Hollywood Actors SNA.ipynb

{"cells":[{"cell_type":"markdown","source":["# **Social Network Analysis. Individual Assignment**\n","\n","\n","---\n","\n","\n","## Author: Alfonso Ferrándiz Hervella\n","## Tutor: Álvaro Romero Miralles\n","## Program: Master in Business Analytics and Big Data - Part-Time\n","## Date: April 2023\n","\n","\n","\n","---\n","\n"],"metadata":{"id":"KKcQOkjH6AcH"}},{"cell_type":"markdown","metadata":{"id":"Dlxjnzd9QUr1"},"source":["#0. Uploading the needed libraries"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XIbYIUEJQbpw","executionInfo":{"status":"ok","timestamp":1680705174480,"user_tz":-120,"elapsed":50377,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"40f2aebb-fba4-4f05-d914-50c782db2363"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting python-igraph\n","  Downloading python-igraph-0.10.4.tar.gz (9.5 kB)\n","  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting igraph==0.10.4\n","  Downloading igraph-0.10.4-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m64.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting texttable>=1.6.2\n","  Downloading texttable-1.6.7-py2.py3-none-any.whl (10 kB)\n","Building wheels for collected packages: python-igraph\n","  Building wheel for python-igraph (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for python-igraph: filename=python_igraph-0.10.4-py3-none-any.whl size=9073 sha256=c4e7c50558618db4d5e4611b1a5a984c02d4fc63d48727cde103a83009e8ed47\n","  Stored in directory: /root/.cache/pip/wheels/dc/07/ac/bff79052fd6222d1239b228cd24a47222f227c2350f9c4df01\n","Successfully built python-igraph\n","Installing collected packages: texttable, igraph, python-igraph\n","Successfully installed igraph-0.10.4 python-igraph-0.10.4 texttable-1.6.7\n","Reading package lists... 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wheel for pycairo (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pycairo: filename=pycairo-1.23.0-cp39-cp39-linux_x86_64.whl size=326975 sha256=489760a58c1365074677ab431a46b8d2ab80fee9072348d74d701e4a5a14aa57\n","  Stored in directory: /root/.cache/pip/wheels/7e/33/44/ed63d7850b41775b9c99e2677fe7cc4c6afd3b3eb767be75e7\n","Successfully built pycairo\n","Installing collected packages: pycairo\n","Successfully installed pycairo-1.23.0\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (3.0)\n"]}],"source":["# Installing the libraries\n","\n","!pip install python-igraph\n","!apt-get install libcairo2-dev libjpeg-dev libgif-dev\n","!pip install pycairo\n","!pip install networkx"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"Cp-EDO7xQs14","executionInfo":{"status":"ok","timestamp":1680705175931,"user_tz":-120,"elapsed":1458,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# Importing the libraries\n","\n","from igraph import *\n","import cairo\n","import pandas as pd\n","import networkx as nx\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from collections import OrderedDict"]},{"cell_type":"markdown","metadata":{"id":"VVz-BL1fQ7xR"},"source":["#1. Loading the data\n","In this section, the goal is loading the datasets given, building the graph and analyzing basics metrics. Include the edge or node attributes you consider."]},{"cell_type":"code","execution_count":3,"metadata":{"id":"dpB7vpYqRI92","executionInfo":{"status":"ok","timestamp":1680705176648,"user_tz":-120,"elapsed":720,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# We start loading the data from our Github repository\n","actors_df = pd.read_csv('https://raw.githubusercontent.com/fonsofhervella/sna_individual/main/imdb_actors_key_sol.csv', sep=';')\n","collaborations_df = pd.read_csv('https://raw.githubusercontent.com/fonsofhervella/sna_individual/main/imdb_actor_edges_sol.csv',sep=';')\n"]},{"cell_type":"markdown","metadata":{"id":"7SPvd00W7k8d"},"source":["We are going to take a quick look at the loaded data\n"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xJncmjlc7TmI","executionInfo":{"status":"ok","timestamp":1680705176650,"user_tz":-120,"elapsed":16,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"e98a3210-fc6a-4664-e61f-97188f2bd7d4"},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 17577 entries, 0 to 17576\n","Data columns (total 5 columns):\n"," #   Column        Non-Null Count  Dtype \n","---  ------        --------------  ----- \n"," 0   id            17577 non-null  object\n"," 1   ActorName     17577 non-null  object\n"," 2   movies_95_04  17577 non-null  int64 \n"," 3   main_genre    17509 non-null  object\n"," 4   genres        17577 non-null  object\n","dtypes: int64(1), object(4)\n","memory usage: 686.7+ KB\n"]}],"source":["actors_df.info()"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"NjyxNOV27uv-","executionInfo":{"status":"ok","timestamp":1680705176651,"user_tz":-120,"elapsed":11,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"12668f40-b4e5-41a7-b900-14ed7e6643e7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["       id            ActorName  movies_95_04 main_genre  \\\n","0  n15629  Rudder, Michael (I)            12   Thriller   \n","1   n5026        Morgan, Debbi            16      Drama   \n","2  n11252         Bellows, Gil            33      Drama   \n","3   n5150         Dray, Albert            20     Comedy   \n","4   n4057      Daly, Shane (I)            18      Drama   \n","\n","                                              genres  \n","0  Action:1,Comedy:1,Drama:1,Fantasy:1,Horror:1,N...  \n","1  Comedy:2,Documentary:1,Drama:6,Horror:2,NULL:3...  \n","2  Comedy:6,Documentary:1,Drama:7,Family:1,Fantas...  \n","3  Comedy:6,Crime:1,Documentary:1,Drama:4,NULL:5,...  \n","4  Comedy:2,Crime:1,Drama:7,Horror:1,Music:1,Musi...  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  Column  Non-Null Count   Dtype \n","---  ------  --------------   ----- \n"," 0   from    287074 non-null  object\n"," 1   to      287074 non-null  object\n"," 2   weight  287074 non-null  int64 \n","dtypes: int64(1), object(2)\n","memory usage: 6.6+ MB\n"]}],"source":["collaborations_df.info()"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"VrHxhTGM77p1","executionInfo":{"status":"ok","timestamp":1680705176985,"user_tz":-120,"elapsed":15,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"0b571fea-6785-4ace-f8dd-a2d460ba31ad"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     from      to  weight\n","0  n17776  n17778       6\n","1   n5578   n9770       3\n","2   n5578    n929       2\n","3   n5578   n9982       2\n","4   n1835   n6278       2"],"text/html":["\n","  <div id=\"df-6b38b735-acc6-44e5-83ac-6be7ee461888\">\n","    <div class=\"colab-df-container\">\n","      <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>from</th>\n","      <th>to</th>\n","      <th>weight</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>n17776</td>\n","      <td>n17778</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>n5578</td>\n","      <td>n9770</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>n5578</td>\n","      <td>n929</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>n5578</td>\n","      <td>n9982</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>n1835</td>\n","      <td>n6278</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b38b735-acc6-44e5-83ac-6be7ee461888')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-6b38b735-acc6-44e5-83ac-6be7ee461888 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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The column movies_95_04 in the actors_df, includes the number of movies that in which the actor has participated during the period.\n","Also, and this is really important, **we have much more weights than movies**. Weights, due to that fact, are not representing number of movies where actors have worked together but other, unknown at this point, variable, as, i.e., time shared in screen"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"Vh5zqHF19L6x","executionInfo":{"status":"ok","timestamp":1680705176986,"user_tz":-120,"elapsed":11,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# Preparing the collaborations_df to use it as our main df for creating the graph\n","collaborations_df = collaborations_df.merge(actors_df[['id', 'ActorName']], left_on='from', right_on='id', how='left')\n","collaborations_df = collaborations_df.merge(actors_df[['id', 'ActorName']], left_on='to', right_on='id', how='left')"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ERlyJj0bS4FJ","executionInfo":{"status":"ok","timestamp":1680705177519,"user_tz":-120,"elapsed":544,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"bfe03fb1-0aa7-4599-ed71-5665cc999059"},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 287074 entries, 0 to 287073\n","Data columns (total 7 columns):\n"," #   Column       Non-Null Count   Dtype \n","---  ------       --------------   ----- \n"," 0   from         287074 non-null  object\n"," 1   to           287074 non-null  object\n"," 2   weight       287074 non-null  int64 \n"," 3   id_x         287074 non-null  object\n"," 4   ActorName_x  287074 non-null  object\n"," 5   id_y         287074 non-null  object\n"," 6   ActorName_y  287074 non-null  object\n","dtypes: int64(1), object(6)\n","memory usage: 17.5+ MB\n"]}],"source":["# Checking that everything has worked fine\n","collaborations_df.info()"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"vawGJPmOTX5E","executionInfo":{"status":"ok","timestamp":1680705177520,"user_tz":-120,"elapsed":14,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# Getting our df ready\n","collaborations_df = collaborations_df.drop(columns=['id_x', 'id_y'])\n","collaborations_df.rename(columns={'ActorName_x': 'from_name', 'ActorName_y': 'to_name'}, inplace=True)"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"dU9YeBR3TXxh","executionInfo":{"status":"ok","timestamp":1680705177520,"user_tz":-120,"elapsed":14,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"7d2cdc12-2285-4d88-9907-d2e5134d79ac"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     from      to  weight           from_name               to_name\n","0  n17776  n17778       6          Wood, T.J.      