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  1. datacreation.ipynb +872 -163
  2. pythonanalysis.ipynb +0 -0
datacreation.ipynb CHANGED
@@ -26,7 +26,8 @@
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  "base_uri": "https://localhost:8080/"
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  },
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  "id": "f48c8f8c",
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- "outputId": "13d0dd5e-82c6-489f-b1f0-e970186a4eb7"
 
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  {
@@ -85,7 +86,7 @@
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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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  "metadata": {
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  "id": "91d52125"
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  },
@@ -96,7 +97,7 @@
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  "import pandas as pd\n",
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  "import time\n",
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  "\n",
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- "base_url = \"https://books.toscrape.com/catalogue/page-{}.html\"\n",
100
  "headers = {\"User-Agent\": \"Mozilla/5.0\"}\n",
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  "\n",
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  "titles, prices, ratings = [], [], []"
@@ -113,7 +114,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 3,
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  "metadata": {
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  "id": "xqO5Y3dnYhxt"
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  },
@@ -145,12 +146,18 @@
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  "cell_type": "code",
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- "execution_count": 4,
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  "metadata": {
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  "id": "l5FkkNhUYTHh"
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  },
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  "outputs": [],
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- "source": []
 
 
 
 
 
 
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  },
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  {
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  "cell_type": "markdown",
@@ -163,7 +170,7 @@
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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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  "id": "lC1U_YHtZifh"
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  },
@@ -187,14 +194,14 @@
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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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  "base_uri": "https://localhost:8080/",
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  "height": 206
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- "outputId": "349b36b0-c008-4fd5-d4a4-dba38ae18337"
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  "outputs": [
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  {
@@ -210,7 +217,7 @@
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  ],
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  "text/html": [
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  "\n",
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- " <div id=\"df-04c87660-4415-45e9-ad3b-3fa19d9402c2\" class=\"colab-df-container\">\n",
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  " <div>\n",
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  "<style scoped>\n",
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  " .dataframe tbody tr th:only-of-type {\n",
@@ -271,7 +278,7 @@
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  " <div class=\"colab-df-buttons\">\n",
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  "\n",
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  " <div class=\"colab-df-container\">\n",
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- " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04c87660-4415-45e9-ad3b-3fa19d9402c2')\"\n",
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  " title=\"Convert this dataframe to an interactive table.\"\n",
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  " style=\"display:none;\">\n",
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  "\n",
@@ -323,12 +330,12 @@
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  "\n",
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  " <script>\n",
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  " const buttonEl =\n",
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- " document.querySelector('#df-04c87660-4415-45e9-ad3b-3fa19d9402c2 button.colab-df-convert');\n",
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  " buttonEl.style.display =\n",
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  " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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  "\n",
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  " async function convertToInteractive(key) {\n",
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- " const element = document.querySelector('#df-04c87660-4415-45e9-ad3b-3fa19d9402c2');\n",
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  " const dataTable =\n",
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  " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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  " [key], {});\n",
@@ -354,14 +361,16 @@
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  "application/vnd.google.colaboratory.intrinsic+json": {
355
  "type": "dataframe",
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  "variable_name": "df_books",
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- "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}"
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  }
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  },
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  "metadata": {},
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- "execution_count": 6
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- "source": []
 
 
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  },
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  "cell_type": "markdown",
@@ -383,7 +392,7 @@
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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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  "id": "-gPXGcRPuV_9"
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  },
@@ -410,7 +419,7 @@
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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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  "id": "mnd5hdAbaNjz"
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  },
@@ -433,12 +442,191 @@
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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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- "id": "V-G3OCUCgR07"
 
 
 
 
 
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  },
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- "outputs": [],
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- "source": []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  },
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  {
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  "cell_type": "markdown",
@@ -451,7 +639,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 10,
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  "metadata": {
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  "id": "kUtWmr8maZLZ"
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  },
@@ -477,85 +665,277 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 11,
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- "metadata": {
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- "id": "tafQj8_7gYCG"
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- },
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- "outputs": [],
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- "source": []
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- },
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- {
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- "cell_type": "markdown",
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- "metadata": {
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- "id": "T8AdKkmASq9a"
491
- },
492
- "source": [
493
- "## **4.** 📈 Generate synthetic book sales data of 18 months"
494
- ]
495
- },
496
- {
497
- "cell_type": "markdown",
498
- "metadata": {
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- "id": "OhXbdGD5fH0c"
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- },
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- "source": [
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- "### *a. Create a generate_sales_profit function that would generate sales patterns based on sentiment_label (with some randomness)*"
503
- ]
504
- },
505
- {
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- "cell_type": "code",
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- "execution_count": 12,
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- "metadata": {
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- "id": "qkVhYPXGbgEn"
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- },
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- "outputs": [],
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- "source": [
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- "def generate_sales_profile(sentiment):\n",
514
- " months = pd.date_range(end=datetime.today(), periods=18, freq=\"M\")\n",
515
- "\n",
516
- " if sentiment == \"positive\":\n",
517
- " base = random.randint(200, 300)\n",
518
- " trend = np.linspace(base, base + random.randint(20, 60), len(months))\n",
519
- " elif sentiment == \"negative\":\n",
520
- " base = random.randint(20, 80)\n",
521
- " trend = np.linspace(base, base - random.randint(10, 30), len(months))\n",
522
- " else: # neutral\n",
523
- " base = random.randint(80, 160)\n",
524
- " trend = np.full(len(months), base + random.randint(-10, 10))\n",
525
- "\n",
526
- " seasonality = 10 * np.sin(np.linspace(0, 3 * np.pi, len(months)))\n",
527
- " noise = np.random.normal(0, 5, len(months))\n",
528
- " monthly_sales = np.clip(trend + seasonality + noise, a_min=0, a_max=None).astype(int)\n",
529
- "\n",
530
- " return list(zip(months.strftime(\"%Y-%m\"), monthly_sales))"
531
- ]
532
- },
533
- {
534
- "cell_type": "markdown",
535
- "metadata": {
536
- "id": "L2ak1HlcgoTe"
537
- },
538
- "source": [
539
- "### *b. Run the function as part of building sales_data*"
540
- ]
541
- },
542
- {
543
- "cell_type": "code",
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- "execution_count": 13,
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  "metadata": {
546
- "id": "SlJ24AUafoDB"
 
