thesis23-24
Science Score: 18.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
○Academic links in README
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Unable to calculate vocabulary similarity
Last synced: 10 months ago
·
JSON representation
·
Repository
Basic Info
- Host: GitHub
- Owner: jdeen33
- Language: HTML
- Default Branch: main
- Size: 43 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 7
- Releases: 0
Created about 3 years ago
· Last pushed over 2 years ago
Metadata Files
Citation
Citation (Citations_txtreuse/Vizualizations_Cleaned_ (1).ipynb)
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OGPioP3C_lLk"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from google.colab import drive\n",
"from bs4 import BeautifulSoup, Tag\n",
"import re"
]
},
{
"cell_type": "code",
"source": [
"!pip install pythonbible"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EnPiF7CXKc06",
"outputId": "09f4b433-0eff-4b08-8aae-61aee0db6658"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting pythonbible\n",
" Downloading pythonbible-0.12.0-py3-none-any.whl (19.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.8/19.8 MB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: pythonbible\n",
"Successfully installed pythonbible-0.12.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import plotly.graph_objs as go\n",
"import plotly.express as px\n",
"import pythonbible as bible"
],
"metadata": {
"id": "jIn28osZ4v5K"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Citation Viz Data Collection"
],
"metadata": {
"id": "aDinjusq6EJ8"
}
},
{
"cell_type": "code",
"source": [
"!unzip /content/tlg0018_first1K-20231003T211502Z-001.zip"
],
"metadata": {
"id": "COKqR7T7djil"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import glob\n",
"grc_files = sorted(glob.glob('/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/*opp-grc1.xml'))\n",
"grc_files"
],
"metadata": {
"id": "sQbJ9LuZcS_E",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6ff1ec45-9de1-44d7-d09c-12a8e3a3b43d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg001.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg002.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg003.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg004.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg005.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg006.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg007.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg008.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg009.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg010.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg011.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg012.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg013.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg014.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg015.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg016.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg017.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg018.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg019.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg020.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg021.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg022.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg023.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg024.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg025.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg026.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg027.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg028.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg029.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg030.opp-grc1.xml',\n",
" '/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg031.opp-grc1.xml']"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"def open_file(x):\n",
" philo = open( x ,encoding='utf-8', errors='ignore')\n",
" philo_soup = BeautifulSoup(philo,'lxml')\n",
" return philo_soup\n"
],
"metadata": {
"id": "AE_EbVou44_Q"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def dir_name(x):\n",
" philo = open(x, encoding='utf-8', errors='ignore')\n",
" nom= philo.name[60:] #arbitrary length..the names of my xml files are very long\n",
" return nom"
],
"metadata": {
"id": "8p8-_lNe9hQe"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def make_refdict(soup):\n",
" ref_dict={}\n",
" for d in opened.find_all('div'):\n",
" ref_dict[d['n']]= d.get_text()\n",
" return ref_dict"
],
"metadata": {
"id": "CC1_ViD_48DE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def pull_citations(soup_dict):\n",
" cits= []\n",
" pattern= '\\s*\\((.*?)\\)\\s*' #pattern generated by chatGPT\n",
" for v in soup_dict.values():\n",
" match= re.findall(pattern,v)\n",
" if match:\n",
" cits.append(match)\n",
" return cits\n",
"#list of lists"
],
"metadata": {
"id": "qNaWs68JRGIr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def list_cleaning(lst_1):\n",
" split_lst= [y.split(\"','\") for x in lst_1 for y in x]\n",
" flat_list = [item for sublist in split_lst for item in sublist]\n",
" return flat_list #still a list of lists"
],
"metadata": {
"id": "1C_MIQyJ51GA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def ibid_corrections(lst_4): #code by ChatGPT\n",
" prev_citation= None\n",
" for i in range(len(lst_4)):\n",
" if 'ib' in lst_4[i]:\n",
" if prev_citation is not None:\n",
" lst_4[i] = prev_citation\n",
" else:\n",
" prev_citation = lst_4[i]\n",
" return lst_4"
],
"metadata": {
