{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas - DataFrame Intro\n",
    "\n",
    "A very useful Python package is\n",
    "[pandas](https://pandas.pydata.org/), which is an open source library\n",
    "providing high-performance, easy-to-use data structures and data\n",
    "analysis tools for Python. **pandas** stands for panel data, a term borrowed from econometrics and is an efficient library for data analysis with an emphasis on tabular data.\n",
    "**pandas** has two major classes, the **DataFrame** class with two-dimensional data objects and tabular data organized in columns and the class **Series** with a focus on one-dimensional data objects. Both classes allow you to index data easily as we will see in the examples below. \n",
    "**pandas** allows you also to perform mathematical operations on the data, spanning from simple reshapings of vectors and matrices to statistical operations. \n",
    "\n",
    "The following simple example shows how we can, in an easy way make tables of our data. Here we define a data set which includes names, place of birth and date of birth, and displays the data in an easy to read way. We will see repeated use of **pandas**, in particular in connection with classification of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Place of birth</th>\n",
       "      <th>Date of Birth T.A.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frodo</td>\n",
       "      <td>Baggins</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Baggins</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aragorn II</td>\n",
       "      <td>Elessar</td>\n",
       "      <td>Eriador</td>\n",
       "      <td>2931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Samwise</td>\n",
       "      <td>Gamgee</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name Last Name Place of birth  Date of Birth T.A.\n",
       "0       Frodo   Baggins          Shire                2968\n",
       "1       Bilbo   Baggins          Shire                2890\n",
       "2  Aragorn II   Elessar        Eriador                2931\n",
       "3     Samwise    Gamgee          Shire                2980"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "data = {'First Name': [\"Frodo\", \"Bilbo\", \"Aragorn II\", \"Samwise\"],\n",
    "        'Last Name': [\"Baggins\", \"Baggins\",\"Elessar\",\"Gamgee\"],\n",
    "        'Place of birth': [\"Shire\", \"Shire\", \"Eriador\", \"Shire\"],\n",
    "        'Date of Birth T.A.': [2968, 2890, 2931, 2980]\n",
    "        }\n",
    "data_pandas = pd.DataFrame(data)\n",
    "display(data_pandas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above we have imported **pandas** with the shorthand **pd**, the latter has become the standard way we import **pandas**. We make then a list of various variables\n",
    "and reorganize the aboves lists into a **DataFrame** and then print out  a neat table with specific column labels as *Name*, *place of birth* and *date of birth*.\n",
    "Displaying these results, we see that the indices are given by the default numbers from zero to three.\n",
    "**pandas** is extremely flexible and we can easily change the above indices by defining a new type of indexing as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Place of birth</th>\n",
       "      <th>Date of Birth T.A.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Frodo</th>\n",
       "      <td>Frodo</td>\n",
       "      <td>Baggins</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbo</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Baggins</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aragorn</th>\n",
       "      <td>Aragorn II</td>\n",
       "      <td>Elessar</td>\n",
       "      <td>Eriador</td>\n",
       "      <td>2931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam</th>\n",
       "      <td>Samwise</td>\n",
       "      <td>Gamgee</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         First Name Last Name Place of birth  Date of Birth T.A.\n",
       "Frodo         Frodo   Baggins          Shire                2968\n",
       "Bilbo         Bilbo   Baggins          Shire                2890\n",
       "Aragorn  Aragorn II   Elessar        Eriador                2931\n",
       "Sam         Samwise    Gamgee          Shire                2980"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
    "display(data_pandas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thereafter we display the content of the row which begins with the index **Aragorn**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First Name            Aragorn II\n",
       "Last Name                Elessar\n",
       "Place of birth           Eriador\n",
       "Date of Birth T.A.          2931\n",
       "Name: Aragorn, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_pandas.loc['Aragorn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily append data to this, for example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Place of birth</th>\n",
       "      <th>Date of Birth T.A.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Frodo</th>\n",
