time_evolution_ref_ut_location.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "#Due to leftoevererrors in Nathan's python installation, some cleaning up occurs here\n",
    "#sys.path.append(\"./code/\")\n",
    "#sys.path.remove('/usr/local/lib/python2.7/site-packages') \n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import copy\n",
    "import datetime\n",
    "import pickle\n",
    "import gzip\n",
    "import os,glob\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "#sys.path\n",
    "\n",
    "import regex as re\n",
    "import datetime\n",
    "import math\n",
    "import time\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "########## to be able to plot offline (without sending the plots to the plotly server every time)\n",
    "import plotly.offline as offline\n",
    "from plotly.graph_objs import *\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "################\n",
    "\n",
    "\n",
    "\n",
    "from IPython.core.display import display,HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))  # to make the notebook use the entire width of the browser\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done loading pickle (1172686, 3)\n",
      "done loading pickle (8907763, 11)\n"
     ]
    }
   ],
   "source": [
    "# ###### load df pickle for time evolution of ref_UTs\n",
    "df_citations_time=pd.read_pickle('/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/data/df_UT_dates_citation_counts.pickle')\n",
    "print (\"done loading pickle\", df_citations_time.shape)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "try:\n",
    "    ref_df=pd.read_pickle('/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/data/df_reference_cite_plos_merged_simplified_columns.pickle')\n",
    "    print (\"done loading pickle\", ref_df.shape)\n",
    "except:\n",
    "\n",
    "    print( \"simplified df not found, loading original\")\n",
    "    ### load the df pickle for location of ref_UTs\n",
    "    ref_df = pickle.load(open('../data/df_reference_cite_plos_merged.pkl', 'rb'))\n",
    "    print (\"done loading pickle\", ref_df.shape)\n",
    "    ######## i select only the rows I care about, to save memory\n",
    "    ref_df = ref_df[['paper_UT','reference_UT','occurence', 'ref_datetime','reference_rank','total_refs','regex_sect_index','paper_char_pos','paper_char_total', 'cite_count','plos_datetime']]\n",
    "\n",
    "\n",
    "\n",
    "    ####### i convert dates\n",
    "    ref_df['plos_pub_date']=ref_df.apply(lambda row: row.plos_datetime.date() if row.plos_datetime !=-1  else row.plos_datetime, axis=1)\n",
    "    ref_df['ref_pub_date']=ref_df.apply(lambda  row: row.ref_datetime.date()  if row.ref_datetime != -1  else row.ref_datetime,  axis=1)\n",
    "    ref_df=ref_df.drop(['plos_datetime', 'ref_datetime'], axis=1)\n",
    "    print (\"done converting dates\")\n",
    "\n",
    "\n",
    "    ref_df.to_pickle('../data/df_reference_cite_plos_merged_simplified_columns.pkl', protocol=2)\n",
    "    print (\"written: ../data/df_reference_cite_plos_merged_simplified_columns.pkl\")\n",
    "    ref_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UT</th>\n",
       "      <th>citation_count</th>\n",
       "      <th>date</th>\n",
       "      <th>ref_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>1</td>\n",
       "      <td>2000-09-15</td>\n",
       "      <td>2000-09-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>2</td>\n",
       "      <td>2000-10-15</td>\n",
       "      <td>2000-10-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-11-01</td>\n",
       "      <td>2000-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-11-15</td>\n",
       "      <td>2000-11-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-08</td>\n",
       "      <td>2000-12-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                UT  citation_count       date    ref_date\n",
       "0  000087539300015               1 2000-09-15  2000-09-15\n",
       "1  000087539300015               2 2000-10-15  2000-10-15\n",
       "2  000087539300015               3 2000-11-01  2000-11-01\n",
       "3  000087539300015               4 2000-11-15  2000-11-15\n",
       "4  000087539300015               5 2000-12-08  2000-12-08"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_citations_time['ref_date']=df_citations_time.apply(lambda row: row.date.date() if row.date !=-1  else row.date, axis=1)\n",
    "df_citations_time.head()   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ref_df['plos_pub_date']=ref_df['plos_datetime'].date()\n",
    "# ref_df['ref_pub_date']=ref_df['ref_datetime'].date()\n",
    "\n",
    "\n",
    "# ref_df['plos_pub_date']=ref_df.apply(lambda row: row.plos_datetime.date() if row.plos_datetime !=-1  else row.plos_datetime, axis=1)\n",
    "# ref_df['ref_pub_date']=ref_df.apply(lambda  row: row.ref_datetime.date()  if row.ref_datetime != -1  else row.ref_datetime,  axis=1)\n",
    "# ref_df=ref_df.drop(['plos_datetime', 'ref_datetime'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-30-3ba67b634ba0>, line 51)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-30-3ba67b634ba0>\"\u001b[0;36m, line \u001b[0;32m51\u001b[0m\n\u001b[0;31m    pass\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#df_citations_time.head()   UT\tcitation_count\tdate   ref_date\n",
    "\n",
    "\n",
    "\n",
    "lista_dict_evol=[]\n",
    "\n",
    "for UT in df_citations_time.UT.unique():   \n",
    "    \n",
    "    print (UT)\n",
    "    \n",
    "    \n",
    "    \n",
    "    df_ref_selct=ref_df[ref_df['reference_UT']==UT]\n",
    "    df_citations_time_select=df_citations_time[df_citations_time['UT']==UT]\n",
    "    \n",
    "    max_cit=max(df_citations_time_select.citation_count)\n",
    "    \n",
    "    \n",
    "    delta_cit=int(max_cit/10.)\n",
    "    \n",
    "   # print (UT, max_cit, delta_cit)\n",
    "    \n",
    "    num_cit=0\n",
