ML-For-Beginners/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EDA\n",
    "import pandas as pd\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_difference_review_avg(row):\n",
    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the hotel reviews from CSV\n",
    "print(\"Loading data file now, this could take a while depending on file size\")\n",
    "start = time.time()\n",
    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
    "end = time.time()\n",
    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What shape is the data (rows, columns)?\n",
    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# value_counts() creates a Series object that has index and values\n",
    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What reviewer nationality is the most common in the dataset?\n",
    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What is the top 10 most common nationalities and their frequencies?\n",
    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
    "print(nationality_freq[0:10].to_string())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many unique nationalities are there?\n",
    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
    "for nat in nationality_freq[:10].index:\n",
    "   # First, extract all the rows that match the criteria into a new dataframe\n",
    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
    "   # Now get the hotel freq\n",
    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
    "# Get rid of all the duplicated rows\n",
    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
    "print()\n",
    "print(hotel_freq_df.to_string())\n",
    "print(str(hotel_freq_df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
    "# Add a new column with the difference between the two average scores\n",
    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
    "# Sort the dataframe to find the lowest and highest average score difference\n",
    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
   ]
  }
 ]
}