2_Data Wrangling & Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "import re\n",
    "import json\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data after Webscraping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Episodes', 'Episode_Names', 'Characters', 'Lines'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json1_file = open(\"Friends_transcripts.json\")\n",
    "json1_str = json1_file.read()\n",
    "json1_data = json.loads(json1_str)\n",
    "json1_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48944 entries, 0 to 48943\n",
      "Data columns (total 4 columns):\n",
      "Episodes         48944 non-null object\n",
      "Episode_Names    48944 non-null object\n",
      "Characters       48944 non-null object\n",
      "Lines            48944 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', -1)\n",
    "data = pd.DataFrame.from_dict(json1_data)\n",
    "data.info()"
   ]
  },
  {
   "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>Episodes</th>\n",
       "      <th>Episode_Names</th>\n",
       "      <th>Characters</th>\n",
       "      <th>Lines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>Monica</td>\n",
       "      <td>There's nothing to tell! He's just some guy\\nI work with!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>Joey</td>\n",
       "      <td>C'mon, you're going out with the guy! There's\\ngotta be something wrong with him!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>Chandler</td>\n",
       "      <td>All right Joey, be\\nnice.  So does he have a hump? A hump and a hairpiece?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>Phoebe</td>\n",
       "      <td>Wait, does he eat chalk?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>Phoebe</td>\n",
       "      <td>Just, 'cause, I don't want her to go through\\nwhat I went through with Carl- oh!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Episodes             Episode_Names Characters  \\\n",
       "0  101       Monica Gets A Roommate   Monica      \n",
       "1  101       Monica Gets A Roommate   Joey        \n",
       "2  101       Monica Gets A Roommate   Chandler    \n",
       "3  101       Monica Gets A Roommate   Phoebe      \n",
       "4  101       Monica Gets A Roommate   Phoebe      \n",
       "\n",
       "                                                                                Lines  \n",
       "0   There's nothing to tell! He's just some guy\\nI work with!                          \n",
       "1   C'mon, you're going out with the guy! There's\\ngotta be something wrong with him!  \n",
       "2   All right Joey, be\\nnice.  So does he have a hump? A hump and a hairpiece?         \n",
       "3   Wait, does he eat chalk?                                                           \n",
       "4   Just, 'cause, I don't want her to go through\\nwhat I went through with Carl- oh!   "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It seems that character names are sometimes written in captial cases. and sometimes in lower case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Episodes', 'Episode_Names', 'Characters', 'Lines', 'Season']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets make the character names consistent and add a new column 'Season'\n",
    "data.Characters = data.Characters.apply (lambda x:  str.upper(x))\n",
    "data['Season']= data.Episodes. apply(lambda x: int(x[0]))\n",
    "data_columns = list(data.columns)\n",
    "data_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total Number of Lines for Top 15 Characters in the Show ')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Characters_per_rank = (data.Characters.value_counts()).index \n",
    "data_for_count_plot = data[data.Characters.isin (Characters_per_rank[0:15])]\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.countplot(x= 'Characters', order = Characters_per_rank[0:15], palette = 'muted', data = data_for_count_plot)\n",
    "plt.xlabel(\"Characters\", fontsize=16)\n",
    "plt.ylabel(\"Numer of Lines\", fontsize=16)\n",
    "plt.xticks(rotation = 90,  fontsize=16)\n",
    "plt.yticks(  fontsize= 16)\n",
    "plt.title(\"Total Number of Lines for Top 15 Characters in the Show \",fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_lines_per_season = pd.pivot_table(data_for_count_plot,index=[\"Characters\",\"Season\"],values=[\"Lines\"],aggfunc='count')\n",
    "data_lines_per_season = data_lines_per_season.reset_index()\n",
