5. Stackoverflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import json\n",
    "import dc_stat_think as dcst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-31    1\n",
      "2021-06-30    2\n",
      "2021-09-30    3\n",
      "2021-12-31    4\n",
      "Freq: Q-DEC, dtype: int64\n",
      "2021-01-31    NaN\n",
      "2021-02-28    NaN\n",
      "2021-03-31    1.0\n",
      "2021-04-30    NaN\n",
      "2021-05-31    NaN\n",
      "2021-06-30    2.0\n",
      "2021-07-31    NaN\n",
      "2021-08-31    NaN\n",
      "2021-09-30    3.0\n",
      "2021-10-31    NaN\n",
      "2021-11-30    NaN\n",
      "2021-12-31    4.0\n",
      "Freq: M, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "dates = pd.date_range(start='2021', periods=4, freq='Q')\n",
    "\n",
    "data=range(1,5)\n",
    "\n",
    "quarterly = pd.Series(data=data, index=dates)\n",
    "print(quarterly)\n",
    "\n",
    "monthly = pd.date_range(start='2021',\n",
    "    periods=12,\n",
    "    freq='M')\n",
    "\n",
    "print(quarterly.reindex(monthly))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-31    1\n",
      "2016-02-29    2\n",
      "Freq: M, dtype: int64\n",
      "2016-01-03    NaN\n",
      "2016-01-10    NaN\n",
      "2016-01-17    NaN\n",
      "2016-01-24    NaN\n",
      "2016-01-31    1.0\n",
      "2016-02-07    NaN\n",
      "2016-02-14    NaN\n",
      "2016-02-21    NaN\n",
      "2016-02-28    NaN\n",
      "Freq: W-SUN, dtype: float64\n",
      "2016-01-03    1\n",
      "2016-01-10    1\n",
      "2016-01-17    1\n",
      "2016-01-24    1\n",
      "2016-01-31    1\n",
      "2016-02-07    2\n",
      "2016-02-14    2\n",
      "2016-02-21    2\n",
      "2016-02-28    2\n",
      "Freq: W-SUN, dtype: int64\n",
      "2016-01-03    NaN\n",
      "2016-01-10    NaN\n",
      "2016-01-17    NaN\n",
      "2016-01-24    NaN\n",
      "2016-01-31    1.0\n",
      "2016-02-07    1.0\n",
      "2016-02-14    1.0\n",
      "2016-02-21    1.0\n",
      "2016-02-28    1.0\n",
      "Freq: W-SUN, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Set start and end dates\n",
    "start = '2016-1-1'\n",
    "end = '2016-2-29'\n",
    "\n",
    "# Create monthly_dates here\n",
    "monthly_dates = pd.date_range(start=start, end=end, freq='M')\n",
    "\n",
    "# Create monthly here\n",
    "monthly = pd.Series(data=[1,2], index=monthly_dates)\n",
    "print(monthly)\n",
    "\n",
    "# Create weekly_dates here\n",
    "weekly_dates = pd.date_range(start=start, end=end, freq='W')\n",
    "\n",
    "# Print monthly, reindexed using weekly_dates\n",
    "print(monthly.reindex(weekly_dates))\n",
    "print(monthly.reindex(weekly_dates, method='bfill'))\n",
    "print(monthly.reindex(weekly_dates, method='ffill'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    value\n",
      "country date             \n",
      "uk      2016-01-01    4.0\n",
      "        2016-01-08    5.0\n",
      "us      2016-01-01    1.0\n",
      "        2016-01-08    1.5\n",
