Code-Example-040.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 4: Data Preprocessing\n",
    "\n",
    "The following tutorial contains Python examples for data preprocessing. You should refer to the \"Data\" chapter of the \"Introduction to Data Mining\" book (slides are available at https://www-users.cs.umn.edu/~kumar001/dmbook/index.php) to understand some of the concepts introduced in this tutorial. The notebook can be downloaded from http://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial4/tutorial4.ipynb.\n",
    "\n",
    "Data preprocessing consists of a broad set of techniques for cleaning, selecting, and transforming data to improve data mining analysis. Read the step-by-step instructions below carefully. To execute the code, click on the corresponding cell and press the SHIFT-ENTER keys simultaneously."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Data Quality Issues\n",
    "\n",
    "Poor data quality can have an adverse effect on data mining. Among the common data quality issues include noise, outliers, missing values, and duplicate data. This section presents examples of Python code to alleviate some of these data quality problems. We begin with an example dataset from the UCI machine learning repository containing information about breast cancer patients. We will first download the dataset using Pandas read_csv() function and display its first 5 data points.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances = 699\n",
      "Number of attributes = 10\n"
     ]
    },
    {
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       "<div>\n",
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       "\n",
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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 of Cell Size</th>\n",
       "      <th>Uniformity of 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 Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>7</td>\n",
       "      <td>10</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</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>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "   Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
       "0                5                        1                         1   \n",
       "1                5                        4                         4   \n",
       "2                3                        1                         1   \n",
       "3                6                        8                         8   \n",
       "4                4                        1                         1   \n",
       "\n",
       "   Marginal Adhesion  Single Epithelial Cell Size Bare Nuclei  \\\n",
       "0                  1                            2           1   \n",
       "1                  5                            7          10   \n",
       "2                  1                            2           2   \n",
       "3                  1                            3           4   \n",
       "4                  3                            2           1   \n",
       "\n",
       "   Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "0                3                1        1      2  \n",
       "1                3                2        1      2  \n",
       "2                3                1        1      2  \n",
       "3                3                7        1      2  \n",
       "4                3                1        1      2  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)\n",
    "data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape',\n",
    "                'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',\n",
    "                'Normal Nucleoli', 'Mitoses','Class']\n",
    "\n",
    "data = data.drop(['Sample code'],axis=1)\n",
    "print('Number of instances = %d' % (data.shape[0]))\n",
    "print('Number of attributes = %d' % (data.shape[1]))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.1 Missing Values\n",
    "\n",
    "It is not unusual for an object to be missing one or more attribute values. In some cases, the information was not collected; while in other cases, some attributes are inapplicable to the data instances. This section presents examples on the different approaches for handling missing values. \n",
    "\n",
    "According to the description of the data (https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original), the missing values are encoded as '?' in the original data. Our first task is to convert the missing values to NaNs. We can then count the number of missing values in each column of the data.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances = 699\n",
      "Number of attributes = 10\n",
      "Number of missing values:\n",
      "\tClump Thickness: 0\n",
      "\tUniformity of Cell Size: 0\n",
      "\tUniformity of Cell Shape: 0\n",
      "\tMarginal Adhesion: 0\n",
      "\tSingle Epithelial Cell Size: 0\n",
      "\tBare Nuclei: 16\n",
      "\tBland Chromatin: 0\n",
      "\tNormal Nucleoli: 0\n",
      "\tMitoses: 0\n",
      "\tClass: 0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = data.replace('?',np.NaN)\n",
    "\n",
    "print('Number of instances = %d' % (data.shape[0]))\n",
    "print('Number of attributes = %d' % (data.shape[1]))\n",
    "\n",
    "print('Number of missing values:')\n",
    "for col in data.columns:\n",
    "    print('\\t%s: %d' % (col,data[col].isna().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe that only the 'Bare Nuclei' column contains missing values. In the following example, the missing values in the 'Bare Nuclei' column are replaced by the median value of that column. The values before and after replacement are shown for a subset of the data points.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before replacing missing values:\n",