    "    while num_cit <= max_cit:\n",
    "    \n",
    "        selection_time_evol= df_citations_time_select[( df_citations_time_select['citation_count']>= num_cit )   &  ( df_citations_time_select['citation_count']< (num_cit + delta_cit) )]\n",
    "               \n",
    "        min_date=min(selection_time_evol.ref_date)\n",
    "        max_date=max(selection_time_evol.ref_date)\n",
    "\n",
    "        \n",
    "\n",
    "        try:            \n",
    "            sub_selection_ref_df =  df_ref_selct[(df_ref_selct['plos_pub_date']>= min_date)  & (df_ref_selct['plos_pub_date']< max_date)]\n",
    "        except TypeError:      \n",
    "            pass\n",
    "        \n",
    "\n",
    "        lista_sect=sub_selection_ref_df.regex_sect_index.tolist()\n",
    "\n",
    "        try:\n",
    "            most_common=max(set(lista_sect), key=lista_sect.count)\n",
    "            \n",
    "            dict_aux={'UT':UT,'num_cit':num_cit,'most_common_sect':most_common}\n",
    "            lista_dict_evol.append(dict_aux)\n",
    "            \n",
    "            \n",
    "            \n",
    "               \n",
    "            \n",
    "            \n",
    "        except ValueError: # if empty list\n",
    "            pass\n",
    "     \n",
    "    \n",
    "        \n",
    "        num_cit += delta_cit\n",
    "    \n",
    "    \n",
    "\n",
    "print (\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>paper_UT</th>\n",
       "      <th>reference_UT</th>\n",
       "      <th>occurence</th>\n",
       "      <th>reference_rank</th>\n",
       "      <th>total_refs</th>\n",
       "      <th>regex_sect_index</th>\n",
       "      <th>paper_char_pos</th>\n",
       "      <th>paper_char_total</th>\n",
       "      <th>cite_count</th>\n",
       "      <th>plos_pub_date</th>\n",
       "      <th>ref_pub_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>324515600133</td>\n",
       "      <td>A1995QY75100004</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>21733</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2013-09-04</td>\n",
       "      <td>1995-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>324515600133</td>\n",
       "      <td>A1995QY75100004</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>16829</td>\n",
       "      <td>21733</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2013-09-04</td>\n",
       "      <td>1995-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>324515600133</td>\n",
       "      <td>A1995QY75100004</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>17257</td>\n",
       "      <td>21733</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2013-09-04</td>\n",
       "      <td>1995-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324515600133</td>\n",
       "      <td>000222499800017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>266</td>\n",
       "      <td>21733</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2013-09-04</td>\n",
       "      <td>2004-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>324515600133</td>\n",
       "      <td>000222499800017</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>444</td>\n",
       "      <td>21733</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2013-09-04</td>\n",
       "      <td>2004-06-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       paper_UT     reference_UT  occurence  reference_rank  total_refs  \\\n",
       "0  324515600133  A1995QY75100004          1               1          37   \n",
       "1  324515600133  A1995QY75100004          2               1          37   \n",
       "2  324515600133  A1995QY75100004          3               1          37   \n",
       "3  324515600133  000222499800017          1               2          37   \n",
       "4  324515600133  000222499800017          2               2          37   \n",
       "\n",
       "   regex_sect_index  paper_char_pos  paper_char_total  cite_count  \\\n",
       "0                 0             139             21733        60.0   \n",
       "1                 3           16829             21733        60.0   \n",
       "2                 3           17257             21733        60.0   \n",
       "3                 0             266             21733        65.0   \n",
       "4                 0             444             21733        65.0   \n",
       "\n",
       "  plos_pub_date ref_pub_date  \n",
       "0    2013-09-04   1995-05-15  \n",
       "1    2013-09-04   1995-05-15  \n",
       "2    2013-09-04   1995-05-15  \n",
       "3    2013-09-04   2004-06-15  \n",
       "4    2013-09-04   2004-06-15  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_citations_time.head()\n",
    "ref_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000087539300015\n",
      "A1987J391400011\n",
      "A1997XH86900011\n",
      "A1997XX70200010\n",
      "000082946400010\n",
      "000084023900023\n",
      "000165228200045\n",
      "A1987J406700007\n",
      "A1991GM61300001\n",
      "A1996UB59500002\n",
      "A1990ED16700008\n",
      "A1997XU79300002\n",
      "A1996TW56700049\n",
      "000083534200033\n",
      "A1997XT82700053\n",
      "A1967A307700002\n",
      "A1996UG35000008\n",
      "000085750800017\n",
      "A1971I616900007\n",
      "A1989T157400011\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "#once a year since plos started, instead of 10 times during the paper's lifespan\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "lista_dict_evol=[]\n",
    "lista_dict_evol_all_values=[]\n",
    "\n",
    "for UT in df_citations_time.UT.unique()[:20]:   \n",
    "    \n",
    "    print (UT)\n",
    "    \n",
    "    \n",
    "    \n",
    "    df_ref_selct=ref_df[ref_df['reference_UT']==UT]\n",
    "    df_citations_time_select=df_citations_time[df_citations_time['UT']==UT]\n",
    "    \n",
    "    max_cit=max(df_citations_time_select.citation_count)\n",
