Ehsan_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Matplotlib Overview Lecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Matplotlib is the \"grandfather\" library of data visualization with Python. It was created by John Hunter. He created it to try to replicate MatLab's (another programming language) plotting capabilities in Python. So if you happen to be familiar with matlab, matplotlib will feel natural to you.\n",
    "\n",
    "It is an excellent 2D and 3D graphics library for generating scientific figures. \n",
    "\n",
    "Some of the major Pros of Matplotlib are:\n",
    "\n",
    "* Generally easy to get started for simple plots\n",
    "* Support for custom labels and texts\n",
    "* Great control of every element in a figure\n",
    "* High-quality output in many formats\n",
    "* Very customizable in general\n",
    "\n",
    "Matplotlib allows you to create reproducible figures programmatically. Let's learn how to use it! Before continuing this lecture, I encourage you just to explore the official Matplotlib web page: http://matplotlib.org/\n",
    "    \n",
    "## Importing\n",
    "Import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll also need to use this line to see plots in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Example\n",
    "\n",
    "Let's walk through a very simple example using two numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 5, 11)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.  ,   0.25,   1.  ,   2.25,   4.  ,   6.25,   9.  ,  12.25,\n",
       "        16.  ,  20.25,  25.  ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Matplotlib Commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGvf/AUJrfmM3R/IeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb821940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'r') # 'r' is the color red\n",
    "plt.xlabel('X Axis Title Here')\n",
    "plt.ylabel('Y Axis Title Here')\n",
    "plt.title('String Title Here')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Multiplots on Same Canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3X2c1PP+//HHa2u1XdK1VBRCdCh1\nclGoqKNUcpKuOEkkfIn4uTyuD8lx6CCdFlFHJSnfinShUypO2wV9qVYipYvt+jq12fb9++OzkXSx\n7c7Me+Yzz/vttrfZmZ2deY4+8/Le97w/75c55xARkcSX4juAiIhEhgq6iEhIqKCLiISECrqISEio\noIuIhIQKuohISKigi4iEhAq6iEhIqKCLiIRE0Vg+WYUKFVyNGjVi+ZSSRObPn7/ROVfRx3Pr2JZo\nyu+xHdOCXqNGDebNmxfLp5QkYmYrfD23jm2Jpvwe25pyEREJCRV0EZGQUEEXEQkJFXQRkZBQQRcR\nCYmjFnQzq25m08ws08wWmVnvvNufMLPVZrYg76tV9OOKxIaZLTezr/OObS1fkajK2pHFZW9fxtqd\nawv1OPkZoecA9zrnagMXAneY2dl5P3vJOVc372tCoZKIxJ+mecd2A99BJNyenvE0s36cxVOfPlWo\nxznqOnTnXBaQlff9DjPLBKoW6llFRITizxRnT86eX64PnDeQgfMGklY0jd2P7D7mxzumOXQzqwHU\nAzLybvofM/vKzAabWdnD/E5PM5tnZvM2bNhwzAFFfnHvvTBoUKyezQGTzWy+mfU81B10bEthze85\nn6qlfx0flyhagq5/6MoPvX8o0OPlu6CbWSlgNHC3c247MBA4DahLMIL/x6F+zzmX7pxr4JxrULGi\nl7OyJQw2boRXXoFvv43VMzZyzp0PtCSYZrz04Dvo2JbCyFiVQathrVizYw2GkVY0jT379lCmWBlO\nLHVigR4zXwXdzFIJivkw59wYAOfcOufcPudcLvA60LBACUTyY+RI+Pln+MtfYvJ0zrk1eZfrgQ/Q\n8S0R4pyj/+z+XPLWJZgZl51yGbc1uI3ZPWbTq36vQn0wetQ5dDMz4E0g0zn34gG3V8mbXwe4BlhY\n4BQiRzNkCJx3XvAVZWZWEkjJ+8yoJNACKNynVSLAlt1b6D62O2OXjKXdWe0Y3HYwZYv/Ols94KoB\nhXr8/GzO1Qi4AfjazBbk3fYw0NnM6hLMNS4Hbi1UEpHDycyEuXPhH4ec1YuGysAHwViGosBw59zE\nWD25hNOc1XPo+H5HVm1fxUt/eoneF/Qm7xiLmPyscpkFHOpZtUxRYiM7G1q1gi5dYvJ0zrllQPT/\nFJCk4Jzj5YyX+X9T/h8nlT6JWd1ncUG1C6LyXDHdPlekQOrWhY8+8p1C5Jht3bOVm8bexAfffEDb\nM9vy1tVvUa54uag9nwq6xLcffoDUVKhWzXcSkWMyb808rht1HSu3r+QfLf7BPRfeE/EploNpLxeJ\nb088AeeeG6xwEUkAzjleyXiFi9+8mH1uHzO7z6TPRX2iXsxBI3SJZzt3wujRwdx5aqrvNCJHtW3P\nNnqM68HozNG0PqM1Q9oNieoUy8FU0CV+ffAB7NoVs7XnIoUxf818rnv/OlZsXcHfm/+dPhf1IcVi\nOwmigi7xa8gQOPVUaNTIdxKRw3LO8drc1+gzuQ+VSlZiRvcZXFz9Yi9ZVNAlPm3aBDNmwCOPQAzm\nHkUKYtuebdwy/hZGLR5Fq1qtGNpuKOVLlPeWRwVd4lP58rBihebOJW59kfUF1426juVbl9Pvin7c\nd/F9MZ9iOZgKusSvKlV8JxD5Hecc/5r3L+6edDcVS1Rk+o3TaXxyY9+xAC1blHg0bx5ccQUsXeo7\nichvbM/eTufRnbl9wu00q9mMBb0WxE0xB43QJR4NGQKzZkGlSr6TiPxiwdoFdBjVgR+2/EDfy/ty\nf6P7vU+xHEwFXeLL3r0wYgS0awfHH+87jSS5rB1ZdHq/E1fVuorHpj9G+RLlmdZtGpecconvaIek\ngi7xZcKEYIWL1p5LHHh02qPM+HEGM36cQYvTWvDva/5NpZLx+5ejCrrEl6FDoXJlaNHCdxJJYgf3\n+gSY/P1kTul/SoF6fcZKfE0AiTRtCg89BEU11hB/BrcdTGrKr0tmC9vrM1b0rpH4cuedvhNIkhsw\nZwC9J/bm+GLHs2XPFooVLVboXp+xooIu8WP8+GCEXqqU7ySShHJyc7hn4j28OvdVrqp1FSmWQvUy\n1elZvyfp89PJ2pl19AfxTAVd4sM330DbtkGbuT59fKeRJLNtzzY6vt+RSd9Pos+FfXi++fMUSSny\ny88L2+szVlTQJT78+99QpEjM2syJ7LdsyzJaD2/N0s1Leb3N69x8/s2+IxWYCrr4l5sbFPQ//QlO\njO85SgmXmStmcs3Ia8h1uUy+fjJNazb1HalQtMpF/Js+HVau1Npziam3F7zN5UMvp3yJ8mTcnJHw\nxRxU0CUeTJ0anBXatq3vJJIEcl0uD37yIN3HdufSUy5ldo/Z1Cpfy3esiFBBF/+eeQYWL4bixX0n\nkZDbtXcX7d9rT7/P+nFr/Vv5uOvHlC1e1nesiNEcuvjlXNDA4qSTfCeRkFu1fRVtRrThq3Vf0f9P\n/bnrgrti0rg5llTQxa9rroGzz4Znn/WdREJs7uq5tH23Lbv27uLDzh/SslZL35GiQlMu4s/KlTBu\nHBx3nO8kEmLvLXqPS9++lLSiaXze4/PQFnNQQRefhg0LplxuuMF3Egkh5xxPf/o0Hd/vSP0q9cm4\nOYM6ler4jhVVmnIRP5wLGlk0bgynneY7jYTMnpw99BjXg+FfD+eGc2/g9TavU6xoMd+xok4FXfyY\nNy843T893XcSCZl1O9fRbmQ7Zq+azTPNnuGhxg+F7sPPw1FBFz8qVoR774UOHXwnkRD5et3XtB7R\nmg27NvB+h/dpf3Z735Fi6qhz6GZW3cymmVmmmS0ys955t5czsylmtjTvMjyLOSX6atSAF16AE07w\nneSwzKyImX1pZh/6ziJH9+G3H3Lx4IvJyc1hZveZSVfMIX8fiuYA9zrnagMXAneY2dnAg8BU51wt\nYGredZGjmzs3ODs0N9d3kqPpDWT6DiGHl7Uji8veuownpj9B2xFtObP8mcy5eQ71T6rvO5oXR51y\ncc5lAVl53+8ws0ygKnA10CTvbkOA6cADUUkp4fLXv8KiRbB8OaTE50IrM6sGXAU8A2g/3zj1xKdP\n/NLzs33t9gy9ZiglUkv4juXNMc2hm1kNoB6QAVTOK/Y457LMLH47p0r8WLoUJk+GJ5+M9zZz/YH7\ngdK+g8jvHarn5+jM0Xy09KO47vkZbfkeHplZKWA0cLdzbvsx/F5PM5tnZvM2bNhQkIwSJoMGBfue\n3xy/e06bWWtgvXNu/lHup2PbkwW3LqBCiQq/XE+Unp/Rlq+CbmapBMV8mHNuTN7N68ysSt7PqwDr\nD/W7zrl051wD51yDihUrRiKzJKrdu2Hw4OB0//jeu6UR0NbMlgPvAs3M7J2D76Rj24+NP22k65iu\nbPppE4aRVjQtYXp+Rlt+VrkY8CaQ6Zx78YAfjQO65X3fDRgb+XgSKkuWQLFicNttvpMckXPuIedc\nNedcDaAT8B/n3PWeYwnBh6BN3m7CwvULubDahdzW4DZm95hNr/q9WLtzre943uVnErMRcAPwtZkt\nyLvtYeA54D0z6wH8CGhBsRxZ3bqwYgWkpvpOIgloxdYVXPHvK8jakcXHXT/+TUOKROn5GW35WeUy\nCzjcaVaXRzaOhNbWrVCqVMJtxOWcm06wgks8WrppKZcPvZzt2duZcsMULqp+ke9IcSk+14xJ+Nx/\nP9SpA/v2+U4iCWbh+oVc+val7M7ZzbRu01TMj0AFXaJv27ZgZ8XGjYMVLiL5NH/NfC57+zIMY8aN\nM6hXpZ7vSHFNBV2ib+hQ+OmnuP8wVOLLrB9n0WxoM0ofV5qZ3WdSu2Jt35Hingq6RJdzMHAg/PGP\nUD85T8eWY/fJsk/40zt/4sRSJzKz+0xOK6ctlvNDBV2ia9YsyMyE22/3nUQSxPgl47lq+FWcVvY0\nZtw4g+rHV/cdKWHE9bnXEgKNGsGECdCkie8kkgBGLhzJ9R9cT70T6zHx+omUK17Od6SEohG6RFdK\nCrRsCcWL+04icW7wl4PpPLozF1e/mE/+8omKeQGooEv0vPoqPPhgImyTK569nPEyPcb1oMVpLfi4\n68eUKVbGd6SEpIIu0ZGTA88/D198Ebdb5Ep86DuzL70n9qbdWe0Y22lsUm9/W1h6p0l0fPQRrFyp\npYpyWM45Hpn6CA//52G6/KEL7137XlI0co4mfSgq0TFwIFStCm3a+E4icSjX5XLPxHt4ec7L3HL+\nLQy8aiBFUnTSWWFphC6R9913MGkS9OwZ700sxIN9ufu4ZdwtvDznZe6+4G4GtR6kYh4herdJdHTp\nEtdNLCT2snZk0fH9jpQrXo6xS8by6KWP8mSTJwl26JZIUEGXyDv99GDvFpEDPD79cWb+OBOAflf0\n4/5G93tOFD4q6BJZs2cH2+TWqeM7icSJQ/X/fOCTB3h8+uNJ3f8zGjSHLpF1993QoUOwh4sI8P2d\n33PqCaf+cl39P6NHBV0i58svISMDevUCzYtKnlfnvsqyrcvU/zMGNOUikTNwYHCKf7duR7+vJIV/\nzv4nfWf1pcbxNWhZqyW31r+V9PnpZO3M8h0tlFTQJTL2N7Ho0gVOOMF3GokDI74ewd2T7uaas65h\nVIdRvyxNVP/P6NGUi0TG/PnBNIu2yRVg8veT6fa/3bjslMsY3n641pnHiEboEhnNmsHatcEKF0lq\nc1bP4c8j/8zZFc9mbKexpBVN8x0paWiELoW3a1ewqkXFPOkt2biEVsNaUalkJT7u+jHHpx3vO1JS\nUUGXwuveHa64QksVk9zq7atp8U4LiqQUYfINk6lSuorvSElHBV0KJysLPvgA6tbVUsUktmX3Fq4c\ndiWbd2/m464fc3q5031HSkqaQ5fCefPNYO/zXr18JxFPfvr5J9qMaMO3m75lQpcJnF/lfN+RkpYK\nuhTcvn2Qng7Nm0OtWr7TiAc5uTl0fL8jn6/8nJHXjuTyUy/3HSmpacpFCm7cODWxSGLOOXqO78mH\n337Iq61epcM5HXxHSnoq6FJwrVrBO+9A27a+k4gHD099mLcWvMVjlz7G7X/U+QfxQFMuUnDFikHX\nrr5TiAf9Z/fnuc+e49b6t/JEkyd8x5E8GqFLwdx0EwwZ4juFeDDsq2HcM+ke2tduz4BWA9SgIo6o\noMuxmz8f3noL1q3znURibOJ3E7lx7I00qdGEd/78jk7pjzNHLehmNtjM1pvZwgNue8LMVpvZgryv\nVtGNKXGlXz84/vjQLlU0szQzm2Nm/2dmi8zsSd+ZfMrakcVlb1/GhG8n0P699tSpVEen9Mep/Myh\nvw28Cgw96PaXnHMvRDyRxLelS2H0aLj/fihTxneaaMkGmjnndppZKjDLzD52zs32HcyHp2c8zcwV\nM8lYlUHVMlX5uOvHlCkW2n/7hHbUgu6cm2FmNaIfRRLCCy9Aair07u07SdQ45xywM+9qat5X0u1r\ncHDruOx92Szbsoya/6yp1nFxqjBz6P9jZl/lTcmUPdydzKynmc0zs3kbNmwoxNNJXGjXDvr2hRPD\n3W3GzIqY2QJgPTDFOZdxiPuE+thedtcyOp3TiZS8MpFWJE2t4+JcQQv6QOA0oC6QBfzjcHd0zqU7\n5xo45xpUrFixgE8ncaNlS7jnHt8pos45t885VxeoBjQ0s991vQ77sV2ldBUWrl9ILrmkpqSyN3ev\nWsfFuQIVdOfcurwDPhd4HWgY2VgSd7Ztg0cfhfXrfSeJKefcVmA6cKXnKDH37sJ3WbhhIedVPo+5\nt8ylV/1erN251ncsOYICnVhkZlWcc/ubAl4DLDzS/SUEBg2Cv/0tmHKpVMl3mqgys4rAz865rWZW\nHLgC6Oc5Vkwt3rCYm8fdTOOTG/Ofv/yH1CKpah2XAI5a0M1sBNAEqGBmq4DHgSZmVpfgg6LlwK1R\nzCi+7dkDL70U7Hlev77vNLFQBRhiZkUI/op9zzn3oedMMbNz706ufe9aSh5XkpHXjiS1SKrvSJJP\n+Vnl0vkQN78ZhSwSr/7976C93Dvv+E4SE865r4B6vnP44JzjlvG3sGTTEj654RNOKn2S70hyDLSX\nixzZvn3w/PPByLxZM99pJMoGzB3Auwvf5dlmz9K0ZlPfceQYqaDLkW3bFnQj6tRJHYlCbvaq2fSZ\n1IfWZ7TmgcYP+I4jBaCCLkdWrhyMGuU7hUTZhl0b6DCqA9XKVGNou6GkmLZ5SkQq6HJ4X38NRYtC\n7dq+k0gU7cvdR9cxXdmwawOf9/icssUPe56gxDkVdDm8Pn1g8WJYsSIo7BJKT336FFOWTSG9dbr6\ngSY4/V0lhzZ/PnzySbBni4p5aE38biJPz3iaG+veyM3n3+w7jhSSCrocWr9+wW6Kt+oUg7BasXUF\nXcd05Q+V/6BGFSGhgi6/t3+L3NtvD/Y9l9DJzsmmw6gO5OTmMPq60ZRILeE7kkSA/paW35s/Pyjk\nId4iN9n1mdSHuWvmMua6MZxe7nTfcSRCNEKX3+vUCVavDv0Wuclq2FfDeG3ea9x30X1cU/sa33Ek\nglTQ5bd+/BGcg+LFfSeRKFi0fhE9P+zJJSdfQt8r+vqOIxGmgi6/2rYN/vAH+OtffSeRCMvakUXj\nwY25+t2rKX1caUZeO5KiKZpxDRv9i8qvBg2C7dvhz3/2nUQi7KlPn+KzlZ8BMK3bNKqUruI5kUSD\nCroEkm+L3KRwcF9QgKZDmpJWNE19QUNIUy4S2L9F7oMP+k4iEbTsrmW0PbPtL9dLFC2hvqAhpoIu\ngSFDtEVuCJ1Y6kS+zPoSgGJFirFn3x71BQ0xTblIYMqUYKmizhYMlZGLRrJy+0oaVW/EgFYDSJ+f\nTtbOrKP/oiQkFfRkl50dFPHixeF0nWASJlt2b6H3xN40OKkBn974KUVSiqgvaMhpyiXZvfxysD3u\n5s2+k0iEPfDJA2z6aRPprdMpklLEdxyJARX0ZLZlCzz7LJx1VtDIQkJj5oqZvP7F69xz4T3Uq5KU\n7VGTkgp6MnvuueBkor46YzBMsnOyufXDWznl+FN4oskTvuNIDGkOPVmtXAn//CfccAOce67vNBJB\n/T7rR+bGTCZ0mUDJ40r6jiMxpBF6snrzzWDPlqee8p1EImjJxiU8M/MZOtXpRMtaLX3HkRhTQU9W\njz0GGRlwyim+k0iEOOfo9VEvSqSWoP+f+vuOIx5oyiUZ7dwJpUpB3bq+k0gEvb3gbaYvn05663Qq\nl6rsO454oBF6svnsM6hWDT7/3HcSiaD1u9Zz35T7aHxyY3qc38N3HPFEBT2ZOAcPPAAlSsB55/lO\nIxHUZ1IfdmTvIL11Oimmt3Wy0pRLMhk/PhihDxoEJbX6ISymfD+FYV8P47FLH6N2xdq+44hH+l95\nssjJgYcegjPOgJtu8p1GIuSnn3+i10e9OKP8GTx0yUO+44hnGqEni2nTYPFieP99KKp/9rB4+tOn\nWbZlGdO6TSOtaJrvOOLZUUfoZjbYzNab2cIDbitnZlPMbGneZdnoxpRCa94cvvhC3Yjywcyqm9k0\nM8s0s0Vm1tt3pkP5at1XvPDfF+hetztNajTxHUfiQH6mXN4GrjzotgeBqc65WsDUvOsSr3buDC7r\n1dP2uPmTA9zrnKsNXAjcYWZne870G6u2raLx4MaUKVaGvzf/u+84EieOWtCdczOAg7fiuxoYkvf9\nEKBdhHNJpGzZAqeeCgMH+k6SMJxzWc65L/K+3wFkAlX9pvqtzqM7s2PvDuqdWI/yJcr7jiNxoqCT\nqZWdc1kQHPxmVimCmSSSnnsONm6ERo18J0lIZlYDqAdk+E0SOLhH6NQfpmJPmnqEChCDVS5m1tPM\n5pnZvA0bNkT76eRA2oCrUMysFDAauNs5t/0QP4/5sb3srmXUrvDr0kT1CJUDFbSgrzOzKgB5l+sP\nd0fnXLpzroFzrkHFihUL+HRSIE88oQ24CsjMUgmK+TDn3JhD3cfHsZ1aJJWlm5YCkFY0TT1C5TcK\nWtDHAd3yvu8GjI1MHImYjRthxAi44w5twHWMzMyAN4FM59yLvvMcqP/s/uS4HDqe05HZPWbTq34v\n1u5c6zuWxImjzqGb2QigCVDBzFYBjwPPAe+ZWQ/gR6BDNENKAVSoEKw7L1PGd5JE1Ai4AfjazBbk\n3fawc26Cx0xs2b2FlzNe5tqzr+Xda98FUI9Q+Y2jFnTnXOfD/OjyCGeRSNm/m2KNGr6TJCTn3Cwg\n7tZ3vpzxMjv27uCvl/zVdxSJUzr1P2ycgyuvhBtv9J1EImh79nb6Z/Tn6jOv5rwTtbGaHJoKetjs\n34Droot8J5EIenXOq2zds5VHL33UdxSJYyroYZKdHWyPqw24QmXn3p28+N8XaVWrFfVPqu87jsQx\n7dIUJv36wTffwEcfQWqq7zQSIQPnDmTT7k0anctRaYQeFtnZQePnTp2gVSvfaSRCfvr5J1747ws0\nP7U5F1a70HcciXMaoYdFsWKwYAHs2+c7iURQ+vx01u9ar9G55ItG6GGwYAH8/DOULRusP5dQ2JOz\nh+c/e54mNZpwySmX+I4jCUAFPdGtWQOXXQa943LLbimEN794k6ydWRqdS76poCe6u+6CvXuhTx/f\nSSSCsnOyee6z52hUvRFNazT1HUcShObQE9nYsTB6NDz7LJx+uu80EkFD/m8Iq7av4o02b2BqSiL5\npBF6otq+Pdh469xz4b77fKeRCPp538/0ndWXhlUb0uK0Fr7jSALRCD1RrVsXfACanq415yEz7Oth\nLN+6nFdavqLRuRwTFfREVatW0PQ5RX9khcnKbSu57aPbqFOxDlfVusp3HEkwqgaJZu9eePJJ2LZN\nxTyEuo/tzp6cPVQtU1WjczlmGqEnmn79gk5EDRtCy5a+00iEHNwrdNL3k9QrVI6ZhniJ5Jtv4G9/\ng44dVcxDZtldy2hV69ctG9QrVApCBT1R5OZCz55QsmTQ+FlCpUrpKmTtyAKgWJFi6hUqBaIpl0Tx\nxhswc2awAVflyr7TSIQ551iycQnVy1RnfOfxpM9PJ2tnlu9YkmBU0BNF8+bw0EPQvbvvJBIFGasz\n+CnnJ15r+hrnnXieeoVKgaigJwLnoGbN4IxQCaXhXw+nWJFiXFP7Gt9RJIFpDj3ejR0LrVvDpk2+\nk0iU5OTm8N6i92hzZhvKFCvjO44kMI3Q49n+0/vLl4cyeqOH1bQfprFu1zo61+nsO4okOBX0ePbQ\nQ8H2uGPG6PT+EBuxcARlipX5zbJFkYLQlEu8+vxzGDgw2B63YUPfaSRK9uTsYXTmaP5c+8+kFU3z\nHUcSnAp6vHr8cahWLTiRSEJrwtIJbM/eTpc6XXxHkRDQlEu8evddWLkSSpXynUSiaMTCEVQuWZmm\nNdXEQgpPI/R4M2VKsAFX+fJQt67vNBJF27O3M37JeK475zqKpmhsJYWngh5Ppk6FK6+Evn19J5EY\n+N9v/pfsfdl0+YOmWyQyVNDjxcqV0KkTnHUW3Huv7zQSA8O/Hk7NE2pyQdULfEeRkFBBjwfZ2dCh\nA+zZE/QI1bx56K3ftZ5Pln1C5zqdte+5RIwm7uLBgw9CRgaMGhWM0CX0Ri0axT63T9MtElGFKuhm\nthzYAewDcpxzDSIRKun06BEsUbz2Wt9JBDCzwUBrYL1zrk6kHz9rRxYP/+dhzip/FudUOifSDy9J\nLBJTLk2dc3VVzAtg8+bgsk4dzZvHl7eBK6P14PdPuZ/t2ds5Pu34aD2FJClNufiydStccAG0bw/P\nPec7jRzAOTfDzGpE+nEPbjOXsTpDbeYkogo7QnfAZDObb2Y9D3UHM+tpZvPMbN6GDRsK+XQhkZsL\nN94Iy5dDmza+00gBHeuxveyuZXSp04WUvLed2sxJpBW2oDdyzp0PtATuMLNLD76Dcy7dOdfAOdeg\nYsWKhXy6kHj++WBb3BdegEaNfKeRAjrWY7tK6SqUOq4UueRSxIqozZxEXKGmXJxza/Iu15vZB0BD\nYEYkgoXW1KnwyCNBo+e77vKdRmJs6ealADzb7FlWbFuhNnMSUQUu6GZWEkhxzu3I+74F8FTEkoXV\n7t3B7olvvAFaf5x0Op7TkWnLp9HhnA7ULFvTdxwJmcJMuVQGZpnZ/wFzgI+ccxMjEyvEWrcOtsbV\nyUNxy8xGAP8FzjSzVWbWI1KPPWf1HCqUqECNE2pE6iFFflHgEbpzbhlwXgSzhFufPlCjRjDNopF5\nXHPORa110Jw1c2hYtaHODpWo0Kn/sTBsGLz0Evz4o+8k4tHOvTtZvGExfzzpj76jSEipoEfbV1/B\nLbfApZdqvXmS+yLrC3JdLg2rqgOVRIcKejRlZkKLFlC2LIwcCUV1Hlcym7N6DoBG6BI1KujR9N//\nQkpKsFTxRK01TnZzVs+h5gk1qVhS52NIdKigR8PPPweXN90E33yjHRQFCAr6H6tqdC7Ro4IeaYsX\nBwV8Rt75VWXK+M0jcWH9rvWs2LaChidp/lyiR5O6kbR4MTRrFixLrFTJdxqJI3NXzwXQB6ISVRqh\nR8qBxXzaNE2zyG/MWT2HFEvh/Crn+44iIaaCHgk//qhiLkc0d81czql4DiWPK+k7ioSYCnokVK0a\nNHhWMZdDcM4xZ/UcTbdI1GkOvTAyM4MPPatWhf79faeROPXD1h/YtHuTCrpEnUboBbV4MTRtCp07\ng3O+00gcm/TdJABqnqDdFSW6VNALYv8HoADp6dpsS47oX/P+BcDozNGek0jYacrlWB1YzKdP15y5\nHNbBPUQHzR/EoPmD1ENUokYj9GN1zz3BpYq5HMX+HqL7qYeoRJtG6PnlXDC1MmwYbNoEZ57pO5HE\nuSqlq1C6WGkAilpR9RCVqFNBP5rc3KCZ8/Tp8OGHUKFC8CWSD2t2rAHgzgvuJDsnWz1EJao05XIk\nq1bBFVfAAw9AiRJBP1CRY/BKy1cAqFOpDgOuGsCYjmM8J5IwU0E/nDFj4NxzYc4cePNNGDUKSuos\nPzk2W/ZsAaBc8XKek0gy0JSGEjDjAAAF+0lEQVTLoezeHXz4edppMHw41KrlO5EkqM27NwMq6BIb\nKugH+uqr4MPO4sWDphQnnwzHHec7lSSw/QW9bFpZz0kkGWjKBYIPPp9/Hho0+LXv5+mnq5hLoWmE\nLrGkEfqqVfCXvwQba7VvD3fe6TuRhIgKusRSchf0yZODXRKzs+GNN4KWcTqNXyJo8+7NpBVNo3hq\ncd9RJAkkd0E/6SSoUyco5mec4TuNhNDm3Zs1OpeYSa6Cnp0NI0fCt9/C3/4WFPNPP9WoXKJGBV1i\nKTk+FF2zBh57LFi10q1bsMZ8w4bgZyrmEkUq6BJL4S/o48fDKacEI/ILLgjmzRctgooVfSeTJKCC\nLrEUvoKenQ1Dh8LEicH1Ro3grrtg6VIYNw6aN9eoXGJm8+7NlEtTQZfYCE9BP3haZciQ4PZy5eAf\n/wjO+hSJMY3QJZYKVdDN7EozW2Jm35nZg5EKdcweffT30yrDh3uLI4kvEsf27p93sztntwq6xEyB\nV7mYWRFgANAcWAXMNbNxzrnFkQr3C+eCPci/+y6YOtl/+cwzULNmcLr+nXfCHXdoJC6FFqlj+5uN\n3wBQxIpEPKPIoRRm2WJD4Dvn3DIAM3sXuBooWEF3DjZvDgr1/qLdvn2w4+FHH0GbNr/eNyUlGJGv\nXRsU9OuvD75EIiMix/ZznwXbSEz9YSr3N74/0hlFfqcwBb0qsPKA66uACwr0SN98AxddBFu3/npb\nSgqcempQ0OvVgxdfDHY9PP30oIgXK1aI6CJHVKhj++BeopOXTcaeNPUSlagrTEE/1FIR97s7mfUE\negKcfPLJh36kqlWhc+egYB+qaFet+msvT5HoK9SxveyuZdw3+T7GZI5hz749pBVNo33t9rzQ4oWo\nBRaBwhX0VUD1A65XA9YcfCfnXDqQDtCgQYPfvSkAKF0aXnutEFFEIqpQx3aV0lUoU6wMe3P3klY0\njb379qqXqMREYVa5zAVqmVlNMzsO6ASMi0wsEa8KfWyv27WOXvV7MbvHbHrV78XanWujElTkQAUe\noTvncszsf4BJQBFgsHNuUcSSiXgSiWP7wN6hA64aENmAIodRqM25nHMTgAkRyiISN3RsSyIKz5mi\nIiJJTgVdRCQkVNBFREJCBV1EJCRU0EVEQsKcO/S5PlF5MrMNwIrD/LgCsDFmYWIrzK8N4uf1neKc\n89K55AjHdrz8t4mWML++eHpt+Tq2Y1rQj8TM5jnnGvjOEQ1hfm0Q/tdXGGH/bxPm15eIr01TLiIi\nIaGCLiISEvFU0NN9B4iiML82CP/rK4yw/7cJ8+tLuNcWN3PoIiJSOPE0QhcRkULwXtDjptF0FJhZ\ndTObZmaZZrbIzHr7zhRpZlbEzL40sw99Z4k3YTu2zWywma03s4UH3FbOzKaY2dK8y7I+MxbU4d6r\nifb6vBb0A5rxtgTOBjqb2dk+M0VYDnCvc642cCFwR8heH0BvINN3iHgT0mP7beDKg257EJjqnKsF\nTM27nogO915NqNfne4T+SzNe59xeYH8z3lBwzmU5577I+34HQeGr6jdV5JhZNeAq4A3fWeJQ6I5t\n59wMYPNBN18NDMn7fgjQLqahIuQI79WEen2+C/qhmvGGpuAdyMxqAPWADL9JIqo/cD+Q6ztIHEqW\nY7uycy4LgqIIVPKcp9AOeq8m1OvzXdDz1Yw30ZlZKWA0cLdzbrvvPJFgZq2B9c65+b6zxKmkOLbD\nJtHfq74Ler6a8SYyM0slOECGOefGHO3+CaQR0NbMlhNMJzQzs3f8RooroT+286wzsyoAeZfrPecp\nsMO8VxPq9fku6KFuNG1mBrwJZDrnXvSdJ5Kccw8556o552oQ/Lv9xzl3vedY8STUx/YBxgHd8r7v\nBoz1mKXAjvBeTajX57WgO+dygP3NeDOB90LWaLoRcAPB6HVB3lcr36Ek+sJ4bJvZCOC/wJlmtsrM\negDPAc3NbCnQPO96IjrcezWhXp/OFBURCQnfUy4iIhIhKugiIiGhgi4iEhIq6CIiIaGCLiISEiro\nIiIhoYIuIhISKugiIiHx/wEhI1ZwFzYgoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb821898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplot(nrows, ncols, plot_number)\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x, y, 'r--') \n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(y, x, 'g*-');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Object Oriented Method\n",
    "\n",
    "The main idea in using the more formal Object Oriented method is to create figure objects and then just call methods or attributes off of that object. This approach is nicer when dealing with a canvas that has multiple plots on it. \n",
    "\n",
    "To begin we create a figure instance. Then we can add axes to that figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEJCAYAAACQZoDoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd15cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # Create Figure (empty canvas)\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Set Title')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEjCAYAAADOsV1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9be780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Figure (empty canvas)\n",
    "fig = plt.figure()\n",
    "\n",
    "# Add set of axes to figure\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
    "\n",
    "# Plot on that set of axes\n",
    "axes.plot(x, y, 'b')\n",
    "axes.set_xlabel('Set X Label') # Notice the use of set_ to begin methods\n",
    "axes.set_ylabel('Set y Label')\n",
    "axes.set_title('Set Title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## subplots()\n",
    "\n",
    "The plt.subplots() object will act as a more automatic axis manager.\n",
    "\n",
