[1-2]_Convert_SciGlass_GComp_file_to_Pandas_DataFrame_(wt%).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <u>Convert GComp.csv file to composition table as is to use by Pandas dataframe (wt%)</u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some notes here:\n",
    "### (1) SciGlass mdb files are publised by EPAM under ODC Open Database License (ODbL). Use the information and codes below following the EPAM's license and also at your own risk. Understand that there is no guarantee for any problems that may occur.\n",
    "### (2) The following code is written to work with csv files. Access 2.0 mdb files published by EPAM should be converted to csv files and then executed. Please convert the mdb files to csv files first and use them.\n",
    "### (3) File names and contents (presumed)\n",
    "    - Gcomp:      composition data\n",
    "    - SciGK:      property data together with typical compositions (wt% and mol%)\n",
    "    - Reference:  source data such as journals or patents\n",
    "    - Kod2Ref:    connection keys from \"kod\" to \"Refer_ID\" to quote source data in the \"Reference\" file\n",
    "\n",
    "### (4) You need only \"GComp.csv\" file in this notebook.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libralies\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(422879, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Kod</th>\n",
       "      <th>GlasNo</th>\n",
       "      <th>Composition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>204</td>\n",
       "      <td>20000</td>\n",
       "      <td>SiO260.08100.100.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>205</td>\n",
       "      <td>20001</td>\n",
       "      <td>SiO260.0835.945.35P2O5141.9421.2311.35...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205</td>\n",
       "      <td>20002</td>\n",
       "      <td>SiO260.0830.1739.99P2O5141.9420.1711.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>205</td>\n",
       "      <td>20003</td>\n",
       "      <td>SiO260.0839.48.64P2O5141.9416.998.97Z...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>205</td>\n",
       "      <td>20004</td>\n",
       "      <td>SiO260.0838.3348.24P2O5141.9416.088.57...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Kod  GlasNo                                        Composition\n",
       "0  204   20000                             SiO260.08100.100.\n",
       "1  205   20001  SiO260.0835.945.35P2O5141.9421.2311.35...\n",
       "2  205   20002  SiO260.0830.1739.99P2O5141.9420.1711.3...\n",
       "3  205   20003  SiO260.0839.48.64P2O5141.9416.998.97Z...\n",
       "4  205   20004  SiO260.0838.3348.24P2O5141.9416.088.57..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load \"GComp.csv\" file\n",
    "\n",
    "df = pd.read_csv('data_SciGlass/GComp.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\x7fSiO2\\x7f60.08\\x7f35.9\\x7f45.35\\x7fP2O5\\x7f141.94\\x7f21.23\\x7f11.35\\x7fZrO2\\x7f123.22\\x7f15.12\\x7f9.31\\x7fNa2O\\x7f61.98\\x7f27.76\\x7f33.99\\x7f'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data in the 2nd row and the 3rd column as an example\n",
    "\n",
    "comp_example_1 = df.iloc[1, 2]\n",
    "comp_example_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The example above shows that element, molecular mass, wt%, and mol% are lined up in one set between the separater \"\\x7f\". Then split them by the separater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                             [, SiO2, 60.08, 100., 100., ]\n",
       "1         [, SiO2, 60.08, 35.9, 45.35, P2O5, 141.94, 21....\n",
       "2         [, SiO2, 60.08, 30.17, 39.99, P2O5, 141.94, 20...\n",
       "3         [, SiO2, 60.08, 39., 48.64, P2O5, 141.94, 16.9...\n",
       "4         [, SiO2, 60.08, 38.33, 48.24, P2O5, 141.94, 16...\n",
       "                                ...                        \n",
       "422874    [, SiO2, 60.08, 55.26, 62.01, Al2O3, 101.96, 1...\n",
       "422875    [, SiO2, 60.08, 64., 72.95, Al2O3, 101.96, 16....\n",
       "422876    [, SiO2, 60.08, 61.9, 71.78, Al2O3, 101.96, 15...\n",
       "422877    [, SiO2, 60.08, 58.2, 66.83, Al2O3, 101.96, 29...\n",
       "422878    [, SiO2, 60.08, 62.2, 68.28, Al2O3, 101.96, 20...\n",
       "Name: Composition, Length: 422879, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split by \"\\x7f\"\n",
    "\n",
    "comp_0 = df['Composition'].str.split('\\x7f')\n",
    "comp_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'SiO2', '60.08', '35.9', '45.35', 'P2O5', '141.94', '21.23', '11.35', 'ZrO2', '123.22', '15.12', '9.31', 'Na2O', '61.98', '27.76', '33.99', '']\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "# Check the data in the 2nd row and the 3rd column as an example\n",
    "\n",
    "comp_ex = list(comp_0[1])\n",
    "print(comp_ex)\n",
    "print(len(comp_ex))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Composition,  wt%\n",
      "SiO2 35.9\n",
      "P2O5 21.23\n",
      "ZrO2 15.12\n",
      "Na2O 27.76\n"
     ]
    }
   ],
   "source": [
    "# Extract composition and mol% data utilizing the remainder divided by 4\n",
    "\n",
    "print('Composition,  wt%')\n",
    "for i in range(len(comp_ex)//4):\n",
    "    print(comp_ex[i * 4 + 1], comp_ex[i * 4 + 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now element, molecular mass, wt%, and mol% can be extraced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162\n"
     ]
    }
   ],
   "source": [
    "# Check the number of data in the longest row.\n",
    "\n",
    "max_compostion_num = 0\n",
    "line_length = []\n",
    "\n",
    "for j in list(comp_0):\n",
    "    if len(j) > max_compostion_num:\n",
    "        max_compostion_num = len(j)\n",
