{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FrTmjbt6J-Mb"
   },
   "source": [
    "# Analysis of the dialogs in \"L'avare\" of Molière\n",
    "Student : Hugo Alexander Gonzalez Reyes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hstvx_-lLKzB"
   },
   "source": [
    "## 1. \n",
    "Classify the characters according to their amount of speech using a syntactic analysis of the text (scenes / lines / words). In particular, which character speaks the most? Which one does not speak at all? Note that the names of the characters are not necessarily uniform, case and the presence of accents may vary, for example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SUXKCVGJM31_"
   },
   "source": [
    "Before doing any work let's install and import all the libraries that we will use trought the document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3YBzlxmQNGC_",
    "outputId": "e1c962a4-ba6f-4467-eea8-cb03877160ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    }
   ],
   "source": [
    "#It is better to be sure that we have all the required pakages\n",
    "!pip install numpy --upgrade\n",
    "!pip install urllib3\n",
    "!pip install pandas --upgrade\n",
    "!pip install nltk\n",
    "!pip install pandas plotnine\n",
    "!pip install networkx\n",
    "\n",
    "import urllib.request #library to make web requests and retrieve files\n",
    "import unicodedata #built-in tool to work with different text formats (e.g. UTF-8)\n",
    "import pandas as pd #library that allows to work with dataframes\n",
    "import numpy as np #library that provides tools to work with matrices\n",
    "import string #built-in tool to work with strings\n",
    "import nltk #tokenizer library\n",
    "nltk.download('punkt') #download the tokenizer models\n",
    "from plotnine import * #library to plot the text analisys\n",
    "import networkx as nx #graphs library\n",
    "import matplotlib.pyplot as plt # library to plot the graphs built with networkx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ntDYq3TKS5oH"
   },
   "source": [
    "With the enviroment set and done we proceed to download the book. We convert the document to a readable format and we print a fragment of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vTesI8XBDMY-",
    "outputId": "d3ead83a-1f3f-4622-ab9f-05f2bf7825ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "identifier: moliere_avare  \n",
      "creator: Molière.  \n",
      "date: 1668  \n",
      "title: L'Avare. Comédie  \n",
      "---\n",
      "L'AVARE,\n",
      "COMÉDIE.\n",
      "Par J.B.P. MOLIÈRE.\n",
      "À PARIS, Chez JEAN RIBOU, au Palais, vis à vis la Porte de l'Église de la Sainte Chapelle, à l'Image Saint-Louis. M. DC. LXIX. *AVEC PRIVILÈGE DU ROI*\n",
      "# ACTEURS.\n",
      " – Harpagon, Père de Cléante et d'Élise, et Amoureux de Mariane.\n",
      " – Cléante, Fils d'Harpagon, Amant de Mariane.\n",
      " – Élise, Fille d'Harpagon, Amante de Valère.\n",
      " – Valère, Fils d'Anselme, et Amant d'Élise.\n",
      " – Mariane, Amante de Cléante, et aimée d'Harpagon.\n",
      " – Anselme, Père de Valère et de Mariane.\n",
      " – Frosine, Femme d'Intrigue.\n",
      " – Maitre Simon, Courtier.\n",
      " – Maitre Jacques, Cuisinier et Cocher d'Harpagon.\n",
      " – La Flèche, Valet de Cléante.\n",
      " – Dame Claude, Servante d'Harpagon.\n",
      " – Brindavoine, laquais d'Harpagon.\n",
      " – La Merluche, laquais d'Harpagon.\n",
      " – Le commissaire, et son clerc.\n",
      "La Scène est à Paris.\n",
      "# L'Avare, *Comédie.*.\n",
      "## Acte Premier.\n",
      "### Scène Première.\n",
      "Valère, Élise\n",
      "    VALÈRE.\n",
      "Hé quoi, charmante Élise, vous devenez mélancolique, après les obligeantes assurances que vous avez eu la bonté de me donner de votre foi ?Je vous vois soupirer, hélas, au milieu de ma joie !Est-ce du regret, dites-moi, de m'avoir fait heureux ? et vous repentez-vous de cet engagement où mes feux ont pu vous contraindre ?\n",
      "    ÉLISE.\n",
      "Non, Valère, je ne puis pas me repentir de tout ce que je fais pour vous. Je m'y sens entraîner par une trop douce puissance, et je n'ai pas même la force de souhaiter que les choses ne fussent pas. Mais, à vous dire vrai, le succès me donne de l'inquiétude ; et je crains fort de vous aimer un peu plus que je ne devrais.\n",
      "    VALÈRE.\n",
      "Hé que pouvez-vous craindre, Élise, dans les bontés que vous avez pour moi ?\n",
      "    ÉLISE.\n",
      "Hélas ! cent choses à la fois : L'emportement d'un Père ; les reproches d'une Famille ; les censures du monde ; mais plus que tout, Valère, le changement de votre cœur ; et cette froideur criminelle dont ceux de votre Sexe payent le plus souvent les témoignages trop ardents d'une innocente amour.\n",
      "    VALÈRE.\n",
      "Ah ! ne me faites pas ce tort, de juger de moi par les autres.Soupçonnez-moi de tout, Élise, plutôt que de manquer à ce que je vous dois.Je vous aime trop pour cela ; et mon amour pour vous, durera autant que ma vie.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "url = \"http://dramacode.github.io/markdown/moliere_avare.txt\"\n",
    "\n",
    "file = urllib.request.urlopen(url)\n",
    "readable_doc = []\n",
    "for line in file:\n",
    "    decoded_line = line.decode(\"utf-8\") #we decode the line\n",
    "    decoded_line = unicodedata.normalize(\"NFKD\", decoded_line) #we normalize some weird characters that might appear when working with strings\n",
    "    if decoded_line != '\\n':\n",
    "      readable_doc.append(decoded_line) \n",
    "print(''.join(readable_doc[0:40]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c0KxOFhwWEDb"
   },
   "source": [
    "We observe that the book follows a structure.\n",
    "* Each act is announced with a \"##\".\n",
    "* Each scene is announced with a \"###\".\n",
    "* The line after the scene presents all the actors playing in it.\n",
    "* For each dialog, the name of the actor is shown starting with four blank spaces and followed by their corresponding lines.\n",
    "\n",
    "Following this guidelines we can make a first list structure of the whole text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_BX4QcBxTGhn",
    "outputId": "fbb1a7eb-19b6-4043-9366-846bd4b9ab9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Premier', 'Première', 'VALÈRE, ÉLISE', 'VALÈRE', \"Hé quoi, charmante Élise, vous devenez mélancolique, après les obligeantes assurances que vous avez eu la bonté de me donner de votre foi ?Je vous vois soupirer, hélas, au milieu de ma joie !Est-ce du regret, dites-moi, de m'avoir fait heureux ? et vous repentez-vous de cet engagement où mes feux ont pu vous contraindre ?\"]\n",
      "['Premier', 'Première', 'VALÈRE, ÉLISE', 'ÉLISE', \"Non, Valère, je ne puis pas me repentir de tout ce que je fais pour vous. Je m'y sens entraîner par une trop douce puissance, et je n'ai pas même la force de souhaiter que les choses ne fussent pas. Mais, à vous dire vrai, le succès me donne de l'inquiétude ; et je crains fort de vous aimer un peu plus que je ne devrais.\"]\n"
     ]
    }
   ],
   "source": [
    "acte = \"\"\n",
    "scene = \"\"\n",
    "acteur = \"\"\n",
    "set_acteurs = \"\"\n",
    "dialogues = []\n",
    "\n",
    "start = False\n",
