Facies Prediction ND.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Facies Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you really want to drill me?\n",
      "Do you really want to make me cry\n",
      "AH AH AH AH\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "import sys\n",
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "from plotly import graph_objs as go\n",
    "from plotly import tools\n",
    "# Import our custom modules\n",
    "sys.path.append(\"..\")\n",
    "from mysticbit import ml, munging, plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "well_data = pd.read_csv(\"C:\\\\Users\\\\Toyin Obasoro\\\\Desktop\\\\Kunle practise\\\\Well 4.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>NEU</th>\n",
       "      <th>CAL</th>\n",
       "      <th>FDC</th>\n",
       "      <th>GR</th>\n",
       "      <th>RES_D</th>\n",
       "      <th>SP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4398.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4398.5</td>\n",
       "      <td>0.54</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4399.0</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4399.5</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DEPTH   NEU     CAL     FDC      GR   RES_D      SP\n",
       "0  4398.0  0.54 -999.25 -999.25 -999.25 -999.25 -999.25\n",
       "1  4398.5  0.54 -999.25 -999.25 -999.25 -999.25 -999.25\n",
       "2  4399.0  0.53 -999.25 -999.25 -999.25 -999.25 -999.25\n",
       "3  4399.5  0.53 -999.25 -999.25 -999.25 -999.25 -999.25\n",
       "4  4400.0  0.51 -999.25 -999.25 -999.25 -999.25 -999.25"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "well_log = pd.read_csv(\"C:\\\\Users\\\\Toyin Obasoro\\\\Desktop\\\\Kunle practise\\\\freeman-well.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TVDSS</th>\n",
       "      <th>DEPT</th>\n",
       "      <th>DEN</th>\n",
       "      <th>DT</th>\n",
       "      <th>GR</th>\n",
       "      <th>NEUT</th>\n",
       "      <th>RES</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1717.0908</td>\n",
       "      <td>5633.5</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>12.159700</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1717.2432</td>\n",
       "      <td>5634.0</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>44.075001</td>\n",
       "      <td>-999.25</td>\n",
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       "      <td>2</td>\n",
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       "      <td>5634.5</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>44.075001</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1717.5480</td>\n",
       "      <td>5635.0</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>44.075001</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1717.7004</td>\n",
       "      <td>5635.5</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>43.715900</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       TVDSS    DEPT     DEN      DT         GR    NEUT     RES\n",
       "0  1717.0908  5633.5 -999.25 -999.25  12.159700 -999.25 -999.25\n",
       "1  1717.2432  5634.0 -999.25 -999.25  44.075001 -999.25 -999.25\n",
       "2  1717.3956  5634.5 -999.25 -999.25  44.075001 -999.25 -999.25\n",
       "3  1717.5480  5635.0 -999.25 -999.25  44.075001 -999.25 -999.25\n",
       "4  1717.7004  5635.5 -999.25 -999.25  43.715900 -999.25 -999.25"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "well_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TVDSS    0\n",
       "DEPT     0\n",
       "DEN      0\n",
       "DT       0\n",
       "GR       0\n",
       "NEUT     0\n",
       "RES      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_log.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "well_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5613, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "well_log = well_log.rename(columns={'DEN': 'RHOB','NEUT':'NPHI'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>DT</th>\n",
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       "      <td>0</td>\n",
       "      <td>1717.0908</td>\n",
       "      <td>5633.5</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>12.159700</td>\n",
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       "      <td>1</td>\n",
       "      <td>1717.2432</td>\n",
       "      <td>5634.0</td>\n",
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       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>44.075001</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
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       "      <td>1717.5480</td>\n",
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       "      <td>-999.25</td>\n",
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       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
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       "      <td>1717.7004</td>\n",
       "      <td>5635.5</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>43.715900</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
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      ],
      "text/plain": [
       "       TVDSS    DEPT    RHOB      DT         GR    NPHI     RES\n",
       "0  1717.0908  5633.5 -999.25 -999.25  12.159700 -999.25 -999.25\n",
       "1  1717.2432  5634.0 -999.25 -999.25  44.075001 -999.25 -999.25\n",
       "2  1717.3956  5634.5 -999.25 -999.25  44.075001 -999.25 -999.25\n",
       "3  1717.5480  5635.0 -999.25 -999.25  44.075001 -999.25 -999.25\n",
       "4  1717.7004  5635.5 -999.25 -999.25  43.715900 -999.25 -999.25"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Wrong number of items passed 8, placement implies 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Facies'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mset\u001b[1;34m(self, item, value)\u001b[0m\n\u001b[0;32m   1068\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1069\u001b[1;33m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1070\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Facies'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-0210bf33f1ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mwell_log\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Facies'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_facies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwell_log\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3470\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3471\u001b[0m             \u001b[1;31m# set column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3472\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3473\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3474\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3548\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3549\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3550\u001b[1;33m         \u001b[0mNDFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3551\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3552\u001b[0m         \u001b[1;31m# check if we are modifying a copy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3380\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3381\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3382\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3383\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mset\u001b[1;34m(self, item, value)\u001b[0m\n\u001b[0;32m   1070\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1071\u001b[0m             \u001b[1;31m# This item wasn't present, just insert at