newsgrab Example Analysis

newsgrab_example_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing newsgrab JSON Data with spaCy and pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Data Preparation](#Data-Preparation)\n",
    "\n",
    "[Separate and Quanitfy Keyword Tokens](#Separate-and-Quantify-Keyword-Tokens)\n",
    "\n",
    "[Quantify Noun Chunks](#Quantify-Noun-Chunks)\n",
    "\n",
    "[Quantify Named Entities](#Quantify-Named-Entities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary libraries and load in each of the JSON files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import re\n",
    "import spacy\n",
    "from collections import Counter\n",
    "\n",
    "# Load English language pretrained model from spacy\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "# Set the dataframe row column widths to show full text\n",
    "from pandas.io.json import json_normalize\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "with open('output_1.json',encoding=\"utf8\") as f1, open('output_2.json',encoding=\"utf8\") as f2, open('output_3.json',encoding=\"utf8\") as f3, open('output_special.json',encoding=\"utf8\") as f4:\n",
    "    list1 = json.load(f1)\n",
    "    list2 = json.load(f2)\n",
    "    list3 = json.load(f3)\n",
    "    list4 = json.load(f4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the JSON files with pandas and return dataframe objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_data1 = pd.json_normalize(list1, record_path='results', meta='search_term',\n",
    "                                     record_prefix='results')\n",
    "search_data2 = pd.json_normalize(list2, record_path='results', meta='search_term',\n",
    "                                     record_prefix='results')\n",
    "search_data3 = pd.json_normalize(list3, record_path='results', meta='search_term',\n",
    "                                     record_prefix='results')\n",
    "search_data4 = pd.json_normalize(list4, record_path='results', meta='search_term',\n",
    "                                     record_prefix='results')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the four dataframes into one using pandas concatenate function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>results0</th>\n",
       "      <th>search_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oxford's Emma Fogarty makes Adelphi University dean's list</td>\n",
       "      <td>adelphi university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wilton Resident Sophia Walsh Named to Adelphi University ...</td>\n",
       "      <td>adelphi university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Local Students Named to Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           results0  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "2        Oxford's Emma Fogarty makes Adelphi University dean's list   \n",
       "3      Wilton Resident Sophia Walsh Named to Adelphi University ...   \n",
       "4        Local Students Named to Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term  \n",
       "0  adelphi university  \n",
       "1  adelphi university  \n",
       "2  adelphi university  \n",
       "3  adelphi university  \n",
       "4  adelphi university  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [search_data1, search_data2, search_data3, search_data4]\n",
    "df = pd.concat(frames)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the results column to exclude the 0 and create a column with lowercase results output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns = {\"results0\":\"results\"})\n",
    "df['results_lower'] = df['results'].apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use spaCy to get noun chunks, named entities, and tokens from the results text and append these to respective lists. Turn these into new columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "noun_chunks = []\n",
    "named_entity = []\n",
    "tokens = []\n",
    "\n",
    "for doc in nlp.pipe(df['results_lower'].astype('unicode').values, batch_size=50,\n",
    "                        n_process=5):\n",
    "    if doc.is_parsed:\n",
    "        noun_chunks.append([chunk.text for chunk in doc.noun_chunks])\n",
    "        named_entity.append([ent.text for ent in doc.ents])\n",
    "        tokens.append([token.text for token in doc if not token.is_stop and not token.is_punct])\n",
    "    else:\n",
    "        noun_chunks.append(None)\n",
    "        named_entity.append(None)       \n",
    "        tokens.append(None)\n",
    "        \n",
    "df['results_noun_chunks'] = noun_chunks\n",
    "df['results_named_entities'] = named_entity\n",
    "df['results_tokens_clean'] = tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separate and Quantify Keyword Tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean the output from the tokens column in its entirety by flattening the list, remove special characters, and remove line breaks and the leftovers of ampersands. Then, put in a string list and use the Counter from Python's build-in collections. Get a sample of the 15 most common words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('college', 289), ('new', 159), ('university', 116), ('2020', 93), ('students', 87), ('suny', 86), ('covid19', 67), ('school', 66), ('news', 63), ('fall', 63), ('colleges', 59), ('york', 53), ('president', 46), ('named', 44), ('coronavirus', 41)]\n"
     ]
    }
   ],
   "source": [
    "flattened_list = [y for x in tokens for y in x]\n",
    "\n",
    "#remove special characters from tokens list before analyzing\n",
    "list_cleaned = [re.sub(r\"[^a-zA-Z0-9]\", \"\", file) for file in flattened_list]\n",
    "\n",
    "#remove the remainders of what was ampersands and paragraph breaks: \\n and amp\n",
    "list_cleaned2 = [re.sub('\\n', \"\", file) for file in list_cleaned]\n",
    "\n",
    "list_cleaned3 = [re.sub('amp', \"\", file) for file in list_cleaned2]\n",
    "\n",
    "str_list = list(filter(None, list_cleaned3))\n",
    "\n",
    "word_freq = Counter(str_list)\n",
    "common_words = word_freq.most_common(15)\n",
    "print(common_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a copy of the token list to use in further processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_str_list = str_list.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile a list of words to exclude from the tokens list, using the original search document text. Read in the text file, split on the separator, then join on it to return a string, and replace the commas with a space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compile list of words to exclude from college list\n",