    "save_aut_set = False\n",
    "\n",
    "for decoded_line in readable_doc:\n",
    "      if save_aut_set:\n",
    "          set_acteurs = decoded_line.replace(\"\\n\",\"\").replace(\".\",\"\").strip().upper()\n",
    "          save_aut_set = False\n",
    "      if decoded_line[:3] == \"###\":\n",
    "          start = False \n",
    "          scene = decoded_line[4:-2].replace(\"Scène \",\"\").strip()\n",
    "          save_aut_set = True\n",
    "      elif decoded_line[:2] == \"##\":\n",
    "          acte = decoded_line[3:-2].replace(\"Acte \",\"\").strip()\n",
    "      elif decoded_line[:4] == \"    \":\n",
    "          start = True\n",
    "          acteur = decoded_line.strip()[:-1]\n",
    "      elif start:\n",
    "          dialogues.append([acte, scene, set_acteurs, acteur,decoded_line.replace(\"\\n\",\"\")])\n",
    "print(dialogues[0])\n",
    "print(dialogues[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "73p48k8-YMpB"
   },
   "source": [
    "We can proceed to build the dataframe to process this structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Jtafl5qBEHhJ"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dialogues, columns=[\"acte\", \"scene\",\"set_acteurs\" ,\"acteur\",\"text\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F26xcuE0bsUx"
   },
   "source": [
    "### Cleaning data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G5riMa_uYUch"
   },
   "source": [
    "First thing we can do is to normalize the acts and scenes in the document. We use roman numbers for all of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yqrMLDSMFOPQ",
    "outputId": "042f0653-cda7-4cca-acfc-a3120de4cbac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Premier', 'II', 'III', 'IV', 'V']\n",
      "['I', 'II', 'III', 'IV', 'V']\n"
     ]
    }
   ],
   "source": [
    "print(df[\"acte\"].unique().tolist())\n",
    "df.loc[df.acte == \"Premier\", \"acte\"] = \"I\"\n",
    "print(df[\"acte\"].unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xiNJ5UfXF5V3",
    "outputId": "9ddfaf55-216c-4606-afc9-61f4891415c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Première', 'II', 'III', 'IV', 'V', 'VI', 'VII', 'VIII', 'IX']\n",
      "['I', 'II', 'III', 'IV', 'V', 'VI', 'VII', 'VIII', 'IX']\n"
     ]
    }
   ],
   "source": [
    "print(df[\"scene\"].unique().tolist())\n",
    "df.loc[df.scene == \"Première\", \"scene\"] = \"I\"\n",
    "print(df[\"scene\"].unique().tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aBaQWxtvYfCh"
   },
   "source": [
    "The roman numbers are good to display the values, but to make the processing of the document easier we add columns with integers for each act and scene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "xh-tL3p2CIVh",
    "outputId": "5c2073a8-aec3-48ec-baae-54e85c0a3baa"
   },
   "outputs": [
    {
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       "      <th>acteur</th>\n",
       "      <th>text</th>\n",
       "      <th>acte_n</th>\n",
       "      <th>scene_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>Hé quoi, charmante Élise, vous devenez méla...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>Non, Valère, je ne puis pas me repentir de to...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>Hé que pouvez-vous craindre, Élise, dans les...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>Hélas ! cent choses à la fois : L'emportemen...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>Ah ! ne me faites pas ce tort, de juger de moi...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>MAÎTRE JACQUES</td>\n",
       "      <td>Hélas ! comment faut-il donc faire ? On me do...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>ANSELME</td>\n",
       "      <td>Seigneur Harpagon, il faut lui pardonner cette...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>Vous payerez donc le Commissaire ?</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>ANSELME</td>\n",
       "      <td>Soit. Allons vite faire part de notre joie à ...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>Et moi, voir ma chère Cassette.&lt; Fin &gt;</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1007 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     acte scene                                        set_acteurs  \\\n",
       "0       I     I                                    VALÈRE, ÉLISE   \n",
       "1       I     I                                    VALÈRE, ÉLISE   \n",
       "2       I     I                                    VALÈRE, ÉLISE   \n",
       "3       I     I                                    VALÈRE, ÉLISE   \n",
       "4       I     I                                    VALÈRE, ÉLISE   \n",
       "...   ...   ...                                                ...   \n",
       "1002    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1003    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1004    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1005    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1006    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "\n",
       "               acteur                                               text  \\\n",
       "0             VALÈRE  Hé quoi, charmante Élise, vous devenez méla...   \n",
       "1              ÉLISE  Non, Valère, je ne puis pas me repentir de to...   \n",
       "2             VALÈRE  Hé que pouvez-vous craindre, Élise, dans les...   \n",
       "3              ÉLISE  Hélas ! cent choses à la fois : L'emportemen...   \n",
       "4             VALÈRE  Ah ! ne me faites pas ce tort, de juger de moi...   \n",
       "...               ...                                                ...   \n",
       "1002  MAÎTRE JACQUES  Hélas ! comment faut-il donc faire ? On me do...   \n",
       "1003          ANSELME  Seigneur Harpagon, il faut lui pardonner cette...   \n",
       "1004         HARPAGON                 Vous payerez donc le Commissaire ?   \n",
       "1005          ANSELME  Soit. Allons vite faire part de notre joie à ...   \n",
       "1006         HARPAGON            Et moi, voir ma chère Cassette.< Fin >   \n",
       "\n",
       "      acte_n  scene_n  \n",
       "0          1        1  \n",
       "1          1        1  \n",
       "2          1        1  \n",
       "3          1        1  \n",
       "4          1        1  \n",
       "...      ...      ...  \n",
       "1002       5        6  \n",
       "1003       5        6  \n",
       "1004       5        6  \n",
       "1005       5        6  \n",
       "1006       5        6  \n",
       "\n",
       "[1007 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conditions_a = [\n",
    "    (df['acte'] == \"I\"),\n",
    "    (df['acte'] == \"II\"),\n",
    "    (df['acte'] == \"III\"),\n",
    "    (df['acte'] == \"IV\"),\n",
    "    (df['acte'] == \"V\"),\n",
    "    (df['acte'] == \"VI\"),\n",
    "    (df['acte'] == \"VII\"),\n",
    "    (df['acte'] == \"VIII\"),\n",
    "    (df['acte'] == \"IX\"),\n",
    "    ]\n",
    "conditions_s = [\n",
    "    (df['scene'] == \"I\"),\n",
    "    (df['scene'] == \"II\"),\n",
    "    (df['scene'] == \"III\"),\n",
    "    (df['scene'] == \"IV\"),\n",
    "    (df['scene'] == \"V\"),\n",
    "    (df['scene'] == \"VI\"),\n",