    "    line_length.append(len(j))\n",
    "\n",
    "print(max_compostion_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The longest row in the \"GComp\" contains 40 compositions (162 devided by 4) and more than the number of composition in \"SciGK\" (that is only 17). \"GComp\" should be used as the composition data, so make the matrix that composition forms a column, and glass forms a row with \"GlasNo\" (glass ID) and \"Kod\" (Reference_ID)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "275302"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of the lomgest row\n",
    "\n",
    "max_idx = np.argmax(line_length)\n",
    "max_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Composition, wt%\n",
      "\n",
      "SiO2 48.32\n",
      "B2O3 14.19\n",
      "Al2O3 2.51\n",
      "Li2O 2.78\n",
      "Na2O 14.07\n",
      "MgO 1.84\n",
      "CaO 4.4\n",
      "TiO2 0.96\n",
      "Se 0.016\n",
      "Rb2O 0.053\n",
      "SrO 0.095\n",
      "Y2O3 0.12\n",
      "ZrO2 0.63\n",
      "MoO3 0.86\n",
      "RuO2 0.5\n",
      "Rh2O3 0.12\n",
      "PdO 0.25\n",
      "Ag2O 0.021\n",
      "CdO 0.015\n",
      "SnO 0.012\n",
      "Sb2O3 0.002\n",
      "TeO2 0.12\n",
      "Cs2O 0.57\n",
      "BaO 0.44\n",
      "La2O3 0.41\n",
      "CeO2 0.33\n",
      "Pr2O3 0.28\n",
      "Nd2O3 1.24\n",
      "Sm2O3 0.24\n",
      "Eu2O3 0.025\n",
      "Gd2O3 0.061\n",
      "UO3 1.21\n",
      "Cr2O3 0.47\n",
      "MnO 0.33\n",
      "FeO 1.79\n",
      "NiO 0.28\n",
      "CuO 0.003\n",
      "ZnO 0.002\n",
      "K2O 0.044\n",
      "P2O5 0.36\n",
      "\n",
      "Total wt% =  99.969\n"
     ]
    }
   ],
   "source": [
    "# Check the comosition and wt% in the longest row and its total\n",
    "\n",
    "comp_longest = comp_0[max_idx]\n",
    "comp_total = 0\n",
    "print('Composition, wt%\\n')\n",
    "\n",
    "for i in range(len(comp_longest)//4):\n",
    "    print(comp_longest[i * 4 + 1], comp_longest[i * 4 + 3])\n",
    "    comp_total = comp_total + float(comp_longest[i * 4 + 3])\n",
    "    \n",
    "print('\\nTotal wt% = ', comp_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a list of element contained in \"GComp\" without any omission and duplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row number in transaction :   0\n",
      "row number in transaction :   50000\n",
      "row number in transaction :   100000\n",
      "row number in transaction :   150000\n",
      "row number in transaction :   200000\n",
      "row number in transaction :   250000\n",
      "row number in transaction :   300000\n",
      "row number in transaction :   350000\n",
      "row number in transaction :   400000\n",
      "\n",
      "complete!\n"
     ]
    }
   ],
   "source": [
    "composition_items = []\n",
    "\n",
    "for k in range(len(df)):\n",
    "    composition_k = list(comp_0[k])\n",
    "    for l in range(len(composition_k)//4):\n",
    "        composition_item_kl = composition_k[l * 4 + 1]\n",
    "        composition_items.append(composition_item_kl)             # Add composition name to the list\n",
    "        composition_items_update = set(composition_items)         # Delete the duplication\n",
    "        composition_items = list(composition_items_update)        # Convert back to list\n",
    "    \n",
    "    if k%50000 == 0:                                              # Show the transaction status\n",
    "        print('row number in transaction :  ', k)\n",
    "\n",
    "#composition_items_update\n",
    "\n",
    "print('\\ncomplete!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements (all) =   726 \n",
      "\n",
      " (NH4)2SO4 (NH4)3PO4 Ag Ag2CO3 Ag2MoO4 Ag2O Ag2S Ag2SO4 Ag2Se Ag2Se5 Ag2Te Ag4SSe AgAsS2 AgBr AgCl AgF AgGaS2 AgI AgNO3 AgPO3 Al Al(PO3)3 Al2(SO4)3 Al2N3 Al2O3 Al2O3+Fe2O3 Al2S3 Al2Y2O6 AlCl3 AlF3 AlN AlPO4 Am Am2O3 AmO2 Ar As As2O3 As2O5 As2S3 As2S5 As2Se3 As2Se5 As2Te As2Te3 As2Te5 AsBr3 AsF5 AsI3 AsS AsS2 AsSBr AsSI AsSe AsSe2 AsSeI AsTe AsTe3 Au Au2O Au2O3 AuCl AuCl3 B B2O3 B2S3 B2Se3 BF3 BN BOF BPO4 BS2 Ba Ba(H2PO4)2 Ba(PO3)2 Ba3N2 BaB2O4 BaBr2 BaCl2 BaF2 BaGeO3 BaHPO4 BaI2 BaO BaO2 BaPO3F BaS BaSO4 BaSe Be BeF2 BeO BeSO4 Bi Bi2O3 Bi2O5 Bi2S3 Bi2Se3 Bi2Te3 BiBr3 BiCl3 BiF3 BiI3 BiNbO4 BiOBr BiOCl BiOF BiPO4 BiTe Br C C2H5OH C6H12O6 CO2 Ca Ca(NO3)2 Ca3N2 CaBr2 CaC2 CaCO3 CaCl2 CaF2 CaI2 CaO CaO+MgO CaS CaSO4 Cd Cd(NO3)2 CdAs2 CdBr2 CdCl2 CdF2 CdGeO3 CdI2 CdO CdS CdSO4 CdSe CdTe Ce Ce2O3 Ce2S3 CeCl3 CeF3 CeF4 CeO CeO2 CeSe Cl Cl2 Co Co2O3 Co3O4 CoBr2 CoCl2 CoF2 CoO CoS CoSO4 Cr Cr2O3 Cr2Se3 Cr3O4 CrCl3 CrF3 CrO CrO3 Cs Cs2O Cs2S Cs2SO4 CsBr CsCl CsF CsHSO4 CsI Cu Cu2MoO4 Cu2O Cu2S Cu2Se Cu2Te Cu2WO4 Cu3PO4 CuBr CuCl CuCl2 CuF2 CuI CuNbOF5 CuO CuPO3 CuS CuSO4 Dy Dy2O3 Dy2S3 Dy2Se3 DyCl3 DyF3 Er Er2(SO4)3 Er2O3 Er2S3 ErCl3 ErF3 ErI3 ErO2 ErPO4 Eu Eu2O3 EuCl2 EuCl3 EuF2 EuF3 EuO EuS F F2 Fe Fe2(SO4)3 Fe2O3 Fe3O4 FeBr2 FeCl2 FeCl3 FeF2 FeF3 FeO FeO+Fe2O3 FeS FeS2 FemOn Ga Ga2O3 Ga2S3 Ga2Se3 Ga2Te3 GaBr3 GaF3 GaI3 GaS2 GaSe GaSe2 GaTe GaTe3 Gd Gd2(SO4)3 Gd2O3 Gd2S3 GdCl3 GdF3 Ge Ge2S3 Ge2Se2Te Ge2Se3 GeAs GeBr4 GeF4 GeI2 GeI4 GeO GeO2 GeS GeS2 GeS3 GeSBr2 GeSb GeSb2 GeSe GeSe2 GeSe3 GeSe4 GeSeBr2 GeTe GeTe2 GeTe4 GeTe4.3 H H2 H2O H2S H2SO4 H3BO3 HF+H2O Hf HfF4 HfO2 Hg Hg2O HgBr2 HgCl2 HgF HgI2 HgO HgS HgSe HgTe Ho Ho2(SO4)3 Ho2O3 Ho2S3 Ho2Se3 HoAs HoCl3 HoF3 HoSe I In In2O3 In2S3 In2Se3 In2Te6 InF3 InI3 InSe InTe Ir K K2CO3 K2NbOF5 K2O K2S K2S2O8 K2SO4 K2SiF6 K3N KAsO3 KBF4 KBO2 KBr KCl KClO4 KF KHC4H4O6 KHF2 KHSO4 KI KNO3 KOH KPO3 La La2(SO4)3 La2O3 La2S3 La2Se3 LaBr3 LaCl3 LaF3 Li Li2B4O7 Li2CO3 Li2MoO4 Li2O Li2O+Na2O+K2O Li2S Li2SO3 Li2SO4 Li2Se Li2WO4 Li3N Li3PO4 LiBr LiCl LiF LiHSO4 LiI LiNO3 LiSiON Lu Lu2O3 LuF3 Mg Mg3N2 MgBr2 