{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activity - Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the breast cancer dataset for this activity. The main goal is to create visualization for finding the attributes that could predict or classify the type of tumor."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Attribute                   | Domain  |\n",
    "|-----------------------------|---------|\n",
    "| Sample code number          | id number |\n",
    "| Clump Thickness             | 1 - 10 |\n",
    "| Uniformity of Cell Size     | 1 - 10 |\n",
    "| Uniformity of Cell Shape    | 1 - 10 |\n",
    "| Marginal Adhesion           | 1 - 10 |\n",
    "| Single Epithelial Cell Size | 1 - 10 |\n",
    "| Bare Nuclei                 | 1 - 10 |\n",
    "| Bland Chromatin             | 1 - 10 |\n",
    "| Normal Nucleoli             | 1 - 10 |\n",
    "| Mitoses                     |  1 - 10 |\n",
    "| Class                       |  (2 for benign, 4 for malignant) |\n",
    "\n",
    "More details [here](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>uniformity_cell_size</th>\n",
       "      <th>uniformity_cell_shape</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>single_epithelial_cell_size</th>\n",
       "      <th>bare_nuclei</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_cucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000025</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002945</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015425</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016277</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017023</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         clump_thickness  uniformity_cell_size  uniformity_cell_shape  \\\n",
       "code                                                                    \n",
       "1000025                5                     1                      1   \n",
       "1002945                5                     4                      4   \n",
       "1015425                3                     1                      1   \n",
       "1016277                6                     8                      8   \n",
       "1017023                4                     1                      1   \n",
       "\n",
       "         marginal_adhesion  single_epithelial_cell_size bare_nuclei  \\\n",
       "code                                                                  \n",
       "1000025                  1                            2           1   \n",
       "1002945                  5                            7          10   \n",
       "1015425                  1                            2           2   \n",
       "1016277                  1                            3           4   \n",
       "1017023                  3                            2           1   \n",
       "\n",
       "         bland_chromatin  normal_cucleoli  mitoses  class  \n",
       "code                                                       \n",
       "1000025                3                1        1      2  \n",
       "1002945                3                2        1      2  \n",
       "1015425                3                1        1      2  \n",
       "1016277                3                7        1      2  \n",
       "1017023                3                1        1      2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_data = pd.read_csv(\n",
    "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data\",\n",
    "    names=[\n",
    "        \"code\",\n",
    "        \"clump_thickness\",\n",
    "        \"uniformity_cell_size\",\n",
    "        \"uniformity_cell_shape\",\n",
    "        \"marginal_adhesion\",\n",
    "        \"single_epithelial_cell_size\",\n",
    "        \"bare_nuclei\",\n",
    "        \"bland_chromatin\",\n",
    "        \"normal_cucleoli\",\n",
    "        \"mitoses\",\n",
    "        \"class\",\n",
    "    ],\n",
    "    index_col=0\n",
    ")\n",
    "breast_cancer_data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for this time we will drop the `bare_nublei` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clump_thickness                 int64\n",
       "uniformity_cell_size            int64\n",
       "uniformity_cell_shape           int64\n",
       "marginal_adhesion               int64\n",
       "single_epithelial_cell_size     int64\n",
       "bare_nuclei                    object\n",
       "bland_chromatin                 int64\n",
       "normal_cucleoli                 int64\n",
       "mitoses                         int64\n",
       "class                           int64\n",
       "class_name                     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "breast_cancer_data.drop(columns=\"bare_nuclei\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clump_thickness                 int64\n",
       "uniformity_cell_size            int64\n",
       "uniformity_cell_shape           int64\n",
       "marginal_adhesion               int64\n",
       "single_epithelial_cell_size     int64\n",
       "bland_chromatin                 int64\n",
       "normal_cucleoli                 int64\n",
       "mitoses                         int64\n",
       "class                           int64\n",
       "class_name                     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_data.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's add a categorical column with the type of tumor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_dict = {2: \"benign\", 4: \"malignant\"}\n",
    "breast_cancer_data[\"class_name\"] = breast_cancer_data[\"class\"].map(class_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>uniformity_cell_size</th>\n",
       "      <th>uniformity_cell_shape</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>single_epithelial_cell_size</th>\n",
       "      <th>bare_nuclei</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_cucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "      <th>class_name</th>\n",
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       "    <tr>\n",
       "      <th>code</th>\n",
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       "      <th>1000025</th>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>benign</td>\n",
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       "    <tr>\n",
       "      <th>1002945</th>\n",
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       "      <td>benign</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016277</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017023</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         clump_thickness  uniformity_cell_size  uniformity_cell_shape  \\\n",
       "code                                                                    \n",
       "1000025                5                     1                      1   \n",
       "1002945                5                     4                      4   \n",
       "1015425                3                     1                      1   \n",
       "1016277                6                     8                      8   \n",
       "1017023                4                     1                      1   \n",
       "\n",
       "         marginal_adhesion  single_epithelial_cell_size bare_nuclei  \\\n",
       "code                                                                  \n",
       "1000025                  1                            2           1   \n",
       "1002945                  5                            7          10   \n",
       "1015425                  1                            2           2   \n",
       "1016277                  1                            3           4   \n",
       "1017023                  3                            2           1   \n",
       "\n",
       "         bland_chromatin  normal_cucleoli  mitoses  class class_name  \n",
       "code                                                                  \n",
       "1000025                3                1        1      2     benign  \n",
       "1002945                3                2        1      2     benign  \n",
       "1015425                3                1        1      2     benign  \n",
       "1016277                3                7        1      2     benign  \n",
       "1017023                3                1        1      2     benign  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='clump_thickness', ylabel='uniformity_cell_size'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(\n",
    "    data=breast_cancer_data,\n",
    "    x=\"clump_thickness\",\n",
    "    y=\"uniformity_cell_size\",\n",
    "    hue=\"class_name\"\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your turn!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "casbbi-nrt-ds",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}