    "list_names = open('ny_colleges.txt',encoding=\"utf8\").read().split(',')\n",
    "str1 = ','.join(list_names)\n",
    "other_str = str1.replace(',', ' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tokenize the list of search terms to exclude using spaCy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#must tokenize the list that was brought in\n",
    "new_college_list = []\n",
    "doc2 = nlp(other_str)\n",
    "for token in doc2:\n",
    "    new_college_list.append(token.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a final list of tokens that exclude the search terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_words = [word for word in new_str_list if word not in new_college_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the Counter from collections to quantify the frequency of each keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_freq2 = Counter(new_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe for the keywords and their frequency counts. Sort from high to low count, reset the index, and rename the columns. Save the dataframe as a CSV if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_word_counts = pd.DataFrame.from_dict(word_freq2, orient='index',\n",
    "                       columns=['keyword_count'])\n",
    "results_words = results_word_counts.sort_values(by=['keyword_count'],ascending=False)\n",
    "results_words.reset_index(inplace=True)\n",
    "results_words.rename(columns = {\"index\":\"keyword\",\"keyword_count\":\"count\"}, inplace=True)\n",
    "#results_words.to_csv('keyword_counts.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose all keywords with a frequency greater than or equal to 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>keyword</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>students</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>covid19</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>news</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fall</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    keyword  count\n",
       "0      2020     93\n",
       "1  students     87\n",
       "2   covid19     67\n",
       "3      news     63\n",
       "4      fall     63"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from the above dataframe, choose all keywords with a frequency greater than or equal to 10\n",
    "results_words = results_words.loc[results_words['count'] >= 10]\n",
    "results_words.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removed an unrecognizable word in the dataframe ('cus') after noticing it above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_words = results_words.loc[~results_words['keyword'].str.contains(\"cus\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataframe for the top 24 keywords and include both original columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_words_top = results_words.iloc[0:24, 0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a bar plot of the keyword data using seaborn and save the figure image, if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.barplot(x = results_words_top['count'],y = results_words_top['keyword'],palette=\"Blues_d\")\n",
    "\n",
    "sb.despine(fig=None, ax=None, top=True, right=True, left=False, trim=False)\n",
    "sb.set(rc={'figure.figsize':(6,7.2)})\n",
    "\n",
    "ax.set_ylabel('')    \n",
    "ax.set_xlabel('')\n",
    "ax.set_title('Keyword Counts for NY 4-Year College Headlines on 7/1/20', fontsize=18, fontweight='heavy')\n",
    "sb.set(font_scale = 1.4)\n",
    "ax.axes.get_xaxis().set_visible(True)\n",
    "ax.set_frame_on(True)\n",
    "\n",
    "#all data that had over 22 frequency counts    \n",
    "#plt.savefig('keyword_countplot.jpg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantify Noun Chunks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataframe focusing on columns for noun chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>results</th>\n",
       "      <th>search_term</th>\n",
       "      <th>results_noun_chunks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[student, ozark, adelphi university spring 2020 dean]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[kaitlyn grant, adelphi university spring]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oxford's Emma Fogarty makes Adelphi University dean's list</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[oxford's emma fogarty, adelphi university dean's list]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wilton Resident Sophia Walsh Named to Adelphi University ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[wilton resident sophia walsh, university]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Local Students Named to Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[local students, university spring]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            results  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "2        Oxford's Emma Fogarty makes Adelphi University dean's list   \n",
       "3      Wilton Resident Sophia Walsh Named to Adelphi University ...   \n",
       "4        Local Students Named to Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term                                      results_noun_chunks  \n",
       "0  adelphi university    [student, ozark, adelphi university spring 2020 dean]  \n",
       "1  adelphi university               [kaitlyn grant, adelphi university spring]  \n",
       "2  adelphi university  [oxford's emma fogarty, adelphi university dean's list]  \n",
       "3  adelphi university               [wilton resident sophia walsh, university]  \n",
       "4  adelphi university                      [local students, university spring]  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nouns = df[['results','search_term','results_noun_chunks']].copy()\n",
    "nouns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the noun chunks column to a string type and create a new column that will be separated. Use the explode function from pandas to separate the items in the sep column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>results</th>\n",
       "      <th>search_term</th>\n",
       "      <th>results_noun_chunks</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>['student'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>'ozark'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>'adelphi university spring 2020 dean']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university spring']</td>\n",