 
 
 
 
547
  },
548
- "outputs": [],
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- "source": [
550
- "sales_data = []\n",
551
- "for _, row in df_books.iterrows():\n",
552
- " records = generate_sales_profile(row[\"sentiment_label\"])\n",
553
- " for month, units in records:\n",
554
- " sales_data.append({\n",
555
- " \"title\": row[\"title\"],\n",
556
- " \"month\": month,\n",
557
- " \"units_sold\": units,\n",
558
- " \"sentiment_label\": row[\"sentiment_label\"]\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
559
  " })"
560
  ]
561
  },
@@ -565,18 +945,197 @@
565
  "id": "4IXZKcCSgxnq"
566
  },
567
  "source": [
568
- "### *c. ✋🏻🛑⛔️ Create a df_sales DataFrame from sales_data*"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
569
  ]
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  },
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- {
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- "cell_type": "code",
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- "execution_count": 14,
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- "metadata": {
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- "id": "wcN6gtiZg-ws"
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- },
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- "outputs": [],
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- "source": []
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- },
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  {
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  "cell_type": "markdown",
582
  "metadata": {
@@ -588,13 +1147,13 @@
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  },
589
  {
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  "cell_type": "code",
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- "execution_count": 15,
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  "metadata": {
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  "colab": {
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  "base_uri": "https://localhost:8080/"
595
  },
596
  "id": "MzbZvLcAhGaH",
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- "outputId": "c692bb04-7263-4115-a2ba-c72fe0180722"
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  },
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  "outputs": [
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  {
@@ -602,11 +1161,11 @@
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  "name": "stdout",
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  "text": [
604
  " title month units_sold sentiment_label\n",
605
- "0 A Light in the Attic 2024-08 100 neutral\n",
606
- "1 A Light in the Attic 2024-09 109 neutral\n",
607
- "2 A Light in the Attic 2024-10 102 neutral\n",
608
- "3 A Light in the Attic 2024-11 107 neutral\n",
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- "4 A Light in the Attic 2024-12 108 neutral\n"
610
  ]
611
  }
612
  ],
@@ -636,7 +1195,7 @@
636
  },
637
  {
638
  "cell_type": "code",
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- "execution_count": 16,
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  "metadata": {
641
  "id": "b3cd2a50"
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  },
@@ -672,7 +1231,7 @@
672
  },
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  {
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  "cell_type": "code",
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- "execution_count": 17,
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  "metadata": {
677
  "id": "l2SRc3PjuTGM"
678
  },
@@ -683,7 +1242,8 @@
683
  " title = row['title']\n",
684
  " sentiment_label = row['sentiment_label']\n",
685
  " review_pool = synthetic_reviews_by_sentiment[sentiment_label]\n",
686
- " sampled_reviews = random.sample(review_pool, 10)\n",
 
687
  " for review_text in sampled_reviews:\n",
688
  " review_rows.append({\n",
689
  " \"title\": title,\n",
@@ -691,7 +1251,7 @@
691
  " \"review_text\": review_text,\n",
692
  " \"rating\": row['rating'],\n",
693
  " \"popularity_score\": row['popularity_score']\n",
694
- " })"
695
  ]
696
  },
697
  {
@@ -705,7 +1265,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 18,
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  "metadata": {
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  "id": "ZUKUqZsuumsp"
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  },
@@ -726,13 +1286,13 @@
726
  },
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  {
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  "cell_type": "code",
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- "execution_count": 19,
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  "metadata": {
731
  "colab": {
732
  "base_uri": "https://localhost:8080/"
733
  },
734
  "id": "3946e521",
735
- "outputId": "514d7bef-0488-4933-b03c-953b9e8a7f66"
736
  },
737
  "outputs": [
738
  {
@@ -858,36 +1418,202 @@
858
  },
859
  {
860
  "cell_type": "code",
861
- "execution_count": 20,
862
  "metadata": {
863
  "colab": {
864
- "base_uri": "https://localhost:8080/"
 
