{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27324b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from dsutil.plotting import add_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c980166f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1.3.2', '3.4.3')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__, matplotlib.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce10df8a",
   "metadata": {},
   "source": [
    "## Plot date histogram per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c95f349e",
   "metadata": {},
   "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>name</th>\n",
       "      <th>date_of_admission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alice</td>\n",
       "      <td>2021-10-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bob</td>\n",
       "      <td>2022-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>charlie</td>\n",
       "      <td>2022-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>david</td>\n",
       "      <td>2022-04-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>edward</td>\n",
       "      <td>2022-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>felicia</td>\n",
       "      <td>2022-03-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name date_of_admission\n",
       "0    alice        2021-10-28\n",
       "1      bob        2022-01-24\n",
       "2  charlie        2022-03-25\n",
       "3    david        2022-04-11\n",
       "4   edward        2022-03-01\n",
       "5  felicia        2022-03-30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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": [
    "import pandas as pd\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'name':[\n",
    "        'alice', 'bob', 'charlie', 'david', 'edward', 'felicia'\n",
    "    ],\n",
    "    'date_of_admission':[\n",
    "        '2021-10-28', '2022-01-24', '2022-03-25', '2022-04-11', '2022-03-01', '2022-03-30'\n",
    "    ],\n",
    "})\n",
    "\n",
    "\n",
    "display(df)\n",
    "\n",
    "# convert column to datetime\n",
    "df['date_of_admission'] = pd.to_datetime(df['date_of_admission'])\n",
    "\n",
    "# create a representation of the month with strfmt\n",