MgCO3 MgCl2 MgF2 MgI2 MgO MgS MgSO4 Mn Mn2O3 Mn2O7 Mn3O4 MnBr2 MnCl2 MnF2 MnNbOF5 MnO MnO2 MnS MnSO4 MnSe Mo Mo2O3 Mo2O5 MoF5 MoO MoO2 MoO3 MoO3+WO3 N N2 N2O5 NH4Cl NH4F NH4H2PO4 NH4HF2 NH4NO3 NH4PF6 NO2 NO3 Na Na2B4O7 Na2CO3 Na2HPO4 Na2MoO4 Na2O Na2O+K2O Na2S Na2SO4 Na2Se Na2WO4 Na3AlF6 Na3N Na3PO4 Na4As2O7 NaBF4 NaBH4 NaBr NaCN NaCl NaClO3 NaF NaHSO4 NaI NaPO3 Nb Nb2O3 Nb2O5 NbF5 NbO2F NbPO5 Nd Nd2(SO4)3 Nd2O3 Nd2S3 NdCl3 NdF3 Ni Ni2O3 NiBr2 NiCl2 NiF2 NiI2 NiO NiSO4 NiTe Np2O3 NpO2 O OH P P2O3 P2O5 P2S3 P2S5 P2Se3 P2Se5 P3N5 P4S3 P4S7 P4Se10 P4Se3 P4Se4 PCl5 PF3 PF5 PF6 PON Pb Pb(PO3)2 Pb3(PO4)2 Pb3O4 PbBr2 PbCl2 PbF2 PbF3 PbI2 PbO PbO2 PbS PbSO4 PbSe PbTe Pd PdO Pr Pr2(SO4)3 Pr2O3 Pr2S3 Pr2Se3 Pr6O11 PrCl3 PrF3 PrO2 Pt PtCl2 PtO PtO2 Pu2O3 PuO2 R2O R2O3 RO Ra Rb Rb2O Rb2S Rb2SO4 RbBr RbCl RbF RbI RbNO3 RbV2O5 Re2O7 ReO3 Rh Rh2O3 RhO2 RmOn Ru RuO2 S SO2 SO3 SO4 Sb Sb(PO3)3 Sb2O3 Sb2O5 Sb2S3 Sb2Se3 Sb2Te2 Sb2Te3 Sb2Te4 SbBr3 SbCl3 SbF3 SbI3 SbO SbO2 SbS2 SbSI Sc Sc(PO3)3 Sc2O3 ScF3 Se Se4I SeO2 SeO3 SeS2 Si Si2N4 Si3N4 SiC SiCl4 SiF4 SiO SiO2 SiS2 SiSe2 Sm Sm2(SO4)3 Sm2O3 Sm2S3 SmCl3 SmF3 Sn Sn2O3 Sn2S3 SnCl2 SnF2 SnF4 SnI2 SnI4 SnO SnO2 SnS SnS2 SnSe SnSe2 SnTe Sr SrB2O4 SrBr2 SrCl2 SrF2 SrO SrS SrSO4 SrSe Ta Ta2O3 Ta2O5 TaF5 TaO2F TaPO5 TaS2 Tb Tb2O3 Tb2S3 Tb2Se3 Tb3O7 Tb4O7 TbCl3 TbF3 TbO2 TcO2 Te TeCl4 TeF4 TeI TeO TeO2 TeO3 Th Th(SO4)2 ThCl4 ThF4 ThO2 Ti Ti2O3 Ti2S3 Ti3B4 TiC TiF3 TiF4 TiO TiO2 TiS2 Tl Tl2O Tl2O3 Tl2S Tl2S3 Tl2Se Tl2Se3 Tl2Te Tl2Te3 Tl2TiS3 TlAsS2 TlAsSe2 TlAsTe2 TlBr TlCl TlF TlI TlS TlS2 TlSe TlTe Tm Tm2O3 Tm2S3 TmCl3 TmF3 U U2O5 U3O8 UF2 UF4 UO2 UO2F2 UO3 V V2O3 V2O5 V2S3 VCl3 VF3 VF5 VN VO2 VO6 VOSO4 W WCl6 WO3 Y Y2(SO4)3 Y2O3 Y2S3 YF3 Yb Yb2O3 Yb2S3 YbCl3 YbF3 YbO2 YbSe Zn Zn(PO3)2 Zn3(PO4)2 Zn3As2 ZnBr2 ZnCl2 ZnF2 ZnI2 ZnO ZnS ZnSO4 ZnSe ZnTe Zr ZrF4 ZrO2 ZrS2 ZrSe2 ZrSiO4\n"
     ]
    }
   ],
   "source": [
    "# Check the element names in the list made above\n",
    "\n",
    "composition_items_sorted = sorted(composition_items)\n",
    "print('Number of elements (all) =  ', len(composition_items_sorted), '\\n')\n",
    "print(*composition_items_sorted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 726 element are contained, but it includes symbols like + or R2O."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Al2O3+Fe2O3',\n",
       " 'CaO+MgO',\n",
       " 'FeO+Fe2O3',\n",
       " 'HF+H2O',\n",
       " 'Li2O+Na2O+K2O',\n",
       " 'MoO3+WO3',\n",
       " 'Na2O+K2O']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check elements that include the symbol \"+\" \n",
    "\n",
    "[s for s in composition_items_sorted if '+' in s]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R2O',\n",
       " 'R2O3',\n",
       " 'RO',\n",
       " 'Ra',\n",
       " 'Rb',\n",
       " 'Rb2O',\n",
       " 'Rb2S',\n",
       " 'Rb2SO4',\n",
       " 'RbBr',\n",
       " 'RbCl',\n",
       " 'RbF',\n",
       " 'RbI',\n",
       " 'RbNO3',\n",
       " 'RbV2O5',\n",
       " 'Re2O7',\n",
       " 'ReO3',\n",
       " 'Rh',\n",
       " 'Rh2O3',\n",
       " 'RhO2',\n",
       " 'RmOn',\n",
       " 'Ru',\n",
       " 'RuO2']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check elements that include the symbol \"R\"\n",
    "\n",
    "[s for s in composition_items_sorted if 'R' in s]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The column list that contains all composition is made, so make the matrix with \"GlasNo\" and \"Kod\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kod</th>\n",
       "      <th>GlasNo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>204</td>\n",
       "      <td>20000</td>\n",
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       "      <td>205</td>\n",
       "      <td>20001</td>\n",
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       "      <td>205</td>\n",
       "      <td>20002</td>\n",
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       "      <td>20003</td>\n",
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       "      <td>205</td>\n",
       "      <td>20004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>44933</td>\n",
       "      <td>611695</td>\n",
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       "    <tr>\n",
       "      <th>422876</th>\n",
       "      <td>44933</td>\n",
       "      <td>611696</td>\n",
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       "    <tr>\n",
       "      <th>422877</th>\n",
       "      <td>44933</td>\n",
       "      <td>611697</td>\n",
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       "    <tr>\n",
       "      <th>422878</th>\n",
       "      <td>44933</td>\n",
       "      <td>611698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>422879 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Kod  GlasNo\n",
       "0         204   20000\n",
       "1         205   20001\n",
       "2         205   20002\n",
       "3         205   20003\n",
       "4         205   20004\n",
       "...       ...     ...\n",
       "422874  44933  611694\n",
       "422875  44933  611695\n",
       "422876  44933  611696\n",
       "422877  44933  611697\n",
       "422878  44933  611698\n",
       "\n",
       "[422879 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract Kod and GlasNo columns from the original dataframe\n",
    "\n",
    "df_rebuild_0 = df.iloc[:, :2]\n",
    "df_rebuild_0 = df_rebuild_0.astype(np.int32)\n",
    "df_rebuild_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a matrix (422879 x 726) with the number of rows in the \"GComp\", the columns of all elements, and zeros. (The zeroth column was blank and now zeros, but ignore because of no impact.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(422879, 726)\n"
