{"cells": [{"cell_type": "markdown", "metadata": {"id": "4ba6aba8"}, "source": ["# \ud83e\udd16 **Data Collection, Creation, Storage, and Processing**\n"]}, {"cell_type": "markdown", "metadata": {"id": "jpASMyIQMaAq"}, "source": ["## **1.** \ud83d\udce6 Install required packages"]}, {"cell_type": "code", "execution_count": 1, "metadata": {"colab": {"base_uri": "https://localhost:8080/"}, "id": "f48c8f8c", "outputId": "13d0dd5e-82c6-489f-b1f0-e970186a4eb7"}, "outputs": [{"output_type": "stream", "name": "stdout", "text": ["Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.12/dist-packages (4.13.5)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n", "Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n", "Requirement already satisfied: textblob in /usr/local/lib/python3.12/dist-packages (0.19.0)\n", "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4) (2.8.3)\n", "Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4) (4.15.0)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n", "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.3)\n", "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.61.1)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.0)\n", "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n", "Requirement already satisfied: nltk>=3.9 in /usr/local/lib/python3.12/dist-packages (from textblob) (3.9.1)\n", "Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (8.3.1)\n", "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (1.5.3)\n", "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (2025.11.3)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (4.67.3)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"]}], "source": ["!pip install beautifulsoup4 pandas matplotlib seaborn numpy textblob"]}, {"cell_type": "markdown", "metadata": {"id": "lquNYCbfL9IM"}, "source": ["## **2.** \u26cf Web-scrape all book titles, prices, and ratings from books.toscrape.com"]}, {"cell_type": "markdown", "metadata": {"id": "0IWuNpxxYDJF"}, "source": ["### *a. Initial setup*\n", "Define the base url of the website you will scrape as well as how and what you will scrape"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"id": "91d52125"}, "outputs": [], "source": ["import requests\n", "from bs4 import BeautifulSoup\n", "import pandas as pd\n", "import time\n", "\n", "base_url = \"https://books.toscrape.com/catalogue/page-{}.html\"\n", "headers = {\"User-Agent\": \"Mozilla/5.0\"}\n", "\n", "titles, prices, ratings = [], [], []"]}, {"cell_type": "markdown", "metadata": {"id": "oCdTsin2Yfp3"}, "source": ["### *b. Fill titles, prices, and ratings from the web pages*"]}, {"cell_type": "code", "execution_count": 3, "metadata": {"id": "xqO5Y3dnYhxt"}, "outputs": [], "source": ["# Loop through all 50 pages\n", "for page in range(1, 51):\n", " url = base_url.format(page)\n", " response = requests.get(url, headers=headers)\n", " soup = BeautifulSoup(response.content, \"html.parser\")\n", " books = soup.find_all(\"article\", class_=\"product_pod\")\n", "\n", " for book in books:\n", " titles.append(book.h3.a[\"title\"])\n", " prices.append(float(book.find(\"p\", class_=\"price_color\").text[1:]))\n", " ratings.append(book.p.get(\"class\")[1])\n", "\n", " time.sleep(0.5) # polite scraping delay"]}, {"cell_type": "markdown", "metadata": {"id": "T0TOeRC4Yrnn"}, "source": ["### *c. \u270b\ud83c\udffb\ud83d\uded1\u26d4\ufe0f Create a dataframe df_books that contains the now complete \"title\", \"price\", and \"rating\" objects*"]}, {"cell_type": "code", "execution_count": 4, "metadata": {"id": "l5FkkNhUYTHh"}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {"id": "duI5dv3CZYvF"}, "source": ["### *d. Save web-scraped dataframe either as a CSV or Excel file*"]}, {"cell_type": "code", "execution_count": 5, "metadata": {"id": "lC1U_YHtZifh"}, "outputs": [], "source": ["# \ud83d\udcbe Save to