dataqc_lasfiles_firstpart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9fcda6ea",
   "metadata": {},
   "source": [
    "# Data QC, first part: re-structure of .las files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4c9e8e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "from IPython.display import display, HTML\n",
    "from IPython.display import Image\n",
    "import numpy as np\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60eb457b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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W0d/dTeqS620tczVVNHrNJuXeF3DcRkDPOK8b8f8AxHvtMFivhzWpHcWYnZzBCi3PXB/eEE5wchBxXZvqq6n/AMIfq9nLHLNdy7GMJyrRtExcfQMq/lXHWy2tQpQqz2lfvdW73S3KU03b5nZUUUV5hYUUUUAV79S2nzhRklDgD6UVNL/qm+lFAGFUKRWyzHy44RIOu1RkVNWTALSW/ja2MaKjN8wb5pWOcj1IoA0Va3abchiMuMZBG6nN941SS2gXVkWCGNFgjLHaoHLcD9M1db7xoAStWOytbyyg+120M+zlfNjDbT6jPSsqqPkabe6ldr4jkSMQKhszLKYwqFeXXkfNuzz14FAHUf2bY75X+x2+6YYlPlLmQejcc/jULaFpD436XZNtGBm3Q4Hp0rzTWzAviKJ/D9vc6g3h/T42FzDPGS78FfMLOuRsU84P366Sa40C88baRqhltcy6fLOJGlA5Bi2Hr1ALY/Gmtf69QOql0jTZ9nn6fay+WoVN8CnaPQZHAqnc/wDCRLcOunx6V9mHEfmtIGx74GKwPEB8Pa1JoOoXQtz9qmQgzOFYxFWIBGema7WMKsaiPGwABcdMUgKEOmR3Kx3GsWNhJfD70kcQbGOmCwzV2S3hmZGmijkaNtyFlB2n1Hoa8v8AEFrqcvizX/7DuF8i68q21EG5C/Z4vLVjKATwfvLx/eHpXZeHNR03T/CXh+Ka9giN1bxRwB5RmZ9gJAz1PU0LVX9BtWNi50ywvZBJeWNtcOBgNLCrED6kUv8AZ1j5ry/Y7fzHXY7+UuWX0JxyPaua8Y2WiT6rpDaott5rXKo3myBSY8McHnpmue8Ti1sfEWr3kcivZrZxR3tqr5BRgwEmM9VYKc+hNK9lcLXdj05VCKFUBVAwABwBS1m+HI0i8M6akQwgtYyAPdQa0qqSs7Ep3VwooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAqOW3hmZDNFHIY23IXUHafUehqSsjXJ/JutMH9uR6V5l0F8p0Q/bOD+6G7oT1454oA16KKKAKOoWelSYn1W3s32/KJLlFOPbLU6TTdMvik01laXHygJI0Svx2wcdK5XxtaQX3iTw/b3esNZwtLKWh/clTiJyGIkRvp6fjVrWJ5bXXtKsLXXxp9vNBIsiBYeigbWXK8E5+ntR0BnTT2VrcmM3NtDMYzlDJGG2H2z0ph02xMrSmztzI7BmfylyxHQk46iuevLu6W+ltbrXm0/7HAjpL5cQ+1Eg5YhgeMjGFwfeud/tnXr2/UjxC9iJZYY3tfJhYwF4C5AJXOdw4znv1pXHY9Jhhit4hFbxpFGvREUKB+ApHt4ZCxkhjcsuxiyg5X0PtVLw/evqXhzT7yZ1klnt0d2ToWKjP615t46ujpfizUTBCjIbVby+Lpu3WwGwqD2y2Dx/dpy0dgSueoXOl2F46veWNtOyjCtLCrED05FPWwtEuPtCWsCzbdvmCMBsemeuK4jVNI0SHTPDMOpLb+ajQxN5smCU2nOeeRms7xRbQt4s1BbHTYbt002FxItyVe2+Zx5iqOWIAB4weBQ9PvEtT0F9D0mQgyaXZuQMAtbocD8qq3C+IIpymmRaUtquBEJWkDAY6YAx+VcPqltYap4wLyTaZc2p063aea4mIcqS4LR843YHf2r0PRp4LnQ7GezMht5LdGiMn3ipUYz74oAhFjDdwwt4htNOluz8owgdfopcZrSVQihVAVQMAAcCua8WNp8F7pM9y9vHcC+hAaRgGC7j69q6UEMoKkEHkEd6Olw62GyRRy7fNjV9rbl3LnB9R71FNYWdzMstxaQSyKMK7xhiB6ZNWKKAKcuj6ZcbPP060l2KFXfAp2gdAMjgVJJYWcvl+baQP5QxHujB2D0HpViigCtJptjLHFHLZ27pD/q1aJSE+gxxTp7CzuWVrm1gmZSCpkjDEfTNT0UAFQXNla3m37ZbQz7DlfNjDbT7ZqeigCu+n2UkjySWkDvImx2aJSWX+6Tjke1NOl6exjLWNsTGu1Mwr8g9BxwKtUUAVf7MsOf9Btvmj8s/uV5T+706e1UdW8NW2rQwQ+dJaQwqVEdvFFgqeCMshK8f3SK2KKAKz6bZSwwxTWsMyQACMSxhtmPTNOnsrW5ZGubaGZo+UMkYYr9M9KnooAZFDHBEscEaxxr0RFAA/AUyeztrl0e5toZmT7jSRhiv0z0qaigCq+mWEior2Vsyx42AxKduOmOOKLnS9PvJA95Y21w4GA0sKsQPTJFWqKAKx02xLMTZW5LR+WT5S8p/d6dPaqGreGrbVYYIfOktIYQVEdvFFgqeCMshK/8BINbFFADIYkggSGIYSNQqj0AqK50+zvHRry0guGT7pliDFfpkcVYooAqXGkabdy+ZdafazyYxvkhVjj0yRS/2ZYAsRZW2XTy2/dL8y/3Tx09qtUUAVJNK06VYllsLV1hGIw0KkIPbjiqd2fEQunGnppf2f8Ag85pA344GK16KAMyLSoryJZdd0/TprzozJEHGO3LDNWJNI02V0eXT7V2jAVGaFSVA6AccVbooAq3OmWF66teWVtcMowpliViB6ciln02xufL+02VvN5YwnmRK2we2RxVmigAAwMDgVDeWcGoWclreRiWGVdroe4qaijYCpcaVYXew3dlb3BRdqtNErkD6kVJNY2lwsYuLWGURHMYeMNs+melT0UAVbnTLC8kEl5Y207qMBpYVYgfUirQAAAAwB0AoooAq3Ol2F7IJLyxtrhwMBpYVYgfUilOmWBYk2Vvkx+UT5S8p/d6dParNFAFZ9NsZIYopLK3eOH/AFaNEpCfQY4pZNPspZGkltIHd08tmaJSWX+6Tjp7VYooApSaNpcyoJtNtJBGu1A0CnaPQccCpJNNsZlRZbO3cR4CBolO3HTHHFWaKAIbiztruNUu7eKdFOVWVAwB9eaSextLpUW5tYZhGcoJIw236Z6VPRQBXfT7OWVpJLSB3dPLZmiBLL/dJx09qR9OspbdLeSzt3hj+5G0SlV+gxgVZooAQAKoCgADoB2qKeztrpkNzbxTFDlTIgbb9M9KmopptO6Aq/2ZYbUH2G2xHnYPJX5c9cccU9rC0e2W3e1gaBfuxGMFR9B0qeinzy7gQS2NpOUM1rDIYxhC8YO0e3pUY0yzW6t50gRHtkZIQowqBsZwBx2q3RRzSta4BRRRUgFFFFADZf8AVN9KKJf9U30ooAwqhSK2WY+XHCJB12qMipqyYBaSX8bWpjRUZvnDfNKxzkepFAGoGQyMFK7+4B5pG+8ayrMAXsRwmS8nKn5+/wB72/8ArVqt940AJWrHZWt5ZQfa7aGfZyvmxhtp9RnpWVWPc2WhT+K5W1cwKyabG4Ly7Srb35HPXAH6UD6HZf2bY75X+xW+6cYlbylzIPRuOfxqFtB0h8b9Ksm2jAzbocD06Vwc2jWV0NEu/EMiPe6jIgcTttYKISAMZ7kBvqa9D060+waZa2nmNL9niWPzG6ttAGT+VAiOXR9MuNnn6daS+WoVN8CnaPQZHAqnc/8ACRLcOunx6V9mHEfmvIGx7gDFbFFAGfaacjQO+o2Vj9qnGLgwx5WQehJGT+NWVsbRI4kW1hVITmJRGMRn1Udvwpt1qNlYy28d5dQwPcyeXAsjhTI+M7Vz1NclrFppUWt+IJNSWJGn09ChkbG/hs7eeTkL09qJXSuNLWx1lzpdheyCS8sba4cDAaWFWIH1Ips1pptus13PbWsYEZEszRqPkA5BPpgVwGqWdr9hht/tFo0tlpke+wuZnheEkE742H8Z6d+QK6O4I1G10fRIhIEmhSe6WQ5dYVA4Y+rNge/NXGN5W/rqTfRM6aPZ5S+Vt2YG3b0x2xTq8/8AFenpf/ELT4Ei0+ZmssyR3kjDK+ao+UDq2CcV22mafDpWnRWVsXMUIITeckAknGfxqN1cOti1RRRQMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACo5beGdkM0UchjbchdQdp9R6GszXJ/JutMH9uR6V5l0F8p0Q/bOD+6G7oT1454rXoAKjmnhto/MuJUiTONzsFH5mpK4nxtaQX3iTw/b3esNZwtLKWh/clTiJyGIkRvp6fjQM6ufT9O1EpNc2drdHb8skkSvx7EjpRPpWnXUivdWFrM6jCtJCrED0yRXPaxPLa69pVhaa8NPt5oJFkQLD0UDay5XgnP09qdeXd2t7Na3WvNp/2OBHSXy4h9qJByxDA8ZGMLg+9K6FudHPZWt0Yzc20MxjOUMkYbYfbPSmnTbEytKbK3MjsGZzEuWI6EnHUV55/bOvXt+pHiB7ESywxva+TCxgLwFyASuc7hxnPfrXeeH719S8OafeTOsks9ujuydCxUZ/Wn3AuwwxW8Qit40ijXoiKFA/AUj28MrMZIY3LLsYsoOV9D7V5T46ujpfizUTBEjIbVby+Lpu3WwGwqD2y2Dx/drc1TSNEh03wzDqK2/mK0MTebJglNpJzzyM0LUHodpc6Xp946veWNtcMowrSwqxA9ORT1sLNLj7QlrAs23b5gjAbHpnrivOPFFtC3izURY6bDdummwuJEuSr23zOPMVRyxAAPGDwKTVLax1TxgzvNptzatp1u08085DlSXBeMZxuwB19qVx21PQX0PSZCDJpdk5AwC1uhwPyqndNrtvceVp6aRHa5CwrM7q2MdMAY/KrujTwXOh2U9mZDbyQI0Rk+8VKjGffFY3is6fDe6RPcvbx3AvoQGkYBguT69qpr3rE30uaqaal9BG+vWNhPdKCCVj3qBnsWGa0FUIoVQFUDAAHAoBDKCpBB5BHeuF8YwQaTf3V0sCvJrdqbKPdk5nz8g/EHP/AKQ0d3Ub3EKTJC80ayv91CwDN9B3rzzW9Sn8K2C6fot21uNItFaUTXESJIxGejqzvnnOCv1re1+6055tBu55bQTm6iZZGZdwUqehPOKOvzDWx1NFch4g1ttPuNX8zWvscI01bi1b938r5fJXKndnC8HNZl94g1y5v40sLyKGNLOKZGa5hiWcsMliGjbIzx8pWgdv6/r1PQqK4rXtX1REtZ7fVLe2RbQTXEUE8YYMRncPMU719ANpOOtddYymbT7eUuzl41bcy7S2R1I7fSgRPTRLG0jRq6l1ALKDyM9Miub1W+mk8QSWUmtHRoIbdZkZVjzMSSDkyAjAx0GDz1rFvGmXW7u8sPEEcUlvosc8k0cUWboqXIZsg4X2GOvUUIdrnoFFee+I/F15Fbi50y9ELW1mlxKGuIUicsMgAMrM//AAEr9at6nqWoTanePZeIfskEGlJerCiROC/zdyM7DgZ5z6EUAlc7eiuG1LxBdTWl5dLraaZ9msEuIEVEZbhmQkk7gSRkYwuKp3Ws+IftcNvBfhPKs4phJNcQw/aGYEksGjOQDx8u2h6C/r+vvPQ3dI4y8jKiKMlmOAKVWDqGQhlIyCDkGuG1jU7i9s71L/WIdNWGxSQQqsbx3JZCWyXGWXPHy4NdL4XdZPCelMjKym0j5Xp90U7b/wBdw7ef/ANWiuS8Q6w+mXmrpPrJsYl01Z7ct5Y2SAuDt3Kc5wvBzVTV9a1FrWxubLWIY4hZLNOscsSyEkZ3YdWDDjoNvPel0uOx2c1zBbtEs8qRmZxHGGbG9sZwPU8Gpa5TVdRWF9Bun1fyxJLEJIZliUSBgfnII3KfoRVfW76/Ou6gtj4h+yW9tpovFiVIny2W7kZ2nAzz9CKBLVnZ0Vw2o+ILqa0urpdbTTBbWCXECKiMtwzISSdwJIBGMLg11mitK+h2b3M73ErwqzyuACxIz0AA/SnYOxdoriPFmh6S+u2V1qckTyXl3HHtlfbtQI+QOeQTg1jalodpF4si0+Z7CaC3023SeS8lKybAzAsgzjdgd/apvp/Xa47anqFFeU67PYSm2l0GSR4rrTYzqpZwXisuMSYzxJjPTtk9hnV1nQvDpuNIuTNBLb3c0MUIaXgQrG2MHPIPBp/52/Gwj0Giqul2Q03SbWyWRpRbxLGHbq2BjNcH4lsLW9+IUqXA04w/YoTcfapirqu9wTGM43Y9fah6MaVz0aiuAWHw1qWt2M940Zt20eOSJrmXa4G75WPPXHemX8Gn32ieHZ/EIhN288aM08m1miyevI6jBNNqzsLp/Xa56FRXC+J9B0X+1tPub14WF1cxRKkj4Cxqj8DnkE4P5V2Gl2Q03SbWyWRpRbxLGHbq2BjNJa3BlksFUliAB1J7UyWeGCLzJ5Ujj/vuwA/M1geOJHXw6kakiKe7ghnI7RNIob9DWZ43sre71rw9a3GrNZQNcPuhAhKnELkMRIjfT0/GqcfdUg62O0R0kQPGyujDIZTkGnVy15cPFfNYrrH9k29pbJJC6pEBcZznO5cYGBwoHWql1q19cXGgeZrH9ktf27tcw7Y+CFBBXeMg5PfI9qkDtKimuYLYxCeVIzM4jjDNjex6Aep4NFqkkVpEk8/2iRVAaUqF3n1wOPyrB8S3i2WpaTKdTWAG7RHtn8va6nPzHcNwI9QRR1sHS50dFcbrd7ftr94lj4h+yW8Gmi7ESpE+WBbByRnacc/oRVC/8Ra3eahCthcw20X2KK4Q/aYo1mZgd2d6NlQQB8pBo6XHY9BqKa5gtjELiVIzM4jjDNjex6Aep4NcD4l8RapaSWhsdV/fXFluu7e38t0s1OM3WWXLAf3e/Yda19Uv47ZdBnGvrMjTxI+/ydtwrZ/eH5cg+6kCn/mLodZRXJeKtTvobqJtN1WGCCOEyyxpNEsh9G+dWDLgHgYJPequomSbW9FupPEl5p0E1hISwWGNWb92ckOhwSD09uKQ7Hb0Vweta7q8utiDSL6NLVLWOaGVrqGMXO7OWO6Ntw4H3SvWn3mo6rcahJ5evpZCPSVvHhhWKRfMGehZc7D3/DBFAW1sdzRXD308l7q+h38niK40yG40+Rm8kwiPd8h4Lo3XJ79uO9dnazR3FpFNBMJ43QFZQQQ49eOKdhD3dI0LyMqKoyWY4ApUdZEDxsGVhkMpyCK8w+IeuTyarqGi3mu2eh2CWPmJHdW6yjUC2cp83YYAwvPNcjqHxGvNI8IeH7fwzqvlyQaYjyxokGx2AxtzIcnBUghRkV7uHyStiKcZQavL1sla+rtb7r+ZnKok7f10PfS6qyqzAM33QTyaWvJvFmpTX954O16LxXFo9ncRS5uRDG8cTmPk5bg5+7g+lU/H/wASL7TBYr4c1qR3FmJ2cwQotz1wf3hBOcHhBxUUsmrVnCMGryve6dk02rN2t0D2itc9k3LuC7huIyBnmlrjn1VdT/4Q/V7OWOWa7l2MYTlWjaJi4+gZV/KuxryalN03aW/+RaaaugooorIYUUUUANl/1TfSiiX/AFTfSigDCqFIbZZiY44RIOu1RkVNWTALSS/ja1MaKjN84b5pWOcj1IoA0lEImYoE8z+IjG78aVvvGsmNJYtSjSNITmZmeVWy7Lz1HbFazfeNACVpQaXYXEcVxcWNtLOAMSvCrMMdOSM1m1kXjwPdXz6lBHdS2qxmzgnufIXYVyWU+u7P5Ck9B7nY3GnWV5Ikl3Z287p9xpYlYr9CRxVmvIr0R3eo3E9npIZzpNpKGN4TLZZ3/OAOXIUKTjnivV7OVJ7GGWGZZ0ZARKpyH461QupNRXGatZaFceOLltXMCsunRuC8u0q29+RzwcAV0Ph2W6n8OWEt/u+0NCpcsOT6E+5GDS6A97F6SCGZkaaJJDG25Cyg7T6j0NNns7a5kje5t4pnjOUaRAxQ+oz0pl1qNnYy28d5dQwPcyeXAsjhTI+M7V9TXLay8D6vqTalbxXUtqIzZwXFz5C7CuWZT0zuz+Qod0hnVTafZ3Mqy3FpBLIpyryRBiD7EipFt4UuHnWNRM6hWcDkgZwM/ifzryXUr5dQ1e0v9H0u6abSNMimIguEYpI21lSQs6kgRgjoeXrc1x7HWvFOl3FrZ2mom701nQS3nlY+dNrDrkjJ6e9PXb+uv+Qn/X4f5ndS6fZT3KXE9pBJOn3ZXiBZfoSMirFcRr2laYn/AAj8WvS29xeLJHDNLLJtMihWznnkZrtYwqxqI8bAAFx0xSDqOooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAI5beGdkaaGOQxtuQuoO0+o9DUlZGuT+TdaYP7cj0vzLoL5TojfbOD+6G7oT1454rXoAKo6hZaVLifVbazfb8okuY0OPbLVerifG1nBfeJNAt7vWGs4XllLQ4hKnETkMRIjfT0/GgaOql0zTL4pNNZWlyQoCSPEr8dsHHSpZ7K1ujGbm2hmMRyhkjDbD7Z6VzWsTS2uvaVYWmvDT7eaCRZECw9FA2suV4Jz9PanXl3drezWt1rzaf9jgR0l8uIfaiQcsQwPGRjC4PvRdCR0J02xMrSmytzI7BmcxLliOhJxyRU0MMVvEIreJIo16IihQPwFebf21r17fqR4gaxEssMb2vkwsYC8BcgErnO4cZz3613nh+9fUfDmn3kzrJLPbo7snQsVGf1o7gXHt4ZSxkhjcsuxiyg5X0PtUNzpen3rq95Y21wyjCtLCrED05FeX+Oro6X4s1EwRIyG1W8vi6bt1sBsKj0y2Dx/drc1PSNEh03wzDqK2/mq0MTebLglNpJzzyM0LUHodsthZpc/aEtIFm27fNEYDbfTPXFQPoWkSEGTS7JyBgFrdDgflXAeKLaFvFmoCx06K7dNNhcSJclXtvmceYqjliAAeOeBSapbWOqeMGd5tNurU6fbtPNPOVfaS4LxgHG7AHX2pXHY7e4XxBFOY9Mh0pbVcCISNIrAY6YAwPwqUWEF3DC3iGz06W75UfIHX6KXGam0aeC50OynszIbeSBGiMn3ipUYz74rG8VnT4b3SJ7l4I7gX0IDSMAwXJ9e1VazsTfS50qqEUKoCqBgADgU2SKOXb5savsbcu5c7T6j3pwIZQVIIPII71wvjGGDSdQurpYFeTWrQ2Ue7PM+fkH4g5/4BSGjs5rCzuZlmuLSCWVRtV5IwzAemTVa407RZJooruzsHl27YkkiQttHYAjp9K4nW9Sn8K6ethot20A0i0VpRNcRIkjEE8h1Z3zznBX61va/dac82g3c8loJzdRMsjMu4KVPQnnFGl9O4a21Ohk0+zl8vzbSB/JGI90QOweg44pJNNsZY4o5bK3dIf9WrRKQn0GOK5nxBrbafcav5mtfY4Rpq3Fq37v5Xy+SuVO7OF4OazL7xBrlzfxpYXcUMaWcUyM1zDEs5YZLEOjZGePlIoHb+v69Tup7CzuWVrm0gmZSCpkjDEY9M1Yrite1fVES1nt9Ut7ZFtBNcRQTxBgxGdw8xTvX0A2k46111jKZtPt5S7OXjVtzLtLZHUjt9KBBc2Npe7ftlrDcbDlfNjDbT7ZqNrPTpriXdb2skxQJLlFLFOwPfHHQ1i6rezSeIJLKTWjo0EFusyMqx5mJJByZARgY6DB561iXjTDW7u7sPEEcUlvosc8k0cUWboqXIZsg4X2GOvUUDs2dsdK09jGWsbYmJdkeYV+RfQccCj+y9P5/wBBtvmj8o/uV5T+706e1cP4k8XXkVuLnTL0QtbWaXEoaeFInLDIGGVmf/gJX61b1PUtRm1O8ey8QC0gg0pL1YUSJwX+buRnYcDPOfQigErm9q/hq21aGCHzntIYVKiO3iiwVPBGWQleP7pFX302ylhhintIZkgAEYljDbMema5DUvEF3NaXl0mtppgtrBLiBFRGW4ZkJJO4EkZGMLiqdzrPiH7XDbwXwTyrKKYSTXEMP2hmBJLBozkA8fLtp7C8/wCuh3t1a2UoWW9gt3EPKvMinZ7gnpUkEcMUCJapGkQHyrGAFx7YridY1O4vbO9W+1eHTVhsUkEKrG8dyWQlslxllzx8uDXS+F3WTwnpTIysptI+V6fdFFt/67h2/rsX57K1unR7m2hmaP7jSRhiv0z0qN9LsJFjV7G2ZY8bAYVOzHTHHFc54h1h9MvNXSfWTYxLpqz25byxskBcHbuU5zheDmqmr61qLWtjc2WrwxxCyWadY5YlkJIzuw6kMPYbTnvS6XHbU6m/s9IlmhbU7azkkkYRRGeNWLHkhRkex4qU6ZYFmJsrYlo/KY+UvKf3enT2rntV1EQvoN0+r7BJLEJIZliUSBgfnII3KfoRVfW77UDruoLY+IfslvbaaLxYlSJ8tlu5GdpwM8/QigS1Ztat4attWhgh817SGEFRHbxRYKngjLISv/ASDWtDEkECQxDakahVHoBXE6j4gu5rS7ul1uPTBbWCXECKiMtwzISSdwJIBGMLg11mitK+h2b3M73ErwqzyuACxIz0AA/SnZoOxLc6dZXro15Z29wyfdMsSsV+mRxUdxpGm3cvm3en2s8mMb5YFY49MkVynizQ9JfXbK61OSN5Ly7jj2yvt2oEfIHPIJwaxtS0O0i8WRafM9hPBb6bbpPJeTFZNgZgWQZxuwO/tU30/rsO2p6MNLsAWIsbbLp5bfuV+ZP7p46e1JJpOnTJEsthausIxGGhUhB7ccV5rrs9jIbaXQZJXiutNjOqlnBeGy4xJ14fGenbJ7DOprOheHjcaRcmaCW3u5oYoQ0vAhWNsYOeQeDT/wA/1/r8Rf1/X9djqrs+IhdONPj0s24+55zyBvxwMVJFpMN7Es2u6dp0150ZkiDjHblhmrOl2Q03SbWyWRpRbxLGJG6tgYzXB+JbC1vfiFKlwNPMP2KE3H2qYq6rvcExjON2PX2oeg1qd1JpGmyujy6fau0YCozQKSoHQDjjFOutLsL11a9sba4ZRhTLCrkD05FcUsPhvUtbsZ7xozbto8ckTXMu1wN3yseeuO9Nv4LC+0Tw7P4hEJu3njRmnk2s8WT15HUYJpta2F/X4XO3n0ywuvL+02VvN5QxH5kStsHtkcVaAwMDgVwvibQdF/tbT7i9eFhdXMUSo74Cxqj8DnkE4P5V2Gl2Q03SbWyWRpRbxLGHbq2BjNJa3AffWltfWMttfIskEi4dW4GKr3tjpUkaS6nb2cixqFWS5RWwPq1ZnjiR18OpGuRFPdwQzkdomkUN+hrL8b2UF5rXh61uNWayga4cNCBCVOIXIYiRG+np+NW0+RPz/wAg62Ote0sb2KIyW9vcRphoiyKwX3X0/CkudL0+9kEl5Y21w6jAaWFWIH1IrAvLh4r5rFdY/sm3tLZJIXVIgLjOc53LjAwOFA61UutWv7m40Ayax/ZLX9u5uYdsfBCggrvGQcnvke1QB2gAAAAwB0AqhqFppEssLapbWckkjCKJriNWLMckKMj2PFWrVJIrSJJ5/tEiqA0pULvPrgcflWD4lvFstS0mU6msA+1oj2z+XtdTn5vmG4EeoIo6pB0Nk6ZYFiTY2xLR+UT5K8p/d6dPah9MsJYYopLK3eOH/Vo0SkJ9BjiuX1u9v21+8Sx8Q/ZLeDTRdrEqRPlgW5yRnacc/oRVC/8AEWt3moQrp9zDbR/YorhD9pijWZmBznejEqCAPlIo6XHY7iTTrKWR5JbO3d3Ty2ZolJZf7pOOR7VWu9O0Rfs63tlYDcwhgEsKdeSFXI+vArjvEviLVLSS0ax1X99cWW67t7fy3SzU4zdZK5ZR/d79h1rX1S/S2XQZxryzI08SPv8AJ23Ctn94flyD7qQKdvzF0udHJptjMqLNZW8gjACBolO3HTHHFPuLO2vI1S7toZ0U5VZYwwB9cGuY8ValfQ3UTabqsMEEcJlljSaJZD6N86sGXA6DBJ71W1Eyza3ot1J4kvNPgmsJCWCworMPLOSHQ4JB6e3FIdjr57G0ulRbq1hmEZygkjDbfpnpSPp9lLK0klpA8jp5bO0Sksv90nHT2ri9Z13V5dbEGj30aWqWsc0MpuYYxc7s5Y7o23DgfdK9afeajqtxqEnl6+lkI9JW8eGFYpF8wZ6FlzsPf8MEUBbWx2D6dYy2yW8lnbvBHykTRKVX6DGBVgAKoCgADgAdq4i+mkvdX0O/k8RXGmQ3GnyM3kmER7vkPBdG65Pftx3rs7WaO4tIpoJhPG6ArKCCHHrxxTEMurazmUSX0MEgi5DzIDs98npUaadpskUZSztWRQTGREpAB64471538Q9cnk1XUNFvNds9DsEsPMSO6t1kGoFs5T5uwwBheea5HUPiNeaR4Q8P2/hnVvLkg0xHljRINjsBjbmRgTgqQQoyK9/D5PiK9ODhLWXrZJq+9rfdfzM5TSdv66HuslnYmCO0ltrcxfwQtGu3j0WnS2NpOyGe1hkMYwheMHaPbPSvLfFmpTX954O16LxXDo9ncRS5uRDG8cTmPk5bg5+7g+lVPH/xIvtMFivhzWpHcWYndzBCi3PXB/eEE5weEHFRSynEVXCNOWsr3+JJNNp3drdBe0VrnrA06xS8t5UhjSW3RlhVeAitjdhRx2HNXK459VXU/wDhENXs5Y5ZruXYxhOVaNomLj6BlX8q7GvHqwlB2lvr+ZommroKKKKyGFFFFADZf9U30ool/wBU30ooAwqhSG2WYmOOESDrtUZFTVknyBq1s1v5O1i3MR+cnHO72oA0kWFJGEYRXPLbQMn60rfeNZluFEtsVC/aDK/m4+9jnr7dK02+8aAErUjsLO9s4DeWsFwYxlDLGG2/TPSsusi7eB7q+fUoI7qW1WM2cE9z5C7CuSynpndn8hSegzsxY2i3TXK2sInZdplEY3EemeuKzJY9ft5Wi0q30hLRTiJXaRSB9FXA/CvPL0R3eo3E9npIZzpNpKGN4xlss7/nAHLkKFJxzxXq9nKk9lDLFMs6MgIlU5D8daqwipDpcVysdzrFhYSXw+9IkQfGOmGYZrRrkfGFjotxqukPqYt/Na5VG8yTaTHtY4PPTNdZGFWNRHjYAAuOmKXQOo2SCGZkaaJJDG25Cyg7T6j0NMubCzvShvLWC4KHKebGG2/TPSm3Wo2djLbx3l1DA9zJ5cCyOFMj4ztXPU1y2svA+r6m2pQR3UtqIjZwT3PkLsK5ZlPTO7P5Ch3SGdT/AGZYB5XFlb7phtlbylzIPRuOfxpg0jTVeJ1061DQjEbCBcp9OOK8t1K/GoavaX+j6XdNNpGmRTEQXCMUkbaypKWdSQIwR0PL1ua49lrXinS57WztNR+16azoJbzysfOm1h1yRk/rTtr/AF5/5Cf9fh/mdxdaXp99IJL2xtrhwMBpYVcgfUirKqFUKoAAGAAOlcTr2laYv/CPxa9LBPeLJHDNLLJtMihWznnkZrtYwqxqI8bAAFx0xSDqOooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAI5beGdkaaGOQxtuQuoO0+o9DUlZGuT+TdaYP7cj0vzLoL5TojG84P7obuhPXjnitegAqjqFlpUuJ9VtrN9vyiS5jQ49stV6uJ8bWcF/4k0C3u9Xa0heWUtDiEqcROQxEiN9PT8aBo6mXTNMvik01jaXJCgJI8Kvx2wcdKmnsrW6MZubaGYxHMZkjDbD6jPSub1iaa117SrC014afbzW8iyIEh6KBtZcrwefp7Ut5d3a3s1rc682n/AGOBHSXy4x9qJByxDA8ZGMLg+9K6EdCdMsTK0psrcyOwZnMS5YjoScckVNDBFbxCK3iSKNeiIoUD8BXm39s69e36sPEDWImlhje18iFjAXgLkAlc53DjOe/Wu80C9fUfDun3kzrJLPbo7svQsVGf1p9wZce3hlZjJDG5ZdjFlByvofaoLnStPvXV7ywtrhlGFaWFWIHpyK8w8dXZ0vxXqJgiRkNqt5fF13brYDYVHpltp4/u1uanpGiw6Z4Zh1FYPMVoYm82XBZNpJzzyM0LUHodsthZpc/aEtIFn27PNEYDbfTPXFQPoWkSEGTSrJyBgFrdDgflXAeKLaI+LNQFjp0d26abC6yJdFXtvmceYqjliAAeOeBSapbWWqeMGd5tOubU6fbtPNPOVfaS4LxgHG7AHX2pXHbU7e4HiCKcx6ZDpItFwIhK8isBjpgLgfhUosILyGFvENnp0t390fIHX6KXGam0aeC50OynszI1vJAjRGT7xUqMZ98VjeKzp8N7pE9w8EdwL6EBpGAYLk+vaqtZ2Jvpc6VVCKFQBVAwABgCmyRRy7fNjV9jbl3KDtPqPenAhlBUgg8gjvS0hlebT7O5mWa4tIJZVG1XkiDMB6ZIqKbRtLuNnn6baS7FCpvgVtqjoBkcCrtFAFeTT7ObyvNtIH8kYj3RA7B6Djikk0yxljijlsrd0h/1StEpCfQY4/CrNFAFeews7pla5tIJmUgqZIwxGPTNWKKKAILmxtL3b9stYbjYcr5sYbafbNNfTrKWR5JLO3d5E8t2aJSWX+6Tjke1WaKAKh0rT2MZawtiYl2R5hX5F9BxwKX+y9P5/wBBtvmj8o/uV5T+706e1WqKAMbV/DVtq0MEPmvaQwqVEdvFFgqeCMshK8f3SKvvpllLDDFPaQzJAAIxLGG2Y9M1aooAgnsbS6aNrm1hmaPlDJGGKfTPSpIoYreJYoI0ijX7qIoAH4Cn0UAQz2VrdOj3NtDM0f3GkjDFfpnpUb6Xp8ixrJY2zLHjYGhUhMdMccVaooAqXOladeyCS8sLW4cDAaWFWIHpkilOmWBZibG2JaPymPkryn93p09qtUUAY2r+GrbVoYIfNe0hhBUJbxRYKngjLISv/ASDWtDEkECQxDCRqFUegFPooArXOnWV66PeWdvcMn3TLErFfpkcUy40fTLuXzbvTrSeTGN8sCscemSKuUUAVf7L08FiLG2y6eW37lfmT+6eOntSSaTp0yRLLp9q6wjEYaFSEHtxxVuigDIuz4iF040+PSzbj7nnSSBvxAGKfFpMN7Es2u6bp0150ZkiDjHblhmtSigCnJo+mSujy6daO8YCozQKSoHQDjjFOutL0++dXvbG2uGUYUywq5A9ORVqigCrPplhdeX9psrabyhiPzIlbYPbI4q0BgYHAoooAgvLODULOS1vIxLDKu11PcVHcaVp94U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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"C:/Users/Admin/Documents/Machine_learning_ifp/wells/notebooks/test_pozo.JPG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674d3b09",
   "metadata": {},
   "source": [
    "### The number of columns does not match the number of logs contained in the file. They are overlapped. Now, to fix it:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1994a3c",
   "metadata": {},
   "source": [
    "#### 1. Reading the .las file, without consideration of the header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eba65da0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = \"C:/Users/Admin/Documents/Machine_learning_ifp/wells/team_alwyn/DATASET_full/Dataset_Alwyn_MiniProjets/3_9a-2_wire_all.las\"\n",
    "\n",
    "with open(fp, 'r') as f:\n",
    "    well = f.readlines()\n",
    "\n",
    "well = well[55:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943a99af",
   "metadata": {},
   "source": [
    "#### 2. creating a list per every depth point, considerng the different logs. Then, splitting the list in order to have the correct number of colums. This is an iterative process.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "654d20fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = []\n",
    "for i in range(0, len(well), 6):\n",
    "    temp = []\n",
    "    for j in range(6):\n",
    "        if j == 0:\n",
    "            temp.append(float(well[i]))\n",
    "        else:\n",
    "            temp.extend([float(val) for val in well[i + j].split()])\n",
    "    observations.append(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be8d3ea",
   "metadata": {},
   "source": [
    "####  3. Saving these lists as a data frame and adding the corresponding name of every colums, according to the original file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a1f0706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>CALI_D</th>\n",
       "      <th>CALI_M</th>\n",
       "      <th>DT</th>\n",
       "      <th>GR_D</th>\n",
       "      <th>GR_I</th>\n",
       "      <th>GR_L</th>\n",
       "      <th>ILD</th>\n",
       "      <th>LLD</th>\n",
       "      <th>LLS</th>\n",
       "      <th>MINV</th>\n",
       "      <th>MLL</th>\n",
       "      <th>MNOR</th>\n",
       "      <th>MSFL</th>\n",
       "      <th>NPHI</th>\n",
       "      <th>POTA</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>SFL</th>\n",
       "      <th>SGR</th>\n",
       "      <th>SP_I</th>\n",
       "      <th>SP_L</th>\n",
       "      <th>TENS_D</th>\n",
       "      <th>THOR</th>\n",
       "      <th>URAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2999.9940</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>91.649</td>\n",
       "      <td>38.745</td>\n",
       "      <td>39.465</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.514</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.500</td>\n",
       "      <td>12.946</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>51.612</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",
       "      <th>1</th>\n",
       "      <td>3000.1464</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>82.759</td>\n",
       "      <td>39.847</td>\n",
