metadata_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of CNBC YouTube Video Metadata\n",
    "\n",
    "This notebook is an analysis of the metadata of the CNBC YouTube videos. The dataset contains information about the videos such as the title, description, the number of views, and video duration. The goal of this analysis is to explore the data and find interesting insights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "Import pandas to load and summarize our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>video_id</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>length</th>\n",
       "      <th>view_count</th>\n",
       "      <th>ceo_in_title</th>\n",
       "      <th>duration_seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R9bPE-ksEJw</td>\n",
       "      <td>Disney's boardroom showdown: Here's what you n...</td>\n",
       "      <td>James Stewart, New York Times columnist, joins...</td>\n",
       "      <td>3:53</td>\n",
       "      <td>99 views</td>\n",
       "      <td>False</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L6jFnvl_N3g</td>\n",
       "      <td>Markets have embraced that the Fed is no longe...</td>\n",
       "      <td>Ayako Yoshioka, Wealth Enhancement Group senio...</td>\n",
       "      <td>3:55</td>\n",
       "      <td>897 views</td>\n",
       "      <td>False</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>w1-XmVZmjKc</td>\n",
       "      <td>The likelihood of mortgage rates going down is...</td>\n",
       "      <td>Jay McCanless, Wedbush housing analyst, joins ...</td>\n",
       "      <td>2:30</td>\n",
       "      <td>153 views</td>\n",
       "      <td>False</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nIwobHIEIb4</td>\n",
       "      <td>Stripe co-founder John Collison on startups, s...</td>\n",
       "      <td>Stripe co-founder and president John Collison ...</td>\n",
       "      <td>6:17</td>\n",
       "      <td>521 views</td>\n",
       "      <td>False</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>QJuuBz9-8Rc</td>\n",
       "      <td>House TikTok vote expected today: Here's what ...</td>\n",
       "      <td>CNBC’s Emily Wilkins joins 'Squawk on the Stre...</td>\n",
       "      <td>5:27</td>\n",
       "      <td>593 views</td>\n",
       "      <td>False</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      video_id                                              title  \\\n",
       "0  R9bPE-ksEJw  Disney's boardroom showdown: Here's what you n...   \n",
       "1  L6jFnvl_N3g  Markets have embraced that the Fed is no longe...   \n",
       "2  w1-XmVZmjKc  The likelihood of mortgage rates going down is...   \n",
       "3  nIwobHIEIb4  Stripe co-founder John Collison on startups, s...   \n",
       "4  QJuuBz9-8Rc  House TikTok vote expected today: Here's what ...   \n",
       "\n",
       "                                         description length view_count  \\\n",
       "0  James Stewart, New York Times columnist, joins...   3:53   99 views   \n",
       "1  Ayako Yoshioka, Wealth Enhancement Group senio...   3:55  897 views   \n",