      "20     10\n",
      "21      7\n",
      "22      1\n",
      "23    NaN\n",
      "24      1\n",
      "Name: Bare Nuclei, dtype: object\n",
      "\n",
      "After replacing missing values:\n",
      "20    10\n",
      "21     7\n",
      "22     1\n",
      "23     1\n",
      "24     1\n",
      "Name: Bare Nuclei, dtype: object\n"
     ]
    }
   ],
   "source": [
    "data2 = data['Bare Nuclei']\n",
    "\n",
    "print('Before replacing missing values:')\n",
    "print(data2[20:25])\n",
    "data2 = data2.fillna(data2.median())\n",
    "\n",
    "print('\\nAfter replacing missing values:')\n",
    "print(data2[20:25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of replacing the missing values, another common approach is to discard the data points that contain missing values. This can be easily accomplished by applying the dropna() function to the data frame.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in original data = 699\n",
      "Number of rows after discarding missing values = 683\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows in original data = %d' % (data.shape[0]))\n",
    "\n",
    "data2 = data.dropna()\n",
    "print('Number of rows after discarding missing values = %d' % (data2.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.2 Outliers\n",
    "\n",
    "Outliers are data instances with characteristics that are considerably different from the rest of the dataset. In the example code below, we will draw a boxplot to identify the columns in the table that contain outliers. Note that the values in all columns (except for 'Bare Nuclei') are originally stored as 'int64' whereas the values in the 'Bare Nuclei' column are stored as string objects (since the column initially contains strings such as '?' for representing missing values). Thus, we must  convert the column into numeric values first before creating the boxplot. Otherwise, the column will not be displayed when drawing the boxplot.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fee2f317d60>"
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     "execution_count": 5,
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     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "data2 = data.drop(['Class'],axis=1)\n",
    "data2['Bare Nuclei'] = pd.to_numeric(data2['Bare Nuclei'])\n",
    "data2.boxplot(figsize=(20,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplots suggest that only 5 of the columns (Marginal Adhesion, Single Epithetial Cell Size, Bland Cromatin, Normal Nucleoli, and Mitoses) contain abnormally high values. To discard the outliers, we can compute the Z-score for each attribute and remove those instances containing attributes with abnormally high or low Z-score (e.g., if Z > 3 or Z <= -3). \n",
    "\n",
    "**<font color=\"red\">Code:</font>**\n",
    "\n",
    "The following code shows the results of standardizing the columns of the data. Note that missing values (NaN) are not affected by the standardization process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of 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 Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.917080</td>\n",
       "      <td>-0.044070</td>\n",
       "      <td>-0.406284</td>\n",
       "      <td>2.519152</td>\n",
       "      <td>0.805662</td>\n",
       "      <td>1.771569</td>\n",
       "      <td>0.640688</td>\n",
       "      <td>0.371049</td>\n",
       "      <td>1.405526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.982519</td>\n",
       "      <td>0.611354</td>\n",
       "      <td>0.603167</td>\n",
       "      <td>0.067638</td>\n",
       "      <td>1.257272</td>\n",
       "      <td>0.948266</td>\n",
       "      <td>1.460910</td>\n",
       "      <td>2.335921</td>\n",
       "      <td>-0.343666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.503505</td>\n",
       "      <td>-0.699494</td>\n",
       "      <td>-0.742767</td>\n",
       "      <td>-0.632794</td>\n",
       "      <td>-0.549168</td>\n",
       "      <td>-0.698341</td>\n",
       "      <td>-0.589645</td>\n",
       "      <td>-0.611387</td>\n",
       "      <td>-0.343666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.272227</td>\n",
       "      <td>0.283642</td>\n",
       "      <td>0.603167</td>\n",
       "      <td>-0.632794</td>\n",
       "      <td>-0.549168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.460910</td>\n",
       "      <td>0.043570</td>\n",
       "      <td>-0.343666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-1.213798</td>\n",
       "      <td>-0.699494</td>\n",
       "      <td>-0.742767</td>\n",
       "      <td>-0.632794</td>\n",
       "      <td>-0.549168</td>\n",
       "      <td>-0.698341</td>\n",
       "      <td>-0.179534</td>\n",
       "      <td>-0.611387</td>\n",
       "      <td>-0.343666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
       "20         0.917080                -0.044070                 -0.406284   \n",
       "21         1.982519                 0.611354                  0.603167   \n",
       "22        -0.503505                -0.699494                 -0.742767   \n",
       "23         1.272227                 0.283642                  0.603167   \n",
       "24        -1.213798                -0.699494                 -0.742767   \n",
       "\n",
       "    Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
       "20           2.519152                     0.805662     1.771569   \n",
       "21           0.067638                     1.257272     0.948266   \n",