    "    \n",
    "    \n",
    "    \n",
    "    year=2000\n",
    "   #max_year=2017\n",
    "    date_after = datetime.date(year, 1, 1)\n",
    "    final_date= datetime.date(2017, 12, 31)\n",
    "    delta = datetime.timedelta(days=180)\n",
    "        \n",
    "    while date_after <= final_date :                        \n",
    "       \n",
    "        date_before = date_after + delta\n",
    "        #print (date_after, date_before)\n",
    "        \n",
    "        try:\n",
    "            selection_time_evol= df_citations_time_select[( df_citations_time_select['ref_date']>= date_after )   &  ( df_citations_time_select['ref_date']< date_before )]\n",
    "            num_cit=max(selection_time_evol.citation_count)\n",
    "        except : \n",
    "            num_cit=np.nan\n",
    "                \n",
    "\n",
    "        try:            \n",
    "            sub_selection_ref_df =  df_ref_selct[(df_ref_selct['plos_pub_date']>= date_after)  & (df_ref_selct['plos_pub_date']< date_before)]\n",
    "            \n",
    "            lista_sect=sub_selection_ref_df.regex_sect_index.tolist()\n",
    "\n",
    "            try:\n",
    "                most_common=max(set(lista_sect), key=lista_sect.count)\n",
    "                avg=np.mean(lista_sect)\n",
    "                std=np.std(lista_sect)\n",
    "\n",
    "                dict_aux={'UT':UT,'num_cit':num_cit,'most_common_sect':most_common,'plos_date':date_after,'avg_sect':avg,'std_sect':std,'num_occur':len(lista_sect)}\n",
    "                lista_dict_evol.append(dict_aux)\n",
    "\n",
    "            except ValueError: # if empty list\n",
    "                most_common=np.nan\n",
    "                pass\n",
    "\n",
    "            \n",
    "            \n",
    "            \n",
    "            for i in range(len(lista_sect)):\n",
    "                value_sect=lista_sect[i]\n",
    "                dict_aux={'UT':UT,'num_cit':num_cit,'sect':value_sect}\n",
    "                lista_dict_evol_all_values.append(dict_aux)\n",
    "            \n",
    "            \n",
    "\n",
    "                \n",
    "                \n",
    "        except TypeError:      \n",
    "                pass\n",
    "\n",
    "\n",
    "\n",
    "        date_after += delta\n",
    "    \n",
    "\n",
    "print (\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UT</th>\n",
       "      <th>num_cit</th>\n",
       "      <th>sect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>303</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>303</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>313</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>320</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>320</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>320</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>334</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>340</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>340</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>356</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>356</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>369</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>369</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>384</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5125</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5125</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5148</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5148</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5203</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5203</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5248</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5267</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5267</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5267</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5299</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>5299</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>3406</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2520</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>3406</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2521</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>3406</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2522</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>434</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>434</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2524</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>434</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2525</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>434</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2526</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>565</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>565</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2528</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>651</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>651</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2530</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>651</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2531</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>651</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>695</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>695</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2534</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>695</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2535</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>695</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2536</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2537</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2538</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2539</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2540</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2541</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>766</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2542</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>766</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>793</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2544</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>793</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>822</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2546</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>822</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>822</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2548</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>822</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2549 