actorNetwork.ipynb

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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scikit-network==0.24.0\n",
            "  Downloading scikit_network-0.24.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (8.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 8.2 MB 32.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=1.6.2 in /usr/local/lib/python3.7/dist-packages (from scikit-network==0.24.0) (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.20.2 in /usr/local/lib/python3.7/dist-packages (from scikit-network==0.24.0) (1.21.6)\n",
            "Installing collected packages: scikit-network\n",
            "Successfully installed scikit-network-0.24.0\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-network==0.24.0\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sknetwork.clustering\n",
        "import sknetwork.utils\n",
        "from scipy.sparse import csr_matrix"
      ]
    },
    {
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      "execution_count": 2,
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        {
          "name": "stdout",
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          "text": [
            "total 1059772\n",
            "drwxr-xr-x 1 root root      4096 Oct 18 15:13 .\n",
            "drwxr-xr-x 1 root root      4096 Oct 18 15:12 ..\n",
            "drwxr-xr-x 4 root root      4096 Oct 14 19:04 .config\n",
            "-rw-r--r-- 1 root root 235082849 Oct 18 13:23 name.basics.tsv.gz\n",
            "drwxr-xr-x 1 root root      4096 Oct 14 19:05 sample_data\n",
            "-rw-r--r-- 1 root root 284773528 Oct 18 13:23 title.akas.tsv.gz\n",
            "-rw-r--r-- 1 root root 151270330 Oct 17 13:24 title.basics.tsv.gz\n",
            "-rw-r--r-- 1 root root 414049088 Oct 17 13:24 title.principals.tsv.gz\n"
          ]
        }
      ],
      "source": [
        "# Download the data\n",
        "!rm -f *.tsv.gz\n",
        "!wget -q https://datasets.imdbws.com/name.basics.tsv.gz\n",
        "!wget -q https://datasets.imdbws.com/title.principals.tsv.gz\n",
        "!wget -q https://datasets.imdbws.com/title.basics.tsv.gz\n",
        "!wget -q https://datasets.imdbws.com/title.akas.tsv.gz\n",
        "!ls -la"
      ]
    },
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              "          titleType  startYear\n",
              "tconst                        \n",
              "tt0000001     short       1894\n",
              "tt0000002     short       1892\n",
              "tt0000003     short       1892\n",
              "tt0000004     short       1892\n",
              "tt0000005     short       1893"
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        "# Load the titles\n",
        "title = pd.read_csv('title.basics.tsv.gz', sep='\\t',usecols=['tconst','titleType', 'startYear'], dtype={\n",
        "    'tconst':'str',\n",
        "    'titleType':'str',\n",
        "    'primaryTitle':'str',\n",
        "    'startYear': 'Int64'\n",
        "}, na_values='\\\\N').set_index('tconst')\n",
        "\n",
        "title.head()"
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              "       tconst     nconst category\n",
              "11  tt0000005  nm0443482    actor\n",
              "12  tt0000005  nm0653042    actor\n",
              "16  tt0000007  nm0179163    actor\n",
              "17  tt0000007  nm0183947    actor\n",
              "21  tt0000008  nm0653028    actor"
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        "# Load the cast of each film\n",
        "cast = pd.read_csv('title.principals.tsv.gz',usecols=['tconst', 'nconst', 'category'], sep='\\t')\n",
        "# Only consider actors; shrinks data to about 40%\n",
        "cast = cast[cast.category.isin({'actor', 'actress'})]\n",
        "cast.head()"
      ]
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "          tconst     nconst category\n",
              "80688  tt0011801  nm0459029    actor\n",
              "80689  tt0011801  nm0681726    actor\n",
              "80690  tt0011801  nm0692612  actress\n",
              "80691  tt0011801  nm0726256    actor\n",
              "80692  tt0011801  nm0776458    actor"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Only consider movies\n",
        "movies = title[(title['titleType'] == 'movie') & (title.startYear >2004)]\n",
        "cast = cast[cast['tconst'].isin(movies.index)]\n",
        "\n",
        "cast.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tORsiirfD7Gq",
        "outputId": "8558f651-d406-4398-aacd-a3d20a36e303"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "region\n",
              "IN        3933771\n",
              "dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "region = pd.read_csv('title.akas.tsv.gz', sep='\\t', usecols=['titleId',\"region\"], dtype={\"region\":'str'}).set_index('titleId')\n",
        "region = region[region.region.isin({'IN'})]\n",