       "2  Jay McCanless, Wedbush housing analyst, joins ...   2:30  153 views   \n",
       "3  Stripe co-founder and president John Collison ...   6:17  521 views   \n",
       "4  CNBC’s Emily Wilkins joins 'Squawk on the Stre...   5:27  593 views   \n",
       "\n",
       "   ceo_in_title  duration_seconds  \n",
       "0         False               233  \n",
       "1         False               235  \n",
       "2         False               150  \n",
       "3         False               377  \n",
       "4         False               327  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnbc_metadata = pd.read_csv('data/metadata.csv')\n",
    "\n",
    "cnbc_metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count the number of videos\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 63 rows.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(cnbc_metadata)} rows.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8 videos with CEO in the title.\n"
     ]
    }
   ],
   "source": [
    "num_ceo_videos = cnbc_metadata['ceo_in_title'].sum()\n",
    "print(f'There are {num_ceo_videos} videos with CEO in the title.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Average length of videos by CEO/other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ceo_in_title\n",
       "False    240.200\n",
       "True     254.625\n",
       "Name: duration_seconds, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnbc_metadata.groupby('ceo_in_title')['duration_seconds'].mean()\n",
    "# ^           ^        ^               ^                  ^   \n",
    "# data        group    grouping col    data column        summarization function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3\n",
    "\n",
    "## 3.3 Histogram of video lengths\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnbc_metadata['duration_seconds'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 4: Deeper Analysis\n",
    "\n",
    "## 4.1 Most common words in video titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'to': 22, 'says': 15, 'the': 12, \"Here's\": 11, 'of': 9, 'on': 9, 'and': 8, 'in': 8, 'know': 7, 'a': 7, 'what': 6, 'need': 6, 'have': 6, 'is': 6, 'why': 6, 'you': 5, 'as': 5, 'for': 5, 'Cramer’s': 5, 'AI': 4, 'TikTok': 4, 'Apple': 4, 'Opening': 4, 'Bell:': 4, '2024': 4, 'CEO': 4, 'The': 3, 'rates': 3, 'still': 3, 'new': 3, 'with': 3, 'March': 3, 'CEO:': 3, 'We': 3, 'Disney': 3, 'will': 3, 'Mad': 3, 'Dash:': 3, 'be': 3, 'Stop': 3, 'Trading:': 3, \"Disney's\": 2, 'Fed': 2, 'going': 2, 'consumer': 2, 'House': 2, 'expected': 2, 'Boeing': 2, 'an': 2, 'bill:': 2, 'into': 2, 'should': 2, 'June,': 2, \"Research's\": 2, 'Reddit': 2, 'IPO': 2, 'energy': 2, 'want': 2, \"Nvidia's\": 2, 'stock': 2, 'are': 2, 'oil': 2, 'demand': 2, 'ban': 2, 'Bob': 2, 'wealth': 2, 'this': 2, 'target': 2, 'was': 2, 'Donald': 2, 'home': 2, 'market': 2, 'April': 2, 'Capital': 2, 'Charles': 2, 'price': 2, 'boardroom': 1, 'showdown:': 1, 'Markets': 1, 'embraced': 1, 'that': 1, 'no': 1, 'longer': 1, 'raising': 1, 'rates:': 1, 