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   UT  num_cit  sect\n",
       "0     000087539300015      303     3\n",
       "1     000087539300015      303     0\n",
       "2     000087539300015      313     3\n",
       "3     000087539300015      320     2\n",
       "4     000087539300015      320     3\n",
       "5     000087539300015      320     1\n",
       "6     000087539300015      334     3\n",
       "7     000087539300015      340     1\n",
       "8     000087539300015      340     1\n",
       "9     000087539300015      340     0\n",
       "10    000087539300015      350     1\n",
       "11    000087539300015      356     3\n",
       "12    000087539300015      356     1\n",
       "13    000087539300015      369     0\n",
       "14    000087539300015      369     4\n",
       "15    000087539300015      384     0\n",
       "16    A1987J391400011     5125     1\n",
       "17    A1987J391400011     5125     1\n",
       "18    A1987J391400011     5148     1\n",
       "19    A1987J391400011     5148     1\n",
       "20    A1987J391400011     5203     1\n",
       "21    A1987J391400011     5203     1\n",
       "22    A1987J391400011     5225     1\n",
       "23    A1987J391400011     5225     1\n",
       "24    A1987J391400011     5248     1\n",
       "25    A1987J391400011     5267     1\n",
       "26    A1987J391400011     5267     1\n",
       "27    A1987J391400011     5267     1\n",
       "28    A1987J391400011     5299     1\n",
       "29    A1987J391400011     5299     1\n",
       "...               ...      ...   ...\n",
       "2519  A1971I616900007     3406     1\n",
       "2520  A1971I616900007     3406     1\n",
       "2521  A1971I616900007     3406     1\n",
       "2522  A1989T157400011      434     0\n",
       "2523  A1989T157400011      434     3\n",
       "2524  A1989T157400011      434     3\n",
       "2525  A1989T157400011      434     1\n",
       "2526  A1989T157400011      565     1\n",
       "2527  A1989T157400011      565     1\n",
       "2528  A1989T157400011      651     2\n",
       "2529  A1989T157400011      651     1\n",
       "2530  A1989T157400011      651     1\n",
       "2531  A1989T157400011      651     1\n",
       "2532  A1989T157400011      695     1\n",
       "2533  A1989T157400011      695     1\n",
       "2534  A1989T157400011      695     1\n",
       "2535  A1989T157400011      695     1\n",
       "2536  A1989T157400011      735     1\n",
       "2537  A1989T157400011      735     1\n",
       "2538  A1989T157400011      735     1\n",
       "2539  A1989T157400011      735     1\n",
       "2540  A1989T157400011      735     1\n",
       "2541  A1989T157400011      766     1\n",
       "2542  A1989T157400011      766     7\n",
       "2543  A1989T157400011      793     0\n",
       "2544  A1989T157400011      793     1\n",
       "2545  A1989T157400011      822     1\n",
       "2546  A1989T157400011      822     0\n",
       "2547  A1989T157400011      822     1\n",
       "2548  A1989T157400011      822     1\n",
       "\n",
       "[2549 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_summary_evol_sect_values= pd.DataFrame.from_dict(lista_dict_evol_all_values)\n",
    "df_summary_evol_sect_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UT</th>\n",
       "      <th>avg_sect</th>\n",
       "      <th>most_common_sect</th>\n",
       "      <th>num_cit</th>\n",
       "      <th>num_occur</th>\n",
       "      <th>plos_date</th>\n",
       "      <th>std_sect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>303</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-03</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-30</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>320</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-04-27</td>\n",
       "      <td>0.816497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>334</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>340</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-04-22</td>\n",
       "      <td>0.471405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-10-19</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>356</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-04-17</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>369</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000087539300015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>384</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5125</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-05-13</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5148</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5203</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-11-04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5225</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5248</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-30</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5267</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-04-27</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5299</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5327</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-04-22</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5353</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-10-19</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5381</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>5401</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-04-12</td>\n",