CSV\n", "df_books.to_csv(\"books_data.csv\", index=False)\n", "\n", "# \ud83d\udcbe Or save to Excel\n", "# df_books.to_excel(\"books_data.xlsx\", index=False)"]}, {"cell_type": "markdown", "metadata": {"id": "qMjRKMBQZlJi"}, "source": ["### *e. \u270b\ud83c\udffb\ud83d\uded1\u26d4\ufe0f View first fiew lines*"]}, {"cell_type": "code", "execution_count": 6, "metadata": {"colab": {"base_uri": "https://localhost:8080/", "height": 206}, "id": "O_wIvTxYZqCK", "outputId": "349b36b0-c008-4fd5-d4a4-dba38ae18337"}, "outputs": [{"output_type": "execute_result", "data": {"text/plain": [" title price rating\n", "0 A Light in the Attic 51.77 Three\n", "1 Tipping the Velvet 53.74 One\n", "2 Soumission 50.10 One\n", "3 Sharp Objects 47.82 Four\n", "4 Sapiens: A Brief History of Humankind 54.23 Five"], "text/html": ["\n", "
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titlepricerating
0A Light in the Attic51.77Three
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3Sharp Objects47.82Four
4Sapiens: A Brief History of Humankind54.23Five
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\n"], "application/vnd.google.colaboratory.intrinsic+json": {"type": "dataframe", "variable_name": "df_books", "summary": "{\n \"name\": \"df_books\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.446689669952772,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 903,\n \"samples\": [\n 19.73,\n 55.65,\n 46.31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"One\",\n \"Two\",\n \"Four\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}}, "metadata": {}, "execution_count": 6}], "source": []}, {"cell_type": "markdown", "metadata": {"id": "p-1Pr2szaqLk"}, "source": ["## **3.** \ud83e\udde9 Create a meaningful connection between real & synthetic datasets"]}, {"cell_type": "markdown", "metadata": {"id": "SIaJUGIpaH4V"}, "source": ["### *a. Initial setup*"]}, {"cell_type": "code", "execution_count": 7, "metadata": {"id": "-gPXGcRPuV_9"}, "outputs": [], "source": ["import numpy as np\n", "import random\n", "from datetime import datetime\n", "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "random.seed(2025)\n", "np.random.seed(2025)"]}, {"cell_type": "markdown", "metadata": {"id": "pY4yCoIuaQqp"}, "source": ["### *b. Generate popularity scores based on rating (with some randomness) with a generate_popularity_score function*"]}, {"cell_type": "code", "execution_count": 8, "metadata": {"id": "mnd5hdAbaNjz"}, "outputs": [], "source": ["def generate_popularity_score(rating):\n", " base = {\"One\": 2, \"Two\": 3, \"Three\": 3, \"Four\": 4, \"Five\": 4}.get(rating, 3)\n", " trend_factor = random.choices([-1, 0, 1], weights=[1, 3, 2])[0]\n", " return int(np.clip(base + trend_factor, 1, 5))"]}, {"cell_type": "markdown", "metadata": {"id": "n4-TaNTFgPak"}, "source": ["### *c. \u270b\ud83c\udffb\ud83d\uded1\u26d4\ufe0f Run the function to create a \"popularity_score\" column from \"rating\"*"]}, {"cell_type": "code", "execution_count": 9, "metadata": {"id": "V-G3OCUCgR07"}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {"id": "HnngRNTgacYt"}, "source": ["### *d. Decide on the sentiment_label based on the popularity score with a get_sentiment function*"]}, {"cell_type": "code", "execution_count": 10, "metadata": {"id": "kUtWmr8maZLZ"}, "outputs": [], "source": ["def get_sentiment(popularity_score):\n", " if popularity_score <= 2:\n", " return \"negative\"\n", " elif popularity_score == 3:\n", " return \"neutral\"\n", " else:\n", " return \"positive\""]}, {"cell_type": "markdown", "metadata": {"id": "HF9F9HIzgT7Z"}, "source": ["### *e. \u270b\ud83c\udffb\ud83d\uded1\u26d4\ufe0f Run the function to create a \"sentiment_label\" column from \"popularity_score\"*"]}, {"cell_type": "code", "execution_count": 11, "metadata": {"id": "tafQj8_7gYCG"}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {"id": "T8AdKkmASq9a"}, "source": ["## **4.