fiddle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mat\n",
    "import calmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-06-21 08:00:00    1\n",
      "2018-06-22 10:00:00    1\n",
      "2018-11-23 11:00:00    1\n",
      "2018-07-20 12:00:00    1\n",
      "2018-11-23 15:00:00    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# a small set of random dates, morphed into a series using the date as the index, with the occurences for that date\n",
    "dates = pd.to_datetime([\n",
    "    '2018-06-21 08:00',\n",
    "    '2018-06-21 08:00'\n",
    "    '2018-06-26 08:00',\n",
    "    '2018-11-23 11:00',\n",
    "    '2018-07-20 12:00',\n",
    "    '2018-11-23 15:00'\n",
    "])\n",
    "# dates_series = pd.Series(dates, index=dates).sort_values().value_counts()\n",
    "dates_series = pd.Series(dates)\n",
    "# dates_series = pd.Series(dates_series.index.values, index=dates_series)\n",
    "dates_series = pd.Series(np.ones(len(dates), dtype=int), index=dates)\n",
    "print(dates_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x720 with 1 Axes>,\n",
       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f4c40b0ad68>],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FxJHA8cDhmfkg4NHAd0eN71NgfXFo+rdqrcf3iD+FZvj2wTDvkiRJkjRrDgRuysxbATLzpsz8/qjBfQqsS4EbWWiFCuC1tdaRWqVKKVtoBr24uo21NUuSJEnSrDkPuGdEXBMRb4+I3+kTPPIw7aWUrLWeAhw8tOguwA9G3MbNtdZjaQa+kCRJkqSZkpmbI2ID8AjgGOCsiPifmfn+UeL7/A4WpZSP909xu23cALx6pduRJEmSpNWQmVuBjcDGiLgSeCbNmBI71aeLoCRJkiStaRFxn4g4pDPrMJqfrBpJrxYsSZIkSVrj1gFviYj9gS3Av9EM2DcSCyxJkiRJamXmJcBRy423i6AkSZIkjUlkzs1PUs1NopIkSZLmxlh/Pmqeugj6u1mSJEmSZppdBCVJkiRpTCywJEmSJGlMLLAkSZIkaUwssCRJkiRpTCywxiAiNk87h0mIiK0RcVnncfAO1j06Ij45uezGLyIyIv6+M71HRNw4769rRyLiv7av+77TzmVW7YqfC9h1jnPDdva6I2JjRDxkUvnMml3tmBERL4+IqyPiivZ78GHTzmlWRcQ9IuLjEXFtRPy/iHhzROy1g/VfFBFlkjmOS/t/4LTO9Esj4tQpprSqOueDV0fE5RHxxxFhTdHhP4b6+EVmHtZ5XD/thFbZLcADIuJ27fRjgO/12UBEzNNInQAnAZ9v/44sInZfnXRm0oo/F9IasqxjxjyKiCOB44HDM/NBwKOB7043q9kUEQH8M/CxzDwEuDewDvirHYS9CJjLAgu4FXh8RBww7UQmZHA+eH+a78BjgT+fck4zxQJrTCJiXUScHxGXRsSVEfF77fyDI+LrEfHuttI/r3NiNvciYveI+JuIuLi9ove8zuL9IuLsiPhmRLxjTq9unAMc1z4/CThzsCAijoiIL0bEVyPiwoi4Tzv/5Ij4RER8Bjh/8ikvT0SsAx4OPBs4sZ13dERcsNj7GBGbI+K0iLgcOHJ6mU/Fcj4XF0TEYZ31Ph8RD55o1is03DIdEW+NiJPb59dHxKs7x8A106Kxo9e9K9vBMWOpz8jjIuIbEXFJRJw+h62+BwI3ZeatAJl5U2Z+PyI2RMTn2td1bkQcCL9p3Xxze6X/qog4YqrZT9bvAr/MzPcBZOZW4MXAsyJi34h4Q/tvckVEvCAiXgjcDfhsRHx2inkv1xbgXTSvcRvteeBn2td6fkQcFBHrI+Lbne/TfSPiuxGx56QTX6nM/BFwCvBH0VjyvDAi/rT9frg8Il43vaxX3zye8M6qXwInZObhwDHAae0VHIBDgLe1lf7PgCdMKceVul0sdA/8aDvv2cCmzHwo8FDguRHxW+2yI4AXAIcC/x54/MQzXrl/BE6MiH2ABwEXdZZ9A3hEZv5H4FXAazvLDgeemJm/M7FMV+73gE9l5jXAjyNiQzt/qfdxX+CizHxwZn5+4tlO13I+F+8BTgaIiHsD+2Tm5RPLeDJuao+Bfwe8dNrJaNUtdczYTvt/5Z3AsZm5AbjThHIcp/OAe0bENRHx9oj4nfaE+C00x/sNwHvZtpWmZOZhwPPbZbuK+wOXdGdk5s+B7wDPAQ4GDmtbAj+cmacD3weOycxjJpzruLwN+P2IWD80/y3ABwavFTg9MzcBlwGDc4TjgXMz87aJZTtGmfktYHfgzixxXhgRx9IcMx6WmQ8GXj+1hCfAAmt8AnhtRFwB/Atwd+Au7bLrMvOy9vklNAeWedTtInhCO++xwDMi4jKak8w70hSUAF/OzG+1V67OpLnSOVcy8wqa9+skmlaLrvXAP0XEVcAbab5QBj6dmT+ZSJLjcxJN4UD7d9DlZ6n3cSvwkcmmOBuW+bn4J+D49oTsWcD7J5LsZP1z+3eej3Ma3VLHjMXcF/hWZl7XTp+5g3VnUmZuBjbQXK2/ETgLeB7wAODT7ffgK4B7dMLObGMvoOnVsf9Ek55NRwPvzMwtAHP4XbmotoD8IPDCoUVHAv/QPv8QC9+hZwFPaZ+f2E6vBUudFz4aeF9mVlg77/tS5u3+kFn2+zRX5DZk5m0RcT2wT7vs1s56W4E100WQprB8QWaeu83MiKOBHFp3eHpefAJ4A82Xwh078/8S+GxmnhDNgB8bO8tumVBuYxERd6Dp0vHAiEiaK1EJnM3S7+Mv26JrV9Xrc5GZNSI+TXMF78k0J2rzZgvbXpjbZ2j54Fi3lbX1/bKz173L2cEx4+Os4X+r9pi3EdgYEVcC/x24OjOX6ia9Vr4H+/oa8MTujIjYDzgIuH4aCU3Im4BLgfeNsO4naC7M34Hm++Azq5nYaoqI36Y57v+Ipc8L//M0cpsWW7DGZz3wo7a4Oga417QTmpBzgf826DccEfeOiH3bZUe0zcK70VylmdduZO8FXp2ZVw7NX8/C4AYnTzSj8Xsi8KHMvFdmHpyZ9wSuAx7B2nkfx205n4szgNOBizPzp6ub3qr4NnBoROzdXol/1LQTmpBd9XXvyFLHjN1Y/N/qm8Bvx8Los08Z3uCsi4j7RMQhnVmHAV8H7hTNABhExJ4R0e3N8JR2/sNpuk1tmljC03U+UCLiGfCbgZBOo2m5Pxd4XrSDQLUFBsDNwO0nn+r4tK0y/4emm9zAhbT3KNJcjP/Xdt3NwMXAm4FPzusFy4i4E/AO4K2ZmSx9Xvhp4A+iHSmy876vSRZYK9QeIG6l6Vf7kPaK1jNo7sPYFZxBc6Xq0rZL1DtZuHJ9MfBWmi+g64CPLrqFGZeZN7T9w4e9HvjfEfFV5v9q/Uls//58pJ2/Jt7HcVvO5yIzLwF+zmhXN2fG4DiXmd+lOXm4qv371akmtsp21dc9oqWOGSeyyL9VZv6C5j6kT0XEJTQn0/NWbKwDPhARX2tvBziU5j7LJwJ/Hc2AP5cBR3ViftkeC97Btifda1p7on0C8KSIuBa4huZe9f9Fc97wHeCK9t/sqW3Yu2g+H/M4yEXXaUB3NMEX0BQWVwBPB/5HZ9lZwNOYv+6Bg3vyr6a5LeY84NXtskXPCzPzUzStdl9puw+u6ft0o/k/oOWKZhSwd2fmrjQ6kHYRbVfPl2bm8dPOZS2IiLvRdC+6b2b+esrpjGxXPc7tqq97tUTEuszc3A4A9Tbg2sx847TzWi0RsZHm+PmVaeciabJswVqBiPhDmhtYXzHtXCTNtrarzEXAy+esuNolj3O76uteZc9tr1xfTdOV9p1TzkeSVoUtWJIkSZI0JrZgSZIkSdKYWGBJkiRJ0phYYEmSJEnSmFhgSZIkSdKYWGBJkiRJ0pj8f6H6CWc/oYxAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calmap.calendarplot(\n",
    "    dates_series,\n",
    "    daylabels='MTWTFSS',\n",
    "    fig_kws={'figsize': (12, 10)},\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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}