smoofra/atlantic.ipynb

## atlantic.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import matplotlib\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Imports to Allies</th>\n",
       "      <th>Sunk byU-Boat</th>\n",
       "      <th>Sunk byaircraft</th>\n",
       "      <th>Sunk bywarship or raider</th>\n",
       "      <th>Sunk bymines</th>\n",
       "      <th>Total Alliedshipping sunk</th>\n",
       "      <th>Germansubmarines lost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1939-09-01</th>\n",
       "      <td>3297070.0</td>\n",
       "      <td>153879.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5051.0</td>\n",
       "      <td>29537.0</td>\n",
       "      <td>158930.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939-10-01</th>\n",
       "      <td>3576135.0</td>\n",
       "      <td>134807.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32058.0</td>\n",
       "      <td>29490.0</td>\n",
       "      <td>166865.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939-11-01</th>\n",
       "      <td>4408689.0</td>\n",
       "      <td>51589.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1722.0</td>\n",
       "      <td>120958.0</td>\n",
       "      <td>53311.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939-12-01</th>\n",
       "      <td>4466664.0</td>\n",
       "      <td>80881.0</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>22506.0</td>\n",
       "      <td>82712.0</td>\n",
       "      <td>106336.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940-01-01</th>\n",
       "      <td>4847044.0</td>\n",
       "      <td>111263.0</td>\n",
       "      <td>23693.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77116.0</td>\n",
       "      <td>134956.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0           Imports to Allies  Sunk byU-Boat  Sunk byaircraft  \\\n",
       "date                                                            \n",
       "1939-09-01          3297070.0       153879.0              0.0   \n",
       "1939-10-01          3576135.0       134807.0              0.0   \n",
       "1939-11-01          4408689.0        51589.0              0.0   \n",
       "1939-12-01          4466664.0        80881.0           2949.0   \n",
       "1940-01-01          4847044.0       111263.0          23693.0   \n",
       "\n",
       "0           Sunk bywarship or raider  Sunk bymines  Total Alliedshipping sunk  \\\n",
       "date                                                                            \n",
       "1939-09-01                    5051.0       29537.0                   158930.0   \n",
       "1939-10-01                   32058.0       29490.0                   166865.0   \n",
       "1939-11-01                    1722.0      120958.0                    53311.0   \n",
       "1939-12-01                   22506.0       82712.0                   106336.0   \n",
       "1940-01-01                       0.0       77116.0                   134956.0   \n",
       "\n",
       "0           Germansubmarines lost  \n",
       "date                               \n",
       "1939-09-01                    2.0  \n",
       "1939-10-01                    5.0  \n",
       "1939-11-01                    1.0  \n",
       "1939-12-01                    1.0  \n",
       "1940-01-01                    1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pandas.read_html('https://en.wikipedia.org/wiki/Losses_during_the_Battle_of_the_Atlantic')[0]\n",
    "a.columns = a.iloc[0,:]\n",
    "a = a.iloc[1:,:]\n",
    "def parse(date):\n",
    "    return pandas.to_datetime('19' + re.match('\\d+-\\d+', date).group())\n",
    "a['date'] = a['Month, year'].apply(parse)\n",
    "del a['Month, year']\n",
    "a.set_index('date', inplace=True)\n",
    "a = a.astype('float')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Sri5haSCiIQgz2GtYGdevKaOpczhskLlzeCLi8KWZzBSN5j43F3rdXLe6nM21RVQUONgT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\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a['Sunk byU-Boat'].plot();"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}