C2_Week3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#下一行：如果用这行，下面会提示 Javascript Error: IPython is not defined\n",
    "#%matplotlib notebook \n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#plt.subplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111f83518>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# subplot：一行，两列，前两个数改的是行列比例，比如“1，2”就是行列比=1:2。第三个数是plot的数量，貌似只能是1（至少在这是）\n",
    "#with 1 row, 2 columns, and current axis is 1st subplot axes\n",
    "'''使用subplot的目的往往是为了并列显示多个图表'''\n",
    "plt.subplot(1,2,1) #前两个数越大，图形越小（？）\n",
    "\n",
    "linear_data = np.array([1,2,3,4,5,6,7,8])\n",
    "\n",
    "plt.plot(linear_data, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112135fd0>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exponential_data = linear_data**2 \n",
    "\n",
    "# subplot with 1 row, 2 columns, and current axis is 2nd subplot axes\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(exponential_data, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1121cdc50>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot exponential data on 1st subplot axes\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(exponential_data, '-x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111e931d0>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''如下：使用subplot的目的往往是为了并列显示多个图表'''\n",
    "plt.figure()\n",
    "ax1 = plt.subplot(1, 2, 1)\n",
    "plt.plot(linear_data, '-o')\n",
    "# pass ‘sharey=ax1’ to ensure the two subplots share the same y axis\n",
    "ax2 = plt.subplot(1, 2, 2, sharey=ax1)\n",
    "plt.plot(exponential_data, '-x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiucat/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# the right hand side is equivalent shorthand syntax\n",
    "plt.subplot(1,2,1) == plt.subplot(121)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1120ce390>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 【注意】是复数，subplots，一个新的function \n",
    "# create a 3x3 grid of subplots\n",
    "fig, ((ax1,ax2,ax3), (ax4,ax5,ax6), (ax7,ax8,ax9)) = plt.subplots(3, 3, sharex=True, sharey=True)\n",
    "# plot the linear_data on the 5th subplot axes \n",
    "ax5.plot(linear_data, '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set inside tick labels to visible\n",
    "for ax in plt.gcf().get_axes():\n",
    "    for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "        label.set_visible(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# necessary on some systems to update the plot\n",
    "plt.gcf().canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create 2x2 grid of axis subplots\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True)\n",