    "data_lines_per_season_pivot = data_lines_per_season.pivot(index='Season', columns='Characters', values='Lines')"
   ]
  },
  {
   "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>Characters</th>\n",
       "      <th>ALL</th>\n",
       "      <th>CAROL</th>\n",
       "      <th>CHANDLER</th>\n",
       "      <th>CHARLIE</th>\n",
       "      <th>EMILY</th>\n",
       "      <th>FRANK</th>\n",
       "      <th>JANICE</th>\n",
       "      <th>JOEY</th>\n",
       "      <th>MIKE</th>\n",
       "      <th>MONICA</th>\n",
       "      <th>PAUL</th>\n",
       "      <th>PHOEBE</th>\n",
       "      <th>RACHEL</th>\n",
       "      <th>ROSS</th>\n",
       "      <th>TAG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>817.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>872.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>793.0</td>\n",
       "      <td>925.0</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>766.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.0</td>\n",
       "      <td>908.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>941.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>898.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>813.0</td>\n",
       "      <td>906.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Characters   ALL  CAROL  CHANDLER  CHARLIE  EMILY  FRANK  JANICE   JOEY  MIKE  \\\n",
       "Season                                                                          \n",
       "1           91.0  79.0   817.0    NaN      NaN    NaN     38.0    640.0 NaN     \n",
       "2           8.0   29.0   170.0    NaN      NaN    NaN     7.0     172.0 NaN     \n",
       "3           55.0  27.0   825.0    NaN      NaN     79.0   62.0    775.0  3.0    \n",
       "4           39.0  11.0   912.0    NaN       138.0  28.0   27.0    816.0  4.0    \n",
       "5           47.0  7.0    941.0    NaN       29.0   25.0   14.0    932.0 NaN     \n",
       "\n",
       "Characters  MONICA  PAUL  PHOEBE  RACHEL    ROSS  TAG  \n",
       "Season                                                 \n",
       "1           872.0   17.0  637.0   850.0   952.0  NaN   \n",
       "2           156.0  NaN    136.0   177.0   278.0  NaN   \n",
       "3           847.0  NaN    793.0   925.0   1075.0 NaN   \n",
       "4           766.0  NaN    711.0   908.0   807.0  NaN   \n",
       "5           898.0  NaN    813.0   906.0   870.0  NaN   "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_lines_per_season_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a5e6bd1da0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_lines_per_season_pivot = data_lines_per_season.pivot(index='Season', columns='Characters', values='Lines')\n",
    "data_lines_per_season_pivot.fillna(0)\n",
    "\n",
    "#sns.set()\n",
    "plt.figure(figsize=(12,6))\n",
    "data_lines_per_season_pivot.loc[:,Characters_per_rank[:14]].plot.bar(stacked=True)\n",
    "\n",
    "plt.xlabel( \"Season\",fontsize=16)\n",
    "plt.ylabel(\"Number of Lines\", fontsize=16)\n",
    "plt.xticks(  fontsize= 16,rotation = 90)\n",
    "plt.yticks(  fontsize= 16)\n",
    "plt.title(\"Lines per Season for Top 15 Characters in the Show\",fontsize=16)\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1),prop={'size':10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading averaged_perceptron_tagger: <urlopen error\n",
      "[nltk_data]     [WinError 10060] A connection attempt failed because\n",
      "[nltk_data]     the connected party did not properly respond after a\n",
      "[nltk_data]     period of time, or established connection failed\n",
      "[nltk_data]     because connected host has failed to respond>\n",
      "[nltk_data] Error loading wordnet: <urlopen error [WinError 10060] A\n",
      "[nltk_data]     connection attempt failed because the connected party\n",
      "[nltk_data]     did not properly respond after a period of time, or\n",
      "[nltk_data]     established connection failed because connected host\n",
      "[nltk_data]     has failed to respond>\n"
     ]
    }
   ],
   "source": [
    "# Define a function to return the wordnet object value corresponding to the POS tag\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import wordnet\n",
    "from nltk import pos_tag\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "def get_wordnet_pos(pos_tag):\n",
    "\n",
    "    if pos_tag.startswith('J'):\n",
    "        \n",
    "        return wordnet.ADJ\n",
    "\n",