Black, Rick (II)\n","1   n5578   n9770       3   Gale, Vincent (I)      Gray, G. Michael\n","2   n5578    n929       2   Gale, Vincent (I)     Holden, Mark (IV)\n","3   n5578   n9982       2   Gale, Vincent (I)  Gray-Stanford, Jason\n","4   n1835   n6278       2  Moore, Ashley (II)            Star, Judy"],"text/html":["\n","  <div id=\"df-b4653de0-c474-4f92-9abd-2bb00daf7a54\">\n","    <div class=\"colab-df-container\">\n","      <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>from</th>\n","      <th>to</th>\n","      <th>weight</th>\n","      <th>from_name</th>\n","      <th>to_name</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>n17776</td>\n","      <td>n17778</td>\n","      <td>6</td>\n","      <td>Wood, T.J.</td>\n","      <td>Black, Rick (II)</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>n5578</td>\n","      <td>n9770</td>\n","      <td>3</td>\n","      <td>Gale, Vincent (I)</td>\n","      <td>Gray, G. Michael</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>n5578</td>\n","      <td>n929</td>\n","      <td>2</td>\n","      <td>Gale, Vincent (I)</td>\n","      <td>Holden, Mark (IV)</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>n5578</td>\n","      <td>n9982</td>\n","      <td>2</td>\n","      <td>Gale, Vincent (I)</td>\n","      <td>Gray-Stanford, Jason</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>n1835</td>\n","      <td>n6278</td>\n","      <td>2</td>\n","      <td>Moore, Ashley (II)</td>\n","      <td>Star, Judy</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4653de0-c474-4f92-9abd-2bb00daf7a54')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-b4653de0-c474-4f92-9abd-2bb00daf7a54 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-b4653de0-c474-4f92-9abd-2bb00daf7a54');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":12}],"source":["# Checking results\n","collaborations_df.head()"]},{"cell_type":"code","source":["# We are going to create a list with unique actors names\n","actor_names = list(set(list(collaborations_df['from_name']) + list(collaborations_df['to_name'])))"],"metadata":{"id":"4_HT5yUQFYiA","executionInfo":{"status":"ok","timestamp":1680705177521,"user_tz":-120,"elapsed":13,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["# To be sure that everything is working fine, we will check how many different names do we have in the list\n","\n","l1 = []\n","count = 0\n","\n","for item in actor_names:\n","    if item not in l1:\n","        count += 1\n","        l1.append(item)\n"," \n","print(\"The number of unique items in actor names list is:\", count)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Byyu_CrgFPis","executionInfo":{"status":"ok","timestamp":1680705180647,"user_tz":-120,"elapsed":3139,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"520a9102-bdda-4b97-a0e1-0340eaac6832"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["The number of unique items in actor names list is: 17577\n"]}]},{"cell_type":"code","execution_count":15,"metadata":{"id":"LBz4PYwiaDCI","executionInfo":{"status":"ok","timestamp":1680707981766,"user_tz":-120,"elapsed":2801125,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# For creating the graph, first we define it. Then we add nodes from our actor_names list. Then, we have added an iterrows to \n","# stablish the edges. Despite iterrows is a slow way, I was having issues to get the proper edges without it (each run of the\n","# code they were changing). Finally, we make the graph undirected.\n","\n","g = Graph()\n","\n","# Adding vertices\n","g.add_vertices(actor_names)\n","\n","# Adding edges\n","for i, row in collaborations_df.iterrows():\n","    from_name = row['from_name']\n","    to_name = row['to_name']\n","    weight = row['weight']\n","    g.add_edge(from_name, to_name, weight=weight)\n","\n","g.to_undirected()\n","\n"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"_JT003NdXWQY","executionInfo":{"status":"ok","timestamp":1680707981769,"user_tz":-120,"elapsed":51,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"ea884912-793a-48af-f8d7-506cb140a67f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'IGRAPH UNW- 17577 287074 -- \\n+ attr: name (v), weight (e)'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":16}],"source":["# We check graph properties\n","g.summary()"]},{"cell_type":"markdown","source":["We graph an undirected, named and weighted graph, composed by 17,577 nodes and 287,074 edges"],"metadata":{"id":"ecFpXag0WwbJ"}},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"22e8ZknDiCiy","executionInfo":{"status":"ok","timestamp":1680707981770,"user_tz":-120,"elapsed":48,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"248b8300-26cd-44b1-f615-79fba4b8cb32"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<igraph.Graph at 0x7f78c12dae40>"]},"metadata":{},"execution_count":17}],"source":["# We check graph object\n","g"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"TUgNYLint0Eg","executionInfo":{"status":"ok","timestamp":1680707981770,"user_tz":-120,"elapsed":42,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# We create a vertex dataframe and we named it g_df. This df will be used for showing the degrees in the next exercise\n","g_df= g.get_vertex_dataframe()"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"r2MBEJiPv8VA","executionInfo":{"status":"ok","timestamp":1680707981770,"user_tz":-120,"elapsed":41,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# As the adjancecy matrix is too big we stored it as a pandas DataFrame and we save it\n","# in a csv file. When running this notebook in Google Colab, the csv file can be downloaded\n","# at the left side. Uncomment the next lines if you want to download the adjacency matrix. It\n","# will take a while\n","\n","# adj_matrix = pd.DataFrame(g.get_adjacency().data, columns=g.vs[\"label\"])\n","\n","# adj_matrix.to_csv(\"adj_matrix.csv\", index=False)"]},{"cell_type":"markdown","metadata":{"id":"XcG0y3kRqtrB"},"source":["#2. Degree \n","Analyse the degree distribution. Compute the total degree distribution."]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lDCSP4zLqqmI","executionInfo":{"status":"ok","timestamp":1680707981771,"user_tz":-120,"elapsed":42,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"0bd01963-88e2-4e86-f9a6-4efe9ab4082a"},"outputs":[{"output_type":"stream","name":"stdout","text":["The Minimum degree is: 1\n","The Maximum degree is: 784\n"]}],"source":["# First, we calculate the minimum and maximum degree of the vertices in the graph\n","min_degree = min(g.degree())\n","max_degree = max(g.degree())\n","\n","print(\"The Minimum degree is:\", min_degree)\n","print(\"The Maximum degree is:\", max_degree)"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xhlum4dY2V7q","executionInfo":{"status":"ok","timestamp":1680707981771,"user_tz":-120,"elapsed":37,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"33477885-1152-407d-ba8b-9ac268cda0e2"},"outputs":[{"output_type":"stream","name":"stdout","text":["The Minimum weighted degree is: 2.0\n","The Maximum weighted degree is: 5435.0\n"]}],"source":["# Method degree does not give us the weighted degree. If we want to find the weighted degree\n","# we should use strenght\n","min_weighted_degree = min(g.strength(weights=\"weight\"))\n","max_weighted_degree = max(g.strength(weights=\"weight\"))\n","\n","print(\"The Minimum weighted degree is:\", min_weighted_degree)\n","print(\"The Maximum weighted degree is:\", max_weighted_degree)"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5cCM5Oyy6DB8","executionInfo":{"status":"ok","timestamp":1680707981771,"user_tz":-120,"elapsed":33,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"9a3f80f2-323d-4a07-f9b2-515e6dc89923"},"outputs":[{"output_type":"stream","name":"stdout","text":["The Average degree is: 32.6647323206463\n"]}],"source":["# Calculate the average degree of the graph\n","avg_degree = sum(g.degree()) / len(g.degree())\n","\n","# Print