Code-Example-030.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 3: Data Exploration\n",
    "\n",
    "The following tutorial contains examples of Python code for data exploration. You should refer to the \"Data Exploration\" chapter of the \"Introduction to Data Mining\" book (available at https://www-users.cs.umn.edu/~kumar001/dmbook/index.php) to understand some of the concepts introduced in this tutorial notebook. The notebook can be downloaded from http://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial3/tutorial3.ipynb.\n",
    "\n",
    "Data exploration refers to the preliminary investigation of data in order\n",
    "to better understand its specific characteristics. There are two key motivations for data exploration:\n",
    "1. To help users select the appropriate preprocessing and data analysis technique used.\n",
    "2. To make use of humans’ abilities to recognize patterns in the data.\n",
    "\n",
    "Read the step-by-step instructions below carefully. To execute the code, click on the cell and press the SHIFT-ENTER keys simultaneously."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Summary Statistics\n",
    "\n",
    "Summary statistics are quantities, such as the mean and standard deviation, that capture various characteristics of a potentially large set of values with a single number or a small set of numbers. In this tutorial, we will use the Iris sample data, which contains information on 150 Iris flowers, 50 each from one of three Iris species: Setosa, Versicolour, and Virginica. Each flower is characterized by five attributes:\n",
    "\n",
    "- sepal length in centimeters\n",
    "\n",
    "- sepal width in centimeters\n",
    "\n",
    "- petal length in centimeters\n",
    "\n",
    "- petal width in centimeters\n",
    "\n",
    "- class (Setosa, Versicolour, Virginica) \n",
    "\n",
    "In this tutorial, you will learn how to:\n",
    "\n",
    "- Load a CSV data file into a Pandas DataFrame object.\n",
    "\n",
    "- Compute various summary statistics from the DataFrame.\n",
    "\n",
    "To execute the sample program shown here, make sure you have installed the Pandas library (see Module 2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.** First, you need to download the <a href=\"http://archive.ics.uci.edu/ml/datasets/Iris\">Iris dataset</a> from the UCI machine learning repository.\n",
    "\n",
    "**<font color='red'>Code:</font>** The following code uses Pandas to read the CSV file and store them in a DataFrame object named data. Next, it will display the first five rows of the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width        class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',header=None)\n",
    "data.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'class']\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.** For each quantitative attribute, calculate its average, standard deviation, minimum, and maximum values.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length:\n",
      "\t Mean = 5.84\n",
      "\t Standard deviation = 0.83\n",
      "\t Minimum = 4.30\n",
      "\t Maximum = 7.90\n",
      "sepal width:\n",
      "\t Mean = 3.05\n",
      "\t Standard deviation = 0.43\n",
      "\t Minimum = 2.00\n",
      "\t Maximum = 4.40\n",
      "petal length:\n",
      "\t Mean = 3.76\n",
      "\t Standard deviation = 1.76\n",
      "\t Minimum = 1.00\n",
      "\t Maximum = 6.90\n",
      "petal width:\n",
      "\t Mean = 1.20\n",
      "\t Standard deviation = 0.76\n",
      "\t Minimum = 0.10\n",
      "\t Maximum = 2.50\n"
     ]
    }
   ],
   "source": [
    "from pandas.api.types import is_numeric_dtype\n",
    "\n",
    "for col in data.columns:\n",
    "    if is_numeric_dtype(data[col]):\n",
    "        print('%s:' % (col))\n",
    "        print('\\t Mean = %.2f' % data[col].mean())\n",
    "        print('\\t Standard deviation = %.2f' % data[col].std())\n",
    "        print('\\t Minimum = %.2f' % data[col].min())\n",
    "        print('\\t Maximum = %.2f' % data[col].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.** For the qualitative attribute (class), count the frequency for each of its distinct values.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Iris-setosa        50\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.** It is also possible to display the summary for all the attributes simultaneously in a table using the describe() function. If an attribute is quantitative, it will display its mean, standard deviation and various quantiles (including minimum, median, and maximum) values. If an attribute is qualitative, it will display its number of unique values and the top (most frequent) values. \n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sepal length  sepal width  petal length  petal width            class\n",
       "count     150.000000   150.000000    150.000000   150.000000              150\n",
       "unique           NaN          NaN           NaN          NaN                3\n",
       "top              NaN          NaN           NaN          NaN  Iris-versicolor\n",
       "freq             NaN          NaN           NaN          NaN               50\n",
       "mean        5.843333     3.054000      3.758667     1.198667              NaN\n",
       "std         0.828066     0.433594      1.764420     0.763161              NaN\n",
       "min         4.300000     2.000000      1.000000     0.100000              NaN\n",
       "25%         5.100000     2.800000      1.600000     0.300000              NaN\n",
       "50%         5.800000     3.000000      4.350000     1.300000              NaN\n",