    "    elif pos_tag.startswith('V'):\n",
    "\n",
    "        return wordnet.VERB\n",
    "\n",
    "    elif pos_tag.startswith('N'):\n",
    "\n",
    "        return wordnet.NOUN\n",
    "\n",
    "    elif pos_tag.startswith('R'):\n",
    "\n",
    "        return wordnet.ADV\n",
    "\n",
    "    else:\n",
    "\n",
    "        return wordnet.NOUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# write a function to clean text    \n",
    "import string\n",
    "from nltk import pos_tag\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import WhitespaceTokenizer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "def clean_text(text):\n",
    "\n",
    "    # lower text\n",
    "    text = text.lower()\n",
    "\n",
    "    # tokenize text for space and \\n\n",
    "    text = re.split('\\s+|\\n',text)\n",
    "    \n",
    "    # remove puncutation\n",
    "    text = [word.strip(string.punctuation) for word in text]\n",
    "\n",
    "    # remove words that contain numbers\n",
    "    text = [word for word in text if not any(c.isdigit() for c in word)]\n",
    "\n",
    "    # remove stop words\n",
    "    stop = set(stopwords.words('english'))\n",
    "    text = [x for x in text if x not in stop]\n",
    "\n",
    "    # remove empty tokens\n",
    "    text = [t for t in text if len(t) > 0]\n",
    "\n",
    "    # pos tag text\n",
    "    pos_tags = pos_tag(text)\n",
    "\n",
    "    # create a (word, pos_tag) tuple for each word in text and then lemmatize text\n",
    "    text = [WordNetLemmatizer().lemmatize(t[0], get_wordnet_pos(t[1])) for t in pos_tags]\n",
    "\n",
    "    # remove words with only one letter\n",
    "    text = [t for t in text if len(t) > 1]\n",
    "\n",
    "    # join all\n",
    "    text = \" \".join(text)\n",
    "\n",
    "    return(text)\n",
    "\n",
    "# call the function to clean text data\n",
    "data[\"Lines\"] = data[\"Lines\"].apply(lambda x: clean_text(x))\n",
    "data[\"Lines\"].head()\n",
    "\n",
    "# Eliminate any row with no entry fordata['Lines'] after cleaning \n",
    "data['Lines'].replace('', np.NaN, inplace=True)\n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "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>Episodes</th>\n",
       "      <th>Episode_Names</th>\n",
       "      <th>Characters</th>\n",
       "      <th>Lines</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>MONICA</td>\n",
       "      <td>there's nothing tell he's guy work</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>JOEY</td>\n",
       "      <td>c'mon go guy there's gotta something wrong</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>CHANDLER</td>\n",
       "      <td>right joey nice hump hump hairpiece</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>PHOEBE</td>\n",
       "      <td>wait eat chalk</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>Monica Gets A Roommate</td>\n",
       "      <td>PHOEBE</td>\n",
       "      <td>cause want go go carl oh</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Episodes             Episode_Names Characters  \\\n",
       "0  101       Monica Gets A Roommate   MONICA      \n",
       "1  101       Monica Gets A Roommate   JOEY        \n",
       "2  101       Monica Gets A Roommate   CHANDLER    \n",
       "3  101       Monica Gets A Roommate   PHOEBE      \n",
       "4  101       Monica Gets A Roommate   PHOEBE      \n",
       "\n",
       "                                        Lines  Season  \n",
       "0  there's nothing tell he's guy work          1       \n",
       "1  c'mon go guy there's gotta something wrong  1       \n",
       "2  right joey nice hump hump hairpiece         1       \n",
       "3  wait eat chalk                              1       \n",
       "4  cause want go go carl oh                    1       "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep a list of all words and a set for all characters for future use in the code\n",
    "import itertools\n",
    "line_tokenized = [ re.split(' ',text) for text in data['Lines'].dropna()]\n",
    "all_words = list(itertools.chain.from_iterable(line_tokenized))\n",
    "all_characters = set(data.Characters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive EDA: Visualize Word/Letter Density for Different Actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2465c2dc92354437ae1ace6ccc5c6b0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Character_Name', options=('RACHEL', 'ROSS', 'CHANDLER', 'MONICA', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.word_letter_density_distribution(Character_Name)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create figure\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "def word_letter_density_distribution(Character_Name):\n",