       "      <td>Frodo</td>\n",
       "      <td>Baggins</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbo</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Baggins</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aragorn</th>\n",
       "      <td>Aragorn II</td>\n",
       "      <td>Elessar</td>\n",
       "      <td>Eriador</td>\n",
       "      <td>2931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam</th>\n",
       "      <td>Samwise</td>\n",
       "      <td>Gamgee</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pippin</th>\n",
       "      <td>Peregrin</td>\n",
       "      <td>Took</td>\n",
       "      <td>Shire</td>\n",
       "      <td>2990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         First Name Last Name Place of birth  Date of Birth T.A.\n",
       "Frodo         Frodo   Baggins          Shire                2968\n",
       "Bilbo         Bilbo   Baggins          Shire                2890\n",
       "Aragorn  Aragorn II   Elessar        Eriador                2931\n",
       "Sam         Samwise    Gamgee          Shire                2980\n",
       "Pippin     Peregrin      Took          Shire                2990"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_hobbit = {'First Name': [\"Peregrin\"],\n",
    "              'Last Name': [\"Took\"],\n",
    "              'Place of birth': [\"Shire\"],\n",
    "              'Date of Birth T.A.': [2990]\n",
    "              }\n",
    "data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))\n",
    "display(data_pandas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are other examples where we use the **DataFrame** functionality to handle arrays, now with more interesting features for us, namely numbers. We set up a matrix \n",
    "of dimensionality $10\\times 5$ and compute the mean value and standard deviation of each column. Similarly, we can perform mathematial operations like squaring the matrix elements and many other operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.749765</td>\n",
       "      <td>0.342680</td>\n",
       "      <td>1.153036</td>\n",
       "      <td>-0.252436</td>\n",
       "      <td>0.981321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.514219</td>\n",
       "      <td>0.221180</td>\n",
       "      <td>-1.070043</td>\n",
       "      <td>-0.189496</td>\n",
       "      <td>0.255001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.458027</td>\n",
       "      <td>0.435163</td>\n",
       "      <td>-0.583595</td>\n",
       "      <td>0.816847</td>\n",
       "      <td>0.672721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.104411</td>\n",
       "      <td>-0.531280</td>\n",
       "      <td>1.029733</td>\n",
       "      <td>-0.438136</td>\n",
       "      <td>-1.118318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.618982</td>\n",
       "      <td>1.541605</td>\n",
       "      <td>-0.251879</td>\n",
       "      <td>-0.842436</td>\n",
       "      <td>0.184519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.937082</td>\n",
       "      <td>0.731000</td>\n",
       "      <td>1.361556</td>\n",
       "      <td>-0.326238</td>\n",
       "      <td>0.055676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.222400</td>\n",
       "      <td>-1.443217</td>\n",
       "      <td>-0.756352</td>\n",
       "      <td>0.816454</td>\n",
       "      <td>0.750445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.455947</td>\n",
       "      <td>1.189622</td>\n",
       "      <td>-1.690617</td>\n",
       "      <td>-1.356399</td>\n",
       "      <td>-1.232435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.544439</td>\n",
       "      <td>-0.668172</td>\n",
       "      <td>0.007315</td>\n",
       "      <td>-0.612939</td>\n",
       "      <td>1.299748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.733096</td>\n",
       "      <td>-0.983310</td>\n",
       "      <td>0.357508</td>\n",
       "      <td>-1.613579</td>\n",
       "      <td>1.470714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0 -1.749765  0.342680  1.153036 -0.252436  0.981321\n",
       "1  0.514219  0.221180 -1.070043 -0.189496  0.255001\n",
       "2 -0.458027  0.435163 -0.583595  0.816847  0.672721\n",
       "3 -0.104411 -0.531280  1.029733 -0.438136 -1.118318\n",
       "4  1.618982  1.541605 -0.251879 -0.842436  0.184519\n",
       "5  0.937082  0.731000  1.361556 -0.326238  0.055676\n",
       "6  0.222400 -1.443217 -0.756352  0.816454  0.750445\n",
       "7 -0.455947  1.189622 -1.690617 -1.356399 -1.232435\n",
       "8 -0.544439 -0.668172  0.007315 -0.612939  1.299748\n",
       "9 -1.733096 -0.983310  0.357508 -1.613579  1.470714"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   -0.175300\n",
      "1    0.083527\n",
      "2   -0.044334\n",
      "3   -0.399836\n",
      "4    0.331939\n",
      "dtype: float64\n",
      "0    1.069584\n",
      "1    0.965548\n",
      "2    1.018232\n",
      "3    0.793167\n",
      "4    0.918992\n",
