notebooks/unit04/unit04_06.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was prepared by Cayetano Benavent, 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas 01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Importing library (convention):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data structures in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series: 1 Dimension data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "dt = np.random.random(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.34882491,  0.40815936,  0.37846343,  0.07374071,  0.57645901,\n",
       "        0.73252434,  0.25812427,  0.90573503,  0.20462665,  0.86907253,\n",
       "        0.80300727,  0.80050171,  0.77619599,  0.08859435,  0.15722074,\n",
       "        0.34332281,  0.66705256,  0.21204897,  0.14318771,  0.57384648])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.348825\n",
       "1     0.408159\n",
       "2     0.378463\n",
       "3     0.073741\n",
       "4     0.576459\n",
       "5     0.732524\n",
       "6     0.258124\n",
       "7     0.905735\n",
       "8     0.204627\n",
       "9     0.869073\n",
       "10    0.803007\n",
       "11    0.800502\n",
       "12    0.776196\n",
       "13    0.088594\n",
       "14    0.157221\n",
       "15    0.343323\n",
       "16    0.667053\n",
       "17    0.212049\n",
       "18    0.143188\n",
       "19    0.573846\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import string\n",
    "abc = string.ascii_lowercase[:20]\n",
    "idx = list(abc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'b',\n",
       " 'c',\n",
       " 'd',\n",
       " 'e',\n",
       " 'f',\n",
       " 'g',\n",
       " 'h',\n",
       " 'i',\n",
       " 'j',\n",
       " 'k',\n",
       " 'l',\n",
       " 'm',\n",
       " 'n',\n",
       " 'o',\n",
       " 'p',\n",
       " 'q',\n",
       " 'r',\n",
       " 's',\n",
       " 't']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.348825\n",
       "b    0.408159\n",
       "c    0.378463\n",
       "d    0.073741\n",
       "e    0.576459\n",
       "f    0.732524\n",
       "g    0.258124\n",
       "h    0.905735\n",
       "i    0.204627\n",
       "j    0.869073\n",
       "k    0.803007\n",
       "l    0.800502\n",
       "m    0.776196\n",
       "n    0.088594\n",
       "o    0.157221\n",
       "p    0.343323\n",
       "q    0.667053\n",
       "r    0.212049\n",
       "s    0.143188\n",
       "t    0.573846\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(dt, index=idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Python dict can be converted to a Serie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydict = {'t01': 345, 't02': 212, 't03': 453, 't04': 769, 't05':762, 't06': 911}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t01    345\n",
       "t02    212\n",
       "t03    453\n",
       "t04    769\n",
       "t05    762\n",
       "t06    911\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(mydict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Frame: 2 Dimensions data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydict2 = {'t01': 35, 't02': 2, 't03': 32, 't04': 71, 't05':82, 't06': 21}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_dt = {'d01': pd.Series(mydict), \n",
    "      'd02': pd.Series(mydict2)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(df_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d01</th>\n",
       "      <th>d02</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t01</th>\n",
       "      <td>345</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t02</th>\n",
       "      <td>212</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t03</th>\n",
       "      <td>453</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t04</th>\n",
       "      <td>769</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t05</th>\n",
       "      <td>762</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t06</th>\n",
       "      <td>911</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     d01  d02\n",
       "t01  345   35\n",
       "t02  212    2\n",
       "t03  453   32\n",
       "t04  769   71\n",
       "t05  762   82\n",
       "t06  911   21"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel: 3 Dimensions data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = {'l01': pd.Series(np.random.random(6), index=['t01','t02','t03','t04','t05','t06']), \n",
    "      'l02': pd.Series(np.random.random(6), index=['t01','t02','t03','t04','t05','t06'])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dt_pn = {'p01': df, 'p02': df2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pn = pd.Panel(dt_pn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 2 (items) x 6 (major_axis) x 4 (minor_axis)\n",
       "Items axis: p01 to p02\n",
       "Major_axis axis: t01 to t06\n",
       "Minor_axis axis: d01 to l02"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d01</th>\n",
       "      <th>d02</th>\n",
       "      <th>l01</th>\n",
       "      <th>l02</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.872596</td>\n",
       "      <td>0.279537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081171</td>\n",
       "      <td>0.926469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.339822</td>\n",
       "      <td>0.353231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.960054</td>\n",
       "      <td>0.335430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.337112</td>\n",
       "      <td>0.922273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.755057</td>\n",
       "      <td>0.743222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     d01  d02       l01       l02\n",
       "t01  NaN  NaN  0.872596  0.279537\n",
       "t02  NaN  NaN  0.081171  0.926469\n",
       "t03  NaN  NaN  0.339822  0.353231\n",
       "t04  NaN  NaN  0.960054  0.335430\n",
       "t05  NaN  NaN  0.337112  0.922273\n",
       "t06  NaN  NaN  0.755057  0.743222"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pn['p02']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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