    "Basic use cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAHBFJREFUeJzt3XeYleWZx/HvLWKh2UCCKKIGSSwJ\nuGOL0awoCDZaTLCgxgIWVlAUKxtdaxRrMCKKK0bRGIEFA4soKCWCUixgQEVFQhEGkEVYRYa594/n\nsI44A8PMvOc557y/z3Wda04b3vugvL/zPtXcHRERSa8dYhcgIiJxKQhERFJOQSAiknIKAhGRlFMQ\niIiknIJARCTlFAQi22BmzcxsnZnV2sp73Mx+nM26RGqKgkCkHGa20MxOBnD3Re5ez903ZV57w8wu\niVuhSM1REIiIpJyCQGQLZvZnoBnwcqZJqF+m6WdHM7sTOB4YmHltYDm/v7OZDTCzRWa23MwGmdmu\n2f4cIpWlIBDZgrt3BxYBZ7h7PeDFMq/dDEwBemWai3qV80f8ATgYaAX8GGgK/HvihYtUkYJApAaZ\nmQGXAle7+2p3/wq4C+gWtzKRiu0YuwCRAtMIqAPMCpkAgAEVjjgSiU1BIFK+rS3Lu7XXVgJfA4e6\n+5KaLUkkGWoaEinfcuDA7X3N3UuBJ4AHzWxvADNramanJFKlSA1QEIiU727gFjNbA/x6i9ceBn5t\nZl+a2SPl/O71wAJgupmtBV4DWiZarUg1mDamERFJN10RiIiknIJARCTlFAQiIimnIBARSbm8mEfQ\nsGFDb968eewyRETyyqxZs1a6e6NtvS8vgqB58+bMnDkzdhkiInnFzD6vzPvUNCQiknIKAhGRlFMQ\niIiknIJARCTlFAQiIimXWBCY2X5m9rqZzTOzD8ysd+b5W81siZm9m7mdmlQNIiKybUkOHy0B+rr7\nbDOrT9io49XMaw+6+4AEjy0iIpWU2BWBuy9z99mZ+18B8wh7t4qIyLb87//CVVfBqlWJHyorfQRm\n1hxoDbyVeaqXmb1vZk+Z2R4V/E4PM5tpZjOLi4uzUaaISG5wh0svhYEDYcaMxA+XeBCYWT1gONDH\n3dcCjwEHAa2AZcD95f2euw929yJ3L2rUaJszpEVECseAATBsGNxxB7Rvn/jhEg0CM6tNCIHn3H0E\ngLsvd/dNZbb0OyrJGkRE8sorr8ANN8BZZ8GNN2blkEmOGjJgCDDP3R8o83yTMm/rDMxNqgYRkbzy\n8cfQrRscdhj853+CWVYOm+SooeOA7sAcM3s389xNwNlm1gpwYCHQM8EaRETyw9q10LEj1KoFo0ZB\n3bpZO3RiQeDuU4Hy4mxsUscUEclLpaXQvTt89BG8+ipkedn9vFiGWkSkoN16K4weDY88AieemPXD\na4kJEZGYhg+H22+Hiy6CXr2ilKAgEBGJZc4cuOACOOYY+NOfstY5vCUFgYhIDKtWhc7h3XaDESNg\n552jlaI+AhGRbCspgd/8BpYsgcmToUmTbf9OghQEIiLZdt11MHFimCtw9NGxq1HTkIhIVg0dCg89\nBL17w4UXxq4GUBCIiGTP229Dz57Qpk1YTyhHKAhERLJh2TLo3Bn22QdefBF2zJ2W+dypRESkUG3Y\nAF26wJo1MG0a7LVX7Iq+R0EgIpIkd7jySpg+HV56CX72s9gV/YCahkREkvToozBkCNxyC3TtGrua\ncikIRESS8sYb0KcPnHEG3HZb7GoqpCAQEUnCwoXw61/DwQfDs8/CDrl7us3dykRE8tX69dCpE2za\nFPYWaNAgdkVbpc5iEZGa5B5WEp0zB8aMgRYtYle0TQoCEZGadM89YZ7AvfdmZeP5mqCmIRGRmjJm\nDNx8M5x9Nlx7bexqKk1BICJSE+bPh3POgdat4ckno+0tUBUKAhGR6lqzJuwtsPPOMHIk1KkTu6Lt\noj4CEZHq2LQJzj0XPv00LC3drFnsirabgkBEpDr694exY+Gxx+D442NXUyVqGhIRqaq//AXuvht6\n9IDLLotdTZUpCEREquLdd+F3v4PjjoM//jF2NdWiIBAR2V7FxaFzeK+9YPhw2Gmn2BVVi/oIRES2\nx8aNcNZZsGIFTJkCjRvHrqjaFAQiItvjmmtg0qSwkFxRUexqaoSahkREKmvIEBg4MMwaPvfc2NXU\nGAWBiEhlvPkmXH45tGsX1hMqIAoCEZFtWbIk7C62//7wwgtQq1bsimqU+ghERLbmm2+gc2dYtw5e\new322CN2RTUusSsCM9vPzF43s3lm9oGZ9c48v6eZvWpmH2d+Ft7fqogUhtJSuPhimDEjdA4femjs\nihKRZNNQCdDX3X8KHANcaWaHADcAE9y9BTAh81hEJLe4h07hYcPC7OGOHWNXlJjEgsDdl7n77Mz9\nr4B5QFOgIzA087ahQKekahARqbIBA+DBB+Gqq+D662NXk6isdBabWXOgNfAW0Njdl0EIC2DvCn6n\nh5nNNLOZxcXF2ShTRCQYOhT69YPf/jaEQR7tLVAViQeBmdUDhgN93H1tZX/P3Qe7e5G7FzVq1Ci5\nAkVEyho7NvQLnHxyCIQdCn9wZaKf0MxqE0LgOXcfkXl6uZk1ybzeBFiRZA0iIpU2fXpYPqJVKxgx\nImw0kwJJjhoyYAgwz90fKPPSaOCCzP0LgFFJ1SAiUmnz5sFpp8E++4Srgvr1Y1eUNUnOIzgO6A7M\nMbN3M8/dBNwDvGhmFwOLgLMSrEFEZNsWL4ZTToHateGVV2DvcrsuC1ZiQeDuU4GKelhOSuq4IiLb\n5csvoX37sO/wpElw4IGxK8o6zSwWkfT6+ms44wz4+GP47/+G1q1jVxSFgkBE0qmkBLp1C4vJ/eUv\n0KZN7IqiURCISPq4hz2GR4+GRx8NI4VSrPAHyIqIbKl//7C3QP/+cMUVsauJTkEgIukycCDceSdc\neincdlvsanKCgkBE0uPFF8PaQZ06wZ/+VPBLR1SWgkBE0mHCBDjvPPjlL8OKojuqi3QzBYGIFL7Z\ns8PmMi1bwqhRsOuusSvKKQoCESlsn3wCHTqEncXGjSvIHcaqS0EgIoVr+fKw2fymTWHpiKZNY1eU\nk9RIJiKFae3acCXwxRcwcSL85CexK8pZCgIRKTwbNoQ+gTlz4OWX4eijY1eU0xQEIlJYSkvh/PPD\nVcAzz4QF5WSr1EcgIoXDHXr3DvMF7rsPunePXVFeUBCISOG4++4wc7hvX7j22tjV5A0FgYgUhief\nhJtvDpPG7r03djV5RUEgIvlv9Gjo2TP0Bzz1VCo2nK9J+tsSkfw2dSr89rdQVAR//WvYblK2i4JA\nRPLX3Llhh7H994cxY6BevdgV5SUFgYjkp0WLQlNQnTph1nDDhrEryluaRyAi+WfVKjjlFFi3DqZM\nCVcEUmUKAhHJL+vXw2mnwWefwfjxcPjhsSvKewoCEckfGzfCb34DM2bA8OFwwgmxKyoICgIRyQ/u\ncMklMHYsPP542GVMaoQ6i0UkP9xwQ1g76D/+A3r0iF1NQVEQiEhuc4dbbgmzha+4ItyXGqWmIRHJ\nXe7Qrx8MGACXXgp//KM2nE+ArghEJDe5Q58+IQSuvBIGDdLSEQnR36qI5J7S0tAM9MgjcPXV4UpA\nIZAY/c2KSG7ZtCk0Aw0aFDqI779fzUEJSywIzOwpM1thZnPLPHermS0xs3czt1OTOr6I5KGSErjw\nwrCC6O9/D3fdpRDIgiSvCJ4Gytsj7kF3b5W5jU3w+CKSTzZuhHPPhWefhTvvhFtvVQhkSWKjhtx9\nspk1T+rPF5EC8u230K0bjBwZtpjU7mJZFaOPoJeZvZ9pOtqjojeZWQ8zm2lmM4uLi7NZn4hk0zff\nQJcuIQQeflghEEG2g+Ax4CCgFbAMuL+iN7r7YHcvcveiRo0aZas+Ecmmr7+Gjh3DXgKDBsFVV8Wu\nKJWyOqHM3Zdvvm9mTwB/y+bxRSSHrF8fNpV5443QOfy738WuKLWyekVgZk3KPOwMzK3ovSJSwL76\nCjp0gEmTwvpBCoGoErsiMLPngX8FGprZYuD3wL+aWSvAgYVAz6SOLyI5as2aEAIzZsCwYWG/YYkq\nyVFDZ5fz9JCkjicieWD16rCz2HvvhY3mO3eOXZGgRedEJFtWroS2beEf/4ARI+D002NXJBkKAhFJ\n3vLlcNJJ8Mkn8PLL0K5d7IqkDAWBiCRr6dIQAosWhWGibdrErki2oCAQkeT885/hxP/FFzBuHBx/\nfOyKpBwKAhFJxsKFIQRWrYLx4+HYY2NXJBVQEIhIzVuwIITAunUwYQIUFcWuSLZCQSAiNWv+/NAn\nsGEDTJwIrVrFrki2QUEgIjVn7lw4+eSwzeQbb8Bhh8WuSCpBO5SJSM147z048cSwpeSkSQqBPKIg\nEJHqmzUrhMAuu4QQ+MlPYlck20FBICLVM3166BNo0AAmT4YWLWJXJNtJQSAiVTd1alg2omHDEAIH\nHBC7IqkCBYGIVM3rr4cF5Jo2Dc1BzZrFrkiqaJtBYGa9tralpIik0PjxcOqp0Lx5GB3UtGnsiqQa\nKnNF8CNghpm9aGbtzcySLkpEctjYsXDmmdCyZQiBH/0odkVSTdsMAne/BWhB2EvgQuBjM7vLzA5K\nuDYRyTUvvQSdOoWhoRMngvYTLwiV6iNwdwe+yNxKgD2Al8zs3gRrE5Fc4Q533w1nnQVHHgmvvQZ7\n7hm7Kqkh25xZbGZXARcAK4EngevcfaOZ7QB8DPRLtkQRiWrDBujRI+wtfM45MGRImC8gBaMyS0w0\nBLq4++dln3T3UjPTFkMihWzlyrCd5NSpcNtt0L8/qJuw4GwzCNz937fy2ryaLUdEcsa8eWE7ySVL\n4PnnoVu32BVJQrTonIj80Kuvhv6AXXYJI4OOOSZ2RZIgTSgTke977DHo0CFMEHvrLYVACigIRCQo\nKYHeveGKK6B9e/j732H//WNXJVmgIBARWLs2TBJ75BG4+moYNQrq149dlWSJ+ghE0m7hQjjjjNA5\n/NhjcNllsSuSLFMQiKTZtGlhpvCGDTBuXNhdTFJHTUMiafX882Ezmfr1w54CCoHUUhCIpI073Hpr\nmCV81FEhBLSjWKqpaUgkTb7+Gi66CF54AS64AB5/HHbeOXZVEpmCQCQtli8P/QHTp8M990C/flou\nQgAFgUg6zJkTlosoLobhw6FLl9gVSQ5JrI/AzJ4ysxVmNrfMc3ua2atm9nHmp3Y+E0namDHwi1+E\nCWNTpigE5AeS7Cx+Gmi/xXM3ABPcvQUwIfNYRJLgDg8/HCaKtWgBb78N//IvsauSHJRYELj7ZGD1\nFk93BIZm7g8FOiV1fJFU27gxLBXRp08IgilTtK+wVCjbw0cbu/sygMzPvSt6o5n1MLOZZjazuLg4\nawWK5L01a8LG8oMGwfXXhz6BunVjVyU5LGfnEbj7YHcvcveiRtoXVaRyPvkEjj0WJk2Cp54Ko4N2\nyNl/5pIjsj1qaLmZNXH3ZWbWBFiR5eOLFK7Jk0NHsHvYT+BXv4pdkeSJbH9VGE3Y/5jMz1FZPr5I\nYRo6NCwRsddeYZ6AQkC2Q5LDR58HpgEtzWyxmV0M3AO0NbOPgbaZxyJSVaWlcNNNcOGFcPzxIQRa\ntIhdleSZxJqG3P3sCl46KaljiqTK+vVw/vkwYgT06AEDB0Lt2rGrkjykmcUi+Wjp0jAsdPZseOCB\nMExUy0VIFSkIRPLNhAnQvXvYVWzUqLCpjEg1aFyZSL749tuwUFzbtrDbbvDmmwoBqRG6IhDJB/Pn\nh/0D3nkHevYMzUF16sSuSgqErghEcpk7DB4MRxwBixbByJFhxrBCQGqQrghEctWqVXDppeHkf9JJ\n8MwzsM8+sauSAqQrApFcNGEC/Oxn8Le/wYABMH68QkASoyAQySVlO4QbNIC33oK+fbVekCRKTUMi\nuWL+fDj33DA3QB3CkkX6miESW9kO4c8/V4ewZJ2uCERiUoew5ABdEYjEUrZD+L771CEs0SgIRLKt\nbIdw/fphxdBrr1WHsESjpiGRbPrwwzBDWB3CkkP0FUQkG9zhiSdCh/DCheoQlpyiIBBJ2qpV0LVr\n2DPg2GNhzhzo1Cl2VSL/T0EgkiR1CEseUBCIJEEdwpJH1FksUtO27BC+/36oWzd2VSIV0tcTkZpS\nUYewQkBynIJApCaoQ1jymIJApLomToSf/1wdwpK3FAQiVbVhA1x/PZx8MtSrpw5hyVv6P1akKsaM\ngcMOg3vvDYvGzZoV+gZE8pCCQGR7fPghnHoqnH461KoF48bB44+rQ1jymoJApDLWroXrroPDD4e/\n/z0MCX3/fTjllNiViVSb5hGIbE1pKfz5z6EvYPlyuOgiuOsuaNw4dmUiNUZBIFKRt9+Gf/u38POY\nY+Dll+HII2NXJVLj1DQksqUvvgjf/I8+GhYtgqFDQ3OQQkAKlIJAZLNvvw1t/wcfDM8+G9YK+ugj\nOP98DQmVghalacjMFgJfAZuAEncvilGHyP8bNw769PluVNCDD4ZAEEmBmH0EJ7r7yojHF4EFC+Ca\na0L7f4sWYXbwaafFrkokq3S9K+m0bh3ceCMceii8/jr84Q9hfSCFgKRQrCBwYLyZzTKzHuW9wcx6\nmNlMM5tZXFyc5fKkYLmH9v+WLeGee6Bbt9AP0K8f7Lxz7OpEoogVBMe5+xFAB+BKMzthyze4+2B3\nL3L3okaNGmW/Qik8s2bBL38J3buHReGmTQsjgpo0iV2ZSFRRgsDdl2Z+rgBGAkfFqENSYsWKsB7Q\nkUeGPoEhQ+Ctt8LcABHJfhCYWV0zq7/5PtAOmJvtOiQFNm6Ehx4Ko3+efhquvjo0A110kYaDipQR\nY9RQY2CkmW0+/jB3HxehDilkr70GV10F8+ZBu3YhEH7609hVieSkrAeBu38K/Dzbx5WU+Owz6Ns3\nbBN54IHwX/8FZ54J4YuHiJRD18dSGNavh/79w7f+V16BO++EDz6Ajh0VAiLboEXnJL9t3AgvvAA3\n3QSLF8M554Q5AfvuG7sykbyhIJD89NVX8OSToe1/0SJo1Qqefz4MDxWR7aIgkPyyZAk88kjYFex/\n/gdOOAEGDgwzgjUSSKRKFASSH+bOhQEDYNgw2LQJunYNG8UfpSkoItWlIJDc5Q4TJ4YAGDcO6tSB\nnj3DfIADD4xdnUjBUBBI7tm4Ef761xAA77wDe+8Nd9wBl10Ge+0VuzqRgqMgkNyxZQdwy5bwxBNw\n3nmwyy6xqxMpWAoCiW/p0tABPGiQOoBFIlAQSDxz54atIZ977rsO4L59w17BIpI1CgLJLvewEcx9\n96kDWCRHKAgkOzZuhJdeCh3As2erA1gkhygIJFnqABbJeQoCSYY6gEXyhoJAapY6gEXyjoJAqm/t\nWhgzBp555vsdwH36wEEHxa5ORLZBQSBVs3o1jB4Nw4fD+PHw7bdhE/jbb4fLL1cHsEgeURBI5S1f\nHnb8Gj48DAEtKYFmzeDKK0MT0LHHqv1fJA8pCGTrliyBESPCyX/KFCgthR//OLT7d+0KRUXaAUwk\nzykI5Ic++yyc+IcPh+nTw3OHHgq33BJO/ocfrpO/SAFREEgwf/53J/933gnPtW4d9v7t2jWM/xeR\ngqQgSCt3mDPnu5P/Bx+E5485Jiz/0KWLlnwQSQkFQZq4w8yZ3538FywInbvHHx8mf3XurE3fRVJI\nQVDoSkth2rSwzs+IEWGZh1q1oE2bsNVjp07QuHHsKkUkIgVBISopgcmTw7f+kSNh2TLYaSdo1w5u\nuw3OPBP23DN2lSKSIxQEhWD16tDBO3s2zJoFEybAypWw667QoUPo7D39dGjQIHalIpKDFAT55osv\nwgm/7O3zz797vVkzaNs2nPzbt4e6dePVKiJ5QUGQq9xDe/7mb/qbb8uWffeeFi3CKJ8rroAjjgjD\nPbW0g4hsJwVBLigthU8++eE3/dWrw+s77ACHHBK+6bduHU76rVqpqUdEaoSCINtKSuDDD79/wn/n\nnbCBC0Dt2mHmbpcu4YR/xBHhcZ06cesWkYKlIEjShg1holbZk/5778E334TXd901fLM///zvTvqH\nHBJG+IiIZEmUIDCz9sDDQC3gSXe/J0Yd22XDBvjyy3Bbs+b7P8t7rrg4fPPfuDH8foMG4UR/+eXf\nnfRbtgxj+kVEIsp6EJhZLeBRoC2wGJhhZqPd/R+JHtg9NL9sz8m87P3N3+IrUqcO7LEH7L57+Hng\ngWHI5uaT/gEHaIlmEclJMa4IjgIWuPunAGb2AtARqPkguP12ePrpcDJfsyZ0ylbEDHbbLZzEN5/Q\nDznk+yf3iu7vvruac0Qkb8UIgqbAP8s8Xgz8YENbM+sB9ABo1qxZ1Y7UpEnYK3drJ/HN9xs00Dd2\nEUmlGEFQ3kL2/oMn3AcDgwGKiop+8HqlXHJJuImISIVifAVeDOxX5vG+wNIIdYiICHGCYAbQwswO\nMLOdgG7A6Ah1iIgIEZqG3L3EzHoBrxCGjz7l7h9kuw4REQmizCNw97HA2BjHFhGR79MwGRGRlFMQ\niIiknIJARCTlFAQiIiln7lWbq5VNZlYMfL7NN5avIbCyBsvJB/rM6aDPnA7V+cz7u3ujbb0pL4Kg\nOsxsprsXxa4jm/SZ00GfOR2y8ZnVNCQiknIKAhGRlEtDEAyOXUAE+szpoM+cDol/5oLvIxARka1L\nwxWBiIhshYJARCTlCjoIzKy9mX1oZgvM7IbY9STNzJ4ysxVmNjd2LdlgZvuZ2etmNs/MPjCz3rFr\nSpqZ7WJmb5vZe5nPfFvsmrLFzGqZ2Ttm9rfYtWSDmS00szlm9q6ZzUz0WIXaR2BmtYCPgLaEzXBm\nAGe7e83vjZwjzOwEYB3wjLsfFruepJlZE6CJu882s/rALKBTgf83NqCuu68zs9rAVKC3u0+PXFri\nzOwaoAho4O6nx64naWa2EChy98Qn0BXyFcFRwAJ3/9TdvwVeADpGrilR7j4ZWB27jmxx92XuPjtz\n/ytgHmFP7ILlwbrMw9qZW2F+myvDzPYFTgOejF1LISrkIGgK/LPM48UU+EkizcysOdAaeCtuJcnL\nNJG8C6wAXnX3gv/MwENAP6A0diFZ5MB4M5tlZj2SPFAhB4GV81zBf3NKIzOrBwwH+rj72tj1JM3d\nN7l7K8J+30eZWUE3A5rZ6cAKd58Vu5YsO87djwA6AFdmmn4TUchBsBjYr8zjfYGlkWqRhGTayYcD\nz7n7iNj1ZJO7rwHeANpHLiVpxwFnZtrMXwDamNmzcUtKnrsvzfxcAYwkNHcnopCDYAbQwswOMLOd\ngG7A6Mg1SQ3KdJwOAea5+wOx68kGM2tkZrtn7u8KnAzMj1tVstz9Rnff192bE/4dT3T38yKXlSgz\nq5sZAIGZ1QXaAYmNBizYIHD3EqAX8AqhE/FFd/8gblXJMrPngWlASzNbbGYXx64pYccB3QnfEN/N\n3E6NXVTCmgCvm9n7hC87r7p7KoZTpkxjYKqZvQe8DYxx93FJHaxgh4+KiEjlFOwVgYiIVI6CQEQk\n5RQEIiIppyAQEUk5BYGISMopCEREUk5BICKScgoCkSowsyPN7P3M/gB1M3sDFPSaP1K4NKFMpIrM\n7A5gF2BXYLG73x25JJEqURCIVFFmDasZwDfAL9x9U+SSRKpETUMiVbcnUA+oT7gyEMlLuiIQqSIz\nG01YFvkAwpaZvSKXJFIlO8YuQCQfmdn5QIm7D8vsj/2mmbVx94mxaxPZXroiEBFJOfURiIiknIJA\nRCTlFAQiIimnIBARSTkFgYhIyikIRERSTkEgIpJy/wf+tzpg8YIv0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbccdc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use similar to plt.figure() except use tuple unpacking to grab fig and axes\n",