865
  },
866
  "id": "xfE8NMqOurKo",
867
- "outputId": "191730ba-d5e2-4df7-97d2-99feb0b704af"
868
  },
869
  "outputs": [
870
  {
871
- "output_type": "stream",
872
- "name": "stdout",
873
- "text": [
874
- " title sentiment_label \\\n",
875
- "0 A Light in the Attic neutral \n",
876
- "1 A Light in the Attic neutral \n",
877
- "2 A Light in the Attic neutral \n",
878
- "3 A Light in the Attic neutral \n",
879
- "4 A Light in the Attic neutral \n",
880
- "\n",
881
- " review_text rating popularity_score \n",
882
- "0 Had potential that went unrealized. Three 3 \n",
883
- "1 The themes were solid, but not well explored. Three 3 \n",
884
- "2 It simply lacked that emotional punch. Three 3 \n",
885
- "3 Serviceable but not something I'd go out of my... Three 3 \n",
886
- "4 Standard fare with some promise. Three 3 \n"
887
- ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
888
  }
889
  ],
890
- "source": []
 
 
891
  }
892
  ],
893
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@@ -895,26 +1621,9 @@
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918
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  "provenance": []
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26
  "base_uri": "https://localhost:8080/"
27
  },
28
  "id": "f48c8f8c",
29
+ "outputId": "04e1bdd4-2da2-4c3d-9d91-dc5c25a1cf81",
30
+ "collapsed": true
31
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32
  "outputs": [
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  {
 
86
  },
87
  {
88
  "cell_type": "code",
89
+ "execution_count": 6,
90
  "metadata": {
91
  "id": "91d52125"
92
  },
 
97
  "import pandas as pd\n",
98
  "import time\n",
99
  "\n",
100
+ "base_url = \"http://books.toscrape.com/catalogue/page-{}.html\"\n",
101
  "headers = {\"User-Agent\": \"Mozilla/5.0\"}\n",
102
  "\n",
103
  "titles, prices, ratings = [], [], []"
 
114
  },
115
  {
116
  "cell_type": "code",
117
+ "execution_count": 8,
118
  "metadata": {
119
  "id": "xqO5Y3dnYhxt"
120
  },
 
146
  },
147
  {
148
  "cell_type": "code",
149
+ "execution_count": 10,
150
  "metadata": {
151
  "id": "l5FkkNhUYTHh"
152
  },
153
  "outputs": [],
154
+ "source": [
155
+ "df_books = pd.DataFrame({\n",
156
+ " \"title\": titles,\n",
157
+ " \"price\": prices,\n",
158
+ " \"rating\": ratings\n",
159
+ "})"
160
+ ]
161
  },
162
  {
163
  "cell_type": "markdown",
 
170
  },
171
  {
172
  "cell_type": "code",
173
+ "execution_count": 12,
174
  "metadata": {
175
  "id": "lC1U_YHtZifh"
176
  },
 
194
  },
195
  {
196
  "cell_type": "code",
197
+ "execution_count": 13,
198
  "metadata": {
199
  "colab": {
200
  "base_uri": "https://localhost:8080/",
201
  "height": 206
202
  },
203
  "id": "O_wIvTxYZqCK",
204
+ "outputId": "718c3d95-1dc9-4f67-9940-ebda3ddbf90b"
205
  },
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  "outputs": [
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  {
 
217
  ],
218
  "text/html": [
219
  "\n",
220
+ " <div id=\"df-f3614048-0669-4e42-9703-5871bd840543\" class=\"colab-df-container\">\n",
221
  " <div>\n",
222
  "<style scoped>\n",
223
  " .dataframe tbody tr th:only-of-type {\n",
 
278
  " <div class=\"colab-df-buttons\">\n",
279
  "\n",
280
  " <div class=\"colab-df-container\">\n",
281
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3614048-0669-4e42-9703-5871bd840543')\"\n",
282
  " title=\"Convert this dataframe to an interactive table.\"\n",
283
  " style=\"display:none;\">\n",
284
  "\n",
 
330
  "\n",
331
  " <script>\n",
332
  " const buttonEl =\n",
333
+ " document.querySelector('#df-f3614048-0669-4e42-9703-5871bd840543 button.colab-df-convert');\n",
334
  " buttonEl.style.display =\n",
335
  " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
336
  "\n",
337
  " async function convertToInteractive(key) {\n",
338
+ " const element = document.querySelector('#df-f3614048-0669-4e42-9703-5871bd840543');\n",
339
  " const dataTable =\n",
340
  " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
341
  " [key], {});\n",
 
