482 lines
16 KiB
Plaintext
482 lines
16 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import re\n",
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"import tqdm\n",
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"from IPython.display import Markdown\n",
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"\n",
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"from bz2file import BZ2File\n",
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"from wiktionary_de_parser import Parser\n",
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"\n",
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"from parse import *\n",
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"\n",
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"import wikitextparser as wtp\n",
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"import numpy as np\n",
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"import unidecode"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def extract_data(wikitext: str, title: str):\n",
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"\n",
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" data = {\n",
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" 'word': title\n",
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" }\n",
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"\n",
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" splitted_text = wikitext.split(\"\\n\")\n",
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"\n",
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" senses = []\n",
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" synonyms = []\n",
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" antonyms = []\n",
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" num_translations = 0\n",
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"\n",
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" senses_on = False\n",
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" synonyms_on = False\n",
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" antonyms_on = False\n",
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" translations_on = False\n",
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" first_translation_line = False\n",
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"\n",
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" for i, line in enumerate(splitted_text):\n",
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"\n",
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" if senses_on:\n",
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" try:\n",
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" sense = parse(\":[{}] {}\", line)[1]\n",
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" sense = sense.replace(\"]\",\"\")\n",
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" sense = sense.replace(\"[\",\"\")\n",
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" senses.append(sense)\n",
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" continue\n",
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" except:\n",
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" senses_on = False\n",
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" \n",
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" if synonyms_on:\n",
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" try:\n",
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" synonym = parse(\":[{}] {}\", line)[1]\n",
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" synonym = synonym.replace(\"]\",\"\")\n",
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" synonym = synonym.replace(\"[\",\"\")\n",
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" synonyms.append(synonym)\n",
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" continue\n",
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" except:\n",
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" synonyms_on = False\n",
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" \n",
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" if antonyms_on:\n",
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" try:\n",
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" antonym = parse(\":[{}] {}\", line)[1]\n",
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" antonym = antonym.replace(\"]\",\"\")\n",
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" antonym = antonym.replace(\"[\",\"\")\n",
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" antonyms.append(antonym)\n",
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" continue\n",
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" except:\n",
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" antonyms_on = False\n",
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" \n",
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" if translations_on:\n",
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" if first_translation_line:\n",
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" first_translation_line = False\n",
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" continue\n",
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" try:\n",
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" _, __, ___ = parse(\"{} [{}] {}\", line)\n",
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" num_translations += 2 # very simple heuristic, assuming there are two translation items in each row :D\n",
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" continue\n",
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" except:\n",
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" translations_on = False\n",
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"\n",
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" if \"{{Bedeutungen}}\" in line:\n",
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" senses_on = True\n",
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" continue\n",
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"\n",
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" if \"{{Gegenwörter}}\" in line:\n",
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" antonyms_on = True\n",
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" continue\n",
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"\n",
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" if \"{{Synonyme}}\" in line:\n",
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" synonyms_on = True\n",
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" continue\n",
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"\n",
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" if \"{{Übersetzungen}}\" in line:\n",
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" translations_on = True\n",
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" first_translation_line = True\n",
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" continue\n",
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" \n",
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" data['senses'] = senses\n",
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" data['synonyms'] = synonyms\n",
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" data['antonyms'] = antonyms\n",
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" data['num_translations'] = num_translations\n",
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"\n",
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" \n",
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" return data\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"989351it [04:32, 3636.28it/s]\n"
