dskg-construction/fields_of_application.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writes all paper-ids from the DSKG-beat-version into a txt file\n",
    "import pandas as pd\n",
    "df = pd.read_csv(\"/DSKG_BETA.csv\")\n",
    "\n",
    "with open(\"/MAG_Paper_ID.txt\", \"w\") as outp:\n",
    "    i = 0\n",
    "    while i < len(df):\n",
    "        mag_id = df[\"isReferencedBy\"][i]\n",
    "        mag_id = mag_id.replace(\"http://ma-graph.org/entity/\", \"\")\n",
    "        outp.write(mag_id + \"\\n\")\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The following Linux command searches for the corresponding discipline for each PaperID from the file PaperFieldsOfStudy.txt of the MAG-dump\n",
    "#Only level 0 disciplines are considered, i.e. a total of 19 high level disciplines \n",
    "\n",
    "fgrep -w -f .../PAPER_MAG_Paper_ID.txt .../mag/PaperFieldsOfStudy.txt > .../paper_disciplines_result.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Writes the IDs of the 19 different disciplines into a text file\n",
    "field_of_study_set = set()\n",
    "\n",
    "with open(\"/paper_disciplines_result.txt\") as inp:\n",
    "    for line in inp:\n",
    "        paper_id, field_of_study_id = line.split(\"\\t\")\n",
    "        field_of_study_set.add(field_of_study_id)\n",
    "    \n",
    "with open(\"/paper_MAG_Field_Of_Study_ID.txt\", \"w\") as outp:\n",
    "    for line in field_of_study_set:\n",
    "        outp.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writes the results of the adjustment into the beat version of the dskg\n",
    "import pandas as pd\n",
    "df = pd.read_csv(\"/DSKG_BETA.csv\", keep_default_na=False)\n",
    "\n",
    "df['MAG_Disciplines'] = \"\"\n",
    "\n",
    "\n",
    "i = 0\n",
    "while i < len(df.index):\n",
    "    inp = open(\"/paper_disciplines_result.txt\")\n",
    "    mag_paper = df['isReferencedBy'][i]\n",
    "    mag_paper_id = mag_paper.strip(\"http://ma-graph.org/entity/\").strip()\n",
    "    \n",
    "    for line in inp:\n",
    "        paper_id, field_of_study_id = line.split(\"\\t\")\n",
    "        paper_id = paper_id.strip()\n",
    "        if mag_paper_id == paper_id:\n",
    "            df['MAG_Disciplines'][i] = str(field_of_study_id) + \"\"\n",
    "\n",
    "    i += 1\n",
    "    #print(i)\n",
    "    \n",
    "df.to_csv(\"/DSKG_BETA_PAPER_FOS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write the names of the disciplines in the beat version of the dskg\n",
    "import pandas as pd\n",
    "df = pd.read_csv(\"/DSKG_BETA_PAPER_FOS.csv\")\n",
    "\n",
    "df['MAG_Disciplines_Name'] = \"\"\n",
    "\n",
    "i = 0\n",
    "while i < len(df.index):\n",
    "    inp = open(\"/Users/davidlamprecht/Desktop/DSKG_Paper/MAKG_FOS/MAG_Disciplines_labeld.txt\")\n",
    "    mag_discipline = df['MAG_Disciplines'][i]\n",
    "    mag_discipline = str(mag_discipline).replace(\".0\", \"\")\n",
    "    for line in inp:\n",
    "        mag_discipline_id, mag_discipline_name = line.split(\"\\t\")\n",
    "        if mag_discipline == mag_discipline_id:\n",
    "            df['MAG_Disciplines_Name'][i] = mag_discipline_name\n",
    "\n",
    "    i += 1\n",
    "    print(i)\n",
    "    \n",
    "df.to_csv(\"/DSKG_BETA_PAPER_FOS_Labeled.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate percentage of assigned disciplines and save to new csv file \n",
    "#A dataset will be assigned to an application area only if at least 25% of the linked papers have the same discipline\n",
    "from collections import defaultdict\n",
    "df = pd.read_csv(\"/DSKG_BETA_PAPER_FOS_Labeled.csv\")\n",
    "df = df.applymap(str)\n",
    "\n",
    "df_new = pd.read_csv('/DSKG_BETA.csv\")')\n",
    "df_new['MAG_Disciplines_URI_Percentage'] = \"\"\n",
    "df_new['MAG_Disciplines_Percentage'] = \"\"\n",
    "df_new['MAG_Disciplines_Absolute'] = \"\"\n",
    "\n",
    "dataset_dict = defaultdict(list)\n",
    "\n",
    "i = 0\n",
    "while i < len(df.index):\n",
    "    dataset_uri = df['dataset'][i]\n",
    "    discipline_name = df['MAG_Disciplines_Name'][i] \n",
    "    dataset_dict[dataset_uri].append(discipline_name)\n",