361
  "application/vnd.google.colaboratory.intrinsic+json": {
362
  "type": "dataframe",
363
  "variable_name": "df_books",
364
+ "summary": "{\n \"name\": \"df_books\",\n \"rows\": 840,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 799,\n \"samples\": [\n \"Where'd You Go, Bernadette\",\n \"Mockingjay (The Hunger Games #3)\",\n \"The Mindfulness and Acceptance Workbook for Anxiety: A Guide to Breaking Free from Anxiety, Phobias, and Worry Using Acceptance and Commitment Therapy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.560827565784338,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 740,\n \"samples\": [\n 42.95,\n 20.91,\n 21.8\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}"
365
  }
366
  },
367
  "metadata": {},
368
+ "execution_count": 13
369
  }
370
  ],
371
+ "source": [
372
+ "df_books.head()"
373
+ ]
374
  },
375
  {
376
  "cell_type": "markdown",
 
392
  },
393
  {
394
  "cell_type": "code",
395
+ "execution_count": 14,
396
  "metadata": {
397
  "id": "-gPXGcRPuV_9"
398
  },
 
419
  },
420
  {
421
  "cell_type": "code",
422
+ "execution_count": 16,
423
  "metadata": {
424
  "id": "mnd5hdAbaNjz"
425
  },
 
442
  },
443
  {
444
  "cell_type": "code",
445
+ "execution_count": 17,
446
  "metadata": {
447
+ "id": "V-G3OCUCgR07",
448
+ "colab": {
449
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450
+ "height": 206
451
+ },
452
+ "outputId": "034c1831-bbbb-477d-f8fa-a2fcf6f4159c"
453
  },
454
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455
+ {
456
+ "output_type": "execute_result",
457
+ "data": {
458
+ "text/plain": [
459
+ " title price rating popularity_score\n",
460
+ "0 A Light in the Attic 51.77 Three 3\n",
461
+ "1 Tipping the Velvet 53.74 One 2\n",
462
+ "2 Soumission 50.10 One 2\n",
463
+ "3 Sharp Objects 47.82 Four 4\n",
464
+ "4 Sapiens: A Brief History of Humankind 54.23 Five 3"
465
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466
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470
+ "<style scoped>\n",
471
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487
+ " <th>title</th>\n",
488
+ " <th>price</th>\n",
489
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490
+ " <th>popularity_score</th>\n",
491
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492
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493
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495
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497
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498
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499
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500
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505
+ " <td>One</td>\n",
506
+ " <td>2</td>\n",
507
+ " </tr>\n",
508
+ " <tr>\n",
509
+ " <th>2</th>\n",
510
+ " <td>Soumission</td>\n",
511
+ " <td>50.10</td>\n",
512
+ " <td>One</td>\n",
513
+ " <td>2</td>\n",
514
+ " </tr>\n",
515
+ " <tr>\n",
516
+ " <th>3</th>\n",
517
+ " <td>Sharp Objects</td>\n",
518
+ " <td>47.82</td>\n",
519
+ " <td>Four</td>\n",
520
+ " <td>4</td>\n",
521
+ " </tr>\n",
522
+ " <tr>\n",
523
+ " <th>4</th>\n",
524
+ " <td>Sapiens: A Brief History of Humankind</td>\n",
525
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526
+ " <td>Five</td>\n",
527
+ " <td>3</td>\n",
528
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529
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531
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550
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560
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562
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563
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564
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565
+ " fill: #174EA6;\n",
566
+ " }\n",
567
+ "\n",
568
+ " .colab-df-buttons div {\n",
569
+ " margin-bottom: 4px;\n",
570
+ " }\n",
571
+ "\n",
572
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573
+ " background-color: #3B4455;\n",
574
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575
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576
+ "\n",
577
+ " [theme=dark] .colab-df-convert:hover {\n",
578
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579
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580
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
581
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582
+ " }\n",
583
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584
+ "\n",
585
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586
+ " const buttonEl =\n",
587
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588
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589
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
590
+ "\n",
591
+ " async function convertToInteractive(key) {\n",
592
+ " const element = document.querySelector('#df-749488be-58a0-4a48-abf8-6773255573e7');\n",
593
+ " const dataTable =\n",
594
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
595
+ " [key], {});\n",
596
+ " if (!dataTable) return;\n",
597
+ "\n",
598
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
599
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
600
+ " + ' to learn more about interactive tables.';\n",
601
+ " element.innerHTML = '';\n",
602
+ " dataTable['output_type'] = 'display_data';\n",
603
+ " await google.colab.output.renderOutput(dataTable, element);\n",
604
+ " const docLink = document.createElement('div');\n",
605
+ " docLink.innerHTML = docLinkHtml;\n",
606
+ " element.appendChild(docLink);\n",
607
+ " }\n",
608
+ " </script>\n",
609
+ " </div>\n",
610
+ "\n",
611
+ "\n",
612
+ " </div>\n",
613
+ " </div>\n"
614
+ ],
615
+ "application/vnd.google.colaboratory.intrinsic+json": {
616
+ "type": "dataframe",
617
+ "variable_name": "df_books",
618
+ "summary": "{\n \"name\": \"df_books\",\n \"rows\": 840,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 799,\n \"samples\": [\n \"Where'd You Go, Bernadette\",\n \"Mockingjay (The Hunger Games #3)\",\n \"The Mindfulness and Acceptance Workbook for Anxiety: A Guide to Breaking Free from Anxiety, Phobias, and Worry Using Acceptance and Commitment Therapy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.560827565784338,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 740,\n \"samples\": [\n 42.95,\n 20.91,\n 21.8\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 \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 5,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
619
+ }
620
+ },
621
+ "metadata": {},
622
+ "execution_count": 17
623
+ }
624
+ ],
625
+ "source": [
626
+ "df_books[\"popularity_score\"] = df_books[\"rating\"].apply(generate_popularity_score)\n",
627
+ "\n",
628
+ "df_books.head()"
629
+ ]
630
  },
631
  {
632
  "cell_type": "markdown",
 