       "75%         6.400000     3.300000      5.100000     1.800000              NaN\n",
       "max         7.900000     4.400000      6.900000     2.500000              NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that count refers to the number of non-missing values for each attribute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.** For multivariate statistics, you can compute the covariance and correlation between pairs of attributes.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance:\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length</th>\n",
       "      <td>0.685694</td>\n",
       "      <td>-0.039268</td>\n",
       "      <td>1.273682</td>\n",
       "      <td>0.516904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width</th>\n",
       "      <td>-0.039268</td>\n",
       "      <td>0.188004</td>\n",
       "      <td>-0.321713</td>\n",
       "      <td>-0.117981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length</th>\n",
       "      <td>1.273682</td>\n",
       "      <td>-0.321713</td>\n",
       "      <td>3.113179</td>\n",
       "      <td>1.296387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width</th>\n",
       "      <td>0.516904</td>\n",
       "      <td>-0.117981</td>\n",
       "      <td>1.296387</td>\n",
       "      <td>0.582414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal length  sepal width  petal length  petal width\n",
       "sepal length      0.685694    -0.039268      1.273682     0.516904\n",
       "sepal width      -0.039268     0.188004     -0.321713    -0.117981\n",
       "petal length      1.273682    -0.321713      3.113179     1.296387\n",
       "petal width       0.516904    -0.117981      1.296387     0.582414"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Covariance:')\n",
    "data.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation:\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal length  sepal width  petal length  petal width\n",
       "sepal length      1.000000    -0.109369      0.871754     0.817954\n",
       "sepal width      -0.109369     1.000000     -0.420516    -0.356544\n",
       "petal length      0.871754    -0.420516      1.000000     0.962757\n",
       "petal width       0.817954    -0.356544      0.962757     1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Correlation:')\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Data Visualization\n",
    "\n",
    "Data visualization is the display of information in a graphic or tabular format. Successful visualization requires that the data (information) be converted into a visual format so that the characteristics of the data and the relationships\n",
    "among data items or attributes can be analyzed or reported.\n",
    "\n",
    "In this tutorial, you will learn how to display the Iris data created in Section 3.1. To execute the sample program shown here, make sure you have installed the matplotlib library package (see Module 0 on how to install Python packages)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.** First, we will display the histogram for the sepal length attribute by discretizing it into 8 separate bins and counting the frequency for each bin.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe856eccf70>"
      ]
     },
     "execution_count": 7,
     "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": [
    "%matplotlib inline\n",
    "\n",
    "data['sepal length'].hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.** A boxplot can also be used to show the distribution of values for each attribute.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe856f84c10>"
      ]
     },
     "execution_count": 8,
     "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.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.** For each pair of attributes, we can use a scatter plot to visualize their joint distribution.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(3, 2, figsize=(12,12))\n",
    "index = 0\n",
    "for i in range(3):\n",
    "    for j in range(i+1,4):\n",
    "        ax1 = int(index/2)\n",
    "        ax2 = index % 2\n",
    "        axes[ax1][ax2].scatter(data[data.columns[i]], data[data.columns[j]], color='red')\n",
    "        axes[ax1][ax2].set_xlabel(data.columns[i])\n",
    "        axes[ax1][ax2].set_ylabel(data.columns[j])\n",
    "        index = index + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.** Parallel coordinates can be used to display all the data points simultaneously. Parallel coordinates have one coordinate axis for each attribute, but the different axes are parallel to one other instead of perpendicular, as is traditional. Furthermore, an object is represented as a line instead of as a point. In the example below, the distribution of values for each class can be identified in a separate color.\n",
    "\n",
    "**<font color=\"red\">Code:</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe857609250>"
      ]
     },
     "execution_count": 10,
     "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": [
    "from pandas.plotting import parallel_coordinates\n",
    "%matplotlib inline\n",
    "\n",
    "parallel_coordinates(data, 'class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3. Summary\n",
    "\n",
    "This tutorial presents several examples for data exploration and visualization using the Pandas and matplotlib library packages available in Python. \n",
    "\n",
    "**<font color='blue'>References:</font>**\n",
    "\n",
    "1. Documentation on Pandas. https://pandas.pydata.org/\n",
    "2. Documentation on matplotlib. https://matplotlib.org/\n",
    "3. Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}