     ]
    },
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 726 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        (NH4)2SO4  (NH4)3PO4   Ag  Ag2CO3  Ag2MoO4  Ag2O  Ag2S  Ag2SO4  Ag2Se  \\\n",
       "0  0.0        0.0        0.0  0.0     0.0      0.0   0.0   0.0     0.0    0.0   \n",
       "1  0.0        0.0        0.0  0.0     0.0      0.0   0.0   0.0     0.0    0.0   \n",
       "2  0.0        0.0        0.0  0.0     0.0      0.0   0.0   0.0     0.0    0.0   \n",
       "3  0.0        0.0        0.0  0.0     0.0      0.0   0.0   0.0     0.0    0.0   \n",
       "4  0.0        0.0        0.0  0.0     0.0      0.0   0.0   0.0     0.0    0.0   \n",
       "\n",
       "   ...  ZnS  ZnSO4  ZnSe  ZnTe   Zr  ZrF4  ZrO2  ZrS2  ZrSe2  ZrSiO4  \n",
       "0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "1  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "2  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "3  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "4  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "\n",
       "[5 rows x 726 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_composition_wt = pd.DataFrame(np.zeros(len(df) * len(composition_items_sorted)).reshape(len(df), len(composition_items_sorted)),\n",
    "                                 columns = composition_items_sorted)\n",
    "print(df_composition_wt.shape)\n",
    "df_composition_wt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Place the wt% data in the appropriate places in the table (it takes some time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time =  24.022482872009277 \n",
      "\n",
      "(422879, 726)\n"
     ]
    },
    {
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       "      <td>20.51</td>\n",
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       "<p>5 rows × 726 columns</p>\n",
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      ],
      "text/plain": [
       "        (NH4)2SO4  (NH4)3PO4   Ag  Ag2CO3  Ag2MoO4  Ag2O  Ag2S  Ag2SO4  Ag2Se  \\\n",
       "0  0.0        0.0        0.0  0.0     0.0      0.0   0.0   0.0     0.0    0.0   \n",
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       "4  0.0        0.0        0.0  0.0     0.0      0.0   0.0   0.0     0.0    0.0   \n",
       "\n",
       "   ...  ZnS  ZnSO4  ZnSe  ZnTe   Zr  ZrF4   ZrO2  ZrS2  ZrSe2  ZrSiO4  \n",
       "0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.00   0.0    0.0     0.0  \n",
       "1  ...  0.0    0.0   0.0   0.0  0.0   0.0  15.12   0.0    0.0     0.0  \n",
       "2  ...  0.0    0.0   0.0   0.0  0.0   0.0  23.67   0.0    0.0     0.0  \n",
       "3  ...  0.0    0.0   0.0   0.0  0.0   0.0  18.01   0.0    0.0     0.0  \n",
       "4  ...  0.0    0.0   0.0   0.0  0.0   0.0  20.51   0.0    0.0     0.0  \n",
       "\n",
       "[5 rows x 726 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "t1 = time.time()\n",
    "\n",
    "# Place the numbers\n",
    "\n",
    "for i in range (len(df)):\n",
    "    for j in range(len(comp_0[i])//4):\n",
    "        composition = comp_0[i][j * 4 + 1]\n",
    "        value = comp_0[i][j * 4 + 3]                              # value of wt%\n",
    "        df_composition_wt.at[i, composition] = float(value)       # Place them to the right places\n",
    "       \n",
    "t2 = time.time() \n",
    "\n",
    "print('Elapsed time = ', t2 - t1, '\\n')\n",
    "print(df_composition_wt.shape)\n",
    "df_composition_wt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine the composition data horizontally with the \"Kod\" and \"GlasNo\" tables created earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(422879, 728)\n"
     ]
    },
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      ],
      "text/plain": [
       "          Kod  GlasNo       (NH4)2SO4  (NH4)3PO4   Ag  Ag2CO3  Ag2MoO4  Ag2O  \\\n",
       "0         204   20000  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "1         205   20001  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "2         205   20002  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "3         205   20003  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "4         205   20004  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "...       ...     ...  ...        ...        ...  ...     ...      ...   ...   \n",
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       "422878  44933  611698  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "\n",
       "        Ag2S  ...  ZnS  ZnSO4  ZnSe  ZnTe   Zr  ZrF4   ZrO2  ZrS2  ZrSe2  \\\n",
       "0        0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.00   0.0    0.0   \n",
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       "3        0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0  18.01   0.0    0.0   \n",
       "4        0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0  20.51   0.0    0.0   \n",
       "...      ...  ...  ...    ...   ...   ...  ...   ...    ...   ...    ...   \n",
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       "422875   0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.10   0.0    0.0   \n",
       "422876   0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.10   0.0    0.0   \n",
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       "422878   0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.00   0.0    0.0   \n",
       "\n",
       "        ZrSiO4  \n",
       "0          0.0  \n",
       "1          0.0  \n",
       "2          0.0  \n",
       "3          0.0  \n",
       "4          0.0  \n",
       "...        ...  \n",
       "422874     0.0  \n",
       "422875     0.0  \n",
       "422876     0.0  \n",
