Matplotlib - Scatterplots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23a64adc0f0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a64a632e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,10,40)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x,y, 'o', color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plt.scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x23a64b82550>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a64a47f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y, marker='^')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x23a64d57f28>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OL/QVOL5nnGqlSi6fmYt/M0tZlm/tNytTvQm4OWruAAYlrWukglMHT5Hvak4X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\nKyRyscUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a64b49630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "x = rng.randn(100)\n",
    "y = rng.randn(100)\n",
    "colors = rng.rand(100)\n",
    "sizes = 1000 * rng.rand(100) # Multiplying 1000 to get a decent pixel size\n",
    "\n",
    "# alpha - transperancy of colors\n",
    "# cmap - color coding option\n",
    "# colors - choosing colors from there\n",
    "\n",
    "\n",
    "x = [1,2,3,4]\n",
    "y = [5,6,7,8]\n",
    "colors = [1,3,1,0]\n",
    "#sizes = \n",
    "\n",
    "plt.scatter(x,y, c=colors, s=sizes, alpha=0.5, cmap='plasma')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.76405235,  0.40015721,  0.97873798,  2.2408932 ,  1.86755799,\n",
       "       -0.97727788,  0.95008842, -0.15135721, -0.10321885,  0.4105985 ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "rng.randn(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a dataframe from iris_data\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This returns the coloumn name\n",
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['target', 'DESCR', 'data', 'feature_names', 'target_names'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data corresponding to each coloumn\n",
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# possible flowers\n",
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = iris.data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.1,  4.9,  4.7,  4.6,  5. ,  5.4,  4.6,  5. ,  4.4,  4.9,  5.4,\n",
       "        4.8,  4.8,  4.3,  5.8,  5.7,  5.4,  5.1,  5.7,  5.1,  5.4,  5.1,\n",
       "        4.6,  5.1,  4.8,  5. ,  5. ,  5.2,  5.2,  4.7,  4.8,  5.4,  5.2,\n",
       "        5.5,  4.9,  5. ,  5.5,  4.9,  4.4,  5.1,  5. ,  4.5,  4.4,  5. ,\n",
       "        5.1,  4.8,  5.1,  4.6,  5.3,  5. ,  7. ,  6.4,  6.9,  5.5,  6.5,\n",
       "        5.7,  6.3,  4.9,  6.6,  5.2,  5. ,  5.9,  6. ,  6.1,  5.6,  6.7,\n",
       "        5.6,  5.8,  6.2,  5.6,  5.9,  6.1,  6.3,  6.1,  6.4,  6.6,  6.8,\n",
       "        6.7,  6. ,  5.7,  5.5,  5.5,  5.8,  6. ,  5.4,  6. ,  6.7,  6.3,\n",
       "        5.6,  5.5,  5.5,  6.1,  5.8,  5. ,  5.6,  5.7,  5.7,  6.2,  5.1,\n",
       "        5.7,  6.3,  5.8,  7.1,  6.3,  6.5,  7.6,  4.9,  7.3,  6.7,  7.2,\n",
       "        6.5,  6.4,  6.8,  5.7,  5.8,  6.4,  6.5,  7.7,  7.7,  6. ,  6.9,\n",
       "        5.6,  7.7,  6.3,  6.7,  7.2,  6.2,  6.1,  6.4,  7.2,  7.4,  7.9,\n",
       "        6.4,  6.3,  6.1,  7.7,  6.3,  6.4,  6. ,  6.9,  6.7,  6.9,  5.8,\n",
       "        6.8,  6.7,  6.7,  6.3,  6.5,  6.2,  5.9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1dfb58a5e48>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KS1zckYGsWJA6hL3Y+gKS605fJ32Ibl9FmYyPbdaJKxHZ/jPt6Kb+bdvtF6WM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\nUhARkYSSgoiIJJQUREQk8Qb2384XyY0kMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dfb5876748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#features[0] - sepel length\n",
    "#features[1] - sepel width\n",
    "plt.scatter(features[0], features[1], alpha=0.2, s=100*features[3], c=iris.target, cmap='plasma')\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  20.,   20.,   20.,   20.,   20.,   40.,   30.,   20.,   20.,\n",
       "         10.,   20.,   20.,   10.,   10.,   20.,   40.,   40.,   30.,\n",
       "         30.,   30.,   20.,   40.,   20.,   50.,   20.,   20.,   40.,\n",
       "         20.,   20.,   20.,   20.,   40.,   10.,   20.,   10.,   20.,\n",
       "         20.,   10.,   20.,   20.,   30.,   30.,   20.,   60.,   40.,\n",
       "         30.,   20.,   20.,   20.,   20.,  140.,  150.,  150.,  130.,\n",
       "        150.,  130.,  160.,  100.,  130.,  140.,  100.,  150.,  100.,\n",
       "        140.,  130.,  140.,  150.,  100.,  150.,  110.,  180.,  130.,\n",
       "        150.,  120.,  130.,  140.,  140.,  170.,  150.,  100.,  110.,\n",
       "        100.,  120.,  160.,  150.,  160.,  150.,  130.,  130.,  130.,\n",
       "        120.,  140.,  120.,  100.,  130.,  120.,  130.,  130.,  110.,\n",
       "        130.,  250.,  190.,  210.,  180.,  220.,  210.,  170.,  180.,\n",
       "        180.,  250.,  200.,  190.,  210.,  200.,  240.,  230.,  180.,\n",
       "        220.,  230.,  150.,  230.,  200.,  200.,  180.,  210.,  180.,\n",
       "        180.,  180.,  210.,  160.,  190.,  200.,  220.,  150.,  140.,\n",
       "        230.,  240.,  180.,  180.,  210.,  240.,  230.,  190.,  230.,\n",
       "        250.,  230.,  190.,  200.,  230.,  180.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100* features[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two techniques of plotting - plot & scatter\n",
    "* scatter has more options & thus is less efficient\n",
    "* plot of much optimised "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}