    "    i += 1\n",
    "    \n",
    "\n",
    "index = 0\n",
    "for key, value in dataset_dict.items():\n",
    "    History = 0\n",
    "    Geology = 0\n",
    "    Economics = 0\n",
    "    Geography = 0\n",
    "    Chemistry = 0\n",
    "    Philosophy = 0\n",
    "    Sociology = 0\n",
    "    Materials_science = 0\n",
    "    Mathematics = 0\n",
    "    Biology = 0\n",
    "    Computer_science = 0\n",
    "    Political_science = 0\n",
    "    Engineering = 0\n",
    "    Psychology = 0\n",
    "    Environmental_science = 0\n",
    "    Business = 0\n",
    "    Physics = 0\n",
    "    Medicine = 0\n",
    "    Art = 0\n",
    "    Nan = 0\n",
    "    \n",
    "    for discipline in value:\n",
    "        discipline = str(discipline)\n",
    "        if discipline == \"nan\":\n",
    "            Nan += 1\n",
    "        if discipline == 'History\\n':\n",
    "            History += 1\n",
    "        if discipline == 'Geology\\n':\n",
    "            Geology += 1\n",
    "        if discipline == 'Economics\\n':  \n",
    "            Economics += 1\n",
    "        if discipline == 'Geography\\n':\n",
    "            Geography += 1\n",
    "        if discipline == 'Chemistry\\n': \n",
    "            Chemistry += 1\n",
    "        if discipline == 'Philosophy\\n':\n",
    "            Philosophy += 1\n",
    "        if discipline == 'Sociology\\n':\n",
    "            Sociology += 1\n",
    "        if discipline == 'Materials science\\n':\n",
    "            Materials_science += 1\n",
    "        if discipline == 'Mathematics\\n':\n",
    "            Mathematics += 1\n",
    "        if discipline == 'Biology\\n':\n",
    "            Biology += 1\n",
    "        if discipline == 'Computer science\\n':\n",
    "            Computer_science += 1\n",
    "        if discipline == 'Political science\\n':\n",
    "            Political_science += 1\n",
    "        if discipline == 'Engineering\\n':\n",
    "            Engineering += 1\n",
    "        if discipline == 'Psychology\\n':\n",
    "            Psychology += 1\n",
    "        if discipline == 'Environmental science\\n':\n",
    "            Environmental_science\n",
    "        if discipline == 'Business\\n':\n",
    "            Business += 1\n",
    "        if discipline == 'Physics\\n':\n",
    "            Physics += 1\n",
    "        if discipline == 'Medicine\\n':\n",
    "            Medicine += 1\n",
    "        if discipline == 'Art\\n':\n",
    "            Art += 1\n",
    "       \n",
    "    if History > 0:\n",
    "        History_Percantage = History / (len(list(filter(bool, value))) - Nan)\n",
    "        if History_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(History_Percantage)  + \" \" + \"History\" +   \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(History_Percantage) + \" \" + \"95457728\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"95457728\" +  \", \"\n",
    "    if Geology > 0:\n",
    "        Geology_Percantage = Geology / (len(list(filter(bool, value))) - Nan)\n",
    "        if Geology_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Geology_Percantage) + \" \" + \"Geology\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Geology_Percantage) + \" \" + \"127313418\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"127313418\" +  \", \"\n",
    "    if Economics > 0:\n",
    "        Economics_Percantage = Economics / (len(list(filter(bool, value))) - Nan)\n",
    "        if Economics_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Economics_Percantage)  + \" \" + \"Economics\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Economics_Percantage) + \" \" + \"162324750\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"162324750\" +  \", \"\n",
    "    if Geography > 0:\n",
    "        Geography_Percantage = Geography / (len(list(filter(bool, value))) - Nan)\n",
    "        if Geography_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Geography_Percantage)  + \" \" + \"Geography\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Geography_Percantage) + \" \" + \"205649164\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"205649164\" +  \", \"\n",