       "      <td>0.748331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>A1987J391400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5413</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-24</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-11-20</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-05-13</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-30</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-04-27</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>3.031089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>4</td>\n",
       "      <td>2013-04-22</td>\n",
       "      <td>1.224745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A1997XH86900011</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>A1996UG35000008</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-04-17</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>A1996UG35000008</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>276</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>A1996UG35000008</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>295</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>1.374369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>000085750800017</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>3</td>\n",
       "      <td>210</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-04-27</td>\n",
       "      <td>1.469694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>000085750800017</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>220</td>\n",
       "      <td>8</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>1.391941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>000085750800017</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>241</td>\n",
       "      <td>6</td>\n",
       "      <td>2013-04-22</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>000085750800017</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0</td>\n",
       "      <td>273</td>\n",
       "      <td>16</td>\n",
       "      <td>2014-04-17</td>\n",
       "      <td>1.165922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>000085750800017</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>302</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-04-12</td>\n",
       "      <td>1.224745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>000085750800017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>2055</td>\n",
       "      <td>4</td>\n",
       "      <td>2007-05-24</td>\n",
       "      <td>0.866025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2388</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2475</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-05-08</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2648</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-05-03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2712</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-10-30</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>2808</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-04-27</td>\n",
       "      <td>2.598076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>1</td>\n",
       "      <td>2886</td>\n",
       "      <td>9</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>2.454525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2998</td>\n",
       "      <td>6</td>\n",
       "      <td>2013-04-22</td>\n",
       "      <td>2.828427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>3072</td>\n",
       "      <td>10</td>\n",
       "      <td>2013-10-19</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>1</td>\n",
       "      <td>3141</td>\n",
       "      <td>9</td>\n",
       "      <td>2014-04-17</td>\n",
       "      <td>2.572408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3226</td>\n",
       "      <td>8</td>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>3325</td>\n",
       "      <td>15</td>\n",
       "      <td>2015-04-12</td>\n",
       "      <td>2.467567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>A1971I616900007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3406</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>3</td>\n",
       "      <td>434</td>\n",
       "      <td>4</td>\n",
       "      <td>2007-05-24</td>\n",
       "      <td>1.299038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>565</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>651</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-05-03</td>\n",
       "      <td>0.433013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>695</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-04-27</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>735</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-10-24</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>766</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-04-22</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>793</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-10-19</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>A1989T157400011</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>822</td>\n",
       "      <td>4</td>\n",
       "      <td>2014-04-17</td>\n",
       "      <td>0.433013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  UT  avg_sect  most_common_sect  num_cit  num_occur  \\\n",
       "0    000087539300015  1.500000                 0      303          2   \n",
       "1    000087539300015  3.000000                 3      313          1   \n",