    "fig, axes = plt.subplots()\n",
    "\n",
    "# Now use the axes object to add stuff to plot\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then you can specify the number of rows and columns when creating the subplots() object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJgx8SWrCwJekJv4PcgCmcLyIQvoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc20b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Empty canvas of 1 by 2 subplots\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000C21BBE0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000BD2D3C8>], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Axes is an array of axes to plot on\n",
    "axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can iterate through this array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAEkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc20b438>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for ax in axes:\n",
    "    ax.plot(x, y, 'b')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')\n",
    "\n",
    "# Display the figure object    \n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9f1cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4), dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving to png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Realistic example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gdp_cap=[974.58033839999996,\n",
    " 5937.0295259999984,\n",
    " 6223.3674650000003,\n",
    " 4797.2312670000001,\n",
    " 12779.379639999999,\n",
    " 34435.367439999995,\n",
    " 36126.492700000003,\n",
    " 29796.048340000001,\n",
    " 1391.253792,\n",
    " 33692.605080000001,\n",
    " 1441.2848730000001,\n",
    " 3822.137084,\n",
    " 7446.2988029999997,\n",
    " 12569.851769999999,\n",
    " 9065.8008250000003,\n",
    " 10680.792820000001,\n",
    " 1217.0329939999999,\n",
    " 430.07069159999998,\n",
    " 1713.7786860000001,\n",
    " 2042.0952400000001,\n",
    " 36319.235009999997,\n",
    " 706.01653699999997,\n",
    " 1704.0637240000001,\n",
    " 13171.638849999999,\n",
    " 4959.1148540000004,\n",
    " 7006.5804189999999,\n",
    " 986.14787920000003,\n",
    " 277.55185870000003,\n",
    " 3632.5577979999998,\n",
    " 9645.06142,\n",
    " 1544.7501119999999,\n",
    " 14619.222719999998,\n",
    " 8948.1029230000004,\n",
    " 22833.308509999999,\n",
    " 35278.418740000001,\n",
    " 2082.4815670000007,\n",
    " 6025.3747520000015,\n",
    " 6873.2623260000009,\n",
    " 5581.1809979999998,\n",
    " 5728.3535140000004,\n",
    " 12154.089749999999,\n",
    " 641.36952360000021,\n",
    " 690.80557590000001,\n",
    " 33207.0844,\n",
    " 30470.0167,\n",
    " 13206.48452,\n",
    " 752.74972649999995,\n",
    " 32170.37442,\n",
    " 1327.6089099999999,\n",
    " 27538.41188,\n",
    " 5186.0500030000003,\n",
    " 942.6542111,\n",
    " 579.23174299999982,\n",
    " 1201.637154,\n",
    " 3548.3308460000007,\n",
    " 39724.978669999997,\n",
    " 18008.944439999999,\n",
    " 36180.789190000003,\n",
    " 2452.210407,\n",
    " 3540.6515639999998,\n",
    " 11605.71449,\n",
    " 4471.0619059999999,\n",
    " 40675.996350000001,\n",
    " 25523.277099999999,\n",
    " 28569.719700000001,\n",
    " 7320.8802620000015,\n",
    " 31656.068060000001,\n",
    " 4519.4611709999999,\n",
    " 1463.249282,\n",
    " 1593.06548,\n",
    " 23348.139730000006,\n",
    " 47306.989780000004,\n",
    " 10461.05868,\n",
    " 1569.3314419999999,\n",
    " 414.5073415,\n",
    " 12057.49928,\n",
    " 1044.7701259999999,\n",
    " 759.34991009999999,\n",
    " 12451.6558,\n",
    " 1042.581557,\n",
    " 1803.151496,\n",
    " 10956.991120000001,\n",
    " 11977.57496,\n",
    " 3095.7722710000007,\n",
    " 9253.896111,\n",
    " 3820.1752299999998,\n",
    " 823.68562050000003,\n",
    " 944.0,\n",
    " 4811.0604290000001,\n",
    " 1091.359778,\n",
    " 36797.933319999996,\n",
    " 25185.009109999999,\n",
    " 2749.3209649999999,\n",
    " 619.67689239999982,\n",
    " 2013.9773049999999,\n",
    " 49357.190170000002,\n",
    " 22316.192869999999,\n",
    " 2605.94758,\n",
    " 9809.1856360000002,\n",
    " 4172.8384640000004,\n",
    " 7408.9055609999996,\n",
    " 3190.4810160000002,\n",
    " 15389.924680000002,\n",
    " 20509.64777,\n",
    " 19328.709009999999,\n",
    " 7670.122558,\n",
    " 10808.47561,\n",
    " 863.08846390000019,\n",
    " 1598.4350890000001,\n",
    " 21654.83194,\n",
    " 1712.4721360000001,\n",
    " 9786.5347139999994,\n",
    " 862.54075610000018,\n",
    " 47143.179640000002,\n",
    " 18678.314350000001,\n",
    " 25768.257590000001,\n",
    " 926.14106830000003,\n",
    " 9269.6578079999999,\n",
    " 28821.063699999999,\n",
    " 3970.0954069999998,\n",
    " 2602.3949950000001,\n",
    " 4513.4806429999999,\n",
    " 33859.748350000002,\n",
    " 37506.419070000004,\n",
    " 4184.5480889999999,\n",
    " 28718.276839999999,\n",
    " 1107.482182,\n",
    " 7458.3963269999977,\n",
    " 882.9699437999999,\n",
    " 18008.509239999999,\n",
    " 7092.9230250000001,\n",
    " 8458.2763840000007,\n",
    " 1056.3801209999999,\n",
    " 33203.261279999999,\n",
    " 42951.65309,\n",
    " 10611.46299,\n",
    " 11415.805689999999,\n",
    " 2441.5764039999999,\n",
    " 3025.3497980000002,\n",
    " 2280.769906,\n",
    " 1271.211593,\n",
    " 469.70929810000007]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "life_exp = [43.828000000000003,\n",
    " 76.423000000000002,\n",
    " 72.301000000000002,\n",
    " 42.731000000000002,\n",
    " 75.319999999999993,\n",
    " 81.234999999999999,\n",
    " 79.828999999999994,\n",
    " 75.635000000000005,\n",
    " 64.061999999999998,\n",
    " 79.441000000000003,\n",
    " 56.728000000000002,\n",
    " 65.554000000000002,\n",
    " 74.852000000000004,\n",
    " 50.728000000000002,\n",
    " 72.390000000000001,\n",
    " 73.004999999999995,\n",
    " 52.295000000000002,\n",
    " 49.579999999999998,\n",
    " 59.722999999999999,\n",
    " 50.43,\n",
    " 80.653000000000006,\n",
    " 44.741000000000007,\n",
    " 50.651000000000003,\n",
    " 78.552999999999997,\n",
    " 72.960999999999999,\n",
    " 72.888999999999996,\n",
    " 65.152000000000001,\n",
    " 46.462000000000003,\n",
    " 55.322000000000003,\n",
    " 78.781999999999996,\n",
    " 48.328000000000003,\n",
    " 75.748000000000005,\n",
    " 78.272999999999996,\n",
    " 76.486000000000004,\n",
    " 78.331999999999994,\n",
    " 54.790999999999997,\n",
    " 72.234999999999999,\n",
    " 74.994,\n",
    " 71.338000000000022,\n",
    " 71.878,\n",
    " 51.578999999999994,\n",
    " 58.039999999999999,\n",
    " 52.947000000000003,\n",
    " 79.313000000000002,\n",
    " 80.656999999999996,\n",
    " 56.734999999999999,\n",
    " 59.448,\n",
    " 79.406000000000006,\n",
    " 60.021999999999998,\n",
    " 79.483000000000004,\n",
    " 70.259,\n",
    " 56.006999999999998,\n",
    " 46.388000000000012,\n",
    " 60.915999999999997,\n",
    " 70.198000000000008,\n",
    " 82.207999999999998,\n",
    " 73.338000000000022,\n",
    " 81.757000000000005,\n",
    " 64.698000000000008,\n",
    " 70.650000000000006,\n",
    " 70.963999999999999,\n",
    " 59.545000000000002,\n",
    " 78.885000000000005,\n",
    " 80.745000000000005,\n",
    " 80.546000000000006,\n",
    " 72.566999999999993,\n",
    " 82.602999999999994,\n",
    " 72.534999999999997,\n",
    " 54.109999999999999,\n",
    " 67.296999999999997,\n",
    " 78.623000000000005,\n",
    " 77.588000000000022,\n",
    " 71.992999999999995,\n",
    " 42.591999999999999,\n",
    " 45.677999999999997,\n",
    " 73.951999999999998,\n",
    " 59.443000000000012,\n",
    " 48.302999999999997,\n",
    " 74.241,\n",
    " 54.466999999999999,\n",
    " 64.164000000000001,\n",
    " 72.801000000000002,\n",
    " 76.194999999999993,\n",
    " 66.802999999999997,\n",
    " 74.543000000000006,\n",
    " 71.164000000000001,\n",
    " 42.082000000000001,\n",
    " 62.069000000000003,\n",
    " 52.906000000000013,\n",
    " 63.784999999999997,\n",
    " 79.762,\n",
    " 80.203999999999994,\n",
    " 72.899000000000001,\n",
    " 56.866999999999997,\n",
    " 46.859000000000002,\n",
    " 80.195999999999998,\n",
    " 75.640000000000001,\n",
    " 65.483000000000004,\n",
    " 75.536999999999978,\n",
    " 71.751999999999995,\n",
    " 71.421000000000006,\n",
    " 71.688000000000002,\n",
    " 75.563000000000002,\n",
    " 78.097999999999999,\n",
    " 78.746000000000024,\n",
    " 76.441999999999993,\n",
    " 72.475999999999999,\n",
    " 46.241999999999997,\n",
    " 65.528000000000006,\n",
    " 72.777000000000001,\n",
    " 63.061999999999998,\n",
    " 74.001999999999995,\n",
    " 42.568000000000012,\n",
    " 79.971999999999994,\n",
    " 74.662999999999997,\n",
    " 77.926000000000002,\n",
    " 48.158999999999999,\n",
    " 49.338999999999999,\n",
    " 80.941000000000003,\n",
    " 72.396000000000001,\n",
