pythonanalysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kz8lLSv6mVQo"
      },
      "source": [
        "# **🤖 Data Analysis & Visualization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpASMyIQMaAq"
      },
      "source": [
        "## **1.** 📦 Install required packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f48c8f8c",
        "outputId": "31fb8283-7f35-4fb0-f270-7c040da95ed4",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
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            "Collecting faker\n",
            "  Downloading faker-40.8.0-py3-none-any.whl.metadata (16 kB)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (5.0.0)\n",
            "Collecting vaderSentiment\n",
            "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl.metadata (572 bytes)\n",
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            "Downloading faker-40.8.0-py3-none-any.whl (2.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.0/126.0 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: faker, vaderSentiment\n",
            "Successfully installed faker-40.8.0 vaderSentiment-3.3.2\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas matplotlib seaborn numpy textblob faker transformers vaderSentiment\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZd99NpKkKyp"
      },
      "source": [
        "## **2.** ✅️ Load & inspect input datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_JBLmm508Uq2"
      },
      "source": [
        "### *a. Initial setup*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eBDXPQz18Xrs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IL8lZbMm8m3k"
      },
      "source": [
        "### *b. ✋🏻🛑⛔️ Create the df_reviews dataframe from the synthetic_book_reviews.csv file*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fdgjghfO8uuq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "e14f27ca-3651-4e5f-e8d6-fcb1446e9ec9"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'synthetic_book_reviews.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipykernel_347/448808692.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_reviews\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"synthetic_book_reviews.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1024\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1619\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1620\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1622\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1879\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1880\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1881\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1882\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    871\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    874\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    875\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'synthetic_book_reviews.csv'"
          ]
        }
      ],
      "source": [
        "df_reviews = pd.read_csv(\"synthetic_book_reviews.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-Dl37J0HLhU"
      },
      "source": [
        "### *c. ✋🏻🛑⛔️ Create the df_sales dataframe from the synthetic_sales_data.csv file*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6XZs3P7fHgQe"
      },
      "outputs": [],
      "source": [
        "df_sales = pd.read_csv(\"synthetic_sales_data.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUI3SkmyrGQo"
      },
      "source": [
        "### *d. ✋🏻🛑⛔️ Visualize the first few lines of the two final datasets: df_reviews and df_sales*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p8FdQFXErOqE",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "df_reviews.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales.head()"
      ],
      "metadata": {
        "collapsed": true,
        "id": "EDdSx5KMNjiB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3oqGHsmrQzx"
      },
      "source": [
        "### *d. Run a quality check on the datasets*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VArQGPoKrfLm",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "def quality_check(df, name=\"DataFrame\"):\n",
        "    print(f\"\\n🔍 Quality Check Report for: {name}\")\n",
        "    print(\"=\" * (25 + len(name)))\n",
        "\n",
        "    # Basic info\n",
        "    print(f\"\\n📏 Shape: {df.shape}\")\n",
        "    print(\"\\n🔠 Column Types:\")\n",
        "    print(df.dtypes)\n",
        "\n",
        "    # Missing values\n",
        "    print(\"\\n❓ Missing Values:\")\n",
        "    print(df.isnull().sum())\n",
        "\n",
        "    # Duplicates\n",
        "    duplicate_count = df.duplicated().sum()\n",
        "    print(f\"\\n📋 Duplicate Rows: {duplicate_count}\")\n",
        "\n",
        "    # Summary stats\n",
        "    print(\"\\n📊 Summary Statistics:\")\n",
        "    display(df.describe(include='all').transpose())\n",