    "    (df['scene'] == \"VII\"),\n",
    "    (df['scene'] == \"VIII\"),\n",
    "    (df['scene'] == \"IX\"),\n",
    "    ]\n",
    "\n",
    "# create a list of the values we want to assign for each condition\n",
    "values = [1,2,3,4,5,6,7,8,9]\n",
    "\n",
    "# create a new column and use np.select to assign values to it using our lists as arguments\n",
    "df['acte_n'] = np.select(conditions_a, values)\n",
    "df['scene_n'] = np.select(conditions_s, values)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JSGr7tNDZAsy"
   },
   "source": [
    "When we check the list of actors in our dataframe, we find that some of them were written with some action added to it. We proceed to clean the records and we print againt to see if we have the list we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QDXCBwYqF9Tr",
    "outputId": "80075990-d1e8-4ea5-90ea-4fb79948dc09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['VALÈRE', 'ÉLISE', 'CLÉANTE', 'HARPAGON', 'LA FLÈCHE', 'LA FLÈCHE *lui montrant une des poches de son justaucorps*', 'MAÎTRE SIMON', 'FROSINE', 'MAÎTRE JACQUES', 'LA MERLUCHE', 'BRINDAVOINE', 'HARPAGON, *en lui mettant la main sur la bouche*', 'HARPAGON *en le battant*', 'MARIANE', 'MARIANE *bas à Frosine*', 'MARIANE *à part*', 'MARIANE *à part à Frosine*', 'FROSINE *à Mariane*', 'HARPAGON *bas à Valère*', 'VALÈRE *à Harpagon*', 'HARPAGON *à part à son fils*', 'HARPAGON *à part*', 'CLÉANTE *en empêchant toujours Mariane de rendre la Bague*', 'HARPAGON *bas à son fils*', 'HARPAGON *bas à son Fils, en le menaçant*', 'HARPAGON *bas à son Fils, avec emportement*', 'HARPAGON *bas à son Fils, avec les mêmes grimaces*', 'LA FLÈCHE *sortant du Jardin, avec une Cassette*', 'LE COMMISSAIRE', 'MAÎTRE JACQUES *au bout du théâtre, en se retournant du côté dont il sort*', 'MAÎTRE JACQUES, *à part*', 'ÉLISE, *à genoux devant son père*', 'ANSELME', 'VALÈRE, *en mettant fièrement son Chapeau*']\n",
      "['VALÈRE', 'ÉLISE', 'CLÉANTE', 'HARPAGON', 'LA FLÈCHE', 'MAÎTRE SIMON', 'FROSINE', 'MAÎTRE JACQUES', 'LA MERLUCHE', 'BRINDAVOINE', 'MARIANE', 'LE COMMISSAIRE', 'ANSELME']\n"
     ]
    }
   ],
   "source": [
    "print(df[\"acteur\"].unique().tolist())\n",
    "df.acteur = df.acteur.map(lambda a: a if \"*\" not in a else (a[:a.find('*')]).replace(',','').strip())\n",
    "print(df[\"acteur\"].unique().tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8enyE6NkZpga"
   },
   "source": [
    "In the text part we observe that there are parts that don't correspond to the lines of the actor. For example, brackets that signal the end of the scene. We find those lines and we clean them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iJy82qZkbLKx",
    "outputId": "66f37e64-c142-42c1-a1b3-d3b924feddcf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "268                                  Fin du Premier Acte.\n",
       "422                                   Fin du Second Acte.\n",
       "638     Ô fils impertinent, as-tu envie de me ruiner ...\n",
       "803                               Fin du Quatrième Acte.\n",
       "1006              Et moi, voir ma chère Cassette.< Fin >\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['text'].str.contains(\"Fin du\")) | (df['text'].str.contains(\"<\"))]['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "ChP_i7YRQUd6"
   },
   "outputs": [],
   "source": [
    "df.text = df.text.map(lambda a: a if \"<\" not in a else (a[:a.find('<')]))\n",
    "df = df.drop(df[df['text'].str.contains(\"Fin du\")].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nymhkLGQQKXh"
   },
   "source": [
    "### Analysing the dialogues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CDaYNzoHb0U6"
   },
   "source": [
    "Once the data is cleaned, we proceed to start working with the actual lines from the actors. Although the punctuations are a important part of a script, we will omit them to improve our word analysis. We also set all the text to lowercase to maintain uniformity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "id": "8swjAeKFT5is",
    "outputId": "998c62cc-83c4-4753-c9fd-d3e4007de1f3"
   },
   "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>acte</th>\n",
       "      <th>scene</th>\n",
       "      <th>set_acteurs</th>\n",
       "      <th>acteur</th>\n",
       "      <th>text</th>\n",
       "      <th>acte_n</th>\n",
       "      <th>scene_n</th>\n",
       "      <th>free_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>Hé quoi, charmante Élise, vous devenez méla...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hé quoi  charmante élise  vous devenez méla...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>Non, Valère, je ne puis pas me repentir de to...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>non  valère  je ne puis pas me repentir de to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>Hé que pouvez-vous craindre, Élise, dans les...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hé que pouvez vous craindre  élise  dans les...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>Hélas ! cent choses à la fois : L'emportemen...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hélas   cent choses à la fois   l emportemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>Ah ! ne me faites pas ce tort, de juger de moi...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ah   ne me faites pas ce tort  de juger de moi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>MAÎTRE JACQUES</td>\n",
       "      <td>Hélas ! comment faut-il donc faire ? On me do...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>hélas   comment faut il donc faire   on me do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>ANSELME</td>\n",
       "      <td>Seigneur Harpagon, il faut lui pardonner cette...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>seigneur harpagon  il faut lui pardonner cette...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>Vous payerez donc le Commissaire ?</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>vous payerez donc le commissaire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>ANSELME</td>\n",
       "      <td>Soit. Allons vite faire part de notre joie à ...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>soit  allons vite faire part de notre joie à ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>Et moi, voir ma chère Cassette.</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>et moi  voir ma chère cassette</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     acte scene                                        set_acteurs  \\\n",
       "0       I     I                                    VALÈRE, ÉLISE   \n",
       "1       I     I                                    VALÈRE, ÉLISE   \n",
       "2       I     I                                    VALÈRE, ÉLISE   \n",
       "3       I     I                                    VALÈRE, ÉLISE   \n",
       "4       I     I                                    VALÈRE, ÉLISE   \n",
       "...   ...   ...                                                ...   \n",
       "1002    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1003    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1004    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1005    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1006    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "\n",