** \ud83d\udcc8 Generate synthetic book sales data of 18 months"]}, {"cell_type": "markdown", "metadata": {"id": "OhXbdGD5fH0c"}, "source": ["### *a. Create a generate_sales_profit function that would generate sales patterns based on sentiment_label (with some randomness)*"]}, {"cell_type": "code", "execution_count": 12, "metadata": {"id": "qkVhYPXGbgEn"}, "outputs": [], "source": ["def generate_sales_profile(sentiment):\n months = pd.date_range(end=datetime.today(), periods=18, freq=\"ME\")\n\n if sentiment == \"positive\":\n base = random.randint(200, 300)\n trend = np.linspace(base, base + random.randint(20, 60), len(months))\n elif sentiment == \"negative\":\n base = random.randint(20, 80)\n trend = np.linspace(base, base - random.randint(10, 30), len(months))\n else: # neutral\n base = random.randint(80, 160)\n trend = np.full(len(months), base + random.randint(-10, 10))\n\n seasonality = 10 * np.sin(np.linspace(0, 3 * np.pi, len(months)))\n noise = np.random.normal(0, 5, len(months))\n monthly_sales = np.clip(trend + seasonality + noise, a_min=0, a_max=None).astype(int)\n\n return list(zip(months.strftime(\"%Y-%m\"), monthly_sales))"]}, {"cell_type": "markdown", "metadata": {"id": "L2ak1HlcgoTe"}, "source": ["### *b. Run the function as part of building sales_data*"]}, {"cell_type": "code", "execution_count": 13, "metadata": {"id": "SlJ24AUafoDB"}, "outputs": [], "source": ["sales_data = []\n", "for _, row in df_books.iterrows():\n", " records = generate_sales_profile(row[\"sentiment_label\"])\n", " for month, units in records:\n", " sales_data.append({\n", " \"title\": row[\"title\"],\n", " \"month\": month,\n", " \"units_sold\": units,\n", " \"sentiment_label\": row[\"sentiment_label\"]\n", " })"]}, {"cell_type": "markdown", "metadata": {"id": "4IXZKcCSgxnq"}, "source": ["### *c. \u270b\ud83c\udffb\ud83d\uded1\u26d4\ufe0f Create a df_sales DataFrame from sales_data*"]}, {"cell_type": "code", "execution_count": 14, "metadata": {"id": "wcN6gtiZg-ws"}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {"id": "EhIjz9WohAmZ"}, "source": ["### *d. Save df_sales as synthetic_sales_data.csv & view first few lines*"]}, {"cell_type": "code", "execution_count": 15, "metadata": {"colab": {"base_uri": "https://localhost:8080/"}, "id": "MzbZvLcAhGaH", "outputId": "c692bb04-7263-4115-a2ba-c72fe0180722"}, "outputs": [{"output_type": "stream", "name": "stdout", "text": [" title month units_sold sentiment_label\n", "0 A Light in the Attic 2024-08 100 neutral\n", "1 A Light in the Attic 2024-09 109 neutral\n", "2 A Light in the Attic 2024-10 102 neutral\n", "3 A Light in the Attic 2024-11 107 neutral\n", "4 A Light in the Attic 2024-12 108 neutral\n"]}], "source": ["df_sales.to_csv(\"synthetic_sales_data.csv\", index=False)\n", "\n", "print(df_sales.head())"]}, {"cell_type": "markdown", "metadata": {"id": "7g9gqBgQMtJn"}, "source": ["## **5.** \ud83c\udfaf Generate synthetic customer reviews"]}, {"cell_type": "markdown", "metadata": {"id": "Gi4y9M9KuDWx"}, "source": ["### *a. \u270b\ud83c\udffb\ud83d\uded1\u26d4\ufe0f Ask ChatGPT to create a list of 50 distinct generic book review texts for the sentiment labels \"positive\", \"neutral\", and \"negative\" called synthetic_reviews_by_sentiment*"]}, {"cell_type": "code", "execution_count": 16, "metadata": {"id": "b3cd2a50"}, "outputs": [], "source": ["synthetic_reviews_by_sentiment = {\n", " \"positive\": [\n", " \"A compelling and heartwarming read that stayed with me long after I finished.\",\n", " \"Brilliantly written! The characters were unforgettable and the plot was engaging.\",\n", " \"One of the best books I've read this year \u2014 inspiring and emotionally rich.\",\n", " ],\n", " \"neutral\": [\n", " \"An average book \u2014 not great, but not bad either.\",\n", " \"Some parts really stood out, others felt a bit flat.\",\n", " \"It was okay overall. A decent way to pass the time.