       "22          -0.632794                    -0.549168    -0.698341   \n",
       "23          -0.632794                    -0.549168          NaN   \n",
       "24          -0.632794                    -0.549168    -0.698341   \n",
       "\n",
       "    Bland Chromatin  Normal Nucleoli   Mitoses  \n",
       "20         0.640688         0.371049  1.405526  \n",
       "21         1.460910         2.335921 -0.343666  \n",
       "22        -0.589645        -0.611387 -0.343666  \n",
       "23         1.460910         0.043570 -0.343666  \n",
       "24        -0.179534        -0.611387 -0.343666  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = (data2-data2.mean())/data2.std()\n",
    "Z[20:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=\"red\">Code:</font>**\n",
    "\n",
    "The following code shows the results of discarding columns with Z > 3 or Z <= -3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows before discarding outliers = 699\n",
      "Number of rows after discarding missing values = 632\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows before discarding outliers = %d' % (Z.shape[0]))\n",
    "\n",
    "Z2 = Z.loc[((Z > -3).sum(axis=1)==9) & ((Z <= 3).sum(axis=1)==9),:]\n",
    "print('Number of rows after discarding missing values = %d' % (Z2.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.3 Duplicate Data\n",
    "\n",
    "Some datasets, especially those obtained by merging multiple data sources, may contain duplicates or near duplicate instances. The term deduplication is often used to refer to the process of dealing with duplicate data issues. \n",
    "\n",
    "**<font color=\"red\">Code:</font>**\n",
    "\n",
    "In the following example, we first check for duplicate instances in the breast cancer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows = 236\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of 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 Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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 of Cell Size  Uniformity of Cell Shape  \\\n",
       "11                2                        1                         1   \n",
       "28                2                        1                         1   \n",
       "\n",
       "    Marginal Adhesion  Single Epithelial Cell Size Bare Nuclei  \\\n",
       "11                  1                            2           1   \n",
       "28                  1                            2           1   \n",
       "\n",
       "    Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "11                2                1        1      2  \n",
       "28                2                1        1      2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dups = data.duplicated()\n",
    "print('Number of duplicate rows = %d' % (dups.sum()))\n",
    "data.loc[[11,28]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The duplicated() function will return a Boolean array that indicates whether each row is a duplicate of a previous row in the table. The results suggest there are 236 duplicate rows in the breast cancer dataset. For example, the instance with row index 11 has identical attribute values as the instance with row index 28. Although such duplicate rows may correspond to samples for different individuals, in this hypothetical example, we assume that the duplicates are samples taken from the same individual and illustrate below how to remove the duplicated rows.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows before discarding duplicates = 699\n",
      "Number of rows after discarding duplicates = 463\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows before discarding duplicates = %d' % (data.shape[0]))\n",
    "data2 = data.drop_duplicates()\n",
    "print('Number of rows after discarding duplicates = %d' % (data2.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Aggregation\n",
    "\n",
    "Data aggregation is a preprocessing task where the values of two or more objects are combined into a single object. The motivation for aggregation includes (1) reducing the size of data to be processed, (2) changing the granularity of analysis (from fine-scale to coarser-scale), and (3) improving the stability of the data.\n",
    "\n",
    "In the example below, we will use the daily precipitation time series data for a weather station located at Detroit Metro Airport. The raw data was obtained from the Climate Data Online website (https://www.ncdc.noaa.gov/cdo-web/). The daily precipitation time series will be compared against its monthly values.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**\n",
    "\n",
    "The code below will load the precipitation time series data and draw a line plot of its daily time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Daily Precipitation (variance = 0.0530)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily = pd.read_csv('datasets/DTW_prec.csv', header='infer')\n",
    "daily.index = pd.to_datetime(daily['DATE'])\n",
    "daily = daily['PRCP']\n",
    "ax = daily.plot(kind='line',figsize=(15,3))\n",
    "ax.set_title('Daily Precipitation (variance = %.4f)' % (daily.var()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe that the daily time series appear to be quite chaotic and varies significantly from one time step to another. The time series can be grouped and aggregated by month to obtain the total monthly precipitation values. The resulting time series appears to vary more smoothly compared to the daily time series.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Monthly Precipitation (variance = 2.4241)')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly = daily.groupby(pd.Grouper(freq='M')).sum()\n",