        "region.value_counts().head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "YPvwN81QUsJi"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "xjDUPVwLEZYZ"
      },
      "outputs": [],
      "source": [
        "name = pd.read_csv('name.basics.tsv.gz', sep='\\t', na_values='\\\\N', dtype={'birthYear': float}).set_index('nconst')[['primaryName', 'birthYear']]\n",
        "name_freq = cast['nconst'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "BQr9NMl2EbD7"
      },
      "outputs": [],
      "source": [
        "def get_pairs(lang=None, min_acted=25, min_pairings=4):\n",
        "    '''\n",
        "    Returns an adjacency matrix and actor mapping of actor pairs where:\n",
        "    - Each actor has acted in at least min_acted films\n",
        "    - The two actors have acted together in at least min_pairings films\n",
        "    - And (optionally), belong to a region `lang` (IN, UN, etc)\n",
        "    '''\n",
        "    graph = cast\n",
        "    if lang is not None:\n",
        "        graph = graph[graph['tconst'].isin(region[region == lang].index)]\n",
        "    top_names = name_freq[name_freq >= min_acted]\n",
        "    top_actors = graph[graph['nconst'].isin(top_names.index)]\n",
        "\n",
        "    p = top_actors.copy()\n",
        "    p['title'] = p['tconst'].astype('category')\n",
        "    p['name'] = p['nconst'].astype('category')\n",
        "\n",
        "    row = p['title'].cat.codes.values\n",
        "    col = p['name'].cat.codes.values\n",
        "    data = np.ones(len(p), dtype='int')\n",
        "\n",
        "    matrix = csr_matrix((data, (row, col)))\n",
        "    square = matrix.T * matrix\n",
        "    square.setdiag(0)\n",
        "    square = square.tocoo()\n",
        "\n",
        "    pairs = pd.DataFrame({\n",
        "        'row': square.row,\n",
        "        'col': square.col,\n",
        "        'n': square.data\n",
        "    })\n",
        "    pairs = pairs[pairs.n >= min_pairings].reset_index(drop=True)\n",
        "    return pairs, name.reindex(p['name'].cat.categories)\n",
        "\n",
        "def lookup(pairs, cat):\n",
        "    pairs = pd.concat([\n",
        "        pairs,\n",
        "        cat.iloc[pairs.row].reset_index(drop=True),\n",
        "        cat.iloc[pairs.col].reset_index(drop=True),\n",
        "    ], axis=1)\n",
        "    pairs = pairs.drop(columns=['row', 'col'])\n",
        "    pairs.columns = ['count', 'name1', 'year1', 'name2', 'year2']\n",
        "    return pairs.sort_values('count', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "ZxYt9_EoExyE",
        "outputId": "ee7609c1-922a-4a08-f7ad-9e823751f28c"
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              "      <td>NaN</td>\n",
              "      <td>Amin Hajee</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>70192</th>\n",
              "      <td>1</td>\n",
              "      <td>Sachin Deshpande</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Amin Hajee</td>\n",
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              "      <td>Amin Hajee</td>\n",
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              "    <tr>\n",
              "      <th>100971</th>\n",
              "      <td>1</td>\n",
              "      <td>Vin Diesel</td>\n",
              "      <td>1967.0</td>\n",
              "      <td>Sam Heughan</td>\n",
              "      <td>1980.0</td>\n",
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              "<p>201942 rows × 5 columns</p>\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ],
            "text/plain": [
              "        count             name1   year1             name2   year2\n",
              "17981      32      Brahmanandam  1956.0      Mohammad Ali  1968.0\n",
              "23993      32      Mohammad Ali  1968.0      Brahmanandam  1956.0\n",
              "83521      20      Brahmanandam  1956.0  Krishna Bhagavan     NaN\n",
              "88942      20      Mohammad Ali  1968.0        Raghu Babu     NaN\n",
              "17957      20        Raghu Babu     NaN      Mohammad Ali  1968.0\n",
              "...       ...               ...     ...               ...     ...\n",
              "70191       1    Kishori Ballal     NaN        Amin Hajee     NaN\n",
              "70192       1  Sachin Deshpande     NaN        Amin Hajee     NaN\n",
              "70193       1     Celina Jaitly  1981.0        Amin Hajee     NaN\n",
              "70194       1      Amrita Arora  1981.0        Amin Hajee     NaN\n",
              "100971      1        Vin Diesel  1967.0       Sam Heughan  1980.0\n",
              "\n",
              "[201942 rows x 5 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pairs_in, cat_in = get_pairs(lang='IN', min_acted=3, min_pairings=1)\n",
        "lookup(pairs_in, cat_in)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
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        },