      "dtype: float64\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.061679</td>\n",
       "      <td>0.117430</td>\n",
       "      <td>1.329492</td>\n",
       "      <td>0.063724</td>\n",
       "      <td>0.962990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.264421</td>\n",
       "      <td>0.048920</td>\n",
       "      <td>1.144993</td>\n",
       "      <td>0.035909</td>\n",
       "      <td>0.065026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.209789</td>\n",
       "      <td>0.189367</td>\n",
       "      <td>0.340583</td>\n",
       "      <td>0.667239</td>\n",
       "      <td>0.452553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.010902</td>\n",
       "      <td>0.282259</td>\n",
       "      <td>1.060349</td>\n",
       "      <td>0.191963</td>\n",
       "      <td>1.250636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.621102</td>\n",
       "      <td>2.376547</td>\n",
       "      <td>0.063443</td>\n",
       "      <td>0.709698</td>\n",
       "      <td>0.034047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.878123</td>\n",
       "      <td>0.534362</td>\n",
       "      <td>1.853835</td>\n",
       "      <td>0.106431</td>\n",
       "      <td>0.003100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.049462</td>\n",
       "      <td>2.082875</td>\n",
       "      <td>0.572069</td>\n",
       "      <td>0.666597</td>\n",
       "      <td>0.563167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.207888</td>\n",
       "      <td>1.415201</td>\n",
       "      <td>2.858185</td>\n",
       "      <td>1.839818</td>\n",
       "      <td>1.518895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.296414</td>\n",
       "      <td>0.446453</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.375694</td>\n",
       "      <td>1.689345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.003620</td>\n",
       "      <td>0.966899</td>\n",
       "      <td>0.127812</td>\n",
       "      <td>2.603636</td>\n",
       "      <td>2.162999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  3.061679  0.117430  1.329492  0.063724  0.962990\n",
       "1  0.264421  0.048920  1.144993  0.035909  0.065026\n",
       "2  0.209789  0.189367  0.340583  0.667239  0.452553\n",
       "3  0.010902  0.282259  1.060349  0.191963  1.250636\n",
       "4  2.621102  2.376547  0.063443  0.709698  0.034047\n",
       "5  0.878123  0.534362  1.853835  0.106431  0.003100\n",
       "6  0.049462  2.082875  0.572069  0.666597  0.563167\n",
       "7  0.207888  1.415201  2.858185  1.839818  1.518895\n",
       "8  0.296414  0.446453  0.000054  0.375694  1.689345\n",
       "9  3.003620  0.966899  0.127812  2.603636  2.162999"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "np.random.seed(100)\n",
    "# setting up a 10 x 5 matrix\n",
    "rows = 10\n",
    "cols = 5\n",
    "a = np.random.randn(rows,cols)\n",
    "df = pd.DataFrame(a)\n",
    "display(df)\n",
    "print(df.mean())\n",
    "print(df.std())\n",
    "display(df**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thereafter we can select specific columns only and plot final results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>Fourth</th>\n",
       "      <th>Fifth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.749765</td>\n",
       "      <td>0.342680</td>\n",
       "      <td>1.153036</td>\n",
       "      <td>-0.252436</td>\n",
       "      <td>0.981321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.514219</td>\n",
       "      <td>0.221180</td>\n",
       "      <td>-1.070043</td>\n",
       "      <td>-0.189496</td>\n",
       "      <td>0.255001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.458027</td>\n",
       "      <td>0.435163</td>\n",
       "      <td>-0.583595</td>\n",
       "      <td>0.816847</td>\n",
       "      <td>0.672721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.104411</td>\n",
       "      <td>-0.531280</td>\n",
       "      <td>1.029733</td>\n",
       "      <td>-0.438136</td>\n",
       "      <td>-1.118318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.618982</td>\n",
       "      <td>1.541605</td>\n",
       "      <td>-0.251879</td>\n",
       "      <td>-0.842436</td>\n",
       "      <td>0.184519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.937082</td>\n",
       "      <td>0.731000</td>\n",
       "      <td>1.361556</td>\n",
       "      <td>-0.326238</td>\n",
       "      <td>0.055676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.222400</td>\n",
       "      <td>-1.443217</td>\n",
       "      <td>-0.756352</td>\n",
       "      <td>0.816454</td>\n",
       "      <td>0.750445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.455947</td>\n",
       "      <td>1.189622</td>\n",
       "      <td>-1.690617</td>\n",
       "      <td>-1.356399</td>\n",
       "      <td>-1.232435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.544439</td>\n",
       "      <td>-0.668172</td>\n",
       "      <td>0.007315</td>\n",
       "      <td>-0.612939</td>\n",
       "      <td>1.299748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.733096</td>\n",