the result\n","print(\"The Average degree is:\", avg_degree)"]},{"cell_type":"markdown","source":["Average degree is an important measure in Undirected Graphs, as it is related to graph density."],"metadata":{"id":"NKqADksQXmvn"}},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RPbf2Xpa5np3","executionInfo":{"status":"ok","timestamp":1680707981771,"user_tz":-120,"elapsed":29,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"cf1981ac-5173-4286-ffb3-a7467783f847"},"outputs":[{"output_type":"stream","name":"stdout","text":["The Average weighted degree is: 107.17972350230414\n"]}],"source":["# Calculate the average weighted degree of the graph\n","avg_weighted_degree = sum(g.strength(weights=\"weight\")) / len(g.strength(weights=\"weight\"))\n","\n","# Print the result\n","print(\"The Average weighted degree is:\", avg_weighted_degree)"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"pkJ__yiS3vG0","executionInfo":{"status":"ok","timestamp":1680707982561,"user_tz":-120,"elapsed":815,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"8df404be-31da-4bf1-cf6b-93dbf35e6e8f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# We will plot a histogram for checking the degree distribution\n","\n","bins = 20\n","plt.hist(g.degree(), bins)\n","plt.show()"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"-pnqVMJSSH9k","executionInfo":{"status":"ok","timestamp":1680707982562,"user_tz":-120,"elapsed":40,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"5d5f2cd4-1ead-4878-828b-a9026e75c92b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Now, we will plot a histogram but for the weighted degrees\n","bins = 20\n","plt.hist(g.strength(weights=\"weight\"), bins)\n","plt.show()"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gqh_FTVTUCHv","executionInfo":{"status":"ok","timestamp":1680707982563,"user_tz":-120,"elapsed":35,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"5ef933b9-5ac1-488d-c011-c68e6f6663f8"},"outputs":[{"output_type":"stream","name":"stdout","text":["The person with the highest degree is Davis, Mark (V)\n","The person with the highest weighted degree is Seldano, Jose\n"]}],"source":["# We insert the degrees in our previously built dataframe,\n","g_df[\"degree\"] = g.degree()\n","g_df[\"weighted_degree\"] = g.strength(weights=\"weight\")\n","\n","# With that, we extract the actors with highest degree and highest weighted degree\n","highest_degree = g_df['degree'].max()  \n","highest_degree_name = g_df.loc[g_df['degree'] == highest_degree, 'name'].values[0]  \n","print(f\"The person with the highest degree is {highest_degree_name}\")\n","\n","highest_weighted_degree = g_df['weighted_degree'].max()  \n","highest_weighted_degree_name = g_df.loc[g_df['weighted_degree'] == highest_degree, 'name'].values[0]  \n","print(f\"The person with the highest weighted degree is {highest_weighted_degree_name}\")"]},{"cell_type":"code","source":["g_df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"33ahyHqvcBpX","executionInfo":{"status":"ok","timestamp":1680707982563,"user_tz":-120,"elapsed":29,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"e896c290-0b3d-4cf0-bb85-8ee9411dcc58"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 17577 entries, 0 to 17576\n","Data columns (total 3 columns):\n"," #   Column           Non-Null Count  Dtype  \n","---  ------           --------------  -----  \n"," 0   name             17577 non-null  object \n"," 1   degree           17577 non-null  int64  \n"," 2   weighted_degree  17577 non-null  float64\n","dtypes: float64(1), int64(1), object(1)\n","memory usage: 549.3+ KB\n"]}]},{"cell_type":"markdown","metadata":{"id":"vLSRUlbcVjYi"},"source":["#3.Network Diameter and Average Path Length\n","\n","You have functions in igraph to calculate the diameter and the average path length. Think if you should consider the weights, the directions, etc."]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kD2tAPvsWlAo","executionInfo":{"status":"ok","timestamp":1680707982564,"user_tz":-120,"elapsed":25,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"d1cdc8f9-bbd9-4098-ff75-67c6ff3f2bf8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of components existing in our graph: 19\n","Component 0: Has 17455 nodes\n","Component 1: Has 22 nodes\n","Component 2: Has 23 nodes\n","Component 3: Has 33 nodes\n","Component 4: Has 2 nodes\n","Component 5: Has 3 nodes\n","Component 6: Has 4 nodes\n","Component 7: Has 2 nodes\n","Component 8: Has 11 nodes\n","Component 9: Has 3 nodes\n","Component 10: Has 2 nodes\n","Component 11: Has 2 nodes\n","Component 12: Has 2 nodes\n","Component 13: Has 2 nodes\n","Component 14: Has 2 nodes\n","Component 15: Has 2 nodes\n","Component 16: Has 3 nodes\n","Component 17: Has 2 nodes\n","Component 18: Has 2 nodes\n"]}],"source":["# As we saw in class, the graph obtained has different clusters or components unconnected between them. Igraph provides us with the components method for that objective.\n","components = g.components()\n","print(f\"Number of components existing in our graph: {len(components)}\")\n","\n","# Now, we will print each component and how many nodes does it have\n","for i, component in enumerate(components):\n","    num_nodes = len(component)\n","    print(f\"Component {i}: Has {num_nodes} nodes\")"]},{"cell_type":"markdown","metadata":{"id":"7rsSvK1OuVl5"},"source":["As can be seen, from our 19 components, one represents 99,3% of the total nodes. Now we have 2 options: analyzing just this big component, assuming that it will be a good representation of the total network or analyzing each component separately.\n","\n","First, we will compute diameter and average path length for each component"]},{"cell_type":"code","execution_count":29,"metadata":{"id":"qdkCZr7WgxCV","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680708014266,"user_tz":-120,"elapsed":31723,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"f6ad2465-bcad-471c-df3d-c37e8564e13b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Diameter of component 0: 16\n","Diameter of component 1: 8\n","Diameter of component 2: 6\n","Diameter of component 3: 8\n","Diameter of component 4: 1\n","Diameter of component 5: 1\n","Diameter of component 6: 1\n","Diameter of component 7: 1\n","Diameter of component 8: 4\n","Diameter of component 9: 2\n","Diameter of component 10: 1\n","Diameter of component 11: 1\n","Diameter of component 12: 1\n","Diameter of component 13: 1\n","Diameter of component 14: 1\n","Diameter of component 15: 1\n","Diameter of component 16: 2\n","Diameter of component 17: 1\n","Diameter of component 18: 1\n"]}],"source":["# We calculate the diameter of each component, iterating among them\n","for i, component in enumerate(components):\n","    subgraph = g.subgraph(component)\n","    diameter = subgraph.diameter(directed=False)\n","    print(f\"Diameter of component {i}: {diameter}\")"]},{"cell_type":"code","execution_count":30,"metadata":{"id":"7bL75WPthXWn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680708046834,"user_tz":-120,"elapsed":32580,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"9e3bc5c1-c31e-4b14-d1be-f3ad50a9c383"},"outputs":[{"output_type":"stream","name":"stdout","text":["Average path length of component 0: 4.890557890566182\n","Average path length of component 1: 3.329004329004329\n","Average path length of component 2: 3.225296442687747\n","Average path length of component 3: 3.3011363636363638\n","Average path length of component 4: 1.0\n","Average path length of component 5: 1.0\n","Average path length of component 6: 1.0\n","Average path length of component 7: 1.0\n","Average path length of component 8: 1.9090909090909092\n","Average path length of component 9: 1.3333333333333333\n","Average path length of component 10: 1.0\n","Average path length of component 11: 1.0\n","Average path length of component 12: 1.0\n","Average path length of component 13: 1.0\n","Average path length of component 14: 1.0\n","Average path length of component 15: 1.0\n","Average path length of component 16: 1.3333333333333333\n","Average path length of component 17: 1.0\n","Average path length of component 18: 1.0\n"]}],"source":["# We calculate the average path length of each component, iterating among them\n","for i, component in enumerate(components):\n","    subgraph = g.subgraph(component)\n","    avg_path_length = subgraph.average_path_length(directed=False)\n","    print(f\"Average path length of component {i}: {avg_path_length}\")"]},{"cell_type":"markdown","metadata":{"id":"uOxMENu7w6SW"},"source":["10 out of 19 components are just integrated by 2 actors. The information provided in the previous analysis is not being useful for them, as both metrics are getting 1 as a result. For next analysis, we will go for the largest one due to the next reasons:\n","\n","\n","1.   We believe that in this specific situation, the big component provides insights into the overall structure of the network, the key actors, and the patterns of collaboration among them.\n","2.    