feature_engineering/yarl_example.ipynb

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    "%load_ext nb_black"
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   },
   "outputs": [],
   "source": [
    "!pip install yarl wordninja"
   ]
  },
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     "text": [
      "Downloading url-classification-dataset-dmoz.zip to /home/khuyen/Data-science/feature_engineering\n",
      "100%|██████████████████████████████████████| 69.6M/69.6M [00:07<00:00, 12.4MB/s]\n",
      "100%|██████████████████████████████████████| 69.6M/69.6M [00:07<00:00, 10.1MB/s]\n"
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       "                var nbb_unformatted_code = \"!kaggle datasets download shawon10/url-classification-dataset-dmoz\";\n",
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   "source": [
    "!kaggle datasets download shawon10/url-classification-dataset-dmoz"
   ]
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   "id": "c0d431a8",
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     "text": [
      "/home/khuyen/Data-science/venv/lib/python3.8/site-packages/numpy/lib/arraysetops.py:583: FutureWarning:\n",
      "\n",
      "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "\n"
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       "                                                       url        type\n",
       "212798                         http://www.sairegion15.org/        Arts\n",
       "579328                               http://www.s3host.com   Computers\n",
       "805934           http://www.kreisjugendfeuerwehr-gotha.de/        Kids\n",
       "28647                          http://www.rotlichthits.de/       Adult\n",
       "856878                           http://www.flytheboat.com  Recreation\n",
       "215786   http://www.qesnrecit.qc.ca/schools/olp/2mmsolv...        Arts\n",
       "799537   http://videogames.yahoo.com/gamereview?cid=199...        Kids\n",
       "469135                                http://www.pmdm.com/    Business\n",
       "142055   http://members.tripod.com/thescottstappsite/in...        Arts\n",
       "1097568    http://nobelprize.org/economics/laureates/2002/     Science"
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       "\n",
       "            setTimeout(function() {\n",
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       "                var nbb_unformatted_code = \"import pandas as pd\\nfrom yarl import URL\\n\\ndf = pd.read_csv(\\n    \\\"url-classification-dataset-dmoz/URL Classification.csv\\\", index_col=0, header=None\\n)\\ndf.columns = [\\\"url\\\", \\\"type\\\"]\\ndf = df.reset_index(drop=True).dropna()\\ndf.sample(10)\";\n",
       "                var nbb_formatted_code = \"import pandas as pd\\nfrom yarl import URL\\n\\ndf = pd.read_csv(\\n    \\\"url-classification-dataset-dmoz/URL Classification.csv\\\", index_col=0, header=None\\n)\\ndf.columns = [\\\"url\\\", \\\"type\\\"]\\ndf = df.reset_index(drop=True).dropna()\\ndf.sample(10)\";\n",
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   "source": [
    "import pandas as pd\n",
    "from yarl import URL\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"url-classification-dataset-dmoz/URL Classification.csv\", index_col=0, header=None\n",
    ")\n",
    "df.columns = [\"url\", \"type\"]\n",
    "df = df.reset_index(drop=True).dropna()\n",
    "df.sample(10)"
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     "start_time": "2021-11-12T18:52:25.446155Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://www.geocities.com/peterfullman2001/concord_youth.html'"
      ]
     },
     "execution_count": 133,
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    "url1 = df[\"url\"][1545858]\n",
    "url1"
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   "id": "dc29daab",
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    {
     "data": {
      "text/plain": [
       "'http'"
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     "execution_count": 134,
     "metadata": {},
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     "data": {
      "text/plain": [
       "'www.geocities.com'"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/peterfullman2001/concord_youth.html'"
      ]
     },
     "execution_count": 136,
     "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/', 'peterfullman2001', 'concord_youth.html')"
      ]
     },
     "execution_count": 145,
     "metadata": {},
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       "            setTimeout(function() {\n",
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       "                var nbb_formatted_code = \"URL(url1).parts\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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       "                        }\n",
       "                        break;\n",
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       "            "
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    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 246;\n",
       "                var nbb_unformatted_code = \"df[\\\"host\\\"] = df.url.apply(lambda url: URL(str(url)).host)\\ndf[\\\"parts\\\"] = df.url.apply(lambda url: URL(str(url)).parts)\";\n",