       "      <td>-0.983310</td>\n",
       "      <td>0.357508</td>\n",
       "      <td>-1.613579</td>\n",
       "      <td>1.470714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      First    Second     Third    Fourth     Fifth\n",
       "0 -1.749765  0.342680  1.153036 -0.252436  0.981321\n",
       "1  0.514219  0.221180 -1.070043 -0.189496  0.255001\n",
       "2 -0.458027  0.435163 -0.583595  0.816847  0.672721\n",
       "3 -0.104411 -0.531280  1.029733 -0.438136 -1.118318\n",
       "4  1.618982  1.541605 -0.251879 -0.842436  0.184519\n",
       "5  0.937082  0.731000  1.361556 -0.326238  0.055676\n",
       "6  0.222400 -1.443217 -0.756352  0.816454  0.750445\n",
       "7 -0.455947  1.189622 -1.690617 -1.356399 -1.232435\n",
       "8 -0.544439 -0.668172  0.007315 -0.612939  1.299748\n",
       "9 -1.733096 -0.983310  0.357508 -1.613579  1.470714"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08352721390288316\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10 entries, 0 to 9\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   First   10 non-null     float64\n",
      " 1   Second  10 non-null     float64\n",
      " 2   Third   10 non-null     float64\n",
      " 3   Fourth  10 non-null     float64\n",
      " 4   Fifth   10 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 480.0 bytes\n",
      "None\n",
      "           First     Second      Third     Fourth      Fifth\n",
      "count  10.000000  10.000000  10.000000  10.000000  10.000000\n",
      "mean   -0.175300   0.083527  -0.044334  -0.399836   0.331939\n",
      "std     1.069584   0.965548   1.018232   0.793167   0.918992\n",
      "min    -1.749765  -1.443217  -1.690617  -1.613579  -1.232435\n",
      "25%    -0.522836  -0.633949  -0.713163  -0.785061   0.087887\n",
      "50%    -0.280179   0.281930  -0.122282  -0.382187   0.463861\n",
      "75%     0.441264   0.657041   0.861676  -0.205231   0.923602\n",
      "max     1.618982   1.541605   1.361556   0.816847   1.470714\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n",
    "df.index = np.arange(10)\n",
    "\n",
    "display(df)\n",
    "print(df['Second'].mean() )\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "\n",
    "from pylab import plt, mpl\n",
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "\n",
    "df.cumsum().plot(lw=2.0, figsize=(10,6))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "df.plot.bar(figsize=(10,6), rot=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can produce a $4\\times 4$ matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]]\n",
      "    0   1   2   3\n",
      "0   0   1   2   3\n",
      "1   4   5   6   7\n",
      "2   8   9  10  11\n",
      "3  12  13  14  15\n"
     ]
    }
   ],
   "source": [
    "b = np.arange(16).reshape((4,4))\n",
    "print(b)\n",
    "df1 = pd.DataFrame(b)\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and many other operations. \n",
    "\n",
    "The **Series** class is another important class included in\n",
    "**pandas**. You can view it as a specialization of **DataFrame** but where\n",
    "we have just a single column of data. It shares many of the same features as _DataFrame. As with **DataFrame**,\n",
    "most operations are vectorized, achieving thereby a high performance when dealing with computations of arrays, in particular labeled arrays.\n",
    "As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in.\n",
    "For multidimensional arrays, we recommend strongly [xarray](http://xarray.pydata.org/en/stable/). **xarray** has much of the same flexibility as **pandas**, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both **pandas** and **xarray**. \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "In order to study various Machine Learning algorithms, we need to\n",
    "access data. Acccessing data is an essential step in all machine\n",
    "learning algorithms. In particular, setting up the so-called **design\n",
    "matrix** (to be defined below) is often the first element we need in\n",
    "order to perform our calculations. To set up the design matrix means\n",
    "reading (and later, when the calculations are done, writing) data\n",
    "in various formats, The formats span from reading files from disk,\n",
    "loading data from databases and interacting with online sources\n",
    "like web application programming interfaces (APIs).\n",
    "\n",
    "In handling various input formats, as discussed above, we will mainly stay with **pandas**,\n",
    "a Python package which allows us, in a seamless and painless way, to\n",
    "deal with a multitude of formats, from standard **csv** (comma separated\n",
    "values) files, via **excel**, **html** to **hdf5** formats.  With **pandas**\n",
    "and the **DataFrame**  and **Series** functionalities we are able to convert text data\n",
    "into the calculational formats we need for a specific algorithm. And our code is going to be \n",
    "pretty close the basic mathematical expressions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}