{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "#Please adjust to your file\n",
    "df=pd.read_excel(\"/Users/davidmika/Desktop/First.xlsx\",\"Sheet1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heat Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEFCAYAAADzHRw3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACyZJREFUeJzt3WuspeVZx+H/PQwNp0JhpkAKiLSW2GgibSYxmiZFjDU1\n0tZqv6mFlpLUNKSNJEbSlrHGiB+0Yk1EqJGWmjaNtSZEU+MBEazRQKiVECE1BWwE5FDLYQZkmNsP\nexMnc2DetWbttWY/c13Jypr97if7vZPZ85s3zzpVdweAzW/LqgcAYDEEHWAQgg4wCEEHGISgAwxC\n0AEGIegAgxB0gEEIOsAgti7zZKedfFKfve30ZZ4SJtn93O6ccJzrG44+e7Zsybcee/yJ7n7t4dYu\nNegXbD8td//6h5Z5Spjky7fekUtr16rHgAPccdL2/ORn//yhKWtdkgAMQtABBiHoAIMQdIBBCDrA\nIAQdYBCCDjAIQQcYhKADDELQAQYh6ACDEHSAQQg6wCAEHWAQgg4wCEEHGISgAwxC0AEGIegAg5g7\n6FX181XV67crFjkUALObK+hVdV6S30/y7GLHAWBeMwe9qirJHyd5MskNC58IgLnMc4V+VZJLklye\n5LnFjgPAvGYKelW9Kcl1Sa7v7n/YmJEAmMfkoFfV1iS3JHk4yTUbNhEAc9k6w9pPJHlzkrd29+4N\nmgeAOU26Qq+qH87aVflvd/c/zXKCqrqyqu6qqruefHbXPDMCMMFhg76+1fK5JA8k+fisJ+juG7t7\nR3fv2HbKSXOMCMAUU67QT0lyYZI3JXl+nxcTdZJr19fctH7sdzdqUABe2ZQ99BeS/NEhvveWrO2r\n35nk/iQzbccAsDiHDfr6A6AHfWl/Ve3MWtA/292fWexoAMzCm3MBDELQAQZxREHv7p3dXbZbAFbP\nFTrAIAQdYBCCDjAIQQcYhKADDELQAQYh6ACDEHSAQQg6wCAEHWAQgg4wCEEHGISgAwxC0AEGIegA\ngxB0gEEIOsAgBB1gEFuXebKnX3gxt97/X8s8JUxy7xPfyXGnnbzqMeAAD77wzOS1Sw36qSedmEt/\n9KJlnhIm2fPoI3nXKXtXPQYc4PZ+9eS1tlwABiHoAIMQdIBBCDrAIAQdYBCCDjAIQQcYhKADDELQ\nAQYh6ACDEHSAQQg6wCAEHWAQgg4wCEEHGISgAwxC0AEGIegAgxB0gEEIOsAgJge9qn6rqv62qv6z\nqnZX1VNVdU9VXVtV2zZySAAOb5Yr9I8mOTnJXye5PsmfJNmTZGeSb1TVeQufDoDJts6w9tTufn7/\ng1X1G0muSfKrSX5pUYMBMJvJV+gHi/m6L63fv/HIxwFgXot4UPTS9ftvLOBnATCnWbZckiRVdXWS\nU5KclmRHkrdmLebXLXY0AGYxc9CTXJ3krH2+/mqSy7r78YMtrqork1yZJOduP32O0wEwxcxbLt19\ndndXkrOTvCfJ65PcU1VvOcT6G7t7R3fv2Pbqk49sWgAOae499O5+rLu/kuTtSbYl+dzCpgJgZkf8\noGh3P5TkviQ/UFXbj3wkAOaxqJf+v279/qUF/TwAZjQp6FV1YVWddpDjW9ZfWHRmkq9193cWPSAA\n00x9lstPJfnNqrozybeSPJm1Z7q8LWsPij6a5IMbMiEAk0wN+t8k+b6sPef8zUlek+S5JA8kuSXJ\n73X3UxsyIQCTTAp6d9+b5MMbPAsAR8D7oQMMQtABBiHoAIMQdIBBCDrAIAQdYBCCDjAIQQcYhKAD\nDELQAQYh6ACDEHSAQQg6wCAEHWAQgg4wCEEHGISgAwxi6kfQLcRLezvP7n5+maeESXa/8GKefVWv\negw4wK69/zt5bXUv75f4Nce/qi85/cylnQ+mevHE47N99+5VjwEH2HPW2fn8vf96d3fvONzapV6h\nb93zUs58/OllnhIm6Tecm3Mf++6qx4AD7DrvxMlr7aEDDELQAQYh6ACDEHSAQQg6wCAEHWAQgg4w\nCEEHGISgAwxC0AEGIegAgxB0gEEIOsAgBB1gEIIOMAhBBxiEoAMMQtABBiHoAIOYFPSq2lZVV1TV\nV6rqm1W1u6q+W1V3VtUHqsp/DAArNvVDot+b5A+SPJLktiQPJzkryXuSfCbJO6rqvd3dGzIlAIc1\nNegPJHlnkr/o7r0vH6yqa5L8S5KfzVrcv7zwCQGYZNJWSXf/XXffum/M148/muSG9S8vXvBsAMxg\nEXvfL67f71nAzwJgTkcU9KramuQX17/86pGPA8C8pu6hH8p1SX4wyV92918dbEFVXZnkyiQ5OXWE\npwPgUOa+Qq+qq5L8cpJ/T/ILh1rX3Td2947u3nGCoANsmLmCXlUfTnJ9kvuS/Fh3P7XQqQCY2cxB\nr6qPJPl0knuzFvNHFz4VADObKehV9StJPpXk61mL+X9vyFQAzGxy0Kvq41l7EPTuJD/e3U9s2FQA\nzGzSs1yq6n1JPpnkpSR3JLmq6oAHOB/s7psXOh0Ak0192uIF6/fHJfnIIdbcnuTmIx0IgPlMfen/\nzu6uw9wu3uBZAXgF3vYWYBCCDjAIQQcYhKADDELQAQYh6ACDEHSAQQg6wCAEHWAQgg4wCEEHGISg\nAwxC0AEGIegAgxB0gEEIOsAgBB1gEIIOMIipnym6EFtOPSUnve2SZZ4SJnno3ntzwgXnrHoMOMAL\n2Tt57VKD/rrzvye/88Wbl3lKmORTb780Fz3wH6seAw7wzW1nTF5rywVgEIIOMAhBBxiEoAMMQtAB\nBiHoAIMQdIBBCDrAIAQdYBCCDjAIQQcYhKADDELQAQYh6ACDEHSAQQg6wCAEHWAQgg4wCEEHGMSk\noFfVz1XVp6vqjqp6uqq6qj6/0cMBMN3UD4n+WJIfSvJskm8n+f4NmwiAuUzdcvlokguTnJrkQxs3\nDgDzmnSF3t23vfznqtq4aQCYmwdFAQYh6ACDEHSAQWx40Kvqyqq6q6ruevKppzb6dADHrA0Penff\n2N07unvHtjPO2OjTARyzbLkADELQAQYh6ACDmPTCoqp6d5J3r3959vr9j1TVzet/fqK7r17wbADM\nYOp7uVyU5H37HXv9+i1JHkoi6AArNGnLpbt3dne9wu17N3hOAA7DHjrAIAQdYBCCDjAIQQcYhKAD\nDELQAQYh6ACDEHSAQQg6wCAEHWAQgg4wCEEHGISgAwxC0AEGIegAgxB0gEEIOsAgBB1gEFM/U3Qh\nnntuV/7+jn9c5ilhkseO25L7zztn1WPAAZ48+aTJa6u7N3CU/U5W9XjWPlCaxdie5IlVDwEH4Xdz\nsc7v7tcebtFSg85iVdVd3b1j1XPA/vxuroY9dIBBCDrAIAR9c7tx1QPAIfjdXAF76ACDcIUOMAhB\nBxiEoB/lqqrXb3ur6g2vsO62fdZetsQRYd/f00PdLlv1jMeCpb5SlLntydrf1QeSXLP/N6vqjUku\n3mcdrMqvHeL415c6xTHKP/7N4bEkjyS5vKo+0d179vv+Fev3tyb5maVOBvvo7p2rnuFYZstl87gp\nydlJfnrfg1V1fJLLknwtyX3LHws4Wgj65vGFJM/l/6/GX/bOJGdmLfjAMcyWyybR3c9U1ReTXFZV\n53b3t9e/9cEkTyf5Ug6yvw7LVFU7D3L4we6+ecmjHJMEfXO5KWsPjL4/ySer6vwkP5HkD7t7V1Wt\ndDhIcu1Bjt2e5OYlz3FMsuWyiXT3Pyf5tyTvr6otWdt+2RLbLRwlursOcrt41XMdKwR987kpyflJ\n3pHk8iR3d/c9qx0JOBoI+uZzS5LdSW5Ick68CRKwTtA3me7+nyR/muTcrD3r5QurnQg4WnhQdHP6\nWJI/S/J4dz+z6mGAo4Ogb0Ld/XCSh1c9B3B0seUCMAgfcAEwCFfoAIMQdIBBCDrAIAQdYBCCDjAI\nQQcYhKADDELQAQYh6ACDEHSAQfwfB2BLwOYjOXwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111f35dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data = np.random.rand(4,2)\n",
    "#Define rows here\n",
    "rows = list('1234')\n",
    "#Define column categories here\n",
    "columns = list('MF')\n",
    "fig,ax=plt.subplots()\n",
    "ax.pcolor(data,cmap=plt.cm.Reds,edgecolors='k')\n",
    "ax.set_xticks(np.arange(0,2)+0.5)\n",
    "ax.set_yticks(np.arange(0,4)+0.5)\n",
    "ax.xaxis.tick_bottom()\n",
    "ax.yaxis.tick_left()\n",
    "ax.set_xticklabels(columns,minor=False,fontsize=20)\n",
    "ax.set_yticklabels(rows,minor=False,fontsize=20)\n",
    "#Show heat map\n",
    "plt.show()"
   ]
  },
  {
   "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}