      "DatetimeIndex(['2015-01-02', '2015-01-09', '2015-01-16', '2015-01-23',\n",
      "               '2015-01-30', '2015-02-06', '2015-02-13', '2015-02-20',\n",
      "               '2015-02-27', '2015-03-06', '2015-03-13', '2015-03-20',\n",
      "               '2015-03-27', '2015-04-03', '2015-04-10', '2015-04-17',\n",
      "               '2015-04-24', '2015-05-01', '2015-05-08', '2015-05-15',\n",
      "               '2015-05-22', '2015-05-29', '2015-06-05', '2015-06-12',\n",
      "               '2015-06-19', '2015-06-26', '2015-07-03', '2015-07-10',\n",
      "               '2015-07-17', '2015-07-24', '2015-07-31', '2015-08-07',\n",
      "               '2015-08-14', '2015-08-21', '2015-08-28', '2015-09-04',\n",
      "               '2015-09-11', '2015-09-18', '2015-09-25', '2015-10-02',\n",
      "               '2015-10-09', '2015-10-16', '2015-10-23', '2015-10-30',\n",
      "               '2015-11-06', '2015-11-13', '2015-11-20', '2015-11-27',\n",
      "               '2015-12-04', '2015-12-11', '2015-12-18', '2015-12-25',\n",
      "               '2016-01-01'],\n",
      "              dtype='datetime64[ns]', freq='7D')\n",
      "                    value\n",
      "country                  \n",
      "uk      2015-01-02    NaN\n",
      "        2015-01-09    NaN\n",
      "        2015-01-16    NaN\n",
      "        2015-01-23    NaN\n",
      "        2015-01-30    NaN\n",
      "...                   ...\n",
      "us      2015-12-04    NaN\n",
      "        2015-12-11    NaN\n",
      "        2015-12-18    NaN\n",
      "        2015-12-25    NaN\n",
      "        2016-01-01    1.0\n",
      "\n",
      "[106 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "data=[('uk' ,'2016-01-01',4.0),\n",
    "('uk', '2016-01-08',  5.0),\n",
    "('us', '2016-01-01', 1.0),\n",
    "('us', '2016-01-08', 1.5)\n",
    "]\n",
    "df=pd.DataFrame(data,columns=['country','date','value'])\n",
    "df['date']=pd.to_datetime(df['date'])\n",
    "df=df.set_index(['country','date'])\n",
    "print(df)\n",
    "\n",
    "min_df_date=df.index.levels[1].min()\n",
    "weekly_dates=pd.date_range(start='2015-01-02',end=min_df_date,freq='7d')\n",
    "print(weekly_dates)\n",
    "\n",
    "country=df.index.levels[0]\n",
    "print(df.reindex(pd.MultiIndex.from_product([country,weekly_dates])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df=pd.read_csv('https://raw.githubusercontent.com/Mrvrinder/AbnormalBehaviourDetection-/main/BobPatientDataCSV.csv')\n",
    "#$df.to_csv('BobPatientDataCSV.csv')\n",
    "#df=pd.read_csv('BobPatientDataCSV.csv',parse_dates=['Start time','End time'], index_col=['Start time','End time'])\n",
    "df=pd.read_csv('BobPatientDataCSV.csv',parse_dates=['Start time'])\n",
    "df.drop('End time',axis=1)\n",
    "df=df.set_index('Start time')\n",
    "\n",
    "#monthly=df.resample('M').mean()\n",
    "#print(monthly)\n",
    "\n",
    "grouped=df.groupby(['Activity',df.index.dayofyear])['Duration (Minutes)'].mean()\n",