    "axs = [ax1,ax2,ax3,ax4]\n",
    "\n",
    "# draw n = 10, 100, 1000, and 10000 samples from the normal distribution and plot corresponding histograms\n",
    "for n in range(0,len(axs)): #跑个循环，令n=axs中的值的数量\n",
    "    sample_size = 10**(n+1) #然后令sample_size等于10的1次方，2次方，3次方...等等\n",
    "    sample = np.random.normal(loc=0.0, scale=1.0, size=sample_size)\n",
    "    axs[n].hist(sample)\n",
    "    axs[n].set_title('n={}'.format(sample_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# repeat with number of bins set to 100 【把bar的数量扩大到100，上面是默认的10】\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True)\n",
    "axs = [ax1,ax2,ax3,ax4]\n",
    "\n",
    "for n in range(0,len(axs)):\n",
    "    sample_size = 10**(n+1)\n",
    "    sample = np.random.normal(loc=0.0, scale=1.0, size=sample_size)\n",
    "    axs[n].hist(sample, bins=100)\n",
    "    axs[n].set_title('n={}'.format(sample_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1127dec50>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''把x值定义为不同的分布方式，可以看到不同的图形效果'''\n",
    "plt.figure()\n",
    "Y = np.random.normal(loc=0.0, scale=1.0, size=10000)#y值定义为正态分布\n",
    "X = np.random.normal(loc=0.0, scale=1.0, size=10000)#x值定义为正态分布\n",
    "#X = np.random.random(size=10000)#或者x值定义为随机分布\n",
    "plt.scatter(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# use gridspec to partition the figure into subplots\n",
    "import matplotlib.gridspec as gridspec #使用 matplotlib下面的 gridspec\n",
    "\n",
    "plt.figure()\n",
    "gspec = gridspec.GridSpec(3, 3)\n",
    "\n",
    "top_histogram = plt.subplot(gspec[0, 1:])\n",
    "side_histogram = plt.subplot(gspec[1:, 0])\n",
    "lower_right = plt.subplot(gspec[1:, 1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y = np.random.normal(loc=0.0, scale=1.0, size=10000)\n",
    "X = np.random.random(size=10000)\n",
    "lower_right.scatter(X, Y)\n",
    "top_histogram.hist(X, bins=100)\n",
    "s = side_histogram.hist(Y, bins=100, orientation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiucat/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# clear the histograms and plot normed histograms\n",
    "top_histogram.clear()\n",
    "top_histogram.hist(X, bins=100, normed=True)\n",
    "side_histogram.clear()\n",
    "side_histogram.hist(Y, bins=100, orientation='horizontal', normed=True)\n",
    "# flip the side histogram's x axis\n",
    "side_histogram.invert_xaxis()\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change axes limits\n",
    "for ax in [top_histogram, lower_right]:\n",