The smaller components in collaboration networks represent isolated groups of actors that are not directly connected to the main structure of the network. \n","\n","For that reasons, I have considered that due to the small size of the components, calculating the centrality or distance measures for all the nodes and then applying some normalizing measures can bias how almost the complete network is working\n","\n"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"82fq-jKZzDe7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680708046835,"user_tz":-120,"elapsed":37,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"31db29ab-e4cb-48bd-e55d-eca317a4d221"},"outputs":[{"output_type":"stream","name":"stdout","text":["IGRAPH UNW- 17455 286911 -- \n","+ attr: name (v), weight (e)\n"]}],"source":["# With this snippet of code we are extracting the biggest component (0) to calculate the next metrics directly upon it\n","component_0 = [i for i, c in enumerate(components.membership) if c == 0]\n","subgraph_bc = g.subgraph(component_0)\n","print(subgraph_bc.summary())"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"_B7fcYE4zH03","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680708079126,"user_tz":-120,"elapsed":646,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"769ea443-1730-45a5-f1b1-a3cc12e43d83"},"outputs":[{"output_type":"stream","name":"stdout","text":["The diameter of the big component, which for us is reprensentative of the whole network, is: 16\n"]}],"source":["# We calculate again the diameter of the biggest component, to check if the previous snippet of code is working\n","diameter = subgraph_bc.diameter(directed= False)\n","\n","print(f\"The diameter of the big component, which for us is reprensentative of the whole network, is: {diameter}\")"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"oam6Kyny3HST","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680708345360,"user_tz":-120,"elapsed":233813,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"a9a1ac94-5881-4a84-f80c-a81d4354e0b6"},"outputs":[{"output_type":"stream","name":"stdout","text":["The Average path length of the big component, which for us is reprensentative of the whole network, is: 4.890557890566182\n"]}],"source":["# We calculate again the average path length of the biggest component, to double check if the previous snippet of code is working\n","avg_path_length_bc = subgraph_bc.average_path_length(directed=False)\n","\n","print(f\"The Average path length of the big component, which for us is reprensentative of the whole network, is: {avg_path_length_bc}\")"]},{"cell_type":"code","execution_count":34,"metadata":{"id":"JUg2zri1mNKT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680708345602,"user_tz":-120,"elapsed":20,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"aa0f4c26-847a-46e9-bf6c-37c237ec1a9d"},"outputs":[{"output_type":"stream","name":"stdout","text":["The Weighted Diameter of the big component, representative of the whole network: 39.0\n"]}],"source":["# Now, we will calculate the weighted diameter of the biggest component\n","weighted_diameter = subgraph_bc.diameter(directed=False, weights='weight')\n","print(f\"The Weighted Diameter of the big component, representative of the whole network: {weighted_diameter}\")"]},{"cell_type":"code","execution_count":35,"metadata":{"id":"e-b2Ph6R6lR_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680708574671,"user_tz":-120,"elapsed":447,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"bdd74370-2e50-49c5-e544-312ea1262acc"},"outputs":[{"output_type":"stream","name":"stdout","text":["The Weighted Average path length of the big component, representative of the whole network: 10.260319096491864\n"]}],"source":["# Now, we will calculate the weighted average path lenght of the biggest component\n","weighted_avg_path_length = subgraph_bc.average_path_length(directed=False, weights='weight')\n","print(f\"The Weighted Average path length of the big component, representative of the whole network: {weighted_avg_path_length}\")"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"8m1335Cq4SOf","executionInfo":{"status":"ok","timestamp":1680708574672,"user_tz":-120,"elapsed":46,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/","height":238},"outputId":"a5161019-88ad-40ab-a8fe-c23ec6758f0a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                              name\n","vertex ID                         \n","0          Bouvet, Jean-Christophe\n","1                   Castel, France\n","2                 Vallance, Louise\n","3                Fernhout, Roeland\n","4                    Hayes, Tanner"],"text/html":["\n","  <div id=\"df-fc94683f-c910-4902-bfd6-986d17b7f5f1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th 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Node importance: Centrality measures\n","\n","(Optional but recommended): Obtain the distribution of the number of movies made by an actor and the number of genres in which an actor starred in. It may be useful to analyze and discuss the results to be obtained in the following exercises."]},{"cell_type":"code","source":["# We will start creating a copy of our bcg_df. That's because we want to analyze just the big component (despite the\n","# other ones are small) and we don't want to mess our bcg_df, which will be used later for plotting centrality metrics\n","bcg_df_copy = bcg_df.copy()"],"metadata":{"id":"l6VNbeQf-XKG","executionInfo":{"status":"ok","timestamp":1680708574672,"user_tz":-120,"elapsed":38,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"execution_count":38,"outputs":[]},{"cell_type":"code","source":["# We are going to add the columns with information of the actors\n","bcg_df_copy = bcg_df_copy.merge(actors_df[['ActorName','movies_95_04', 'main_genre', 'genres']], \n","                      how='left', \n","                      left_on='name', \n","                      right_on='ActorName')\n","\n","# Drop the redundant ActorName column\n","bcg_df_copy = bcg_df_copy.drop(columns=['ActorName'])"],"metadata":{"id":"MvfGuX0n-qLY","executionInfo":{"status":"ok","timestamp":1680708574673,"user_tz":-120,"elapsed":38,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"execution_count":39,"outputs":[]},{"cell_type":"code","execution_count":40,"metadata":{"id":"rlKv9sTQ5KDz","executionInfo":{"status":"ok","timestamp":1680708583868,"user_tz":-120,"elapsed":9233,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# We have to extract all the information that is stored in the 'genres' column. For that we are going to start by creating a list of unique genres\n","unique_genres = set()\n","for row in bcg_df_copy['genres']:\n","    genres = row.split(',')\n","    for genre in genres:\n","        unique_genres.add(genre.split(':')[0])\n","unique_genres = list(unique_genres)\n","\n","# Once we have the list, we can add the list of all the genres that are presented in the df as new columns\n","for genre in unique_genres:\n","     bcg_df_copy[genre] = 0\n","\n","# Now we fill the values of each genre with the quantity that was already present in the genres column for each actor\n","for index, row in  bcg_df_copy.iterrows():\n","    genres = row['genres'].split(',')\n","    for genre in genres:\n","        name, value = genre.split(':')\n","        bcg_df_copy.loc[index, name] = int(value)"]},{"cell_type":"code","execution_count":41,"metadata":{"id":"hi4xtOsy64yO","executionInfo":{"status":"ok","timestamp":1680708583868,"user_tz":-120,"elapsed":54,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# We can drop column genres as we have extract the information in different columns\n","bcg_df_copy.drop('genres', axis=1, inplace=True)"]},{"cell_type":"code","execution_count":42,"metadata":{"id":"SQKySHmW7mX4","executionInfo":{"status":"ok","timestamp":1680708583869,"user_tz":-120,"elapsed":53,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"25263b30-e349-4796-8ab3-358281c81d22"},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 17455 entries, 0 to 17454\n","Data columns (total 24 columns):\n"," #   Column        Non-Null Count  Dtype \n","---  ------        --------------  ----- \n"," 0   name          17455 non-null  object\n"," 1   movies_95_04  17455 non-null  int64 \n"," 2   main_genre    17387 non-null  object\n"," 3   Adult         17455 non-null  int64 \n"," 4   Family        17455 non-null  int64 \n"," 5   Animation     17455 non-null  int64 \n"," 6   Drama         17455 non-null  int64 \n"," 7   Mystery       17455 non-null  int64 \n"," 8   Short         17455 non-null  int64 \n"," 9   Adventure     17455 non-null  int64 \n"," 10  Action        17455 non-null  int64 \n"," 11  Horror        17455 non-null  int64 \n"," 12  Romance       17455 non-null  int64 \n"," 13  Western       17455 non-null  int64 \n"," 14  NULL          17455 non-null  int64 \n"," 15  Comedy        17455 non-null  int64 \n"," 16  Sci-Fi        17455 non-null  int64 \n"," 17  Fantasy       17455 non-null  int64 \n"," 18  Music         17455 