    "    if Chemistry > 0:\n",
    "        Chemistry_Percantage = Chemistry / (len(list(filter(bool, value))) - Nan)\n",
    "        if Chemistry_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Chemistry_Percantage)  + \" \" + \"Chemistry\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Chemistry_Percantage) + \" \" + \"185592680\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"185592680\" +  \", \"\n",
    "    if Philosophy > 0:\n",
    "        Philosophy_Percantage = Philosophy / (len(list(filter(bool, value))) - Nan)\n",
    "        if Philosophy_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Philosophy_Percantage)  + \" \" + \"Philosophy\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Philosophy_Percantage) + \" \" + \"138885662\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"138885662\" +  \", \"\n",
    "    if Sociology > 0:\n",
    "        Sociology_Percantage = Sociology / (len(list(filter(bool, value))) - Nan)\n",
    "        if Sociology_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Sociology_Percantage)  + \" \" + \"Sociology\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Sociology_Percantage) + \" \" + \"144024400\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"144024400\" +  \", \"\n",
    "    if Materials_science > 0:\n",
    "        Materials_science_Percantage = Materials_science / (len(list(filter(bool, value))) - Nan)\n",
    "        if Materials_science_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Materials_science_Percantage)  + \" \" + \"Materials science\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Materials_science_Percantage) + \" \" + \"192562407\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"192562407\" +  \", \"\n",
    "    if Mathematics > 0:\n",
    "        Mathematics_Percantage = Mathematics / (len(list(filter(bool, value))) - Nan)\n",
    "        if Mathematics_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Mathematics_Percantage)  + \" \" + \"Mathematics\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Mathematics_Percantage) + \" \" + \"33923547\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"33923547\" +  \", \"\n",
    "    if Biology > 0:\n",
    "        Biology_Percantage = Biology / (len(list(filter(bool, value))) - Nan)\n",
    "        if Biology_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Biology_Percantage)  + \" \" + \"Biology\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Biology_Percantage) + \" \" + \"86803240\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"86803240\" +  \", \"\n",
    "    if Computer_science > 0:\n",
    "        Computer_science_Percantage = Computer_science / (len(list(filter(bool, value))) - Nan)\n",
    "        if Computer_science_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Computer_science_Percantage)  + \" \" + \"Computer science\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Computer_science_Percantage) + \" \" + \"41008148\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"41008148\" +  \", \"\n",
    "    if Political_science > 0:\n",
    "        Political_science_Percantage = Political_science / (len(list(filter(bool, value))) - Nan)\n",
    "        if Political_science_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Political_science_Percantage)  + \" \" + \"Political science\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Political_science_Percantage) + \" \" + \"17744445\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"17744445\" +  \", \"\n",
    "    if Engineering > 0:\n",
    "        Engineering_Percantage = Engineering / (len(list(filter(bool, value))) - Nan)\n",
    "        if Engineering_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Engineering_Percantage)  + \" \" + \"Engineering\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Engineering_Percantage) + \" \" + \"127413603\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"127413603\" +  \", \"\n",
    "    if Psychology > 0:\n",
    "        Psychology_Percantage = Psychology / (len(list(filter(bool, value))) - Nan)\n",