639
  },
640
  {
641
  "cell_type": "code",
642
+ "execution_count": 18,
643
  "metadata": {
644
  "id": "kUtWmr8maZLZ"
645
  },
 
665
  },
666
  {
667
  "cell_type": "code",
668
+ "execution_count": 19,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
669
  "metadata": {
670
+ "id": "tafQj8_7gYCG",
671
+ "colab": {
672
+ "base_uri": "https://localhost:8080/",
673
+ "height": 206
674
+ },
675
+ "outputId": "dbb4cead-9675-4491-db0e-0fedb34a10b9"
676
  },
677
+ "outputs": [
678
+ {
679
+ "output_type": "execute_result",
680
+ "data": {
681
+ "text/plain": [
682
+ " title price rating popularity_score \\\n",
683
+ "0 A Light in the Attic 51.77 Three 3 \n",
684
+ "1 Tipping the Velvet 53.74 One 2 \n",
685
+ "2 Soumission 50.10 One 2 \n",
686
+ "3 Sharp Objects 47.82 Four 4 \n",
687
+ "4 Sapiens: A Brief History of Humankind 54.23 Five 3 \n",
688
+ "\n",
689
+ " sentiment_label \n",
690
+ "0 neutral \n",
691
+ "1 negative \n",
692
+ "2 negative \n",
693
+ "3 positive \n",
694
+ "4 neutral "
695
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696
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697
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698
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
+ " <td>Tipping the Velvet</td>\n",
736
+ " <td>53.74</td>\n",
737
+ " <td>One</td>\n",
738
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739
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740
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741
+ " <tr>\n",
742
+ " <th>2</th>\n",
743
+ " <td>Soumission</td>\n",
744
+ " <td>50.10</td>\n",
745
+ " <td>One</td>\n",
746
+ " <td>2</td>\n",
747
+ " <td>negative</td>\n",
748
+ " </tr>\n",
749
+ " <tr>\n",
750
+ " <th>3</th>\n",
751
+ " <td>Sharp Objects</td>\n",
752
+ " <td>47.82</td>\n",
753
+ " <td>Four</td>\n",
754
+ " <td>4</td>\n",
755
+ " <td>positive</td>\n",
756
+ " </tr>\n",
757
+ " <tr>\n",
758
+ " <th>4</th>\n",
759
+ " <td>Sapiens: A Brief History of Humankind</td>\n",
760
+ " <td>54.23</td>\n",
761
+ " <td>Five</td>\n",
762
+ " <td>3</td>\n",
763
+ " <td>neutral</td>\n",
764
+ " </tr>\n",
765
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766
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767
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768
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769
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770
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777
+ " </svg>\n",
778
+ " </button>\n",
779
+ "\n",
780
+ " <style>\n",
781
+ " .colab-df-container {\n",
782
+ " display:flex;\n",
783
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784
+ " }\n",
785
+ "\n",
786
+ " .colab-df-convert {\n",
787
+ " background-color: #E8F0FE;\n",
788
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+ " cursor: pointer;\n",
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800
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801
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802
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803
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804
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805
+ " margin-bottom: 4px;\n",
806
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807
+ "\n",
808
+ " [theme=dark] .colab-df-convert {\n",
809
+ " background-color: #3B4455;\n",
810
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811
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812
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813
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814
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815
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816
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817
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818
+ " }\n",
819
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820
+ "\n",
821
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822
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823
+ " document.querySelector('#df-4de4fca6-e8b0-45a4-aeb2-25e946439896 button.colab-df-convert');\n",
824
+ " buttonEl.style.display =\n",
825
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
826
+ "\n",
827
+ " async function convertToInteractive(key) {\n",
828
+ " const element = document.querySelector('#df-4de4fca6-e8b0-45a4-aeb2-25e946439896');\n",
829
+ " const dataTable =\n",
830
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
831
+ " [key], {});\n",
832
+ " if (!dataTable) return;\n",
833
+ "\n",
834
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
835
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
836
+ " + ' to learn more about interactive tables.';\n",
837
+ " element.innerHTML = '';\n",
838
+ " dataTable['output_type'] = 'display_data';\n",
839
+ " await google.colab.output.renderOutput(dataTable, element);\n",
840
+ " const docLink = document.createElement('div');\n",
841
+ " docLink.innerHTML = docLinkHtml;\n",
842
+ " element.appendChild(docLink);\n",
843
+ " }\n",
844
+ " </script>\n",
845
+ " </div>\n",
846
+ "\n",
847
+ "\n",
848
+ " </div>\n",
849
+ " </div>\n"
850
+ ],
851
+ "application/vnd.google.colaboratory.intrinsic+json": {
852
+ "type": "dataframe",
853
+ "variable_name": "df_books",
854
+ "summary": "{\n \"name\": \"df_books\",\n \"rows\": 840,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 799,\n \"samples\": [\n \"Where'd You Go, Bernadette\",\n \"Mockingjay (The Hunger Games #3)\",\n \"The Mindfulness and Acceptance Workbook for Anxiety: A Guide to Breaking Free from Anxiety, Phobias, and Worry Using Acceptance and Commitment Therapy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.560827565784338,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 740,\n \"samples\": [\n 42.95,\n 20.91,\n 21.8\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 \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 5,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
855
+ }
856
+ },
857
+ "metadata": {},
858
+ "execution_count": 19
859
+ }
860
+ ],
861
+ "source": [
862
+ "df_books[\"sentiment_label\"] = df_books[\"popularity_score\"].apply(get_sentiment)\n",
863
+ "\n",
864
+ "df_books.head()"
865
+ ]
866
+ },
867
+ {
868
+ "cell_type": "markdown",
869
+ "metadata": {
870
+ "id": "T8AdKkmASq9a"
871
+ },
872
+ "source": [
873
+ "## **4.** 📈 Generate synthetic book sales data of 18 months"
874
+ ]
875
+ },
876
+ {
877
+ "cell_type": "markdown",
878
+ "metadata": {
879
+ "id": "OhXbdGD5fH0c"
880
+ },
881
+ "source": [
882
+ "### *a. Create a generate_sales_profit function that would generate sales patterns based on sentiment_label (with some randomness)*"
883
+ ]
884
+ },
885
+ {
886
+ "cell_type": "code",
887
+ "execution_count": 20,
888
+ "metadata": {
889
+ "id": "qkVhYPXGbgEn"
890
+ },
891
+ "outputs": [],
892
+ "source": [
893
+ "def generate_sales_profile(sentiment):\n",
894
+ " months = pd.date_range(end=datetime.today(), periods=18, freq=\"M\")\n",
895
+ "\n",
896
+ " if sentiment == \"positive\":\n",
897
+ " base = random.randint(200, 300)\n",
898
+ " trend = np.linspace(base, base + random.randint(20, 60), len(months))\n",
899
+ " elif sentiment == \"negative\":\n",
900
+ " base = random.randint(20, 80)\n",
901
+ " trend = np.linspace(base, base - random.randint(10, 30), len(months))\n",
902
+ " else: # neutral\n",
903
+ " base = random.randint(80, 160)\n",
904
+ " trend = np.full(len(months), base + random.randint(-10, 10))\n",
905
+ "\n",
906
+ " seasonality = 10 * np.sin(np.linspace(0, 3 * np.pi, len(months)))\n",
907
+ " noise = np.random.normal(0, 5, len(months))\n",
908
+ " monthly_sales = np.clip(trend + seasonality + noise, a_min=0, a_max=None).astype(int)\n",
909
+ "\n",
910
+ " return list(zip(months.strftime(\"%Y-%m\"), monthly_sales))"
911
+ ]
912
+ },
913
+ {
914
+ "cell_type": "markdown",
915
+ "metadata": {
916
+ "id": "L2ak1HlcgoTe"
917
+ },
918
+ "source": [
919
+ "### *b. Run the function as part of building sales_data*"
920
+ ]
921
+ },
922
+ {
923
+ "cell_type": "code",
924
+ "execution_count": 21,
925
+ "metadata": {
926
+ "id": "SlJ24AUafoDB"
927
+ },
928
+ "outputs": [],
929
+ "source": [
930
+ "sales_data = []\n",
931
+ "for _, row in df_books.iterrows():\n",
932
+ " records = generate_sales_profile(row[\"sentiment_label\"])\n",
933
+ " for month, units in records:\n",
934
+ " sales_data.append({\n",
935
+ " \"title\": row[\"title\"],\n",
936
+ " \"month\": month,\n",
937
+ " \"units_sold\": units,\n",
938
+ " \"sentiment_label\": row[\"sentiment_label\"]\n",
939
  " })"
940
  ]
941
  },
 