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]
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}
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],
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"source": [
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"bzfile_path = \"dewiktionary-20210201-pages-articles.xml.bz2\"\n",
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"bz = BZ2File(bzfile_path)\n",
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"\n",
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"db = {}\n",
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"\n",
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"for i, record in enumerate(tqdm.tqdm(Parser(bz))):\n",
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" if 'langCode' not in record or record['langCode'] != 'de':\n",
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" continue\n",
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"\n",
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" word = record['title']\n",
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" word = word.replace(\"ä\", \"ae\")\n",
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" word = word.replace(\"ü\", \"ue\")\n",
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" word = word.replace(\"ö\", \"oe\")\n",
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" word = word.replace(\"ß\", \"ss\")\n",
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"\n",
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" \n",
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" data = extract_data(record['wikitext'], word)\n",
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" \n",
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"\n",
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" title = word.lower()\n",
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" key = title\n",
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"\n",
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" j = 0\n",
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"\n",
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" while key in db:\n",
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" key = title + str(j)\n",
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" j += 1\n",
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" \n",
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" db[key] = data\n",
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"\n",
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" i += 1\n",
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"\n"
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]
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},
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{
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"source": [
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"## filter database from links and only keep \"relevant\" items"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def clean_text(s:str):\n",
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"\n",
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" if s.startswith(\"{{\"):\n",
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" raw_categories, text = parse(\"{{{{{}}}}}{}\", s)\n",
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" categories = raw_categories.split('|')\n",
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" if \"K\" in categories:\n",
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" categories.remove(\"K\")\n",
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" \n",
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" categories_text = f\"({', '.join(categories)}):\"\n",
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" else:\n",
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" categories_text = \"\"\n",
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" text = s\n",
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"\n",
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" cleaned_text = text\n",
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" while \"<ref \" in cleaned_text and not \"</ref>\" in cleaned_text:\n",
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" pre = \"\"\n",
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" post = \"\"\n",
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" try:\n",
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" pre, _, post = parse(\"{}<ref{}/>{}\", cleaned_text)\n",
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" except:\n",
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" try:\n",
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" _, post = parse(\"<ref{}/>{}\", cleaned_text)\n",
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" except:\n",
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" try:\n",
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" pre, _ = parse(\"{}<ref{}/>\", cleaned_text)\n",
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" except:\n",
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" print(\"cannot clean inline ref from text\")\n",
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" break\n",
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" cleaned_text = pre + post\n",
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"\n",
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" while \"<ref\" in cleaned_text:\n",
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" pre = \"\"\n",
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" post = \"\"\n",
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" try:\n",
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" pre, _, post = parse(\"{}<ref{}</ref>{}\", cleaned_text)\n",
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" except:\n",
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" try:\n",
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" _, post = parse(\"<ref{}</ref>{}\", cleaned_text)\n",
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" except:\n",
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" try:\n",
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" pre, _ = parse(\"{}<ref{}</ref>\", cleaned_text)\n",
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" except:\n",
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" print(\"cannot clean ref from text\")\n",
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" break\n",
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" cleaned_text = pre + post\n",
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"\n",
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" cleaned_text = cleaned_text.replace(\"'\",\"\")\n",
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"\n",
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" text = categories_text + cleaned_text\n",
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"\n",
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" text = text.replace(\"::\", \":\")\n",
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"\n",
