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137 lines
4.6 KiB
137 lines
4.6 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": "2025-09-03T20:58:38+00:00",
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"source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
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"language_code": "pl"
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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**Zastrzeżenie**: \nTen dokument został przetłumaczony za pomocą usługi tłumaczenia AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dokładamy wszelkich starań, aby zapewnić poprawność tłumaczenia, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za autorytatywne źródło. W przypadku informacji o kluczowym znaczeniu zaleca się skorzystanie z profesjonalnego tłumaczenia przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z użycia tego tłumaczenia.\n"
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]
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}
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]
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} |