945
  "id": "4IXZKcCSgxnq"
946
  },
947
  "source": [
948
+ "### *c. ✋🏻🛑⛔️ Create a df_sales DataFrame from sales_data*"
949
+ ]
950
+ },
951
+ {
952
+ "cell_type": "code",
953
+ "execution_count": 22,
954
+ "metadata": {
955
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960
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961
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962
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963
+ {
964
+ "output_type": "execute_result",
965
+ "data": {
966
+ "text/plain": [
967
+ " title month units_sold sentiment_label\n",
968
+ "0 A Light in the Attic 2024-09 127 neutral\n",
969
+ "1 A Light in the Attic 2024-10 136 neutral\n",
970
+ "2 A Light in the Attic 2024-11 129 neutral\n",
971
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972
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973
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974
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995
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996
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997
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998
+ " <th>sentiment_label</th>\n",
999
+ " </tr>\n",
1000
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1006
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1007
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1009
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1014
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1016
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1017
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1018
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1020
+ " <td>129</td>\n",
1021
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1022
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1023
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1024
+ " <th>3</th>\n",
1025
+ " <td>A Light in the Attic</td>\n",
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1027
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1029
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1030
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1031
+ " <th>4</th>\n",
1032
+ " <td>A Light in the Attic</td>\n",
1033
+ " <td>2025-01</td>\n",
1034
+ " <td>135</td>\n",
1035
+ " <td>neutral</td>\n",
1036
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1037
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1038
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+ "\n",
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+ " background-color: #E8F0FE;\n",
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1080
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1096
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1097
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1100
+ " const element = document.querySelector('#df-a851ff68-be2f-4e9a-849f-62e1c2a663f1');\n",
1101
+ " const dataTable =\n",
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+ " [key], {});\n",
1104
+ " if (!dataTable) return;\n",
1105
+ "\n",
1106
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
1107
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1108
+ " + ' to learn more about interactive tables.';\n",
1109
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+ " dataTable['output_type'] = 'display_data';\n",
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1122
+ ],
1123
+ "application/vnd.google.colaboratory.intrinsic+json": {
1124
+ "type": "dataframe",
1125
+ "variable_name": "df_sales",
1126
+ "summary": "{\n \"name\": \"df_sales\",\n \"rows\": 15120,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 799,\n \"samples\": [\n \"Where'd You Go, Bernadette\",\n \"Mockingjay (The Hunger Games #3)\",\n \"The Mindfulness and Acceptance Workbook for Anxiety: A Guide to Breaking Free from Anxiety, Phobias, and Worry Using Acceptance and Commitment Therapy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"month\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 18,\n \"samples\": [\n \"2024-09\",\n \"2024-10\",\n \"2025-05\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"units_sold\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 98,\n \"min\": 0,\n \"max\": 362,\n \"num_unique_values\": 355,\n \"samples\": [\n 247,\n 302,\n 56\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
1127
+ }
1128
+ },
1129
+ "metadata": {},
1130
+ "execution_count": 22
1131
+ }
1132
+ ],
1133
+ "source": [
1134
+ "df_sales = pd.DataFrame(sales_data)\n",
1135
+ "\n",
1136
+ "df_sales.head()"
1137
  ]
1138
  },
 