\",\n", " ],\n", " \"negative\": [\n", " \"I struggled to get through this one \u2014 it just didn\u2019t grab me.\",\n", " \"The plot was confusing and the characters felt underdeveloped.\",\n", " \"Disappointing. I had high hopes, but they weren't met.\",\n", " ]\n", "}"]}, {"cell_type": "markdown", "metadata": {"id": "fQhfVaDmuULT"}, "source": ["### *b. Generate 10 reviews per book using random sampling from the corresponding 50*"]}, {"cell_type": "code", "execution_count": 17, "metadata": {"id": "l2SRc3PjuTGM"}, "outputs": [], "source": ["review_rows = []\n", "for _, row in df_books.iterrows():\n", " title = row['title']\n", " sentiment_label = row['sentiment_label']\n", " review_pool = synthetic_reviews_by_sentiment[sentiment_label]\n", " sampled_reviews = random.sample(review_pool, 10)\n", " for review_text in sampled_reviews:\n", " review_rows.append({\n", " \"title\": title,\n", " \"sentiment_label\": sentiment_label,\n", " \"review_text\": review_text,\n", " \"rating\": row['rating'],\n", " \"popularity_score\": row['popularity_score']\n", " })"]}, {"cell_type": "markdown", "metadata": {"id": "bmJMXF-Bukdm"}, "source": ["### *c. Create the final dataframe df_reviews & save it as synthetic_book_reviews.csv*"]}, {"cell_type": "code", "execution_count": 18, "metadata": {"id": "ZUKUqZsuumsp"}, "outputs": [], "source": ["df_reviews = pd.DataFrame(review_rows)\n", "df_reviews.to_csv(\"synthetic_book_reviews.csv\", index=False)"]}, {"cell_type": "markdown", "source": ["### *c. inputs for R*"], "metadata": {"id": "_602pYUS3gY5"}}, {"cell_type": "code", "execution_count": 19, "metadata": {"colab": {"base_uri": "https://localhost:8080/"}, "id": "3946e521", "outputId": "514d7bef-0488-4933-b03c-953b9e8a7f66"}, "outputs": [{"output_type": "stream", "name": "stdout", "text": ["\u2705 Wrote synthetic_title_level_features.csv\n", "\u2705 Wrote synthetic_monthly_revenue_series.csv\n"]}], "source": ["import numpy as np\n", "\n", "def _safe_num(s):\n", " return pd.to_numeric(\n", " pd.Series(s).astype(str).str.replace(r\"[^0-9.]\", \"\", regex=True),\n", " errors=\"coerce\"\n", " )\n", "\n", "# --- Clean book metadata (price/rating) ---\n", "df_books_r = df_books.copy()\n", "if \"price\" in df_books_r.columns:\n", " df_books_r[\"price\"] = _safe_num(df_books_r[\"price\"])\n", "if \"rating\" in df_books_r.columns:\n", " df_books_r[\"rating\"] = _safe_num(df_books_r[\"rating\"])\n", "\n", "df_books_r[\"title\"] = df_books_r[\"title\"].astype(str).str.strip()\n", "\n", "# --- Clean sales ---\n", "df_sales_r = df_sales.copy()\n", "df_sales_r[\"title\"] = df_sales_r[\"title\"].astype(str).str.strip()\n", "df_sales_r[\"month\"] = pd.to_datetime(df_sales_r[\"month\"], errors=\"coerce\")\n", "df_sales_r[\"units_sold\"] = _safe_num(df_sales_r[\"units_sold\"])\n", "\n", "# --- Clean reviews ---\n", "df_reviews_r = df_reviews.copy()\n", "df_reviews_r[\"title\"] = df_reviews_r[\"title\"].astype(str).str.strip()\n", "df_reviews_r[\"sentiment_label\"] = df_reviews_r[\"sentiment_label\"].astype(str).str.lower().str.strip()\n", "if \"rating\" in df_reviews_r.columns:\n", " df_reviews_r[\"rating\"] = _safe_num(df_reviews_r[\"rating\"])\n", "if \"popularity_score\" in df_reviews_r.columns:\n", " df_reviews_r[\"popularity_score\"] = _safe_num(df_reviews_r[\"popularity_score\"])\n", "\n", "# --- Sentiment shares per title (from reviews) ---\n", "sent_counts = (\n", " df_reviews_r.groupby([\"title\", \"sentiment_label\"])\n", " .size()\n", " .unstack(fill_value=0)\n", ")\n", "for lab in [\"positive\", \"neutral\", \"negative\"]:\n", " if lab not in sent_counts.columns:\n", " sent_counts[lab] = 0\n", "\n", "sent_counts[\"total_reviews\"] = sent_counts[[\"positive\", \"neutral\", \"negative\"]].sum(axis=1)\n", "den = sent_counts[\"total_reviews\"].replace(0, np.nan)\n", "sent_counts[\"share_positive\"] = sent_counts[\"positive\"] / den\n", "sent_counts[\"share_neutral\"] = sent_counts[\"neutral\"] / den\n", "sent_counts[\"share_negative\"] = sent_counts[\"negative\"] / den\n", "sent_counts = sent_counts.reset_index()\n", "\n", "# --- Sales aggregation per title ---\n", "sales_by_title = (\n", " df_sales_r.dropna(subset=[\"title\"])\n", " .groupby(\"title\", as_index=False)\n", " .agg(\n", " months_observed=(\"month\", \"nunique\"),\n", " avg_units_sold=(\"units_sold\", \"mean\"),\n", " total_units_sold=(\"units_sold\", \"sum\"),\n", " )\n", ")\n", "\n", "# --- Title-level features (join sales + books + sentiment) ---\n", "df_title = (\n", " sales_by_title\n", " .merge(df_books_r[[\"title\", \"price\", \"rating\"]], on=\"title\", how=\"left\")\n", " .merge(sent_counts[[\"title\", \"share_positive\", \"share_neutral\", \"share_negative\", \"total_reviews\"]],\n", " on=\"title\", how=\"left\")\n", ")\n", "\n", "df_title[\"avg_revenue\"] = df_title[\"avg_units_sold\"] * df_title[\"price\"]\n", "df_title[\"total_revenue\"] = df_title[\"total_units_sold\"] * df_title[\"price\"]\n", "\n", "df_title.to_csv(\"synthetic_title_level_features.csv\", index=False)\n", "print(\"\u2705 Wrote synthetic_title_level_features.csv\")\n", "\n", "# --- Monthly revenue series (proxy: units_sold * price) ---\n", "monthly_rev = (\n", " df_sales_r.merge(df_books_r[[\"title\", \"price\"]], on=\"title\", how=\"left\")\n", ")\n", "monthly_rev[\"revenue\"] = monthly_rev[\"units_sold\"] * monthly_rev[\"price\"]\n", "\n", "df_monthly = (\n", " monthly_rev.dropna(subset=[\"month\"])\n", " .groupby(\"month\", as_index=False)[\"revenue\"]\n", " .sum()\n", " .rename(columns={\"revenue\": \"total_revenue\"})\n", " .sort_values(\"month\")\n", ")\n", "# if revenue is all NA (e.g., missing price), fallback to units_sold as a teaching proxy\n", "if df_monthly[\"total_revenue\"].notna().sum() == 0:\n", " df_monthly = (\n", " df_sales_r.dropna(subset=[\"month\"])\n", " .groupby(\"month\", as_index=False)[\"units_sold\"]\n", " .sum()\n", " .rename(columns={\"units_sold\": \"total_revenue\"})\n", " .sort_values(\"month\")\n", " )\n", "\n", "df_monthly[\"month\"] = pd.to_datetime(df_monthly[\"month\"], errors=\"coerce\").dt.strftime(\"%Y-%m-%d\")\n", "df_monthly.to_csv(\"synthetic_monthly_revenue_series.csv\", index=False)\n", "print(\"\u2705 Wrote synthetic_monthly_revenue_series.csv\")\n"]}, {"cell_type": "markdown", "metadata": {"id": "RYvGyVfXuo54"}, "source": ["### *d. \u270b\ud83c\udffb\ud83d\uded1\u26d4\ufe0f View the first few lines*"]}, {"cell_type": "code", "execution_count": 20, "metadata": {"colab": {"base_uri": "https://localhost:8080/"}, "id": "xfE8NMqOurKo", "outputId": "191730ba-d5e2-4df7-97d2-99feb0b704af"}, "outputs": [{"output_type": "stream", "name": "stdout", "text": [" title sentiment_label \\\n", "0 A Light in the Attic neutral \n", "1 A Light in the Attic neutral \n", "2 A Light in the Attic neutral \n", "3 A Light in the Attic neutral \n", "4 A Light in the Attic neutral \n", "\n", " review_text rating popularity_score \n", "0 Had potential that went unrealized. Three 3 \n", "1 The themes were solid, but not well explored. Three 3 \n", "2 It simply lacked that emotional punch. Three 3 \n", "3 Serviceable but not something I'd go out of my... Three 3 \n", "4 Standard fare with some promise. Three 3 \n"]}], "source": []}], "metadata": {"colab": {"collapsed_sections": ["jpASMyIQMaAq", "lquNYCbfL9IM", "0IWuNpxxYDJF", "oCdTsin2Yfp3", "T0TOeRC4Yrnn", "duI5dv3CZYvF", "qMjRKMBQZlJi", "p-1Pr2szaqLk", "SIaJUGIpaH4V", "pY4yCoIuaQqp", "n4-TaNTFgPak", "HnngRNTgacYt", "HF9F9HIzgT7Z", "T8AdKkmASq9a", "OhXbdGD5fH0c", "L2ak1HlcgoTe", "4IXZKcCSgxnq", "EhIjz9WohAmZ", "Gi4y9M9KuDWx", "fQhfVaDmuULT", "bmJMXF-Bukdm", "RYvGyVfXuo54"], "provenance": []}, "kernelspec": {"display_name": "Python 3", "name": "python3"}, "language_info": {"name": "python"}}, "nbformat": 4, "nbformat_minor": 0}