        "\n",
        "    # Sample rows\n",
        "    print(\"\\n👀 Sample Rows:\")\n",
        "    display(df.sample(5))\n",
        "\n",
        "# Run checks\n",
        "quality_check(df_reviews, \"df_reviews\")\n",
        "quality_check(df_sales, \"df_sales\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTxUKDYINPxV"
      },
      "source": [
        "## **3.** 🎭 Perform sentiment analysis using VADER"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqhYU8rDxQRT"
      },
      "source": [
        "### *a. Initial setup*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DNk5w8mNxSZ6"
      },
      "outputs": [],
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "# 🤖 Initialize VADER analyzer\n",
        "analyzer = SentimentIntensityAnalyzer()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P123TwSWxVAr"
      },
      "source": [
        "### *b. Create a function get_sentiment_label that will return the label negative, neutral, or positive based on the VADER analyzer's scoring of the text*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "89809e6f"
      },
      "outputs": [],
      "source": [
        "def get_sentiment_label(text):\n",
        "    score = analyzer.polarity_scores(text)[\"compound\"]\n",
        "    if score >= 0.05:\n",
        "        return \"positive\"\n",
        "    elif score <= -0.05:\n",
        "        return \"negative\"\n",
        "    else:\n",
        "        return \"neutral\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DS9eCZ95yQn3"
      },
      "source": [
        "### *c. ✋🏻🛑⛔️ Apply get_sentiment_label to df_reviews column named review_text to get sentiment_label column*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SpXzFaDfyM7I"
      },
      "outputs": [],
      "source": [
        "# Create sentiment_label column using VADER sentiment analysis\n",
        "df_reviews[\"sentiment_label\"] = df_reviews[\"review_text\"].apply(get_sentiment_label)\n",
        "\n",
        "# Preview results\n",
        "df_reviews.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cnPCFFnyXN6"
      },
      "source": [
        "### *d. ✋🏻🛑⛔️ View the first few lines of the resulting table df_reviews*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODGyfjBSyZEO"
      },
      "outputs": [],
      "source": [
        "df_reviews.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qy3Hqm-FojvT"
      },
      "source": [
        "## **4.** 📊 Use the following data visualization code snippets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcjGSw2bzqtZ"
      },
      "source": [
        "### *a. Initial setup*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p5LV2o1rzsiC"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib.dates as mdates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tvaBtswpGS__"
      },
      "outputs": [],
      "source": [
        "# ----------------------------\n",
        "# Outputs (for Hugging Face app)\n",
        "# ----------------------------\n",
        "# In the notebook: you still SEE interactive tables/plots inline.\n",
        "# For the Space dashboard: we also SAVE the same outputs as files.\n",
        "\n",
        "from pathlib import Path\n",
        "\n",
        "ART_DIR = Path(\"artifacts\")\n",
        "PY_FIG = ART_DIR / \"py\" / \"figures\"\n",
        "PY_TAB = ART_DIR / \"py\" / \"tables\"\n",
        "\n",
        "for p in [PY_FIG, PY_TAB]:\n",
        "    p.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "print(\"✅ Output folders:\")\n",
        "print(\" -\", PY_FIG.resolve())\n",
        "print(\" -\", PY_TAB.resolve())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9T1rkBe0AJU"
      },
      "source": [
        "### *b. Sample of 5 books for each popularity level for visualizations*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sLdFmGqXqo_t"
      },
      "outputs": [],
      "source": [
        "sampled_titles = []\n",
        "for pop_score in sorted(df_reviews[\"popularity_score\"].dropna().unique()):\n",
        "    all_titles = df_reviews[df_reviews[\"popularity_score\"] == pop_score][\"title\"].unique()\n",
        "    sampled = random.sample(list(all_titles), min(5, len(all_titles)))\n",
        "    sampled_titles.extend(sampled)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xq7-C8m70mMH"
      },
      "source": [
        "### *c. Copy relevant sales, reviews, and book names*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "laDdMece0qrq"
      },
      "outputs": [],
      "source": [
        "sampled_sales = df_sales[df_sales[\"title\"].isin(sampled_titles)].copy()\n",
        "sampled_reviews = df_reviews[df_reviews[\"title\"].isin(sampled_titles)].copy()\n",
        "sampled_books = df_reviews[df_reviews[\"title\"].isin(sampled_titles)].copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YtfkG_A0wTy"
      },
      "source": [
