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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": "2025-09-03T20:58:34+00:00",
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"source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
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"language_code": "lt"
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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**Atsakomybės apribojimas**: \nŠis dokumentas buvo išverstas naudojant AI vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama naudoti profesionalų žmogaus vertimą. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus aiškinimus, atsiradusius dėl šio vertimo naudojimo.\n"
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]
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}
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]
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} |