       "      <td>37.834</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.537</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.501</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>51.552</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",
       "      <th>2</th>\n",
       "      <td>3000.2988</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>86.213</td>\n",
       "      <td>40.398</td>\n",
       "      <td>36.855</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.534</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.489</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>51.262</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",
       "      <th>3</th>\n",
       "      <td>3000.4512</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>84.139</td>\n",
       "      <td>39.594</td>\n",
       "      <td>35.691</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.518</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.505</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>51.262</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",
       "      <th>4</th>\n",
       "      <td>3000.6036</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>89.080</td>\n",
       "      <td>38.897</td>\n",
       "      <td>35.538</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.502</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.513</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>51.664</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",
       "      <th>5</th>\n",
       "      <td>3000.7560</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>89.324</td>\n",
       "      <td>38.553</td>\n",
       "      <td>35.946</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.486</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.499</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>52.124</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",
       "      <th>6</th>\n",
       "      <td>3000.9084</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>88.878</td>\n",
       "      <td>38.189</td>\n",
       "      <td>36.368</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.470</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.486</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>52.459</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",
       "      <th>7</th>\n",
       "      <td>3001.0608</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>86.684</td>\n",
       "      <td>37.534</td>\n",
       "      <td>36.790</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.454</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.505</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>52.766</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",
       "      <th>8</th>\n",
       "      <td>3001.2132</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>88.709</td>\n",
       "      <td>35.698</td>\n",
       "      <td>37.107</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.438</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.521</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>52.975</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",
       "      <th>9</th>\n",
       "      <td>3001.3656</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>85.527</td>\n",
       "      <td>33.022</td>\n",
       "      <td>36.959</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>5.422</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",
       "      <td>-999.25</td>\n",
       "      <td>-9.9925</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>2.490</td>\n",
       "      <td>20.000</td>\n",
       "      <td>-999.25</td>\n",
       "      <td>52.888</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>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pandas.DataFrame(observations)\n",
    "\n",
    "l = ['DEPTH','CALI_D','CALI_M','DT','GR_D', 'GR_I','GR_L', 'ILD','LLD','LLS','MINV','MLL','MNOR','MSFL','NPHI','POTA','RHOB','SFL','SGR','SP_I','SP_L','TENS_D','THOR','URAN']\n",
    "n = dict(zip(range(len(l)), l))\n",
    "\n",
    "df_final = df.rename(n, axis=1)\n",
    "\n",
    "df_final_show= df_final.head(10)\n",
    "\n",
    "display(HTML(df_final_show.to_html()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87edca9",
   "metadata": {},
   "source": [
    "### Prior to conducting additional data analysis or machine learning, it is critical to identify and handle missing data. In this case, we have -999.25 as \"not a number\" value. \n",
    "\n",