       "                var nbb_formatted_code = \"df[\\\"host\\\"] = df.url.apply(lambda url: URL(str(url)).host)\\ndf[\\\"parts\\\"] = df.url.apply(lambda url: URL(str(url)).parts)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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    "df[\"host\"] = df.url.apply(lambda url: URL(str(url)).host)\n",
    "df[\"parts\"] = df.url.apply(lambda url: URL(str(url)).parts)"
   ]
  },
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     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 268;\n",
       "                var nbb_unformatted_code = \"df.dropna(axis=0, subset=[\\\"host\\\"], inplace=True)\";\n",
       "                var nbb_formatted_code = \"df.dropna(axis=0, subset=[\\\"host\\\"], inplace=True)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 247;\n",
       "                var nbb_unformatted_code = \"import plotly.express as px\";\n",
       "                var nbb_formatted_code = \"import plotly.express as px\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "ca30f2ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:29:01.200974Z",
     "start_time": "2021-11-12T19:29:01.150849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Arts          253840\n",
       "Society       243942\n",
       "Business      240176\n",
       "Computers     117961\n",
       "Science       110286\n",
       "Recreation    106586\n",
       "Sports        101328\n",
       "Shopping       95270\n",
       "Health         60097\n",
       "Reference      58247\n",
       "Games          56477\n",
       "Kids           46182\n",
       "Adult          35325\n",
       "Home           28269\n",
       "News            8989\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 248;\n",
       "                var nbb_unformatted_code = \"df.type.value_counts()\";\n",
       "                var nbb_formatted_code = \"df.type.value_counts()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "abb9e66d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:29:02.320099Z",
     "start_time": "2021-11-12T19:29:02.316835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 253;\n",
       "                var nbb_unformatted_code = \"import wordninja\\nfrom pipe import traverse\";\n",
       "                var nbb_formatted_code = \"import wordninja\\nfrom pipe import traverse\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import wordninja\n",
    "from pipe import traverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "7d1c6235",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:29:02.326123Z",
     "start_time": "2021-11-12T19:29:02.321170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['angel', 'fire']"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 254;\n",
       "                var nbb_unformatted_code = \"wordninja.split(\\\"angelfire\\\")\";\n",
       "                var nbb_formatted_code = \"wordninja.split(\\\"angelfire\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordninja.split(\"angelfire\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "27d88fbb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:29:02.331332Z",
     "start_time": "2021-11-12T19:29:02.327579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['new', 'advent']"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 255;\n",
       "                var nbb_unformatted_code = \"wordninja.split(\\\"newadvent\\\")\";\n",
       "                var nbb_formatted_code = \"wordninja.split(\\\"newadvent\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "wordninja.split(\"newadvent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "2d86df9f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:29:02.337274Z",
     "start_time": "2021-11-12T19:29:02.332093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['familytree', 'maker']"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 256;\n",
       "                var nbb_unformatted_code = \"wordninja.split(\\\"familytreemaker\\\")\";\n",
       "                var nbb_formatted_code = \"wordninja.split(\\\"familytreemaker\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordninja.split(\"familytreemaker\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "02478d0d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:29:02.387794Z",
     "start_time": "2021-11-12T19:29:02.338098Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 257;\n",
       "                var nbb_unformatted_code = \"df.host.isnull().sum()\";\n",
       "                var nbb_formatted_code = \"df.host.isnull().sum()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.host.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "59f6e6ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:29:02.393851Z",
     "start_time": "2021-11-12T19:29:02.388742Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 258;\n",
       "                var nbb_unformatted_code = \"def split_url(url: str):\\n    parts = url.split(\\\".\\\")\\n    return list([wordninja.split(part) for part in parts] | traverse)\\n    \";\n",
       "                var nbb_formatted_code = \"def split_url(url: str):\\n    parts = url.split(\\\".\\\")\\n    return list([wordninja.split(part) for part in parts] | traverse)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def split_url(url: str):\n",