    "df['year_month_of_admission'] = df['date_of_admission'].map(lambda dt: dt.strftime('%Y-%m'))\n",
    "\n",
    "grouped_df = df.groupby('year_month_of_admission').size().to_frame(\"count\").reset_index()\n",
    "\n",
    "grouped_df.plot(kind='bar', x='year_month_of_admission', y='count', rot=60)\n",
    "add_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73de4c68",
   "metadata": {},
   "source": [
    "## Fill missing days in between"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e71ff689",
   "metadata": {},
   "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>item_id</th>\n",
       "      <th>purchase_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>273</td>\n",
       "      <td>1988-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>231</td>\n",
       "      <td>1988-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133</td>\n",
       "      <td>1988-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>555</td>\n",
       "      <td>1988-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123</td>\n",
       "      <td>1988-12-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>234</td>\n",
       "      <td>1988-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>342</td>\n",
       "      <td>1988-12-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  item_id purchase_date\n",
       "0     273    1988-12-28\n",
       "1     231    1988-12-24\n",
       "2     133    1988-12-25\n",
       "3     555    1988-12-25\n",
       "4     123    1988-12-26\n",
       "5     234    1988-12-28\n",
       "6     342    1988-12-30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1988-12-24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1988-12-25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1988-12-26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1988-12-28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1988-12-30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  purchase_date  count\n",
       "0    1988-12-24      1\n",
       "1    1988-12-25      2\n",
       "2    1988-12-26      1\n",
       "3    1988-12-28      2\n",