    "#print(grouped)\n",
    "\n",
    "index=grouped.index.to_flat_index()\n",
    "activities=[]\n",
    "month=[]\n",
    "for a,b in index:\n",
    "    activities.append(a)\n",
    "    month.append(b)\n",
    "\n",
    "values=grouped.values\n",
    "\n",
    "fig,ax =plt.subplots(figsize=(20,10))\n",
    "sns.barplot(x=activities, y=values,hue=month,ax=ax)\n",
    "plt.yscale(\"log\")\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2880x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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Sz8/iX5zOTdKyOLxwVZH8XUn+zfC+uWexBuvT9TNZ/AvQO7Zcf52kZ/F8rpAfzu4qMIuu9breneQPh68LL0ry4iy+be2fJ7mkeH55km/P4i+o555F13pdP7Ql/5MM/8E1yXVJrqqQZ/EXfY/M4p8vJWfUddY1OJjFf1z7/7L4i7X/M8mzktyQ5AeL5x8qNIuu9bpemeQpSb4ii79Qe9aw56/P4s/D7Pkw47cOHUrOaA1Kdn1Pkvdv+e+tv5Tk85N8MMkfFMn/KIuDFxcUmEXXel0PZXEI+OTe/+7vDk5O8v4sDjNUzv80i3/OvKfALLrW6/qKLJ7xq/x7oePNN+nv0jap67LW4Kqs/u/7qv390jrks3QdnnVXHH7uuWY6bzH3AK5aV5K/zeL/4v/1bddnhveNyT+TxRc8O+Vj770u+Vxr8LEkd2/5ff7uLP6PnOuSXD5R/tdZvCrE5Sv8nHPluh7/va/dco+DW95/RZU8wxcpWfzFX8kZrUHJrv9jFv8C9C1bspuG/J1F8tsLzaJrva5bn9lXbnn78nz2z0nJPJ/9M3nF3LPoWrLrtfnsoYZ3JzlpS359hXzIHjb8WHJGXWddg8uTXDu8/fgk/3LI7knys8Xzv0rygSQ/W2AWXWt3vWXb8/zKCnk++8+iK+eexRqsVdercv+vRS7bkr+vQp7Pfm30vrln0bVk1xu35lvef2OSGyvnh3++U15lRl1n7fq3WbwS9q9ntX9Hczz5Z7I5f5e2SV2XtQZz/H1ftb9fWod8rq5P2pq75rlmH8BV68rii/Qv2yW/bUx++N7Z8i+jy7r3uuQzrsG1O3zOb8ziP3Rtn3FZ+Qez+D+Qb1vh55wr13XEvZP8wJb3XZ3kmgp5kquHH6+ZexZrsD5dh59fl8X/Mfa7WfzlwgeG/CFV8kqz6FqraxYvx/yqLP7PiQ/nsy9rekcW/4JfNt8y+8fmnkXXkl2/K4v/+PG9WfzfZa9P8oIs/o+k24vk/z6LlyZ+S4FZdK23Bh9L8oqtX3Ns+brjpyrnWRyg++Id8jIz6jpr1/dk8S0i/kUWX2P/WJJTh48/WCR/dxZ/2XFTgVmswfp0vTaLV1h4bJIfSvL6Yc+/PYtvWTV7PmTfncV/5yk5o66zdr0wi1f4febwvscOb9+YxZ6vnL81i/++8/YCs+har+sdSd68w9ck/h5J13VZgzn+vq/s3y8VzufqenCnfeNa7XX4+zZDkqS19tVJPtR7v3mH/GG99zfuNT987ySP6r0fWOa91yWfcQ1+LMmneu8XbMt/McnX9N6fsex8+JyfTHJu7/2bVvE558p13dO9fybJV/bev35b/pNJTum9v2TuvLX24ixe2vR7e+8/WnFGa1Cy6z9I8oO99x9trT0tySuzONi6f8vHlMkrzaJrja5J/nnu7+Le+12ttR9O8k1Jfq9qnuTbknzBlrzcjLrO2vUxSX4qi69jnpxkXxYvq//7WfwHke8vkl+a5EuKzKJrrTV4VO/9O7NNa+2Vvfd/VjmvNMvUeaVZps6XeO/TkvxkFt8e86ez+BavL8ziINpHs/j2mnPnL07y4Cz+feFHi85oDWp2/Zskp2Xxajr/ovd+W2vtv0vy6iy+vpk1z+Kw86uSPD2Lb8tcbkZdZ+36mCT/OskTsjhU1pLckuRNWfjWwvmhLP5MPiKLQxcVZ9R1vq7XJXl17/1gtmj+HknX9VmDlf9937Ly5u/SVjHL05L8wtbPyeo5gAMAMJHWWkvy0N77J6vmlWbRtV5XAAAAAADg2DiAwwO01p6d5LlZnAruSW5N8obh7bH5oSSPm+je65Jv0hroWmtGXXW1BrrqWnNGXa2BrrrqWnNGa6CrrvN1/f0sVMlvyeKVTCrMYg3Wu+sm/Tmulut6nPfuvb85O2it/ave+79Zx7zSLFPnlWaZOq80y9R5pVmmzivNMldeaZap80qzTJ3PNQur4wAO99Na++UsXub6dVl8oZ4svlj/34e3zx+RvyzJWVl838KXL/ne65Jv0hroWmtGXXW1BrrqWnNGXa2BrrrqWnNGa6CrrrpuYldroGu1GXWdr+sLktzYe/+RbNNau7n3/vh1zCvNMnVeaZap80qzTJ1XmmXqvNIsc+WVZpk6rzTL1Plcs7A6DuBwP6219/fen7xTnsV+OWOv+ZB9cZL375CPuve65Ju0BrrWmlFXXa2BrrrqOveM1kBXXXXdxK7WQNdqM+qqqzXQVVddj/Hen0zy+Uk+lft76PDj3YXzh255e6e8wozLynWtNeOycl1rzTh1vklroOv0s7QkD+697wuzsfhs9+nW2tm990u35W24tjue/NNJzht+XPa91yXfpDXQtdaMy8p1rTXj1PkmrYGutWZcVq5rrRmnzjdpDXStNeOycl1rzTh1vklroGutGZeV61prxqnzTVoDXWvNuKxc1+O/918l+XDv