NER_parsing.ipynb

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      "source": [
        "#! python -m spacy download ru_core_news_lg"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import spacy\n",
        "from spacy.lang.ru.examples import sentences \n",
        "from spacy.matcher import Matcher\n",
        "\n",
        "import ru_core_news_lg  \n",
        "nlp = ru_core_news_lg.load()"
      ],
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        "id": "2w8PZLgNSZj0"
      },
      "execution_count": 2,
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      "source": [
        "# Содержание. Content\n",
        "1. Загрузка данных. Load data\n",
        "\n",
        "2. Извлечение реплик с приветствием – где менеджер поздоровался. \n",
        "Extract greeting lines - where the manager said hello\n",
        "\n",
        "3. Извлечение реплик, где менеджер представил себя.\n",
        "Extract the lines where the manager introduced himself.\n",
        "\n",
        "4. Извлечение имени менеджера.\n",
        "Retrieve the manager's name.\n",
        "\n",
        "5. Извлечение названия компании\n",
        "Company name extraction\n",
        "\n",
        "6. Извлечение реплик, где менеджер попрощался.\n",
        "Extraction of replicas where the manager said goodbye.\n",
        "\n",
        "7. Проверка требований к менеджеру: «В каждом диалоге обязательно необходимо поздороваться и попрощаться с клиентом»\n",
        "Checking the requirements for the manager: “In every dialogue, it is imperative to say hello and say goodbye to the client”"
      ],
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      "source": [
        "## 1. Загрузка данных. Load data"
      ],
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      "source": [
        "data = pd.read_csv('/content/test_data.csv')\n",
        "data.head(2)"
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          "data": {
            "text/plain": [
              "   dlg_id  line_n     role               text\n",
              "0       0       0   client               Алло\n",
              "1       0       1  manager  Алло здравствуйте"
            ],
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        "data.shape"
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              "(480, 4)"
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          "execution_count": 4
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        "data.isnull().sum()"
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dlg_id    0\n",
              "line_n    0\n",
              "role      0\n",
              "text      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
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      "source": [
        "data.duplicated().sum()"
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              "0"
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          "metadata": {},
          "execution_count": 6
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    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
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        "id": "x3lvZE46Tan1",
        "outputId": "96285617-4796-45c3-d297-a803f247426f"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dlg_id     int64\n",
              "line_n     int64\n",
              "role      object\n",
              "text      object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
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    {
      "cell_type": "code",
      "source": [
        "data['text'] = data['text'].astype('str')\n",
        "data['role'] = data['role'].astype('str')"
      ],
      "metadata": {
        "id": "StZ-9jM4SZn0"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.dlg_id.nunique()"
      ],
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        "id": "GYIDxFCSSZrt",
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      "outputs": [
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              "6"
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          "metadata": {},
          "execution_count": 9
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      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "ax = sns.countplot(x=\"dlg_id\", data=data)\n",
        "\n",
        "plt.title('Distribution of amount text line in a dialogs')\n",
        "plt.xlabel('Dialog id')\n",
        "plt.ylabel('Count')\n",
        "for p in ax.patches:\n",
        "    ax.annotate('{:.0f}'.format(p.get_height()), (p.get_x()+0.15, p.get_height()+1))"
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      "execution_count": 10,