       "4    1988-12-30      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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jI4MzzjiD6urqObd1Nk/05wHXAn8TkcfCde8DNgGo6i3AXcBzcZMZDwCvCbd1iMhHcRMqA3wk8WI27kQ95GicGA6itiA+WFx4LC48M70nyMrKIgicYIn3CwmS2+BnZmYyOjo65/bN+ESvqg+oqqjq01X1jPDvLlW9JUzyqOMtqrpVVZ+mqvuTyt+qqtvCv2/M+RkcJ46ke/F8Z3B0Yf9ET8biwrPQ4+LSSy/lhz/8Ie3t7YgIHR0dnHvuudx+++0AfPe73+WCCy4A3NDrjz76KAB33HEHIyMjM37/0qVL6e3tnRNbbQiEFPT29rJy5cqozYgF6xYpNX0L+6JOYHHhiVNcRPGy/7TTTuP9738/F110ERkZGTzjGc/g85//PK95zWv45Cc/Of4yFuANb3gDV111Fbt372bfvn0sWbJkxu+/4YYb2LdvHxs2bDjsZeyREss5Y+PQ6mZwcDD2nSBmw1y0RliRo3QNH9sFPV9a3VhceKKMi6lanURJEARkZKRvRJkjbXVjY92koLa2NmoTYsNZq+P3MBAVFhceiwtPcp17HLFEnwIbodBzT0M8fp7HAYsLj8WFJ12Dmh0tluhTEPWATXHiiiJ7cktgceGJOi7iVO08NDSUtmMdzXlbok9BXMbwiAN311uYJLC48EQZF3l5ebS3t8cm2adjLHrw49Ef6fGs1U0KEuPRGLCvKOBXluwBi4tkooyLoqIi6uvraWtri+T4kxkZGUlb09vEDFNHgiX6FBzJ7C3znQearS42gcWFJ8q4yM7OjpUvhoaGYl1Pb49pKWhsbIzahNiwe1U8fh7HAYsLj8WFJ+5xYYk+BdYpxlO9wAevSsbiwmNx4Yl7XFiiT8HAwEDUJsSGglx7cktgceGxuPDEPS4s0acgnb3c4k5g1/M4FhceiwtP3OMi3tZFSE5OTtQmxIa+EfuJnsDiwmNx4Yl7XFiiT0FPT0/UJsSGDUvs0S2BxYXH4sIT97iwRJ+CNWvWRG1CbHiqy57cElhceCwuPHGPC0v0KbDBqzzPWmNPbgksLjwWF564x8WMHaZE5Fbg+UCrqp4+xfZ3Aa9K+r5TgNXh7FLVQC8wBoymGkIzjlhXd48NgeCxuPBYXHjiHhez8dRtwL5UG1X1k4mZp4D3Ar+bNF3gJeH2EybJA5SWlkZtQmy4ssjmjEtgceGxuPDEPS5mM5Xg74HZzvN6DfD9Y7IoJsT9Dp1O7MnNY3HhsbjwxD0u5sxTIrIY9+T/46TVCvxaRB4VkRvm6ljpIO536HRiT24eiwuPxYUn7nExl7fkFwAPTqq2OV9VnwE8B3iLiFyYqrCI3CAi+0Vkf3NzM319fZSXlzM6OjouYmlpKSMjIxw4cIDe3l4aGhpoa2ujvb2duro6+vv7KS8vZ2xsbEKZ4eFhKisr6enpobGxkdbWVjo6OqitrWVgYICysjKCIJhQZv369VRWVtLd3U1jYyMtLS10dHRQU1PD4OAgpaWlqOqEMocOHaKqqoquri6amppobm6ms7OT6urq8TLgRkBMlBkcHKS6uprOzk6am5tpamqiq6uLqqoqDh06NOH7E8cbHBykpqaGjo4OWlpaaGxspLu7m8rKSoaGhiaUyRDl8o0BBTnKWasDivOVncsDdq8MWLdIuWBdwKJMHb9orywKyBLlsg0Bq3KVvYUBDf2wY1nAGasC1i5SLloXkDepTHaGcumGgMI8ZU9hwNalyrZlAWeuClidp1RUVDA6Ojrh3EdGRqioqKC3t3d8JMKDBw9SV1c3wf+JMiUlJeP+7+npoaGhgdbWVtrb26mtraW/v5+ysrJZ+7+mpoaBgQFKS0sP8//Q0BBVVVV0d3fT1NRES0sLnZ2dZGZmTuv/6upqurq6aG5uprm5ma6uLqqrqw/zZbL/a2pq6OzspKWlhaamJrq7u6mqqmJoaGiCXtP5v6enh8rKSoaHhyfEWRAElJWVMTAwQG1tLR0dHbS2to77/8Ip/J+dMdH/W5Yq20NfrslTLl4fkJup5GboeJmcDOWS9c7XewoDti1zf3sK3bpL1rt9ko+Tm6lHdS0n/D/dtZys22T/z3Qtl5SUpPR/cplkX46MjByR/yf7MpX/k8skYnRgYGDc/62treP+n45ZzRkrIsXAnVO9jE3a5yfAD1X1eym2fxjoU9VPzXS8OMwZW11dTXFxcaQ2zAVzMTfoOWsCHmo9tmeC+TJnrMWFx+LCE4e4OO5zxorIcuAi4GdJ65aIyNLEZ+AK4Im5OF46WLFiRdQmxIa6fmsvncDiwmNx4Yl7XMymeeX3gYuBQhGpBz4EZAOo6i3hbi8Gfq2q/UlF1wI/EZHEcb6nqr+aO