    "    ax.set_xlim(0, 1)\n",
    "for ax in [side_histogram, lower_right]:\n",
    "    ax.set_ylim(-5, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src='http://educationxpress.mit.edu/sites/default/files/journal/WP1-Fig13.jpg' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<img src='http://educationxpress.mit.edu/sites/default/files/journal/WP1-Fig13.jpg' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box and Whisker Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "normal_sample = np.random.normal(loc=0.0, scale=1.0, size=10000)\n",
    "random_sample = np.random.random(size=10000)\n",
    "gamma_sample = np.random.gamma(2, size=10000)\n",
    "\n",
    "df = pd.DataFrame({'normal': normal_sample, \n",
    "                   'random': random_sample, \n",
    "                   'gamma': gamma_sample})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normal</th>\n",
       "      <th>random</th>\n",
       "      <th>gamma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.006632</td>\n",
       "      <td>0.499012</td>\n",
       "      <td>2.006245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.002688</td>\n",
       "      <td>0.288691</td>\n",
       "      <td>1.427543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.818317</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.018429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.662700</td>\n",
       "      <td>0.245275</td>\n",
       "      <td>0.961656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.010786</td>\n",
       "      <td>0.495878</td>\n",
       "      <td>1.690056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.678806</td>\n",
       "      <td>0.750434</td>\n",
       "      <td>2.698152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.295112</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>14.405026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             normal        random         gamma\n",
       "count  10000.000000  10000.000000  10000.000000\n",
       "mean       0.006632      0.499012      2.006245\n",
       "std        1.002688      0.288691      1.427543\n",
       "min       -3.818317      0.000028      0.018429\n",
       "25%       -0.662700      0.245275      0.961656\n",
       "50%        0.010786      0.495878      1.690056\n",
       "75%        0.678806      0.750434      2.698152\n",
       "max        4.295112      0.999968     14.405026"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# create a boxplot of the normal data, assign the output to a variable to supress output\n",