    " 58.555999999999997,\n",
    " 39.613,\n",
    " 80.884,\n",
    " 81.701000000000022,\n",
    " 74.143000000000001,\n",
    " 78.400000000000006,\n",
    " 52.517000000000003,\n",
    " 70.616,\n",
    " 58.420000000000002,\n",
    " 69.819000000000003,\n",
    " 73.923000000000002,\n",
    " 71.777000000000001,\n",
    " 51.542000000000002,\n",
    " 79.424999999999997,\n",
    " 78.242000000000004,\n",
    " 76.384,\n",
    " 73.747,\n",
    " 74.248999999999995,\n",
    " 73.421999999999997,\n",
    " 62.698,\n",
    " 42.383999999999993,\n",
    " 43.487000000000002]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop= [31.889923,\n",
    " 3.6005229999999999,\n",
    " 33.333216,\n",
    " 12.420476000000001,\n",
    " 40.301926999999999,\n",
    " 20.434176000000001,\n",
    " 8.199783,\n",
    " 0.70857300000000001,\n",
    " 150.448339,\n",
    " 10.392226000000001,\n",
    " 8.0783140000000007,\n",
    " 9.1191519999999997,\n",
    " 4.5521979999999997,\n",
    " 1.6391309999999999,\n",
    " 190.01064700000001,\n",
    " 7.3228580000000001,\n",
    " 14.326203,\n",
    " 8.3905049999999992,\n",
    " 14.131857999999999,\n",
    " 17.696293000000001,\n",
    " 33.390141,\n",
    " 4.3690379999999998,\n",
    " 10.238807,\n",
    " 16.284741,\n",
    " 1318.683096,\n",
    " 44.227550000000001,\n",
    " 0.71096000000000004,\n",
    " 64.606758999999997,\n",
    " 3.8006099999999998,\n",
    " 4.1338840000000001,\n",
    " 18.013408999999999,\n",
    " 4.4933120000000004,\n",
    " 11.416987000000001,\n",
    " 10.228744000000001,\n",
    " 5.4681199999999999,\n",
    " 0.49637399999999998,\n",
    " 9.3196220000000007,\n",
    " 13.75568,\n",
    " 80.264543000000003,\n",
    " 6.9396880000000003,\n",
    " 0.55120100000000005,\n",
    " 4.9065849999999998,\n",
    " 76.511887000000002,\n",
    " 5.2384599999999999,\n",
    " 61.083916000000002,\n",
    " 1.4548669999999999,\n",
    " 1.6883589999999999,\n",
    " 82.400996000000006,\n",
    " 22.873338,\n",
    " 10.706289999999999,\n",
    " 12.572927999999999,\n",
    " 9.9478139999999993,\n",
    " 1.4720409999999999,\n",
    " 8.5028140000000008,\n",
    " 7.4837629999999997,\n",
    " 6.9804120000000003,\n",
    " 9.9561080000000004,\n",
    " 0.301931,\n",
    " 1110.3963309999999,\n",
    " 223.547,\n",
    " 69.453569999999999,\n",
    " 27.499638000000001,\n",
    " 4.1090859999999996,\n",
    " 6.426679,\n",
    " 58.147733000000002,\n",
    " 2.780132,\n",
    " 127.467972,\n",
    " 6.0531930000000003,\n",
    " 35.610177,\n",
    " 23.301725000000001,\n",
    " 49.044789999999999,\n",
    " 2.5055589999999999,\n",
    " 3.921278,\n",
    " 2.0126490000000001,\n",
    " 3.1939419999999998,\n",
    " 6.0369140000000003,\n",
    " 19.167653999999999,\n",
    " 13.327078999999999,\n",
    " 24.821286000000001,\n",
    " 12.031795000000001,\n",
    " 3.2700650000000002,\n",
    " 1.250882,\n",
    " 108.700891,\n",
    " 2.8741270000000001,\n",
    " 0.68473600000000001,\n",
    " 33.757174999999997,\n",
    " 19.951656,\n",
    " 47.761980000000001,\n",
    " 2.0550799999999998,\n",
    " 28.901789999999998,\n",
    " 16.570613000000002,\n",
    " 4.1157709999999996,\n",
    " 5.6753559999999998,\n",
    " 12.894864999999999,\n",
    " 135.03116399999999,\n",
    " 4.6279260000000004,\n",
    " 3.2048969999999999,\n",
    " 169.27061699999999,\n",
    " 3.2421730000000002,\n",
    " 6.6671469999999999,\n",
    " 28.674757,\n",
    " 91.077286999999998,\n",
    " 38.518241000000003,\n",
    " 10.642836000000001,\n",
    " 3.942491,\n",
    " 0.79809399999999997,\n",
    " 22.276056000000001,\n",
    " 8.8605879999999999,\n",
    " 0.19957900000000001,\n",
    " 27.601037999999999,\n",
    " 12.267493,\n",
    " 10.150264999999999,\n",
    " 6.1445619999999996,\n",
    " 4.5530090000000003,\n",
    " 5.4475020000000001,\n",
    " 2.0092449999999999,\n",
    " 9.1187729999999991,\n",
    " 43.997827999999998,\n",
    " 40.448191000000001,\n",
    " 20.378239000000001,\n",
    " 42.292929000000001,\n",
    " 1.1330659999999999,\n",
    " 9.0310880000000004,\n",
    " 7.5546610000000003,\n",
    " 19.314747000000001,\n",
    " 23.174294,\n",
    " 38.13964,\n",
    " 65.068149000000005,\n",
    " 5.7015789999999997,\n",
    " 1.056608,\n",
    " 10.276158000000001,\n",
    " 71.158647000000002,\n",
    " 29.170397999999999,\n",
    " 60.776237999999999,\n",
    " 301.13994700000001,\n",
    " 3.4474960000000001,\n",
    " 26.084662000000002,\n",
    " 85.262355999999997,\n",
    " 4.018332,\n",
    " 22.211742999999998,\n",
    " 11.746034999999999,\n",
    " 12.311143]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "life_exp1950 =[28.8,\n",
    " 55.23,\n",
    " 43.08,\n",
    " 30.02,\n",
    " 62.48,\n",
    " 69.12,\n",
    " 66.8,\n",
    " 50.94,\n",
    " 37.48,\n",
    " 68.0,\n",
    " 38.22,\n",
    " 40.41,\n",
    " 53.82,\n",
    " 47.62,\n",
    " 50.92,\n",
    " 59.6,\n",
    " 31.98,\n",
    " 39.03,\n",
    " 39.42,\n",
    " 38.52,\n",
    " 68.75,\n",
    " 35.46,\n",
    " 38.09,\n",
    " 54.74,\n",
    " 44.0,\n",
    " 50.64,\n",
    " 40.72,\n",
    " 39.14,\n",
    " 42.11,\n",
    " 57.21,\n",
    " 40.48,\n",
    " 61.21,\n",
    " 59.42,\n",
    " 66.87,\n",
    " 70.78,\n",
    " 34.81,\n",
    " 45.93,\n",
    " 48.36,\n",
    " 41.89,\n",
    " 45.26,\n",
    " 34.48,\n",
    " 35.93,\n",
    " 34.08,\n",
    " 66.55,\n",
    " 67.41,\n",
    " 37.0,\n",
    " 30.0,\n",
    " 67.5,\n",
    " 43.15,\n",
    " 65.86,\n",
    " 42.02,\n",
    " 33.61,\n",
    " 32.5,\n",
    " 37.58,\n",
    " 41.91,\n",
    " 60.96,\n",
    " 64.03,\n",
    " 72.49,\n",
    " 37.37,\n",
    " 37.47,\n",
    " 44.87,\n",
    " 45.32,\n",
    " 66.91,\n",
    " 65.39,\n",
    " 65.94,\n",
    " 58.53,\n",
    " 63.03,\n",
    " 43.16,\n",
    " 42.27,\n",
    " 50.06,\n",
    " 47.45,\n",
    " 55.56,\n",
    " 55.93,\n",
    " 42.14,\n",
    " 38.48,\n",
    " 42.72,\n",
    " 36.68,\n",
    " 36.26,\n",
    " 48.46,\n",
    " 33.68,\n",
    " 40.54,\n",
    " 50.99,\n",
    " 50.79,\n",
    " 42.24,\n",
    " 59.16,\n",
    " 42.87,\n",
    " 31.29,\n",
    " 36.32,\n",
    " 41.72,\n",
    " 36.16,\n",
    " 72.13,\n",
    " 69.39,\n",
    " 42.31,\n",
    " 37.44,\n",
    " 36.32,\n",
    " 72.67,\n",
    " 37.58,\n",
    " 43.44,\n",
    " 55.19,\n",
    " 62.65,\n",
    " 43.9,\n",
    " 47.75,\n",
    " 61.31,\n",
    " 59.82,\n",
    " 64.28,\n",
    " 52.72,\n",
    " 61.05,\n",
    " 40.0,\n",
    " 46.47,\n",
    " 39.88,\n",
    " 37.28,\n",
    " 58.0,\n",
    " 30.33,\n",
    " 60.4,\n",
    " 64.36,\n",
    " 65.57,\n",
    " 32.98,\n",
    " 45.01,\n",
    " 64.94,\n",
    " 57.59,\n",
    " 38.64,\n",
    " 41.41,\n",
    " 71.86,\n",
    " 69.62,\n",
    " 45.88,\n",
    " 58.5,\n",
    " 41.22,\n",
    " 50.85,\n",
    " 38.6,\n",
    " 59.1,\n",
    " 44.6,\n",
    " 43.58,\n",
    " 39.98,\n",
    " 69.18,\n",
    " 68.44,\n",
    " 66.07,\n",
    " 55.09,\n",
    " 40.41,\n",
    " 43.16,\n",
    " 32.55,\n",
    " 42.04,\n",
    " 48.45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ax\t axes\t fig\t gdp_cap\t life_exp\t life_exp1950\t np\t plt\t pop\t \n",
      "x\t y\t \n"
     ]
    }
   ],
   "source": [
    "%who"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdp_cap</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>life_exp1950</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>974.580338</td>\n",
       "      <td>43.828</td>\n",
       "      <td>28.80</td>\n",
       "      <td>31.889923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5937.029526</td>\n",
       "      <td>76.423</td>\n",
       "      <td>55.23</td>\n",
       "      <td>3.600523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6223.367465</td>\n",
       "      <td>72.301</td>\n",
       "      <td>43.08</td>\n",
       "      <td>33.333216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4797.231267</td>\n",
       "      <td>42.731</td>\n",
       "      <td>30.02</td>\n",
       "      <td>12.420476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12779.379640</td>\n",
       "      <td>75.320</td>\n",
       "      <td>62.48</td>\n",
       "      <td>40.301927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34435.367440</td>\n",
       "      <td>81.235</td>\n",
       "      <td>69.12</td>\n",
       "      <td>20.434176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36126.492700</td>\n",
       "      <td>79.829</td>\n",
       "      <td>66.80</td>\n",
       "      <td>8.199783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29796.048340</td>\n",
       "      <td>75.635</td>\n",
       "      <td>50.94</td>\n",
       "      <td>0.708573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1391.253792</td>\n",
       "      <td>64.062</td>\n",
       "      <td>37.48</td>\n",
       "      <td>150.448339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33692.605080</td>\n",
       "      <td>79.441</td>\n",
       "      <td>68.00</td>\n",