9OPL4cOHYrahNiwPFupxy5qsLhIxuLCE/e4mDHRq+o1s9jnNlwzzOR1lcDuozXMiBF2LRtTYXFxwmDto1KQrjkgTwS6h+2KTmBx4bG48MQ9LizRp6CrqytqE2LDJhu8ahyLC4/FhSfucWGJPgXr1q2L2oTY8LdOe3JLYHHhsbjwxD0uLNGnoKamJmoTYsO5a+3JLYHFhcfiwhP3uLBEn4K4d2lOJ9bV3WNx4bG48MQ9LsxTKYh7l+Z0Yl3dPRYXHosLT9zjwhJ9Cnbs2BG1CbHh7nqri01gceGxuPDEPS4s0aegrKwsahNiw5VFVhebwOLCY3HhiXtcWKJPwaZNm6I2ITb8sdWe3BJYXHgsLjxxjwtL9ClobW2N2oTYcMoKe3JLYHHhsbjwxD0uLNGnYNmyZVGbEBsaB+zJLYHFhcfiwhP3uLBEn4Lh4eGoTYgN+Vn25JbA4sJjceGJe1xYok9BEFjTsQSZ9uA2jsWFx+LCE/e4sESfgsWLF0dtQmzoGLIrOoHFhcfiwhP3uLBEn4KOjtnOhz7/2bzUfqInsLjwWFx44h4XluhTsGHDhqhNiA1/abcntwQWFx6LC0/c48ISfQqqq6ujNiE2nL/OntwSWFx4LC48cY+LGRO9iNwqIq0iMuV8ryJysYh0i8hj4d8Hk7btE5FSEakQkZvm0vDjTdwHKUonNniVx+LCY3HhiXtczMZTtwH7Ztjn/1T1jPDvIwAikgl8EXgOcCpwjYiceizGppO4D1KUTmzwKo/FhcfiwhP3uJgx0avq74GjedNwFlChqpWqOgzcDlx1FN8TCdu3b4/ahNhwjw1eNY7FhcfiwhP3uJir317niMhfReSXInJauG4jUJe0T324bkpE5AYR2S8i+5ubm+nr66O8vJzR0dHxu2VpaSkjIyMcOHCA3t5eGhoaaGtro729nbq6Ovr7+ykvL2dsbGxCmeHhYSorK+np6aGxsZHW1lY6Ojqora1lYGCAsrIygiCYUOapp56isrKS7u5uGhsbaWlpoaOjg5qaGgYHByktLUVVJ5Q5dOgQVVVVdHV10dTURHNzM52dnVRXV4+XASgpKRkvMzg4SHV1NZ2dnTQ3N9PU1ERXVxdVVVUcOnRowvcnjjc4OEhNTQ0dHR20tLTQ2NhId3c3lZWVDA0NTSiTIcrlGwMKcpSzVgcU5ys7lwfsXhmwbpFywbqARZk6/nR2ZVFAliiXbQhYlavsLQy4ekvAjmUBZ6wKWLtIuWhdQN6kMtkZyqUbAgrzlD2FAVuXKtuWBZy5KmB1nlJRUcHo6OiEcx8ZGaGiooLe3l7q6+tpa2vj4MGD1NXVTfB/okxJScm4/3t6emhoaKC1tZX29nZqa2vp7++nrKxs1v6vqalhYGCA0tLSw/w/NDREVVUV3d3dNDU10dLSQmdnJ4888si0/q+urqarq4vm5maam5vp6uqiurr6MF8m+7+mpobOzk5aWlpoamqiu7ubqqoqhoaGJug1nf97enqorKxkeHh4QpwFQUBZWRkDAwPU1tbS0dFBa2vruP8vnML/2RkT/b9lqbI99OWaPOXi9QG5mcqbTvFlcjKUS9Y7X+8pDNi2zP3tKXTrLlnv9kk+Tm6mHtW1nPD/dNdysm6T/T/TtVxSUpLS/8llkn350EMPHZH/J/sylf+TyyRidGBgYNz/ra2t4/6fDlGd+YWKiBQDd6rq6VNsWwYEqtonIs8FPqeq20XkamCfqr4+3O9a4GxVvXGm4+3du1f3798/o13Hk4GBgdi3jZ0NxTf94pi/oyBH6TzGiaCrb37eMdsRBywuPBYXnjjEhYg8qqp7p9p2zE/0qtqjqn3h57uAbBEpBBqAk5J2LQrXnRC0tbVFbUJs2LHcWlcksLjwWFx44h4Xx5zoRWSdiEj4+azwO9uBR4DtIrJZRHKAVwB3HOvx0sXSpUujNiE2NA9aXWwCiwuPxYUn7nGRNdMOIvJ94GKgUETqgQ8B2QCqegtwNfBmERkFBoFXqKsPGhWRG4G7gUzgVlV98ricxXFgdHQ0ahNiw6JMBeyiBouLZCwuPHGPixkTvapeM8P2LwBfSLHtLuCuozMtWuLuuHSSkxm1BfHB4sJjceGJe1xYj4cU5OfnR21CbGg7ZE9tCSwuPBYXnrjHhSX6FBw8eDBqE2LDtmX20i2BxYXH4sIT97iwRJ+CoqKiqE2IDY8etCe3BBYXHosLT9zjwhJ9CqqqqqI2ITZcYINXjWNx4bG48MQ9LizRpyDugxSlExu8ymNx4bG48MQ9LsxTKYj