    "_ = plt.boxplot(df['normal'], whis='range')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# clear the current figure\n",
    "plt.clf()\n",
    "# plot boxplots for all three of df's columns\n",
    "_ = plt.boxplot([ df['normal'], df['random'], df['gamma'] ], whis='range')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "_ = plt.hist(df['gamma'], bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mpl_toolkits.axes_grid1.inset_locator as mpl_il\n",
    "\n",
    "plt.figure()\n",
    "plt.boxplot([ df['normal'], df['random'], df['gamma'] ], whis='range')\n",
    "# overlay axis on top of another \n",
    "ax2 = mpl_il.inset_axes(plt.gca(), width='60%', height='40%', loc=2)\n",
    "ax2.hist(df['gamma'], bins=100)\n",
    "ax2.margins(x=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# switch the y axis ticks for ax2 to the right side\n",
    "ax2.yaxis.tick_right()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if `whis` argument isn't passed, boxplot defaults to showing 1.5*interquartile (IQR) whiskers with outliers\n",
    "plt.figure()\n",
    "_ = plt.boxplot([ df['normal'], df['random'], df['gamma'] ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFplJREFUeJzt3XuQ3XV5x/HPc/aSze7mRhJCQsJFCMhFBYxUe1G5TBtpB7RqB6dUbRnT2tHenLZWptXWaadT2zrt1Bmajoy24/2CUEERKoh2AA2CmBCigXDJBRLMbbPJZm9P/9i1yWDI7vOc3/md5cv7NZOZ3ex5fs/3fM/vPPnld77Pfs3dBQAoV6PdAwAAtBaFHgAKR6EHgMJR6AGgcBR6ACgchR4ACkehB4DCUegBoHAUegAoXGc7knZbj/dYXyzohdDBaxaPqfN51Tm+TK4MzgtUaYa/VgPa86y7L47GtaXQ91ifXt21OhTjI8MtGk11rKs7HFPn86pzfJlcGZwXqNJMf63u8C8+kYnj1g0AFI5CDwCFo9ADQOEo9ABQOAo9ABSOQg8AhaPQA0Dh2rKOXu7htad1rcuW8utiM3EdCxaEY8YPDIZjpNz4svM+09eBN/qCDXuSxgdn9rzP9J4MaeafF5nxZc4lKX8+ZXBFDwCFo9ADQOGaLvRm1mNm3zWzH5jZBjP76yoGBgCoRhX36A9LutTdD5hZl6TvmNnX3P3eCo4NAGhS04Xe3V3Sgclvuyb/8Kv3AGCGqOQevZl1mNmDknZKut3d76viuACA5lVS6N19zN0vkLRc0sVmdv5zH2Nma8xsnZmtG9HhKtICAKah0nX07r7XzO6StFrS+uf8bK2ktZI0t7HQo+twrburolG2TmYN7tiePS0YybGV+PvoszJrmGf6eukXwtr2zuUnh2PGntkVjsk+pzr7K1Ln04GpH3LMXLmwI8xssZnNn/x6tqTLJT3S7HEBANWo4op+qaRPmlmHJv7h+Ly7f7WC4wIAKlDFqpuHJF1YwVgAAC1AZywAFI5CDwCFo9ADQOEo9ABQOAo9ABSuLRuPWEeHGifMD8WM796bylXnZhuZTUQyDVOZPJLkwzVuTJFocGssiJ0TkjS+J3deZPjwSCou0xhj3fFzMLshTWp8/bnmsdGt2+K5Eu/HdPNY4jXOvh/rbJbkih4ACkehB4DCUegBoHAUegAoHIUeAApHoQeAwlHoAaBwFHoAKFxbGqbkLgUbExrJBg0pEZfczcoTDSuZZots45MtXxoP2rojlSvVXJSY92zjTqOvNxzjgwdTuTLnhSXGl71qy5xPmeckSY3zXxqOsW3PxGMWzAvHSArXJUkanz8nlapx8pJ40A9TqbiiB4DSUegBoHAUegAoHIUeAApHoQeAwlHoAaBwTRd6M1thZnea2UYz22Bmf1jFwAAA1ahiHf2opPe5+/fNbI6k+83sdnd/uIJjAwCa1HShd/cdknZMfj1gZhslnSzp+Qt9oxFuCPG+2anx2eChcMzY4vhOR5LUODg3nmte/Hl1bt8djpEkT8SMn/eSVK7OZwdScVFjpyaaTiTZyHg4ptGVe7v4ssXhmH1n9Idj5n57SzhGkhqJ5qLhZbn3SNeGJ+JBiUa68d6eeB5JPi/egNfYviuVK9OclVXpPXozO03ShZLuq/K4AIC8yn4Fgpn1S/qSpD9y9/3H+PkaSWskqacj1zIMAIir5IrezLo0UeQ/5e5fPtZj3H2tu69y91XdjdxtGABAXBWrbkzSxyVtdPd/bn5IAIAqVXFF/wuSfkvSpWb24OSfKyo4LgCgAlWsuvmOJKtgLACAFqAzFgAK15aNR7ynS4dXxtY/j87uSOXqPBRf2969/WcWDU3LyOL42udDJ82KJzolvimFJHUeTKwdH82svs85vCi+9jk7vq69h8Mxw8vi51JW/6MHwjH7f+n0VK7u/WOpuAy/ID7GTM9D1uGF3eGYxtLc+7HvkcT6+1wLDVf0AFA6Cj0AFI5CDwCFo9ADQOEo9ABQOAo9ABSOQg8AhaPQA0Dh2tIwNdJr2nVBrDnGRnO5lt4Z3wBj74WLUrkyzTsDK+KNYIdzez7ohI31/aaKoRPip9Zwf3x84/E9KSbiOuONMb27co07g0vj11MDZ8af2Pz1ueu23efEz8H+p3KNaocX1HMOdgzlxjfeWd97ZLQ3sWnOo7lcXNEDQOEo9ABQOAo9ABSOQg8AhaPQA0DhKPQAUDgKPQAUjkIPAIVrS8OUebwBaijXw6RN74p3FzVGcrlsNP7vZtdgPM9oX32NO10DycaTRCPT0KJ4s8rQSbndkToG4nMxPD93XTS0KP56zdkcb2La/4uHwjFZw3tnp+IyjY/7XxnfDWzRXfGGOEkaXBqPOXRS7v04tCixa94XUqm4ogeA0lVS6M3sBjPbaWbrqzgeAKA6VV3Rf0LS6oqOBQCoUCWF3t3vVnp/cgBAK3GPHgAKV1uhN7M1ZrbOzNaNHkosNQEApNRW6N19rbuvcvdVnbP76koLAC96bVlHP9bj2ndebEFtx5zc4nYfiC/oXv3zP0jluuPWV4ZjTrnkiXDMk7sXhGMkaWDRrHDM7E3xGEk6tDy+vv3Us54OxyzpjW8sI0n3f/vscMzwvNx66cai+Drw/rP3hWMGNp0YjpGk/lPjucYvG0rlOnfxM+GYh3YsC8eMvTl3XhzedEI45qyXPZXKteWeU1JxGVUtr/yMpHsknW1mW83s2iqOCwBoXiVX9O7+tiqOAwCoHqtuAKBwFHoAKByFHgAKR6EHgMJR6AGgcBR6AChcWxqmZs8e1ivOiTUK9XTmGqaGRuMNU5sHcrucjJ4Z3/hh12C8S3joQK6JqX/+wXDMm66+N5Xr7p1nhmOe2Baf99deuDkcI0kPv3RJOCbT7CNJZ/fH4zLzl90w51UnxRt+th2cl8r1493x1/itKx8Ix1zSvzEcI0lrBq8Jxzy2c2Eql86o71fBcEUPAIWj0ANA4Sj0AFA4Cj0AFI5CDwCFo9ADQOEo9ABQOAo9ABSuLQ1ThwZnaf33Tg/F/MsbP5HK9eVnV4VjHhtINkDsiDcyvfeVt4RjvjL3wnCMJD21P97k8oUf53K9fOn2cMx558V3mLpxy8vDMZL0u2d9Jxxz3qxtqVzXP/36cMzqkx4Oxxw8Mdc8dseO+G5bew70pnKNjsavLbceiu+otnP2nHCMJF173j3hmI9veE1tuT6QysQVPQAUj0IPAIWj0ANA4Sj0AFA4Cj0AFK6SQm9mq81sk5ltNrP3V3FMAEA1mi70ZtYh6WOS3iDpXElvM7Nzmz0uAKAaVVzRXyxps7s/5u7Dkj4r6aoKjgsAqEAVDVMnSzp6i5qtkn7uuQ8yszWS1kjSrBPnaMUrdoSSPD68ODW4bz26Mhzzm+d/N5XrM8vjDUmf3f6qcMzXXxpvspKkuw7F/12/Zd8rUrl6O4bDMZnGnczuSJK0cXBZOGbnyNxUrr7EXNyz+yXhmDcuie/EJEkfOPPRcMw/bvmVVK4lvQPhmMGx7nDMiR3xPJJ06fx4o9+PTovvViZJ58/OnbsZVVzR2zH+zn/mL9zXuvsqd1/VNT/XVQcAiKui0G+VtOKo75dLiv+zCABoiSoK/fckrTSz082sW9LVkm6u4LgAgAo0fY/e3UfN7D2SbpPUIekGd9/Q9MgAAJWo5LdXuvutkm6t4lgAgGrRGQsAhaPQA0Dh2rLxyNi4af9QfJOOjDOX7gzH3L3zzFSuzs7xcMz2/fG12Zn18JL0+MiicMyNGy9I5ertG0rFRX3zwVwT9uxFB8Mxf3b+N1K5MpujHNgbX4K8ck78XJekH+6N93Js+0m8Z0SSVi18Mhxz049eFo75+8NvCMdIuffjwcGeVK6L5j6Risvgih4ACkehB4DCUegBoHAUegAoHIUeAApHoQeAwlHoAaBwFHoAKFxbGqYa5urpGg3FfPTWX03lGp/9M78af0odA7l//+y0wXDM0Lb+cMyaoWvCMZJ08sJ94ZixQx2pXAtPjDckbb0/vhmIJV5fSTrlhD3hmL97cHUq1/hYfA67tsU327ipJ95YJEmjz8Sbs2Y/nbxGPCsekhnfpr25JibrGQvHzN6Ua/7876XxRjrpzlQurugBoHAUegAoHIUeAApHoQeAwlHoAaBwFHoAKByFHgAKR6EHgMI11TBlZm+V9CFJ50i62N3XTSdu9GCXnr