    "        if Psychology_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Psychology_Percantage)  + \" \" + \"Psychology\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Psychology_Percantage) + \" \" + \"15744967\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"15744967\" +  \", \"\n",
    "    if Environmental_science > 0:\n",
    "        Environmental_science_Percantage = Environmental_science / (len(list(filter(bool, value))) - Nan)\n",
    "        if Environmental_science_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Environmental_science_Percantage)  + \" \" + \"Environmental science\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Environmental_science_Percantage) + \" \" + \"39432304\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"39432304\" +  \", \"\n",
    "    if Business > 0:\n",
    "        Business_Percantage = Business / (len(list(filter(bool, value))) - Nan)\n",
    "        if Business_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Business_Percantage)  + \" \" + \"Business\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Business_Percantage) + \" \" + \"144133560\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"144133560\" +  \", \"\n",
    "    if Physics > 0:\n",
    "        Physics_Percantage = Physics / (len(list(filter(bool, value))) - Nan)\n",
    "        if Physics_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Physics_Percantage)  + \" \" + \"Physics\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Physics_Percantage) + \" \" + \"121332964\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"121332964\" +  \", \"\n",
    "    if Medicine > 0:\n",
    "        Medicine_Percantage = Medicine / (len(list(filter(bool, value))) - Nan)\n",
    "        if Medicine_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Medicine_Percantage)  + \" \" + \"Medicine\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Medicine_Percantage) + \" \" + \"71924100\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"71924100\" +  \", \"\n",
    "    if Art > 0:\n",
    "        Art_Percantage = Art / (len(list(filter(bool, value))) - Nan)\n",
    "        if Art_Percantage >= 0.25:\n",
    "            df_new['MAG_Disciplines_Percentage'][index] = df_new['MAG_Disciplines_Percentage'][index] + str(Art_Percantage)  + \" \" + \"Art\" + \", \"\n",
    "            df_new['MAG_Disciplines_URI_Percentage'][index] = df_new['MAG_Disciplines_URI_Percentage'][index] + str(Art_Percantage) + \" \" + \"142362112\" +  \", \"\n",
    "            df_new['MAG_Disciplines_Absolute'][index] = df_new['MAG_Disciplines_Absolute'][index] + \"142362112\" +  \", \"\n",
    "        \n",
    "    index +=1\n",
    "    print(index)\n",
    "        \n",
    "            \n",
    "\n",
    "df_new.to_csv(\"/DSKG_BETA.csv\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deletes interfering characters (commas) at the end of the cell entries\n",
    "import pandas as pd\n",
    "df = pd.read_csv(\"/DSKG_BETA.csv\")\n",
    "\n",
    "i = 0\n",
    "while i < len(df.index):\n",
    "    \n",
    "    if str(df[\"MAG_Disciplines_URI_Percentage\"][i]).endswith(\", \"):\n",
    "        max_len = len(str(df[\"MAG_Disciplines_URI_Percentage\"][i]))\n",
    "        unwanted_cut_off = max_len - 2\n",
    "        df[\"MAG_Disciplines_URI_Percentage\"][i] = str(df[\"MAG_Disciplines_URI_Percentage\"][i])[0:unwanted_cut_off]\n",
    "        \n",
    "    if str(df[\"MAG_Disciplines_Percentage\"][i]).endswith(\", \"):\n",
    "        max_len = len(str(df[\"MAG_Disciplines_Percentage\"][i]))\n",
    "        unwanted_cut_off = max_len - 2\n",
    "        df[\"MAG_Disciplines_Percentage\"][i] = str(df[\"MAG_Disciplines_Percentage\"][i])[0:unwanted_cut_off]\n",
    "        \n",
    "    if str(df[\"MAG_Disciplines_Absolute\"][i]).endswith(\", \"):\n",
    "        max_len = len(str(df[\"MAG_Disciplines_Absolute\"][i]))\n",
    "        unwanted_cut_off = max_len - 2\n",
    "        df[\"MAG_Disciplines_Absolute\"][i] = str(df[\"MAG_Disciplines_Absolute\"][i])[0:unwanted_cut_off]\n",
    "    i += 1\n",
    "\n",
    "    \n",
    "df.to_csv(\"/DSKG_BETA.csv\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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