Friends-data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import string\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from textblob import TextBlob # for sentiment analysis\n",
    "from collections import Counter \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.path.isfile('Friends_Pilot-Analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset\n",
    "\n",
    "df = pd.read_csv('Book4.csv',encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 305 entries, 0 to 304\n",
      "Data columns (total 2 columns):\n",
      "character    305 non-null object\n",
      "text         303 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 4.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Provides Concise Summary of our Dataset\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clear unwanted characters\n",
    "df = df[df['character'] != '[Scene']\n",
    "df = df[df['character'] != 'Customer']\n",
    "df = df[df['character'] != 'Commercial Break']\n",
    "df = df[df['character'] != 'Waitress']\n",
    "df = df[df['character'] != 'End']\n",
    "df = df[df['character'] != 'Ross and Rachel']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Word Count Column\n",
    "df['word_count'] = df['text'].str.split().str.len()\n",
    "#print(df.character.value_counts().iloc[:8].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which Character has most lines?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Friends: Character Line Count')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x = 'character', data = df, order = df.character.value_counts().iloc[:8].index)\n",
    "plt.title('Friends: Character Line Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which Character uses most words on average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Friends : Average Character Word Counts')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x='character',y='word_count',data=df, order = df.character.value_counts().iloc[:8].index)\n",
    "plt.title('Friends : Average Character Word Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which character uses the most words in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "total_sum = df.groupby(['character'])['word_count'].sum().reset_index()\n",
    "#print(total_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_sum = total_sum.sort_values(by='word_count', ascending=False)\n",
    "#print(total_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Friends : Total Character Word Counts')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x='character',y='word_count', data=total_sum, order = df.character.value_counts().iloc[:8].index)\n",
    "plt.title('Friends : Total Character Word Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre Processing\n",
    "# Clean and Normalize Text\n",
    "# - tokenize\n",
    "# - lowercase\n",
    "# - remove punctuation\n",
    "# - remove numeric characters\n",
    "# - remove stopwords\n",
    "\n",
    "stopwords = set(stopwords.words('english'))\n",
    "#df['text'].dropna(inplace=True)\n",
    "#df['new_text'] = df['text'].apply(word_tokenize)\n",
    "\n",
    "def clean(text):\n",
    "    text = str(text)\n",
    "    text = word_tokenize(text)\n",
    "    text = [word.lower() for word in text]\n",
    "    punct = str.maketrans('', '', string.punctuation) \n",
    "    text = [word.translate(punct) for word in text] \n",
    "    text = [word for word in text if word.isalpha()]\n",
    "    text = [word for word in text if not word in stopwords]\n",
    "    return \" \".join(text)\n",
    "\n",
    "df['clean_text'] = df['text'].apply(clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                                  nothing tell guy work\n",
      "1                  cmon going guy got ta something wrong\n",
      "2                    right joey nice hump hump hairpiece\n",
      "3                                         wait eat chalk\n",
      "4                          cause nt want go went carl oh\n",
      "                             ...                        \n",
      "298                                              serving\n",
      "299                                 yeah yeah cup coffee\n",
      "300                             kids new dream las vegas\n",
      "302    ugh excuse could give guy go ahead thank sorry...\n",
      "303                          okay las vegas liza minelli\n",
      "Name: clean_text, Length: 297, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Create Word Count Column for Clean Text\n",
    "\n",
    "df['clean_word_count'] = df['clean_text'].str.split().str.len()\n",
    "print(df['clean_text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Words vs Clean Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(6, 7))\n",
    "\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=\"word_count\", y=\"character\", data=df,\n",
    "            label=\"Total Word Count\", color=\"b\")\n",
    "\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x=\"clean_word_count\", y=\"character\", data=df,\n",
    "            label=\"Clean Word Count\", color=\"b\")\n",
    "\n",
    "ax.legend( loc=\"lower right\", frameon=True)\n",
    "ax.set(xlim=(0, 24), ylabel=\"\",\n",
    "       xlabel=\"Total Words vs Clean Words\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    character                                               text  word_count  \\\n",
      "0      Monica   There's nothing to tell! He's just some guy I...          11   \n",
      "1        Joey   C'mon, you're going out with the guy! There's...          14   \n",
      "2    Chandler   All right Joey, be nice.  So does he have a h...          16   \n",
      "3      Phoebe                           Wait, does he eat chalk?           5   \n",
      "4      Phoebe   Just, 'cause, I don't want her to go through ...          16   \n",
      "..        ...                                                ...         ...   \n",
      "298    Rachel                               I'm just serving it.           4   \n",
      "299       All             Yeah. Yeah, I'll have a cup of coffee.           8   \n",
      "300  Chandler              Kids, new dream... I'm in Las Vegas.            7   \n",
      "302    Rachel   Ugh.  Excuse me, could you give this to that ...          20   \n",
      "303  Chandler    Okay, so, I'm in Las Vegas... I'm Liza Minelli-           9   \n",
      "\n",
      "                                            clean_text  clean_word_count  \\\n",