1/SSjXrAMWHp8kHV4Y3/XJV+R2R+q5P978lGm1GH86nkeSHj8j3oQzd0M8RpK29cd3IMrMuycbujY9tjQcMyvRxCRJsw7EY0YSL/H4433xIElznom/tw4uyTWqfemhi8IxJ/wwMb6ludI20pc7nzIy52BWs1f06yX9uqS7KxgLAKAFmrqid/eNkmSWu9oGALQe9+gBoHBTXtGb2R2STjrGj65z95umm8jM1khaI0md8xdMe4AAgOZMWejd/fIqErn7WklrJaln+YrcJzkAgDBu3QBA4Zoq9Gb2JjPbKuk1km4xs9uqGRYAoCrNrrq5UdKNFY0FANACbdlhqmvAdfK3RkIxB5Z1pXLN2xKP6dmda5o4eGK8OasR22hLktS3NdfQtW/H7ERU7uOUrjtyzTtRnUP1fdzTGI2/vpK07/T4f5w7Ei/xid+P744kSftPiZeBxQ/m5r1va3yMuy6ML9/ufyo3vsML4rk6kufggq/H68yTqUzcoweA4lHoAaBwFHoAKByFHgAKR6EHgMJR6AGgcBR6AChcW9bRy6Txzth61ex66Z7d8YXq3bsOpXIdWDY3HNM5lFubnbHg4fgOGI2DsX6Hnxo8Iz4XQwvi64p7dufWjg/PjecaT75b5m2Jv8bz1u8Nx3hXrv+jN7E4u3HwcCrX05cuDscsfuBgKlfGwGk94ZhsbRrur+86myt6ACgchR4ACkehB4DCUegBoHAUegAoHIUeAApHoQeAwlHoAaBwbWmYagyNqu+RXaGY7DYWPhhvthhZuSyVK9OQ1LEv3pw1srg/HJPNZSOJnVEkde/vDcf0PjmYypUx69nEZiBPPJPK5YtPCMdk5t127Q7HSJL1xV+r8d54Y5EknfTN2PtekoZOnR+O6Xki3nAmSQu3x+PG5uWqk43kmv0yuKIHgMJR6AGgcBR6AChcU4XezD5iZo+Y2UNmdqOZxW+mAQBaqtkr+tslne/uL5f0I0l/0fyQAABVaqrQu/s33P2nywPulbS8+SEBAKpU5T3635H0tQqPBwCowJTr6M3sDkknHeNH17n7TZOPuU7SqKRPHec4ayStkaSezjmpwQIA4qYs9O5++fF+bmbvkPRrki5z9+fdasXd10paK0nzepd5tOGisXcg9PhmdAzmdlXq2Jdo+BmO5+oezO2AlWke04J5qVzdD28Nx2Qad1LPSdL47nhjjC2J744kSb7lqXhQf7wJxw/kGs6sqysc00ict5I0unVbOGbW1vhcpF+rxPnUSJ6DdWqqM9bMVkv6c0mvc/eZ/2wB4EWo2Xv0/yZpjqTbzexBM7u+gjEBACrU1BW9u59Z1UAAAK1BZywAFI5CDwCFo9ADQOEo9ABQuLZsPKLhEdm22CYOz7tAvwUa2+ObI0iSJ9YWjyfWPjcSa6wlyYeH4zHP5ObCurvjuYb3xfMk1/krsY5+LDkX2dcrKjPnUm7t+NiePalcjb7EmvjE8xpPvlaZ97B1x/sQsrmyuKIHgMJR6AGgcBR6ACgchR4ACkehB4DCUegBoHAUegAoHIUeAArXloYpHxsLN1xYV64ZJCPbADE+mGh+SjSQ1Nmskm3qqKvxZPyp7eGYbK7sXGRerzrP98YJ8+MxiXNJys1hJqauJjUpf16kxrg7lYoregAoHYUeAApHoQeAwlHoAaBwFHoAKByFHgAKR6EHgMI1VejN7MNm9pCZPWhm3zCzZVUNDABQjWYbpj7i7n8pSWb2B5L+StLvTRVkjYYavbFmgUwzUla2YSrT5JJ5XtlmmtROPcl5zzTU1NVwls1VZxNTncae2Vlbrswc+kh8Z7TxA+GQdK7seZFtfMxo6ore3fcf9W2f6t3xDwAwDU3/CgQz+1tJb5e0T9IlTY8IAFCpKa/ozewOM1t/jD9XSZK7X+fuKyR9StJ7jnOcNWa2zszWDftQdc8AAHBcU17Ru/vl0zzWpyXdIumDz3OctZLWStK8jkXc4gGAmjS76mblUd9eKemR5oYDAKhas/fo/97MzpY0LukJTWPFDQCgXk0Vend/c1UDAQC0Bp2xAFC49uwwNT5eWwNUXY07LwR1NmjUNYfZPHU17mTVmavORrC6nlc2T6lNcVzRA0DhKPQAUDgKPQAUjkIPAIWj0ANA4Sj0AFA4Cj0AFK4t6+jr3HikxDXxda6xLhVzeARzcUSpc8EVPQAUjkIPAIWj0ANA4Sj0AFA4Cj0AFI5CDwCFo9ADQOEo9ABQOHP3+pOaDUjaVHvimWmRpGfbPYgZgrk4grk4grk44mx3nxMNaktnrKRN7r6qTblnFDNbx1xMYC6OYC6OYC6OMLN1mThu3QBA4Sj0AFC4dhX6tW3KOxMxF0cwF0cwF0cwF0ek5qItH8YCAOrDrRsAKFxLC72ZrTazTWa22czef4yfzzKzz03+/D4zO62V42mnaczFn5jZw2b2kJn9j5md2o5x1mGquTjqcW8xMzezYldcTGcuzOw3Js+NDWb26brHWJdpvEdOMbM7zeyByffJFe0YZ6uZ2Q1mttPM1j/Pz83M/nVynh4ys4umPKi7t+SPpA5Jj0p6iaRuST+QdO5zHvP7kq6f/PpqSZ9r1Xja+Weac3GJpN7Jr9/9Yp6LycfNkXS3pHslrWr3