       "422877     0.0  \n",
       "422878     0.0  \n",
       "\n",
       "[422879 rows x 728 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SciGlass_wt = df_rebuild_0.join(df_composition_wt)\n",
    "print(df_SciGlass_wt.shape)\n",
    "df_SciGlass_wt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check if the process was done correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 728)\n"
     ]
    },
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Kod, GlasNo, , (NH4)2SO4, (NH4)3PO4, Ag, Ag2CO3, Ag2MoO4, Ag2O, Ag2S, Ag2SO4, Ag2Se, Ag2Se5, Ag2Te, Ag4SSe, AgAsS2, AgBr, AgCl, AgF, AgGaS2, AgI, AgNO3, AgPO3, Al, Al(PO3)3, Al2(SO4)3, Al2N3, Al2O3, Al2O3+Fe2O3, Al2S3, Al2Y2O6, AlCl3, AlF3, AlN, AlPO4, Am, Am2O3, AmO2, Ar, As, As2O3, As2O5, As2S3, As2S5, As2Se3, As2Se5, As2Te, As2Te3, As2Te5, AsBr3, AsF5, AsI3, AsS, AsS2, AsSBr, AsSI, AsSe, AsSe2, AsSeI, AsTe, AsTe3, Au, Au2O, Au2O3, AuCl, AuCl3, B, B2O3, B2S3, B2Se3, BF3, BN, BOF, BPO4, BS2, Ba, Ba(H2PO4)2, Ba(PO3)2, Ba3N2, BaB2O4, BaBr2, BaCl2, BaF2, BaGeO3, BaHPO4, BaI2, BaO, BaO2, BaPO3F, BaS, BaSO4, BaSe, Be, BeF2, BeO, BeSO4, Bi, Bi2O3, Bi2O5, Bi2S3, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 728 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check duplicates ->　No duplicate\n",
    "\n",
    "df_overlap_all = df_SciGlass_wt[df_SciGlass_wt.duplicated(keep = False)]\n",
    "print(df_overlap_all.shape)\n",
    "df_overlap_all.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in total   =  422879\n",
      "Unique number of 'GlasNo' =  420731\n",
      "The gap                   =  2148\n"
     ]
    }
   ],
   "source": [
    "# Compare the unique number of \"GlasNo\" and the number of rows\n",
    "\n",
    "print(\"Number of rows in total   = \", len(df_SciGlass_wt))\n",
    "print(\"Unique number of 'GlasNo' = \", df_SciGlass_wt['GlasNo'].nunique())\n",
    "print(\"The gap                   = \", len(df_SciGlass_wt) - df_SciGlass_wt['GlasNo'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4225, 728)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Kod</th>\n",
       "      <th>GlasNo</th>\n",
       "      <th></th>\n",
       "      <th>(NH4)2SO4</th>\n",
       "      <th>(NH4)3PO4</th>\n",
       "      <th>Ag</th>\n",
       "      <th>Ag2CO3</th>\n",
       "      <th>Ag2MoO4</th>\n",
       "      <th>Ag2O</th>\n",
       "      <th>Ag2S</th>\n",
       "      <th>...</th>\n",
       "      <th>ZnS</th>\n",
       "      <th>ZnSO4</th>\n",
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       "      <th>ZrSe2</th>\n",
       "      <th>ZrSiO4</th>\n",
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       "    <tr>\n",
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       "<p>5 rows × 728 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Kod  GlasNo       (NH4)2SO4  (NH4)3PO4   Ag  Ag2CO3  Ag2MoO4  Ag2O  \\\n",
       "2209  375   21951  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "2210  375   21952  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "2211  375   21953  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "2212  375   21954  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "2213  375   21955  0.0        0.0        0.0  0.0     0.0      0.0   0.0   \n",
       "\n",
       "      Ag2S  ...  ZnS  ZnSO4  ZnSe  ZnTe   Zr  ZrF4  ZrO2  ZrS2  ZrSe2  ZrSiO4  \n",
       "2209   0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "2210   0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "2211   0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "2212   0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "2213   0.0  ...  0.0    0.0   0.0   0.0  0.0   0.0   0.0   0.0    0.0     0.0  \n",
       "\n",
       "[5 rows x 728 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the place where \"GlasNo\"  are overlapped\n",
    "\n",
    "df_overlap = df_SciGlass_wt[df_SciGlass_wt.duplicated(subset = ['GlasNo'], keep = False)]\n",
    "print(df_overlap.shape)\n",
    "df_overlap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is not enough to understand, so check the overlaps in the original table as well. (Sorted by \"GlasNo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4225, 3)\n"
     ]
    },
    {
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       "      <td>3819</td>\n",
       "      <td>9191</td>\n",
       "      <td>SiO260.0825.55.32PbO223.275.44.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30729</th>\n",
       "      <td>3831</td>\n",
       "      <td>9191</td>\n",
       "      <td>SiO260.0825.55.32PbO223.275.44.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9417</th>\n",
       "      <td>1851</td>\n",
       "      <td>9191</td>\n",
       "      <td>SiO260.0825.55.32PbO223.275.44.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20655</th>\n",
       "      <td>3047</td>\n",
       "      <td>9648</td>\n",
       "      <td>SiO260.0850.4476.Rb2O186.9349.5624.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23365</th>\n",
       "      <td>3375</td>\n",
       "      <td>9648</td>\n",
       "      <td>SiO260.0850.4476.Rb2O186.9349.5624.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40139</th>\n",
       "      <td>4862</td>\n",
       "      <td>9651</td>\n",
       "      <td>SiO260.0888.9480.Li2O29.8811.0620.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25145</th>\n",
       "      <td>3481</td>\n",
       "      <td>9651</td>\n",
       "      <td>SiO260.0888.9480.Li2O29.8811.0620.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40140</th>\n",
       "      <td>4862</td>\n",
       "      <td>9653</td>\n",
       "      <td>SiO260.0871.8480.K2O94.228.1620.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25151</th>\n",