/Sn3++DWbk5ycu/91Kr5kD0jyYHe+2kVZ9RVV103r6s10HUVswzvu2V7xmq17hVw2KK19vQkr87i5Nzhl5w8Lcm9w9snj8jPSLI/yR1JDi753uuSb9Ia6FprRl11tQa66lpzRl2tga666lpzRmugq666bmJXa6BrtRl1na/rw5Kc33t/XbZorf1Mksf33l9QNR+yi5N8Z+/9JyrOqKuuum5eV2ug6ypmGd7381s/J6vnAA47aq09JsmpSVqSQ733jywrn/Le65JXmkVXXXXV1RroOvcsuupqDXTVVde5Z7EGuuqqq67WQNf5Z9G1XlcAAI5T793lOuKV5Kenyqe897rklWbRVVdddbUGNWfRVVddrYGuNWfRVVdroKuuNWfR1RroqquuNWexBrrqqquu1mDuz7lpXV2rv2YfwFX/SnL5VPmU916XvNIsuuqqq67WoOYsuuqqqzXQteYsuupqDXTVteYsuloDXXXVteYs1kBXXXXV1RrM/Tk3ratr9ddJgaNrE+ZT3ntd8kqzTJ1XmmXqvNIsU+eVZpk6rzTLXHmlWabOK80ydV5plqnzSrNMnVeaZa680ixT55VmmTqvNMvUeaVZps4rzTJXXmmWqfNKs0ydV5pl6rzSLFPnlWaZK680y9R5pVmmzivNMnVeaZap80qzzJVXmmXqvNIsU+eVZpk6rzTL1HmlWebKK80ydV5plqnzuWZhxdpwIgp21Vo7qff+mSnyKe+9LnmlWabOK80ydV5plqnzSrNMnVeaZa680ixT55VmmTqvNMvUeaVZps4rzTJXXmmWqfNKs0ydV5pl6rzSLFPnlWaZK680y9R5pVmmzivNMnVeaZap80qzzJVXmmXqvNIsU+eVZpk6rzTL1HmlWebKK80ydV5plqnzSrNMnVeaZeq80ixz5ZVmmTqvNMvU+VyzsHpeAYejOvyHtbX2r5adT3nvdckrzaKrrrrqag10nXsWXXW1Brrqquvcs1gDXXXVVVdroOv8s+iqqzXQVVdd557FGuh6InRlBr3A98FyrceV5Oap8invvS55pVl01VVXXa1BzVl01VVXa6BrzVl01dUa6KprzVl0tQa66qprzVmsga666qqrNZj7c25aV9fqLt+CivtprX1yl3c9dPjx7hH5Q7e8vVM+5t7rkm/SGuhaa8Zl5brWmnHqfJPWQNdaMy4r17XWjFPnm7QGutaacVm5rrVmnDrfpDXQtdaMy8p1rTXj1PkmrYGutWZcVq5rrRmnzjdpDXStNeOycl1rzTh1vklroOv0s7QkD+697wvzmfsEkKvWleTmJI/eJf/wmPzwvZPcsux7r0u+SWuga60ZddXVGuiqa80ZdbUGuuqqa80ZrYGuuuq6iV2tga7VZtRVV2ugq641Z9TVGuhas+vwvlt2yl2ru5x+YrvXJXlCktt3yB+/y8cfa3743r89wb3XJd+kNdC11ozLynWtNePU+Satga61ZlxWrmutGafON2kNdK0147JyXWvNOHW+SWuga60Zl5XrWmvGqfNNWgNda824rFzXWjNOnW/SGuhaa8Zl5brWmnHqfJPWQNfpZ8kOn5MV8y2oAAAAAAAAAABghJPmHoD10Vr7kqnyKe+9LnmlWabOK80ydV5plqnzSrNMnVeaZa680ixT55VmmTqvNMvUeaVZps4rzTJXXmmWqfNKs0ydV5pl6rzSLFPnlWaZK680y9R5pVmmzivNMnVeaZap80qzzJVXmmXqvNIsU+eVZpk6rzTL1HmlWebKK80ydV5plqnzSrNMnVeaZeq80ixz5ZVmmTqvNMvU+VyzsEJzfw8s1/pcSW6eKp/y3uuSV5pFV1111dUa1JxFV111tQa61pxFV12tga661pxFV2ugq6661pzFGuiqq666WoO5P+emdXWt7toX2KK19qpd3vU1SR69w/uPJ/9HSdoO+TLuvS75Jq2BrrVmXFaua60Zp843aQ10rTXjsnJda804db5Ja6BrrRmXletaa8ap801aA11rzbisXNdaM06db9Ia6FprxmXlutaacep8k9ZA11ozLivXtdaMU+ebtAa6Tj9LS/KFYVYO4LDdP0ny40nu2ZZ/f5K/SXLZiPz7k1yY5O/vkI+997rkm7QGutaacVm5rrVmnDrfpDXQtdaMy8p1rTXj1PkmrYGutWZcVq5rrRmnzjdpDXStNeOycl1rzTh1vklroGutGZeV61prxqnzTVoDXWvNuKxc11ozTp1v0hroOv0sSfL8HTJWae6X4HHVupL8YZKv3CW/dUx++N5Jblr2vdcl36Q10LXWjLrqag101bXmjLpaA1111bXmjNZAV1113cSu1kDXajPqqqs10FXXmjPqag10rdl1eN9NO+Wu1V2zD+CqdSV5RJLPmyKf8t7rkleaRVddddXVGtScRVdddbUGutacRVddrYGuutacRVdroKuuutacxRroqquuulqDuT/npnV11bja8JsEAAAAAAAAAADswb65B6CW1toXJDk/yXOT7B/iO5K8eXj7m0fkH03y8SRfNFzLvPe65Ju0BrrWmlFXXa2BrrrWnFFXa6CrrrrWnNEa6KqrrpvY1RroWm1GXXW1BrrqWnNGXa2BrjW7viHJz/XePxHmM/dL8LhqXUnekuQnkjxmS/aYJO9PcuPI/B1J3pTknRPce13yTVoDXWvNqKuu1kBXXWvOqKs10FVXXWvOaA101VXXTexqDXStNqOuuloDXXWtOaOu1kDXml1/IsnbDmeuea6TAvd3eu/953vvHzkcDG/3JJ8Zkyd5bO/9W7J4ACz13uuSb9Ia6FprRl11tQa66lpzRl2tga666lpzRmugq666bmJXa6BrtRl11dUa6KprzRl1tQa61uzae//5JI8Ps3IAh+0+1Fr7l621Rx8OhrdbkpPG5Elua629Kcnty773uuSbtAa61ppRV12tga661pxRV2ugq6661pzRGuiqq66b2NUa6FptRl11tQa66lpzRl2tga41u7bWfiLJLWFW++YegHL+pyQvTfLO4Q9tz+Lh8KYs/jCPye9McleSR7TW7lryvdcl36Q10LXWjLrqag101bXmjLpaA1111bXmjNZAV1113cSu1kDXajPqqqs10FXXmjPqag10rdn14iTfFebVC3wfLFetK8mXJPnGJA/Zln/f2Hy490unuPe65Ju0BrrWmlFXXa2BrrrWnFFXa6CrrrrWnNEa6KqrrpvY1RroWm1GXXW1BrrqWnNGXa2BrmW7Pmfrz12rv2YfwFXrSvLDSW5I8vtJPpjknC35p8fkW+79iWXfe13yTVoDXWvNqKuu1kBXXWvOqKs10FVXXWvOaA101VXXTexqDXStNqOuuloDXXWtOaOu1kDXml2H911++G3XTOct5h7AVetK8t4MJ+WSnJ7kQJIfGfKrRua3JnlIkismuPe65Ju0BrrWmlFXXa2BrrrWnFFXa6CrrrrWnNEa6KqrrpvY1RroWm1GXXW1BrrqWnNGXa2BrgW7Dj+/ovf5zxxs8jX7AK5aV5Lrtv38IUnenORjSa4cmf9lklcezpd873XJN2kNdK01o666WgNdda05o67WQFddda05ozXQVVddN7GrNdC12oy66moNdNW15oy6WgNda3Z95dbcNc81+wCuWleSP0xy5rZsX5Lbktw3Mn9Hkj/Ymi/x3uuSb9Ia6FprRl11tQa66lpzRl2tga666lpzRmugq666bmJXa6BrtRl11dUa6KprzRl1tQa61uz6uu25a/XXvsD9vSDJvVuD3vu9rbVnJHnamDzJ9wz3ftKy770u+Satga61ZtRVV2ugq641Z9TVGuiqq641Z7QGuuqq6yZ2tQa6VptRV12tga661pxRV2uga82uSV7QWvsPYVatL05EAQAAAAAAAAAAe3DS3AMAAAAAAAAAAMA6cwAHAAAAAAAAAABGcAAHAAAAAAAAAABGcAAHAAAAAAAAAABG+P8BrTudGWEsmFwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2880x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.read_csv('BobPatientDataCSV.csv',parse_dates=['Start time'])\n",
    "df.drop('End time',axis=1)\n",
    "df=df.set_index('Start time')\n",
    "\n",
    "grouped=df.groupby(['Activity',df.index.dayofyear]).groups\n",
    "#print(grouped)\n",
    "summary_df=pd.DataFrame(columns=['Activity','DayOfYear','Duration (Minutes)','Date'])\n",
    "for index, group in grouped.items():\n",
    "    activity=index[0]\n",
    "    dayofyear=index[1]\n",
    "    filter=(df['Activity']==activity) & (df.index.dayofyear==dayofyear)\n",
    "    #print(activity)\n",
    "    duration=df[filter]['Duration (Minutes)'].mean()\n",
    "    date=df[filter].index.date.max()\n",
    "    summary_df.loc[len(summary_df)]=[activity,dayofyear,duration,date]\n",
    "    \n",
    "#\n",
    "\n",
    "summary_df=summary_df.set_index('Date')\n",
    "\n",
    "min_date=df.index.min()\n",
    "max_date=df.index.max()\n",
    "\n",
    "#print(min_date,max_date)\n",
    "\n",
    "daily=pd.date_range(start=min_date,end=max_date,freq='D')\n",
    "activity=['Breakfast','Sleeping']\n",
    "filter=summary_df.Activity=='Breakfast'\n",
    "breakfast=summary_df[filter]\n",
    "breakfast=breakfast.reindex(daily, method='bfill')\n",
    "#print(breakfast.index,breakfast.DayOfYear)\n",
    "\n",
    "filter=summary_df.Activity=='Sleeping'\n",
    "sleeping=summary_df[filter]\n",
    "sleeping=sleeping.reindex(daily,method='bfill')\n",
    "#print(sleeping.index,sleeping.DayOfYear)\n",
    "\n",
    "filter=summary_df.Activity=='Spare_Time/TV'\n",
    "spare_time=summary_df[filter]\n",
    "spare_time=spare_time.reindex(daily,method='bfill')\n",
    "\n",
    "\n",
    "plt.figure(figsize=(40,6))\n",
    "plt.bar(daily,breakfast['Duration (Minutes)'],color='green')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xticks(daily)\n",
    "plt.show()\n",
    "plt.figure(figsize=(40,6))\n",