    "    parts = url.split(\".\")\n",
    "    return list([wordninja.split(part) for part in parts] | traverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "82814407",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:29:02.398564Z",
     "start_time": "2021-11-12T19:29:02.394751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['familytree', 'maker', 'genealogy', 'com']"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 259;\n",
       "                var nbb_unformatted_code = \"split_url(\\\"familytreemaker.genealogy.com\\\")\";\n",
       "                var nbb_formatted_code = \"split_url(\\\"familytreemaker.genealogy.com\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "split_url(\"familytreemaker.genealogy.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "414695ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:33:51.497033Z",
     "start_time": "2021-11-12T19:33:51.347439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 263;\n",
       "                var nbb_unformatted_code = \"df[\\\"host\\\"].isnull().sum()\";\n",
       "                var nbb_formatted_code = \"df[\\\"host\\\"].isnull().sum()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"host\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "8e496292",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T19:37:36.577986Z",
     "start_time": "2021-11-12T19:35:57.399977Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 269;\n",
       "                var nbb_unformatted_code = \"df[\\\"host_parts\\\"] = df[\\\"host\\\"].apply(split_url)\";\n",
       "                var nbb_formatted_code = \"df[\\\"host_parts\\\"] = df[\\\"host\\\"].apply(split_url)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"host_parts\"] = df[\"host\"].apply(split_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "46121bee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-12T22:00:01.133822Z",
     "start_time": "2021-11-12T22:00:01.067028Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 352;\n",
       "                var nbb_unformatted_code = \"def filter_type(df: pd.DataFrame, type_: str, column: str):\\n    return df.loc[df[\\\"type\\\"] == type_, column]\\n\\n\\ndef get_top_host_for_type(df: pd.DataFrame, type_: str, n=10):\\n    s = filter_type(df, type_, \\\"host\\\")\\n    return s.value_counts().nlargest(n)\\n\\n\\ndef get_top_host_part_for_type(df: pd.DataFrame, type_: str, n=10):\\n    s = filter_type(df, type_, \\\"host_parts\\\")\\n    return s.explode().value_counts().nlargest(n)\\n\\n\\ndef get_top_part_for_type(df: pd.DataFrame, type_: str, n=10):\\n    parts_to_exclude = [\\\"/\\\", \\\"\\\"]\\n    s = filter_type(df, type_, \\\"parts\\\")\\n    s = s.explode()\\n    s = s[~s.isin(parts_to_exclude)]\\n    s = s.value_counts().nlargest(n)\\n    return s\\n\\n\\ndef plot_series(s: pd.Series):\\n    plot = px.bar(x=s.index, y=s.values)\\n    plot.show()\";\n",
       "                var nbb_formatted_code = \"def filter_type(df: pd.DataFrame, type_: str, column: str):\\n    return df.loc[df[\\\"type\\\"] == type_, column]\\n\\n\\ndef get_top_host_for_type(df: pd.DataFrame, type_: str, n=10):\\n    s = filter_type(df, type_, \\\"host\\\")\\n    return s.value_counts().nlargest(n)\\n\\n\\ndef get_top_host_part_for_type(df: pd.DataFrame, type_: str, n=10):\\n    s = filter_type(df, type_, \\\"host_parts\\\")\\n    return s.explode().value_counts().nlargest(n)\\n\\n\\ndef get_top_part_for_type(df: pd.DataFrame, type_: str, n=10):\\n    parts_to_exclude = [\\\"/\\\", \\\"\\\"]\\n    s = filter_type(df, type_, \\\"parts\\\")\\n    s = s.explode()\\n    s = s[~s.isin(parts_to_exclude)]\\n    s = s.value_counts().nlargest(n)\\n    return s\\n\\n\\ndef plot_series(s: pd.Series):\\n    plot = px.bar(x=s.index, y=s.values)\\n    plot.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def filter_type(df: pd.DataFrame, type_: str, column: str):\n",
    "    return df.loc[df[\"type\"] == type_, column]\n",
    "\n",
    "\n",
    "def get_top_host_for_type(df: pd.DataFrame, type_: str, n=10):\n",
    "    s = filter_type(df, type_, \"host\")\n",
    "    return s.value_counts().nlargest(n)\n",
    "\n",
    "\n",
    "def get_top_host_part_for_type(df: pd.DataFrame, type_: str, n=10):\n",
    "    s = filter_type(df, type_, \"host_parts\")\n",
    "    return s.explode().value_counts().nlargest(n)\n",
    "\n",
    "\n",
    "def get_top_part_for_type(df: pd.DataFrame, type_: str, n=10):\n",
    "    parts_to_exclude = [\"/\", \"\"]\n",
    "    s = filter_type(df, type_, \"parts\")\n",
    "    s = s.explode()\n",
    "    s = s[~s.isin(parts_to_exclude)]\n",
    "    s = s.value_counts().nlargest(n)\n",
    "    return s\n",
    "\n",
    "\n",
    "def plot_series(s: pd.Series):\n",
    "    plot = px.bar(x=s.index, y=s.values)\n",
    "    plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "a9a07d34",
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       "host                           type   \n",
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       "host_parts  type\n",
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