Raw_Analysis_NASA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ___Raw Analysis___\n",
    "---\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['name', 'id', 'nametype', 'recclass', 'mass', 'fall', 'year', 'reclat',\n",
      "       'reclong', 'GeoLocation'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aachen</td>\n",
       "      <td>1</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>50.77500</td>\n",
       "      <td>6.08333</td>\n",
       "      <td>(50.775000, 6.083330)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aarhus</td>\n",
       "      <td>2</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H6</td>\n",
       "      <td>720.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>56.18333</td>\n",
       "      <td>10.23333</td>\n",
       "      <td>(56.183330, 10.233330)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abee</td>\n",
       "      <td>6</td>\n",
       "      <td>Valid</td>\n",
       "      <td>EH4</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>54.21667</td>\n",
       "      <td>-113.00000</td>\n",
       "      <td>(54.216670, -113.000000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acapulco</td>\n",
       "      <td>10</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Acapulcoite</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>16.88333</td>\n",
       "      <td>-99.90000</td>\n",
       "      <td>(16.883330, -99.900000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Achiras</td>\n",
       "      <td>370</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>780.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>-33.16667</td>\n",
       "      <td>-64.95000</td>\n",
       "      <td>(-33.166670, -64.950000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name   id nametype     recclass      mass  fall    year    reclat  \\\n",
       "0    Aachen    1    Valid           L5      21.0  Fell  1880.0  50.77500   \n",
       "1    Aarhus    2    Valid           H6     720.0  Fell  1951.0  56.18333   \n",
       "2      Abee    6    Valid          EH4  107000.0  Fell  1952.0  54.21667   \n",
       "3  Acapulco   10    Valid  Acapulcoite    1914.0  Fell  1976.0  16.88333   \n",
       "4   Achiras  370    Valid           L6     780.0  Fell  1902.0 -33.16667   \n",
       "\n",
       "     reclong               GeoLocation  \n",
       "0    6.08333     (50.775000, 6.083330)  \n",
       "1   10.23333    (56.183330, 10.233330)  \n",
       "2 -113.00000  (54.216670, -113.000000)  \n",
       "3  -99.90000   (16.883330, -99.900000)  \n",
       "4  -64.95000  (-33.166670, -64.950000)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/meteorite-landings.csv')\n",
    "print(data.columns)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Look\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data described: \n",
      "\n",
      "                 id          mass          year        reclat       reclong\n",
      "count  45716.000000  4.558500e+04  45428.000000  38401.000000  38401.000000\n",
      "mean   26889.735104  1.327808e+04   1991.772189    -39.122580     61.074319\n",
      "std    16860.683030  5.749889e+05     27.181247     46.378511     80.647298\n",
      "min        1.000000  0.000000e+00    301.000000    -87.366670   -165.433330\n",
      "25%    12688.750000  7.200000e+00   1987.000000    -76.714240      0.000000\n",
      "50%    24261.500000  3.260000e+01   1998.000000    -71.500000     35.666670\n",
      "75%    40656.750000  2.026000e+02   2003.000000      0.000000    157.166670\n",
      "max    57458.000000  6.000000e+07   2501.000000     81.166670    354.473330\n",
      "\n",
      "\n",
      "Data info: \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45716 entries, 0 to 45715\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   name         45716 non-null  object \n",
      " 1   id           45716 non-null  int64  \n",
      " 2   nametype     45716 non-null  object \n",
      " 3   recclass     45716 non-null  object \n",
      " 4   mass         45585 non-null  float64\n",
      " 5   fall         45716 non-null  object \n",
      " 6   year         45428 non-null  float64\n",
      " 7   reclat       38401 non-null  float64\n",
      " 8   reclong      38401 non-null  float64\n",
      " 9   GeoLocation  38401 non-null  object \n",
      "dtypes: float64(4), int64(1), object(5)\n",
      "memory usage: 3.5+ MB\n",
      "None\n",
      "\n",
      "\n",
      "Data types: \n",
      "\n",
      "name            object\n",
      "id               int64\n",
      "nametype        object\n",
      "recclass        object\n",
      "mass           float64\n",
      "fall            object\n",
      "year           float64\n",
      "reclat         float64\n",
      "reclong        float64\n",
      "GeoLocation     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"Data described: \\n\")\n",
    "print(data.describe())\n",
    "print('\\n')\n",
    "print(\"Data info: \\n\")\n",
    "print(data.info())\n",
    "print('\\n')\n",
    "print(\"Data types: \\n\")\n",
    "print(data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Valid     45641\n",
       "Relict       75\n",
       "Name: nametype, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nametype.value_counts()\n",
    "# Under NameType, 'valid' is for most meteorites and 'relict'\n",
    "# are for objects that were once meteorites but are now highly\n",
    "# altered by weathering on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>class</th>\n",
       "      <th>mass</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aachen</td>\n",
       "      <td>1</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>50.77500</td>\n",
       "      <td>6.08333</td>\n",
       "      <td>(50.775000, 6.083330)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aarhus</td>\n",
       "      <td>2</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H6</td>\n",
       "      <td>720.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>56.18333</td>\n",
       "      <td>10.23333</td>\n",
       "      <td>(56.183330, 10.233330)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abee</td>\n",
       "      <td>6</td>\n",
       "      <td>Valid</td>\n",
       "      <td>EH4</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>54.21667</td>\n",
