dataset_cleaning.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = df[(df.days == 0 ) | (df.days == 365)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>days</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
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       "<p>775200 rows × 4 columns</p>\n",
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      "text/plain": [
       "               id  days         lon        lat\n",
       "0               0     0 -175.496200 -21.259300\n",
       "365             0   365  178.258940 -23.204432\n",
       "366             1     0 -175.536900 -21.052700\n",
       "731             1   365  167.681800 -25.262985\n",
       "732             2     0 -174.884000 -21.178800\n",
       "...           ...   ...         ...        ...\n",
       "141860867  387597   365   91.396355  21.316368\n",
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       "\n",
       "[775200 rows x 4 columns]"
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     },
     "execution_count": 41,
     "metadata": {},
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    }
   ],
   "source": [
    "start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = start[['lon', 'lat']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = array.reshape(len(array)//2, 4)\n",
    "\n",
    "d1 = pd.DataFrame(array, columns=['lon_x', 'lat_x', 'lon_y', 'lay_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = d1.dropna()\n",
    "d1.to_csv('cleaned_data.csv')"
   ]
  }
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