    "    f, ax1 = plt.subplots(figsize=(12,5))\n",
    "\n",
    "    if Character_Name in all_characters:\n",
    "        letter_count  = data[\"Lines\"][data.Characters == Character_Name].str.len()\n",
    "        word_count    =  data[\"Lines\"][data.Characters == Character_Name].str.split(' ').str.len()\n",
    "    else:\n",
    "        letter_count  = data[\"Lines\"].str.len()\n",
    "        word_count    =  data[\"Lines\"].str.split(' ').str.len()\n",
    "    plt1 = sns.distplot(letter_count[letter_count<1000], ax=ax1, label='letters')\n",
    "    plt2 = sns.distplot(word_count[word_count<1000], ax=ax1, color='orange', label='words')\n",
    "\n",
    "    # add legend, title, and labels\n",
    "    legend = plt.legend( prop={'size': 16})\n",
    "    title = plt.title('Density of word and letter counts in lines of the transcripts')\n",
    "    labels = ax1.set(xlabel='Number of Letters or Words', ylabel='Density')\n",
    "\n",
    "    ax1.set_xlim(0, 200)\n",
    "    ax1.set_ylim(0, 0.25)\n",
    "\n",
    "    for item in ([ax1.title, ax1.xaxis.label, ax1.yaxis.label] +\n",
    "             ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "        item.set_fontsize(16)\n",
    "      \n",
    "# features to visualize\n",
    "Selection_Options = list(Characters_per_rank[:20])\n",
    "Selection_Options.append('All Characters')\n",
    "style = {'description_width': 'initial'}\n",
    "warnings.filterwarnings('ignore')\n",
    "interact(word_letter_density_distribution, Character_Name = Selection_Options,style = style)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cumulative Distribution of Word Density for Lines in the Transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def ECDF(Character_Name1):\n",
    "    if Character_Name1 in all_characters:\n",
    "        word_count1    =  data[\"Lines\"][data.Characters == Character_Name1].str.split(' ').str.len() \n",
    "    else: \n",
    "        word_count1    =  data[\"Lines\"].str.split(' ').str.len()\n",
    "    \n",
    "           \n",
    "    x1 = np.sort(word_count1)\n",
    "    n1 = len(word_count1)\n",
    "    y1 = np.arange(1, n1+1) / n1\n",
    "    return x1,y1\n",
    "    \n",
    "\n",
    "x1,y1 = ECDF('ROSS')\n",
    "x2,y2 = ECDF('PHOEBE')\n",
    "x3,y3 = ECDF ('JANICE')\n",
    "x4,y4 = ECDF ('MIKE')\n",
    "x5,y5 = ECDF('ALL')\n",
    "\n",
    "plt.figure(figsize = (12,5))\n",
    "\n",
    "plt.plot(x1 , y1,marker = '.' , linestyle = 'none', label = 'ROSS')\n",
    "plt.plot(x2 , y2 ,marker = '.' , linestyle = 'none', label = 'PHOEBE')\n",
    "plt.plot(x3 , y3 ,marker = '.' , linestyle = 'none', label = 'JANICE')\n",
    "plt.plot(x4 , y4,marker = '.' , linestyle = 'none', label = 'MIKE')\n",
    "plt.plot(x5 , y5 ,marker = '.' , linestyle = 'none', label = 'All Characters')\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Word Count', fontsize= 16)\n",
    "plt.ylabel('ECDF',fontsize= 16)\n",
    "plt.xticks(  fontsize= 16)\n",
    "plt.yticks(  fontsize= 16)\n",
    "plt.title(\"CDF for Word Count per Line\",fontsize=16)\n",
    "#plt.legend(loc='lower right', bbox_to_anchor=(1.3, 1),prop={'size':10})\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlim([0,60])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive EDA: WordCloud for Leading Actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from resizeimage import resizeimage\n",
    "from collections import Counter\n",
    "\n",
    "#define a function to plot word cloud\n",
    "\n",
    "def show_wordcloud(data,Filter_Common_Words, Desired_Character = 'All Friends'):\n",
    "    \n",
    "    \n",
    "    #define the image mask for word_cloud plot\n",
    "  \n",
    "    image = Image.open('Figures\\{0}.png'.format( Desired_Character))\n",
    "        \n",
    "    mask = np.array(image)\n",
    "    def transform_format(val):\n",
    "        if val == 0:\n",
    "            return 255\n",
    "        else:\n",
    "            return val\n",
    "    \n",
    "    transformed_mask = np.ndarray((mask.shape[0],mask.shape[1]), np.int32)\n",
    "\n",
    "    for i in range(len(mask)):\n",
    "        transformed_mask[i] = list(map(transform_format, mask[i]))\n",
