Zarathushtra
4 weeks ago
parent
bbfa03c2d1
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7a50781922
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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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"id": "3b3e3368",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loading the data file now, could take a while...\n",
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"Loading took5.67 seconds\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import time\n",
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"print(\"Loading the data file now, could take a while...\")\n",
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"start_time = time.time()\n",
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"df = pd.read_csv('../data/Hotel_Reviews.csv')\n",
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"end = time.time()\n",
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"print(\"Loading took\" + str(round(end - start_time, 2)) + \" seconds\")"
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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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"id": "f78a15e0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The shap of the data (rows, cols) is (515738, 17)\n"
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]
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}
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],
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"source": [
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"print (\"The shap of the data (rows, cols) is \" + str(df.shape))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e244a843",
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"metadata": {},
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"source": [
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"### Calculate the frequency count for reviewer nationalities:\n",
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"\n",
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"How many distinct values are there for the column Reviewer_Nationality and what are they?\n",
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"What reviewer nationality is the most common in the dataset (print country and number of reviews)?"
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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": null,
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"id": "a9d66aa9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"There are 227different nationalities in the dataset\n",
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"Reviewer_Nationality\n",
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"United Kingdom 245246\n",
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"United States of America 35437\n",
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"Australia 21686\n",
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"Ireland 14827\n",
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"United Arab Emirates 10235\n",
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" ... \n",
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"Cape Verde 1\n",
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"Northern Mariana Islands 1\n",
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"Tuvalu 1\n",
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"Guinea 1\n",
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"Palau 1\n",
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"Name: count, Length: 227, dtype: int64\n"
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]
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}
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],
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"source": [
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"nationality_freq = df['Reviewer_Nationality'].value_counts()\n",
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"print(\"There are \" + str(nationality_freq.size) + \" different nationalities in the dataset\")\n",
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"print(nationality_freq)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "770fa9b5",
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"metadata": {},
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"source": [
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"### What are the next top 10 most frequently found nationalities, and their frequency count?"
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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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"id": "908dc6d0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The next 10 highest frequency reviewer nationalities are:\n",
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"Reviewer_Nationality\n",
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"United States of America 35437\n",
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"Australia 21686\n",
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"Ireland 14827\n",
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"United Arab Emirates 10235\n",
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"Saudi Arabia 8951\n",
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"Netherlands 8772\n",
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"Switzerland 8678\n",
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"Germany 7941\n",
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"Canada 7894\n",
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"France 7296\n"
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]
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}
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],
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"source": [
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"print(\"The next 10 highest frequency reviewer nationalities are:\")\n",
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"print(nationality_freq[1:11].to_string())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cbde61cf",
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"metadata": {},
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"source": [
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"### What was the most frequently reviewed hotel for each of the top 10 most reviewer nationalities?"
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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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"id": "f8c4d995",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n",
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n",
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The most reviewed hotel for United Kingdom was Britannia International Hotel Canary Wharf with 3833 reviews.\n",
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"The most reviewed hotel for United States of America was Hotel Esther a with 423 reviews.\n",
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"The most reviewed hotel for Australia was Park Plaza Westminster Bridge London with 167 reviews.\n",
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"The most reviewed hotel for Ireland was Copthorne Tara Hotel London Kensington with 239 reviews.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n",
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n",
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The most reviewed hotel for United Arab Emirates was Millennium Hotel London Knightsbridge with 129 reviews.\n",
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"The most reviewed hotel for Saudi Arabia was The Cumberland A Guoman Hotel with 142 reviews.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n",
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n",
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The most reviewed hotel for Netherlands was Jaz Amsterdam with 97 reviews.\n",
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"The most reviewed hotel for Switzerland was Hotel Da Vinci with 97 reviews.\n",
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"The most reviewed hotel for Germany was Hotel Da Vinci with 86 reviews.\n",
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"The most reviewed hotel for Canada was St James Court A Taj Hotel London with 61 reviews.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23868\\2970136166.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\")\n"
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]
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}
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],
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"source": [
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"for nat in nationality_freq[:10].index:\n",
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" # First, extract all the rows that match the criteria into a new dataframe\n",
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" nat_df = df[df[\"Reviewer_Nationality\"] == nat] \n",
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" # Now get the hotel freq\n",
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" freq = nat_df[\"Hotel_Name\"].value_counts()\n",
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" print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") "
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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": null,
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"id": "9dbc5464",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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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": "3.13.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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