       "      <td>3481</td>\n",
       "      <td>9653</td>\n",
       "      <td>SiO260.0871.8480.K2O94.228.1620.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38961</th>\n",
       "      <td>4724</td>\n",
       "      <td>11168</td>\n",
       "      <td>SiO260.0882.82.45Na2O61.9818.17.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38953</th>\n",
       "      <td>4723</td>\n",
       "      <td>11168</td>\n",
       "      <td>SiO260.0882.82.45Na2O61.9818.17.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Kod  GlasNo  \\\n",
       "21872   3203     510   \n",
       "21863   3202     510   \n",
       "21881   3204     518   \n",
       "21871   3202     518   \n",
       "21880   3203     526   \n",
       "21888   3204     526   \n",
       "21889   3205     526   \n",
       "9764    1887     667   \n",
       "86226   9853     667   \n",
       "86254   9854     667   \n",
       "86258   9854     668   \n",
       "9765    1887     668   \n",
       "86234   9853     668   \n",
       "20648   3047    1733   \n",
       "23361   3375    1733   \n",
       "20651   3047    1736   \n",
       "23357   3375    1736   \n",
       "3230     486    2813   \n",
       "24762   3462    2813   \n",
       "3236     486    2814   \n",
       "24763   3462    2814   \n",
       "3242     486    2815   \n",
       "24764   3462    2815   \n",
       "24765   3462    2816   \n",
       "3247     486    2816   \n",
       "24766   3462    2817   \n",
       "3250     486    2817   \n",
       "91141  10461    3285   \n",
       "91145  10462    3285   \n",
       "30569   3819    9191   \n",
       "30729   3831    9191   \n",
       "9417    1851    9191   \n",
       "20655   3047    9648   \n",
       "23365   3375    9648   \n",
       "40139   4862    9651   \n",
       "25145   3481    9651   \n",
       "40140   4862    9653   \n",
       "25151   3481    9653   \n",
       "38961   4724   11168   \n",
       "38953   4723   11168   \n",
       "\n",
       "                                                             Composition  \n",
       "21872     SiO260.0875.64.72CaO56.0810.9.25Li2O29.8815.26.03  \n",
       "21863     SiO260.0875.64.72CaO56.0810.9.25Li2O29.8815.26.03  \n",
       "21881     SiO260.0875.74.81CaO56.0810.10.69Na2O61.9815.14.5  \n",
       "21871     SiO260.0875.74.81CaO56.0810.10.69Na2O61.9815.14.5  \n",
       "21880      SiO260.0875.78.71CaO56.0810.11.25K2O94.215.10.04  \n",
       "21888      SiO260.0875.78.71CaO56.0810.11.25K2O94.215.10.04  \n",
       "21889      SiO260.0875.78.71CaO56.0810.11.25K2O94.215.10.04  \n",
       "9764                           SiO260.0865.6875.K2O94.234.3225.  \n",
       "86226                          K2O94.234.3225.SiO260.0865.6875.  \n",
       "86254                          K2O94.234.3225.SiO260.0865.6875.  \n",
       "86258                          K2O94.243.5733.SiO260.0856.4367.  \n",
       "9765                           SiO260.0856.4367.K2O94.243.5733.  \n",
       "86234                          K2O94.243.5733.SiO260.0856.4367.  \n",
       "20648                        SiO260.0875.4376.Na2O61.9824.5724.  \n",
       "23361                        SiO260.0875.4376.Na2O61.9824.5724.  \n",
       "20651                          SiO260.0866.8976.K2O94.233.1124.  \n",
       "23357                          SiO260.0866.8976.K2O94.233.1124.  \n",
       "3230    SiO260.0840.40.21Al2O3101.9610.5.92CaO56.0850.53.86  \n",
       "24762   SiO260.0840.40.21CaO56.0850.53.86Al2O3101.9610.5.92  \n",
       "3236    SiO260.0840.41.21Al2O3101.9615.9.11CaO56.0845.49.68  \n",
       "24763   SiO260.0840.41.21CaO56.0845.49.68Al2O3101.9615.9.11  \n",
       "3242   SiO260.0840.42.26Al2O3101.9620.12.45CaO56.0840.45.28  \n",
       "24764  SiO260.0840.42.26CaO56.0840.45.28Al2O3101.9620.12.45  \n",
       "24765  SiO260.0840.43.37CaO56.0835.40.66Al2O3101.9625.15.97  \n",
       "3247   SiO260.0840.43.37Al2O3101.9625.15.97CaO56.0835.40.66  \n",
       "24766  SiO260.0840.44.53CaO56.0830.35.79Al2O3101.9630.19.68  \n",
       "3250   SiO260.0840.44.53Al2O3101.9630.19.68CaO56.0830.35.79  \n",
       "91141                                           As2O3197.84100.100.  \n",
       "91145                                           As2O3197.84100.100.  \n",
       "30569                         SiO260.0825.55.32PbO223.275.44.68  \n",
       "30729                         SiO260.0825.55.32PbO223.275.44.68  \n",
       "9417                          SiO260.0825.55.32PbO223.275.44.68  \n",
       "20655                       SiO260.0850.4476.Rb2O186.9349.5624.  \n",
       "23365                       SiO260.0850.4476.Rb2O186.9349.5624.  \n",
       "40139                        SiO260.0888.9480.Li2O29.8811.0620.  \n",
       "25145                        SiO260.0888.9480.Li2O29.8811.0620.  \n",
       "40140                          SiO260.0871.8480.K2O94.228.1620.  \n",
       "25151                          SiO260.0871.8480.K2O94.228.1620.  \n",
       "38961                        SiO260.0882.82.45Na2O61.9818.17.55  \n",
       "38953                        SiO260.0882.82.45Na2O61.9818.17.55  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_overlap_s = df_overlap.sort_values('GlasNo')\n",
    "overlap_idx_list = df_overlap_s.index\n",
    "df_overlap = df.loc[overlap_idx_list]\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", 300)\n",
    "print(df_overlap.shape)\n",
    "df_overlap[0:40]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## They were classified by the combination of \"Kod\" and \"GlasNo\", so no problems with the allocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check if the numbers are applied correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Typical compositions found in the \"SciGK\" file\n",
