identifier is present\n",
    "df=df[~df['status'].isnull()]\n",
    "\n",
    "# get column names to rename\n",
    "cols = list(df)\n",
    "\n",
    "# remove duplicate 'record_id'\n",
    "cols[2] = 'num_pts'\n",
    "\n",
    "# rename all columns appropriately\n",
    "df.columns = cols\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Cleaning: Removal and Conversion of Bad Values </h3>\n",
    "<p>\n",
    "Much of the data set still contains many non-useful values that need to be dealt with. The purpose of these actions is to remove any values that would make computation difficult.\n",
    "</p>\n",
    "<ul>\n",
    "    <li>\n",
    "        Step 1: \"UNNAMED\" hurricane name values must be replaced with NaN values.\n",
    "    </li>\n",
    "    <li>\n",
    "        Step 2: Any -999 pressure record must also be replaced with NaN      values.\n",
    "    </li>\n",
    "      <li>\n",
    "        Step 3: Each date and time entry need be converted to a timestamp format.\n",
    "    </li>\n",
    "     <li>\n",
    "        Step 4: Latitude and longitude values must be converted to numeric representations.\n",
    "    </li>\n",
    " \n",
    " \n",
    "    \n",
    "</ul>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Names and Pressure Values </h3>\n",
    "<p> This cleanup is easy with a simple sweep and replace method applied to the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace specified cases with NaN\n",
    "df = df.replace(\"UNNAMED\", np.nan)\n",
    "df = df.replace(-999, np.nan)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Dates </h3>\n",
    "<p>\n",
    "The orignial datetime information was formated as a string. It will be neccesary at this point to convert from a string to a PANDAS Timestamp type. Doing this will allow for operations on those data. \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# concat date and time into datetime\n",
    "df['datetime'] = df['date'].map(str)+df['time'].map(str)\n",
    "\n",
    "# remove old date and time columns\n",
    "df =df.drop(['date','time'], axis =1)\n",
    "\n",
    "# convert to date format\n",
    "df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "\n",
    "\n",
    "# reorder columns\n",
    "\n",
    "# this list was gathered from list(df)\n",
    "df = df[['identifier',\n",
    " 'name',\n",
    " 'num_pts',\n",
    " 'datetime',\n",
    " 'record_id',\n",
    " 'status',\n",
    " 'latitude',\n",
    " 'longitude',\n",
    " 'max_wind',\n",
    " 'min_pressure',\n",
    " 'ne34ktr',\n",
    " 'se34ktr',\n",
    " 'sw34ktr',\n",
    " 'nw34ktr',\n",
    " 'ne50ktr',\n",
    " 'se50ktr',\n",
    " 'sw50ktr',\n",
    " 'nw50ktr',\n",
    " 'ne64ktr',\n",
    " 'se64ktr',\n",
    " 'sw64ktr',\n",
    " 'nw64ktr',\n",
    " ]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Latitudes and Longitudes </h3>\n",
    "<p> \n",
    "Converting the latitudes and longitudes is a slightly less straightfoward process. The first issue with this conversion is the designation of direction. To differentiate between north and south and east and west a 1 or -1 (respectively) are multiplied into the values presented. This will allow a purely numeric conversion to occur over this column.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Get multipliers for latitude and longitude columns\n",
    "\n",
    "lats = df['latitude']\n",
    "latDirections = lats.str.slice(start=-1)\n",
    "\n",
    "d = {'N': 1, 'S': -1}\n",
    "\n",
    "latDirections = latDirections.map(d)\n",
    "\n",
    "longs = df['longitude']\n",
    "longDirections = longs.str.slice(start=-1)\n",
    "\n",
    "\n",
    "c = {'E': 1, 'W': -1}\n",
    "\n",
    "longDirections = longDirections.map(c)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Remove compass directions from original set\n",
    "\n",
    "df[[\"latitude\"]] = df[\"latitude\"].str.slice(stop = -1)\n",
    "df[[\"longitude\"]] = df[\"longitude\"].str.slice(stop = -1)\n",
    "\n",
    "# reassigned because sometimes I was having issues when re-running code\n",
    "edit1 = df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# multiply in direction magnitudes\n",
    "edit1.longitude = edit1.longitude.astype('float64')*longDirections\n",
    "edit1.latitude = edit1.latitude.astype('float64')*latDirections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>identifier</th>\n",
       "      <th>name</th>\n",
       "      <th>num_pts</th>\n",
       "      <th>datetime</th>\n",
       "      <th>record_id</th>\n",
       "      <th>status</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>max_wind</th>\n",
       "      <th>min_pressure</th>\n",
       "      <th>...</th>\n",
       "      <th>sw34ktr</th>\n",
       "      <th>nw34ktr</th>\n",
       "      <th>ne50ktr</th>\n",
       "      <th>se50ktr</th>\n",
       "      <th>sw50ktr</th>\n",
       "      <th>nw50ktr</th>\n",
       "      <th>ne64ktr</th>\n",
       "      <th>se64ktr</th>\n",
       "      <th>sw64ktr</th>\n",
       "      <th>nw64ktr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>1851-06-25 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-94.8</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>1851-06-25 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-95.4</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>1851-06-25 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-96.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>1851-06-25 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.1</td>\n",
       "      <td>-96.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL011851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>1851-06-25 21:00:00</td>\n",
       "      <td>L</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.2</td>\n",
       "      <td>-96.8</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  identifier name num_pts            datetime record_id status  latitude  \\\n",