       "               acteur                                               text  \\\n",
       "0             VALÈRE  Hé quoi, charmante Élise, vous devenez méla...   \n",
       "1              ÉLISE  Non, Valère, je ne puis pas me repentir de to...   \n",
       "2             VALÈRE  Hé que pouvez-vous craindre, Élise, dans les...   \n",
       "3              ÉLISE  Hélas ! cent choses à la fois : L'emportemen...   \n",
       "4             VALÈRE  Ah ! ne me faites pas ce tort, de juger de moi...   \n",
       "...               ...                                                ...   \n",
       "1002  MAÎTRE JACQUES  Hélas ! comment faut-il donc faire ? On me do...   \n",
       "1003          ANSELME  Seigneur Harpagon, il faut lui pardonner cette...   \n",
       "1004         HARPAGON                 Vous payerez donc le Commissaire ?   \n",
       "1005          ANSELME  Soit. Allons vite faire part de notre joie à ...   \n",
       "1006         HARPAGON                   Et moi, voir ma chère Cassette.   \n",
       "\n",
       "      acte_n  scene_n                                          free_text  \n",
       "0          1        1  hé quoi  charmante élise  vous devenez méla...  \n",
       "1          1        1  non  valère  je ne puis pas me repentir de to...  \n",
       "2          1        1  hé que pouvez vous craindre  élise  dans les...  \n",
       "3          1        1  hélas   cent choses à la fois   l emportemen...  \n",
       "4          1        1  ah   ne me faites pas ce tort  de juger de moi...  \n",
       "...      ...      ...                                                ...  \n",
       "1002       5        6  hélas   comment faut il donc faire   on me do...  \n",
       "1003       5        6  seigneur harpagon  il faut lui pardonner cette...  \n",
       "1004       5        6                 vous payerez donc le commissaire    \n",
       "1005       5        6  soit  allons vite faire part de notre joie à ...  \n",
       "1006       5        6                   et moi  voir ma chère cassette   \n",
       "\n",
       "[1004 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "punct = set(string.punctuation)\n",
    "df['free_text']= df['text'].apply(lambda x :''.join(ch.lower() if ch not in punct else ' ' for ch in x ))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YymrAyeSdAYD"
   },
   "source": [
    "Having this, we can finally tokenize the cleaned text. One token corresponds to one word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 684
    },
    "id": "fOaO-Bg8W09i",
    "outputId": "a8f6d7e3-00bd-4622-dfd4-3378712ada27"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acte</th>\n",
       "      <th>scene</th>\n",
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       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>Hélas ! cent choses à la fois : L'emportemen...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hélas   cent choses à la fois   l emportemen...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>Ah ! ne me faites pas ce tort, de juger de moi...</td>\n",
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       "      <td>ah   ne me faites pas ce tort  de juger de moi...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>MAÎTRE JACQUES</td>\n",
       "      <td>Hélas ! comment faut-il donc faire ? On me do...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>hélas   comment faut il donc faire   on me do...</td>\n",
       "      <td>[hélas, comment, faut, il, donc, faire, on, m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>ANSELME</td>\n",
       "      <td>Seigneur Harpagon, il faut lui pardonner cette...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>seigneur harpagon  il faut lui pardonner cette...</td>\n",
       "      <td>[seigneur, harpagon, il, faut, lui, pardonner,...</td>\n",
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       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>Vous payerez donc le Commissaire ?</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>vous payerez donc le commissaire</td>\n",
       "      <td>[vous, payerez, donc, le, commissaire]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>ANSELME</td>\n",
       "      <td>Soit. Allons vite faire part de notre joie à ...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>soit  allons vite faire part de notre joie à ...</td>\n",
       "      <td>[soit, allons, vite, faire, part, de, notre, j...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>Et moi, voir ma chère Cassette.</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>et moi  voir ma chère cassette</td>\n",
       "      <td>[et, moi, voir, ma, chère, cassette]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     acte scene                                        set_acteurs  \\\n",
       "0       I     I                                    VALÈRE, ÉLISE   \n",
       "1       I     I                                    VALÈRE, ÉLISE   \n",
       "2       I     I                                    VALÈRE, ÉLISE   \n",
       "3       I     I                                    VALÈRE, ÉLISE   \n",
       "4       I     I                                    VALÈRE, ÉLISE   \n",
       "...   ...   ...                                                ...   \n",
       "1002    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1003    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1004    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1005    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1006    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "\n",
       "               acteur                                               text  \\\n",
       "0             VALÈRE  Hé quoi, charmante Élise, vous devenez méla...   \n",
       "1              ÉLISE  Non, Valère, je ne puis pas me repentir de to...   \n",
       "2             VALÈRE  Hé que pouvez-vous craindre, Élise, dans les...   \n",
       "3              ÉLISE  Hélas ! cent choses à la fois : L'emportemen...   \n",
       "4             VALÈRE  Ah ! ne me faites pas ce tort, de juger de moi...   \n",
       "...               ...                                                ...   \n",
       "1002  MAÎTRE JACQUES  Hélas ! comment faut-il donc faire ? On me do...   \n",
       "1003          ANSELME  Seigneur Harpagon, il faut lui pardonner cette...   \n",
       "1004         HARPAGON                 Vous payerez donc le Commissaire ?   \n",
       "1005          ANSELME  Soit. Allons vite faire part de notre joie à ...   \n",
       "1006         HARPAGON                   Et moi, voir ma chère Cassette.   \n",
       "\n",
       "      acte_n  scene_n                                          free_text  \\\n",
       "0          1        1  hé quoi  charmante élise  vous devenez méla...   \n",
       "1          1        1  non  valère  je ne puis pas me repentir de to...   \n",
       "2          1        1  hé que pouvez vous craindre  élise  dans les...   \n",
       "3          1        1  hélas   cent choses à la fois   l emportemen...   \n",
       "4          1        1  ah   ne me faites pas ce tort  de juger de moi...   \n",