    "plt.bar(daily,sleeping['Duration (Minutes)'],color='blue')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xticks(daily)\n",
    "plt.show()\n",
    "plt.figure(figsize=(40,6))\n",
    "plt.bar(daily,spare_time['Duration (Minutes)'],color='blue')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xticks(daily)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 6]\n",
      "[1, 5, 9, 12]\n",
      "[6, 21]\n",
      "[27]\n"
     ]
    }
   ],
   "source": [
    "myval=\"01234566\"\n",
    "\n",
    "def walkTheTree(alist):\n",
    "    queue=[]\n",
    "    if(len(alist))==1: \n",
    "        return\n",
    "    while len(alist):\n",
    "        value=alist.pop(0)\n",
    "        value_next=alist.pop(0)\n",
    "        queue.append(value+value_next)\n",
    "    print(queue)      \n",
    "    walkTheTree(queue)\n",
    "    #(mystring)\n",
    "    #walkTheTree(mystring)\n",
    "\n",
    "alist=[]\n",
    "for char in myval:\n",
    "    alist.append(int(char))\n",
    "print(alist)\n",
    "\n",
    "walkTheTree(alist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attrib1 {'x': 1, 'y': 2, 'z': [{'p': 100, 'c': 200}, {'p': 500, 'c': 600}]}\n",
      "attrib2 {'R': 222, 'K': 320}\n",
      "attrib3 hello\n"
     ]
    }
   ],
   "source": [
    "myDct={ \"attrib1\":{ \"x\":1, \"y\":2, \"z\":[{\"p\":100,\"c\":200},{\"p\":500,\"c\":600}] }, \"attrib2\":{\"R\":222,\"K\":320}, \"attrib3\":\"hello\" }\n",
    "for key,value in myDct.items():\n",
    "    print(key,value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date\n",
      "0 2019-10-31\n",
      "1 2019-11-30\n",
      "2 2019-12-31\n",
      "DatetimeIndex(['2019-10-31', '2019-11-30', '2019-12-31'], dtype='datetime64[ns]', freq='M')\n"
     ]
    }
   ],
   "source": [
    "sdate = pd.to_datetime('2020-01-31') \n",
    "date_range = pd.date_range(start = sdate + pd.DateOffset(months=-3), \n",
    "                           end = sdate + pd.DateOffset(months=-1), freq = 'M')\n",
    "\n",
    "df=pd.DataFrame({'date':pd.Series(date_range)})\n",
    "print(df)\n",
    "\n",
    "sdate=pd.Timestamp('2019-10-01')\n",
    "index=pd.date_range(start=sdate,periods=3,freq='M')\n",
    "print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2021-03-25 10:00:00', '2021-03-25 11:00:00',\n",
      "               '2021-03-25 12:00:00', '2021-03-25 13:00:00',\n",
      "               '2021-03-25 14:00:00', '2021-03-25 15:00:00',\n",
      "               '2021-03-25 16:00:00', '2021-03-25 17:00:00',\n",
      "               '2021-03-25 18:00:00', '2021-03-25 19:00:00',\n",
      "               '2021-03-25 20:00:00', '2021-03-25 21:00:00',\n",
      "               '2021-03-25 22:00:00', '2021-03-25 23:00:00',\n",
      "               '2021-03-26 00:00:00', '2021-03-26 01:00:00',\n",
      "               '2021-03-26 02:00:00', '2021-03-26 03:00:00',\n",
      "               '2021-03-26 04:00:00', '2021-03-26 05:00:00',\n",
      "               '2021-03-26 06:00:00', '2021-03-26 07:00:00',\n",
      "               '2021-03-26 08:00:00', '2021-03-26 09:00:00'],\n",