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            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "В данных представлены скрипты  для 6 диалогов между менеджером и клиентом.\n",
        "\n",
        "Размер датафрейма 480 x 4.\n",
        "\n",
        "Самый котроткий диалог - 4-й, содержит всего 35 строк.\n",
        "\n",
        "Долгий диалог  - диалог №5.Он состоит их 143 строк. \n",
        "\n",
        "Данные предобработаны и достаточно \"чистые\":  \n",
        "\n",
        "*   отсутствуют пропуски и пустые значения\n",
        "*   отсутствуют html-теги и эмодзи\n",
        "*   отсутствуют знаки пунктуации"
      ],
      "metadata": {
        "id": "V0xY_UboUG_E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2. Извлечение реплик с приветствием – где менеджер поздоровался. \n",
        "Extract greeting lines - where the manager said hello"
      ],
      "metadata": {
        "id": "y5BGxxF4opyl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Посмотрим на первую реплику в 0 диалоге\n",
        "# Let's look at the first text line in a dialog №0\n",
        "\n",
        "data.query('dlg_id == 0 & role == \"manager\"').reset_index().text[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KCKffVQLSZwf",
        "outputId": "3304a01f-d5bd-40cd-9dcd-2bbce62d99b0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Алло здравствуйте'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# вызовем Matcher()\n",
        "# call the Matcher() method\n",
        "matcher = Matcher(nlp.vocab)\n",
        "\n",
        "# пропишем правила для поиска приветствий\n",
        "# write rules for finding greetings\n",
        "pattern_gr = [{\"TEXT\": {\"REGEX\": \"(привет|здравствуйте|здравствуй|приветствую)\"}}]\n",
        "matcher.add(\"greetings\", [pattern_gr])\n",
        "\n",
        "pattern_gr2 = [{\"LEMMA\": {\"IN\": [\"добрый\", \"доброе\"]}}, {\"POS\": \"NOUN\", \"LENGTH\": {\"<=\": 5}} ]\n",
        "matcher.add(\"greetings\", [pattern_gr2])\n",
        "\n",
        "pattern_gr3 = [{\"POS\": \"NOUN\", \"LENGTH\": {\"<=\": 5}}, {\"LEMMA\": {\"IN\": [\"добрый\", \"доброе\"]}} ]\n",
        "matcher.add(\"greetings\", [pattern_gr3])"
      ],
      "metadata": {
        "id": "gs8EyrVXSZyk"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# функция для поиска приветствий\n",
        "# function for searching greetings\n",
        "\n",
        "def get_greeting(string_):\n",
        "  doc_ = nlp(string_)\n",
        "  matches = matcher(doc_, as_spans=True)\n",
        "  for span in matches:\n",
        "    return span.text\n"
      ],
      "metadata": {
        "id": "uT58VvwgwxFg"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# добавим колонку в датафрейме для записи приветствий\n",
        "# let's find and adding greetings in dataframe\n",
        "\n",
        "data['greetings'] =  ['no' for el in data.text]"
      ],
      "metadata": {
        "id": "QowOxvR_xtQw"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Найдем приветствие менеджера и сохраним в датафрейм\n",
        "# Find greetings and add them to dataframe\n",
        "\n",
        "dlg_number = data.dlg_id.unique()  # all dialogs\n",
        "\n",
        "for dlg in dlg_number:\n",
        "  texts = data.query('dlg_id == @dlg & role == \"manager\"').text[0:1]\n",
        "  indexes = texts.index\n",
        "  for num, dlg_text in enumerate(texts):    \n",
        "    name = get_greeting(dlg_text)\n",
        "    if name is not None:\n",
        "      data.at[indexes[num],'greetings'] = name  # update value in dataframe"
      ],
      "metadata": {
        "id": "HrqNO9uixtdQ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# уникальные значения\n",
        "# unique values\n",
        "data.greetings.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzG6cYjO4PRw",
        "outputId": "5fb7fd2b-a322-42d9-9e51-7a272115de95"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'здравствуйте', 'добрый день'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
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          "height": 112
        },
        "id": "xt4Em6qd2Wg3",
        "outputId": "86a8367a-1372-4ccd-ab00-5ca3a76e1a27"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   dlg_id  line_n     role               text     greetings\n",
              "0       0       0   client               Алло            no\n",
              "1       0       1  manager  Алло здравствуйте  здравствуйте"
            ],
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3. \tИзвлечение реплик, где менеджер представил себя.\n",
        "Extract the lines where the manager introduced himself."