uNt4XqyU9ICkBZPfn9jucbdxLtZKevfk1+dKerzd427RXLxW0kWS1j/Pz6+Q9DVJJunVku6b6pitvKK/WNJmd3/M3YclfVbSVc95zFWSPjn59RclXWZm1sIxtcuUc+Hud7r7wclv75W0vOYx1mU654UkfVjSP0gaqnNwNZvOXLxL0sfcfY8kufvOmsdYl+nMhUuaO/n1PEnbaxxfbdz9bkm7j/OQqyT9p0+4V9J8M1t6vGO2stCfLOmpo77fOvl3x3yMu49K2idpYQvH1C7TmYujXauJf7FLNOVcmNmFkla4+1frHFgbTOe8OEvSWWb2v2Z2r5mtrm109ZrOXHxI0jVmtlXSrZLeW8/QZpxoPWlpZ+yxrsyfu8RnOo8pwbSfp5ldI2mVpNe1dETtc9y5MLOGpI9KemddA2qj6ZwXnZq4ffN6Tfwv79tmdr67723x2Oo2nbl4m6RPuPs/mdlrJP3X5FyMt354M0q4brbyin6rpBVHfb9cP/tfrf9/jJl1auK/Y8f7L8sL1XTmQmZ2uaTrJF3p7odrGlvdppqLOZLOl3SXmT2uiXuQNxf6gex03yM3ufuIu2/RxO+IWlnT+Oo0nbm4VtLnJcnd75HUo4nfg/NiM616crRWFvrvSVppZqebWbcmPmy9+TmPuVnSOya/foukb/rkpw2FmXIuJm9X/Lsminyp92GlKebC3fe5+yJ3P83dT9PE5xVXunvqd3zMcNN5j3xFEx/Uy8wWaeJWzmO1jrIe05mLJyVdJklmdo4mCv2uWkc5M9ws6e2Tq29eLWmfu+84XkDLbt24+6iZvUfSbZr4RP0Gd99gZn8jaZ273yzp45r479dmTVzJX92q8bTTNOfiI5L6JX1h8vPoJ939yrYNukWmORcvCtOci9sk/bKZPSxpTNKfuvtP2jfq1pjmXLxP0n+Y2R9r4lbFO0u8MDSzz2jiVt2iyc8jPiipS5Lc/XpNfD5xhaTNkg5K+u0pj1ngPAEAjkJnLAAUjkIPAIWj0ANA4Sj0AFA4Cj0AFI5CDwCFo9ADQOEo9ABQuP8DCvQNafgZuEwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "Y = np.random.normal(loc=0.0, scale=1.0, size=10000)\n",
    "#X = np.random.normal(loc=0.0, scale=1.0, size=10000)\n",
    "X = np.random.random(size=10000)\n",
    "_ = plt.hist2d(X, Y, bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "_ = plt.hist2d(X, Y, bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "No mappable was found to use for colorbar creation. First define a mappable such as an image (with imshow) or a contour set (with contourf).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-ef28a2e434fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# add a colorbar legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mcolorbar\u001b[0;34m(mappable, cax, ax, **kw)\u001b[0m\n\u001b[1;32m   2319\u001b[0m         \u001b[0mmappable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2320\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmappable\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2321\u001b[0;31m             raise RuntimeError('No mappable was found to use for colorbar '\n\u001b[0m\u001b[1;32m   2322\u001b[0m                                \u001b[0;34m'creation. First define a mappable such as '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2323\u001b[0m                                \u001b[0;34m'an image (with imshow) or a contour set ('\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: No mappable was found to use for colorbar creation. First define a mappable such as an image (with imshow) or a contour set (with contourf)."