       "      <td>['kaitlyn grant'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university spring']</td>\n",
       "      <td>'adelphi university spring']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            results  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "2  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "3      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "4      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term  \\\n",
       "0  adelphi university   \n",
       "1  adelphi university   \n",
       "2  adelphi university   \n",
       "3  adelphi university   \n",
       "4  adelphi university   \n",
       "\n",
       "                                           results_noun_chunks  \\\n",
       "0  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "1  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "2  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "3               ['kaitlyn grant', 'adelphi university spring']   \n",
       "4               ['kaitlyn grant', 'adelphi university spring']   \n",
       "\n",
       "                                       sep  \n",
       "0                               ['student'  \n",
       "1                                  'ozark'  \n",
       "2   'adelphi university spring 2020 dean']  \n",
       "3                         ['kaitlyn grant'  \n",
       "4             'adelphi university spring']  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nouns['results_noun_chunks'] = nouns['results_noun_chunks'].astype(str)\n",
    "nouns['sep'] = nouns['results_noun_chunks'].str.split(pat=',')\n",
    "nouns = nouns.explode('sep').reset_index(drop=True)\n",
    "nouns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove bracket and quotation characters from the sep column incrementally, then remove the columns used for cleaning, and rename the final version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>results</th>\n",
       "      <th>search_term</th>\n",
       "      <th>results_noun_chunks</th>\n",
       "      <th>noun_segments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>ozark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>adelphi university spring 2020 dean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university spring']</td>\n",
       "      <td>kaitlyn grant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university spring']</td>\n",
       "      <td>adelphi university spring</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            results  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "2  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "3      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "4      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term  \\\n",
       "0  adelphi university   \n",
       "1  adelphi university   \n",
       "2  adelphi university   \n",
       "3  adelphi university   \n",
       "4  adelphi university   \n",
       "\n",
       "                                           results_noun_chunks  \\\n",
       "0  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "1  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "2  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "3               ['kaitlyn grant', 'adelphi university spring']   \n",
       "4               ['kaitlyn grant', 'adelphi university spring']   \n",
       "\n",
       "                          noun_segments  \n",
       "0                               student  \n",
       "1                                 ozark  \n",
       "2   adelphi university spring 2020 dean  \n",
       "3                         kaitlyn grant  \n",
       "4             adelphi university spring  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nouns['sep2'] = nouns['sep'].str.replace(\"'\", '')\n",
    "nouns['sep3'] = nouns['sep2'].str.replace(\"[\",'')\n",
    "nouns['sep4'] = nouns['sep3'].str.replace(\"]\", '')\n",
    "nouns.drop(columns = ['sep','sep2','sep3'], inplace=True)\n",
    "nouns = nouns.rename(columns = {\"sep4\":\"noun_segments\"})\n",
    "nouns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strip the noun chunks of any preceding spaces, then return all results not equal to 'amp' from leftover ampersands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "nouns['noun_segments'] = nouns['noun_segments'].map(lambda x:x.lstrip(' '))\n",
    "nouns = nouns.loc[nouns['noun_segments'] != 'amp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a variable for the value counts of the noun segments, convert it to a dictionary, and then map a column in the dataframe to specify the frequency of each noun chunk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>results</th>\n",
       "      <th>search_term</th>\n",
       "      <th>results_noun_chunks</th>\n",
       "      <th>noun_segments</th>\n",
       "      <th>noun_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>student</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>ozark</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['student', 'ozark', 'adelphi university spring 2020 dean']</td>\n",
       "      <td>adelphi university spring 2020 dean</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university spring']</td>\n",
       "      <td>kaitlyn grant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university spring']</td>\n",
       "      <td>adelphi university spring</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            results  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "2  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "3      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "4      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term  \\\n",
       "0  adelphi university   \n",
       "1  adelphi university   \n",
       "2  adelphi university   \n",
       "3  adelphi university   \n",
       "4  adelphi university   \n",
       "\n",
       "                                           results_noun_chunks  \\\n",
       "0  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "1  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "2  ['student', 'ozark', 'adelphi university spring 2020 dean']   \n",
       "3               ['kaitlyn grant', 'adelphi university spring']   \n",
       "4               ['kaitlyn grant', 'adelphi university spring']   \n",
       "\n",