        "### *d. Plot sales trends over time for the sampled books*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1iTVzflW0Rkw"
      },
      "outputs": [],
      "source": [
        "# 🕒 Ensure datetime format\n",
        "df_sales[\"month\"] = pd.to_datetime(df_sales[\"month\"])\n",
        "# 🎨 Color mapping\n",
        "popularity_colors = {\n",
        "    1: \"darkred\", 2: \"orangered\", 3: \"gold\", 4: \"mediumseagreen\", 5: \"royalblue\"\n",
        "}\n",
        "\n",
        "# 📈 Plot 1: Sales trends\n",
        "plt.figure(figsize=(20, 8))\n",
        "for title in sampled_titles:\n",
        "    row = sampled_books[sampled_books[\"title\"] == title].iloc[0]\n",
        "    color = popularity_colors.get(row[\"popularity_score\"], \"gray\")\n",
        "    subset = sampled_sales[sampled_sales[\"title\"] == title]\n",
        "    plt.plot(subset[\"month\"], subset[\"units_sold\"], label=f\"{title} (Pop. {row['popularity_score']})\", color=color)\n",
        "\n",
        "plt.title(\"📈 Sales Trends Over Time (5 per Popularity Level)\")\n",
        "plt.xlabel(\"Month\")\n",
        "plt.ylabel(\"Units Sold\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize='small')\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n",
        "plt.savefig(PY_FIG / 'sales_trends_sampled_titles.png', dpi=150)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDpMkjDP1K6j"
      },
      "source": [
        "### *e. Plot sentiment_label distribution per book*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dn1Jgd5R1KLu"
      },
      "outputs": [],
      "source": [
        "# 🎨 Give a new name to each book that includes the rating together with the title\n",
        "sampled_reviews[\"grouped_title\"] = sampled_reviews[\"rating\"].astype(str) + \"★ | \" + sampled_reviews[\"title\"]\n",
        "\n",
        "# 📊 Aggregate sentiment counts\n",
        "sentiment_counts = (\n",
        "    sampled_reviews.groupby([\"grouped_title\", \"sentiment_label\"])\n",
        "    .size()\n",
        "    .unstack(fill_value=0)[[\"negative\", \"neutral\", \"positive\"]]  # consistent order\n",
        ")\n",
        "\n",
        "# 💾 Save table for HF dashboard\n",
        "sentiment_counts.reset_index().to_csv(PY_TAB / 'sentiment_counts_sampled.csv', index=False)\n",
        "\n",
        "\n",
        "# ✅ Plot stacked horizontal bars\n",
        "fig, ax = plt.subplots(figsize=(12, 14))\n",
        "sentiment_counts.plot.barh(\n",
        "    stacked=True,\n",
        "    ax=ax,\n",
        "    color={\"negative\": \"royalblue\", \"neutral\": \"lightgray\", \"positive\": \"crimson\"}\n",
        ")\n",
        "\n",
        "plt.title(\"💬 Sentiment Distribution in Reviews (5 Books per Popularity Level)\", fontsize=14)\n",
        "plt.xlabel(\"Number of Reviews\")\n",
        "plt.ylabel(\"Book Title (Grouped by Popularity Score)\")\n",
        "plt.legend(title=\"Sentiment\", loc=\"lower right\")\n",
        "plt.grid(axis=\"x\", linestyle=\"--\", alpha=0.6)\n",
        "plt.tight_layout()\n",
        "plt.savefig(PY_FIG / 'sentiment_distribution_sampled_titles.png', dpi=150)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmgylC1ENCHy"
      },
      "source": [
        "## **5.** 🔮 Forecast book sales with the following  ARIMA code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFV4JE1R3FKH"
      },
      "source": [
        "### *a. Initial setup*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mh8Alha03H22"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "import statsmodels.api as sm\n",
        "from itertools import product\n",
        "import matplotlib.cm as cm\n",
        "import warnings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHucD8OW3U0w"
      },
      "source": [
        "### *b. Define function find_best_arima to try different ARIMA parameter values and return the best combination for each book's price forecast*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "477fa43f"
      },
      "outputs": [],
      "source": [
        "def find_best_arima(series, p_range=(0, 5), d_range=(0, 2), q_range=(0, 1)):\n",
        "    best_aic = float(\"inf\")\n",
        "    best_order = None\n",
        "    best_model = None\n",
        "\n",
        "    for p, d, q in product(range(p_range[0], p_range[1] + 1),\n",
        "                           range(d_range[0], d_range[1] + 1),\n",
        "                           range(q_range[0], q_range[1] + 1)):\n",
        "        try:\n",
        "            model = sm.tsa.ARIMA(series, order=(p, d, q))\n",
        "            results = model.fit()\n",
        "            if results.aic < best_aic:\n",
        "                best_aic = results.aic\n",
        "                best_order = (p, d, q)\n",
        "                best_model = results\n",
        "        except:\n",
        "            continue\n",
        "\n",
        "    return best_order, best_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rq5t1Hey3jkD"