       "...      ...      ...                                                ...   \n",
       "1002       5        6  hélas   comment faut il donc faire   on me do...   \n",
       "1003       5        6  seigneur harpagon  il faut lui pardonner cette...   \n",
       "1004       5        6                 vous payerez donc le commissaire     \n",
       "1005       5        6  soit  allons vite faire part de notre joie à ...   \n",
       "1006       5        6                   et moi  voir ma chère cassette    \n",
       "\n",
       "                                         tokenized_text  \n",
       "0     [hé, quoi, charmante, élise, vous, devenez, ...  \n",
       "1     [non, valère, je, ne, puis, pas, me, repentir...  \n",
       "2     [hé, que, pouvez, vous, craindre, élise, dan...  \n",
       "3     [hélas, cent, choses, à, la, fois, l, emport...  \n",
       "4     [ah, ne, me, faites, pas, ce, tort, de, juger,...  \n",
       "...                                                 ...  \n",
       "1002  [hélas, comment, faut, il, donc, faire, on, m...  \n",
       "1003  [seigneur, harpagon, il, faut, lui, pardonner,...  \n",
       "1004             [vous, payerez, donc, le, commissaire]  \n",
       "1005  [soit, allons, vite, faire, part, de, notre, j...  \n",
       "1006              [et, moi, voir, ma, chère, cassette]  \n",
       "\n",
       "[1004 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tokenized_text'] = df.apply(lambda row: nltk.word_tokenize(row['free_text']), axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i8OnTdHVdGjh"
   },
   "source": [
    "With the text tonenized, we can count the tokens to know the ammount of words in each line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 771
    },
    "id": "BxW1Jo57XnmS",
    "outputId": "2aa5f767-4f7d-4637-d61d-2c0e0853e7bc"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
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       "      <th>1</th>\n",
       "      <td>I</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hé que pouvez vous craindre  élise  dans les...</td>\n",
       "      <td>[hé, que, pouvez, vous, craindre, élise, dan...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>Hélas ! cent choses à la fois : L'emportemen...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hélas   cent choses à la fois   l emportemen...</td>\n",
       "      <td>[hélas, cent, choses, à, la, fois, l, emport...</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>I</td>\n",
       "      <td>VALÈRE, ÉLISE</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>Ah ! ne me faites pas ce tort, de juger de moi...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ah   ne me faites pas ce tort  de juger de moi...</td>\n",
       "      <td>[ah, ne, me, faites, pas, ce, tort, de, juger,...</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>MAÎTRE JACQUES</td>\n",
       "      <td>Hélas ! comment faut-il donc faire ? On me do...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>hélas   comment faut il donc faire   on me do...</td>\n",
       "      <td>[hélas, comment, faut, il, donc, faire, on, m...</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>ANSELME</td>\n",
       "      <td>Seigneur Harpagon, il faut lui pardonner cette...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>seigneur harpagon  il faut lui pardonner cette...</td>\n",
       "      <td>[seigneur, harpagon, il, faut, lui, pardonner,...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>Vous payerez donc le Commissaire ?</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>vous payerez donc le commissaire</td>\n",
       "      <td>[vous, payerez, donc, le, commissaire]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>ANSELME</td>\n",
       "      <td>Soit. Allons vite faire part de notre joie à ...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>soit  allons vite faire part de notre joie à ...</td>\n",
       "      <td>[soit, allons, vite, faire, part, de, notre, j...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>V</td>\n",
       "      <td>VI</td>\n",
       "      <td>CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>Et moi, voir ma chère Cassette.</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>et moi  voir ma chère cassette</td>\n",
       "      <td>[et, moi, voir, ma, chère, cassette]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     acte scene                                        set_acteurs  \\\n",
       "0       I     I                                    VALÈRE, ÉLISE   \n",
       "1       I     I                                    VALÈRE, ÉLISE   \n",
       "2       I     I                                    VALÈRE, ÉLISE   \n",
       "3       I     I                                    VALÈRE, ÉLISE   \n",
       "4       I     I                                    VALÈRE, ÉLISE   \n",
       "...   ...   ...                                                ...   \n",
       "1002    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1003    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1004    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1005    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "1006    V    VI  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
       "\n",
       "               acteur                                               text  \\\n",
       "0             VALÈRE  Hé quoi, charmante Élise, vous devenez méla...   \n",
       "1              ÉLISE  Non, Valère, je ne puis pas me repentir de to...   \n",
       "2             VALÈRE  Hé que pouvez-vous craindre, Élise, dans les...   \n",
       "3              ÉLISE  Hélas ! cent choses à la fois : L'emportemen...   \n",
       "4             VALÈRE  Ah ! ne me faites pas ce tort, de juger de moi...   \n",
       "...               ...                                                ...   \n",
       "1002  MAÎTRE JACQUES  Hélas ! comment faut-il donc faire ? On me do...   \n",
       "1003          ANSELME  Seigneur Harpagon, il faut lui pardonner cette...   \n",
       "1004         HARPAGON                 Vous payerez donc le Commissaire ?   \n",
       "1005          ANSELME  Soit. Allons vite faire part de notre joie à ...   \n",
       "1006         HARPAGON                   Et moi, voir ma chère Cassette.   \n",
       "\n",
       "      acte_n  scene_n                                          free_text  \\\n",
       "0          1        1  hé quoi  charmante élise  vous devenez méla...   \n",
       "1          1        1  non  valère  je ne puis pas me repentir de to...   \n",
       "2          1        1  hé que pouvez vous craindre  élise  dans les...   \n",
       "3          1        1  hélas   cent choses à la fois   l emportemen...   \n",
       "4          1        1  ah   ne me faites pas ce tort  de juger de moi...   \n",
       "...      ...      ...                                                ...   \n",
       "1002       5        6  hélas   comment faut il donc faire   on me do...   \n",
       "1003       5        6  seigneur harpagon  il faut lui pardonner cette...   \n",
       "1004       5        6                 vous payerez donc le commissaire     \n",