      ],
      "metadata": {
        "id": "eriSBUa_3PI6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Под \"представлением себя\" будем понимать имя и место работы менеджера \n",
        "\n",
        "By \"introducing yourself\", we mean the name and place of work of the manager"
      ],
      "metadata": {
        "id": "VIlCmny0TkZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.query('dlg_id == 0 & role==\"manager\"').reset_index().text[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EpX4l28p2Wi-",
        "outputId": "c437a544-5b9e-48dc-f80f-e3cd58384107"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Меня зовут ангелина компания диджитал бизнес звоним вам по поводу продления лицензии а мы с серым у вас скоро срок заканчивается'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def self_presentation(string_):\n",
        "  tmp_results = []\n",
        "  doc = nlp(string_)\n",
        "\n",
        "  matcher = Matcher(nlp.vocab)\n",
        "  # шаблоны для поиска \n",
        "  # Да это анастасия\n",
        "  pattern = [{\"POS\": \"PRON\"}, {\"POS\": \"PROPN\"}]\n",
        "  matcher.add(\"sich_vorschtellen1\", [pattern])\n",
        "\n",
        "  # меня зовут ангелина компания диджитал бизнес\n",
        "  pattern2 = [{\"POS\": \"PRON\"},{\"POS\": \"VERB\"}, {\"POS\": \"PROPN\"}, {\"POS\": \"NOUN\"}, {\"POS\": \"VERB\"}, {\"POS\": \"NOUN\"}]\n",
        "  matcher.add(\"sich_vorschtellen2\", [pattern2])\n",
        "\n",
        "  # меня ангелина зовут компания китобизнес\n",
        "  pattern3 = [{\"POS\": \"PRON\"},{\"POS\": \"PROPN\"}, {\"POS\": \"VERB\"}, {\"POS\": \"NOUN\"}, {\"POS\": \"PROPN\"}]  \n",
        "  matcher.add(\"sich_vorschtellen3\", [pattern3])\n",
        "\n",
        "  # ангелина меня зовут\n",
        "  pattern4 = [{\"POS\": \"PROPN\"},{\"POS\": \"PRON\"}, {\"POS\": \"VERB\"}]\n",
        "  matcher.add(\"sich_vorschtellen2\", [pattern4])\n",
        "\n",
        "  # ангелина зовут меня\n",
        "  pattern5 = [{\"POS\": \"PROPN\"},{\"POS\": \"VERB\"}, {\"POS\": \"PRON\"}]\n",
        "  matcher.add(\"sich_vorschtellen2\", [pattern5])\n",
        "\n",
        "   # меня ангелина зовут компания диджитал бизнес\n",
        "  pattern6 = [{\"POS\": \"PRON\"},{\"POS\": \"PROPN\"}, {\"POS\": \"VERB\"}, {\"POS\": \"NOUN\"}, {\"POS\": \"VERB\"}, {\"POS\": \"NOUN\"}]  \n",
        "  matcher.add(\"sich_vorschtellen6\", [pattern6])\n",
        "\n",
        "  # меня зовут ангелина компания китобизнес\n",
        "  pattern7 = [{\"POS\": \"PRON\"},{\"POS\": \"VERB\"}, {\"POS\": \"PROPN\"},{\"POS\": \"NOUN\"}, {\"POS\": \"PROPN\"}]\n",
        "  matcher.add(\"sich_vorschtellen2\", [pattern7])\n",
        "\n",
        "  matches = matcher(doc, as_spans=True)\n",
        "  for span in matches:\n",
        "    tmp_results.append(span.text)\n",
        "  \n",
        "  try:\n",
        "    max_len = max(len(x) for x in tmp_results) # find out the max length\n",
        "    res = [x for x in tmp_results if len(x) == max_len]\n",
        "    return res[0]\n",
        "  except:\n",
        "    return None"
      ],
      "metadata": {
        "id": "8M_p7qhyTpnW"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# добавим колонку в датафрейме для записи представлений менеджеров\n",
        "# add new column \n",
        "\n",
        "data['self_presentation'] =  ['no' for el in data.text]"
      ],
      "metadata": {
        "id": "CbnHreoYVEQf"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Найдем представления менеджеров для каждого диалога и сохраним в таблицу \n",
        "# Find managers self-presentation and add them to dataframe\n",
        "\n",
        "dlg_number = data.dlg_id.unique()  # all dialogs\n",
        "\n",
        "for dlg in dlg_number:\n",
        "  texts = data.query('dlg_id == @dlg & role == \"manager\"').text[0:2]\n",
        "  indexes = texts.index\n",
        "  for num, dlg_text in enumerate(texts): \n",
        "    name = self_presentation(dlg_text)\n",
        "    if name is not None:\n",
        "      data.at[indexes[num], 'self_presentation'] = name  # update value in dataframe"
      ],
      "metadata": {
        "id": "OyEnWxXeTppe"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# проверим результат\n",
        "# check the result\n",
        "\n",
        "data.loc[251:252,['role','text', 'self_presentation']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0KbQRLCuTprT",
        "outputId": "1dc4e59b-9ad8-41b4-950b-ce1ea41f6f54"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        role                                               text  \\\n",
              "251  manager  Добрый меня максим зовут компания китобизнес у...   \n",
              "252   client                                          Да удобно   \n",
              "\n",
              "                         self_presentation  \n",
              "251  меня максим зовут компания китобизнес  \n",
              "252                                     no  "
            ],
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              "\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": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4. Извлечение имени менеджера.\n",
        "Retrieve the manager's name."