    "ax = monthly.plot(kind='line',figsize=(15,3))\n",
    "ax.set_title('Monthly Precipitation (variance = %.4f)' % (monthly.var()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example below, the daily precipitation time series are grouped and aggregated by year to obtain the annual precipitation values. \n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Annual Precipitation (variance = 23.6997)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "annual = daily.groupby(pd.Grouper(freq='Y')).sum()\n",
    "ax = annual.plot(kind='line',figsize=(15,3))\n",
    "ax.set_title('Annual Precipitation (variance = %.4f)' % (annual.var()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Sampling\n",
    "\n",
    "Sampling is an approach commonly used to facilitate (1) data reduction for exploratory data analysis and scaling up algorithms to big data applications and (2) quantifying uncertainties due to varying data distributions. There are various methods available for data sampling, such as sampling without replacement, where each selected instance is removed from the dataset, and sampling with replacement, where each selected instance is not removed, thus allowing it to be selected more than once in the sample.\n",
    "\n",
    "In the example below, we will apply sampling with replacement and without replacement to the breast cancer dataset obtained from the UCI machine learning repository.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**\n",
    "\n",
    "We initially display the first five records of the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of 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 Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>1</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>2</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>3</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>4</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 of Cell Size  Uniformity of Cell Shape  \\\n",
       "0                5                        1                         1   \n",
       "1                5                        4                         4   \n",
       "2                3                        1                         1   \n",
       "3                6                        8                         8   \n",
       "4                4                        1                         1   \n",
       "\n",
       "   Marginal Adhesion  Single Epithelial Cell Size Bare Nuclei  \\\n",
       "0                  1                            2           1   \n",
       "1                  5                            7          10   \n",
       "2                  1                            2           2   \n",
       "3                  1                            3           4   \n",
       "4                  3                            2           1   \n",
       "\n",
       "   Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "0                3                1        1      2  \n",
       "1                3                2        1      2  \n",
       "2                3                1        1      2  \n",
       "3                3                7        1      2  \n",
       "4                3                1        1      2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following code, a sample of size 3 is randomly selected (without replacement) from the original data.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "      <th></th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of 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 Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
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       "      <th>627</th>\n",
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       "      <th>101</th>\n",
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       "      <td>4</td>\n",
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       "      <th>486</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
       "627                2                        1                         1   \n",
       "101                2                        3                         4   \n",
       "486                3                        1                         1   \n",
       "\n",
       "     Marginal Adhesion  Single Epithelial Cell Size Bare Nuclei  \\\n",
       "627                  1                            2           5   \n",
       "101                  4                            2           5   \n",
       "486                  1                            1           1   \n",
       "\n",
       "     Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "627                1                1        1      2  \n",
       "101                2                5        1      4  \n",
       "486                2                1        1      2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = data.sample(n=3)\n",
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next example, we randomly select 1% of the data (without replacement) and display the selected samples. The random_state argument of the function specifies the seed value of the random number generator.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "     Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
       "584                5                        1                         1   \n",
       "417                1                        1                         1   \n",
       "606                4                        1                         1   \n",
       "349                4                        2                         3   \n",