       "                         noun_segments  noun_count  \n",
       "0                              student           3  \n",
       "1                                ozark           1  \n",
       "2  adelphi university spring 2020 dean           2  \n",
       "3                        kaitlyn grant           1  \n",
       "4            adelphi university spring           1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = nouns['noun_segments'].value_counts()\n",
    "z1 = z.to_dict()\n",
    "nouns['noun_count'] = nouns['noun_segments'].map(z1)\n",
    "nouns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataframe with the noun segments sorted by frequency count. Then, create another with only the noun chunks and their respective counts. Drop any duplicates and blanks, then reset the index. Inspect the dataframe and create a CSV, if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>noun_segments</th>\n",
       "      <th>noun_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>students</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>college</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>colleges</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>what</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>campus</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>coronavirus</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>university</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sports</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>it</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>covid-19</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>who</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>jobs</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>universities</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>you</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fall semester</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cuny</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>the class</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>we</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>new york</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>art</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>pandemic</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fall</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>diversity</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>\"deans list\"</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>the fall</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>i</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>equity</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>class</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>college notes</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>person</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>june</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>| news</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>us</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>president</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>july</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>community</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ny</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>new york college students</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>purchase college</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>pace university</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>america</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>utica college</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>petition</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>cornell</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>covid-19 pandemic</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                noun_segments  noun_count\n",
       "0                    students          34\n",
       "1                     college          26\n",
       "2                    colleges          22\n",
       "3                        what          20\n",
       "4                      campus          20\n",
       "5                 coronavirus          19\n",
       "6                  university          15\n",
       "7                      sports          15\n",
       "8                          it          15\n",
       "9                    covid-19          15\n",
       "10                        who          14\n",
       "11                       jobs          14\n",
       "12               universities          12\n",
       "13                        you          12\n",
       "14              fall semester          12\n",
       "15                       cuny          11\n",
       "16                  the class          10\n",
       "17                         we          10\n",
       "18                   new york          10\n",
       "19                        art           9\n",
       "20                   pandemic           9\n",
       "21                       fall           9\n",
       "22                  diversity           8\n",
       "23               \"deans list\"           8\n",
       "24                   the fall           7\n",
       "25                          i           7\n",
       "26                     equity           7\n",
       "27                      class           7\n",
       "28              college notes           7\n",
       "29                     person           7\n",
       "30                       june           7\n",
       "31                     | news           6\n",
       "32                         us           6\n",
       "33                  president           6\n",
       "34                       july           6\n",
       "35                  community           6\n",
       "36                         ny           6\n",
       "37  new york college students           6\n",
       "38           purchase college           6\n",
       "39            pace university           6\n",
       "40                    america           6\n",
       "41              utica college           6\n",
       "42                   petition           5\n",
       "43                    cornell           5\n",
       "44          covid-19 pandemic           5"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort values by noun count, remove duplicates, and reset the index\n",
    "sorted_df = nouns.sort_values(by = 'noun_count', ascending=False)\n",