       "1005       5        6  soit  allons vite faire part de notre joie à ...   \n",
       "1006       5        6                   et moi  voir ma chère cassette    \n",
       "\n",
       "                                         tokenized_text  number_of_words  \n",
       "0     [hé, quoi, charmante, élise, vous, devenez, ...               58  \n",
       "1     [non, valère, je, ne, puis, pas, me, repentir...               68  \n",
       "2     [hé, que, pouvez, vous, craindre, élise, dan...               14  \n",
       "3     [hélas, cent, choses, à, la, fois, l, emport...               51  \n",
       "4     [ah, ne, me, faites, pas, ce, tort, de, juger,...               45  \n",
       "...                                                 ...              ...  \n",
       "1002  [hélas, comment, faut, il, donc, faire, on, m...               23  \n",
       "1003  [seigneur, harpagon, il, faut, lui, pardonner,...                8  \n",
       "1004             [vous, payerez, donc, le, commissaire]                5  \n",
       "1005  [soit, allons, vite, faire, part, de, notre, j...               11  \n",
       "1006              [et, moi, voir, ma, chère, cassette]                6  \n",
       "\n",
       "[1004 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['number_of_words'] = df.tokenized_text.apply(lambda x: len(x))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cowTAUt-dqdr"
   },
   "source": [
    "We can find the number of words for every character. We find that Harpagon is the character that speaks the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "Sp334nz0ZeFS",
    "outputId": "789c07a5-a16e-4517-cd00-416265c4c406"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\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>acteur</th>\n",
       "      <th>number_of_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>6256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CLÉANTE</td>\n",
       "      <td>3364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>2772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FROSINE</td>\n",
       "      <td>2363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAÎTRE JACQUES</td>\n",
       "      <td>1717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LA FLÈCHE</td>\n",
       "      <td>1522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MARIANE</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ANSELME</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LE COMMISSAIRE</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MAÎTRE SIMON</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LA MERLUCHE</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BRINDAVOINE</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             acteur  number_of_words\n",
       "0          HARPAGON             6256\n",
       "1          CLÉANTE             3364\n",
       "2           VALÈRE             2772\n",
       "3           FROSINE             2363\n",
       "4   MAÎTRE JACQUES             1717\n",
       "5        LA FLÈCHE             1522\n",
       "6            ÉLISE             1071\n",
       "7           MARIANE              919\n",
       "8           ANSELME              517\n",
       "9    LE COMMISSAIRE              294\n",
       "10    MAÎTRE SIMON              197\n",
       "11      LA MERLUCHE               55\n",
       "12      BRINDAVOINE               43"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors_num = df.groupby('acteur')['number_of_words'].sum().sort_values(ascending=False).to_frame().reset_index()\n",
    "actors_num"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_XhOAvWtexeq"
   },
   "source": [
    "We check if all the characters in the book's description have actual lines. We find that although DAME CLAUDE is in the scenes she doesn't have any lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wGas5NPJfSu1",
    "outputId": "ae0a9978-ee64-4390-c2cd-de9065cb668c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DAME CLAUDE'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors_d = readable_doc[11:25]\n",
    "actors_d = set([x[3:x.find(',')].upper().replace(\"MAITRE \",\"MAÎTRE \") for x in actors_d])\n",
    "actors_t =  set(actors_num['acteur'])\n",
    "actors_d - actors_t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "niiTcyPci-yW"
   },
   "source": [
    "## 2. \n",
    "Make a graph that shows the number of words each actor says in each scene. You can get inspiration for this from the study of \"L’avare\" carried out by OBVIL (left graph). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "76yTHt5qjSV-"
   },
   "source": [
    "To obtain percentages for every actor for every scene it is necessary to group the data and divide it by the total ammount of lines in each scene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "JlEeDa3fkV66",
    "outputId": "b61bcc15-4710-4f28-fd0c-f484233cd0a0"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acte_n</th>\n",
       "      <th>scene_n</th>\n",
       "      <th>acte</th>\n",
       "      <th>scene</th>\n",
       "      <th>acteur</th>\n",
       "      <th>number_of_words</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>I</td>\n",
       "      <td>V</td>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>36</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>I</td>\n",
       "      <td>V</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>278</td>\n",
       "      <td>27.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>I</td>\n",
       "      <td>V</td>\n",
       "      <td>VALÈRE</td>\n",
       "      <td>710</td>\n",
       "      <td>69.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>I</td>\n",
       "      <td>IV</td>\n",
       "      <td>CLÉANTE</td>\n",
       "      <td>216</td>\n",
       "      <td>14.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>I</td>\n",
       "      <td>IV</td>\n",
       "      <td>ÉLISE</td>\n",
       "      <td>166</td>\n",
       "      <td>10.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>II</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>182</td>\n",
       "      <td>26.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>II</td>\n",
       "      <td>LE COMMISSAIRE</td>\n",
       "      <td>159</td>\n",
       "      <td>23.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>II</td>\n",
       "      <td>MAÎTRE JACQUES</td>\n",
       "      <td>348</td>\n",
       "      <td>50.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>I</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>89</td>\n",
       "      <td>44.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>I</td>\n",
       "      <td>LE COMMISSAIRE</td>\n",
       "      <td>109</td>\n",
       "      <td>55.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    acte_n  scene_n acte scene           acteur  number_of_words   perc\n",
       "9        1        5    I     V           ÉLISE               36   3.52\n",
       "10       1        5    I     V         HARPAGON              278  27.15\n",
       "11       1        5    I     V          VALÈRE              710  69.34\n",
       "6        1        4    I    IV         CLÉANTE              216  14.01\n",
       "7        1        4    I    IV           ÉLISE              166  10.77\n",