        "id": "W4APGmaMGgci",
        "outputId": "701f6c41-45df-4624-b84b-a069646176d1"
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        {
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              "      <td>NaN</td>\n",
              "      <td>Amin Hajee</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70192</th>\n",
              "      <td>1</td>\n",
              "      <td>Sachin Deshpande</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Amin Hajee</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70193</th>\n",
              "      <td>1</td>\n",
              "      <td>Celina Jaitly</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>Amin Hajee</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70194</th>\n",
              "      <td>1</td>\n",
              "      <td>Amrita Arora</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>Amin Hajee</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100971</th>\n",
              "      <td>1</td>\n",
              "      <td>Vin Diesel</td>\n",
              "      <td>1967.0</td>\n",
              "      <td>Sam Heughan</td>\n",
              "      <td>1980.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>201942 rows × 5 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        count             name1   year1             name2   year2\n",
              "17981      32      Brahmanandam  1956.0      Mohammad Ali  1968.0\n",
              "23993      32      Mohammad Ali  1968.0      Brahmanandam  1956.0\n",
              "83521      20      Brahmanandam  1956.0  Krishna Bhagavan     NaN\n",
              "88942      20      Mohammad Ali  1968.0        Raghu Babu     NaN\n",
              "17957      20        Raghu Babu     NaN      Mohammad Ali  1968.0\n",
              "...       ...               ...     ...               ...     ...\n",
              "70191       1    Kishori Ballal     NaN        Amin Hajee     NaN\n",
              "70192       1  Sachin Deshpande     NaN        Amin Hajee     NaN\n",
              "70193       1     Celina Jaitly  1981.0        Amin Hajee     NaN\n",
              "70194       1      Amrita Arora  1981.0        Amin Hajee     NaN\n",
              "100971      1        Vin Diesel  1967.0       Sam Heughan  1980.0\n",
              "\n",
              "[201942 rows x 5 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pairs_us, cat_us = get_pairs(lang='US', min_acted=3, min_pairings=1)\n",
        "lookup(pairs_us, cat_us)"
      ]
    },
    {
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        {
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              "    <tr>\n",
              "      <th>19679</th>\n",
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              "      <td>14</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-49c52ee2-ec83-405e-a771-0af329ecb2c4');\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",
              "  "
            ],
            "text/plain": [
              "           index          primaryName  birthYear  cluster  freq\n",
              "0      nm0000002        Lauren Bacall     1924.0        0     3\n",
              "1      nm0000056          Paul Newman     1925.0        0     3\n",
              "2      nm0000084              Gong Li     1965.0       11    10\n",
              "3      nm0000090  Armin Mueller-Stahl     1930.0        0     7\n",
              "4      nm0000092          John Cleese     1939.0        0    17\n",
              "...          ...                  ...        ...      ...   ...\n",
              "19677  nm9969854                Rekha        NaN        2     8\n",
              "19678  nm9972198        Caylin Turner        NaN        4     3\n",
              "19679  nm9986430    Sebastian Cabanas        NaN       14     3\n",
              "19680  nm9988815        Nat Kitcharit        NaN       13     3\n",
              "19681  nm9989234       Happy BanMajra        NaN      909     5\n",
              "\n",
              "[19682 rows x 5 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "algo = sknetwork.clustering.Louvain()\n",
        "adjacency = sknetwork.utils.edgelist2adjacency(pairs_in)\n",
        "labels = algo.fit_transform(adjacency)\n",
        "clusters_in = pd.concat([\n",
        "    cat_in.reset_index(),\n",
        "    pd.Series(labels, name='cluster')], axis=1)\n",
        "\n",
        "clusters_in = pd.concat([\n",
        "    cat_in.reset_index(),\n",
        "    pd.Series(labels, name='cluster'),\n",
        "    pd.Series(clusters_in['index'].map(name_freq), name='freq'),\n",
        "], axis=1)\n",
        "clusters_in\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "5JbkKOCHsBIP",
        "outputId": "2ab9e0c4-1622-42a0-bf00-282dab7db0b9"
      },
      "outputs": [
        {
          "data": {
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            ],
            "text/plain": [