 
 
 
 
 
 
 
 
1139
  {
1140
  "cell_type": "markdown",
1141
  "metadata": {
 
1147
  },
1148
  {
1149
  "cell_type": "code",
1150
+ "execution_count": 23,
1151
  "metadata": {
1152
  "colab": {
1153
  "base_uri": "https://localhost:8080/"
1154
  },
1155
  "id": "MzbZvLcAhGaH",
1156
+ "outputId": "385401c5-fa65-4eae-fff5-e8ecbcc99fc4"
1157
  },
1158
  "outputs": [
1159
  {
 
1161
  "name": "stdout",
1162
  "text": [
1163
  " title month units_sold sentiment_label\n",
1164
+ "0 A Light in the Attic 2024-09 127 neutral\n",
1165
+ "1 A Light in the Attic 2024-10 136 neutral\n",
1166
+ "2 A Light in the Attic 2024-11 129 neutral\n",
1167
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1168
+ "4 A Light in the Attic 2025-01 135 neutral\n"
1169
  ]
1170
  }
1171
  ],
 
1195
  },
1196
  {
1197
  "cell_type": "code",
1198
+ "execution_count": 24,
1199
  "metadata": {
1200
  "id": "b3cd2a50"
1201
  },
 
1231
  },
1232
  {
1233
  "cell_type": "code",
1234
+ "execution_count": 27,
1235
  "metadata": {
1236
  "id": "l2SRc3PjuTGM"
1237
  },
 
1242
  " title = row['title']\n",
1243
  " sentiment_label = row['sentiment_label']\n",
1244
  " review_pool = synthetic_reviews_by_sentiment[sentiment_label]\n",
1245
+ " # Sample at most the number of available reviews to avoid ValueError\n",
1246
+ " sampled_reviews = random.sample(review_pool, min(10, len(review_pool)))\n",
1247
  " for review_text in sampled_reviews:\n",
1248
  " review_rows.append({\n",
1249
  " \"title\": title,\n",
 