       "..     ...      ...  ...   ...              ...              ...    ...\n",
       "73       5        2    V    II         HARPAGON              182  26.42\n",
       "74       5        2    V    II   LE COMMISSAIRE              159  23.08\n",
       "75       5        2    V    II  MAÎTRE JACQUES              348  50.51\n",
       "71       5        1    V     I         HARPAGON               89  44.95\n",
       "72       5        1    V     I   LE COMMISSAIRE              109  55.05\n",
       "\n",
       "[95 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg = df.groupby(['acte_n','scene_n','acte','scene','acteur'])['number_of_words'].sum().reset_index()\n",
    "aggb = (df.groupby(['acte_n','scene_n','acte','scene','acteur'])['number_of_words'].sum()/(df.groupby(['acte_n','scene_n','acte','scene'])['number_of_words'].sum())).reset_index()\n",
    "agg['perc'] = round(aggb['number_of_words']*100,2)\n",
    "agg = agg.sort_values(['acte_n','scene_n'], ascending=[True, False])\n",
    "agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r_WK67XM1TXA"
   },
   "source": [
    "We also need to know how big is the scene in terms of number of words, compared to other scenes in the play.\n",
    "\n",
    "We find the width of every scene taking its total number of words and dividing it by the value of the longest scene in all acts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "AhT4FXKDKxK1",
    "outputId": "519da0a4-cd4d-41ec-9fd1-cd77703cc1ee"
   },
   "outputs": [
    {
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       "      <td>V</td>\n",
       "      <td>VALÈRE</td>\n",
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       "      <td>CLÉANTE</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>II</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>182</td>\n",
       "      <td>26.42</td>\n",
       "      <td>0.334304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>II</td>\n",
       "      <td>LE COMMISSAIRE</td>\n",
       "      <td>159</td>\n",
       "      <td>23.08</td>\n",
       "      <td>0.334304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>II</td>\n",
       "      <td>MAÎTRE JACQUES</td>\n",
       "      <td>348</td>\n",
       "      <td>50.51</td>\n",
       "      <td>0.334304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>I</td>\n",
       "      <td>HARPAGON</td>\n",
       "      <td>89</td>\n",
       "      <td>44.95</td>\n",
       "      <td>0.096070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>I</td>\n",
       "      <td>LE COMMISSAIRE</td>\n",
       "      <td>109</td>\n",
       "      <td>55.05</td>\n",
       "      <td>0.096070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    acte_n  scene_n acte scene           acteur  number_of_words   perc  \\\n",
       "9        1        5    I     V           ÉLISE               36   3.52   \n",
       "10       1        5    I     V         HARPAGON              278  27.15   \n",
       "11       1        5    I     V          VALÈRE              710  69.34   \n",
       "6        1        4    I    IV         CLÉANTE              216  14.01   \n",
       "7        1        4    I    IV           ÉLISE              166  10.77   \n",
       "..     ...      ...  ...   ...              ...              ...    ...   \n",
       "73       5        2    V    II         HARPAGON              182  26.42   \n",
       "74       5        2    V    II   LE COMMISSAIRE              159  23.08   \n",
       "75       5        2    V    II  MAÎTRE JACQUES              348  50.51   \n",
       "71       5        1    V     I         HARPAGON               89  44.95   \n",
       "72       5        1    V     I   LE COMMISSAIRE              109  55.05   \n",
       "\n",
       "           w  \n",
       "9   0.496846  \n",
       "10  0.496846  \n",
       "11  0.496846  \n",
       "6   0.748180  \n",
       "7   0.748180  \n",
       "..       ...  \n",
       "73  0.334304  \n",
       "74  0.334304  \n",
       "75  0.334304  \n",
       "71  0.096070  \n",
       "72  0.096070  \n",
       "\n",
       "[95 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg2 = (agg.groupby(['acte_n','scene_n','acte','scene'])['number_of_words'].transform('sum'))\n",
    "agg['w'] = agg2\n",
    "agg['w'] = agg['w']/(agg['w'].max())\n",
    "agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ab9josNQvsgO"
   },
   "source": [
    "We display the graph with the information of the number of words per act per scene.  \n",
    "\n",
    "Each bar represents a scene. And inside each bar there is a section for all the actors that said something in the scene. Each section is proportional to the total ammount of words said in the scene.  \n",
    "\n",
    "The width of each bar represents, how long is the scene when it is compared to all the other scenes in terms of number of words. So the thicker the bar, the larger the ammount of words said in the scene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "lV_XquU4l2sl",
    "outputId": "085be369-802a-4b55-cdec-4b5bb4e7f6f3"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "actes = agg['acte'].unique()\n",
    "for sc in list(actes):\n",
    "  aggb = agg[agg['acte'] == sc]\n",
    "  fig = (ggplot(aggb, aes(x='scene', fill = 'acteur', y = 'perc', width='w')) + \\\n",
    "      geom_bar(stat = \"identity\") \\\n",
    "      + scale_x_discrete(limits=aggb['scene'].unique())\\\n",
    "      +geom_text(aes(label='perc'),size = 9, position = position_stack(vjust = 0.5),format_string='\\n\\n{:.1f}%')\\\n",
    "      +geom_text(aes(label='acteur'),size = 9, position = position_stack(vjust = 0.5))\\\n",
    "      +geom_text(aes(label='number_of_words'),size = 9, position = position_stack(vjust = 0.5),format_string='\\n\\n\\n\\n{}' )\\\n",
    "      + labs(y='percentage', x='scene', title='Acte {}'.format(sc))\n",
    "      + coord_flip()).draw()\n",
    "  sc_num = len(list(agg[agg['acte'] == sc]['scene'].unique()))\n",
    "  fig.set_figheight(sc_num)\n",
    "  fig.set_figwidth(25)\n",
    "  fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fX65YPhgr8MG"
   },
   "source": [
    "## 3. \n",
    "**Optional**: Construct an interlocution graph to visualize the exchanges between the characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d7_zG_COsBum"
   },
   "source": [
    "To display the graph of interactions. We assume that, in every scene, each time an actor speaks all the other actors in the scene are hearing the line. With that in mind we create a list of all the lines, with the author that says it as source, and all the other actors in the scene as the destination. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tItOf13nBcya",
    "outputId": "e9da851e-bbad-42c9-b701-00136800c54b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    acte_n  scene_n                                        set_acteurs  \\\n",
      "0        1        1                                    VALÈRE, ÉLISE   \n",
      "1        1        1                                    VALÈRE, ÉLISE   \n",
      "2        1        2                                   CLÉANTE, ÉLISE   \n",
      "3        1        2                                   CLÉANTE, ÉLISE   \n",