    "nouns_only = sorted_df[['noun_segments','noun_count']].copy()\n",
    "nouns_only.drop_duplicates('noun_segments', inplace=True)\n",
    "nouns_only = nouns_only.loc[nouns_only['noun_segments'] != '']\n",
    "nouns_only = nouns_only.reset_index(drop=True)\n",
    "nouns_only.head(45)\n",
    "#nouns_only.to_csv('noun_counts.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose the top 20 noun segments to use for a visual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nouns_count_top = nouns_only.iloc[0:19, 0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a bar plot of the noun chunk data using seaborn and save the figure image, if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x518.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.barplot(x = nouns_count_top['noun_count'],y = nouns_count_top['noun_segments'],palette=\"Blues_d\")\n",
    "\n",
    "sb.despine(fig=None, ax=None, top=True, right=True, left=False, trim=False)\n",
    "sb.set(rc={'figure.figsize':(6,7.2)})\n",
    "\n",
    "ax.set_ylabel('')    \n",
    "ax.set_xlabel('')\n",
    "ax.set_title('Noun Chunk Counts for NY 4-Year College Headlines on 7/1/20', fontsize=18, fontweight='heavy')\n",
    "sb.set(font_scale = 1.8)\n",
    "ax.axes.get_xaxis().set_visible(True)\n",
    "ax.set_frame_on(True)\n",
    "\n",
    "#plt.savefig('noun_countplot.jpg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantify Named Entities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataframe for the named entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>results</th>\n",
       "      <th>search_term</th>\n",
       "      <th>results_named_entities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[adelphi university, 2020, dean]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[kaitlyn grant, adelphi university, spring 2020]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oxford's Emma Fogarty makes Adelphi University dean's list</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[oxford, fogarty, adelphi university dean's]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wilton Resident Sophia Walsh Named to Adelphi University ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[wilton, sophia walsh, adelphi university]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Local Students Named to Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[adelphi university, spring 2020]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            results  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "2        Oxford's Emma Fogarty makes Adelphi University dean's list   \n",
       "3      Wilton Resident Sophia Walsh Named to Adelphi University ...   \n",
       "4        Local Students Named to Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term                            results_named_entities  \n",
       "0  adelphi university                  [adelphi university, 2020, dean]  \n",
       "1  adelphi university  [kaitlyn grant, adelphi university, spring 2020]  \n",
       "2  adelphi university      [oxford, fogarty, adelphi university dean's]  \n",
       "3  adelphi university        [wilton, sophia walsh, adelphi university]  \n",
       "4  adelphi university                 [adelphi university, spring 2020]  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "named = df[['results','search_term','results_named_entities']].copy()\n",
    "named.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform cleaning. Save the named entities column as a string, split based on commas, then use the explode function from pandas and reset the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>results</th>\n",
       "      <th>search_term</th>\n",
       "      <th>results_named_entities</th>\n",
       "      <th>named_ent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>['adelphi university'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>'2020'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>'dean']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university', 'spring 2020']</td>\n",
       "      <td>['kaitlyn grant'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university', 'spring 2020']</td>\n",
       "      <td>'adelphi university'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            results  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "2  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "3      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "4      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term                                  results_named_entities  \\\n",
       "0  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "1  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "2  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "3  adelphi university  ['kaitlyn grant', 'adelphi university', 'spring 2020']   \n",
       "4  adelphi university  ['kaitlyn grant', 'adelphi university', 'spring 2020']   \n",
       "\n",
       "               named_ent  \n",
       "0  ['adelphi university'  \n",
       "1                 '2020'  \n",
       "2                'dean']  \n",
       "3       ['kaitlyn grant'  \n",
       "4   'adelphi university'  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "named['results_named_entities'] = named['results_named_entities'].astype(str)\n",
    "named['named_ent'] = named['results_named_entities'].str.split(pat=',')\n",
    "named = named.explode('named_ent').reset_index(drop=True)\n",
    "named.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove bracket and quotation characters from the named_ent column incrementally, then remove the columns used for cleaning, and rename the final version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove quotation marks and brackets from sep\n",
    "named['named_ent2'] = named['named_ent'].str.replace(\"'\", '')\n",
    "named['named_ent3'] = named['named_ent2'].str.replace(\"[\",'')\n",
    "named['named_ent4'] = named['named_ent3'].str.replace(\"]\", '')\n",
    "\n",
    "named.drop(columns = ['named_ent','named_ent2','named_ent3'], inplace=True)\n",
    "\n",
    "named = named.rename(columns = {\"named_ent4\":\"named_entity\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove any preceding spaces from the named entities and remove all those equal to 'nan'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#named['named_entity'].value_counts()\n",
    "#named['named_entity'] = named['named_entity'].astype(str)\n",
    "named['named_entity'] = named['named_entity'].map(lambda x:x.lstrip(' '))\n",