       "134                3                        1                         1   \n",
       "502                4                        1                         1   \n",
       "117                4                        5                         5   \n",
       "\n",
       "     Marginal Adhesion  Single Epithelial Cell Size Bare Nuclei  \\\n",
       "584                  6                            3           1   \n",
       "417                  1                            2           1   \n",
       "606                  2                            2           1   \n",
       "349                  5                            3           8   \n",
       "134                  1                            3           1   \n",
       "502                  2                            2           1   \n",
       "117                 10                            4          10   \n",
       "\n",
       "     Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "584                1                1        1      2  \n",
       "417                2                1        1      2  \n",
       "606                1                1        1      2  \n",
       "349                7                6        1      4  \n",
       "134                2                1        1      2  \n",
       "502                2                1        1      2  \n",
       "117                7                5        8      4  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = data.sample(frac=0.01, random_state=1)\n",
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we perform a sampling with replacement to create a sample whose size is equal to 1% of the entire data. You should be able to observe duplicate instances in the sample by increasing the sample size.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Uniformity of Cell Size</th>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "     Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
       "37                 6                        2                         1   \n",
       "235                3                        1                         4   \n",
       "72                 1                        3                         3   \n",
       "645                3                        1                         1   \n",
       "144                2                        1                         1   \n",
       "129                1                        1                         1   \n",
       "583                3                        1                         1   \n",
       "\n",
       "     Marginal Adhesion  Single Epithelial Cell Size Bare Nuclei  \\\n",
       "37                   1                            1           1   \n",
       "235                  1                            2         NaN   \n",
       "72                   2                            2           1   \n",
       "645                  1                            2           1   \n",
       "144                  1                            2           1   \n",
       "129                  1                           10           1   \n",
       "583                  1                            2           1   \n",
       "\n",
       "     Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "37                 7                1        1      2  \n",
       "235                3                1        1      2  \n",
       "72                 7                2        1      2  \n",
       "645                2                1        1      2  \n",
       "144                2                1        1      2  \n",
       "129                1                1        1      2  \n",
       "583                1                1        1      2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = data.sample(frac=0.01, replace=True, random_state=1)\n",
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Discretization\n",
    "\n",
    "Discretization is a data preprocessing step that is often used to transform a continuous-valued attribute to a categorical attribute. The example below illustrates two simple but widely-used unsupervised discretization methods (equal width and equal depth) applied to the 'Clump Thickness' attribute of the breast cancer dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we plot a histogram that shows the distribution of the attribute values. The value_counts() function can also be applied to count the frequency of each attribute value.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     145\n",
       "2      50\n",
       "3     108\n",
       "4      80\n",
       "5     130\n",
       "6      34\n",
       "7      23\n",
       "8      46\n",
       "9      14\n",
       "10     69\n",
       "Name: Clump Thickness, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Clump Thickness'].hist(bins=10)\n",
    "data['Clump Thickness'].value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the equal width method, we can apply the cut() function to discretize the attribute into 4 bins of similar interval widths. The value_counts() function can be used to determine the number of instances in each bin.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.991, 3.25]    303\n",
       "(3.25, 5.5]      210\n",
       "(5.5, 7.75]       57\n",
       "(7.75, 10.0]     129\n",
       "Name: Clump Thickness, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.cut(data['Clump Thickness'],4)\n",
    "bins.value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the equal frequency method, the qcut() function can be used to partition the values into 4 bins such that each bin has nearly the same number of instances.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.999, 2.0]    195\n",