    "    \n",
    "    # Select a list of most common words to filter from the word cloud\n",
    "    \n",
    "    if Filter_Common_Words>0:\n",
    "        \n",
    "        #sort all word per their frequency and make a list to filter\n",
    "        list1=all_words\n",
    "        counts = Counter(list1)\n",
    "        most_common = sorted(counts, key=counts.get, reverse=True)[0:Filter_Common_Words]\n",
    "        \n",
    "        #make sure that names of the characters are not in the filter\n",
    "        most_common = [word  for word in most_common if not word.upper() in all_characters]\n",
    "        most_common.append('NaN')\n",
    "\n",
    "        wordcloud = WordCloud (stopwords=most_common,\n",
    "                                width=800, height=400,\n",
    "                                background_color = 'white',\n",
    "                                scale =3,\n",
    "                                max_words = 200, \n",
    "                                mask = transformed_mask,\n",
    "                                random_state = 42, \n",
    "                                contour_width=3, contour_color='steelblue'\n",
    "                             ).generate(str(data))\n",
    "    else:\n",
    "         wordcloud = WordCloud (width=800, height=400,\n",
    "                                background_color = 'white',\n",
    "                                scale =3,\n",
    "                                max_words = 250, \n",
    "                                mask = transformed_mask,\n",
    "                                random_state = 42, \n",
    "                                contour_width=3, contour_color='steelblue',\n",
    "                                collocations = False,\n",
    "                             ).generate(str(data))\n",
    "        \n",
    "    # plot wordcloud\n",
    "    fig = plt.figure(1, figsize = (10, 10))\n",
    "    plt.title('WordCloud: {0}'.format(Desired_Character), FontSize = 32, loc= 'center')\n",
    "    plt.axis('off')\n",
    "    \n",
    "    fig.subplots_adjust(top = 2.3)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    # show plot\n",
    "    plt.imshow(wordcloud,interpolation='bilinear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make a list of most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "322ae06c601c4bd9ab0143bdab0357d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Desired_Character', options=('ROSS', 'MONICA', 'RACHEL', 'PHOEBE',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.wordcloud_generator(Desired_Character, Filter_Common_Words)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "from ipywidgets import interact\n",
    "\n",
    "def wordcloud_generator(Desired_Character, Filter_Common_Words):\n",
    "    if Desired_Character in all_characters:\n",
    "        show_wordcloud(data[\"Lines\"][data.Characters == Desired_Character], Filter_Common_Words, Desired_Character)\n",
    "    else:\n",
    "         show_wordcloud(data[\"Lines\"], Filter_Common_Words)\n",
    "     \n",
    "# features to visualize\n",
    "\n",
    "\n",
    "style = {'description_width': 'initial'}\n",
    "slider_range = widgets.IntSlider(value=1000, min=0, max=15149,style=style)\n",
    "slider_range.style.handle_color = 'lightblue'\n",
    "\n",
    "origin = widgets.Dropdown(\n",
    "    options=list( ['ROSS','MONICA','RACHEL','PHOEBE','JOEY','CHANDLER', 'All Friends']),\n",
    "    description='Desired_Character',style = style)\n",
    "    \n",
    "interact(wordcloud_generator, Desired_Character = origin, Filter_Common_Words = slider_range,style = style)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export DataFrame for Further Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#There are 305 chacracters, lets limit the analysis for top 15 to simplify the project\n",
    "# import data\n",
    "data.to_csv('data_clean.csv', encoding = 'utf-8')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 15\n",
    "data = data[data.Characters.isin(Characters_per_rank[0:threshold])]\n",
    "tokenized_data = data[\"Lines\"].apply(lambda text: re.split(' ',text))\n",
    "my_tags = Characters_per_rank[0:threshold]\n",
    "# Label Character\n",
    "from sklearn import preprocessing\n",
    "label = preprocessing.LabelEncoder()\n",
    "target = label.fit_transform(data['Characters'].astype(str))"
   ]
  }
 ],
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