end\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1072\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1073\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1074\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36minsert\u001b[1;34m(self, loc, item, value, allow_duplicates)\u001b[0m\n\u001b[0;32m   1179\u001b[0m         \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1181\u001b[1;33m         \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1182\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1183\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mblkno\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcount\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_fast_count_smallints\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_blknos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mmake_block\u001b[1;34m(values, placement, klass, ndim, dtype, fastpath)\u001b[0m\n\u001b[0;32m   3265\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDatetimeArray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3266\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3267\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplacement\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, values, placement, ndim)\u001b[0m\n\u001b[0;32m   2773\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2774\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2775\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplacement\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2776\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2777\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, values, placement, ndim)\u001b[0m\n\u001b[0;32m    126\u001b[0m             raise ValueError(\n\u001b[0;32m    127\u001b[0m                 \u001b[1;34m\"Wrong number of items passed {val}, placement implies \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m                 \u001b[1;34m\"{mgr}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmgr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmgr_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m             )\n\u001b[0;32m    130\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Wrong number of items passed 8, placement implies 1"
     ]
    }
   ],
   "source": [
    "well_log['Facies'] = ml.create_facies(well_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8214 entries, 0 to 8213\n",
      "Data columns (total 8 columns):\n",
      "TVDSS     8214 non-null float64\n",
      "DEPT      8214 non-null float64\n",
      "RHOB      8214 non-null float64\n",
      "DT        8214 non-null float64\n",
      "GR        8214 non-null float64\n",
      "NPHI      8214 non-null float64\n",
      "RES       8214 non-null float64\n",
      "facies    8214 non-null object\n",
      "dtypes: float64(7), object(1)\n",
      "memory usage: 513.5+ KB\n"
     ]
    }
   ],
   "source": [
    "well_log.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>TVDSS</th>\n",
       "      <th>DEPT</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>DT</th>\n",
       "      <th>GR</th>\n",
       "      <th>NPHI</th>\n",
       "      <th>RES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>8214.000000</td>\n",
       "      <td>8214.000000</td>\n",
       "      <td>8214.000000</td>\n",
       "      <td>8214.000000</td>\n",
       "      <td>8214.000000</td>\n",
       "      <td>8214.000000</td>\n",
       "      <td>8214.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2342.921400</td>\n",
       "      <td>7686.750000</td>\n",
       "      <td>-28.426668</td>\n",
       "      <td>96.234156</td>\n",
       "      <td>78.585432</td>\n",
       "      <td>-32.925822</td>\n",
       "      <td>-22.860791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>361.389456</td>\n",
       "      <td>1185.660944</td>\n",
       "      <td>172.371419</td>\n",
       "      <td>222.241806</td>\n",
       "      <td>95.708795</td>\n",
       "      <td>179.859278</td>\n",
       "      <td>181.192382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1717.090800</td>\n",
       "      <td>5633.500000</td>\n",
       "      <td>-999.250000</td>\n",
       "      <td>-999.250000</td>\n",
       "      <td>-999.250000</td>\n",
       "      <td>-999.250000</td>\n",
       "      <td>-999.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2030.006100</td>\n",
       "      <td>6660.125000</td>\n",
       "      <td>2.093500</td>\n",
       "      <td>129.800003</td>\n",
       "      <td>80.451977</td>\n",
       "      <td>0.454700</td>\n",
       "      <td>0.887100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>2342.921400</td>\n",
       "      <td>7686.750000</td>\n",
       "      <td>2.187000</td>\n",
       "      <td>137.899994</td>\n",
       "      <td>89.145000</td>\n",
       "      <td>0.516600</td>\n",
       "      <td>1.043350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2655.836700</td>\n",
       "      <td>8713.375000</td>\n",
       "      <td>2.257800</td>\n",
       "      <td>150.800003</td>\n",
       "      <td>97.775078</td>\n",
       "      <td>0.600700</td>\n",
       "      <td>1.282300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2968.752000</td>\n",
       "      <td>9740.000000</td>\n",
       "      <td>2.634200</td>\n",
       "      <td>209.800003</td>\n",
       "      <td>125.198998</td>\n",
       "      <td>1.795600</td>\n",
       "      <td>1950.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TVDSS         DEPT         RHOB           DT           GR  \\\n",
       "count  8214.000000  8214.000000  8214.000000  8214.000000  8214.000000   \n",
       "mean   2342.921400  7686.750000   -28.426668    96.234156    78.585432   \n",
       "std     361.389456  1185.660944   172.371419   222.241806    95.708795   \n",
       "min    1717.090800  5633.500000  -999.250000  -999.250000  -999.250000   \n",
       "25%    2030.006100  6660.125000     2.093500   129.800003    80.451977   \n",
       "50%    2342.921400  7686.750000     2.187000   137.899994    89.145000   \n",
       "75%    2655.836700  8713.375000     2.257800   150.800003    97.775078   \n",
       "max    2968.752000  9740.000000     2.634200   209.800003   125.198998   \n",
       "\n",
       "              NPHI          RES  \n",
       "count  8214.000000  8214.000000  \n",
       "mean    -32.925822   -22.860791  \n",
       "std     179.859278   181.192382  \n",
       "min    -999.250000  -999.250000  \n",
       "25%       0.454700     0.887100  \n",
       "50%       0.516600     1.043350  \n",
       "75%       0.600700     1.282300  \n",
       "max       1.795600  1950.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_log.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cd54c11c8>"
      ]
     },
     "execution_count": 51,
     "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": [
    "sns.distplot(well_log.RES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'well' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-5a0088e4a684>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0max3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwell\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'GR'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'well' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "\n",
    "ax1 = fig.add_subplot(2, 2, 1)\n",
    "\n",
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "\n",
    "ax3 = fig.add_subplot(2, 2, 3)\n",
    "\n",
    "_ = ax1.hist(well.log(['GR']), bin=20, alpha=0.3)\n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22cd57ba448>]"
      ]
     },
     "execution_count": 58,
     "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": [
    "plt.plot(well_log.GR, well_log.DEPT )"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}