      ],
      "metadata": {
        "id": "oVkEfGhr3XLA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# функция для извлечения сушностей \"Person\"\n",
        "# function for getting Person entities\n",
        "\n",
        "def get_entities(doc_):\n",
        "  for ent in doc_.ents:\n",
        "    if ent.label_ == 'PER':\n",
        "      return ent.text"
      ],
      "metadata": {
        "id": "BAHbKAcd23n0"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# напишем правила для поиска персон в тексте\n",
        "# write rules for searching \"person\" in text\n",
        "\n",
        "matcher = Matcher(nlp.vocab)\n",
        "pattern = [{\"LOWER\": \"это\"}, {\"POS\": \"PROPN\"}]\n",
        "matcher.add(\"PER_\", [pattern])"
      ],
      "metadata": {
        "id": "xC5Vd43O23qJ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# функция для получения имени менеджера\n",
        "# function for getting manager name\n",
        "\n",
        "def get_manager_name(string_):\n",
        "  NoneType = type(None)\n",
        "  doc__ = nlp(string_)\n",
        "  ner = get_entities(doc__) \n",
        "\n",
        "  if isinstance(ner, NoneType):\n",
        "    matches = matcher(doc__, as_spans=True)\n",
        "    for span in matches:\n",
        "      return span.text[4:]\n",
        "  else:\n",
        "    return ner"
      ],
      "metadata": {
        "id": "knppOGkE23r-"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# создадим колонку для сохранения результатов\n",
        "# create table for saving managers name\n",
        "\n",
        "data['manager_name'] =  ['no' for el in data.text]"
      ],
      "metadata": {
        "id": "8sOR9gU023uE"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Найдем имена менеджеров для каждого диалога и сохраним в таблицу \n",
        "# Find managers name and add them to dataframe\n",
        "\n",
        "dlg_number = data.dlg_id.unique()  # all dialogs\n",
        "\n",
        "for dlg in dlg_number:\n",
        "  texts = data.query('dlg_id == @dlg & role == \"manager\"').text[0:2]\n",
        "  indexes = texts.index\n",
        "  for num, dlg_text in enumerate(texts):    \n",
        "    name = get_manager_name(dlg_text)\n",
        "    if name is not None:\n",
        "      data.at[indexes[num],'manager_name'] = name  # update value in dataframe"
      ],
      "metadata": {
        "id": "nQ24iML823v3"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# проверим\n",
        "# check\n",
        "\n",
        "data.loc[338:340,['role','text', 'manager_name']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "XObI11uR23yD",
        "outputId": "ccc94fa0-d7c8-4e79-f7e2-b8c261541ea0"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        role                                               text manager_name\n",
              "338  manager                                   Да это анастасия    анастасия\n",
              "339   client  Смотрите мы сейчас уже нашли как бы да мы всю ...           no\n",
              "340  manager                                                Угу           no"
            ],
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              "      <td>Смотрите мы сейчас уже нашли как бы да мы всю ...</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>manager</td>\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",
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              "  "
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    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "yhCgm9fd230D",
        "outputId": "3cfd7f5b-d9e7-4005-f597-79d95146150b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   dlg_id  line_n     role               text     greetings self_presentation  \\\n",
              "0       0       0   client               Алло            no                no   \n",
              "1       0       1  manager  Алло здравствуйте  здравствуйте                no   \n",
              "\n",
              "  manager_name  \n",
              "0           no  \n",
              "1           no  "
            ],
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              "\n",
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              "      <th></th>\n",
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              "      <th>0</th>\n",
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              "      <td>Алло</td>\n",
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              "      <th>1</th>\n",
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              "    }\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",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e3199de8-79d9-4095-903f-836118bb84b8');\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",
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              "          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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# посмотрим на уникальные значения\n",
        "# lets look for unique values\n",