      },
      "source": [
        "### *c. Plot the figure*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DmxGdvLE3dHQ"
      },
      "outputs": [],
      "source": [
        "# 🎨 Generate 25 highly distinct colors using HUSL (hue-saturation-lightness)\n",
        "colors = sns.color_palette(\"tab10\", len(sampled_titles))\n",
        "\n",
        "plt.figure(figsize=(16, 10))\n",
        "\n",
        "for i, title in enumerate(sampled_titles):\n",
        "    book_sales = sampled_sales[sampled_sales[\"title\"] == title].copy()\n",
        "    book_sales[\"month\"] = pd.to_datetime(book_sales[\"month\"])\n",
        "    book_sales = book_sales.sort_values(\"month\").set_index(\"month\")\n",
        "\n",
        "    with warnings.catch_warnings():\n",
        "        warnings.simplefilter(\"ignore\")\n",
        "        best_order, best_model = find_best_arima(book_sales[\"units_sold\"])\n",
        "        if best_model is not None:\n",
        "            forecast = best_model.get_forecast(steps=6)\n",
        "            forecast_index = pd.date_range(start=book_sales.index[-1] + pd.DateOffset(months=1), periods=6, freq='MS')\n",
        "\n",
        "            # 🟦 Plot observed sales (solid line)\n",
        "            plt.plot(book_sales.index, book_sales[\"units_sold\"], color=colors[i], label=title, linewidth=2)\n",
        "\n",
        "            # 🟠 Plot forecast (dotted line, same color)\n",
        "            plt.plot(forecast_index, forecast.predicted_mean, linestyle=\"--\", color=colors[i], linewidth=2)\n",
        "\n",
        "# 📈 Final formatting\n",
        "plt.title(\"📈 ARIMA Forecasts for Sampled Books (1 per Popularity Level)\", fontsize=14)\n",
        "plt.xlabel(\"Month\")\n",
        "plt.ylabel(\"Units Sold\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n",
        "plt.grid(True)\n",
        "plt.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), fontsize=\"small\")\n",
        "plt.tight_layout()\n",
        "plt.savefig(PY_FIG / 'arima_forecasts_sampled_titles.png', dpi=150)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKBcx3fyCFly"
      },
      "source": [
        "## **6.** 🏷️ Decide on price changes with a rule-based approach based on sentiment and future revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nY-vV2JJDZqu"
      },
      "source": [
        "### *a. Calculate average sales per book*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nbDT_RHaDD2R"
      },
      "outputs": [],
      "source": [
        "avg_sales = df_sales.groupby(\"title\")[\"units_sold\"].mean().reset_index()\n",
        "avg_sales.columns = [\"title\", \"avg_units_sold\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94wi-RvkDf2z"
      },
      "source": [
        "### *b. Calculate sentiment distribution per book*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fWjQ9IOXDk-M"
      },
      "outputs": [],
      "source": [
        "sentiment_counts = df_reviews.groupby([\"title\", \"sentiment_label\"]).size().unstack(fill_value=0)\n",
        "sentiment_counts[\"total\"] = sentiment_counts.sum(axis=1)\n",
        "sentiment_counts[\"positive_ratio\"] = sentiment_counts.get(\"positive\") / sentiment_counts[\"total\"]\n",
        "sentiment_counts[\"negative_ratio\"] = sentiment_counts.get(\"negative\") / sentiment_counts[\"total\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vm10ym_iDtEW"
      },
      "source": [
        "### *c. Merge the calculated sales and sentiment characteristics*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-zlh6rBDpxg"
      },
      "outputs": [],
      "source": [
        "df_decision = avg_sales.merge(sentiment_counts, on=\"title\", how=\"left\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WIWDojyD7fK"
      },
      "source": [
        "### *d. ✋🏻🛑⛔️ Create the pricing_decision function as a basic rule-based pricing decider based on sentiment and revenue*\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5qJCb46Dxfb"
      },
      "source": [
        "*   If there are 120 or more average units sold and 0.6 or higher positive ratio, the decision should be to increase price.\n",
        "*   If there are 60 or less average units sold and 0.4 or higher negative ratio, the decision should be to decrease price.\n",
        "*   Otherwise, the price should be kept the same."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XBzozedwD6yx"
      },
      "outputs": [],
      "source": [
        "def pricing_decision(row):\n",
        "    avg_units = row[\"avg_units_sold\"]\n",
        "    positive_ratio = row.get(\"positive_ratio\", 0)\n",
        "    negative_ratio = row.get(\"negative_ratio\", 0)\n",
        "\n",
        "    if avg_units >= 120 and positive_ratio >= 0.6:\n",
        "        return \"increase price\"\n",
        "    elif avg_units <= 60 and negative_ratio >= 0.4:\n",
        "        return \"decrease price\"\n",