'Wealth': 1, \"Enhancement's\": 1, 'Yoshioka': 1, 'likelihood': 1, 'mortgage': 1, 'down': 1, 'pretty': 1, 'low,': 1, \"Wedbush's\": 1, 'Jay': 1, 'McCanless': 1, 'Stripe': 1, 'co-founder': 1, 'John': 1, 'Collison': 1, 'startups,': 1, 'state': 1, 'impact': 1, 'vote': 1, 'today:': 1, \"HSBC's\": 1, 'Max': 1, 'Kettner:': 1, 'High-yield': 1, 'credit': 1, 'equities': 1, 'overall': 1, 'look': 1, \"'pretty\": 1, \"good'\": 1, 'ISM': 1, 'PMI': 1, 'comes': 1, 'cooler': 1, 'than': 1, 'needs': 1, 'find': 1, \"'outsider'\": 1, 'CEO,': 1, \"Yale's\": 1, 'Gautam': 1, 'Mukunda': 1, 'passes': 1, 'What': 1, 'investors': 1, 'EU': 1, 'launches': 1, 'investigation': 1, 'Meta,': 1, 'Alphabet': 1, 'app': 1, 'store': 1, 'by': 1, 'Melius': 1, 'Ben': 1, 'Reitzes': 1, 'Commerce': 1, 'Treasury': 1, 'Departments': 1, 'release': 1, 'recommendations': 1, 'regulating': 1, '25,': 1, 'aims': 1, 'raise': 1, 'nearly': 1, '$750': 1, 'million': 1, 'upcoming': 1, 'begin': 1, 'search': 1, 'EQT': 1, 'fix': 1, 'grid': 1, 'if': 1, 'we': 1, 'fuel': 1, 'boom': 1, 'board': 1, 'battle': 1, 'final': 1, 'days:': 1, 'Room': 1, 'consolidate': 1, 'absorb': 1, 'momentum,': 1, 'Renaissance': 1, \"Macro's\": 1, 'deGraaf': 1, 'There': 1, 'some': 1, 'serious': 1, 'problems': 1, \"'TikTok\": 1, \"ban'\": 1, \"SentinelOne's\": 1, 'Alex': 1, 'Stamos': 1, 'Constructive': 1, 'Canadian': 1, 'complex': 1, 'peak': 1, 'hits': 1, 'next': 1, 'decade,': 1, \"Goldman's\": 1, 'Mehta': 1, '47%': 1, 'CNBC': 1, 'All-America': 1, 'Economic': 1, 'Survey': 1, 'respondents': 1, 'support': 1, 'or': 1, 'sale': 1, 'Broader': 1, 'markets': 1, 'likely': 1, 'pause': 1, 'long': 1, 'tick': 1, 'up,': 1, 'Wells': 1, \"Fargo's\": 1, 'Sameer': 1, 'Samana': 1, 'Faber': 1, 'Report:': 1, 'Jamie': 1, 'Dimon': 1, 'endorses': 1, 'Iger': 1, 'proxy': 1, 'fight': 1, 'Trian': 1, 'Partners': 1, 'Citi': 1, 'downgrades': 1, 'tech': 1, 'stocks,': 1, \"here's\": 1, 'Household': 1, 'reaches': 1, 'all-time': 1, 'high': 1, '$156': 1, 'trillion': 1, 'Strategic': 1, 'Petroleum': 1, 'Reserve': 1, 'become': 1, 'live': 1, 'issue': 1, 'Biden': 1, 'summer,': 1, \"RBC's\": 1, 'Croft': 1, 'under': 1, 'regulatory': 1, 'pressure:': 1, 'BofA': 1, 'maintains': 1, 'its': 1, 'S&P': 1, '500': 1, 'year-end': 1, '5,400': 1, 'Investors': 1, 'separate': 1, 'political': 1, 'views': 1, 'from': 1, 'investment': 1, 'themes,': 1, 'Morgan': 1, \"Stanley's\": 1, 'Sherry': 1, 'Paul': 1, 'Baltimore': 1, 'bridge': 1, 'collapse': 1, \"'completely\": 1, \"preventable',\": 1, 'Broughton': 1, 'given': 1, 'Chinese': 1, 'government': 1, 'direct': 1, 'tool': 1, 'all': 1, 'our': 1, \"kid's\": 1, 'pockets,': 1, 'Bradley': 1, 'Tusk': 1, 'Tyson': 1, 'Foods': 1, 'Squawk': 1, 'Pod:': 1, 'California’s': 1, '$20': 1, 'minimum': 1, 'wage': 1, '&': 1, 'wins': 1, 'DC': 1, 'sports': 1, '-': 1, '04/01/24': 1, '|': 1, 'Audio': 1, 'Only': 1, 'Ellevest': 1, 'It': 1, \"won't\": 1, 'too': 1, 'many': 1, 'years': 1, 'before': 1, 'women': 1, 'majority': 1, 'U.S.': 1, \"Neuberger's\": 1, 'Kei': 1, 'Okamura:': 1, \"Japan's\": 1, 'equity': 1, 'looks': 1, \"'quite\": 1, \"attractive'\": 1, 'Expect': 1, 'first': 1, 