    "\n",
    "main_element = ['Kod', 'GlasNo','SiO2', 'Al2O3', 'B2O3', 'CaO', 'K2O', 'Na2O', 'PbO', 'Li2O', 'MgO', 'SrO', 'BaO', 'ZnO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(422879, 14)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kod</th>\n",
       "      <th>GlasNo</th>\n",
       "      <th>SiO2</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>B2O3</th>\n",
       "      <th>CaO</th>\n",
       "      <th>K2O</th>\n",
       "      <th>Na2O</th>\n",
       "      <th>PbO</th>\n",
       "      <th>Li2O</th>\n",
       "      <th>MgO</th>\n",
       "      <th>SrO</th>\n",
       "      <th>BaO</th>\n",
       "      <th>ZnO</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>204</td>\n",
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       "      <td>205</td>\n",
       "      <td>20001</td>\n",
       "      <td>35.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.76</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205</td>\n",
       "      <td>20002</td>\n",
       "      <td>30.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.99</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>205</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>26.00</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>205</td>\n",
       "      <td>20004</td>\n",
       "      <td>38.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Kod  GlasNo    SiO2  Al2O3  B2O3  CaO  K2O   Na2O  PbO  Li2O  MgO  SrO  \\\n",
       "0  204   20000  100.00    0.0   0.0  0.0  0.0   0.00  0.0   0.0  0.0  0.0   \n",
       "1  205   20001   35.90    0.0   0.0  0.0  0.0  27.76  0.0   0.0  0.0  0.0   \n",
       "2  205   20002   30.17    0.0   0.0  0.0  0.0  25.99  0.0   0.0  0.0  0.0   \n",
       "3  205   20003   39.00    0.0   0.0  0.0  0.0  26.00  0.0   0.0  0.0  0.0   \n",
       "4  205   20004   38.33    0.0   0.0  0.0  0.0  25.08  0.0   0.0  0.0  0.0   \n",
       "\n",
       "   BaO  ZnO  \n",
       "0  0.0  0.0  \n",
       "1  0.0  0.0  \n",
       "2  0.0  0.0  \n",
       "3  0.0  0.0  \n",
       "4  0.0  0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The same compositions from the dataframe made from the \"GComp\" file\n",
    "\n",
    "df_SciGlass_wt_major = df_SciGlass_wt[main_element]\n",
    "print(df_SciGlass_wt_major.shape)\n",
    "df_SciGlass_wt_major.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmm\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3326: DtypeWarning: Columns (176,179) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(422949, 186)\n"
     ]
    },
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205</td>\n",
       "      <td>20002</td>\n",
       "      <td>23.06</td>\n",
       "      <td>79.64</td>\n",
       "      <td>39.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.39</td>\n",
       "      <td>...</td>\n",
       "      <td>130 420 700</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>205</td>\n",
       "      <td>20003</td>\n",
       "      <td>22.31</td>\n",
       "      <td>74.93</td>\n",
       "      <td>48.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.43</td>\n",
       "      <td>...</td>\n",
       "      <td>130 420 700</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>205</td>\n",
       "      <td>20004</td>\n",
       "      <td>22.62</td>\n",
       "      <td>75.62</td>\n",
       "      <td>48.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.60</td>\n",
       "      <td>...</td>\n",
       "      <td>130 420 700</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 186 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   KOD  GLASNO   A_WT   M_WT    SIO2  AL2O3  B2O3  CAO  K2O   NA2O  ...  \\\n",
       "0  204   20000  20.02  60.08  100.00    0.0   0.0  0.0  0.0   0.00  ...   \n",
       "1  205   20001  21.77  71.76   45.35    0.0   0.0  0.0  0.0  33.99  ...   \n",
       "2  205   20002  23.06  79.64   39.98    0.0   0.0  0.0  0.0  33.39  ...   \n",
       "3  205   20003  22.31  74.93   48.64    0.0   0.0  0.0  0.0  31.43  ...   \n",
       "4  205   20004  22.62  75.62   48.24    0.0   0.0  0.0  0.0  30.60  ...   \n",
       "\n",
       "      Prop_Code  GForm  any_n  Trademark  Tmax  Vmax  Tn  Io  tcr  tx  \n",
       "0      400 400       1    NaN        NaN   NaN   NaN NaN NaN  NaN NaN  \n",
       "1  130 420 700       1    NaN        NaN   NaN   NaN NaN NaN  NaN NaN  \n",
       "2  130 420 700       1    NaN        NaN   NaN   NaN NaN NaN  NaN NaN  \n",
       "3  130 420 700       1    NaN        NaN   NaN   NaN NaN NaN  NaN NaN  \n",
       "4  130 420 700       1    NaN        NaN   NaN   NaN NaN NaN  NaN NaN  \n",
       "\n",
       "[5 rows x 186 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the \"SciGK\" file\n",
    "\n",
    "df_scigk = pd.read_csv('data_SciGlass/SciGK.csv', encoding = 'latin1')\n",
    "print(df_scigk.shape)\n",
    "df_scigk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23 34\n"
     ]
    }
   ],
   "source": [
    "# Determine the column number to extract the same compositions from the \"SciGK\" file\n",
    "\n",
    "slice_1 = df_scigk.columns.get_loc('WSIO2')\n",
    "slice_2 = df_scigk.columns.get_loc('WZNO')\n",
    "print(slice_1, slice_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['KOD', 'GLASNO', 'WSIO2', 'WAL2O3', 'WB2O3', 'WCAO', 'WK2O', 'WNA2O', 'WPBO', 'WLi2O', 'WMgO', 'WSRO', 'WBAO', 'WZNO']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmm\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1851: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, val, pi)\n"
     ]
    },