      "0                                nothing tell guy work                 4   \n",
      "1                cmon going guy got ta something wrong                 7   \n",
      "2                  right joey nice hump hump hairpiece                 6   \n",
      "3                                       wait eat chalk                 3   \n",
      "4                        cause nt want go went carl oh                 7   \n",
      "..                                                 ...               ...   \n",
      "298                                            serving                 1   \n",
      "299                               yeah yeah cup coffee                 4   \n",
      "300                           kids new dream las vegas                 5   \n",
      "302  ugh excuse could give guy go ahead thank sorry...                12   \n",
      "303                        okay las vegas liza minelli                 5   \n",
      "\n",
      "     polarity  \n",
      "0        0.00  \n",
      "1       -0.50  \n",
      "2        0.44  \n",
      "3        0.00  \n",
      "4        0.00  \n",
      "..        ...  \n",
      "298      0.00  \n",
      "299      0.00  \n",
      "300      0.14  \n",
      "302     -0.02  \n",
      "303      0.50  \n",
      "\n",
      "[297 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Apply Sentiment Polarity to Text with TextBlob\n",
    "\n",
    "df['polarity'] = [round(TextBlob(word).sentiment.polarity, 2) for word in df['clean_text']]\n",
    "print(df)\n",
    "df['sentiment'] = ['positive' if polarity > 0 \n",
    "                             else 'negative' if polarity < 0 \n",
    "                                 else 'neutral' \n",
    "                                     for polarity in df['polarity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the sentiment of our character's lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200efb9d308>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sentiment.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a counter function to count words\n",
    "\n",
    "def counter(text):\n",
    "    cnt = Counter()\n",
    "    for msgs in text:\n",
    "        for msg in msgs:\n",
    "            cnt[msg] += 1\n",
    "    return cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Isolate Monica's Lines and use Counter to determine the Most Common Words\n",
    "\n",
    "import sys\n",
    "\n",
    "if not sys.warnoptions:\n",
    "    import warnings\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "\n",
    "monica = df.groupby('character')\n",
    "monica = monica.get_group(('Monica'))\n",
    "monica['clean_text'] = monica['clean_text'].apply(lambda x: word_tokenize(x))\n",
    "text_cnt = counter(monica['clean_text'])\n",
    "m = text_cnt.most_common()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are Monica's Most Common Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Monica's Most Common Words\")"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = pd.DataFrame(m, columns = ['Words', 'Counts'])\n",
    "m = m.sort_values(by='Counts', ascending=False)[:20]\n",
    "colors = sns.color_palette(\"winter\", len(m))\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(y = 'Words', x = 'Counts', data=m, palette=colors)\n",
    "plt.title('''Monica's Most Common Words''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are Joey's Most Common Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Isolate Joey's Lines and plot\n",
    "joey = df.groupby('character')\n",
    "joey = joey.get_group(('Joey'))\n",
    "joey['clean_text'] = joey['clean_text'].apply(lambda x: word_tokenize(x))\n",
    "joey_cnt = counter(joey['clean_text'])\n",
    "j = joey_cnt.most_common()\n",
    "j = pd.DataFrame(j, columns = ['Words', 'Counts'])\n",
    "j = j.sort_values(by='Counts', ascending=False)[:20]\n",
    "\n",
    "colors = sns.color_palette(\"gnuplot\", len(j))\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(y = 'Words' , x = 'Counts', data=j, palette=colors)\n",
    "plt.title('''Joey's Most Common Words''')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chandler 's most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chan = df.groupby('character')\n",
    "chan = chan.get_group(('Chandler'))\n",
    "chan['clean_text'] = chan['clean_text'].apply(lambda x: word_tokenize(x))\n",
    "chan_cnt = counter(chan['clean_text'])\n",
    "c = chan_cnt.most_common()\n",
    "c = pd.DataFrame(c, columns = ['Words', 'Counts'])\n",
    "c = c.sort_values(by='Counts', ascending=False)[:20]\n",
    "\n",
    "colors = sns.color_palette(\"YlGnBu\", len(c))\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(y = 'Words' , x = 'Counts', data=j, palette=colors)\n",
    "plt.title('''Chandler's Most Common Words''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ross 's most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Ross's Most Common Words\")"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ross = df.groupby('character')\n",
    "ross = ross.get_group(('Ross'))\n",
    "ross['clean_text'] = ross['clean_text'].apply(lambda x: word_tokenize(x))\n",
    "ross_cnt = counter(ross['clean_text'])\n",
    "r = ross_cnt.most_common()\n",
    "r = pd.DataFrame(r, columns = ['Words', 'Counts'])\n",
    "r = r.sort_values(by='Counts', ascending=False)[:20]\n",
    "\n",
    "colors = sns.color_palette(\"YlGnBu\", len(r))\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(y = 'Words' , x = 'Counts', data=r, palette=colors)\n",
    "plt.title('''Ross's Most Common Words''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phoebe 's most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Phoebe's Most Common Words\")"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "phoebs = df.groupby('character')\n",
    "phoebs = phoebs.get_group(('Phoebe'))\n",
    "phoebs['clean_text'] = phoebs['clean_text'].apply(lambda x: word_tokenize(x))\n",
    "phoebs_cnt = counter(phoebs['clean_text'])\n",
    "p = phoebs_cnt.most_common()\n",
    "p = pd.DataFrame(p, columns = ['Words', 'Counts'])\n",
    "p = p.sort_values(by='Counts', ascending=False)[:20]\n",
    "\n",
    "colors = sns.color_palette(\"PRGn\", len(r))\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(y = 'Words' , x = 'Counts', data=p, palette=colors)\n",
    "plt.title('''Phoebe's Most Common Words''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}