        "data.manager_name.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LA8nmpK22315",
        "outputId": "eb3b950f-2bd8-4c7a-dad0-6ffb445d9759"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'ангелина', 'максим', 'анастасия'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##5. Извлечение названия компании\n",
        "Company name extraction"
      ],
      "metadata": {
        "id": "kXYFAq6q4pMy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# функция для получения имени компании (встроенный метод в spacy)\n",
        "# function for getting companys name (NER in spacy)\n",
        "\n",
        "def get_entities_ORG(string_):\n",
        "  doc_ = nlp(string_)\n",
        "  for ent in doc_.ents:\n",
        "    if ent.label_ == 'ORG':\n",
        "      return ent.text"
      ],
      "metadata": {
        "id": "CciIoQxEgxkX"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# опишем правила по которым будем искать названия компаний\n",
        "# write the rules for finding a companys name\n",
        "\n",
        "matcher = Matcher(nlp.vocab)\n",
        "pattern = [{\"LEMMA\": {\"IN\": [\"компания\", \"агентство\"]}}, {\"POS\": \"PROPN\" }]\n",
        "matcher.add(\"Company\", [pattern])\n",
        "\n",
        "pattern1 = [{\"LEMMA\": {\"IN\": [\"компания\", \"агентство\"]}}, {\"POS\": \"VERB\" }, {\"POS\": \"NOUN\" }]\n",
        "matcher.add(\"Company\", [pattern1])\n",
        "\n",
        "pattern2 = [{\"LEMMA\": {\"IN\": [\"компания\", \"агентство\"]}}, {\"POS\": \"NOUN\" }, {\"POS\": \"CONJ\" }, {\"POS\": \"NOUN\" }]\n",
        "matcher.add(\"Company\", [pattern2])\n",
        "\n",
        "pattern3 = [{\"LEMMA\": {\"IN\": [\"компания\", \"агентство\"]}}, {\"POS\": \"ADJ\" }, {\"POS\": \"NOUN\" }]\n",
        "matcher.add(\"Company\", [pattern3])"
      ],
      "metadata": {
        "id": "JmblBeO1_c_c"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# функция для поиска имен компаний с использованием описанных выше правил\n",
        "# geting company name by using Matcher()\n",
        "\n",
        "def match_company_name(string_):\n",
        "  doc_ = nlp(string_)\n",
        "  matches = matcher(doc_)\n",
        "  matches = matcher(doc_, as_spans=True)\n",
        "  for span in matches:\n",
        "    return span.text"
      ],
      "metadata": {
        "id": "Z04tywxqgxoa"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# итоговая функция для получения имен компаний\n",
        "# combine 2 previous functions for getting company name\n",
        "\n",
        "def get_company_name(string_):\n",
        "  ner_name = get_entities_ORG(string_)\n",
        "  if ner_name is None:\n",
        "    return match_company_name(string_)\n",
        "  else:\n",
        "    return ner_name"
      ],
      "metadata": {
        "id": "Po-wo3c24D25"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# добавим колонку в датафрейме для записи приветствий \n",
        "# add new column\n",
        "\n",
        "data['company'] =  ['no' for el in data.text]"
      ],
      "metadata": {
        "id": "EMT3WCWpBfgL"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Найдем имена компаний для каждого диалога и сохраним в таблицу \n",
        "# Find company names and add them to dataframe\n",
        "\n",
        "dlg_number = data.dlg_id.unique()  # all dialogs\n",
        "\n",
        "for dlg in dlg_number:\n",
        "  texts = data.query('dlg_id == @dlg & role == \"manager\"').text[0:2]\n",
        "  indexes = texts.index\n",
        "  for num, dlg_text in enumerate(texts):    \n",
        "    name = get_company_name(dlg_text)\n",
        "    if name is not None:\n",
        "      data.at[indexes[num],'company'] = name  # update value in dataframe"
      ],
      "metadata": {
        "id": "VaOxevYvBBeV"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# посмотрим на перечень уникальных компаний\n",
        "# let's look to the unique companys\n",
        "\n",
        "data.company.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zmrm3pogBBgx",
        "outputId": "b9d40e61-24e9-43de-fe5c-94da29c66cb2"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'компания диджитал бизнес', 'компания китобизнес'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# посмотрим на резкльтат\n",
        "# check the result\n",
        "\n",
        "data.loc[167:168,['role','text', 'company']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "fYZHeeibgxiJ",
        "outputId": "670e98c8-6442-4c57-873b-d12eb337471f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        role                                               text  \\\n",
              "167  manager  Меня зовут ангелина компания диджитал бизнес з...   \n",
              "168  manager     А нет я еще не отправляла я решила вам набрать   \n",
              "\n",
              "                      company  \n",
              "167  компания диджитал бизнес  \n",
              "168                        no  "
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##6. Извлечение реплик, где менеджер попрощался.\n",
        "Extraction of replicas where the manager said goodbye."
      ],
      "metadata": {
        "id": "iLRel2H14zia"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# вызовем Matcher()\n",
        "# call the method Matcher()\n",
        "\n",
        "matcher = Matcher(nlp.vocab)\n",
        "\n",
        "# пропишем правила для поиска прощаний  (берем официальную лексику, используемую при прощании)\n",
        "# write rules for finding farewells\n",
        "\n",
        "pattern_farewell = [{\"LOWER\": \"до\"}, {\"LEMMA\": {\"IN\": [\"свидание\", \"встреча\"]}}]\n",
        "matcher.add(\"farewell\", [pattern_farewell])\n",
        "\n",
        "pattern_farewell2 = [{\"LOWER\": \"всего\"}, {\"LEMMA\": {\"IN\": [\"добрый\", \"наилучший\", \"хороший\"]}}]\n",
        "matcher.add(\"farewell\", [pattern_farewell2])"
      ],
      "metadata": {
        "id": "CFII12mP50Mc"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# функция для поиска \n",
        "# function for finfing farewells\n",
        "\n",
        "def get_farewell(string_):\n",
        "  farewell = []\n",
        "  doc_ = nlp(string_)\n",
        "  matches = matcher(doc_, as_spans=True)\n",
        "  for span in matches:\n",
        "    farewell.append(span.text)\n",
        "  if len(farewell)==1:\n",
        "    return farewell[0]\n",
        "  if len(farewell)>1:\n",
        "    return ' '.join(farewell)\n",
        "  else:\n",
        "    return None"
      ],
      "metadata": {
        "id": "OIiHE9xwGikZ"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# добавим колонку в датафрейме для записи прощаний\n",
        "# add new column in dataframe\n",
        "\n",
        "data['farewells'] =  ['no' for el in data.text]"
      ],
      "metadata": {
        "id": "YmZXyNVjHMVN"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверим есть ли в диалогах прощание с клиентом. Обновим данные в датафрейме\n",
        "# Find farewells and add them to dataframe\n",
        "\n",
        "dlg_number = data.dlg_id.unique()  # all dialogs\n",
        "\n",
        "for dlg in dlg_number:\n",
        "  texts = data.query('dlg_id == @dlg & role == \"manager\"').text[-1:]\n",
        "  indexes = texts.index\n",
        "  for num, dlg_text in enumerate(texts):    \n",
        "    name = get_farewell(dlg_text)\n",
        "    if name is not None:\n",
        "      data.at[indexes[num],'farewells'] = name  # update value in dataframe"
      ],
      "metadata": {
        "id": "kIkpTcqtIQX5"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# посмотрим на уникальные значения в колонке с прощаниями\n",
        "# let look at the unique values in farewell column\n",
        "\n",
        "data.farewells.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PeZJGVf2L1qi",
        "outputId": "67357639-f24c-4f62-d30a-add9819fe34b"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'Всего хорошего до свидания', 'До свидания', 'всего доброго',\n",
              "       'до свидания'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the result\n",
        "\n",
        "data.loc[300:301,['role','text', 'farewells']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "hr9miv7lFYdZ",
        "outputId": "0a5c8a98-36ac-4ae8-80bf-11c2e40a50d1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        role                                               text      farewells\n",
              "300  manager  Угу все хорошо да понедельника тогда всего доб...  всего доброго\n",
              "301   client                                     Да до свидания             no"
            ],
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              "\n",
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              "          const element = document.querySelector('#df-69f3b9fe-88cb-40bd-8005-e3fb18051adb');\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",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##7. Проверка требований к менеджеру: «В каждом диалоге обязательно необходимо поздороваться и попрощаться с клиентом»\n",
        "Checking the requirements for the manager: “In every dialogue, it is imperative to say hello and say goodbye to the client”\n"
      ],
      "metadata": {
        "id": "j9YGwDGQNLMk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "f1hXm2icHOv0",
        "outputId": "3e7debef-c2b4-40f2-88d8-cdc3a560a215"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     dlg_id  line_n     role  \\\n",
              "0         0       0   client   \n",
              "1         0       1  manager   \n",
              "2         0       2   client   \n",
              "3         0       3  manager   \n",
              "4         0       4   client   \n",
              "..      ...     ...      ...   \n",
              "475       5     138  manager   \n",
              "476       5     139  manager   \n",
              "477       5     140   client   \n",
              "478       5     141   client   \n",
              "479       5     142  manager   \n",
              "\n",
              "                                                  text     greetings  \\\n",
              "0                                                 Алло            no   \n",
              "1                                    Алло здравствуйте  здравствуйте   \n",
              "2                                          Добрый день            no   \n",
              "3    Меня зовут ангелина компания диджитал бизнес з...            no   \n",
              "4                                                  Ага            no   \n",
              "..                                                 ...           ...   \n",