"id": "-UXpYcmFThjB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def format_citations(lst3):\n",
" citations= []\n",
" try:\n",
" for v in lst3:\n",
" references= bible.get_references(v)\n",
" formatted_reference= bible.format_scripture_references(references)\n",
" citations.append(formatted_reference)\n",
" except:\n",
" pass\n",
" return citations"
],
"metadata": {
"id": "L8nDfnELEnYl"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def splitting_chapter_verses(lst):\n",
" splits= []\n",
" split_2= []\n",
" for item in lst:\n",
" split= item.split(':')\n",
" splits.append(split)\n",
" return splits\n",
" for item in splits:\n",
" for y in item:\n",
" again= y.split(';')\n",
" split_2.append(again)\n",
" return split_2\n",
"\n",
"\n",
"\n"
],
"metadata": {
"id": "599iuhidW62z"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def flatten_chap_vers(lst):\n",
" flat= [item for sublist in lst for item in sublist]\n",
" return flat"
],
"metadata": {
"id": "eYNArFjhi2ez"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def remove_verses(lst):\n",
" for item in lst:\n",
" if '-' in item:\n",
" lst.remove(item)\n",
" return lst"
],
"metadata": {
"id": "HS8K9HxPdI-y"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def make_df(lst_6,p):\n",
" couns= pd.Series(lst_6).value_counts()\n",
" counts= pd.DataFrame(couns)\n",
" #counts.reset_index(inplace=True)\n",
" #counts.rename(columns={'index': \"Citation\", 0: \"count\"})\n",
" counts.to_csv(f'/content/drive/MyDrive/Thesis2023/CSV_Files/Citations/{p}.csv')\n"
],
"metadata": {
"id": "mg4Z56bJ8RR_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for x in grc_files:\n",
" opened= open_file(x)\n",
" named= dir_name(x)\n",
" ref_dict= make_refdict(opened)\n",
" cited= pull_citations(ref_dict)\n",
" cleaned= list_cleaning(cited)\n",
" ibids= ibid_corrections(cleaned)\n",
" formatted= format_citations(ibids)\n",
" chaps= splitting_chapter_verses(formatted)\n",
" flattened= flatten_chap_vers(chaps)\n",
" jchaps= remove_verses(flattened)\n",
" frame= make_df(jchaps,named)"
],
"metadata": {
"id": "clIjsfg_VocG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"opened= open_file('/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg002.opp-grc1.xml')\n",
"named= dir_name('/content/drive/MyDrive/Thesis2023/XML Files/tlg0018_first1K/tlg0018.tlg002.opp-grc1.xml')\n",
"ref_dict= make_refdict(opened)\n",
"cited= pull_citations(ref_dict)\n",
"cleaned= list_cleaning(cited)\n",
"ibids= ibid_corrections(cleaned)\n",
"formatted= format_citations(ibids)\n",
"chaps= splitting_chapter_verses(formatted)\n",
"flattened= flatten_chap_vers(chaps)\n",
"jchaps= remove_verses(flattened)\n",
"#frame= make_df(jchaps,named)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hlb0iqCYCP27",
"outputId": "6f8f9d66-4a4d-46cc-8955-f305c2cb8cb1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/bs4/builder/__init__.py:545: XMLParsedAsHTMLWarning: It looks like you're parsing an XML document using an HTML parser. If this really is an HTML document (maybe it's XHTML?), you can ignore or filter this warning. If it's XML, you should know that using an XML parser will be more reliable. To parse this document as XML, make sure you have the lxml package installed, and pass the keyword argument `features=\"xml\"` into the BeautifulSoup constructor.\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"jchaps"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oRLPmbuPCED5",
"outputId": "7afeca99-3a33-41d2-cee4-027477aaaeaf"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
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" <div class=\"colab-df-buttons\">\n",
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" <div class=\"colab-df-container\">\n",
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"\n",
" <style>\n",
" .colab-df-container {\n",
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" border: none;\n",
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" display: none;\n",
" fill: #1967D2;\n",
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"\n",
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" background-color: #E2EBFA;\n",
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" + ' to learn more about interactive tables.';\n",
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" 30% {\n",
" border-color: transparent;\n",
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" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
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" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
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]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"new= chapters_df.rename(columns={'index':'Citation',0: \"Count\"})"
],
"metadata": {
"id": "3R-uxb90ExdG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"new"
],
"metadata": {
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"height": 424
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"id": "GqwPbiOBFrJK",
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{
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"data": {
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" Citation Count\n",
"0 188\n",
"1 Genesis 3 47\n",
"2 Genesis 2 35\n",
"3 Exodus 16 14\n",
"4 Genesis 15 10\n",
".. ... ...\n",
"93 23 1\n",
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" }\n",
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},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"source": [
"chapters_df.name= 'Allegorical Interpretations, Books 1-3'"
],
"metadata": {
"id": "OJ70AW4cFOyC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"new1= new.dropna()"