      "4        1        3                               HARPAGON, LA FLÈCHE   \n",
      "..     ...      ...                                                ...   \n",
      "90       5        6  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
      "91       5        6  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
      "92       5        6  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
      "93       5        6  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
      "94       5        6  CLÉANTE, VALÈRE, MARIANE, ÉLISE, FROSINE, H...   \n",
      "\n",
      "             acteur  number_of_words  \n",
      "0            ÉLISE              491  \n",
      "1           VALÈRE              630  \n",
      "2          CLÉANTE              762  \n",
      "3            ÉLISE              154  \n",
      "4          HARPAGON              492  \n",
      "..              ...              ...  \n",
      "90         CLÉANTE              130  \n",
      "91         HARPAGON               89  \n",
      "92   LE COMMISSAIRE               26  \n",
      "93  MAÎTRE JACQUES               23  \n",
      "94          MARIANE               36  \n",
      "\n",
      "[95 rows x 5 columns]\n",
      "('VALÈRE, ÉLISE', 'ÉLISE', 491)\n"
     ]
    }
   ],
   "source": [
    "aggc = (df.groupby(['acte_n','scene_n','set_acteurs','acteur'])['number_of_words'].sum()).reset_index()\n",
    "print(aggc)\n",
    "records = aggc[['set_acteurs','acteur','number_of_words']].to_records(index=False)\n",
    "result = list(records)\n",
    "print(result[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jFrAIJgesnCH"
   },
   "source": [
    "We want to create an adjacency matrix for the whole play. We create a dictionary with indexes for each actor to facilitate the filling of the matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZH6IHis7JTav",
    "outputId": "f9ac32a1-e36d-4219-802d-99b34ac12006"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'VALÈRE': 0, 'ÉLISE': 1, 'CLÉANTE': 2, 'HARPAGON': 3, 'LA FLÈCHE': 4, 'MAÎTRE SIMON': 5, 'FROSINE': 6, 'DAME CLAUDE': 7, 'MAÎTRE JACQUES': 8, 'BRINDAVOINE': 9, 'LA MERLUCHE': 10, 'MARIANE': 11, 'LE COMMISSAIRE': 12, 'SON CLERC': 13, 'ANSELME': 14}\n",
      "{0: 'VALÈRE', 1: 'ÉLISE', 2: 'CLÉANTE', 3: 'HARPAGON', 4: 'LA FLÈCHE', 5: 'MAÎTRE SIMON', 6: 'FROSINE', 7: 'DAME CLAUDE', 8: 'MAÎTRE JACQUES', 9: 'BRINDAVOINE', 10: 'LA MERLUCHE', 11: 'MARIANE', 12: 'LE COMMISSAIRE', 13: 'SON CLERC', 14: 'ANSELME'}\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "dc = {}\n",
    "index = 0\n",
    "for record in result:\n",
    "  for ch in record[0].split(','):\n",
    "    if ch.strip() not in dc:\n",
    "      dc[ch.strip()] = index\n",
    "      index +=1\n",
    "dc_inv = {v: k for k, v in dc.items()}\n",
    "print(dc)\n",
    "print(dc_inv)\n",
    "print(len(dc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n_nL7ZZ1s_Ov"
   },
   "source": [
    "We fill the adjacency matrix, taking into account that every row is for when the character is the source of the message and every column for when the character is the destination of the message. We observe that the diagonal corresponds to the total number of words said by each character."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eQWxOC4BLdja",
    "outputId": "d7c06cc6-908d-4fee-cacf-50c10b67ad61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2760\t2022\t284\t2033\t0\t0\t410\t272\t1420\t272\t279\t410\t1039\t1039\t376\n",
      "673\t1071\t384\t368\t0\t0\t221\t3\t146\t3\t3\t221\t143\t143\t0\n",
      "206\t2090\t3364\t1961\t653\t127\t1036\t76\t376\t76\t116\t1036\t130\t130\t130\n",
      "1974\t3091\t3216\t6256\t752\t171\t1550\t784\t2040\t807\t857\t974\t1183\t1183\t347\n",
      "0\t0\t962\t270\t1522\t12\t302\t0\t0\t0\t0\t0\t0\t0\t0\n",
      "0\t0\t197\t197\t197\t197\t0\t0\t0\t0\t0\t0\t0\t0\t0\n",
      "4\t490\t477\t1587\t130\t0\t2363\t0\t23\t0\t0\t726\t4\t4\t0\n",
      "0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\n",
      "1040\t831\t1135\t1508\t23\t0\t48\t794\t1717\t794\t794\t48\t396\t396\t30\n",
      "23\t43\t43\t43\t0\t0\t20\t23\t23\t43\t23\t20\t0\t0\t0\n",
      "26\t55\t55\t55\t0\t0\t29\t26\t26\t26\t55\t29\t0\t0\t0\n",
      "228\t734\t507\t498\t36\t0\t919\t0\t228\t0\t0\t919\t228\t228\t228\n",
      "26\t26\t26\t294\t26\t0\t26\t0\t185\t0\t0\t26\t294\t294\t26\n",
      "0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\n",
      "517\t517\t114\t517\t114\t0\t517\t0\t517\t0\t0\t517\t517\t517\t517\n",
      "6256\n",
      "3216\n"
     ]
    }
   ],
   "source": [
    "adj = np.zeros((len(dc),len(dc)),dtype=int)\n",
    "for record in result:\n",
    "    for acteur_ecoute in record[0].split(','):\n",
    "      adj[dc[record[1]]][dc[acteur_ecoute.strip()]] += record[2]\n",
    "print('\\n'.join(['\\t'.join([str(cell) for cell in row]) for row in adj]))\n",
    "max_node_size = np.max(adj*np.identity(len(dc),dtype=int))\n",
    "max_edge_w = np.max(adj*((np.identity(len(dc),dtype=int)-1)**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uMJL5iN0tZaj"
   },
   "source": [
    "Finally we create our graph. Every cell in the diagonal corresponds to a node. Every other cell corresponds to a directed edge if the value is different from Zero. The size of the nodes and the weigths of the edges can be also taken into consideration, normalizing them by the max values present in the adjacency matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 751
    },
    "id": "mSRm2YkGP2dk",
    "outputId": "52bd8cd0-3775-4ad7-8f6e-d2bc68099d26"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = nx.DiGraph()\n",
    "#add nodes\n",
    "for i in range(len(dc)):\n",
    "  G.add_node(dc_inv[i], size = adj[i][i])\n",
    "#add edges\n",
    "for i in range(len(dc)):\n",
    "  for j in range(len(dc)):\n",
    "    if i != j and adj[i][j] != 0:\n",
    "      G.add_weighted_edges_from([(dc_inv[i], dc_inv[j], adj[i][j]/max_edge_w)])\n",
    "\n",
    "\n",
    "edge_width = [a[2]['weight'] for a in G.edges(data=True)]\n",
    "sizes = [a[1]['size'] for a in G.nodes(data=True)]\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "pos = nx.circular_layout(G)\n",
    "nx.draw(G,pos, with_labels=True, width=edge_width, node_size=sizes, arrowsize=10, connectionstyle='arc3, rad = 0.02')\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0WJPVQNruePP"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "Performing text analysis is not an easy task. Even when the text presents a \"structure\", the final document can be full of format exceptions that can cause bad data interpretation. There is a lot of factors to take into consideration and they will change for every single text. It is important then to adquire experience in the field, so that the next time we can have an idea of the possible scenarios that we migth encounter.\\\n",
    "\\\n",
    "Some improvements to the analysis can be done. First, we could omit somme common words in order to work with only meaninful sentences, opening the door to semantic analysis. Second, We can try to estimate better the way the dialogues are routed, instead of making all the people in the scene the destination of every line.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "La_Moliere.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}