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add a colorbar legend\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Animations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.animation as animation\n",
    "\n",
    "n = 100\n",
    "x = np.random.randn(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create the function that will do the plotting, where curr is the current frame\n",
    "def update(curr):\n",
    "    # check if animation is at the last frame, and if so, stop the animation a\n",
    "    if curr == n: \n",
    "        a.event_source.stop()\n",
    "    plt.cla()\n",
    "    bins = np.arange(-4, 4, 0.5)\n",
    "    plt.hist(x[:curr], bins=bins)\n",
    "    plt.axis([-4,4,0,30])\n",
    "    plt.gca().set_title('Sampling the Normal Distribution')\n",
    "    plt.gca().set_ylabel('Frequency')\n",
    "    plt.gca().set_xlabel('Value')\n",
    "    plt.annotate('n = {}'.format(curr), [3,27])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "a = animation.FuncAnimation(fig, update, interval=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "data = np.random.rand(10)\n",
    "plt.plot(data)\n",
    "\n",
    "def onclick(event):\n",
    "    plt.cla()\n",
    "    plt.plot(data)\n",
    "    plt.gca().set_title('Event at pixels {},{} \\nand data {},{}'.format(event.x, event.y, event.xdata, event.ydata))\n",
    "\n",
    "# tell mpl_connect we want to pass a 'button_press_event' into onclick when the event is detected\n",
    "plt.gcf().canvas.mpl_connect('button_press_event', onclick)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.883774</td>\n",
       "      <td>0.277204</td>\n",
       "      <td>Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.762695</td>\n",
       "      <td>0.695827</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.249583</td>\n",
       "      <td>0.987766</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.044110</td>\n",
       "      <td>0.518764</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.047255</td>\n",
       "      <td>0.263612</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.325055</td>\n",
       "      <td>0.399767</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.435533</td>\n",
       "      <td>0.621337</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.230448</td>\n",
       "      <td>0.309990</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.729762</td>\n",
       "      <td>0.760538</td>\n",
       "      <td>Iraq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.984443</td>\n",
       "      <td>0.994132</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     height    weight   origin\n",
       "0  0.883774  0.277204   Mexico\n",
       "1  0.762695  0.695827    India\n",
       "2  0.249583  0.987766    China\n",
       "3  0.044110  0.518764   Brazil\n",
       "4  0.047255  0.263612    Chile\n",
       "5  0.325055  0.399767      USA\n",
       "6  0.435533  0.621337       UK\n",
       "7  0.230448  0.309990   Canada\n",
       "8  0.729762  0.760538     Iraq\n",
       "9  0.984443  0.994132  Germany"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from random import shuffle\n",
    "origins = ['China', 'Brazil', 'India', 'USA', 'Canada', 'UK', 'Germany', 'Iraq', 'Chile', 'Mexico']\n",
    "\n",
    "shuffle(origins)\n",
    "\n",
    "df = pd.DataFrame({'height': np.random.rand(10),\n",
    "                   'weight': np.random.rand(10),\n",
    "                   'origin': origins})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# picker=5 means the mouse doesn't have to click directly on an event, but can be up to 5 pixels away\n",
    "plt.scatter(df['height'], df['weight'], picker=5)\n",
    "plt.gca().set_ylabel('Weight')\n",
    "plt.gca().set_xlabel('Height')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def onpick(event):\n",
    "    origin = df.iloc[event.ind[0]]['origin']\n",
    "    plt.gca().set_title('Selected item came from {}'.format(origin))\n",
    "\n",
    "# tell mpl_connect we want to pass a 'pick_event' into onpick when the event is detected\n",
    "plt.gcf().canvas.mpl_connect('pick_event', onpick)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}