              "          index         primaryName  birthYear  cluster  freq\n",
              "709   nm0001803         Danny Trejo     1944.0        0   101\n",
              "2687  nm0290556        James Franco     1978.0        0    60\n",
              "23    nm0000115        Nicolas Cage     1964.0        0    60\n",
              "125   nm0000246        Bruce Willis     1955.0        0    58\n",
              "233   nm0000448     Lance Henriksen     1940.0        0    55\n",
              "217   nm0000418        Danny Glover     1946.0        0    55\n",
              "182   nm0000353        Willem Dafoe     1955.0        0    55\n",
              "2902  nm0332709     Olivier Gourmet     1963.0        0    54\n",
              "1194  nm0023832     Mathieu Amalric     1965.0        0    52\n",
              "63    nm0000168   Samuel L. Jackson     1948.0        0    50\n",
              "7782  nm1249052  Riccardo Scamarcio     1979.0        0    49\n",
              "309   nm0000579         Ron Perlman     1950.0        0    49\n",
              "2230  nm0202966         Keith David     1956.0        0    49\n",
              "293   nm0000553         Liam Neeson     1952.0        0    48\n",
              "553   nm0001376    Isabelle Huppert     1953.0        0    46\n",
              "1292  nm0038355      Tadanobu Asano     1973.0        0    45\n",
              "75    nm0000185      Dolph Lundgren     1957.0        0    44\n",
              "227   nm0000437     Woody Harrelson     1961.0        0    43\n",
              "723   nm0001845     Forest Whitaker     1961.0        0    43\n",
              "485   nm0001136          Bruce Dern     1936.0        0    42"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clusters_in[clusters_in['cluster']==0].sort_values('freq', ascending=False).head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
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          "height": 676
        },
        "id": "83tpiczlsvNf",
        "outputId": "3f0c9b41-e464-4493-adfe-e53b627ec32e"
      },
      "outputs": [
        {
          "data": {
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              "      <td>56</td>\n",
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              "    <tr>\n",
              "      <th>3516</th>\n",
              "      <td>nm0451272</td>\n",
              "      <td>Mushtaq Khan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4848</th>\n",
              "      <td>nm0712546</td>\n",
              "      <td>Paresh Rawal</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>826</th>\n",
              "      <td>nm0004569</td>\n",
              "      <td>Sanjay Dutt</td>\n",
              "      <td>1959.0</td>\n",
              "      <td>1</td>\n",
              "      <td>54</td>\n",
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              "    <tr>\n",
              "      <th>9978</th>\n",
              "      <td>nm1853497</td>\n",
              "      <td>Monalisa</td>\n",
              "      <td>1982.0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
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              "    <tr>\n",
              "      <th>2331</th>\n",
              "      <td>nm0222144</td>\n",
              "      <td>Rahul Dev</td>\n",
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              "      <td>1</td>\n",
              "      <td>51</td>\n",
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              "    <tr>\n",
              "      <th>13977</th>\n",
              "      <td>nm3792384</td>\n",
              "      <td>Karamjit Anmol</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>50</td>\n",
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              "  "
            ],
            "text/plain": [
              "           index           primaryName  birthYear  cluster  freq\n",
              "3565   nm0457410           Ravi Kishan     1971.0        1   114\n",
              "3530   nm0451600           Anupam Kher     1955.0        1    76\n",
              "3649   nm0474774          Akshay Kumar     1967.0        1    72\n",
              "398    nm0000821      Amitabh Bachchan     1942.0        1    70\n",
              "1928   nm0149822    Mithun Chakraborty     1950.0        1    68\n",
              "1068   nm0006763         Jackie Shroff     1957.0        1    66\n",
              "1078   nm0007106         Shakti Kapoor     1952.0        1    66\n",
              "5210   nm0792116        Jimmy Shergill     1970.0        1    62\n",
              "797    nm0004109        Gulshan Grover     1955.0        1    59\n",
              "2337   nm0222426            Ajay Devgn     1969.0        1    58\n",
              "6543   nm1056425  Rajpal Naurang Yadav     1971.0        1    57\n",
              "4196   nm0592799         Sanjay Mishra     1963.0        1    56\n",
              "4771   nm0700875               Om Puri     1950.0        1    56\n",