       "2    000087539300015  2.000000                 1      320          3   \n",
       "3    000087539300015  3.000000                 3      334          1   \n",
       "4    000087539300015  0.666667                 1      340          3   \n",
       "5    000087539300015  1.000000                 1      350          1   \n",
       "6    000087539300015  2.000000                 1      356          2   \n",
       "7    000087539300015  2.000000                 0      369          2   \n",
       "8    000087539300015  0.000000                 0      384          1   \n",
       "9    A1987J391400011  1.000000                 1     5125          2   \n",
       "10   A1987J391400011  1.000000                 1     5148          2   \n",
       "11   A1987J391400011  1.000000                 1     5203          2   \n",
       "12   A1987J391400011  1.000000                 1     5225          2   \n",
       "13   A1987J391400011  1.000000                 1     5248          1   \n",
       "14   A1987J391400011  1.000000                 1     5267          3   \n",
       "15   A1987J391400011  1.000000                 1     5299          4   \n",
       "16   A1987J391400011  1.000000                 1     5327          1   \n",
       "17   A1987J391400011  1.000000                 1     5353          3   \n",
       "18   A1987J391400011  1.000000                 1     5381          1   \n",
       "19   A1987J391400011  1.200000                 1     5401          5   \n",
       "20   A1987J391400011  1.000000                 1     5413          1   \n",
       "21   A1997XH86900011  7.000000                 7       75          1   \n",
       "22   A1997XH86900011  7.000000                 7       80          1   \n",
       "23   A1997XH86900011  0.000000                 0       94          1   \n",
       "24   A1997XH86900011  0.000000                 0      103          1   \n",
       "25   A1997XH86900011  7.000000                 7      116          1   \n",
       "26   A1997XH86900011  1.000000                 0      124          3   \n",
       "27   A1997XH86900011  1.750000                 0      132          4   \n",
       "28   A1997XH86900011  1.000000                 0      139          4   \n",
       "29   A1997XH86900011  1.500000                 0      169          4   \n",
       "..               ...       ...               ...      ...        ...   \n",
       "179  A1996UG35000008  1.500000                 0      262          2   \n",
       "180  A1996UG35000008  1.000000                 1      276          1   \n",
       "181  A1996UG35000008  1.666667                 3      295          6   \n",
       "182  000085750800017  1.800000                 3      210          5   \n",
       "183  000085750800017  1.250000                 0      220          8   \n",
       "184  000085750800017  2.000000                 3      241          6   \n",
       "185  000085750800017  0.875000                 0      273         16   \n",
       "186  000085750800017  1.000000                 0      302          4   \n",
       "187  000085750800017  0.000000                 0      311          1   \n",
       "188  A1971I616900007  1.500000                 1     2055          4   \n",
       "189  A1971I616900007  1.000000                 1     2388          1   \n",
       "190  A1971I616900007  1.000000                 1     2475          1   \n",
       "191  A1971I616900007  1.000000                 1     2648          4   \n",
       "192  A1971I616900007  1.000000                 1     2712          2   \n",
       "193  A1971I616900007  2.500000                 1     2808          4   \n",
       "194  A1971I616900007  2.555556                 1     2886          9   \n",
       "195  A1971I616900007  3.000000                 1     2998          6   \n",
       "196  A1971I616900007  0.900000                 1     3072         10   \n",
       "197  A1971I616900007  2.222222                 1     3141          9   \n",
       "198  A1971I616900007  1.000000                 1     3226          8   \n",
       "199  A1971I616900007  2.333333                 1     3325         15   \n",
       "200  A1971I616900007  1.000000                 1     3406          4   \n",
       "201  A1989T157400011  1.750000                 3      434          4   \n",
       "202  A1989T157400011  1.000000                 1      565          2   \n",
       "203  A1989T157400011  1.250000                 1      651          4   \n",
       "204  A1989T157400011  1.000000                 1      695          4   \n",
       "205  A1989T157400011  1.000000                 1      735          5   \n",
       "206  A1989T157400011  4.000000                 1      766          2   \n",
       "207  A1989T157400011  0.500000                 0      793          2   \n",
       "208  A1989T157400011  0.750000                 1      822          4   \n",
       "\n",
       "      plos_date  std_sect  \n",
       "0    2011-05-03  1.500000  \n",
       "1    2011-10-30  0.000000  \n",
       "2    2012-04-27  0.816497  \n",
       "3    2012-10-24  0.000000  \n",
       "4    2013-04-22  0.471405  \n",
       "5    2013-10-19  0.000000  \n",
       "6    2014-04-17  1.000000  \n",
       "7    2014-10-14  2.000000  \n",
       "8    2015-10-09  0.000000  \n",
       "9    2009-05-13  0.000000  \n",
       "10   2009-11-09  0.000000  \n",
       "11   2010-11-04  0.000000  \n",
       "12   2011-05-03  0.000000  \n",
       "13   2011-10-30  0.000000  \n",
       "14   2012-04-27  0.000000  \n",
       "15   2012-10-24  0.000000  \n",
       "16   2013-04-22  0.000000  \n",
       "17   2013-10-19  0.000000  \n",
       "18   2014-10-14  0.000000  \n",
       "19   2015-04-12  0.748331  \n",
       "20   2015-10-09  0.000000  \n",
       "21   2007-05-24  0.000000  \n",
       "22   2007-11-20  0.000000  \n",