non-null  int64 \n"," 19  War           17455 non-null  int64 \n"," 20  Thriller      17455 non-null  int64 \n"," 21  Documentary   17455 non-null  int64 \n"," 22  Crime         17455 non-null  int64 \n"," 23  Musical       17455 non-null  int64 \n","dtypes: int64(22), object(2)\n","memory usage: 3.8+ MB\n"]}],"source":["# We check that everything has worked as expected\n","bcg_df_copy.info()"]},{"cell_type":"code","execution_count":43,"metadata":{"id":"vJySQARX77QR","executionInfo":{"status":"ok","timestamp":1680708583869,"user_tz":-120,"elapsed":46,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"4f6986d4-f4bf-46a7-e18e-1bbec249d783"},"outputs":[{"output_type":"stream","name":"stdout","text":["The total number of movies based on genres is: 359588\n","[55149, 8577, 4687, 56818, 3923, 14281, 3369, 11744, 4530, 19071, 1046, 61811, 34150, 8464, 6004, 8142, 2957, 21099, 19839, 12455, 1472]\n"]}],"source":["# We get a list with how many movies have each genre column, to check importance\n","genre_sums =  bcg_df_copy.iloc[:, 3:].sum().tolist()\n","total_sum = sum(genre_sums)\n","print (f\"The total number of movies based on genres is: {total_sum}\")\n","print(genre_sums)"]},{"cell_type":"code","execution_count":44,"metadata":{"id":"NhJTVSQeoPZb","executionInfo":{"status":"ok","timestamp":1680708583870,"user_tz":-120,"elapsed":42,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"2aa42c70-b58b-4557-ae19-c41f41534f05"},"outputs":[{"output_type":"stream","name":"stdout","text":["The total number of movies in the big component is: 359588\n"]}],"source":["# We are going to get the sum of movies from the movies column, to check if a movie can be appearing in more than one genre\n","sum_movies =  bcg_df_copy[\"movies_95_04\"].sum()\n","print (f\"The total number of movies in the big component is: {sum_movies}\")"]},{"cell_type":"markdown","metadata":{"id":"FlDNjd53ol3j"},"source":["As we can see, there are 359,588 movies if we sum the movies_95_04 column, and 359,588 movies in the genres columns, so each movie will be assigned just at one genre."]},{"cell_type":"code","execution_count":45,"metadata":{"id":"NS9I19Bs8bYA","executionInfo":{"status":"ok","timestamp":1680708583870,"user_tz":-120,"elapsed":37,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/","height":552},"outputId":"e6ac553b-2401-4cba-ca3c-58b976fa69d6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# As previous information was quite difficult to read, we are going to plot a histogram: getting the genre names, creating a dict with genre names and genre sums, ordering it and plotting\n","genre_names = bcg_df_copy.columns[3:].tolist()\n","\n","genre_dict = dict(zip(genre_names, genre_sums))\n","sorted_genre_dict = dict(sorted(genre_dict.items(), key=lambda item: item[1], reverse=True))\n","\n","\n","plt.bar(range(len(sorted_genre_dict)), list(sorted_genre_dict.values()), align='center')\n","plt.xticks(range(len(sorted_genre_dict)), list(sorted_genre_dict.keys()), rotation=90)\n","plt.xlabel('Genre')\n","plt.ylabel('Number of Movies')\n","plt.title('Genre Distribution')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"nEAp9BZO_G2x"},"source":["As we can see in the previous graph, unluckily we have plenty of null values. The main genre is Drama, followed really closed by Adult and then by comedy. In a more smaller amount, we find Thriller and Documentary in the 4th and 5th place (without considering Null)"]},{"cell_type":"code","source":["# Now, we will plot a graph with the number of actors in each main genre. First, we count distinct values in genres, then number of actors in each genre and then we plot the graph\n","\n","def count_distinct(bcg_df_copy, col):\n","    genre_names = bcg_df_copy.columns[3:].tolist()\n","    counts = []\n","    for genre in genre_names:\n","        counts.append(bcg_df_copy[bcg_df_copy[genre]==1][col].nunique())\n","    return dict(zip(genre_names, counts))\n","\n","actor_counts = count_distinct(bcg_df_copy, \"name\")\n","\n","# Here we order the data\n","sorted_actor_counts = dict(sorted(actor_counts.items(), key=lambda item: item[1], reverse=True))\n","\n","plt.bar(range(len(sorted_actor_counts)), list(sorted_actor_counts.values()), align='center')\n","plt.xticks(range(len(sorted_actor_counts)), list(sorted_actor_counts.keys()), rotation=90)\n","plt.xlabel('Genre')\n","plt.ylabel('Number of Distinct Actors')\n","plt.title('Distinct Actor Counts by Genre')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":552},"id":"6H0aIwNNijPb","executionInfo":{"status":"ok","timestamp":1680708584615,"user_tz":-120,"elapsed":780,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"228d0be7-3a86-4ad2-a0f8-89f73c76c557"},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["This is a really interesting graph, especially when we contrast it with the number of films. Adult is the second genre with the highest number of movies, but with the lowest number of actors, which seems to explain the high number of movies in which each actor in the adult genre has participated.  In addition, the genres with the highest number of actors such as Romance, Thriller, Short or Family are not among the Top 3 genres with the highest number of movies produced. This seems to indicate that each of these films requires a larger number of actors than, for example, adult film.\n"],"metadata":{"id":"KY8y_MLi0G69"}},{"cell_type":"code","execution_count":47,"metadata":{"id":"QKtzkgEF83yN","executionInfo":{"status":"ok","timestamp":1680708584616,"user_tz":-120,"elapsed":37,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/","height":424},"outputId":"a383edfc-1bc9-4678-89cd-84ca35ce8d5d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                     ActorName  movies_95_04 main_genre  Adult  Family  \\\n","0      Bouvet, Jean-Christophe            44     Comedy      0       0   \n","1               Castel, France            19      Drama      0       0   \n","2             Vallance, Louise            21   Thriller      0       2   \n","3            Fernhout, Roeland            14      Drama      0       0   \n","4                Hayes, Tanner            15      Adult     11       0   \n","...                        ...           ...        ...    ...     ...   \n","17450        Vitali, Élisabeth            25      Drama      0       0   \n","17451        Tabatabai, Jasmin            24      Drama      0       2   \n","17452      McAlister, Scarlett            16      Drama      0       0   \n","17453           Curry, Stephen            19     Comedy      0       0   \n","17454         Greenhouse, Kate            13      Drama      0       0   \n","\n","       Animation  Drama  Mystery  Short  Adventure  ...  Comedy  Sci-Fi  \\\n","0              0      5        0      8          0  ...      15       0   \n","1              0      7        0      0          0  ...       1       0   \n","2              3      1        0      0          1  ...       1       1   \n","3              0      6        1      0          0  ...       0       0   \n","4              0      0        0      0          0  ...       1       0   \n","...          ...    ...      ...    ...        ...  ...     ...     ...   \n","17450          0      6        0      3          0  ...       3       0   \n","17451          0      6        0      3          0  ...       3       0   \n","17452          0      4        1      0          0  ...       1       1   \n","17453          0      3        0      1          0  ...       9       0   \n","17454          0      5        0      0          0  ...       0       2   \n","\n","       Fantasy  Music  War  Thriller  Documentary  Crime  Musical  \\\n","0            1      0    0         1            0      0        0   \n","1            0      0    1         1            0      1        0   \n","2            0      0    0         4            0      0        0   \n","3            1      0    0         3            0      0        0   \n","4            0      0    0         0            0      0        0   \n","...        ...    ...  ...       ...          ...    ...      ...   \n","17450        0      0    0         0            0      0        0   \n","17451        0      0    0         1            0      2        0   \n","17452        1      0    0         1            0      0        0   \n","17453        0      1    1         0            1      0        0   \n","17454        1      0    0         3            0      0        0   \n","\n","       num_different_genres  \n","0                         7  \n","1                         7  \n","2                        10  \n","3                         6  \n","4                         3  \n","...                     ...  \n","17450                     5  \n","17451                     8  \n","17452                     9  \n","17453                     8  \n","17454                     6  \n","\n","[17455 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-8152abcd-518d-43f5-b512-4126b4216a56\">\n","    <div class=\"colab-df-container\">\n","      <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>ActorName</th>\n","      <th>movies_95_04</th>\n","      <th>main_genre</th>\n","      <th>Adult</th>\n","      <th>Family</th>\n","      <th>Animation</th>\n","      <th>Drama</th>\n","      <th>Mystery</th>\n","      <th>Short</th>\n","      <th>Adventure</th>\n","      <th>...</th>\n","      <th>Comedy</th>\n","      <th>Sci-Fi</th>\n","      <th>Fantasy</th>\n","      <th>Music</th>\n","      <th>War</th>\n","      <th>Thriller</th>\n","      <th>Documentary</th>\n","      <th>Crime</th>\n","      <th>Musical</th>\n","      <th>num_different_genres</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Bouvet, Jean-Christophe</td>\n","      <td>44</td>\n","      <td>Comedy</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>8</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>15</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Castel, France</td>\n","      <td>19</td>\n","      <td>Drama</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Vallance, Louise</td>\n","      <td>21</td>\n","      <td>Thriller</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Fernhout, Roeland</td>\n","      <td>14</td>\n","      <td>Drama</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Hayes, Tanner</td>\n","      <td>15</td>\n","      <td>Adult</td>\n","      <td>11</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</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","      <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","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>17450</th>\n","      <td>Vitali, Élisabeth</td>\n","      <td>25</td>\n","      <td>Drama</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>17451</th>\n","      <td>Tabatabai, Jasmin</td>\n","      <td>24</td>\n","      <td>Drama</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>17452</th>\n","      <td>McAlister, Scarlett</td>\n","      <td>16</td>\n","      <td>Drama</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>17453</th>\n","      <td>Curry, Stephen</td>\n","      <td>19</td>\n","      <td>Comedy</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>9</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>17454</th>\n","      <td>Greenhouse, Kate</td>\n","      <td>13</td>\n","      <td>Drama</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17455 rows × 25 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8152abcd-518d-43f5-b512-4126b4216a56')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 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The objective is \n","# to check if an actor is versatile or specialized in a genre\n","\n","bcg_df_copy['num_different_genres'] = (bcg_df_copy.iloc[:, 3:] != 0).sum(axis=1)\n","\n","# I also rename 'name' column because was causing problems in the next merge\n","bcg_df_copy = bcg_df_copy.rename(columns={\"name\": \"ActorName\"})\n","\n","bcg_df_copy"]},{"cell_type":"code","source":["# First, we calculate the mean of the number of different genres an actor has took part in, which will be useful for further analysis\n","average_num_genres = bcg_df_copy['num_different_genres'].mean()\n","print(\"The Average number of genres an actor has took part in is:\", average_num_genres)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O9MuhkRd4lE-","executionInfo":{"status":"ok","timestamp":1680708584617,"user_tz":-120,"elapsed":35,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"84073604-4247-4962-f84e-aa8321e8008a"},"execution_count":48,"outputs":[{"output_type":"stream","name":"stdout","text":["The Average number of genres an actor has took part in is: 6.548037811515325\n"]}]},{"cell_type":"code","source":["\n","# We plot a graph showing, for each genre, the average number of different genres where they actors have participated\n","\n","# Group the DataFrame by main genre and calculate the mean of the num_different_genres column for each group\n","grouped = bcg_df_copy.groupby(['main_genre'])['num_different_genres'].mean().reset_index()\n","\n","# Sort the grouped DataFrame by the average number of different genres in descending order\n","grouped = grouped.sort_values(by='num_different_genres', ascending=False)\n","\n","# In this case, we will create a bar plot using Seaborn\n","plt.figure(figsize=(10, 6))\n","sns.barplot(x='main_genre', y='num_different_genres', data=grouped)\n","\n","plt.xticks(rotation=45, ha='right', fontsize=12)\n","plt.title('Average number of different genres by main genre', fontsize=16)\n","plt.xlabel('Main genre', fontsize=14)\n","plt.ylabel('Average number of different genres', fontsize=14)\n","\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":631},"id":"T0WCVg7NZVL1","executionInfo":{"status":"ok","timestamp":1680708584617,"user_tz":-120,"elapsed":28,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"7563caa5-293e-4253-a8ae-4004ce6b2c5c"},"execution_count":49,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["The average number of different genres an actor has took part in is 6.5. As we can see in the previous graph, actors in Sci-Fi, Animation, Fantasy and thriller are a bit more versatile, in average. On the other hand, actors in the adult genre are much less likely to be involved in other genres."],"metadata":{"id":"8QuZcKeV0Ick"}},{"cell_type":"code","execution_count":50,"metadata":{"id":"XQ1VZBJEuFx5","executionInfo":{"status":"ok","timestamp":1680708584618,"user_tz":-120,"elapsed":26,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# We add the columns movies_95_04, main genre and num of different genres to our bcg_df (the one with the vertex for our big component)\n","bcg_df = bcg_df.merge(bcg_df_copy[['ActorName','movies_95_04', 'main_genre', 'num_different_genres']], \n","                      how='left', \n","                      left_on='name', \n","                      right_on='ActorName')\n","\n","# As it is no longer needed, we drop the ActorName column\n","bcg_df=bcg_df.drop(columns=['ActorName'])\n"]},{"cell_type":"code","source":["g_df = g_df.rename(columns={\"name\":\"ActorName\"})\n","\n","bcg_df = bcg_df.merge(g_df[['ActorName', 'degree', 'weighted_degree']], \n","                      how='left', \n","                      left_on='name', \n","                      right_on='ActorName')\n","\n","# As it is no longer needed, we drop the ActorName column\n","bcg_df=bcg_df.drop(columns=['ActorName'])"],"metadata":{"id":"slfMOiIUcI6e","executionInfo":{"status":"ok","timestamp":1680708584618,"user_tz":-120,"elapsed":25,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"execution_count":51,"outputs":[]},{"cell_type":"code","execution_count":52,"metadata":{"id":"uYbOmxskuRQA","executionInfo":{"status":"ok","timestamp":1680708585026,"user_tz":-120,"elapsed":433,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"9d5286ef-a89d-4cff-925d-66a4460c418e"},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 17455 entries, 0 to 17454\n","Data columns (total 6 columns):\n"," #   Column                Non-Null Count  Dtype  \n","---  ------                --------------  -----  \n"," 0   name                  17455 non-null  object \n"," 1   movies_95_04          17455 non-null  int64  \n"," 2   main_genre            17387 non-null  object \n"," 3   num_different_genres  17455 non-null  int64  \n"," 4   degree                17455 non-null  int64  \n"," 5   weighted_degree       17455 non-null  float64\n","dtypes: float64(1), int64(3), object(2)\n","memory usage: 954.6+ KB\n"]}],"source":["# checking everything has worked\n","bcg_df.info()"]},{"cell_type":"markdown","metadata":{"id":"qj-g8g06rHbC"},"source":["Obtain three vectors with the degree, betweeness and closeness for each vertex of the actors' graph."]