       "      <td>10.392226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1441.284873</td>\n",
       "      <td>56.728</td>\n",
       "      <td>38.22</td>\n",
       "      <td>8.078314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3822.137084</td>\n",
       "      <td>65.554</td>\n",
       "      <td>40.41</td>\n",
       "      <td>9.119152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7446.298803</td>\n",
       "      <td>74.852</td>\n",
       "      <td>53.82</td>\n",
       "      <td>4.552198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12569.851770</td>\n",
       "      <td>50.728</td>\n",
       "      <td>47.62</td>\n",
       "      <td>1.639131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9065.800825</td>\n",
       "      <td>72.390</td>\n",
       "      <td>50.92</td>\n",
       "      <td>190.010647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10680.792820</td>\n",
       "      <td>73.005</td>\n",
       "      <td>59.60</td>\n",
       "      <td>7.322858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1217.032994</td>\n",
       "      <td>52.295</td>\n",
       "      <td>31.98</td>\n",
       "      <td>14.326203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>430.070692</td>\n",
       "      <td>49.580</td>\n",
       "      <td>39.03</td>\n",
       "      <td>8.390505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1713.778686</td>\n",
       "      <td>59.723</td>\n",
       "      <td>39.42</td>\n",
       "      <td>14.131858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2042.095240</td>\n",
       "      <td>50.430</td>\n",
       "      <td>38.52</td>\n",
       "      <td>17.696293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>36319.235010</td>\n",
       "      <td>80.653</td>\n",
       "      <td>68.75</td>\n",
       "      <td>33.390141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>706.016537</td>\n",
       "      <td>44.741</td>\n",
       "      <td>35.46</td>\n",
       "      <td>4.369038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1704.063724</td>\n",
       "      <td>50.651</td>\n",
       "      <td>38.09</td>\n",
       "      <td>10.238807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13171.638850</td>\n",
       "      <td>78.553</td>\n",
       "      <td>54.74</td>\n",
       "      <td>16.284741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4959.114854</td>\n",
       "      <td>72.961</td>\n",
       "      <td>44.00</td>\n",
       "      <td>1318.683096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7006.580419</td>\n",
       "      <td>72.889</td>\n",
       "      <td>50.64</td>\n",
       "      <td>44.227550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>986.147879</td>\n",
       "      <td>65.152</td>\n",
       "      <td>40.72</td>\n",
       "      <td>0.710960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>277.551859</td>\n",
       "      <td>46.462</td>\n",
       "      <td>39.14</td>\n",
       "      <td>64.606759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3632.557798</td>\n",
       "      <td>55.322</td>\n",
       "      <td>42.11</td>\n",
       "      <td>3.800610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9645.061420</td>\n",
       "      <td>78.782</td>\n",
       "      <td>57.21</td>\n",
       "      <td>4.133884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>862.540756</td>\n",
       "      <td>42.568</td>\n",
       "      <td>30.33</td>\n",
       "      <td>6.144562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>47143.179640</td>\n",
       "      <td>79.972</td>\n",
       "      <td>60.40</td>\n",
       "      <td>4.553009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>18678.314350</td>\n",
       "      <td>74.663</td>\n",
       "      <td>64.36</td>\n",
       "      <td>5.447502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>25768.257590</td>\n",
       "      <td>77.926</td>\n",
       "      <td>65.57</td>\n",
       "      <td>2.009245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>926.141068</td>\n",
       "      <td>48.159</td>\n",
       "      <td>32.98</td>\n",
       "      <td>9.118773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>9269.657808</td>\n",
       "      <td>49.339</td>\n",
       "      <td>45.01</td>\n",
       "      <td>43.997828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>28821.063700</td>\n",
       "      <td>80.941</td>\n",
       "      <td>64.94</td>\n",
       "      <td>40.448191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>3970.095407</td>\n",
       "      <td>72.396</td>\n",
       "      <td>57.59</td>\n",
       "      <td>20.378239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2602.394995</td>\n",
       "      <td>58.556</td>\n",
       "      <td>38.64</td>\n",
       "      <td>42.292929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>4513.480643</td>\n",
       "      <td>39.613</td>\n",
       "      <td>41.41</td>\n",
       "      <td>1.133066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>33859.748350</td>\n",
       "      <td>80.884</td>\n",
       "      <td>71.86</td>\n",
       "      <td>9.031088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>37506.419070</td>\n",
       "      <td>81.701</td>\n",
       "      <td>69.62</td>\n",
       "      <td>7.554661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>4184.548089</td>\n",
       "      <td>74.143</td>\n",
       "      <td>45.88</td>\n",
       "      <td>19.314747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>28718.276840</td>\n",
       "      <td>78.400</td>\n",
       "      <td>58.50</td>\n",
       "      <td>23.174294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1107.482182</td>\n",
       "      <td>52.517</td>\n",
       "      <td>41.22</td>\n",
       "      <td>38.139640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>7458.396327</td>\n",
       "      <td>70.616</td>\n",
       "      <td>50.85</td>\n",
       "      <td>65.068149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>882.969944</td>\n",
       "      <td>58.420</td>\n",
       "      <td>38.60</td>\n",
       "      <td>5.701579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>18008.509240</td>\n",
       "      <td>69.819</td>\n",
       "      <td>59.10</td>\n",
       "      <td>1.056608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7092.923025</td>\n",
       "      <td>73.923</td>\n",
       "      <td>44.60</td>\n",
       "      <td>10.276158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>8458.276384</td>\n",
       "      <td>71.777</td>\n",
       "      <td>43.58</td>\n",
       "      <td>71.158647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1056.380121</td>\n",
       "      <td>51.542</td>\n",
       "      <td>39.98</td>\n",
       "      <td>29.170398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>33203.261280</td>\n",
       "      <td>79.425</td>\n",
       "      <td>69.18</td>\n",
       "      <td>60.776238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>42951.653090</td>\n",
       "      <td>78.242</td>\n",
       "      <td>68.44</td>\n",
       "      <td>301.139947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>10611.462990</td>\n",
       "      <td>76.384</td>\n",
       "      <td>66.07</td>\n",
       "      <td>3.447496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>11415.805690</td>\n",
       "      <td>73.747</td>\n",
       "      <td>55.09</td>\n",
       "      <td>26.084662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2441.576404</td>\n",
       "      <td>74.249</td>\n",
       "      <td>40.41</td>\n",
       "      <td>85.262356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>3025.349798</td>\n",
       "      <td>73.422</td>\n",
       "      <td>43.16</td>\n",
       "      <td>4.018332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2280.769906</td>\n",
       "      <td>62.698</td>\n",
       "      <td>32.55</td>\n",
       "      <td>22.211743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1271.211593</td>\n",
       "      <td>42.384</td>\n",
       "      <td>42.04</td>\n",
       "      <td>11.746035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>469.709298</td>\n",
       "      <td>43.487</td>\n",
       "      <td>48.45</td>\n",
       "      <td>12.311143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gdp_cap  life_exp  life_exp1950          pop\n",
       "0      974.580338    43.828         28.80    31.889923\n",
       "1     5937.029526    76.423         55.23     3.600523\n",
       "2     6223.367465    72.301         43.08    33.333216\n",
       "3     4797.231267    42.731         30.02    12.420476\n",
       "4    12779.379640    75.320         62.48    40.301927\n",
       "5    34435.367440    81.235         69.12    20.434176\n",
       "6    36126.492700    79.829         66.80     8.199783\n",
       "7    29796.048340    75.635         50.94     0.708573\n",
