mark zuckerberg testimony.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import nltk.corpus as corpus\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"mark.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_count(x):\n",
    "    return len(nltk.word_tokenize(x))\n",
    "data['len']=data['Text'].map(get_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Person</th>\n",
       "      <th>Text</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GRASSLEY:</td>\n",
       "      <td>I'll start with you.\\r\\n\\r\\nFacebook handles ...</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZUCKERBERG:</td>\n",
       "      <td>Mr. Chairman, thank you.\\r\\n\\r\\nAs I mentione...</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GRASSLEY:</td>\n",
       "      <td>OK (ph).\\r\\n\\r\\nHave you ever required an aud...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ZUCKERBERG:</td>\n",
       "      <td>Mr. Chairman, yes we have. I don't have the e...</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GRASSLEY:</td>\n",
       "      <td>I was going to assume that, sitting here toda...</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Person                                               Text  len\n",
       "0    GRASSLEY:   I'll start with you.\\r\\n\\r\\nFacebook handles ...  139\n",
       "1  ZUCKERBERG:   Mr. Chairman, thank you.\\r\\n\\r\\nAs I mentione...  166\n",
       "2    GRASSLEY:   OK (ph).\\r\\n\\r\\nHave you ever required an aud...   29\n",
       "3  ZUCKERBERG:   Mr. Chairman, yes we have. I don't have the e...  143\n",
       "4    GRASSLEY:   I was going to assume that, sitting here toda...   55"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# marks response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total words spoken by Mark were 19959 words\n",
      "The average length of his response weas 51.71 words\n",
      "The maximum length of Mark's response was 524 words\n"
     ]
    }
   ],
   "source": [
    "print(\"The total words spoken by Mark were {} words\".format(data.query(\"Person=='ZUCKERBERG:'\")['len'].sum()))\n",
    "print(\"The average length of his response weas {} words\".format(round(data.query(\"Person=='ZUCKERBERG:'\")['len'].mean(),2)))\n",
    "print(\"The maximum length of Mark's response was {} words\".format(data.query(\"Person=='ZUCKERBERG:'\")['len'].max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# which senator spoke most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Count')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2zmhp4PD8fHQeg1R1ziw65kA6lI5xJPXGF9VcQUfXx+ck/W85IA9WPoeOFqXHSYOZu+tC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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c07099588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.query(\"Person !='ZUCKERBERG:'\").groupby(\"Person\").sum().rename(columns={'len':'Total Words'}).sort_values(\"Total Words\",ascending=False).head(20).plot(kind=\"barh\",colormap=\"Set2\",figsize=(8,8))\n",
    "plt.title(\"Total Words Spoken\",fontsize=30)\n",
    "plt.ylabel(\"Senator\",fontsize=25)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.xlabel(\"Count\",fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##  Most commonly used words by Mark\n",
    "from sklearn.feature_extraction import text\n",
    "def get_imp(bow,mf,ngram):\n",
    "    tfidf=text.CountVectorizer(bow,ngram_range=(ngram,ngram),max_features=mf,stop_words='english')\n",
    "    matrix=tfidf.fit_transform(bow)\n",
    "    return pd.Series(np.array(matrix.sum(axis=0))[0],index=tfidf.get_feature_names()).sort_values(ascending=False).head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# most common bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make sure              31\n",
       "senator think          29\n",
       "yes senator            18\n",
       "cambridge analytica    17\n",
       "senator yes            14\n",
       "senator don            12\n",
       "don know               11\n",
       "making sure            11\n",
       "team follow            11\n",
       "app developer          10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mark=data[data['Person']==\"ZUCKERBERG:\"]['Text'].tolist()\n",
    "get_imp(mark,mf=5000,ngram=2).head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# most common trigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "want make sure                    8\n",
       "senator don know                  5\n",
       "security content review           5\n",
       "20 000 people                     5\n",
       "going 20 000                      4\n",
       "app access large                  4\n",
       "working security content          4\n",
       "foreign interference elections    4\n",
       "don sell data                     3\n",
       "single app access                 3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_imp(mark,mf=5000,ngram=3).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_imp_tf(bow,mf,ngram):\n",
    "    tfidf=text.TfidfVectorizer(bow,ngram_range=(ngram,ngram),max_features=mf,stop_words='english')\n",
    "    matrix=tfidf.fit_transform(bow)\n",
    "    return pd.Series(np.array(matrix.sum(axis=0))[0],index=tfidf.get_feature_names()).sort_values(ascending=False).head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# common bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "senator yes            4.427712\n",
       "senator think          4.278710\n",
       "yes senator            4.127852\n",
       "senator believe        3.834089\n",
       "make sure              3.620440\n",
       "senator don            2.736966\n",
       "team follow            2.645200\n",
       "don know               2.490640\n",
       "cambridge analytica    2.239803\n",
       "senator know           2.201193\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_imp_tf(mark,mf=5000,ngram=2).head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# common trigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "senator don know              1.772555\n",
       "want make sure                1.681610\n",
       "forward having team           1.172937\n",
       "look forward having           1.172937\n",
       "senator look forward          1.129662\n",
       "team follow information       1.110503\n",
       "believe government ability    1.000000\n",
       "commit political view         1.000000\n",
       "certainly want case           1.000000\n",
       "options yes cortez            1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_imp_tf(mark,mf=5000,ngram=3).head(10)"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}