      "              dtype='datetime64[ns]', freq='60T')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "from datetime import date\n",
    "import datetime as datetime\n",
    "previous_day=pd.datetime.today().date()-datetime.timedelta(days=1)\n",
    "start_datetime=datetime.datetime.strptime(str(previous_day)+' 10:00:00','%Y-%m-%d %H:%M:%S')\n",
    "date_range = pd.date_range(start_datetime, freq='60min', periods=24)\n",
    "print(date_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Timestamp('2015-11-04 00:00:00', freq='D'), Timestamp('2015-11-05 00:00:00', freq='D'), Timestamp('2015-11-11 00:00:00', freq='D'), Timestamp('2015-11-12 00:00:00', freq='D'), Timestamp('2015-11-18 00:00:00', freq='D'), Timestamp('2015-11-19 00:00:00', freq='D'), Timestamp('2015-11-25 00:00:00', freq='D'), Timestamp('2015-11-26 00:00:00', freq='D'), Timestamp('2015-12-02 00:00:00', freq='D'), Timestamp('2015-12-03 00:00:00', freq='D'), Timestamp('2015-12-09 00:00:00', freq='D'), Timestamp('2015-12-10 00:00:00', freq='D')]\n"
     ]
    }
   ],
   "source": [
    "date_range=pd.date_range(start=\"2015-11-02\", end=\"2015-12-14\",freq=\"D\")\n",
    "date_range=[x for x in [x if x.dayofweek in [2,3] else None for x in date_range] if x]\n",
    "print(date_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        Value\n",
      "Date                         \n",
      "2014-02-24 16:16:00  1.391424\n",
      "2014-02-24 16:18:00  1.048143\n",
      "2014-02-24 16:19:00 -0.823974\n",
      "2014-02-24 16:22:00 -0.689560\n",
      "2014-02-24 16:24:00 -0.323252\n",
      "2014-02-24 16:26:00 -1.095331\n",
      "2014-02-24 16:29:00 -0.185681\n"
     ]
    }
   ],
   "source": [
    "data=[('2014-02-24 16:16:47.204000',    1.391424)\n",
    ",('2014-02-24 16:18:48.296000',    1.048143)\n",
    ",('2014-02-24 16:19:52.346000',  -0.823974)\n",
    ",('2014-02-24 16:22:13.665000',   -0.689560)\n",
    ",('2014-02-24 16:24:13.760000',   -0.323252)\n",
    ",('2014-02-24 16:26:15.155000',   -1.095331)\n",
    ",('2014-02-24 16:29:58.235000',   -0.185681)]\n",
    "\n",
    "df=pd.DataFrame(data,columns=['Date','Value'])\n",
    "df['Date']=pd.to_datetime(df['Date'])\n",
    "seconds=df.resample('1Min',on='Date').mean().dropna()\n",
    "\n",
    "print(seconds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14. , 13.2])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=[10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "confidence=[5, 4]\n",
    "np.percentile(data,confidence )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3. 4. 5. 6.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [154, 400, 1124, 82, 94, 108]\n",
    "#print (np.percentile(data,[0.5,95])) # gives the 95th percentile\n",
    "\n",
    "bs_data = dcst.draw_bs_reps(data, np.mean, size=6*10)\n",
    "\n",
    "#print(np.reshape(bs_data,(24,6)))\n",
    "\n",
    "x= np.linspace(1,6,6)\n",
    "print(x)\n",
    "for (item1,item2,item3,item4,item5,item6) in bs_data.reshape((10,6)):\n",