       "8     1391.253792    64.062         37.48   150.448339\n",
       "9    33692.605080    79.441         68.00    10.392226\n",
       "10    1441.284873    56.728         38.22     8.078314\n",
       "11    3822.137084    65.554         40.41     9.119152\n",
       "12    7446.298803    74.852         53.82     4.552198\n",
       "13   12569.851770    50.728         47.62     1.639131\n",
       "14    9065.800825    72.390         50.92   190.010647\n",
       "15   10680.792820    73.005         59.60     7.322858\n",
       "16    1217.032994    52.295         31.98    14.326203\n",
       "17     430.070692    49.580         39.03     8.390505\n",
       "18    1713.778686    59.723         39.42    14.131858\n",
       "19    2042.095240    50.430         38.52    17.696293\n",
       "20   36319.235010    80.653         68.75    33.390141\n",
       "21     706.016537    44.741         35.46     4.369038\n",
       "22    1704.063724    50.651         38.09    10.238807\n",
       "23   13171.638850    78.553         54.74    16.284741\n",
       "24    4959.114854    72.961         44.00  1318.683096\n",
       "25    7006.580419    72.889         50.64    44.227550\n",
       "26     986.147879    65.152         40.72     0.710960\n",
       "27     277.551859    46.462         39.14    64.606759\n",
       "28    3632.557798    55.322         42.11     3.800610\n",
       "29    9645.061420    78.782         57.21     4.133884\n",
       "..            ...       ...           ...          ...\n",
       "112    862.540756    42.568         30.33     6.144562\n",
       "113  47143.179640    79.972         60.40     4.553009\n",
       "114  18678.314350    74.663         64.36     5.447502\n",
       "115  25768.257590    77.926         65.57     2.009245\n",
       "116    926.141068    48.159         32.98     9.118773\n",
       "117   9269.657808    49.339         45.01    43.997828\n",
       "118  28821.063700    80.941         64.94    40.448191\n",
       "119   3970.095407    72.396         57.59    20.378239\n",
       "120   2602.394995    58.556         38.64    42.292929\n",
       "121   4513.480643    39.613         41.41     1.133066\n",
       "122  33859.748350    80.884         71.86     9.031088\n",
       "123  37506.419070    81.701         69.62     7.554661\n",
       "124   4184.548089    74.143         45.88    19.314747\n",
       "125  28718.276840    78.400         58.50    23.174294\n",
       "126   1107.482182    52.517         41.22    38.139640\n",
       "127   7458.396327    70.616         50.85    65.068149\n",
       "128    882.969944    58.420         38.60     5.701579\n",
       "129  18008.509240    69.819         59.10     1.056608\n",
       "130   7092.923025    73.923         44.60    10.276158\n",
       "131   8458.276384    71.777         43.58    71.158647\n",
       "132   1056.380121    51.542         39.98    29.170398\n",
       "133  33203.261280    79.425         69.18    60.776238\n",
       "134  42951.653090    78.242         68.44   301.139947\n",
       "135  10611.462990    76.384         66.07     3.447496\n",
       "136  11415.805690    73.747         55.09    26.084662\n",
       "137   2441.576404    74.249         40.41    85.262356\n",
       "138   3025.349798    73.422         43.16     4.018332\n",
       "139   2280.769906    62.698         32.55    22.211743\n",
       "140   1271.211593    42.384         42.04    11.746035\n",
       "141    469.709298    43.487         48.45    12.311143\n",
       "\n",
       "[142 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "lifeExp = pd.DataFrame({'gdp_cap':gdp_cap,'life_exp':life_exp,'life_exp1950': life_exp1950, 'pop':pop})\n",
    "lifeExp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdp_cap</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>life_exp1950</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>142.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11680.071820</td>\n",
       "      <td>67.007423</td>\n",
       "      <td>49.057535</td>\n",
       "      <td>44.021220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12859.937337</td>\n",
       "      <td>12.073021</td>\n",
       "      <td>12.225867</td>\n",
       "      <td>147.621398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>277.551859</td>\n",
       "      <td>39.613000</td>\n",
       "      <td>28.800000</td>\n",
       "      <td>0.199579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1624.842248</td>\n",
       "      <td>57.160250</td>\n",
       "      <td>39.057500</td>\n",
       "      <td>4.508034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6124.371109</td>\n",
       "      <td>71.935500</td>\n",
       "      <td>45.135000</td>\n",
       "      <td>10.517531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18008.835640</td>\n",
       "      <td>76.413250</td>\n",
       "      <td>59.765000</td>\n",
       "      <td>31.210042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49357.190170</td>\n",
       "      <td>82.603000</td>\n",
       "      <td>72.670000</td>\n",
       "      <td>1318.683096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gdp_cap    life_exp  life_exp1950          pop\n",
       "count    142.000000  142.000000    142.000000   142.000000\n",
       "mean   11680.071820   67.007423     49.057535    44.021220\n",
       "std    12859.937337   12.073021     12.225867   147.621398\n",
       "min      277.551859   39.613000     28.800000     0.199579\n",
       "25%     1624.842248   57.160250     39.057500     4.508034\n",
       "50%     6124.371109   71.935500     45.135000    10.517531\n",
       "75%    18008.835640   76.413250     59.765000    31.210042\n",
       "max    49357.190170   82.603000     72.670000  1318.683096"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lifeExp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xecec240>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUKCLiGTE/wNeZCw7UUsL6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe975a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pop,life_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 15.,  20.,  18.,  38.,  51.]),\n",
       " array([ 39.613,  48.211,  56.809,  65.407,  74.005,  82.603]),\n",
       " <a list of 5 Patch objects>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEwZdkpr4P1l/G8epplpVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb1f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(life_exp,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb1f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   7.,   2.,   5.,   4.,   6.,   5.,   5.,   4.,   6.,   3.,\n",
       "          5.,   5.,   0.,  12.,  21.,  13.,  10.,  17.,  11.]),\n",
       " array([ 39.613 ,  41.7625,  43.912 ,  46.0615,  48.211 ,  50.3605,\n",
       "         52.51  ,  54.6595,  56.809 ,  58.9585,  61.108 ,  63.2575,\n",
       "         65.407 ,  67.5565,  69.706 ,  71.8555,  74.005 ,  76.1545,\n",
       "         78.304 ,  80.4535,  82.603 ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvyQ1xuCXpMYY/JLUGINfkhrzf8BNFPnECxzjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xee23f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(life_exp,bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,   6.,   6.,   7.,   6.,   7.,   7.,   4.,   7.,   5.,   6.,\n",
       "         27.,  18.,  17.,  16.]),\n",
       " array([ 39.613,  42.479,  45.345,  48.211,  51.077,  53.943,  56.809,\n",
       "         59.675,  62.541,  65.407,  68.273,  71.139,  74.005,  76.871,\n",
       "         79.737,  82.603]),\n",
       " <a list of 15 Patch objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xea49ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(life_exp,bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.,   8.,  14.,  17.,  20.,  11.,   7.,   7.,   4.,   7.,   9.,\n",
       "          6.,  11.,  11.,   5.]),\n",
       " array([ 28.8       ,  31.72466667,  34.64933333,  37.574     ,\n",
       "         40.49866667,  43.42333333,  46.348     ,  49.27266667,\n",
       "         52.19733333,  55.122     ,  58.04666667,  60.97133333,\n",
       "         63.896     ,  66.82066667,  69.74533333,  72.67      ]),\n",
