{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checkerboard Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to plot a checkerboard plot / heat map via matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `from mlxtend.plotting import checkerboard plot`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to plot a checkerboard plot / heat map via matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "- -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+DQ0NevDBB5Wdna2kpCRNnjw5hklxvjozoyUlJXr77be1b98+HTp0SB988IGeeuqpGKbF\nuTCj0UMhiy+XSdosqUySec7i3dixYzV37lzNmDFDt9xyi6qrq7Vq1SpdffXVZzxmxYoVGjlypB55\n5BENHjxY3/72t7Vly5YYpsaZLF++XFOmTNGsWbO0adMm5ebmasyYMWpqaupw/6NHj+qaa67RtGnT\nlJeXF+O0OB+dndFDhw7p5Zdf1vDhwzVkyBD9+Mc/1gsvvKBHH300xsnREWY0ysyMFYdLUljS3V08\nNl8nCl1cr2AwaPPnz2+3rb6+3qZOndrh/mPGjLH9+/fblVde6T17tFYoFLJ4NWzYMJs0aVLb1+Fw\n2Pr27Wtz5sw557EjRoywp59+OprxYqqiosL7z5KPGe1o/eu//quVl5d7v5ZLfT7NmNFTQqHQqe9p\nvkXw9zp3yBCXkpOTVVBQoMrKynbbV69ereLi4g6Pueuuu7Rx40Y988wzqq+vV21trV588UV17949\nFpFxFseOHVMoFFJpaWnbNuecRo0apWAw6DEZuqorM3q6vLw8FRcXa82aNVFIiM5gRqMv2XcAoCsy\nMjKUlJSkxsbGdtsbGxuVnZ3d4THXXnuthg8friNHjuiee+5RRkaGFi5cqJ49e+qxxx6LRWycQVNT\nk1pbW5WZmdlue2ZmJi8px6muzOgpO3fuVK9evZSUlKSZM2eqvLw8iklxPpjR6KOQ4ZIRCAQUDof1\nwAMP6NChQ5KkyZMna8WKFSorK1NLS4vnhAAk6bbbbtPll1+uoqIizZkzRx999JH+5V/+xXcsIKoo\nZIhLZ/trraGhocNj9uzZo927d7eVMUmqqamRc079+vXTtm3bopoZZ3a2uylZWVmeUuFCdGVGT9m5\nc6ck6YMPPlBWVpZmzpxJIfOMGY0+niFDXDp+/PiXnmeQpNLSUq1du7bDY6qqqtSnTx+lpaW1bcvO\nzlY4HNauXbuimhdnl5KS8qXnjcxMlZWVKikp8ZgMXdWVGe1IUlISz3leBJjR6OMOWRxxzl0m6XpJ\n7uSma51zuZI+NbN6f8n8eOmll1ReXq5QKKT169fr6aefVnp6etvzJrNnz1afPn00YcIESdIbb7yh\nH/3oR/rFL36hmTNnqlevXnrxxRf1+uuv83LlRWDy5MmaMGGCCgoKNHToUM2bN0/Nzc1t37/nnntO\nn3zyiZYsWdJ2THV1tcxMn3/+ufbt26fq6mp169ZNOTk5nq4CX9TZGZ04caJ27typ2tpaSdIdd9yh\nKVOmaP78+Z6uAF/EjEZZJN+yyYruknSHTnzcRetp6+edPE9CfOyFJJs4caLV1dVZc3OzrV271goK\nCtr+289//nOrrKxst/8NN9xgf/jDH+zgwYO2fft2mzNnjnXr1s37dURqxfvb6hcsWGADBw601NRU\nKyoqsg0bNrT9twkTJtjIkSPb7e+cs0Ag0G4NGjQo1rEjLlE+9qKzM/rkk0/a+++/bwcOHLBPP/3U\nNmzYYN/5zne8XwPz+d+Y0eh97IWzE7+gcQlxzuVLCvnOgcgLhULKz8/3HQMXaOnSpRo3bpzvGIgw\n5jMxvPfeeyooKJCkAjN7L1Ln5RkyAAAAzyhkAAAAnlHIAAAAPKOQAQAAeEYhAwAA8IxCBgAA4BmF\nDAAAwDMKGQAAgGcUMgAAAM8oZAAAAJ5RyAAAADyjkAEAAHhGIQMAAPCMQgYAAOAZhQwAAMAzChkA\nAIBnFDIAAADPKGQAAACeUcgAAAA8o5ABAAB4RiEDAADwjEIGAADgGYUMAADAMwoZAACAZ8m+AwCI\nnJUrV6qmpsZ3DFygqqoq3xEQBcxnYqirq4vKeZ2ZReXEuHg554oCgUAwHA77jgLgDAKBgJhR4KJW\nbGbvRupk3CG7NLWEw2FVVFQoJyfHdxZEwMqVKzVt2jTfMRBBzGjiYD4TVkskT0Yhu4Tl5OQoPz/f\ndwxEAC+DJCZmNDEwnzgfPNQPAADgGYUMAADAMwoZAACAZxQyAAAAzyhkAAAAnlHIAAAAPKOQAQAA\neEYhAwAA8IxCBgAA4BmFDAAAwDMKGQAAgGcUMgAAAM8oZAAAAJ5RyAAAADyjkAEAAHhGIQMAAPCM\nQgYAAOAZhQwAAMAzChkAAIBnFDIAAADPKGQAAACeUcgAAAA8o5ABAAB4RiGLI86555xz651zB5xz\njc65/3DODfady6cFCxZo0KBBSktLU1FRkTZs2HDGfd966y0FAoF2KykpSXv37o1hYpxNWVmZtm3b\npubmZgWDQRUWFp51/5SUFL3wwguqq6vT4cOH9fHHH+vhhx+OUVqcj87M6BdVVVUpJSVF+fn5UU6I\nzmBGo4dCFl+GS3pZ0jBJoySlSPp/zrk0r6k8Wb58uaZMmaJZs2Zp06ZNys3N1ZgxY9TU1HTGY5xz\n2rp1qxoaGtTQ0KA9e/bommuuiWFqnMnYsWM1d+5czZgxQ7fccouqq6u1atUqXX311Wc8ZsWKFRo5\ncqQeeeQRDR48WN/+9re1ZcuWGKbG2XRlRiXps88+08MPP6xRo0bFKCnOBzMaZWbGitMlKUNSWNJt\nnTwuX5KFQiGLZ8OGDbNJkya1fR0Oh61v3742Z86cDvdfs2aNBQIB++yzz2IVMWYqKipMUlyvYDBo\n8+fPb7etvr7epk6d2uH+Y8aMsf3799uVV17pPXu01qU2o6d861vfsunTp9vMmTPtlltuiXbMqEuE\n+WRGO1z5FsHf6dwhi29f0Ykfik99B4m1Y8eOKRQKqbS0tG2bc06jRo1SMBg843Fmpry8PPXp00ej\nR4/W2rVrYxEX55CcnKyCggJVVla227569WoVFxd3eMxdd92ljRs36plnnlF9fb1qa2v14osvqnv3\n7rGIjHPo6oz+4he/UF1dnWbMmBGLmDhPzGj0JfsOgK5xzjlJ8yW9Y2Yf+M4Ta01NTWptbVVmZma7\n7ZmZmWe8Hd67d2+9+uqrKiws1NGjR/Xaa69pxIgRWr9+vfLy8mIRG2eQkZGhpKQkNTY2ttve2Nio\n7OzsDo+59tprNXz4cB05ckT33HOPMjIytHDhQvXs2VOPPfZYLGLjLLoyo1u3btU//uM/6p133lEg\nwP2CiwkzGn0Usvj1iqSvSbrVd5B4MXjwYA0e/N/vgSgqKtLHH3+sefPmacmSJR6ToSsCgYDC4bAe\neOABHTp0SJI0efJkrVixQmVlZWppafGcEJ0RDof14IMPatasWbruuusk6dQjFohTzGjn8CdIHHLO\n/UzS30oaYWZ7fOfx4Wx/rWVlZZ33eYYOHaqPPvoo0vHQSWe7m9LQ0NDhMXv27NHu3bvb/qGXpJqa\nGjnn1K9fv6jmxbl1dkYPHjyojRs36nvf+55SUlKUkpKiH//4x9q8ebO6deumNWvWxCg5OsKMRh+F\nLM6cLGN/J2mkme30nceXlJSULz3PYGaqrKxUSUnJeZ9n8+bN6t27dzQiohOOHz/+peeNJKm0tPSM\nz/lVVVWpT58+Skv77zcZZ2dnKxwOa9euXVHNi3Pr7IxeccUV+tOf