convert.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM5YRkNfVoN/hjyw5y/S6R0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pgmcdonald/cautious-spork/blob/main/convert.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Steps \n",
        "1. Upload XML file into the /content folder. \n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "2. Update the variable XML_FILE_NAME on Line #9 of the XML file named uploaded.\n",
        "\n",
        "3. Download diss_from_xml_export.xlsx after running code\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RbHGnqL306RM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install xmltodict pandas xlsxwriter"
      ],
      "metadata": {
        "id": "KzwEn0RnubEv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bca39f06-6026-4937-93a5-7d91384b8422"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting xmltodict\n",
            "  Downloading xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.5)\n",
            "Collecting xlsxwriter\n",
            "  Downloading XlsxWriter-3.0.3-py3-none-any.whl (149 kB)\n",
            "\u001b[K     |████████████████████████████████| 149 kB 7.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Installing collected packages: xmltodict, xlsxwriter\n",
            "Successfully installed xlsxwriter-3.0.3 xmltodict-0.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "import os\n",
        "import datetime\n",
        "import xmltodict\n",
        "import pandas as pd\n",
        "import getopt\n",
        "\n",
        "#UPDATE THIS FILE NAME BEFORE RUNNING\n",
        "XML_FILE_NAME= 'CombineXML.xml'\n",
        "\n",
        "def main():\n",
        "\n",
        "    now = datetime.datetime.now()\n",
        "    run_dt = now.strftime(\"%Y%m%d\")\n",
        "    \n",
        "    #List of Dictionsary\n",
        "    dictList=[]\n",
        "    \n",
        "    with open(XML_FILE_NAME) as fd:\n",
        "        doc = xmltodict.parse(fd.read())\n",
        "    \n",
        "    for item in doc['root']['DISS_submission']:\n",
        "        data_dict={}\n",
        "        diss_Title = item['DISS_description']['DISS_title']\n",
        "        diss_FullText_Url = \"\"\n",
        "        diss_Binary= item['DISS_content']['DISS_binary']['#text']\n",
        "        diss_Keyword = item['DISS_description']['DISS_categorization']['DISS_keyword']\n",
        "        diss_Abstract= item['DISS_content']['DISS_abstract']['DISS_para']\n",
        "        diss_FName=item['DISS_authorship']['DISS_author']['DISS_name']['DISS_fname']\n",
        "        diss_Middle=item['DISS_authorship']['DISS_author']['DISS_name']['DISS_middle']\n",
        "        diss_SurName=item['DISS_authorship']['DISS_author']['DISS_name']['DISS_surname']\n",
        "        diss_Suffix=item['DISS_authorship']['DISS_author']['DISS_name']['DISS_suffix']\n",
        "    \n",
        "        if(isinstance(item['DISS_authorship']['DISS_author']['DISS_contact'], dict)):\n",
        "            diss_Email=item['DISS_authorship']['DISS_author']['DISS_contact']['DISS_email']\n",
        "        #if there are many emails pick the \"future\" email over current. \n",
        "        else:\n",
        "            for contacts in item['DISS_authorship']['DISS_author']['DISS_contact']:\n",
        "                if contacts['@type'] == 'future':\n",
        "                  diss_Email=contacts['DISS_email']\n",
        "    \n",
        "\n",
        "        diss_Institution= item['DISS_description']['DISS_institution']['DISS_inst_name']\n",
        "\n",
        "        advisor_list = [\"\", \"\", \"\"]\n",
        "        index=0\n",
        "        if(isinstance(item['DISS_description']['DISS_advisor'], dict)):\n",
        "            diss_Advisor_fname=item['DISS_description']['DISS_advisor']['DISS_name']['DISS_fname']\n",
        "            diss_Surname=item['DISS_description']['DISS_advisor']['DISS_name']['DISS_surname']\n",
        "            diss_Advisor_name = diss_Advisor_fname+\" \"+diss_Surname\n",
        "            advisor_list.insert(index,diss_Advisor_name)\n",
        "        else:\n",
        "            for advisor in item['DISS_description']['DISS_advisor']:\n",
        "                diss_Advisor_fname=advisor['DISS_name']['DISS_fname']\n",
        "                diss_Surname=advisor['DISS_name']['DISS_surname']\n",
        "                diss_Advisor_name = diss_Advisor_fname+\" \"+diss_Surname\n",
        "                advisor_list.insert(index,diss_Advisor_name)\n",
        "                index = index + 1\n",
        "\n",
        "        #diss_Advisor_fname=item['DISS_description']['DISS_advisor']['DISS_name']['DISS_fname']\n",
        "\n",
        "\n",
        "        diss_Degree= item['DISS_description']['DISS_degree']\n",
        "        diss_Contact= item['DISS_description']['DISS_institution']['DISS_inst_contact']\n",
        "        diss_Type= item['DISS_description']['@type']\n",
        "        diss_Binary_Type= item['DISS_content']['DISS_binary']['@type']\n",