    "named = named.loc[named['named_entity'] != 'nan']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use spaCy to get the entity types for each named entity. Create a new column in the dataframe for these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "named_entity_type = []\n",
    "\n",
    "for doc in nlp.pipe(named['named_entity'].astype('unicode').values, batch_size=50,\n",
    "                        n_process=5):\n",
    "    if doc.is_parsed:\n",
    "        named_entity_type.append([ent.label_ for ent in doc.ents])\n",
    "    else:\n",
    "        named_entity_type.append(None)        \n",
    "\n",
    "named['named_entities_type'] = named_entity_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>results</th>\n",
       "      <th>search_term</th>\n",
       "      <th>results_named_entities</th>\n",
       "      <th>named_entity</th>\n",
       "      <th>named_entities_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[ORG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>2020</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>dean</td>\n",
       "      <td>[ORG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university', 'spring 2020']</td>\n",
       "      <td>kaitlyn grant</td>\n",
       "      <td>[ORG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university', 'spring 2020']</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>[ORG]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            results  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "2  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "3      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "4      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term                                  results_named_entities  \\\n",
       "0  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "1  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "2  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "3  adelphi university  ['kaitlyn grant', 'adelphi university', 'spring 2020']   \n",
       "4  adelphi university  ['kaitlyn grant', 'adelphi university', 'spring 2020']   \n",
       "\n",
       "         named_entity named_entities_type  \n",
       "0  adelphi university               [ORG]  \n",
       "1                2020                  []  \n",
       "2                dean               [ORG]  \n",
       "3       kaitlyn grant               [ORG]  \n",
       "4  adelphi university               [ORG]  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "named.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the named entity types as strings, then exlore the unique types in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "named['named_entities_type'] = named['named_entities_type'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"['ORG']\", '[]', \"['PERSON']\", \"['ORDINAL']\", \"['DATE']\",\n",
       "       \"['GPE']\", \"['CARDINAL']\", \"['EVENT']\", \"['LOC']\", \"['MONEY']\",\n",
       "       \"['NORP']\", \"['PERCENT']\", \"['TIME']\", \"['QUANTITY']\",\n",
       "       \"['ORG', 'PERSON']\", \"['PERSON', 'PERSON']\", \"['WORK_OF_ART']\",\n",
       "       \"['ORG', 'GPE']\", \"['PERSON', 'ORG']\"], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "named['named_entities_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a variable for the value counts of the named entities, convert it to a dictionary, and then map a column in the dataframe to specify the frequency of each named entity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>results</th>\n",
       "      <th>search_term</th>\n",
       "      <th>results_named_entities</th>\n",
       "      <th>named_entity</th>\n",
       "      <th>named_entities_type</th>\n",
       "      <th>entity_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>2020</td>\n",
       "      <td>[]</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Student from Ozark on Adelphi University Spring 2020 Dean’s List</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['adelphi university', '2020', 'dean']</td>\n",
       "      <td>dean</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university', 'spring 2020']</td>\n",
       "      <td>kaitlyn grant</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kaitlyn Grant Named Among Adelphi University Spring 2020 ...</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['kaitlyn grant', 'adelphi university', 'spring 2020']</td>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            results  \\\n",
       "0  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "1  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "2  Student from Ozark on Adelphi University Spring 2020 Dean’s List   \n",
       "3      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "4      Kaitlyn Grant Named Among Adelphi University Spring 2020 ...   \n",
       "\n",
       "          search_term                                  results_named_entities  \\\n",
       "0  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "1  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "2  adelphi university                  ['adelphi university', '2020', 'dean']   \n",
       "3  adelphi university  ['kaitlyn grant', 'adelphi university', 'spring 2020']   \n",
       "4  adelphi university  ['kaitlyn grant', 'adelphi university', 'spring 2020']   \n",
       "\n",
       "         named_entity named_entities_type  entity_count  \n",
       "0  adelphi university             ['ORG']             6  \n",
       "1                2020                  []            62  \n",
       "2                dean             ['ORG']            12  \n",
       "3       kaitlyn grant             ['ORG']             1  \n",
       "4  adelphi university             ['ORG']             6  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = named['named_entity'].value_counts()\n",
    "q1 = q.to_dict()\n",
    "named['entity_count'] = named['named_entity'].map(q1)\n",
    "named.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataframe with the named entities sorted by frequency count. Then, create another with only the named entities and their respective counts. Drop any duplicates and blanks, then reset the index. Inspect the dataframe and create a CSV, if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>named_entity</th>\n",
       "      <th>named_entities_type</th>\n",
       "      <th>entity_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>[]</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>suny</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>covid-19</td>\n",