],
"metadata": {
"id": "n2tMY_wXFyOF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"new1= new.drop(0)"
],
"metadata": {
"id": "3SVrDg1vF5al"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
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"new1"
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"id": "4P3LX49cGMV4",
"outputId": "4eef6c0f-c530-4953-9451-83840b47f940"
},
"execution_count": null,
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{
"output_type": "execute_result",
"data": {
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" Citation Count\n",
"1 Genesis 3 47\n",
"2 Genesis 2 35\n",
"3 Exodus 16 14\n",
"4 Genesis 15 10\n",
"5 Numbers 12 10\n",
".. ... ...\n",
"93 23 1\n",
"94 Numbers 31 1\n",
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"96 1,25 1\n",
"97 16 1\n",
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"[97 rows x 2 columns]"
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" <th>Citation</th>\n",
" <th>Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Genesis 3</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Genesis 2</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Exodus 16</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Genesis 15</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Numbers 12</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>Numbers 31</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>Exodus 7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>1,25</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
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"<p>97 rows × 2 columns</p>\n",
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" const charts = await google.colab.kernel.invokeFunction(\n",
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]
},
"metadata": {},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"source": [
"fig = px.treemap(new1, path=[px.Constant(f'{chapters_df.name}'), 'Citation'],\n",
" values='Count', color='Citation')\n",
"fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "SqgBHjaTFLUo",
"outputId": "34f68ff9-6dd6-4f7d-c917-cb141bc448f0"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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" </svg>\n",
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"\n",
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"\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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" const buttonEl =\n",
" document.querySelector('#df-f1d24d54-746f-45e9-a9a9-c121fddb1ddd button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-f1d24d54-746f-45e9-a9a9-c121fddb1ddd');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
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" width=\"24px\">\n",
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" </g>\n",
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"\n",
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" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
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" background-color: var(--bg-color);\n",
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"\n",
" .colab-df-spinner {\n",
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"\n",
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" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
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" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
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" 90% {\n",
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" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-0a3eea0b-ff18-47c0-abe5-7728a15e4d36 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"df_8_1= df_8.rename(columns={'index':'Citation',0: \"Count\"})\n",
"df_8_1"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 394
},
"id": "YGLbuamyJKqD",
"outputId": "9b9e1408-2a84-405e-9cfd-c432fdec7289"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Count\n",
" 20\n",
"Genesis 6 4\n",
"Genesis 2 1\n",
"9,22 1\n",
"Genesis 15 1\n",
"Numbers 28 1\n",
"Genesis 38 1\n",
"Deuteronomy 5 1\n",
"Deuteronomy 15 1\n",
"19,30 1\n",
"Deuteronomy 1 1"
],
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-7bafc2d8-4af5-4b49-8a62-8b72f58feee7');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
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" }\n",
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" width=\"24px\">\n",
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" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
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" --disabled-fill-color: #666;\n",
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"\n",
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" border: none;\n",
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" fill: var(--fill-color);\n",
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" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
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" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
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" spin 1s steps(1) infinite;\n",
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"\n",
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" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
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" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-a8267994-f253-4ef0-bb96-5b53dd50f003 button');\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 47
}
]
}
]
}