        "        diss_Language = item['DISS_description']['DISS_categorization']['DISS_language']\n",
        "        diss_AwardDate = item['DISS_description']['DISS_dates']['DISS_comp_date']\n",
        "        diss_disciplines  = []\n",
        "        if(isinstance(item['DISS_description']['DISS_categorization']['DISS_category'], dict)):\n",
        "            diss_disciplines.append(item['DISS_description']['DISS_categorization']['DISS_category']['DISS_cat_desc'])\n",
        "        else:\n",
        "            for category in item['DISS_description']['DISS_categorization']['DISS_category']:\n",
        "                diss_disciplines.append(category['DISS_cat_desc']) \n",
        "            diss_disciplines = ','.join(diss_disciplines)\n",
        "        \n",
        "\n",
        "        data_dict={  'title': diss_Title, \n",
        "                     'fulltext_url': diss_FullText_Url, \n",
        "                     'filename': diss_Binary,\n",
        "                     'keywords': diss_Keyword,\n",
        "                     'abstract': diss_Abstract,\n",
        "                     'author1_fname':diss_FName,\n",
        "                     'author1_mname':diss_Middle,\n",
        "                     'author1_lname':diss_SurName,\n",
        "                     'author1_suffix':diss_Suffix,\n",
        "                     'author1_email': diss_Email,\n",
        "                     'author1_institution':diss_Institution,\n",
        "                     'advisor1':advisor_list[0],\n",
        "                     'advisor2':advisor_list[1],\n",
        "                     'advisor3':advisor_list[2],\n",
        "                     'award_date':diss_AwardDate,\n",
        "                     'disciplines':diss_disciplines,\n",
        "                     'comments':\"\",\n",
        "                     'copyright_date':\"\",\n",
        "                     'degree_name':diss_Degree,\n",
        "                     'department':diss_Contact,\n",
        "                     'document_type':diss_Type,\n",
        "                     'downloads_prior':\"\",\n",
        "                     'embargo_date':\"\",\n",
        "                     'fileformat':diss_Binary_Type,\n",
        "                     'file_size':\"\",\n",
        "                     'geolocate':\"\",\n",
        "                     'language':diss_Language,\n",
        "                     'latitude':\"\",\n",
        "                     'longitude':\"\",\n",
        "                     'provenance':\"\",\n",
        "                     'publication_date':\"\",\n",
        "                     'season':\"\",\n",
        "                     'publication_date_date_format':\"\",\n",
        "                     'rights_holder':\"\",\n",
        "                   }\n",
        "    \n",
        "        dictList.append(data_dict)\n",
        "        #Debug the Python DIctionary\n",
        "        #Prints each Key:Value\n",
        "        #for key, value in data_dict.items():\n",
        "        #    print(key, ' : ', value)\n",
        "    \n",
        "    df=pd.DataFrame(dictList)\n",
        "    #Print final Dataframe we are writting to Excel. \n",
        "    #print(df)\n",
        "\n",
        "    #EXCEL magic\n",
        "    filename = \"diss_from_xml_export.xlsx\".format(run_dt)\n",
        "\n",
        "    \n",
        "    writer = pd.ExcelWriter(filename, engine='xlsxwriter')\n",
        "    df.to_excel(writer, sheet_name='Export', startrow=1, header=False, index=False)\n",
        "\n",
        "\n",
        "    # Get the xlsxwriter workbook and worksheet objects.\n",
        "    workbook = writer.book\n",
        "    worksheet = writer.sheets['Export']\n",
        "\n",
        "    # Get the dimensions of the dataframe.\n",
        "    (max_row, max_col) = df.shape\n",
        "\n",
        "    # Create a list of column headers, to use in add_table().\\\n",
        "    column_settings = [{'header': column} for column in df.columns]\n",
        "\n",
        "    # Add the Excel table structure. Pandas will add the data.\n",
        "    worksheet.add_table(0, 0, max_row, max_col - 1, {'columns': column_settings, 'style': 'Table Style Medium 1'})   \n",
        "\n",
        "    # Make the columns wider for clarity.\n",
        "    worksheet.set_column(0, max_col - 1, 20) \n",
        "\n",
        "    # Close the Pandas Excel writer and output the Excel file.\n",
        "    writer.close()\n",
        "\n",
        "    print(\"*****   Processing is complete   *****\")\n",
        "    print(\"File Generated:\", filename)\n",
        "main()\n"
      ],
      "metadata": {
        "id": "LvWgpSFSvDwX",
        "outputId": "9a7bf4fc-d20c-4927-a3f7-dd31e5186580",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "*****   Processing is complete   *****\n",
            "File Generated: diss_from_xml_export.xlsx\n"
          ]
        }
      ]
    }
  ]
}