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" return text\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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" 1%|▏ | 9931/726049 [00:00<00:13, 51192.22it/s]cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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" 3%|▎ | 22420/726049 [00:00<00:12, 58324.47it/s]cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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" 5%|▍ | 35021/726049 [00:00<00:11, 60170.85it/s]cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean inline ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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" 10%|▉ | 71566/726049 [00:00<00:05, 112907.67it/s]cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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" 18%|█▊ | 130217/726049 [00:01<00:03, 165772.31it/s]cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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" 33%|███▎ | 237155/726049 [00:01<00:01, 253304.61it/s]cannot clean ref from text\n",
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"cannot clean ref from text\n",
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" 40%|███▉ | 287866/726049 [00:01<00:01, 240537.77it/s]cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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"cannot clean ref from text\n",
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" 53%|█████▎ | 387184/726049 [00:02<00:01, 300131.76it/s]cannot clean ref from text\n",
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" 65%|██████▌ | 474466/726049 [00:02<00:01, 245717.70it/s]cannot clean ref from text\n",
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"cannot clean inline ref from text\n",
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"cannot clean ref from text\n",
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" 85%|████████▌ | 618002/726049 [00:02<00:00, 284270.19it/s]cannot clean ref from text\n",
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"100%|██████████| 726049/726049 [00:03<00:00, 215277.92it/s]\n"
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]
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}
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],
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"source": [
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"filtered_db = {}\n",
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"\n",
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"for key, item in tqdm.tqdm(db.items()):\n",
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" senses = item['senses']\n",
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" synonyms = item['synonyms']\n",
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" antonyms = item['antonyms']\n",
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"\n",
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" def filter_entries(entries: list):\n",
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" new_list = []\n",
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" for entry in entries:\n",
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" try:\n",
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" cleaned_entry = clean_text(entry)\n",
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" # sort out bad hints:\n",
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" if \"{\" in cleaned_entry:\n",
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" continue\n",
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" if item['word'].lower() in cleaned_entry.lower():\n",
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" continue\n",
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" if \"familienname\" in cleaned_entry.lower():\n",
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" continue\n",
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" if \"ortsteil von\" in cleaned_entry.lower():\n",
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" continue\n",
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" if \"dorf in\" in cleaned_entry.lower():\n",
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" continue\n",
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" if entry.isupper(): # try to sort out initialisms\n",
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" continue\n",
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" \n",
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" cleaned_entry = cleaned_entry.replace(\"|\",\"/\")\n",
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"\n",
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" new_list.append(cleaned_entry)\n",
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" except:\n",
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" #print(\"cannot process item\", entry)\n",
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" # just skipping unprocessable items\n",
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" pass\n",
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"\n",
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" \n",
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" return new_list\n",
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" \n",
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" item['senses'] = filter_entries(senses)\n",
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" item['synonyms'] = filter_entries(synonyms)\n",
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" item['antonyms'] = filter_entries(antonyms)\n",
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"\n",
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" # clean key from special characters:\n",
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" unaccented_key = unidecode.unidecode(key)\n",
|
||
|
|
" unaccented_word = unidecode.unidecode(item['word'])\n",
|
||
|
|
"\n",
|
||
|
|
" def has_digit(s: str):\n",
|
||
|
|
" for c in s:\n",
|
||
|
|
" if c.isdigit():\n",
|
||
|
|
" return True\n",
|
||
|
|
" return False\n",
|
||
|
|
"\n",
|
||
|
|
" n_hints = len(item['senses']) + len(item['synonyms']) + len(item['antonyms'])\n",
|
||
|
|
"\n",
|
||
|
|
" if (n_hints == 1 and item['num_translations'] >= 6) or n_hints > 1:\n",
|
||
|
|
" if not has_digit(item['word']):\n",
|
||
|
|
" item['word'] = unaccented_word\n",
|
||
|
|
" filtered_db[unaccented_key] = item\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"{'word': 'Muenchen',\n",
|
||
|
|
" 'senses': ['Stadt in Deutschland, die Hauptstadt von Bayern',\n",
|
||
|
|
" 'Ortsteil von Bad Berka in Thüringen',\n",
|
||
|
|
" 'Ortsteil von Hutthurm in Bayern',\n",
|
||
|
|
" 'Ortsteil von Hirschbach in Bayern',\n",
|
||
|
|
" 'Ortsteil von Uebigau-Wahrenbrück in Brandenburg',\n",
|
||
|
|
" 'Dorf in Nordböhmen (Mnichov u Lučního Chvojna)',\n",
|
||
|
|
" 'Wüstung (Mnichov) bei Zahrádky u České Lípy in Nordböhmen'],\n",
|
||
|
|
" 'synonyms': [],\n",
|
||
|
|
" 'antonyms': [],\n",
|
||
|
|
" 'num_translations': 72}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 13
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"db['muenchen']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"64385"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 16
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"len(filtered_db)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"with open('de.json', 'w') as f:\n",
|
||
|
|
" json.dump(filtered_db, f, indent = 4)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "error",
|
||
|
|
"ename": "ValueError",
|
||
|
|
"evalue": "list.remove(x): x not in list",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[0;32m<ipython-input-25-0101445e8506>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
|
|
"\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"l = [1,2,3]\n",
|
||
|
|
"l.remove(4)\n",
|
||
|
|
"l"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"name": "python3",
|
||
|
|
"display_name": "Python 3.9.5 64-bit"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.5"
|
||
|
|
},
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|