              "9933   nm1836657       Gurpreet Ghuggi     1971.0        1    56\n",
              "3516   nm0451272          Mushtaq Khan        NaN        1    55\n",
              "4848   nm0712546          Paresh Rawal     1950.0        1    55\n",
              "826    nm0004569           Sanjay Dutt     1959.0        1    54\n",
              "9978   nm1853497              Monalisa     1982.0        1    53\n",
              "2331   nm0222144             Rahul Dev     1968.0        1    51\n",
              "13977  nm3792384        Karamjit Anmol        NaN        1    50"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clusters_in[clusters_in['cluster']==1].sort_values('freq', ascending=False).head(20)"
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    },
    {
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              "      <th>12703</th>\n",
              "      <td>nm3132784</td>\n",
              "      <td>Kishore Kumar G.</td>\n",
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              "      <td>2</td>\n",
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              "      <th>11495</th>\n",
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              "      <th>5224</th>\n",
              "      <td>nm0793851</td>\n",
              "      <td>Sayaji Shinde</td>\n",
              "      <td>1959.0</td>\n",
              "      <td>2</td>\n",
              "      <td>71</td>\n",
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              "      <th>1344</th>\n",
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              "      <td>2</td>\n",
              "      <td>65</td>\n",
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            "text/plain": [
              "           index         primaryName  birthYear  cluster  freq\n",
              "1696   nm0103977        Brahmanandam     1956.0        2   264\n",
              "4742   nm0695177         Prakash Raj     1965.0        2   159\n",
              "1170   nm0019382        Mohammad Ali     1968.0        2   128\n",
              "4388   nm0621937              Nassar     1958.0        2   124\n",
              "1541   nm0080238   Tanikella Bharani     1954.0        2    98\n",
              "17391  nm6489058           Yogi Babu     1985.0        2    92\n",
              "8071   nm1298052          Raghu Babu        NaN        2    84\n",
              "10697  nm2128968     'Ganja' Karuppu        NaN        2    80\n",
              "5682   nm0896573    Ashish Vidyarthi     1962.0        2    78\n",
              "10708  nm2132667        M.S. Bhaskar        NaN        2    74\n",
              "12703  nm3132784    Kishore Kumar G.     1974.0        2    71\n",
              "11495  nm2496992                Ajay        NaN        2    71\n",
              "5224   nm0793851       Sayaji Shinde     1959.0        2    71\n",
              "1344   nm0045075      Jagapathi Babu     1962.0        2    69\n",
              "8969   nm1521381          Nayanthara     1984.0        2    67\n",
              "6132   nm1007581            Manobala        NaN        2    66\n",
              "10265  nm1962272           Santhanam     1980.0        2    65\n",
              "820    nm0004469  Kota Srinivasa Rao     1944.0        2    65\n",
              "10258  nm1961459    Tamannaah Bhatia     1989.0        2    61\n",
              "7623   nm1223449    Krishna Bhagavan        NaN        2    61"
            ]
          },
          "execution_count": 19,
          "metadata": {},
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        }
      ],
      "source": [
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    {
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        },
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              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>1328</th>\n",
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              "      <th>12060</th>\n",
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              "      <td>144</td>\n",
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              "      <th>9433</th>\n",
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              "      <td>133</td>\n",
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              "      <th>16667</th>\n",
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              "      <td>Achyuth Kumar</td>\n",
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              "      <td>3</td>\n",
              "      <td>97</td>\n",
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              "      <th>9490</th>\n",
              "      <td>nm1693209</td>\n",
              "      <td>Ramesh Bhat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>97</td>\n",
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              "      <th>4349</th>\n",
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              "      <td>Anant Nag</td>\n",