    "#### 4. We are working in Python: we need to read those values as \"NaN\". If the missing data in your dataframe is represented by something other than NaN, we can do the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c04f5fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_save = df_final.replace(-999.25, np.NaN)\n",
    "#display(HTML(df_save.to_html())) , if you want to display the data frame and confirm the change of the NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908a908b",
   "metadata": {},
   "source": [
    "#### 5. Once the missing values are now NaN type, we can work properly with the data. A visualization of the statistics of the data frame is recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8023b598",
   "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>DEPTH</th>\n",
       "      <th>CALI_D</th>\n",
       "      <th>CALI_M</th>\n",
       "      <th>DT</th>\n",
       "      <th>GR_D</th>\n",
       "      <th>GR_I</th>\n",
       "      <th>GR_L</th>\n",
       "      <th>ILD</th>\n",
       "      <th>LLD</th>\n",
       "      <th>LLS</th>\n",
       "      <th>...</th>\n",
       "      <th>NPHI</th>\n",
       "      <th>POTA</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>SFL</th>\n",
       "      <th>SGR</th>\n",
       "      <th>SP_I</th>\n",
       "      <th>SP_L</th>\n",
       "      <th>TENS_D</th>\n",
       "      <th>THOR</th>\n",
       "      <th>URAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6004.000000</td>\n",
       "      <td>2297.000000</td>\n",
       "      <td>2297.000000</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>5778.000000</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>2291.000000</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>2297.000000</td>\n",
       "      <td>2297.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>2297.000000</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>2293.000000</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>2297.00000</td>\n",
       "      <td>2297.000000</td>\n",
       "      <td>2297.000000</td>\n",
       "      <td>2297.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3457.422300</td>\n",
       "      <td>9.754584</td>\n",
       "      <td>9.893040</td>\n",
       "      <td>84.143494</td>\n",
       "      <td>74.691151</td>\n",
       "      <td>67.498635</td>\n",
       "      <td>77.455551</td>\n",
       "      <td>7.116857</td>\n",
       "      <td>20.333988</td>\n",
       "      <td>8.712920</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.074376</td>\n",
       "      <td>2.435803</td>\n",
       "      <td>2.435459</td>\n",
       "      <td>9.481280</td>\n",
       "      <td>74.732252</td>\n",
       "      <td>64.410952</td>\n",
       "      <td>80.63885</td>\n",
       "      <td>7717.730772</td>\n",
       "      <td>5.933271</td>\n",
       "      <td>1.897731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>264.162342</td>\n",
       "      <td>1.482439</td>\n",
       "      <td>1.766285</td>\n",
       "      <td>10.856137</td>\n",
       "      <td>32.968277</td>\n",
       "      <td>26.034678</td>\n",
       "      <td>30.239909</td>\n",
       "      <td>6.841553</td>\n",
       "      <td>77.041639</td>\n",
       "      <td>16.818884</td>\n",
       "      <td>...</td>\n",
       "      <td>4.978360</td>\n",
       "      <td>0.886555</td>\n",
       "      <td>0.165004</td>\n",
       "      <td>5.787746</td>\n",
       "      <td>30.640902</td>\n",
       "      <td>4.474778</td>\n",
       "      <td>4.57327</td>\n",
       "      <td>454.789017</td>\n",
       "      <td>3.491880</td>\n",
       "      <td>1.623510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2999.994000</td>\n",
       "      <td>8.517000</td>\n",
       "      <td>7.764000</td>\n",
       "      <td>62.619000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>18.740000</td>\n",
       "      <td>21.546000</td>\n",
       "      <td>1.514000</td>\n",
       "      <td>1.352000</td>\n",
       "      <td>1.389000</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.992500</td>\n",
       "      <td>0.315000</td>\n",
       "      <td>1.438000</td>\n",
       "      <td>0.182000</td>\n",
       "      <td>20.856000</td>\n",
       "      <td>48.961000</td>\n",
       "      <td>71.88000</td>\n",
       "      <td>4622.819800</td>\n",
       "      <td>-0.686000</td>\n",
       "      <td>-0.481000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3228.708150</td>\n",
       "      <td>8.681000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>76.687000</td>\n",
       "      <td>47.239250</td>\n",
       "      <td>45.532250</td>\n",
       "      <td>53.374500</td>\n",
       "      <td>3.836750</td>\n",
       "      <td>4.148000</td>\n",
