rashivkp/presentation-jupyter.ipynb

## presentation-jupyter.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Jupyter\n",
    "\n",
    "![Jupyter Logo](files/main-logo.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## About Me\n",
    "\n",
    "--\n",
    "\n",
    "### Mohamed Rashid\n",
    "\n",
    "*rashivkp at gmail dot com*\n",
    "\n",
    "@rashivkp\n",
    "\n",
    "http://github.com/rashivkp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Contents\n",
    "\n",
    "- iPython\n",
    "- jupyter\n",
    "- some libraries for scientific python\n",
    "- demo using jupyter    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### IPython \n",
    "\n",
    "http://ipython.org/ipython-doc/stable/interactive/tutorial.html\n",
    "\n",
    "- interactive shell for python\n",
    "- started in 2001 as gist by Fenando Perez with 259 loc\n",
    "- revised BSD license"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### features\n",
    "- System shell commands\n",
    "- introspection\n",
    "- tab completion\n",
    "- access to python's help system \n",
    "- Magic functions\n",
    "- better debugging\n",
    "- rich history\n",
    "- parallel computing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### ipython notebook\n",
    "a web based shell to ipython\n",
    "\n",
    "- supports markdown\n",
    "- latex  -- $\\sum\\limits^{i=3}_{i=1}X_i = X_1+X_2+X_3$\n",
    "- immediate results\n",
    "- graphical plots\n",
    "- sharing and viewing through nbviewer.- http://nbviewer.jupyter.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Jupyter    https://github.com/jupyter\n",
    "\n",
    "- a language agnostic notebook platform\n",
    "- the big split http://blog.jupyter.org/2015/04/15/the-big-split\n",
    "- support 54 kernels, and counting\n",
    "    \n",
    "    * IJulia\n",
    "    * IR\n",
    "    * IHaskell\n",
    "    * IRuby\n",
    "    * lot more - https://github.com/ipython/ipython/wiki/IPython-kernels-for-other-languages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### scientific python\n",
    "\n",
    "- NumPy\n",
    "    n-dimensional array\n",
    "- SciPy\n",
    "    mathematics\n",
    "- matplotlib\n",
    "    plots in 2D and 3D\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "#### Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Thank You"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "$c = \\sqrt{a^2 + b^2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Math, Latex\n",
    "display(Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{align}\n",
       "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
       "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
       "\\end{align}"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\\begin{align}\n",
    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['colors']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "pd.set_option('display.mpl_style', 'default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_weather = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Month</th>\n",
       "      <th>Panjab</th>\n",
       "      <th>UP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>July</td>\n",
       "      <td>39</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>38</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Panjab  UP\n",
       "0    Jan      40  42\n",
       "1    Feb      41  43\n",
       "2    Mar      45  46\n",
       "3    Apr      41  42\n",
       "4    May      42  43\n",
       "5    Jun      40  45\n",
       "6   July      39  43\n",
       "7    Aug      38  42\n",
       "8    Sep      37  40\n",
       "9    Oct      34  37\n",
       "10   Nov      30  32\n",
       "11   Dec      25  28"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Month</th>\n",
       "      <th>Panjab</th>\n",
       "      <th>UP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month  Panjab  UP\n",
       "0   Jan      40  42\n",
       "1   Feb      41  43\n",
       "2   Mar      45  46\n",
       "3   Apr      41  42\n",
       "4   May      42  43"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_weather.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Month</th>\n",
       "      <th>Panjab</th>\n",
       "      <th>UP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>38</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Panjab  UP\n",
       "7    Aug      38  42\n",
       "8    Sep      37  40\n",
       "9    Oct      34  37\n",
       "10   Nov      30  32\n",
       "11   Dec      25  28"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_weather.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Panjab</th>\n",
       "      <th>UP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.666667</td>\n",
       "      <td>40.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.565042</td>\n",
       "      <td>5.361903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.250000</td>\n",
       "      <td>39.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.500000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Panjab         UP\n",
       "count  12.000000  12.000000\n",
       "mean   37.666667  40.250000\n",
       "std     5.565042   5.361903\n",
       "min    25.000000  28.000000\n",
       "25%    36.250000  39.250000\n",
       "50%    39.500000  42.000000\n",
       "75%    41.000000  43.000000\n",
       "max    45.000000  46.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_weather.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84641a0d10>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6NKV5zViGzdvCe6v2cMrhLnS7YM23JAIx54AfAKp7PHj+3Ix7xQo8K1bi2ZOO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\nIgj9HwQf7U0Clw/cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f846435c750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import brewer2mpl\n",
    "colors = brewer2mpl.get_map('Set1', 'qualitative', 8).mpl_colors\n",
    "mpl.rcParams['axes.color_cycle'] = colors\n",
    "# there was a bug, with plot\n",
    "\n",
    "\n",
    "avg_weather.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X4nZXxAE33NDAwIHeLPk6pawMyspijBgRASKceurnL9BauXIlRxxxhDudS4GG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\nFxHxASV7EREfULIXEfGB/w/gGIsjv1gSqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f846406f750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [0,2,4,6,8,4]\n",
    "y = [0]*len(x)\n",
    "s = [20*4**n for n in range(len(x))]\n",
    "plt.scatter(x,y,s=s)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8463b0cc10>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8463cc4e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}