       "(2.0, 4.0]      188\n",
       "(4.0, 6.0]      164\n",
       "(6.0, 10.0]     152\n",
       "Name: Clump Thickness, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.qcut(data['Clump Thickness'],4)\n",
    "bins.value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 Principal Component Analysis\n",
    "\n",
    "Principal component analysis (PCA) is a classical method for reducing the number of attributes in the data by projecting the data from its original high-dimensional space into a lower-dimensional space. The new attributes (also known as components) created by PCA have the following properties: (1) they are linear combinations of the original attributes, (2) they are orthogonal (perpendicular) to each other, and (3) they capture the maximum amount of variation in the data.\n",
    "\n",
    "The example below illustrates the application of PCA to an image dataset. There are 16 RGB files, each of which has a size of 111 x 111 pixels. The example code below will read each image file and convert the RGB image into a 111 x 111 x 3 = 36963 feature values. This will create a data matrix of size 16 x 36963.    \n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'pics/Picture1.jpg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-0d1231e895e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnumImages\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'pics/Picture'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.jpg'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\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     13\u001b[0m     \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, format)\u001b[0m\n\u001b[1;32m   1462\u001b[0m             raise ValueError('Only know how to handle PNG; with Pillow '\n\u001b[1;32m   1463\u001b[0m                              'installed, Matplotlib can handle more images')\n\u001b[0;32m-> 1464\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mimage\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   1465\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mpil_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1466\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_png\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2876\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2877\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2878\u001b[0;31m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\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   2879\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'pics/Picture1.jpg'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 504x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "\n",
    "numImages = 16\n",
    "fig = plt.figure(figsize=(7,7))\n",
    "imgData = np.zeros(shape=(numImages,36963))\n",
    "\n",
    "for i in range(1,numImages+1):\n",
    "    filename = 'pics/Picture'+str(i)+'.jpg'\n",
    "    img = mpimg.imread(filename)\n",
    "    ax = fig.add_subplot(4,4,i)\n",
    "    plt.imshow(img)\n",
    "    plt.axis('off')\n",
    "    ax.set_title(str(i))\n",
    "    imgData[i-1] = np.array(img.flatten()).reshape(1,img.shape[0]*img.shape[1]*img.shape[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using PCA, the data matrix is projected to its first two principal components. The projected values of the original image data are stored in a pandas DataFrame object named projected.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "numComponents = 2\n",
    "pca = PCA(n_components=numComponents)\n",
    "pca.fit(imgData)\n",
    "\n",
    "projected = pca.transform(imgData)\n",
    "projected = pd.DataFrame(projected,columns=['pc1','pc2'],index=range(1,numImages+1))\n",
    "projected['food'] = ['burger', 'burger','burger','burger','drink','drink','drink','drink',\n",
    "                      'pasta', 'pasta', 'pasta', 'pasta', 'chicken', 'chicken', 'chicken', 'chicken']\n",
    "projected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we draw a scatter plot to display the projected values. Observe that the images of burgers, drinks, and pastas  are all projected to the same region. However, the images for fried chicken (shown as black squares in the diagram) are harder to discriminate. \n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "colors = {'burger':'b', 'drink':'r', 'pasta':'g', 'chicken':'k'}\n",
    "markerTypes = {'burger':'+', 'drink':'x', 'pasta':'o', 'chicken':'s'}\n",
    "\n",
    "for foodType in markerTypes:\n",
    "    d = projected[projected['food']==foodType]\n",
    "    plt.scatter(d['pc1'],d['pc2'],c=colors[foodType],s=60,marker=markerTypes[foodType])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 Summary\n",
    "\n",
    "This tutorial presents Python programming examples for data preprocessing, including data cleaning (to handle missing values and remove outliers as well as duplicate data), aggregation, sampling, discretization, and dimensionality reduction using principal component analysis.   \n",
    "\n",
    "**<font color='blue'>References:</font>**\n",
    "\n",
    "1. Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. \n",
    "\n",
    "2. Mangasarian, O.L. and Wolberg, W. H. (1990). \"Cancer diagnosis via linear programming\", SIAM News, Volume 23, Number 5, pp 1 & 18.\n",
    "\n",
    "3. Wolberg, W.H. and Mangasarian, O.L. (1990). \"Multisurface method of pattern separation for medical diagnosis applied to breast cytology\", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, pp 9193-9196. \n",
    "\n",
    "4. Climate Data Online [https://www.ncdc.noaa.gov/cdo-web/]. "
   ]
  }
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