1251
  " \"review_text\": review_text,\n",
1252
  " \"rating\": row['rating'],\n",
1253
  " \"popularity_score\": row['popularity_score']\n",
1254
+ " })\n"
1255
  ]
1256
  },
1257
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1265
  },
1266
  {
1267
  "cell_type": "code",
1268
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1269
  "metadata": {
1270
  "id": "ZUKUqZsuumsp"
1271
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1286
  },
1287
  {
1288
  "cell_type": "code",
1289
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1290
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1291
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1292
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1293
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1294
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1295
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1296
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1297
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1298
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1418
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1419
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1420
  "cell_type": "code",
1421
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1422
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1423
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1424
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1425
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1428
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1429
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1430
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1431
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1432
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1433
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1434
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1435
+ " title sentiment_label \\\n",
1436
+ "0 A Light in the Attic neutral \n",
1437
+ "1 A Light in the Attic neutral \n",
1438
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1439
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1440
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1441
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1442
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1443
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1444
+ "1 Some parts really stood out, others felt a bit... Three 3 \n",
1445
+ "2 It was okay overall. A decent way to pass the ... Three 3 \n",
1446
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1447
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1448
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+ " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
1530
+ " </svg>\n",
1531
+ " </button>\n",
1532
+ "\n",
1533
+ " <style>\n",
1534
+ " .colab-df-container {\n",
1535
+ " display:flex;\n",
1536
+ " gap: 12px;\n",
1537
+ " }\n",
1538
+ "\n",
1539
+ " .colab-df-convert {\n",
1540
+ " background-color: #E8F0FE;\n",
1541
+ " border: none;\n",
1542
+ " border-radius: 50%;\n",
1543
+ " cursor: pointer;\n",
1544
+ " display: none;\n",
1545
+ " fill: #1967D2;\n",
1546
+ " height: 32px;\n",
1547
+ " padding: 0 0 0 0;\n",
1548
+ " width: 32px;\n",
1549
+ " }\n",
1550
+ "\n",
1551
+ " .colab-df-convert:hover {\n",
1552
+ " background-color: #E2EBFA;\n",
1553
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1554
+ " fill: #174EA6;\n",
1555
+ " }\n",
1556
+ "\n",
1557
+ " .colab-df-buttons div {\n",
1558
+ " margin-bottom: 4px;\n",
1559
+ " }\n",
1560
+ "\n",
1561
+ " [theme=dark] .colab-df-convert {\n",
1562
+ " background-color: #3B4455;\n",
1563
+ " fill: #D2E3FC;\n",
1564
+ " }\n",
1565
+ "\n",
1566
+ " [theme=dark] .colab-df-convert:hover {\n",
1567
+ " background-color: #434B5C;\n",
1568
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1569
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1570
+ " fill: #FFFFFF;\n",
1571
+ " }\n",
1572
+ " </style>\n",
1573
+ "\n",
1574
+ " <script>\n",
1575
+ " const buttonEl =\n",
1576
+ " document.querySelector('#df-a985ad05-8a6d-442d-a94f-f2cdc781b1f7 button.colab-df-convert');\n",
1577
+ " buttonEl.style.display =\n",
1578
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1579
+ "\n",
1580
+ " async function convertToInteractive(key) {\n",
1581
+ " const element = document.querySelector('#df-a985ad05-8a6d-442d-a94f-f2cdc781b1f7');\n",
1582
+ " const dataTable =\n",
1583
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1584
+ " [key], {});\n",
1585
+ " if (!dataTable) return;\n",
1586
+ "\n",
1587
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
1588
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1589
+ " + ' to learn more about interactive tables.';\n",
1590
+ " element.innerHTML = '';\n",
1591
+ " dataTable['output_type'] = 'display_data';\n",
1592
+ " await google.colab.output.renderOutput(dataTable, element);\n",
1593
+ " const docLink = document.createElement('div');\n",
1594
+ " docLink.innerHTML = docLinkHtml;\n",
1595
+ " element.appendChild(docLink);\n",
1596
+ " }\n",
1597
+ " </script>\n",
1598
+ " </div>\n",
1599
+ "\n",
1600
+ "\n",
1601
+ " </div>\n",
1602
+ " </div>\n"
1603
+ ],
1604
+ "application/vnd.google.colaboratory.intrinsic+json": {
1605
+ "type": "dataframe",
1606
+ "variable_name": "df_reviews",
1607
+ "summary": "{\n \"name\": \"df_reviews\",\n \"rows\": 2520,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 799,\n \"samples\": [\n \"Where'd You Go, Bernadette\",\n \"Mockingjay (The Hunger Games #3)\",\n \"The Mindfulness and Acceptance Workbook for Anxiety: A Guide to Breaking Free from Anxiety, Phobias, and Worry Using Acceptance and Commitment Therapy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"review_text\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"A compelling and heartwarming read that stayed with me long after I finished.\",\n \"Some parts really stood out, others felt a bit flat.\",\n \"I struggled to get through this one \\u2014 it just didn\\u2019t grab me.\"\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 \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 5,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
1608
+ }
1609
+ },
1610
+ "metadata": {},
1611
+ "execution_count": 30
1612
  }
1613
  ],
1614
+ "source": [
1615
+ "df_reviews.head()"
1616
+ ]
1617
  }
1618
  ],
1619
  "metadata": {
 
1621
  "collapsed_sections": [
1622
  "jpASMyIQMaAq",
1623
  "lquNYCbfL9IM",
 
 
 
 
 
1624
  "p-1Pr2szaqLk",
 
 
 
 
 
1625
  "T8AdKkmASq9a",
1626
+ "7g9gqBgQMtJn"
 
 
 
 
 
 
 
1627
  ],
1628
  "provenance": []
1629
  },
pythonanalysis.ipynb CHANGED
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