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              "      <td>3</td>\n",
              "      <td>81</td>\n",
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              "    <tr>\n",
              "      <th>9449</th>\n",
              "      <td>nm1682769</td>\n",
              "      <td>Bank Janardhan</td>\n",
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              "      <td>66</td>\n",
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              "      <th>16184</th>\n",
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              "      <td>64</td>\n",
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              "      <th>3653</th>\n",
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              "      <td>62</td>\n",
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              "      <td>52</td>\n",
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              "      <th>16707</th>\n",
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              "      <td>49</td>\n",
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              "      <th>7651</th>\n",
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              "      <td>49</td>\n",
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              "      <td>3</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9434</th>\n",
              "      <td>nm1679372</td>\n",
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              "      <td>1973.0</td>\n",
              "      <td>3</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
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              "      <th>16444</th>\n",
              "      <td>nm5561908</td>\n",
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              "      <td>44</td>\n",
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              "    <tr>\n",
              "      <th>13888</th>\n",
              "      <td>nm3749108</td>\n",
              "      <td>P. Ravi Shankar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9677</th>\n",
              "      <td>nm1747510</td>\n",
              "      <td>Doddanna</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
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              "    <tr>\n",
              "      <th>14084</th>\n",
              "      <td>nm3837488</td>\n",
              "      <td>Komal Kumar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
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            ],
            "text/plain": [
              "           index         primaryName  birthYear  cluster  freq\n",
              "1328   nm0043199             Avinash        NaN        3   147\n",
              "12060  nm2794335        Sadhu Kokila        NaN        3   144\n",
              "9433   nm1679254     Rangayana Raghu        NaN        3   133\n",
              "16667  nm5724719       Achyuth Kumar     1966.0        3    97\n",
              "9490   nm1693209         Ramesh Bhat        NaN        3    97\n",
              "4349   nm0619047           Anant Nag     1948.0        3    81\n",
              "9449   nm1682769      Bank Janardhan        NaN        3    66\n",
              "16184  nm5308603      Bullet Prakash        NaN        3    64\n",
              "3653   nm0474871           Sai Kumar        NaN        3    62\n",
              "16632  nm5701208  Sharath Lohitashwa        NaN        3    52\n",
              "16707  nm5754203          Tabla Nani        NaN        3    49\n",
              "7651   nm1227718     H.G. Dattatreya        NaN        3    49\n",
              "17394  nm6492899           Chikkanna        NaN        3    49\n",
              "14232  nm3912079           Shobharaj        NaN        3    46\n",
              "12062  nm2794672        Jai Jagdeesh        NaN        3    46\n",
              "9434   nm1679372              Sudeep     1973.0        3    45\n",
              "16444  nm5561908    Suchendra Prasad        NaN        3    44\n",
              "13888  nm3749108     P. Ravi Shankar        NaN        3    42\n",
              "9677   nm1747510            Doddanna        NaN        3    42\n",
              "14084  nm3837488         Komal Kumar        NaN        3    42"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clusters_in[clusters_in['cluster']==3].sort_values('freq', ascending=False).head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "uUzQEk06GqPt"
      },
      "outputs": [],
      "source": [
        "def connectedness(clusters, pairs, cat):\n",
        "    pairs['rowcluster'] = clusters.iloc[pairs.row].cluster.reset_index(drop=True)\n",
        "    pairs['colcluster'] = clusters.iloc[pairs.col].cluster.reset_index(drop=True)\n",
        "\n",
        "    connectedness, coclusters = {}, {}\n",
        "    for index, costars in pairs.groupby('row'):\n",