       "      <td>-113.00000</td>\n",
       "      <td>(54.216670, -113.000000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acapulco</td>\n",
       "      <td>10</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Acapulcoite</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>16.88333</td>\n",
       "      <td>-99.90000</td>\n",
       "      <td>(16.883330, -99.900000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Achiras</td>\n",
       "      <td>370</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>780.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>-33.16667</td>\n",
       "      <td>-64.95000</td>\n",
       "      <td>(-33.166670, -64.950000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name   id nametype        class      mass  fall    year       lat  \\\n",
       "0    Aachen    1    Valid           L5      21.0  Fell  1880.0  50.77500   \n",
       "1    Aarhus    2    Valid           H6     720.0  Fell  1951.0  56.18333   \n",
       "2      Abee    6    Valid          EH4  107000.0  Fell  1952.0  54.21667   \n",
       "3  Acapulco   10    Valid  Acapulcoite    1914.0  Fell  1976.0  16.88333   \n",
       "4   Achiras  370    Valid           L6     780.0  Fell  1902.0 -33.16667   \n",
       "\n",
       "        long               GeoLocation  \n",
       "0    6.08333     (50.775000, 6.083330)  \n",
       "1   10.23333    (56.183330, 10.233330)  \n",
       "2 -113.00000  (54.216670, -113.000000)  \n",
       "3  -99.90000   (16.883330, -99.900000)  \n",
       "4  -64.95000  (-33.166670, -64.950000)  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns\n",
    "data.rename(columns={'recclass':'class', 'reclat':'lat', 'reclong':'long', 'mass (g)':'mass'}, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling\n",
    "### (Test sample size is set to 10%)\n",
    "### Only comment out once all testing / experiment is done, to use real data\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Data Stats: \n",
      "\n",
      "                 id          mass          year           lat          long\n",
      "count  45716.000000  4.558500e+04  45428.000000  38401.000000  38401.000000\n",
      "mean   26889.735104  1.327808e+04   1991.772189    -39.122580     61.074319\n",
      "std    16860.683030  5.749889e+05     27.181247     46.378511     80.647298\n",
      "min        1.000000  0.000000e+00    301.000000    -87.366670   -165.433330\n",
      "25%    12688.750000  7.200000e+00   1987.000000    -76.714240      0.000000\n",
      "50%    24261.500000  3.260000e+01   1998.000000    -71.500000     35.666670\n",
      "75%    40656.750000  2.026000e+02   2003.000000      0.000000    157.166670\n",
      "max    57458.000000  6.000000e+07   2501.000000     81.166670    354.473330\n",
      "\n",
      "--------\n",
      "\n",
      "New Sample Data Stats: \n",
      "\n",
      "                 id          mass         year          lat         long\n",
      "count   4572.000000  4.562000e+03  4536.000000  3793.000000  3793.000000\n",
      "mean   27031.823053  3.495610e+04  1991.572090   -39.626677    64.362791\n",
      "std    16886.665968  1.280904e+06    25.529171    46.221709    80.515703\n",
      "min        6.000000  0.000000e+00  1583.000000   -86.933330  -165.433330\n",
      "25%    12851.750000  7.100000e+00  1987.000000   -76.716670     0.000000\n",
      "50%    24277.500000  3.221000e+01  1998.000000   -71.500000    54.196670\n",
      "75%    44400.250000  2.140000e+02  2003.000000     0.000000   157.166670\n",
      "max    57434.000000  6.000000e+07  2013.000000    76.133330   175.730280\n"
     ]
    }
   ],
   "source": [
    "# *****************************\n",
    "# HIGHLY IMPORTANT\n",
    "# *****************************\n",
    "\n",
    "# Sample data\n",
    "print(\"Original Data Stats: \\n\")\n",
    "print(data.describe())\n",
    "\n",
    "print('\\n--------\\n')\n",
    "\n",
    "print(\"New Sample Data Stats: \\n\")\n",
    "data['year'].fillna(0).astype(int)\n",
    "data['mass'].fillna(0).astype(int)\n",
    "\n",
    "\n",
    "data = data.sample(frac=0.1)  # 10% sample set\n",
    "print(data.describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Fall vs. Fallen*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "data['fall'].hist(bins=3)  # \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Top 10 classifications of meteors*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d653ed0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "top_10_class = data['class'].value_counts()[:10]\n",
    "plt.bar(top_10_class, height = 1)\n",
    "\n",
    "top_10_class.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Geolocation Conversion*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[None, None, 'Canada', None, None, None, None, None, None, None, None, None, None, None, None, 'Nigeria', None, None, None, None]\n"
     ]
    }
   ],
   "source": [
    "geolocator = Nominatim(user_agent=\"project_impact\")\n",
    "\n",
    "lists = []\n",
    "for i in range(20):\n",
    "    lats = data['lat'].get(key = i)\n",
    "    longs = data['long'].get(key = i)\n",
    "    coor = gp.Point(lats, longs)\n",
    "    country = geolocator.reverse(gp.Point(coor)).raw['address'].get('country')\n",
    "    lists.append(country)\n",
    "print(lists)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Year vs Mass*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "ax.scatter(data['year'], data['mass'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equator or Poles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Above Equator: 830 \n",
      "\n",
      "At Equator: 623 \n",
      "\n",
      "Below Equator: 2340 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plt.gca()\n",
    "axes.set_ylim([-90,90])\n",
    "above_equator = data[data.lat >0].shape[0]\n",
    "at_equator = data[data.lat ==0].shape[0]\n",
    "below_equator = data[data.lat <0].shape[0]\n",
    "\n",
    "print(\"Above Equator:\", above_equator, '\\n')\n",
    "print(\"At Equator:\", at_equator, '\\n')\n",
    "print(\"Below Equator:\", below_equator, '\\n')\n",
    "\n",
    "labels = [\"Above\", 'At', 'Below']\n",
    "values = [above_equator, at_equator, below_equator]\n",
    "plt.pie(values, labels=labels)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}