              "475  По поводу виджетов и с ними уже обсудите конкр...            no   \n",
              "476  Все я вам высылаю счет и с вами на связи если ...            no   \n",
              "477                                    Спасибо спасибо            no   \n",
              "478     Да да тогда созвонимся ага спасибо вам давайте            no   \n",
              "479                     Ну до свидания хорошего вечера            no   \n",
              "\n",
              "                                self_presentation manager_name  \\\n",
              "0                                              no           no   \n",
              "1                                              no           no   \n",
              "2                                              no           no   \n",
              "3    Меня зовут ангелина компания диджитал бизнес     ангелина   \n",
              "4                                              no           no   \n",
              "..                                            ...          ...   \n",
              "475                                            no           no   \n",
              "476                                            no           no   \n",
              "477                                            no           no   \n",
              "478                                            no           no   \n",
              "479                                            no           no   \n",
              "\n",
              "                      company    farewells  \n",
              "0                          no           no  \n",
              "1                          no           no  \n",
              "2                          no           no  \n",
              "3    компания диджитал бизнес           no  \n",
              "4                          no           no  \n",
              "..                        ...          ...  \n",
              "475                        no           no  \n",
              "476                        no           no  \n",
              "477                        no           no  \n",
              "478                        no           no  \n",
              "479                        no  до свидания  \n",
              "\n",
              "[480 rows x 9 columns]"
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              "      <td>1</td>\n",
              "      <td>manager</td>\n",
              "      <td>Алло здравствуйте</td>\n",
              "      <td>здравствуйте</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
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              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>client</td>\n",
              "      <td>Добрый день</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>manager</td>\n",
              "      <td>Меня зовут ангелина компания диджитал бизнес з...</td>\n",
              "      <td>no</td>\n",
              "      <td>Меня зовут ангелина компания диджитал бизнес</td>\n",
              "      <td>ангелина</td>\n",
              "      <td>компания диджитал бизнес</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>client</td>\n",
              "      <td>Ага</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</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",
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              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>5</td>\n",
              "      <td>138</td>\n",
              "      <td>manager</td>\n",
              "      <td>По поводу виджетов и с ними уже обсудите конкр...</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>5</td>\n",
              "      <td>139</td>\n",
              "      <td>manager</td>\n",
              "      <td>Все я вам высылаю счет и с вами на связи если ...</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>5</td>\n",
              "      <td>140</td>\n",
              "      <td>client</td>\n",
              "      <td>Спасибо спасибо</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>5</td>\n",
              "      <td>141</td>\n",
              "      <td>client</td>\n",
              "      <td>Да да тогда созвонимся ага спасибо вам давайте</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479</th>\n",
              "      <td>5</td>\n",
              "      <td>142</td>\n",
              "      <td>manager</td>\n",
              "      <td>Ну до свидания хорошего вечера</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>до свидания</td>\n",
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              "  </tbody>\n",
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              "<p>480 rows × 9 columns</p>\n",
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              "                                                     [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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Количество диалогов ',data.dlg_id.nunique())\n",
        "print('Номера диалогов ',data.dlg_id.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5eYlywXzNHN7",
        "outputId": "bee5101b-5f04-4307-f749-567f109c9bf9"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Количество диалогов  6\n",
            "Номера диалогов  [0 1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Закодируем полученные результаты, где 1 - поздоровался\\попрощался и 0 - не поздоровался\\ не попрощался\n",
        "\n",
        "data['greetings_encode'] = pd.Series([1 if x != \"no\"  else 0 for x in data.greetings ])\n",