       "      <td>[]</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>first</td>\n",
       "      <td>['ORDINAL']</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  named_entity named_entities_type  entity_count\n",
       "0         2020                  []            62\n",
       "1         suny             ['ORG']            51\n",
       "2     covid-19                  []            41\n",
       "3     new york             ['GPE']            20\n",
       "4        first         ['ORDINAL']            17"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_df2 = named.sort_values(by = 'entity_count', ascending=False)\n",
    "entity_only = sorted_df2[['named_entity','named_entities_type','entity_count']].copy()\n",
    "entity_only.drop_duplicates('named_entity', inplace=True)\n",
    "entity_only = entity_only.loc[entity_only['named_entity'] != '']\n",
    "entity_only = entity_only.reset_index(drop=True)\n",
    "entity_only.head()\n",
    "#entity_only.to_csv('entity_counts.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataframe with the top 30 named entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "entity_top = entity_only.iloc[0:29, 0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>named_entity</th>\n",
       "      <th>named_entities_type</th>\n",
       "      <th>entity_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>[]</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>suny</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>covid-19</td>\n",
       "      <td>[]</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>first</td>\n",
       "      <td>['ORDINAL']</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>['CARDINAL']</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>brooklyn</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>jewish</td>\n",
       "      <td>['NORP']</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dean</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>new york city</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>summer</td>\n",
       "      <td>[]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>['CARDINAL']</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>u.s.</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cornell</td>\n",
       "      <td>['PERSON']</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>manhattan</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "      <td>['CARDINAL']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>annual</td>\n",
       "      <td>['DATE']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>three</td>\n",
       "      <td>['CARDINAL']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>five</td>\n",
       "      <td>['CARDINAL']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>utica college</td>\n",
       "      <td>[]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>niagara university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>yeshiva university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15</td>\n",
       "      <td>['CARDINAL']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>syracuse university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>eight</td>\n",
       "      <td>['CARDINAL']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>us</td>\n",
       "      <td>[]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>oswego county</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>clarkson university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           named_entity named_entities_type  entity_count\n",
       "0                  2020                  []            62\n",
       "1                  suny             ['ORG']            51\n",
       "2              covid-19                  []            41\n",
       "3              new york             ['GPE']            20\n",
       "4                 first         ['ORDINAL']            17\n",
       "5                   two        ['CARDINAL']            13\n",
       "6              brooklyn             ['GPE']            12\n",
       "7                jewish            ['NORP']            12\n",
       "8                  dean             ['ORG']            12\n",
       "9         new york city             ['GPE']            10\n",
       "10               summer                  []             8\n",
       "11                    5        ['CARDINAL']             8\n",
       "12                 u.s.             ['GPE']             8\n",
       "13              cornell          ['PERSON']             8\n",
       "14            manhattan             ['GPE']             7\n",
       "15                   10        ['CARDINAL']             7\n",
       "16               annual            ['DATE']             7\n",
       "17                three        ['CARDINAL']             7\n",
       "18                 five        ['CARDINAL']             7\n",
       "19        utica college                  []             7\n",
       "20   niagara university             ['ORG']             6\n",
       "21   yeshiva university             ['ORG']             6\n",
       "22                   15        ['CARDINAL']             6\n",
       "23  syracuse university             ['ORG']             6\n",
       "24                eight        ['CARDINAL']             6\n",
       "25                   us                  []             6\n",
       "26        oswego county             ['ORG']             6\n",
       "27   adelphi university             ['ORG']             6\n",
       "28  clarkson university             ['ORG']             6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entity_top.head(32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a barplot of the named entities data using seaborn and save the figure image, if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x518.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vis for above\n",
    "ax = sb.barplot(x = entity_top['entity_count'],y = entity_top['named_entity'],palette=\"Blues_d\")\n",
    "\n",
    "sb.despine(fig=None, ax=None, top=True, right=True, left=False, trim=False)\n",
    "sb.set(rc={'figure.figsize':(6,9.9)})\n",
    "\n",
    "ax.set_ylabel('')    \n",
    "ax.set_xlabel('')\n",
    "ax.set_title('Named Entity Counts for NY 4-Year College Headlines on 7/1/20', fontsize=18, fontweight='heavy')\n",
    "sb.set(font_scale = 1.4)\n",