/qTNmzerurpa1dXV+u53v6sh\nQ4aourpaw4YNi2V8nIYZjYFIvkOAFd2lEy9T/kUnPv4i8wsrtZPnSYh3WS5fvtzS0tJsyZIlVlNT\nY48//rj17NnT9u7da2Zmzz77rI0fP75t//nz59uvf/1r++ijj+xPf/qTPfXUU5acnGxvvvmmpyuI\nnER4F9d9991nhw4dsoceesiys7Nt0aJF1tTUZBkZGSbJZs+ebeXl5W37p6en2/bt223ZsmU2ZMgQ\nGz58uNXW1trChQu9X0uk1qU2o6fjXZYX12JGv7Qi+i5LniGLL9/ViR+CNadtf0TSL2OexrOxY8eq\nqalJ06dPV2Njo/Ly8rRq1Sr16tVLktTQ0KD6+vq2/VtaWjRlyhR98sknSk9P180336zKykrdfvvt\nvi4BX7BixQplZGTo+eefV2ZmpjZv3tzuM6uysrLUv3//tv2bm5v19a9/XS+//LI2bNig/fv3a/ny\n5Zo2bZqvS8BpOjujuLgxo9Hl7MQdE1xCnHP5kkKhUIhPwU4QS5cu1bhx43zHQIQxo4mB+UxYBWb2\nXqROxjNkAAAAnlHIAAAAPKOQAQAAeEYhAwAA8IxCBgAA4BmFDAAAwDMKGQAAgGcUMgAAAM8oZAAA\nAJ5RyAAAADyjkAEAAHhGIQMAAPCMQgYAAOAZhQwAAMAzChkAAIBnFDIAAADPKGQAAACeUcgAAAA8\no5ABAAB4RiEDAADwjEIGAADgGYUMAADAMwoZAACAZxQyAAAAz5J9B4A/K1euVE1Nje8YiICqqirf\nERAFzGhiYD5xPpyZ+c6AGHPOFUkK+s6ByAoEAgqHw75jAOgA85lYTn4/i83s3Uidkztkl6YW3wEQ\neeFwWBUVFcrJyfEdBRdo5cqVmjZtmu8YiCDmM3HU1NRo3LhxUoR/l1LIgASSk5Oj/Px83zFwgXiZ\nMjExnzgbHuoHAADwjEIGAADgGYUMAADAMwoZAACAZxQyAAAAzyhkAAAAnlHIAAAAPKOQAQAAeEYh\nAwAA8IxCBgAA4BmFDAAAwDMKGQAAgGcUMgAAAM8oZAAAAJ5RyAAAADyjkAEAAHhGIQMAAPCMQgYA\nAOAZhQwAAMAzChkAAIBnFDIAAADPKGQAAACeUcgAAAA8o5DFEefcd51z1c65z06utc65O33n8uW2\n227Tr3/9a+3atUutra266667znnMHXfcoY0bN+rw4cPasmWLxo8fH4OkOF8LFizQoEGDlJaWpqKi\nIm3YsOGc+3/ta19Tenq6cnJy9M///M8xSorzVVZWpm3btqm5uVnBYFCFhYXndVxJSYlaWloUCoWi\nnBCdwYxGkZmx4mRJ+oakOyVdJ+l6SS9IOiopp5PnyZdk8b7GjBljs2bNsrvvvtuOHz9ud91111n3\nHzhwoB08eNDmzJljgwcPtrKyMmtpabFRo0Z5v5ZIrVAoZPFq2bJl1r17d1uyZInV1NTY448/bldd\ndZXt27evw/1feeUVu/LKK23FihVWV1dny5Ytsx49eth//ud/xjh55FVUVHj/WYrEGjt2rB0+fNge\neughy87OtkWLFtn+/fvt6quvPutxV1xxhW3dutVWrlxpoVDI+3Uwnycwoyd84Wcy3yL5Oz6SJ2PF\nfknaL+mRTh6TEIXsi6u1tfWchewnP/mJVVdXt9v2xhtv2O9+9zvv+fkH32zYsGE2adKktq/D4bD1\n7dvX5syZ0+H+JSUl9oMf/KDdtilTptjw4cOjmjMWEqWQBYNBmz9/frtt9fX1NnXq1LMe98Ybb9jM\nmTNt+vTpFLKLCDN6QrQKGS9ZxinnXMA59y1J6ZKCvvPEg6KiIq1evbrdtlWrVqm4uNhTIpxy7Ngx\nhUIhlZaWtm1zzmnUqFEKBjv+8T569KhSU1PbbUtNTdX69evV2toa1bw4t+TkZBUUFKiysrLd9tWr\nV5915iZMmKBBgwZp1qxZ0Y6ITmBGo49CFmecczc55w7qxEuVr0j6ppnVeo4VF7KystTY2NhuW2Nj\no