},{"cell_type":"code","execution_count":53,"metadata":{"id":"ZoVTH3nWkowo","executionInfo":{"status":"ok","timestamp":1680708707871,"user_tz":-120,"elapsed":330,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}}},"outputs":[],"source":["# The next snippet of code has been developed to calculate degree centrality, betweenness centrality and closeness. We have created\n","# a dictionary mapping actors name (which are acting as IDs) and the centrality metrics. After that we add them to the bcg_df (which we\n","# calculated previously). We have used the dictionary to avoid messing up data (mixing vertices_ids and centrality metrics and not\n","# getting the real picture)\n","\n","# Calculate centrality measures\n","betweenness_centrality = subgraph_bc.betweenness(directed=False)\n","closeness_centrality = subgraph_bc.closeness()\n","\n","# Create dictionary mapping actor names to centrality measures\n","bc_dict = OrderedDict(zip(subgraph_bc.vs[\"name\"], betweenness_centrality))\n","cc_dict = OrderedDict(zip(subgraph_bc.vs[\"name\"], closeness_centrality))\n","\n","# Add calculated metrics to bcg_df\n","bcg_df[\"betweenness_centrality\"] = bcg_df[\"name\"].map(bc_dict)\n","bcg_df[\"closeness_centrality\"] = bcg_df[\"name\"].map(cc_dict)"]},{"cell_type":"code","execution_count":54,"metadata":{"id":"nVgNzzJ0kons","executionInfo":{"status":"ok","timestamp":1680708707872,"user_tz":-120,"elapsed":71,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/","height":206},"outputId":"1fa56f12-f911-4f25-9f98-55af4a6d022a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                      name  movies_95_04 main_genre  num_different_genres  \\\n","0  Bouvet, Jean-Christophe            44     Comedy                     7   \n","1           Castel, France            19      Drama                     7   \n","2         Vallance, Louise            21   Thriller                    10   \n","3        Fernhout, Roeland            14      Drama                     6   \n","4            Hayes, Tanner            15      Adult                     3   \n","\n","   degree  weighted_degree  betweenness_centrality  closeness_centrality  \n","0      34             78.0            63420.753086              0.222183  \n","1      38             88.0            45150.962914              0.220312  \n","2      59            192.0            14650.536549              0.241014  \n","3      15             34.0            36804.347028              0.183833  \n","4       8             20.0              792.110081              0.165056  "],"text/html":["\n","  <div id=\"df-e0362c66-7434-45a9-a9b0-821094711e90\">\n","    <div class=\"colab-df-container\">\n","      <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>name</th>\n","      <th>movies_95_04</th>\n","      <th>main_genre</th>\n","      <th>num_different_genres</th>\n","      <th>degree</th>\n","      <th>weighted_degree</th>\n","      <th>betweenness_centrality</th>\n","      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It can be useful to show a list with the degree, the name of the actor, the number of movies, the main genre, and the number of genres in which the actor has participated"]},{"cell_type":"markdown","source":["Regarding **degree centrality**:  a node's degree is simply a count of how many social connections (i.e., edges) it has. The degree centrality for a node is simply its degree. Source: https://www.sciencedirect.com/topics/computer-science/degree-centrality\n","Taking this into consideration, we realized we calculated the degree centrality at exercise 2, so we will use the already computed metrics.\n","We will use the degree instead the weighted degree. As we saw at the beginning of the current exercise, weights are not equal to number of movies. I want to be more focus on measure just the collaborations without considering, i.e., the length of that collaboration."],"metadata":{"id":"GGo40phaS852"}},{"cell_type":"code","execution_count":55,"metadata":{"id":"wJK8N5iUkoXG","executionInfo":{"status":"ok","timestamp":1680708707872,"user_tz":-120,"elapsed":68,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"ad5b55f3-6d61-4773-fc13-b7a34b53f24d"},"outputs":[{"output_type":"stream","name":"stdout","text":["                      name  movies_95_04 main_genre  num_different_genres  degree  weighted_degree  betweenness_centrality  closeness_centrality\n","10478      Davis, Mark (V)           540      Adult                    10     784           5435.0            9.318531e+05              0.249300\n","12253    Sanders, Alex (I)           467      Adult                    10     610           3838.0            5.572365e+05              0.245821\n","66        North, Peter (I)           460      Adult                     8     599           4065.0            4.173385e+05              0.241765\n","4773           Marcus, Mr.           435      Adult                     6     584           3433.0            1.463808e+06              0.249964\n","14774       Tedeschi, Tony           364      Adult                    11     561           3143.0            6.721635e+05              0.245693\n","9030            Dough, Jon           300      Adult                     8     555           2960.0            8.636479e+05              0.248562\n","2439       Stone, Lee (II)           403      Adult                     7     545           3380.0            3.393109e+05              0.238488\n","8822         Voyeur, Vince           370      Adult                    10     533           3534.0            3.810606e+05              0.245783\n","10284  Lawrence, Joel (II)           315      Adult                     7     500           2761.0            2.851236e+05              0.241337\n","10290    Steele, Lexington           429      Adult                     8     493           2814.0            2.971735e+05              0.240841\n","4694           Ashley, Jay           309      Adult                     8     490           2537.0            1.794342e+05              0.238661\n","2354             Boy, T.T.           336      Adult                     8     475           2924.0            2.177450e+05              0.240556\n","16811          Jeremy, Ron           280      Adult                    14     471           1805.0            9.748544e+06              0.282720\n","6950   Cannon, Chris (III)           287      Adult                     9     471           2551.0            5.315881e+05              0.247557\n","4391            Bune, Tyce           267      Adult                     5     463           2339.0            3.241937e+05              0.236232\n","902             Hanks, Tom            75     Family                    13     457           1416.0            1.977252e+06              0.305231\n","1247        Michaels, Sean           252      Adult                     6     451           2116.0            3.383991e+05              0.243856\n","14086          Stone, Kyle           278      Adult                     8     450           2286.0            1.032512e+05              0.235648\n","1515         Hardman, Dave           319      Adult                     7     438           2356.0            2.977737e+05              0.240841\n","5059       Surewood, Brian           244      Adult                     6     428           2081.0            1.729722e+05              0.237955\n"]}],"source":["pd.set_option('display.width', 1000)\n","top_20_degree_centrality = bcg_df.sort_values(by='degree', ascending=False).head(20)\n","print(top_20_degree_centrality)"]},{"cell_type":"markdown","metadata":{"id":"bDqlqpPm0Rqd"},"source":["Based on the previous introduction, explaining degree centrality is quite easy. As saw before, adult is the second genre with more movies but the one with less number of actors on it. This means that actors with Adult as their main genre have been quite busy. We can see that the number of movies recorded by them is really big. Also, I imagine that they collaborate usually with at least one person at each movie. Also (and this is not the main cause but also it adds degrees in comparison with other actors of the genre), the actors in this top20 list, have participated in a number of different genres considerably above the average for Adult genre actors. This explains why these actors are the ones with highest degree centrality"]},{"cell_type":"markdown","source":["**Betweenness centrality** is a measure of how important a node is in a network, by calculating the number of shortest paths that pass through that node. Here it is not as important the number of degrees, as in previous measurement, but how interconnected a node is in between a network. Central actors are those that have “control” of other \n","pairs of actors"],"metadata":{"id":"BEeaOkiJx_0w"}},{"cell_type":"code","execution_count":56,"metadata":{"id":"uMsdYhSi0SnX","executionInfo":{"status":"ok","timestamp":1680708707873,"user_tz":-120,"elapsed":62,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"7d6ad39f-8262-46ee-e9e8-52c131caeb04"},"outputs":[{"output_type":"stream","name":"stdout","text":["                       name  movies_95_04 main_genre  num_different_genres  degree  weighted_degree  betweenness_centrality  closeness_centrality\n","16811           Jeremy, Ron           280      Adult                    14     471           1805.0            9.748544e+06              0.282720\n","3738       Chan, Jackie (I)            59     Comedy                    12     135            370.0            4.716909e+06              0.287238\n","1360         Cruz, Penélope            46      Drama                    13     182            473.0            4.330663e+06              0.295555\n","6681       Shahlavi, Darren            16     Action                     9       8             21.0            4.295503e+06              0.193886\n","1949   Del Rosario, Monsour            20     Action                     9       6             14.0            4.267099e+06              0.163154\n","15149     Depardieu, Gérard            56     Comedy                    11     159            403.0            4.037356e+06              0.278351\n","12834     Bachchan, Amitabh            35    Romance                    13      66            188.0            2.570247e+06              0.226349\n","12571    Jackson, Samuel L.            97      