       " <a list of 15 Patch objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAEahJREFUeJzt3X+MZWV9x/H3p/xoK9Lya0BkWVdb\nQkUjK52sGFLDD0F+BWyjLRtrqcWsGkwksWnXNpEW04SmUfsDI9nCFmwUf6OkrMAGbZBEkVlcZBEo\nSNYyLmUXQZBiNavf/jFn23G4szPec2eH2ef9Sm7ueZ7znHOeeTL7uWefOefcVBWSpHb80mJ3QJK0\nZxn8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMbsu9gdGOSwww6rFStWLHY3JGnJ\n2LRp0+NVNTafts/L4F+xYgUTExOL3Q1JWjKSfHe+bZ3qkaTGGPyS1BiDX5IaY/BLUmMMfklqzJzB\nn+ToJF9Jcl+Se5O8p6s/JMnGJA927wfPsv2FXZsHk1w46h9AkvSLmc8Z/07gvVX1cuBE4OIkxwFr\ngVur6hjg1q78c5IcAlwKvAZYBVw62weEJGnPmDP4q+rRqrqrW/4hcB9wFHA+cG3X7FrgjQM2fwOw\nsaqeqKongY3AmaPouCRpOL/QHH+SFcCrgTuAI6rqUZj6cAAOH7DJUcAj08qTXZ0kaZHM+87dJC8E\nPgdcUlVPJ5nXZgPqBn67e5I1wBqA5cuXz7dbWgAr1t440v1tvfycke5PUj/zOuNPsh9Tof/xqvp8\nV/1YkiO79UcC2wdsOgkcPa28DNg26BhVta6qxqtqfGxsXo+bkCQNYT5X9QS4Grivqj40bdUNwK6r\ndC4Evjhg85uBM5Ic3P1R94yuTpK0SOZzxn8S8Fbg1CSbu9fZwOXA6UkeBE7vyiQZT3IVQFU9AXwA\nuLN7XdbVSZIWyZxz/FV1O4Pn6gFOG9B+Anj7tPJ6YP2wHZQkjZZ37kpSYwx+SWqMwS9JjTH4Jakx\nBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPw\nS1Jj5vwGriTrgXOB7VX1yq7uU8CxXZODgB9U1coB224Ffgj8FNhZVeMj6rckaUhzBj9wDXAF8LFd\nFVX1B7uWk3wQeGo3259SVY8P20FJ0mjN5zt3b0uyYtC6JAF+Hzh1tN2SJC2UvnP8vwM8VlUPzrK+\ngFuSbEqypuexJEkjMJ+pnt1ZDVy3m/UnVdW2JIcDG5PcX1W3DWrYfTCsAVi+fHnPbkmSZjP0GX+S\nfYHfAz41W5uq2ta9bweuB1btpu26qhqvqvGxsbFhuyVJmkOfqZ7XA/dX1eSglUkOSHLgrmXgDGBL\nj+NJkkZgzuBPch3wNeDYJJNJLupWXcCMaZ4kL06yoSseAdye5G7gG8CNVXXT6LouSRrGfK7qWT1L\n/R8PqNsGnN0tPwwc37N/kqQR6/vHXT0PrFh742J3QdIS4iMbJKkxBr8kNcbgl6TGGPyS1BiDX5Ia\nY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNmc9XL65P\nsj3Jlml1f5Xke0k2d6+zZ9n2zCQPJHkoydpRdlySNJz5nPFfA5w5oP7DVbWye22YuTLJPsBHgLOA\n44DVSY7r01lJUn9zBn9V3QY8McS+VwEPVdXDVfUT4JPA+UPsR5I0Qn3m+N+d5FvdVNDBA9YfBTwy\nrTzZ1Q2UZE2SiSQTO3bs6NEtSdLuDBv8HwV+A1gJPAp8cECbDKir2XZYVeuqaryqxsfGxobsliRp\nLkMFf1U9VlU/raqfAf/M1LTOTJPA0dPKy4BtwxxPkjQ6QwV/kiOnFX8X2DKg2Z3AMUlemmR/4ALg\nhmGOJ0kanX3napDkOuBk4LAkk8ClwMlJVjI1dbMVeEfX9sXAVVV1dlXtTPJu4GZgH2B9Vd27ID+F\nJGne5gz+qlo9oPrqWdpuA86eVt4APOdST0nS4vHOXUlqjMEvSY0x+CWpMQa/JDXG4Jekxsx5VY9G\na8XaGxe7C5Ia5xm/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY\n/JLUmDmDP8n6JNuTbJlW93dJ7k/yrSTXJzlolm23JrknyeYkE6PsuCRpOPM5478GOHNG3UbglVX1\nKuA/gPftZvtTqmplVY0P10VJ0ijNGfxVdRvwxIy6W6pqZ1f8OrBsAfomSVoAo5jj/xPgS7OsK+CW\nJJuSrNndTpKsSTKRZGLHjh0j6JYkaZBewZ/kL4GdwMdnaXJSVZ0AnAVcnOR1s+2rqtZV1XhVjY+N\njfXpliRpN4YO/iQXAucCb6mqGtSmqrZ179uB64FVwx5PkjQaQwV/kjOBPwfOq6pnZ2lzQJIDdy0D\nZwBbBrWVJO0587mc8zrga8CxSSaTXARcARwIbOwu1byya/viJBu6TY8Abk9yN/AN4MaqumlBfgpJ\n0rzN+Z27VbV6QPXVs7TdBpzdLT8MHN+rd5KkkfPOXUlqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQY\ng1+SGmPwS1JjDH5JaozBL0mNmfORDVJfK9beOPJ9br38nJHvU88vC/F7M2pL9ffQM35JaozBL0mN\nMfglqTEGvyQ1xuCXpMYY/JLUmHkFf5L1SbYn2TKt7pAkG5M82L0fPMu2F3ZtHuy+oF2StIjme8Z/\nDXDmjLq1wK1VdQxwa1f+OUkOAS4FXgOsAi6d7QNCkrRnzCv4q+o24IkZ1ecD13bL1wJvHLDpG4CN\nVfVEVT0JbOS5HyCSpD2ozxz/EVX1KED3fviANkcBj0wrT3Z1z5FkTZKJJBM7duzo0S1J0u4s9B93\nM6CuBjWsqnVVNV5V42NjYwvcLUlqV5/gfyzJkQDd+/YBbSaBo6eVlwHbehxTktRTn+C/Adh1lc6F\nwBcHtLkZOCPJwd0fdc/o6iRJi2S+l3NeB3wNODbJZJKLgMuB05M8CJzelUkynuQqgKp6AvgAcGf3\nuqyrkyQtknk9lrmqVs+y6rQBbSeAt08rrwfWD9U7SdLIeeeuJDXG4Jekxhj8ktQYg1+SGmPwS1Jj\n/LJ1qVGj/jLzpfrF4y3yjF+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhrj\nnbtzGPXdjZK02IY+409ybJLN015PJ7lkRpuTkzw1rc37+3dZktTH0Gf8VfUAsBIgyT7A94DrBzT9\nalWdO+xxJEmjNao5/tOA71TVd0e0P0nSAhlV8F8AXDfLutcmuTvJl5K8YkTHkyQNqXfwJ9kfOA/4\nzIDVdwEvqarjgX8CvrCb/axJMpFkYseOHX27JUmaxSjO+M8C7qqqx2auqKqnq+qZbnkDsF+Swwbt\npKrWVdV4VY2PjY2NoFuSpEFGEfyrmWWaJ8mLkqRbXtUd7/sjOKYkaUi9ruNP8gLgdOAd0+reCVBV\nVwJvAt6VZCfwI+CCqqo+x5Qk9dMr+KvqWeDQGXVXTlu+AriizzEkSaPlnbtakp7vd1SP+vtnn+8/\nr5YWn9UjSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTE+skHSSPhY\niaXDM35JaozBL0mNMfglqTEGvyQ1xuCXpMb0Dv4kW5Pck2RzkokB65PkH5M8lORbSU7oe0xJ0vBG\ndTnnKVX1+CzrzgKO6V6vAT7avUuSFsGemOo5H/hYTfk6cFCSI/fAcSVJA4wi+Au4JcmmJGsGrD8K\neGRaebKrkyQtglFM9ZxUVduSHA5sTHJ/Vd02bX0GbFMzK7oPjTUAy5cvH7oz3j0oaU8Zdd5svfyc\nke5vNr3P+KtqW/e+HbgeWDWjySRw9LTyMmDbgP2sq6rxqhofGxvr2y1J0ix6BX+SA5IcuGsZOAPY\nMqPZDcAfdVf3nAg8VVWP9jmuJGl4fad6jgCuT7JrX5+oqpuSvBOgqq4ENgBnAw8BzwJv63lMSVIP\nvYK/qh4Gjh9Qf+W05QIu7nMcSdLoeOeuJDXG4Jekxhj8ktQYg1+SGmPwS1Jj/M5daQF4B7mezzzj\nl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGjN08Cc5OslX\nktyX5N4k7xnQ5uQkTyXZ3L3e36+7kqS++jykbSfw3qq6q/vC9U1JNlbVt2e0+2pVndvjOJKkERr6\njL+qHq2qu7rlHwL3AUeNqmOSpIUxkjn+JCuAVwN3DFj92iR3J/lSkleM4niSpOH1fh5/khcCnwMu\nqaqnZ6y+C3hJVT2T5GzgC8Axs+xnDbAGYPny5X27JUmaRa8z/iT7MRX6H6+qz89cX1VPV9Uz3fIG\nYL8khw3aV1Wtq6rxqhofGxvr0y1J0m70uaonwNXAfVX1oVnavKhrR5JV3fG+P+wxJUn99ZnqOQl4\nK3BPks1d3V8AywGq6krgTcC7kuwEfgRcUFXV45iSpJ6GDv6quh3IHG2uAK4Y9hiSpNHzzl1JaozB\nL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS\n1BiDX5IaY/BLUmMMfklqTN8vWz8zyQNJHkqydsD6X07yqW79HUlW9DmeJKm/Pl+2vg/wEeAs4Dhg\ndZLjZjS7CHiyqn4T+DDwt8MeT5I0Gn3O+FcBD1XVw1X1E+CTwPkz2pwPXNstfxY4Lcluv6dXkrSw\n+gT/UcAj08qTXd3ANlW1E3gKOLTHMSVJPe3bY9tBZ+41RJuphskaYE1XfCbJAz36tjuHAY8v0L6X\nKsdkMMfluRyTwUYyLuk3Gf6S+TbsE/yTwNHTysuAbbO0mUyyL/DrwBODdlZV64B1PfozL0kmqmp8\noY+zlDgmgzkuz+WYDLbUxqXPVM+dwDFJXppkf+AC4IYZbW4ALuyW3wR8uaoGnvFLkvaMoc/4q2pn\nkncDNwP7AOur6t4klwETVXUDcDXwr0keYupM/4JRdFqSNLw+Uz1U1QZgw4y6909b/h/gzX2OsQAW\nfDppCXJMBnNcnssxGWxJjUuceZGktvjIBklqzF4d/El+Jck3ktyd5N4kf93Vv7R7hMSD3SMl9l/s\nvu5pSfZJ8s0k/9aVmx6TJFuT3JNkc5KJru6QJBu7MdmY5ODF7ueeluSgJJ9Ncn+S+5K8tuVxSXJs\n9zuy6/V0kkuW2pjs1cEP/Bg4taqOB1YCZyY5kalHR3y4qo4BnmTq0RKteQ9w37SyYwKnVNXKaZfl\nrQVu7cbk1q7cmn8Abqqq3wKOZ+p3ptlxqaoHut+RlcBvA88C17PExmSvDv6a8kxX3K97FXAqU4+Q\ngKlHSrxxEbq3aJIsA84BrurKofExmcX0R440NyZJfg14HVNX51FVP6mqH9D4uExzGvCdqvouS2xM\n9urgh/+b0tgMbAc2At8BftA9QgIGP2pib/f3wJ8BP+vKh+KYFHBLkk3dXeQAR1TVowDd++GL1rvF\n8TJgB/Av3bTgVUkOwHHZ5QLgum55SY3JXh/8VfXT7r9ly5h6sNzLBzXbs71aPEnOBbZX1abp1QOa\nNjMmnZOq6gSmnjZ7cZLXLXaHngf2BU4APlpVrwb+m+f5FMae0v0N7DzgM4vdl2Hs9cG/S/df1H8H\nTgQO6h4hAYMfNbE3Owk4L8lWpp6oeipT/wNoeUyoqm3d+3am5mxXAY8lORKge9++eD1cFJPAZFXd\n0ZU/y9QHQevjAlMnCHdV1WNdeUmNyV4d/EnGkhzULf8q8Hqm/jj1FaYeIQFTj5T44uL0cM+rqvdV\n1bKqWsHUf1W/XFVvoeExSXJAkgN3LQNnAFv4+UeONDUmAFX1X8AjSY7tqk4Dvk3j49JZzf9P88AS\nG5O9+gauJK9i6g8t+zD1IffpqrosycuYOts9BPgm8IdV9ePF6+niSHIy8KdVdW7LY9L97Nd3xX2B\nT1TV3yQ5FPg0sBz4T+DNVTXwIYN7qyQrmboIYH/gYeBtdP+WaHRckryAqcfNv6yqnurqltTvyl4d\n/JKk59qrp3okSc9l8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1Jj/BbKTD8+vXhazAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xec554a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(life_exp1950,bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xf20a898>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD8CAYAAACINTRsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRKRCFNRFRCrk/wMFvreLwxLaPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeffe7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gdp_cap, life_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0EVEIqGALiISCQV0EZFIKKCLiETi/wFyHPQbnS5pqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf22ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gdp_cap, life_exp)\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEaCAYAAAAcz1CnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf4c2198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gdp_cap, life_exp)\n",
    "plt.xscale('log')\n",
    "# Strings\n",
    "xlab = 'GDP per Capita [in USD]'\n",
    "ylab = 'Life Expectancy [in years]'\n",
    "title = 'World Development in 2007'\n",
    "# Add axis labels\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "# Add title\n",
    "plt.title(title)\n",
    "# Definition of tick_val and tick_lab\n",
    "tick_val = [1000,10000,100000]\n",
    "tick_lab = ['1k','10k','100k']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0xd1f39e8>,\n",
       "  <matplotlib.axis.XTick at 0xcd7f2b0>,\n",
       "  <matplotlib.axis.XTick at 0xd2ce2e8>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd11aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gdp_cap, life_exp)\n",
    "plt.xscale('log')\n",
    "# Strings\n",
    "xlab = 'GDP per Capita [in USD]'\n",
    "ylab = 'Life Expectancy [in years]'\n",
    "title = 'World Development in 2007'\n",
    "# Add axis labels\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "# Add title\n",
    "plt.title(title)\n",
    "# Definition of tick_val and tick_lab\n",
    "tick_val = [1000,10000,100000]\n",
    "tick_lab = ['1k','10k','100k']\n",
    "# Adapt the ticks on the x-axis\n",
    "plt.xticks(tick_val, tick_lab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np_pop = np.array(pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np_pop = np_pop *2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xd67afd0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns0BgjDEZzgKBMcZkOAsExhiT4f4fpLGAPj02PzgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd544f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xscale('log')\n",
    "# Strings\n",
    "xlab = 'GDP per Capita [in USD]'\n",
    "ylab = 'Life Expectancy [in years]'\n",
    "title = 'World Development in 2007'\n",
    "# Add axis labels\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "# Add title\n",
    "plt.title(title)\n",
    "# Definition of tick_val and tick_lab\n",
    "tick_val = [1000,10000,100000]\n",
    "tick_lab = ['1k','10k','100k']\n",
    "# Adapt the ticks on the x-axis\n",
    "plt.xticks(tick_val, tick_lab)\n",
    "# Update: set s argument to np_pop\n",
    "plt.scatter(gdp_cap, life_exp, s = np_pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gdp_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xd0ff6a0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD8CAYAAACINTRsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc95908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify c and alpha inside plt.scatter()\n",
    "plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, alpha = 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}