       "1   AL011851  NaN      14 1851-06-25 00:00:00       NaN     HU      28.0   \n",
       "2   AL011851  NaN      14 1851-06-25 06:00:00       NaN     HU      28.0   \n",
       "3   AL011851  NaN      14 1851-06-25 12:00:00       NaN     HU      28.0   \n",
       "4   AL011851  NaN      14 1851-06-25 18:00:00       NaN     HU      28.1   \n",
       "5   AL011851  NaN      14 1851-06-25 21:00:00         L     HU      28.2   \n",
       "\n",
       "   longitude  max_wind  min_pressure  ...  sw34ktr  nw34ktr  ne50ktr  se50ktr  \\\n",
       "1      -94.8      80.0           NaN  ...      NaN      NaN      NaN      NaN   \n",
       "2      -95.4      80.0           NaN  ...      NaN      NaN      NaN      NaN   \n",
       "3      -96.0      80.0           NaN  ...      NaN      NaN      NaN      NaN   \n",
       "4      -96.5      80.0           NaN  ...      NaN      NaN      NaN      NaN   \n",
       "5      -96.8      80.0           NaN  ...      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   sw50ktr  nw50ktr  ne64ktr  se64ktr  sw64ktr  nw64ktr  \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "5      NaN      NaN      NaN      NaN      NaN      NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = edit1\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.to_csv(\"final.csv\", sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Exploration</h3>\n",
    "\n",
    "<p> Now that the final data set has been cleaned up, it is now possible to begin to explore the data set and look for relavent information or trends.\n",
    "The first visualization conducted layers the trajectory of hurricane Maria over a map of the united states for geographic comparsion. This process is shown in the cells below.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "alt.data_transformers.enable('csv')\n",
    "\n",
    "from vega_datasets import data\n",
    "\n",
    "states_data = alt.topo_feature(data.us_10m.url, 'states')\n",
    "states = alt.Chart(states_data).mark_geoshape(\n",
    "        fill='lightgray',\n",
    "        stroke='black',\n",
    "        strokeWidth=1\n",
    "    ).transform_filter((alt.datum.id != 2) & (alt.datum.id != 15))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "states;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot data\n",
    "visual = alt.Chart('final.csv').mark_line().encode(\n",
    "    latitude=\"latitude:Q\",\n",
    "    longitude=\"longitude:Q\",\n",
    "    order = \"datetime:O\"\n",
    ").transform_filter((alt.datum.datetime > '2017-01-01') & (alt.datum.name == 'MARIA'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "visual + states;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalUnique = final.drop_duplicates('identifier')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ian/.local/lib/python3.6/site-packages/altair/utils/core.py:284: FutureWarning: A future version of pandas will default to `skipna=True`. To silence this warning, pass `skipna=True|False` explicitly.\n",
      "  attrs['type'] = infer_vegalite_type(data[attrs['field']])\n"
     ]
    },
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "format": {
         "type": "csv"
        },
        "url": "altair-data-c070a8a213b453b1b40ef5f8ec8ff820.csv"
       },
       "encoding": {
        "color": {
         "field": "datetime",
         "timeUnit": "month",
         "type": "ordinal"
        },
        "x": {
         "field": "datetime",
         "timeUnit": "year",
         "type": "ordinal"
        },
        "y": {
         "aggregate": "count",
         "field": "identifier",
         "type": "nominal"
        }
       },
       "mark": "bar"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent = finalUnique[finalUnique['datetime'].dt.year > 1982]\n",
    "\n",
    "alt.Chart(recent).mark_bar().encode(\n",
    "    x='year(datetime):O',\n",
    "    y='count(identifier)',\n",
    "    color='month(datetime):O'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ian/.local/lib/python3.6/site-packages/altair/utils/core.py:284: FutureWarning: A future version of pandas will default to `skipna=True`. To silence this warning, pass `skipna=True|False` explicitly.\n",
      "  attrs['type'] = infer_vegalite_type(data[attrs['field']])\n"
     ]
    },
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "format": {
         "type": "csv"
        },
        "url": "altair-data-1941f56e042e51f0af6f762f8b336884.csv"
       },
       "encoding": {
        "detail": {
         "field": "identifier",
         "type": "nominal"
        },
        "x": {
         "aggregate": "max",
         "field": "max_wind",
         "scale": {
          "zero": false
         },
         "type": "quantitative"
        },
        "y": {
         "aggregate": "min",
         "field": "min_pressure",
         "scale": {
          "zero": false
         },
         "type": "quantitative"
        }
       },
       "mark": "point",
       "selection": {
        "selector001": {
         "bind": "scales",
         "encodings": [
          "x",
          "y"
         ],
         "mark": {
          "fill": "#333",
          "fillOpacity": 0.125,
          "stroke": "white"
         },
         "on": "[mousedown, window:mouseup] > window:mousemove!",
         "resolve": "global",
         "translate": "[mousedown, window:mouseup] > window:mousemove!",
         "type": "interval",
         "zoom": "wheel!"
        }
       },
       "transform": [
        {
         "filter": "(datum.min_pressure !== -999)"
        }
       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(finalUnique).mark_point().encode(\n",
    "    x=alt.X('max(max_wind):Q', scale = alt.Scale(zero=False)),\n",
    "    y=alt.Y('min(min_pressure):Q', scale = alt.Scale(zero=False)),\n",
    "    detail='identifier'\n",
    ").transform_filter(alt.datum.min_pressure != -999).interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}