        "    else:\n",
        "        return \"keep price\"\n",
        "\n",
        "df_decision[\"pricing_action\"] = df_decision.apply(pricing_decision, axis=1)\n",
        "\n",
        "# Preview results\n",
        "print(df_decision.head())\n",
        "\n",
        "# Optional: distribution of decisions\n",
        "print(df_decision[\"pricing_action\"].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmLEdF14EPAA"
      },
      "source": [
        "### *e. ✋🏻🛑⛔️ Run the pricing_decision function and check out the first few decisions*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TZ0ZhgHrEQJB"
      },
      "outputs": [],
      "source": [
        "# Apply the pricing decision rule to each row\n",
        "df_decision[\"pricing_action\"] = df_decision.apply(pricing_decision, axis=1)\n",
        "\n",
        "# Display the first few pricing decisions\n",
        "df_decision[[\"title\", \"avg_units_sold\", \"positive_ratio\", \"negative_ratio\", \"pricing_action\"]].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTkP2_-EApev"
      },
      "source": [
        "\n",
        "## **7.** 💾 Save Python outputs for the Hugging Face dashboard"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EIjfnokGpJv"
      },
      "source": [
        "\n",
        "This section exports **HF-ready artifacts** into a consistent folder structure:\n",
        "\n",
        "- `(root folder)py/figures/` (Python-generated visuals)\n",
        "- `(root folder)py/tables/` (tables/metrics)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZJJ4PMgIApev"
      },
      "outputs": [],
      "source": [
        "\n",
        "import json\n",
        "\n",
        "# -------------------------\n",
        "# 1) Dashboard table (monthly) — reuse if already built\n",
        "# -------------------------\n",
        "if \"df_monthly\" in globals() and df_monthly is not None:\n",
        "    df_dashboard = df_monthly.copy()\n",
        "else:\n",
        "    # fallback: monthly units sold only\n",
        "    df_dashboard = (\n",
        "        df_sales.groupby(\"month\", as_index=False)\n",
        "        .agg(total_units_sold=(\"units_sold\", \"sum\"))\n",
        "        .sort_values(\"month\")\n",
        "    )\n",
        "\n",
        "# Save the single overview dashboard table\n",
        "df_dashboard.to_csv(PY_TAB / \"df_dashboard.csv\", index=False)\n",
        "\n",
        "# -------------------------\n",
        "# 2) KPI summary (small json) — computed from raw df_sales + df_dashboard\n",
        "# -------------------------\n",
        "kpis = {\n",
        "    \"n_titles\": int(df_sales[\"title\"].nunique()),\n",
        "    \"n_months\": int(df_dashboard[\"month\"].nunique()),\n",
        "    \"total_units_sold\": float(df_sales[\"units_sold\"].sum()),\n",
        "}\n",
        "\n",
        "# Only include revenue KPIs if df_dashboard contains it (since you said monthly revenue already exists)\n",
        "if \"total_revenue\" in df_dashboard.columns and df_dashboard[\"total_revenue\"].notna().any():\n",
        "    kpis[\"total_revenue\"] = float(df_dashboard[\"total_revenue\"].sum())\n",
        "\n",
        "with open(PY_FIG / \"kpis.json\", \"w\", encoding=\"utf-8\") as f:\n",
        "    json.dump(kpis, f, indent=2)\n",
        "\n",
        "# -------------------------\n",
        "# 3) Python tables (title-level quick inspection)\n",
        "# -------------------------\n",
        "df_by_title_units = (\n",
        "    df_sales.groupby(\"title\", as_index=False)\n",
        "    .agg(total_units_sold=(\"units_sold\", \"sum\"))\n",
        "    .sort_values(\"total_units_sold\", ascending=False)\n",
        ")\n",
        "df_by_title_units.head(10).to_csv(PY_TAB / \"top_titles_by_units_sold.csv\", index=False)\n",
        "\n",
        "# Optional: title-level revenue table ONLY if df_sales already has per-row revenue\n",
        "if \"revenue\" in df_sales.columns and df_sales[\"revenue\"].notna().any():\n",
        "    df_by_title_rev = (\n",
        "        df_sales.groupby(\"title\", as_index=False)\n",
        "        .agg(total_revenue=(\"revenue\", \"sum\"))\n",
        "        .sort_values(\"total_revenue\", ascending=False)\n",
        "    )\n",
        "    df_by_title_rev.head(10).to_csv(PY_TAB / \"top_titles_by_revenue.csv\", index=False)\n",
        "\n",
        "print(\"✅ Exports written to artifacts/:\")\n",
        "print(\" - common/: df_dashboard.csv, kpis.json\")\n",
        "print(\" - py/tables/: top_titles_by_units_sold.csv (+ optional top_titles_by_revenue.csv)\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0b4e76d3"
      },
      "source": [
        "✅ **Extra outputs for the R notebook**: `(root folder)common/r_input_title_level.csv` and `(root folder)common/r_input_monthly_revenue.csv` (these are the only two files the R portion needs)."
      ]
    }
  ],
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