       "23   2009-05-13  0.000000  \n",
       "24   2009-11-09  0.000000  \n",
       "25   2011-10-30  0.000000  \n",
       "26   2012-04-27  1.414214  \n",
       "27   2012-10-24  3.031089  \n",
       "28   2013-04-22  1.224745  \n",
       "29   2014-10-14  1.500000  \n",
       "..          ...       ...  \n",
       "179  2014-04-17  1.500000  \n",
       "180  2014-10-14  0.000000  \n",
       "181  2015-10-09  1.374369  \n",
       "182  2012-04-27  1.469694  \n",
       "183  2012-10-24  1.391941  \n",
       "184  2013-04-22  1.414214  \n",
       "185  2014-04-17  1.165922  \n",
       "186  2015-04-12  1.224745  \n",
       "187  2015-10-09  0.000000  \n",
       "188  2007-05-24  0.866025  \n",
       "189  2009-11-09  0.000000  \n",
       "190  2010-05-08  0.000000  \n",
       "191  2011-05-03  0.000000  \n",
       "192  2011-10-30  0.000000  \n",
       "193  2012-04-27  2.598076  \n",
       "194  2012-10-24  2.454525  \n",
       "195  2013-04-22  2.828427  \n",
       "196  2013-10-19  0.300000  \n",
       "197  2014-04-17  2.572408  \n",
       "198  2014-10-14  0.000000  \n",
       "199  2015-04-12  2.467567  \n",
       "200  2015-10-09  0.000000  \n",
       "201  2007-05-24  1.299038  \n",
       "202  2009-11-09  0.000000  \n",
       "203  2011-05-03  0.433013  \n",
       "204  2012-04-27  0.000000  \n",
       "205  2012-10-24  0.000000  \n",
       "206  2013-04-22  3.000000  \n",
       "207  2013-10-19  0.500000  \n",
       "208  2014-04-17  0.433013  \n",
       "\n",
       "[209 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_summary_evol_most_common_sect= pd.DataFrame.from_dict(lista_dict_evol)\n",
    "df_summary_evol_most_common_sect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'file:///home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/testing_time_series_evol_sect_location_vs_num_citations_A1997XT82700053.html'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=[]\n",
    "\n",
    "\n",
    "\n",
    "for UT in df_summary_evol.UT.unique()[:-5]:\n",
    "    \n",
    "#     data=[]\n",
    "    \n",
    "    df_plot=df_summary_evol_sect_values[df_summary_evol_sect_values['UT']==UT]\n",
    "  #  df_plot=df_summary_evol_most_common_sect[df_summary_evol_most_common_sect['UT']==UT]\n",
    "    \n",
    "   # if len(df_plot)>10 :# and len(df_plot.most_common_sect.unique()) >=3:\n",
    "\n",
    "        \n",
    "       # print (UT,len(df_plot), df_plot.sect.unique())\n",
    "\n",
    "    trace = go.Scatter( x=df_plot['num_cit'],                       \n",
    "                  # y=df_plot['avg_sect'],  #y=df_plot['most_common_sect'],\n",
    "                   y=df_plot['sect'],\n",
    "                   mode = 'markers',  #'lines+markers'\n",
    "                   name=UT )\n",
    "\n",
    "\n",
    "    data.append(trace)\n",
    "\n",
    "layout = go.Layout(\n",
    "       # title='Plot Title',\n",
    "        xaxis=dict(\n",
    "            title='Number of citations accumulated',\n",
    "#             titlefont=dict(\n",
    "#                 family='Courier New, monospace',\n",
    "#                 size=18,\n",
    "#                 color='#7f7f7f'\n",
    "#             )\n",
    "        ),\n",
    "        yaxis=dict(\n",
    "            title='Most common section in the paper',\n",
    "#             titlefont=dict(\n",
    "#                 family='Courier New, monospace',\n",
    "#                 size=18,\n",
    "#                 color='#7f7f7f'\n",
    "#             )\n",
    "        )\n",
    "    )\n",
    "\n",
    "fig = Figure(data=data, layout=layout)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "iplot(data, filename='testing_time_series_citations.html')\n",
    "\n",
    "offline.plot(fig, auto_open=True, image = 'png', image_filename=\"testing_time_series_evol_sect_location_vs_num_citations\" ,\n",
    "                 output_type='file',  filename='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/testing_time_series_evol_sect_location_vs_num_citations_'+UT+'.html', validate=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
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       " 97,\n",
       " 98,\n",
       " 99]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N=100\n",
    "lista=[i for i in range(N)]\n",
    "lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 7, 10, 11]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista_listas=[[1,2,3],[22,33]]\n",
    "lista=[1,2,3,4,np.nan]\n",
    "max(lista_listas)\n",
    "np.nanmean(lista)\n",
    "\n",
    "x1=[1,3,4,10]\n",
    "y1=[1,2,1,1]\n",
    "\n",
    "x2=[1,2,5,10,10]\n",
    "y2=[3,1,3,2,3]\n",
    "\n",
    "\n",
    "x3=[1,7,10,11]\n",
    "y3=[1,2,5,1,0]\n",
    "\n",
    "list_of_tuplas=[(x1,y1),(x2,y2),(x3,y3)]\n",
    "list_of_tuplas\n",
    "\n",
    "nueva=sorted(set(x1 + x2+x3))\n",
    "nueva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[1, 2, 3, 4, 5, 7, 10, 11], [1, nan, 2, 1, nan, nan, 1, nan]],\n",
       " [[1, 2, 3, 4, 5, 7, 10, 11], [3, 1, nan, nan, 3, nan, 2, nan]],\n",
       " [[1, 2, 3, 4, 5, 7, 10, 11], [1, nan, nan, nan, nan, 2, 5, 1]]]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fill_gaps (list_of_tuplas):\n",
    "    \n",
    "    \n",
    "    #print (list_of_tuplas)\n",
    "    new_lista_tuplas=[]\n",
    "   \n",
    "    combined_x=[]\n",
    "    for tupla in list_of_tuplas:       \n",
    "        x_original=tupla[0]\n",
    "        combined_x += x_original\n",
    "    combined_x = sorted(list(set(combined_x)))\n",
    "       \n",
    "    for tupla in list_of_tuplas:      \n",
    "        \n",
    "        x_original=tupla[0]\n",
    "        y_original=tupla[1]\n",
    "        \n",
    "        x=[]\n",
    "        y=[]                \n",
    "                      \n",
    "        last_value=0\n",
    "        for x_value in combined_x:          \n",