'rate': 1, 'cut': 1, 'come': 1, \"Citi's\": 1, 'Hollenhorst': 1, 'Both': 1, 'gold': 1, 'provide': 1, 'solid': 1, 'geopolitical': 1, 'hedging': 1, 'value,': 1, 'Goldman': 1, \"Sachs'\": 1, 'Daan': 1, 'Struyven': 1, '13,': 1, 'Recession': 1, 'happen': 1, 'Covid': 1, 'savings': 1, 'run': 1, 'out,': 1, \"Crossmark's\": 1, 'Doll': 1, \"EU's\": 1, 'Vestager': 1, 'fine:': 1, 'Quite': 1, 'obvious': 1, 'there': 1, 'harm': 1, 'done': 1, 'Michael': 1, 'Landsberg': 1, 'his': 1, 'top': 1, 'picks:': 1, 'SNPS,': 1, 'ORLY,': 1, 'AXON,': 1, 'RHMB': 1, 'Edward': 1, \"Jones'\": 1, 'Mona': 1, 'Mahajan': 1, 'closely': 1, 'watching': 1, 'CPI': 1, 'data': 1, 'leaders': 1, 'continue': 1, 'lead': 1, 'way,': 1, 'Defiance': 1, 'ETFs': 1, 'Sylvia': 1, 'Jablonski': 1, '27,': 1, 'Oakmark': 1, \"Funds'\": 1, 'Mike': 1, 'Nicolas': 1, 'bullish': 1, 'One': 1, 'Schwab': 1, 'Liberty': 1, 'Media': 1, '$4.5B': 1, 'MotoGP': 1, 'deal:': 1, 'show': 1, 'world': 1, 'how': 1, 'exciting': 1, 'sport': 1, 'Taylor': 1, 'Morrison': 1, \"We're\": 1, 'good': 1, 'place': 1, 'interest': 1, \"'slightly'\": 1, 'moderated': 1, 'Apple:': 1, 'Loop': 1, 'cuts': 1, 'iPhone': 1, \"maker's\": 1, 'weak': 1, 'United': 1, 'Airlines': 1, 'asks': 1, 'pilots': 1, 'take': 1, 'unpaid': 1, 'time': 1, 'off,': 1, 'citing': 1, \"Boeing's\": 1, 'delayed': 1, 'aircraft': 1, 'Election': 1, 'has': 1, 'chance': 1, 'volatility': 1, 'driver': 1, 'equities,': 1, \"Schwab's\": 1, 'Sonders': 1, 'Cantor': 1, \"Fitzgerald's\": 1, 'C.J.': 1, 'Muse': 1, '$1,200': 1, 'target:': 1, 'Still': 1, 'plenty': 1, 'legroom': 1, 'higher': 1, 'Cintas': 1, 'direct-to-consumer': 1, 'forecast': 1, 'profitable': 1, '2026:': 1, 'Wolfe': 1, 'Peter': 1, 'Supino': 1, \"Cramer's\": 1, 'SoFi': 1, 'Micron': 1, 'Former': 1, 'President': 1, 'Trump': 1, 'entitlements:': 1, \"There's\": 1, 'tremendous': 1, 'numbers': 1, 'things': 1, 'can': 1, 'do': 1, 'Citigroup': 1, '3,': 1, 'gaining': 1, 'momentum:': 1, 'reveals': 1, 'terms:': 1, \"BMO's\": 1, 'Simeon': 1, 'Siegel': 1, 'favors': 1, 'Nike': 1, 'Lululemon': 1, 'February': 1, 'sales': 1, 'narrowly': 1, 'miss': 1, 'expectations': 1, 'downside': 1})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "word_counts = Counter()\n",
    "\n",
    "cnbc_metadata['title'].str.split().apply(word_counts.update)\n",
    "print(word_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('to', 22), ('the', 15), ('says', 15), (\"here's\", 12), ('of', 9), ('on', 9), ('and', 8), ('in', 8), ('what', 7), ('know', 7), ('a', 7), ('need', 6), ('have', 6), ('is', 6), ('why', 6), ('you', 5), ('as', 5), ('for', 5), ('cramer’s', 5), ('ai', 4)]\n"
     ]
    }
   ],
   "source": [
    "word_counts = Counter()\n",
    "\n",
    "cnbc_metadata['title'].str.lower().str.split().apply(word_counts.update)\n",
    "\n",
    "print(word_counts.most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word\t\tcount\n",
      "____\t\t_____\n"
     ]
    }
   ],
   "source": [
    "print('word\\t\\tcount')\n",
    "print('____\\t\\t_____')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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