    "ax.axes.get_xaxis().set_visible(True)\n",
    "ax.set_frame_on(True)\n",
    "\n",
    "#plt.savefig('named_entity_countplot.jpg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create another dataframe without the entity types related to numbers (ordinal and cardinal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>named_entity</th>\n",
       "      <th>named_entities_type</th>\n",
       "      <th>entity_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>[]</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>suny</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>covid-19</td>\n",
       "      <td>[]</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>brooklyn</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>jewish</td>\n",
       "      <td>['NORP']</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dean</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>new york city</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>summer</td>\n",
       "      <td>[]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>u.s.</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cornell</td>\n",
       "      <td>['PERSON']</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>manhattan</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>annual</td>\n",
       "      <td>['DATE']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>utica college</td>\n",
       "      <td>[]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>niagara university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>yeshiva university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>syracuse university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>us</td>\n",
       "      <td>[]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>oswego county</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>adelphi university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>clarkson university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cuomo</td>\n",
       "      <td>['PERSON']</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>\"christies\"</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>christian</td>\n",
       "      <td>[]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>june</td>\n",
       "      <td>['DATE']</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>houghton</td>\n",
       "      <td>['PERSON']</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>bronx</td>\n",
       "      <td>['GPE']</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>binghamton university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>upstate medical university</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>bard college</td>\n",
       "      <td>['ORG']</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  named_entity named_entities_type  entity_count\n",
       "0                         2020                  []            62\n",
       "1                         suny             ['ORG']            51\n",
       "2                     covid-19                  []            41\n",
       "3                     new york             ['GPE']            20\n",
       "6                     brooklyn             ['GPE']            12\n",
       "7                       jewish            ['NORP']            12\n",
       "8                         dean             ['ORG']            12\n",
       "9                new york city             ['GPE']            10\n",
       "10                      summer                  []             8\n",
       "12                        u.s.             ['GPE']             8\n",
       "13                     cornell          ['PERSON']             8\n",
       "14                   manhattan             ['GPE']             7\n",
       "16                      annual            ['DATE']             7\n",
       "19               utica college                  []             7\n",
       "20          niagara university             ['ORG']             6\n",
       "21          yeshiva university             ['ORG']             6\n",
       "23         syracuse university             ['ORG']             6\n",
       "25                          us                  []             6\n",
       "26               oswego county             ['ORG']             6\n",
       "27          adelphi university             ['ORG']             6\n",
       "28         clarkson university             ['ORG']             6\n",
       "29                       cuomo          ['PERSON']             5\n",
       "30                 \"christies\"             ['ORG']             5\n",
       "32                   christian                  []             5\n",
       "33                        june            ['DATE']             5\n",
       "34                    houghton          ['PERSON']             5\n",
       "35                       bronx             ['GPE']             5\n",
       "36       binghamton university             ['ORG']             5\n",
       "37  upstate medical university             ['ORG']             5\n",
       "38                bard college             ['ORG']             5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entity_non_num = entity_only.loc[(entity_only['named_entities_type'] != \"['ORDINAL']\") & (entity_only['named_entities_type'] != \"['CARDINAL']\")]\n",
    "entity_non_num.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile the top 21 named entities that are non-numerical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "entity_non_num_top = entity_non_num.iloc[0:21, 0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a barplot of the non-numerical named entities data using seaborn and save the figure image, if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x518.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.barplot(x = entity_non_num_top['entity_count'],y = entity_non_num_top['named_entity'],palette=\"Blues_d\")\n",
    "\n",
    "sb.despine(fig=None, ax=None, top=True, right=True, left=False, trim=False)\n",
    "sb.set(rc={'figure.figsize':(6,7.2)})\n",
    "\n",
    "ax.set_ylabel('')    \n",
    "ax.set_xlabel('')\n",
    "ax.set_title('Named Entity Counts for NY 4-Year College Headlines on 7/1/20 \\n (Non-Numerical)', fontsize=18, fontweight='heavy')\n",
    "sb.set(font_scale = 1.4)\n",
    "ax.axes.get_xaxis().set_visible(True)\n",
    "ax.set_frame_on(True)\n",
    "\n",
    "#plt.savefig('named_entity_non_num_countplot2.jpg', bbox_inches=\"tight\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}