    "            for indice in range(len(x_original)):\n",
    "                \n",
    "                if x_original[indice] == x_value:\n",
    "                    x.append(x_value)\n",
    "                    y.append(y_original[indice])                         \n",
    "                    last_value= x_value\n",
    "                    break\n",
    "                else:\n",
    "                    if x_original[indice] > last_value:\n",
    "                        x.append(x_value)\n",
    "                        y.append(np.nan)                      \n",
    "                        break                   \n",
    "                \n",
    "        if combined_x[-1] > x_original[-1]: \n",
    "            last_value_original=x_original[-1]\n",
    "            for i in range(len(combined_x)):\n",
    "                value= combined_x[i]\n",
    "                if value > last_value_original:                                \n",
    "                    x.append(combined_x[i])\n",
    "                    y.append(np.nan)  \n",
    "                \n",
    "                                                   \n",
    "        new_tupla=[x,y]\n",
    "        new_lista_tuplas.append(new_tupla)\n",
    "        \n",
    "\n",
    "\n",
    "    return new_lista_tuplas     \n",
    "\n",
    "#################################\n",
    "\n",
    "new_list_tuplas=fill_gaps(list_of_tuplas) \n",
    "\n",
    "\n",
    "# x1=[1,3,4,10]\n",
    "# y1=[1,2,1,1]\n",
    "\n",
    "# x2=[1,2,5,10,10]\n",
    "# y2=[3,1,3,2,3]\n",
    "\n",
    "# x3=[1,7,10,11]\n",
    "# y3=[1,2,5,1,0]\n",
    "new_list_tuplas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1, 2, 3, 4, 5, 7, 10, 11],\n",
       " [1.6666666666666667, 1.0, 2.0, 1.0, 3.0, 2.0, 2.6666666666666665, 1.0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "    \n",
    "def get_time_avg_list_values (list_of_tuplas):   # all the list of tuplas have the same lenght and x values (even if empty y values)\n",
    " \n",
    "    list_avg_y=[]\n",
    "    for i in range(len(list_of_tuplas[0][0])):   #loop over lenght of the (combined) time series\n",
    "    \n",
    "        list_y_values=[]\n",
    "        for tupla in list_of_tuplas:  \n",
    "            list_y_values.append(tupla[1][i])\n",
    "        \n",
    "        list_avg_y.append(np.nanmean(list_y_values))\n",
    "    \n",
    "    \n",
    "    new_tupla=(list_of_tuplas[0][0], list_avg_y)   \n",
    "    return new_tupla\n",
    "###########################    \n",
    "    \n",
    "avg=get_time_avg_list_values(new_list_tuplas)\n",
    "avg\n",
    "    \n",
    "    \n",
    "    \n",
    "        \n",
    "######### try average over multiple curves\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      ]
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     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "'file:///home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/time_series_evol_sect_location_vs_num_citations.html'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=[]\n",
    "lista_tuplas_df_xy=[]\n",
    "\n",
    "\n",
    "for UT in df_summary_evol.UT.unique()[:20]:\n",
    "    \n",
    "    df_plot=df_summary_evol[df_summary_evol['UT']==UT]\n",
    "           \n",
    "    x1=df_plot['num_cit'].tolist()\n",
    "    y1=df_plot['avg_sect'].tolist()      #df_plot['most_common_sect']\n",
    "    \n",
    "    trace = go.Scatter( x=x1, \n",
    "                       #y=df_plot['most_common_sect'],\n",
    "                       y=y1,\n",
    "                       name=UT )\n",
    "    \n",
    "   # trace = go.Scatter(x=x1, y=y1 , name=UT )\n",
    "   # data.append(trace)\n",
    "    \n",
    "    tupla_xy=(x1, y1)\n",
    "    lista_tuplas_df_xy.append(tupla_xy)\n",
    "\n",
    "   \n",
    "\n",
    "        \n",
    "\n",
    "new_list_tuplas=fill_gaps(lista_tuplas_df_xy) \n",
    "tupla_avg=get_time_avg_list_values(new_list_tuplas)        \n",
    "        \n",
    "trace = go.Scatter( x=tupla_avg[0], \n",
    "                    y=tupla_avg[1],                      \n",
    "                    name=\"avg\" )\n",
    "data.append(trace)      \n",
    " \n",
    "\n",
    "\n",
    "        \n",
    "layout = go.Layout(\n",
    "       # title='Plot Title',\n",
    "        xaxis=dict(\n",
    "            title='Number of citations accumulated',\n",
    "#             titlefont=dict(\n",
    "#                 family='Courier New, monospace',\n",
    "#                 size=18,\n",
    "#                 color='#7f7f7f'\n",
    "#             )\n",
    "        ),\n",
    "        yaxis=dict(\n",
    "            title='Most common section in the paper',\n",
    "#             titlefont=dict(\n",
    "#                 family='Courier New, monospace',\n",
    "#                 size=18,\n",
    "#                 color='#7f7f7f'\n",
    "#             )\n",
    "        )\n",
    "    )\n",
    "\n",
    "fig = Figure(data=data, layout=layout)\n",
    "\n",
    "    \n",
    "    \n",
    "\n",
    "iplot(data, filename='testing_time_series_citations.html')\n",
    "   \n",
    "offline.plot(fig, auto_open=True, image = 'png', image_filename=\"testing_time_series_evol_sect_location_vs_num_citations\" ,\n",
    "                 output_type='file',  filename='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/time_series_evol_sect_location_vs_num_citations.html', validate=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_df=pd.DataFrame(data=tupla_avg)\n",
    "np.array(tupla_avg)\n",
    "new_df=pd.DataFrame(data=np.array(tupla_avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "UT='A1987J406700007'\n",
    "df_citations_time[df_citations_time['UT']==UT]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "UT='A1987J406700007'\n",
    "ref_df[ref_df['reference_UT']==UT]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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