6644gp169bNUypIUlNTk1pbW5WZmdlue2ZmphoaGjo8ZsyYMVq8eLHee+89SdLGjRv1+uuv69ix\nY2pqaop6ZpxdRkaGkpKSOpy5rKysDo+5/vrrNXv2bD344IOn7ubjIsGMRh+FLP7USsqVNFTSQkm/\ndM4N8RsJiL1p06bpb/7mb1RcXKyUlBR985vf1IQJEyRJgQD/tMUb55yWLl2qGTNmaNu2bW3bEL+Y\n0c7hf5E4Y2bHzWybmW0ysx9Kqpb0lO9c8aChoaHDv+4OHDiglpYWT6kgde1uSmpqqhYvXqzm5mbt\n2LFDO3fu1MCBA9WjRw/16tUrFrFxFp29o9KjRw8VFhbqZz/7mVpaWtTS0qJp06YpLy9PR48e1R13\n3BGr6OgAMxp9FLL4F5DU3XeIeBAMBts9/yBJo0ePPuPzD4idlJSULz1vZGaqrKxUSUnJWY9NSkpS\nnz595JzTsmXLzuvjTxB9x48f/9IzR5JUWlqqtWvXfmn/AwcO6KabblJeXp5yc3OVm5urRYsWqba2\nVrm5uVq3bl2soqMDzGgMRPIdAqzoLkmzJQ2XNFDSTZL+j6Tjkv66k+dJiHdZpqen280332y5ubnW\n2tpqTz3dBJoaAAAK/klEQVT1lN18883Wr18/k2SzZ8+28vLytv0HDhxoBw4csJ/85Cc2ePBgmzhx\noh09etRKS0u9X0ukVjy/i2v58uWWlpbW7i31PXv2tL1795qZ2bPPPmvjx49v2//DDz+0iooK27p1\nq61bt87uv/9+y8jIsB07dvi6hIhJlHdZ3nfffXbo0KF2H3vR1NRkGRkZHc7o6Yt3WV5cmNETovUu\ny2QhnlwjaYmk3pI+k/S+pNFm9kevqTwpLCzUm2++2fbDPHfuXEnSkiVL9OijjyorK0v9+/dv23/H\njh36xje+oXnz5mnSpEnatWuXHn300S+9Cwx+jB07Vk1NTZo+fboaGxuVl5enVatWtb200dDQoPr6\n+rb9W1tbNXfuXH344YdKSUnRyJEjtXbtWg0YMMDXJeA0K1asUEZGhp5//nllZmZq8+bNGjNmTNsD\n3afPKC5uzGh0OTtxxwSXEOdcviQ+/joBhUIh5efn+46BC7R06VKNGzfOdwxEGPOZGN577z0VFBRI\nUoGZvRep8/IMGQAAgGcUMgAAAM8oZAAAAJ5RyAAAADyjkAEAAHhGIQMAAPCMQgYAAOAZhQwAAMAz\nChkAAIBnFDIAAADPKGQAAACeUcgAAAA8o5ABAAB4RiEDAADwjEIGAADgGYUMAADAMwoZAACAZxQy\nAAAAzyhkAAAAnlHIAAAAPKOQAQAAeEYhAwAA8IxCBgAA4BmFDAAAwLNk3wEARM7KlStVU1PjOwYu\nUFVVle8IiALmMzHU1dVF5bzOzKJyYly8nHNFgUAgGA6HfUcBcAaBQEDMKHBRKzazdyN1Mu6QXZpa\nwuGwKioqlJOT4zsLImDlypWaNm2a7xiIIGY0cTCfCaslkiejkF3CcnJylJ+f7zsGIoCXQRITM5oY\nmE+cDx7qBwAA8IxCBgAA4BmFDAAAwDMKGQAAgGcUMgAAAM8oZAAAAJ5RyAAAADyjkAEAAHhGIQMA\nAPCMQgYAAOAZhQwAAMAzChkAAIBnFDIAAADPKGQAAACeUcgAAAA8o5ABAAB4RiEDAADwjEIGAADg\nGYUMAADAMwoZAACAZxQyAAAAzyhkAAAAnlHIAAAAPKOQxSnn3LPOubBz7iXfWXxasGCBBg0apLS0\nNBUVFWnDhg1n3Pett95SIBBot5KSkrR3794YJsbZlJWVadu2bWpublYwGFRhYeF5HVdSUqKWlhaF\nQqEoJ0RndWZGq6qqdNtttykjI0Pp6enKycnR/PnzY5gW59KZGb399tvV2trabh0/fly9evWKYeL4\nQSGLQ865v5L0uKRq31l8Wr58uaZMmaJZs2Zp06ZNys3N1ZgxY9TU1HTGY5xz2rp1qxoaGtTQ0KA9\ne/bommuuiWFqnMnYsWM1d+5czZgxQ7fccouqq6u1atUqXX311Wc97oorrtCSJUu0evXqGCXF+ers\njF522WX6/ve/r7ffflu1tbWaNm2afvSjH2nx4sUxTo6OdGVGzUw33HCDsrKylJWVpd69e2vfvn0x\nTB1HzIwVR0vS5ZK2SPprSW9KeqkL58iXZKFQyOLZsGHDbNKkSW1fh8Nh69u3r82ZM6fD/desWWOB\nQMA+++yzWEWMmYqKCpMU1ysYDNr8+fPbbauvr7epU6ee9bg33njDZs6cadOnT7dQKOT9OiK5LrUZ\n7ci9995r48ePj0a8mEmE+ezKjN5+++12/Phx69Gjh/fsUVr5FsHf79whiz8LJP3WzP7oO4hPx44d\nUygUUmlpads255xGjRqlYDB4xuPMTHl5eerTp49Gjx6ttWvXxiIuziE5OVkFBQWqrKxst3316tUq\nLi4+43ETJkzQoEGDNGvWrGhHRCd1dUa/aNOmTQoGgxoxYkSUUuJ8dXVGnXPavHmzdu/erVWrVp11\n30sdhSyOOOe+JSlP0nO+s/jW1NSk1tZWZWZmttuemZmphoaGDo/p3bu3Xn31Vf3bv/2b/v3f/139\n+/fXiBEjtHnz5lhExllkZGQoKSlJjY2N7bY3NjYqKyurw2Ouv/56zZ49Ww8++OCpO7+4iHRlRk/p\n37+/UlNTNXToUD355JN65JFHohkV56ErM7pnzx498cQT+vu//3vde++9qq+v15o1a5SbmxuLyHEn\n2XcAnB/nXD9J8yWNMrNjvvPEo8GDB2vw4MFtXxcVFenjjz/WvHnztGTJEo/J0FnOOS1dulQzZszQ\ntm3b2rYhMbzzzjv6/PPP9e677+qZZ57R9ddfr/vvv993LHTS1q1btXXr1rav161bp+uuu05PP/20\nJkyY4C/YRYpCFj8KJPWS9J777988SZJud859T1J3u4RuE3Tlr7WODB06VFVVVZGOh07q7N2UHj16\nqLCwUHl5eVqwYIEkKRAIyDmno0ePavTo0Xrrrbdikh0du5AZHThwoCTpxhtvVENDg2bOnEkh8+xC\n7nh+0fr163XrrbdGOl5C4CXL+LFa0v/QiZcsc0+ujZIqJOVeSmVMklJSUr70PIOZqbKyUiUlJed9\nns2bN6t3797RiIhOOH78+JeeN5Kk0tLSDp/zO3DggG666Sbl5eUpNzdXubm5WrRokWpra5Wbm6t1\n69bFKjrOIFIz2traqqNHj0YjIjqhszN6Jnl5edqzZ0+k4yUE7pDFCTM7JOmDL25zzh2StN/Mavyk\n8mvy5MmaMGGCCgoKNHToUM2bN0/Nzc1tt8Kfe+45ffLJJ20vR/70pz/VoEGDdOONN+rIkSN67bXX\n9Oabb+q//uu/PF4FTnnppZdUXl6uUCik9evX6+mnn1Z6errKy8slSbNnz1afPn3avr81Ne1/7Pfu\n3asjR46otrY2xslxJp2d0VdeeUUDBgzQkCFDJJ347MC5c+fqH/7hH3xdAr6gszM6adIk1dXV6c9/\n/rNSU1P1ne98RyNHjtTXv/51fxdxEaOQxbdL6q7Y6caOHaumpiZNnz5djY2NysvL06pVq9o+dLCh\noUH19fVt+7e0tGjKlCn65JNPlJ6erptvvlmVlZW6/fbbfV0CvmDFihXKyMjQ888/r8zMTG3evLnd\nZ1ZlZWWpf//+nlOiMzo7o+FwWM8995y2b9+u5ORkXXfddfqnf/onPf74474uAV/Q2Rnt1q2b5s6d\nqz59+qi5uVnvv/++SktL9fbbb/u6hIuau8Re6YIk51y+pFAoFFJ+fr7vOIiApUuXaty4cb5jIMKY\n0cTAfCasAjN7L1In4xkyAAAAzyhkAAAAnlHIAAAAPKOQAQAAeEYhAwAA8IxCBgAA4BmFDAAAwDMK\nGQAAgGcUMgAAAM8oZAAAAJ5RyAAAADyjkAEAAHhGIQMAAPCMQgYAAOAZhQwAAMAzChkAAIBnFDIA\nAADPKGQAAACeUcgAAAA8o5ABAAB4RiEDAADwjEIGAADgGYUMAADAMwoZAACAZ8m+A8Cfmpoa3xEQ\nIXV1db4jIAqY0cTAfOJ8ODPznQEx5pwbEAgEtoTD4VTfWQB0LBAIKBwO+44BoGNHJGWb2c5InZBC\ndolyzg2QlOE7ByKqm6QW3yEQMXw/Ewvfz8TSFMkyJlHIAAAAvOOhfgAAAM8oZAAAAJ5RyAAAADyj\nkAEAAHhGIQMAAPCMQgYAAOAZhQwAAMAzChkAAIBnFDIAAADPKGQAAACeUcgAAAA8o5ABAAB4RiED\nAADwjEIGAADgGYUMAADAMwoZAACAZxQyAAAAzyhkAAAAnlHIAAAAPKOQAQAAeEYhAwAA8IxCBgAA\n4BmFDAAAwDMKGQAAgGcUMgAAAM8oZAAAAJ5RyAAAADz7/7X9bTO8PfaMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105f78390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import checkerboard_plot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "ary = np.random.random((5, 4))\n",
    "\n",
    "brd = checkerboard_plot(ary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2 - Changing colors and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LL77Zt4eCI9+WtOVt/w8WFcW5zS6o9prDRe3atUlOTmblyu/vaDjnWLVqVYUPZhw5coTo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\nAWjWtgP5ebm8PevPCtNSTnfdVqRbt24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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eab9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import checkerboard_plot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "checkerboard_plot(ary, \n",
    "                  col_labels=['abc', 'def', 'ghi', 'jkl'],\n",
    "                  row_labels=['sample %d' % i for i in range(1, 6)],\n",
    "                  cell_colors=['skyblue', 'whitesmoke'],\n",
    "                  font_colors=['black', 'black'],\n",
    "                  figsize=(4.5, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## checkerboard_plot\n",
      "\n",
      "*checkerboard_plot(ary, cell_colors=('white', 'black'), font_colors=('black', 'white'), fmt='%.1f', figsize=None, row_labels=None, col_labels=None, fontsize=None)*\n",
      "\n",
      "Plot a checkerboard table / heatmap via matplotlib.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `ary` : array-like, shape = [n, m]\n",
      "\n",
      "    A 2D Nnumpy array.\n",
      "\n",
      "- `cell_colors` : tuple or list (default: ('white', 'black'))\n",
