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137 lines
4.5 KiB
137 lines
4.5 KiB
{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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},
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"orig_nbformat": 4,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.7.0 64-bit ('3.7')"
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},
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"interpreter": {
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"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
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},
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"coopTranslator": {
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"original_hash": "341efc86325ec2a214f682f57a189dfd",
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"translation_date": "2025-09-04T09:30:52+00:00",
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"source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
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"language_code": "da"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the hotel reviews from CSV (you can )\n",
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"import pandas as pd \n",
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"\n",
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"df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# We want to find the most useful tags to keep\n",
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"# Remove opening and closing brackets\n",
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"df.Tags = df.Tags.str.strip(\"[']\")\n",
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"# remove all quotes too\n",
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"df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
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"# Now split the strings into a list\n",
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"tag_list_df = df.Tags.str.split(',', expand = True)\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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"outputs": [],
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"source": [
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"# Remove leading and trailing spaces\n",
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"df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
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"df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
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"df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
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"df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
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"df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
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"df[\"Tag_6\"] = tag_list_df[5].str.strip()\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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Merge the 6 columns into one with melt\n",
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"df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\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": 9,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"The shape of the tags with no filtering: (2514684, 2)\n",
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" index count\n",
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"0 Leisure trip 338423\n",
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"1 Couple 205305\n",
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"2 Solo traveler 89779\n",
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"3 Business trip 68176\n",
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"4 Group 51593\n",
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"5 Family with young children 49318\n",
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"6 Family with older children 21509\n",
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"7 Travelers with friends 1610\n",
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"8 With a pet 1078\n"
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]
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}
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],
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"source": [
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"# Get the value counts\n",
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"tag_vc = df_tags.value.value_counts()\n",
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"# print(tag_vc)\n",
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"print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
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"# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
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"df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
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"tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
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"# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
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"print(tag_vc[:10])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n---\n\n**Ansvarsfraskrivelse**: \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på at sikre nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os intet ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.\n"
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]
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}
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]
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} |