        "        coclusters[cat.index[index]] = clusterdist = costars.groupby('colcluster')['n'].sum()\n",
        "        selfcluster = costars.rowcluster.iloc[0]\n",
        "        connectedness[cat.index[index]] = {\n",
        "            'primaryName': cat.primaryName.iloc[index],\n",
        "            # Which cluster do they belong to\n",
        "            'cluster': selfcluster,\n",
        "            # No of clusters they've acted at least 5 times with\n",
        "            'nclusters': (clusterdist >= 5).sum(),\n",
        "            # No of films they've acted in\n",
        "            'titles': name_freq[cat.index[index]],\n",
        "            # No of pairings they've had with other stars\n",
        "            'pairings': clusterdist.sum(),\n",
        "            # % of films within cluster\n",
        "            'incluster': clusterdist.get(selfcluster, 0) / clusterdist.sum()\n",
        "        }\n",
        "    coclusters = pd.DataFrame(coclusters).T\n",
        "    connectedness = pd.DataFrame(connectedness).T\n",
        "    return connectedness.sort_values('incluster'), coclusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "7EO2DupbKWSt"
      },
      "outputs": [],
      "source": [
        "connected_in, coclusters_in = connectedness(clusters_in, pairs_in, cat_in)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "GOQZ8YjzKZG0",
        "outputId": "35ee8397-8307-4746-b023-61257bcb9666"
      },
      "outputs": [
        {
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              "      <td>1</td>\n",
              "      <td>54</td>\n",
              "      <td>10</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nm0006763</th>\n",
              "      <td>Jackie Shroff</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>66</td>\n",
              "      <td>172</td>\n",
              "      <td>0.715116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nm0000407</th>\n",
              "      <td>Vivica A. Fox</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>54</td>\n",
              "      <td>18</td>\n",
              "      <td>0.722222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nm0001744</th>\n",
              "      <td>Tom Sizemore</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>114</td>\n",
              "      <td>33</td>\n",
              "      <td>0.727273</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  "
            ],
            "text/plain": [
              "                  primaryName cluster nclusters titles pairings incluster\n",
              "nm0474609       Atul Kulkarni       7         5     51      120  0.233333\n",
              "nm0000514      Michael Madsen       4         2    104       24  0.333333\n",
              "nm0896573    Ashish Vidyarthi       2         5     78      233  0.377682\n",
              "nm0222144           Rahul Dev       1         5     51      138  0.413043\n",
              "nm0474871           Sai Kumar       3         4     62      187   0.44385\n",
              "nm0348004       Milind Gunaji       7         5     85      285  0.491228\n",
              "nm0149822  Mithun Chakraborty       1         3     68      201  0.507463\n",
              "nm1418952             Bhavana       6         3     57      172  0.540698\n",
              "nm3132784    Kishore Kumar G.       2         4     71      189  0.544974\n",
              "nm0793851       Sayaji Shinde       2         4     71      222  0.563063\n",
              "nm0001698         John Savage      27         2     51       15       0.6\n",
              "nm0000799        Edward Asner       4         1     53        8     0.625\n",
              "nm0482309                 Lal       6         2     63      186  0.666667\n",
              "nm0482320            Mohanlal       6         4     93      285  0.666667\n",
              "nm0000367    Gérard Depardieu      10         2     61       25      0.68\n",
              "nm0419688             Jayaram       6         3     69      215  0.683721\n",
              "nm0332709     Olivier Gourmet       0         1     54       10       0.7\n",
              "nm0006763       Jackie Shroff       1         5     66      172  0.715116\n",
              "nm0000407       Vivica A. Fox      16         1     54       18  0.722222\n",
              "nm0001744        Tom Sizemore       4         2    114       33  0.727273"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Who are the big crossover actors in IN?\n",
        "connected_in[connected_in['titles'] > 50].sort_values('incluster').head(20)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": []
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    "gpuClass": "standard",
    "interpreter": {
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    },
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    "language_info": {
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  "nbformat": 4,
  "nbformat_minor": 0
}