    "    line_data=[item1,item2,item3,item4,item5,item6]\n",
    "    ci=np.percentile(line_data,[.025,.975])\n",
    "    mean_avg=np.mean(line_data)\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(x,line_data)\n",
    "    ax.fill_between(x, (line_data-ci[0]), (line_data+ci[1]), color='b', alpha=.1)\n",
    "    ax.axhline(mean_avg,color='red')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.  28.3]\n"
     ]
    }
   ],
   "source": [
    "data = [12, 12, 13, 13, 15, 16, 17, 22, 23, 25, 26, 27, 28, 28, 29]\n",
    "print (np.percentile(data,[5,95])) # gives the 95th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Num\n",
      "year Val     \n",
      "2014 C      2\n",
      "     T      6\n",
      "     V      1\n",
      "2015 D      6\n",
      "     T      4\n",
      "     U      2\n",
      "2016 A      1\n",
      "     B      2\n",
      "     D      4\n",
      "     S      3\n",
      "     T      5\n",
      "          Num\n",
      "year Val     \n",
      "2015 D      6\n",
      "2014 T      6\n",
      "2016 T      5\n",
      "     D      4\n",
      "2015 T      4\n",
      "2016 S      3\n",
      "     B      2\n",
      "2015 U      2\n",
      "2014 C      2\n",
      "2016 A      1\n",
      "2014 V      1\n"
     ]
    }
   ],
   "source": [
    "data = ({\n",
    "'year' : ['2016','2016','2016','2016','2016','2015','2015','2015','2015','2014','2014','2014','2014'],        \n",
    "    'Val' : ['A','B','D','T','S','D','T','T','U','T','T','V','C'],                 \n",
    "    'Num' : [1,2,4,5,3,6,4,3,2,5,6,1,2],                                     \n",
    "    })\n",
    "\n",
    "df = pd.DataFrame(data = data)\n",
    "\n",
    "grouped = df.groupby(['year','Val']).max()\n",
    "print(grouped)\n",
    "print(grouped.transform(pd.Series.sort_values,ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       date     value   p_value  pp_value  ppp_value\n",
      "0 2013-02-22 00:00:00+00:00  0.280001       NaN       NaN        NaN\n",
      "1 2013-02-25 00:00:00+00:00  0.109999  0.280001       NaN        NaN\n",
      "2 2013-02-26 00:00:00+00:00 -0.150000  0.109999  0.280001        NaN\n",
      "3 2013-02-27 00:00:00+00:00  0.130001 -0.150000  0.109999   0.280001\n",
      "4 2013-02-28 00:00:00+00:00  0.139999  0.130001 -0.150000   0.109999\n"
     ]
    }
   ],
   "source": [
    "data=[('2013-02-22 00:00:00+00:00',    0.280001)\n",
    ",('2013-02-25 00:00:00+00:00',    0.109999)\n",
    ",('2013-02-26 00:00:00+00:00',   -0.150000)\n",
    ",('2013-02-27 00:00:00+00:00',    0.130001)\n",
    ",('2013-02-28 00:00:00+00:00',    0.139999)]\n",
    "df=pd.DataFrame(data=data,columns=['date','value'])\n",
    "df['date']=pd.to_datetime(df['date'])\n",
    "\n",
    "df['p_value']=df.value.shift(1)\n",
    "df['pp_value']=df.value.shift(2)\n",
    "df['ppp_value']=df.value.shift(3)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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