      "\n",
      "    Tuple or list containing the two colors of the\n",
      "    checkerboard pattern.\n",
      "\n",
      "- `font_colors` : tuple or list (default: ('black', 'white'))\n",
      "\n",
      "    Font colors corresponding to the cell colors.\n",
      "\n",
      "- `figsize` : tuple (default: (2.5, 2.5))\n",
      "\n",
      "    Height and width of the figure\n",
      "\n",
      "- `fmt` : str (default: '%.1f')\n",
      "\n",
      "    Python string formatter for cell values.\n",
      "    The default '%.1f' results in floats with 1 digit after\n",
      "    the decimal point. Use '%d' to show numbers as integers.\n",
      "\n",
      "- `row_labels` : list (default: None)\n",
      "\n",
      "    List of the row labels. Uses the array row\n",
      "    indices 0 to n by default.\n",
      "\n",
      "- `col_labels` : list (default: None)\n",
      "\n",
      "    List of the column labels. Uses the array column\n",
      "    indices 0 to m by default.\n",
      "\n",
      "- `fontsize` : int (default: None)\n",
      "\n",
      "    Specifies the font size of the checkerboard table.\n",
      "    Uses matplotlib's default if None.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `fig` : matplotlib Figure object.\n",
      "\n",
      "\n",
      "**Examples**\n",
      "\n",
      "For usage examples, please see\n",
      "    [http://rasbt.github.io/mlxtend/user_guide/plotting/checkerboard_plot/](http://rasbt.github.io/mlxtend/user_guide/plotting/checkerboard_plot/)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/checkerboard_plot.md', 'r') as f:\n",
    "    s = f.read() \n",
    "print(s)"
   ]
  }
 ],
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  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "version": "3.6.4"
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   "title_sidebar": "Contents",
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   "toc_position": {},
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