Drama                    14     427           1271.0            2.539614e+06              0.309265\n","12465     Soualem, Zinedine            65     Comedy                    12     121            311.0            2.368164e+06              0.249825\n","13528       Del Rio, Olivia            84      Adult                     6     168            506.0            2.316388e+06              0.240033\n","11812      Jaenicke, Hannes            66   Thriller                    12      73            165.0            2.136980e+06              0.250736\n","4097           Hayek, Salma            44      Drama                    14     185            470.0            2.117390e+06              0.288701\n","7232                   Pelé            10    Romance                     4       7             15.0            2.098485e+06              0.192619\n","14764        Knaup, Herbert            50      Drama                    11      63            158.0            2.062585e+06              0.231765\n","12073      Goldberg, Whoopi           109     Comedy                    14     398           1122.0            2.051621e+06              0.307760\n","539           Roth, Cecilia            23      Drama                     8      62            151.0            2.019247e+06              0.244677\n","12189      Bellucci, Monica            43      Drama                    12      81            227.0            2.006221e+06              0.273986\n","902              Hanks, Tom            75     Family                    13     457           1416.0            1.977252e+06              0.305231\n","4339       August, Pernilla            31      Drama                    10      44            114.0            1.937362e+06              0.256443\n","14595             Kier, Udo            69      Drama                    15      64            134.0            1.919261e+06              0.281793\n"]}],"source":["top_20_betweenness_centrality = bcg_df.sort_values(by='betweenness_centrality', ascending=False).head(20)\n","print(top_20_betweenness_centrality)"]},{"cell_type":"markdown","metadata":{"id":"T5UsANIb1Z_g"},"source":["This is probably the one with hardest explanation, as results are quite diverse and there are actors with really low degree and low number of different genres. First, we will start by stating that Adult genre seem to be quite isolated of the rest, as it just has 2 representatives here. \n","In general terms, we can state that most actors in the list have collaborated with a large number of other actors in the network. Additionally, they have participated in movies across a diverse range of genres, with an average of 12.65 different genres per actor, significantly higher than the average for all actors in the network (6.5).\n","The reason why we have some actors with low degrees and also low number of different genres, could be answered by the fact that, despite these actors may not collaborate with a large number of other actors or participate in a wide variety of genres, they are crucial for connecting different parts of the network together. They may have worked with actors who themselves have a high degree or are part of different genres, which allows them to act as a bridge between those parts of the network."]},{"cell_type":"markdown","source":["**Closeness centrality** calculates the average shortest path distance between a node and all other nodes in the network. Regarding the main difference with betweenness centrality, closeness centrality measures the direct reachability of a node to all other nodes in the network, while betweenness centrality measures the extent to which a node lies on the shortest paths between pairs of other nodes. As seen in class, for an undirected graph this measure ranges between 0 and 1"],"metadata":{"id":"Wsyeht4Gz7Tf"}},{"cell_type":"code","execution_count":57,"metadata":{"id":"NrCIs5lb0oRU","executionInfo":{"status":"ok","timestamp":1680708707874,"user_tz":-120,"elapsed":55,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"bef57ca5-b0a0-45af-f8f8-7ec1e7558b8e"},"outputs":[{"output_type":"stream","name":"stdout","text":["                         name  movies_95_04 main_genre  num_different_genres  degree  weighted_degree  betweenness_centrality  closeness_centrality\n","12571      Jackson, Samuel L.            97      Drama                    14     427           1271.0            2.539614e+06              0.309265\n","12073        Goldberg, Whoopi           109     Comedy                    14     398           1122.0            2.051621e+06              0.307760\n","8921             Berry, Halle            63     Family                    14     376           1135.0            1.268316e+06              0.305905\n","10375           Diaz, Cameron            59      Drama                    13     361           1006.0            1.431434e+06              0.305669\n","902                Hanks, Tom            75     Family                    13     457           1416.0            1.977252e+06              0.305231\n","10315            Stiller, Ben            66     Comedy                    14     382           1077.0            1.153987e+06              0.304719\n","3827          Myers, Mike (I)            58     Comedy                    10     345           1064.0            1.186416e+06              0.302611\n","14278    Douglas, Michael (I)            41     Family                    11     263            723.0            1.244845e+06              0.302606\n","4683      Lopez, Jennifer (I)            68      Music                    11     410           1311.0            7.463466e+05              0.301217\n","10155         De Niro, Robert            51     Comedy                    13     257            677.0            1.099195e+06              0.300708\n","8820        Willis, Bruce (I)            52   Thriller                    14     137            326.0            1.200133e+06              0.300485\n","11581             Cruise, Tom            46      Music                    11     336           1023.0            9.895053e+05              0.300408\n","7212           Hopper, Dennis           106      Music                    16     231            576.0            1.044104e+06              0.299336\n","16928          Kidman, Nicole            54     Family                    12     280            861.0            7.426381e+05              0.298768\n","4810          Smith, Will (I)            57      Music                    10     326            888.0            6.077963e+05              0.298553\n","8047       Washington, Denzel            49     Family                    10     253            682.0            4.714627e+05              0.298548\n","3921           Travolta, John            63      Drama                    13     335            999.0            6.715298e+05              0.298512\n","4037              Madonna (I)            61      Music                    11     324            911.0            7.666255e+05              0.298359\n","12925  Schwarzenegger, Arnold            70     Family                    13     333            940.0            5.201300e+05              0.297743\n","6960          Hoffman, Dustin            56   Thriller                    12     259            738.0            4.633092e+05              0.297581\n"]}],"source":["top_20_closeness_centrality = bcg_df.sort_values(by='closeness_centrality', ascending=False).head(20)\n","print(top_20_closeness_centrality)"]},{"cell_type":"markdown","metadata":{"id":"urHbaAc11bAK"},"source":["In closeness centrality we can see that all actors have a considerable degree (they have collaborated with many other actors) and also they have participated in a really important number of different genres, in this case, the value of the number of different genres is much higher than the average for all actors (6.5). They act (mainly or have participated) in genres with high number of actors, which will mean that they have easy access to all the nodes in that genre (we can see this in the graph that is plotting number of actors by genre). This means these actors can reach every node of the network faster than the rest."]},{"cell_type":"code","source":["%%shell\n","jupyter nbconvert --to html /content/SNA_Individual_Assignment_AFH.ipynb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T06jIry1FvIe","executionInfo":{"status":"ok","timestamp":1680709491556,"user_tz":-120,"elapsed":1343,"user":{"displayName":"Alfonso Ferrándiz","userId":"06510596590384276037"}},"outputId":"e123d5e2-caf3-4547-ad40-7ef7ae6527fc"},"execution_count":59,"outputs":[{"output_type":"stream","name":"stdout","text":["[NbConvertApp] Converting notebook /content/SNA_Individual_Assignment_AFH.ipynb to html\n","[NbConvertApp] Writing 950148 bytes to /content/SNA_Individual_Assignment_AFH.html\n"]},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":59}]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNuW2tMjJ69ZcCKKhsS2aVF"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"nbformat":4,"nbformat_minor":0}