       "      <td>3.838000</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.992500</td>\n",
       "      <td>1.964000</td>\n",
       "      <td>2.357000</td>\n",
       "      <td>9.259500</td>\n",
       "      <td>49.850000</td>\n",
       "      <td>61.649750</td>\n",
       "      <td>77.26900</td>\n",
       "      <td>7522.717800</td>\n",
       "      <td>2.954000</td>\n",
       "      <td>1.049000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3457.422300</td>\n",
       "      <td>9.163000</td>\n",
       "      <td>9.410000</td>\n",
       "      <td>82.158500</td>\n",
       "      <td>72.531000</td>\n",
       "      <td>65.260000</td>\n",
       "      <td>74.768000</td>\n",
       "      <td>5.899000</td>\n",
       "      <td>5.929000</td>\n",
       "      <td>5.576000</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.992500</td>\n",
       "      <td>2.435000</td>\n",
       "      <td>2.454000</td>\n",
       "      <td>10.075000</td>\n",
       "      <td>71.307000</td>\n",
       "      <td>63.731000</td>\n",
       "      <td>79.69500</td>\n",
       "      <td>7711.354000</td>\n",
       "      <td>5.628000</td>\n",
       "      <td>1.589000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3686.136450</td>\n",
       "      <td>9.991000</td>\n",
       "      <td>10.373000</td>\n",
       "      <td>90.362000</td>\n",
       "      <td>96.324000</td>\n",
       "      <td>86.137500</td>\n",
       "      <td>96.841000</td>\n",
       "      <td>7.711000</td>\n",
       "      <td>11.684000</td>\n",
       "      <td>9.889000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185087</td>\n",
       "      <td>2.985000</td>\n",
       "      <td>2.558000</td>\n",
       "      <td>10.075000</td>\n",
       "      <td>91.860000</td>\n",
       "      <td>66.936750</td>\n",
       "      <td>83.38600</td>\n",
       "      <td>7998.340800</td>\n",
       "      <td>8.534000</td>\n",
       "      <td>2.238000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3914.850600</td>\n",
       "      <td>15.373000</td>\n",
       "      <td>16.999000</td>\n",
       "      <td>139.360000</td>\n",
       "      <td>297.355000</td>\n",
       "      <td>196.661000</td>\n",
       "      <td>195.842000</td>\n",
       "      <td>138.310000</td>\n",
       "      <td>2358.471900</td>\n",
       "      <td>439.094000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.641370</td>\n",
       "      <td>10.219000</td>\n",
       "      <td>2.913000</td>\n",
       "      <td>65.434000</td>\n",
       "      <td>199.716000</td>\n",
       "      <td>76.378000</td>\n",
       "      <td>92.09800</td>\n",
       "      <td>9994.470700</td>\n",
       "      <td>17.502000</td>\n",
       "      <td>24.299000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             DEPTH       CALI_D       CALI_M           DT         GR_D  \\\n",
       "count  6004.000000  2297.000000  2297.000000  6004.000000  5778.000000   \n",
       "mean   3457.422300     9.754584     9.893040    84.143494    74.691151   \n",
       "std     264.162342     1.482439     1.766285    10.856137    32.968277   \n",
       "min    2999.994000     8.517000     7.764000    62.619000     0.295000   \n",
       "25%    3228.708150     8.681000     8.500000    76.687000    47.239250   \n",
       "50%    3457.422300     9.163000     9.410000    82.158500    72.531000   \n",
       "75%    3686.136450     9.991000    10.373000    90.362000    96.324000   \n",
       "max    3914.850600    15.373000    16.999000   139.360000   297.355000   \n",
       "\n",
       "              GR_I         GR_L          ILD          LLD          LLS  ...  \\\n",
       "count  6004.000000  2291.000000  6004.000000  2297.000000  2297.000000  ...   \n",
       "mean     67.498635    77.455551     7.116857    20.333988     8.712920  ...   \n",
       "std      26.034678    30.239909     6.841553    77.041639    16.818884  ...   \n",
       "min      18.740000    21.546000     1.514000     1.352000     1.389000  ...   \n",
       "25%      45.532250    53.374500     3.836750     4.148000     3.838000  ...   \n",
       "50%      65.260000    74.768000     5.899000     5.929000     5.576000  ...   \n",
       "75%      86.137500    96.841000     7.711000    11.684000     9.889000  ...   \n",
       "max     196.661000   195.842000   138.310000  2358.471900   439.094000  ...   \n",
       "\n",
       "              NPHI         POTA         RHOB          SFL          SGR  \\\n",
       "count  6004.000000  2297.000000  6004.000000  6004.000000  2293.000000   \n",
       "mean     -6.074376     2.435803     2.435459     9.481280    74.732252   \n",
       "std       4.978360     0.886555     0.165004     5.787746    30.640902   \n",