        "data['farewells_encode'] = pd.Series([1 if x != \"no\"  else 0 for x in data.farewells ])"
      ],
      "metadata": {
        "id": "fC_em8RiOlO2"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "ImwE0KmuOlQs",
        "outputId": "f34f338a-1ab4-4357-aa73-afd8deb701b9"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   dlg_id  line_n     role               text     greetings self_presentation  \\\n",
              "0       0       0   client               Алло            no                no   \n",
              "1       0       1  manager  Алло здравствуйте  здравствуйте                no   \n",
              "\n",
              "  manager_name company farewells  greetings_encode  farewells_encode  \n",
              "0           no      no        no                 0                 0  \n",
              "1           no      no        no                 1                 0  "
            ],
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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",
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            ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = data.groupby(['dlg_id']).agg({'greetings_encode':'sum','farewells_encode':'sum'})\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "eiLhIy94QTcn",
        "outputId": "1dbba32a-b41a-4389-d831-d235ea0a52f5"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        greetings_encode  farewells_encode\n",
              "dlg_id                                    \n",
              "0                      1                 1\n",
              "1                      1                 1\n",
              "2                      1                 0\n",
              "3                      1                 1\n",
              "4                      0                 1\n",
              "5                      0                 1"
            ],
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>greetings_encode</th>\n",
              "      <th>farewells_encode</th>\n",
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              "    <tr>\n",
              "      <th>dlg_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "      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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dc8288fc-4ab2-44d3-bf81-f7e3fc622827 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-dc8288fc-4ab2-44d3-bf81-f7e3fc622827');\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": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results['conditions_must_be_2'] = results.greetings_encode + results.farewells_encode\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "KAZ9-Lu7QTgr",
        "outputId": "934f10fa-e3a4-4b8f-9c9d-fe7c46f6e4ef"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        greetings_encode  farewells_encode  conditions_must_be_2\n",
              "dlg_id                                                          \n",
              "0                      1                 1                     2\n",
              "1                      1                 1                     2\n",
              "2                      1                 0                     1\n",
              "3                      1                 1                     2\n",
              "4                      0                 1                     1\n",
              "5                      0                 1                     1"
            ],
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              "\n",
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              "          const element = document.querySelector('#df-16b7d6d2-7a03-4a76-95f5-115a0ced0f2c');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
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          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.query('conditions_must_be_2 == 2')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "HjQJvyWGOlSX",
        "outputId": "b8a33e8d-a6e3-4874-cb6a-23ef2068aab2"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        greetings_encode  farewells_encode  conditions_must_be_2\n",
              "dlg_id                                                          \n",
              "0                      1                 1                     2\n",
              "1                      1                 1                     2\n",
              "3                      1                 1                     2"
            ],
            "text/html": [
              "\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        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>greetings_encode</th>\n",
              "      <th>farewells_encode</th>\n",
              "      <th>conditions_must_be_2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dlg_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aff03a33-5e43-4b50-a289-c84660d05078')\"\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-aff03a33-5e43-4b50-a289-c84660d05078 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-aff03a33-5e43-4b50-a289-c84660d05078');\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": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Условия выполнены в диалогах 0, 1, 3"
      ],
      "metadata": {
        "id": "1gTWoH9vRie4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# сохраним результат\n",
        "\n",
        "#data.to_csv('/content/update_test_data.csv')"
      ],
      "metadata": {
        "id": "hubCRvPGMkWV"
      },
      "execution_count": 52,
      "outputs": []
    }
  ]
}