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       "      <th>Kod</th>\n",
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       "      <td>205</td>\n",
       "      <td>20004</td>\n",
       "      <td>38.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>25.07</td>\n",
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      ],
      "text/plain": [
       "   Kod  GlasNo    SiO2  Al2O3  B2O3  CaO  K2O   Na2O  PbO  Li2O  MgO  SrO  \\\n",
       "0  204   20000  100.00    0.0   0.0  0.0  0.0   0.00  0.0   0.0  0.0  0.0   \n",
       "1  205   20001   35.90    0.0   0.0  0.0  0.0  27.75  0.0   0.0  0.0  0.0   \n",
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       "4  205   20004   38.32    0.0   0.0  0.0  0.0  25.07  0.0   0.0  0.0  0.0   \n",
       "\n",
       "   BaO  ZnO  \n",
       "0  0.0  0.0  \n",
       "1  0.0  0.0  \n",
       "2  0.0  0.0  \n",
       "3  0.0  0.0  \n",
       "4  0.0  0.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the composition range from the \"SciGK\" file determined above and change the column to the same name as the new table\n",
    "\n",
    "scigk_columns_to_check = ['KOD', 'GLASNO']\n",
    "\n",
    "for i in range(slice_1, slice_2 + 1):\n",
    "    scigk_columns_to_check.append(df_scigk.columns[i])\n",
    "print(scigk_columns_to_check)\n",
    "df_scigk_check = df_scigk[scigk_columns_to_check]\n",
    "df_scigk_check.columns = main_element\n",
    "\n",
    "df_scigk_check.iloc[:, :2] = df_scigk_check.iloc[:, :2].astype(np.int32)\n",
    "df_scigk_check.iloc[:, 2:] = df_scigk_check.iloc[:, 2:].astype(np.float64)\n",
    "\n",
    "df_scigk_check.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the row number to compare\n",
    "\n",
    "rows_to_see = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 examples from Gcomp wt%\n"
     ]
    },
    {
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       "      <td>71.26</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>14.96</td>\n",
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       "      <th>504</th>\n",
       "      <td>233</td>\n",
       "      <td>123782</td>\n",
       "      <td>74.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.40</td>\n",
       "      <td>0.99</td>\n",
       "      <td>15.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "     Kod  GlasNo   SiO2  Al2O3  B2O3   CaO   K2O   Na2O  PbO  Li2O  MgO  SrO  \\\n",
       "500  233  123778  73.35    0.0   0.0  8.31  0.98  15.40  0.0   0.0  0.0  0.0   \n",
       "501  233  123779  72.64    0.0   0.0  8.23  0.97  15.25  0.0   0.0  0.0  0.0   \n",
       "502  233  123780  71.94    0.0   0.0  8.15  0.96  15.11  0.0   0.0  0.0  0.0   \n",
       "503  233  123781  71.26    0.0   0.0  8.08  0.95  14.96  0.0   0.0  0.0  0.0   \n",
       "504  233  123782  74.07    0.0   0.0  8.40  0.99  15.56  0.0   0.0  0.0  0.0   \n",
       "\n",
       "     BaO  ZnO  \n",
       "500  0.0  0.0  \n",
       "501  0.0  0.0  \n",
       "502  0.0  0.0  \n",
       "503  0.0  0.0  \n",
       "504  0.0  0.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 rows in the \"GComp\"\n",
    "\n",
    "print('5 examples from Gcomp wt%')\n",
    "df_SciGlass_wt_major.loc[int(rows_to_see):int(rows_to_see)+4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 examples from SciGK mol%\n"
     ]
    },
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.31</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>233</td>\n",
       "      <td>123779</td>\n",
       "      <td>72.63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.23</td>\n",
       "      <td>0.96</td>\n",
       "      <td>15.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>233</td>\n",
       "      <td>123780</td>\n",
       "      <td>71.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.15</td>\n",
       "      <td>0.95</td>\n",
       "      <td>15.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>233</td>\n",
       "      <td>123781</td>\n",
       "      <td>71.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.95</td>\n",
       "      <td>14.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>233</td>\n",
       "      <td>123782</td>\n",
       "      <td>74.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.39</td>\n",
       "      <td>0.98</td>\n",
       "      <td>15.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Kod  GlasNo   SiO2  Al2O3  B2O3   CaO   K2O   Na2O  PbO  Li2O  MgO  SrO  \\\n",
       "500  233  123778  73.34    0.0   0.0  8.31  0.97  15.40  0.0   0.0  0.0  0.0   \n",
       "501  233  123779  72.63    0.0   0.0  8.23  0.96  15.25  0.0   0.0  0.0  0.0   \n",
       "502  233  123780  71.94    0.0   0.0  8.15  0.95  15.10  0.0   0.0  0.0  0.0   \n",
       "503  233  123781  71.25    0.0   0.0  8.07  0.95  14.96  0.0   0.0  0.0  0.0   \n",
       "504  233  123782  74.07    0.0   0.0  8.39  0.98  15.55  0.0   0.0  0.0  0.0   \n",
       "\n",
       "     BaO  ZnO  \n",
       "500  0.0  0.0  \n",
       "501  0.0  0.0  \n",
       "502  0.0  0.0  \n",
       "503  0.0  0.0  \n",
       "504  0.0  0.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"5 rows in the SciGK\"\n",
    "\n",
    "print('5 examples from SciGK mol%')\n",
    "df_scigk_check.loc[int(rows_to_see):int(rows_to_see)+4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some disparities are found in the second decimal, but composition data look retrieved correctly from the \"GComp\" file, so save the file (it takes some time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_SciGlass_wt.to_csv('data_SciGlass/SciGlass_comp_wt.csv', index = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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