       "min      -9.992500     0.315000     1.438000     0.182000    20.856000   \n",
       "25%      -9.992500     1.964000     2.357000     9.259500    49.850000   \n",
       "50%      -9.992500     2.435000     2.454000    10.075000    71.307000   \n",
       "75%       0.185087     2.985000     2.558000    10.075000    91.860000   \n",
       "max       0.641370    10.219000     2.913000    65.434000   199.716000   \n",
       "\n",
       "              SP_I        SP_L       TENS_D         THOR         URAN  \n",
       "count  6004.000000  2297.00000  2297.000000  2297.000000  2297.000000  \n",
       "mean     64.410952    80.63885  7717.730772     5.933271     1.897731  \n",
       "std       4.474778     4.57327   454.789017     3.491880     1.623510  \n",
       "min      48.961000    71.88000  4622.819800    -0.686000    -0.481000  \n",
       "25%      61.649750    77.26900  7522.717800     2.954000     1.049000  \n",
       "50%      63.731000    79.69500  7711.354000     5.628000     1.589000  \n",
       "75%      66.936750    83.38600  7998.340800     8.534000     2.238000  \n",
       "max      76.378000    92.09800  9994.470700    17.502000    24.299000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_save.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dcca34a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6004 entries, 0 to 6003\n",
      "Data columns (total 24 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   DEPTH   6004 non-null   float64\n",
      " 1   CALI_D  2297 non-null   float64\n",
      " 2   CALI_M  2297 non-null   float64\n",
      " 3   DT      6004 non-null   float64\n",
      " 4   GR_D    5778 non-null   float64\n",
      " 5   GR_I    6004 non-null   float64\n",
      " 6   GR_L    2291 non-null   float64\n",
      " 7   ILD     6004 non-null   float64\n",
      " 8   LLD     2297 non-null   float64\n",
      " 9   LLS     2297 non-null   float64\n",
      " 10  MINV    2297 non-null   float64\n",
      " 11  MLL     2297 non-null   float64\n",
      " 12  MNOR    2297 non-null   float64\n",
      " 13  MSFL    2297 non-null   float64\n",
      " 14  NPHI    6004 non-null   float64\n",
      " 15  POTA    2297 non-null   float64\n",
      " 16  RHOB    6004 non-null   float64\n",
      " 17  SFL     6004 non-null   float64\n",
      " 18  SGR     2293 non-null   float64\n",
      " 19  SP_I    6004 non-null   float64\n",
      " 20  SP_L    2297 non-null   float64\n",
      " 21  TENS_D  2297 non-null   float64\n",
      " 22  THOR    2297 non-null   float64\n",
      " 23  URAN    2297 non-null   float64\n",
      "dtypes: float64(24)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "# if we want to see how many non-null values we have:\n",
    "\n",
    "df_save.info()\n",
    "\n",
    "#we can notice that we don't have null values. Remember: Null values represent \"nothing\" or \"no value,\" and are not the same as an empty string or zero. \n",
    "#Null values can be used to denote the absence of anything valuable. \"Not a Number\" , on the other hand,is the abbreviation for NaN. \n",
    "#they are not the same, but we will threaten equally for this purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a8d1f7",
   "metadata": {},
   "source": [
    "#### 5. We can also visualize the missing values with the missingno library. \n",
    "\n",
    "#### This is just an example. You should visualize and QC all your data as a prerequisite for any ML computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4c04fd2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_save) \n",
    "#we try with a matrix plot visualization, but you are invited to test the different visualizations available withint this library.\n",
    "#here we can see that all the logs have no missing values (remember NaN and null are different), so we see the full columns in gray.\n",
    "#Usually, this plot helps to understand how the missing values are distributed in the samples and it is especially useful for handling the well data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba25e068",
   "metadata": {},
   "source": [
    "#### 6. saving the data frame to csv (to handle the data more easily in subsequent processes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cbf40f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_save.to_csv ('C:/Users/Admin/Documents/Machine_learning_ifp/wells/team_alwyn/DATASET_full/3_9A-2_corrected.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab7c586",
   "metadata": {},
   "source": [
    "## Authors: Maria Betania Cedeño Tepedino.\n",
    "## Peer review: Imoleayo Fashagba"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}