7IEXpxAav8lhceCwuPHGPC0v0Kdi2bVvUJsSG3zRYXWwCiwuPxYUn7nFhiT4FBw4ciNqE2HDJequLTWBx4bG48MQ9LizRp2DjxpQDbS44/mxTxo1jceGxuPDEPS4s0aegvb09ahNiw1abBHociwuPxYUn7nFhiT4FS5YsidqE2HBwyJ7cElhceCwuPHGPC0v0KRgbG4vahNiQnWFPbgksLjwWF564x4Ul+hSMjIxEbUJsWGyDV41jceGxuPDEPS4s0acg7uNLp5MmG3d8HIsLj8WFJ+5xYYk+BXGfMSad7LKZhMaxuPBYXHjiHheW6FOwadOmqE2IDX9qsye3BBYXHosLT9zjwhJ9CuLeASKdXGQdY8axuPBYXHjiHhczJnoRuVVEWkXkiRTbXyUij4vI30TkDyKyO2lbdbj+MRHZP5eGH2927doVtQmx4Vc2eNU4FhceiwtP3ONiNp66Ddg3zfYq4CJVfRrwUeCrk7ZfoqpnqOreozMxGkpKSqI2ITbss8GrxrG48FhceOIeF7OZM/b3IlI8zfY/JC3+EYj3CPyzJO6DFKWTexutLjaBxYXH4sIT97iY699erwN+mbSswK9F5FERuWG6giJyg4jsF5H9zc3N9PX1UV5ezujo6PgQoKWlpYyMjHDgwAF6e3tpaGigra2N9vZ26urq6O/vp7y8nLGxsQllhoeHqayspKenh8bGRlpbW+no6KC2tpaBgQHKysoIgmBCmfLyciorK+nu7qaxsZGWlhY6OjqoqalhcHCQ0tJSVHVCmUOHDlFVVUVXVxdNTU00NzfT2dlJdXX1eBnwd//S0lIGBweprq6ms7OT5uZmmpqa6OrqoqqqikOHDk34/sTxBgcHqampoaOjg5aWFhobG+nu7qayspKhoaEJZTJEuXxjQEGOctbqgOJ8ZefygN0rA9YtUi5YF7AoU8eHnL2yKCBLlMs2BKzKVfYWBlx1csCOZQFnrApYu0i5aF1A3qQy2RnKpRsCCvOUPYUBW5cq25YFnLkqYHWeUlFRwejo6IRzHxkZoaKigt7eXurr62lra+PgwYPU1dVN8H+iTElJybj/e3p6aGhooLW1lfb2dmpra+nv76esrGzW/q+pqWFgYIDS0tLD/D80NERVVRXd3d00NTXR0tJCZ2cnf/7zn6f1f3V1NV1dXTQ3N9Pc3ExXVxfV1dWH+TLZ/zU1NXR2dtLS0kJTUxPd3d1UVVUxNDQ0Qa/p/N/T00NlZSXDw8MT4iwIAsrKyhgYGKC2tpaOjg5aW1vH/X/hFP7Pzpjo/y1Lle2hL9fkKRevD8jNVF6305fJyVAuWe98vacwYNsy97en0K27ZL3bJ/k4uZl6VNdywv/TXcvJuk32/0zXcklJSUr/J5dJ9uWf/vSnI/L/ZF+m8n9ymUSMDgwMjPu/tbV13P/T5lfVmV+ohE/0d6rq6dPscwnwJeB8VW0P121U1QYRWQPcA7xVVX8/0/H27t2r+/dHW6Xf19cX+wl/Z0PxTb845u8ozFMOHuNE0NU3P++Y7YgDFhceiwtPHOJCRB5NVUU+J0/0IvJ04OvAVYkkD6CqDeH/VuAnwFlzcbx00NnZGbUJseHkfGtdkcDiwmNx4Yl7XBxzoheRTcD/AteqalnS+iUisjTxGbgCmLLlThxZtGhR1CbEhi4bvGociwuPxYUn7nEx48tYEfk+cDFQKCL1wIeAbABVvQX4ILAK+JKIAIyGPx/WAj8J12UB31PVXx2HczguzKZKa6EgooBd1GBxkYzFhSfucTGbVjfXzLD99cDrp1hfCew+vMSJwdDQUNQmxIal2VFbEB8sLjwWF564x4X1eEjB8uXLozYhNjT021NbAosLj8WFJ+5xYYk+BS0tLVGbEBtOLYj3z9J0YnHhsbjwxD0uLNGn4OSTT47ahNjwUIs9uSWwuPBYXHjiHheW6FNQWVkZtQmx4eIN9uSWwOLCY3HhiXtcWKJPwc6dO6M2ITbcbYNXjWNx4bG48MQ9LsxTKYj7IEXpxAav8lhceCwuPHGPC0v0KYj7IEXp5D4bvGociwuPxYUn7nFhiT4F1dXVUZsQG85ba3WxCSwuPBYXnrjHhSX6FKxbty5qE2LDE5325JbA4sJjceGJe1xYok9BV1dX1CbEhqIl9uSWwOLCY3HhiXtcWKJPQV5eXtQmxIbekagtiA8WFx6LC0/c48ISvTEjqvYT3Tgci4sTB0v0KTh06FDUJsSGFbn2Ez2BxYXH4sIT97iwRJ+CFStWRG1CbKjpsye3BBYXHosLT9zjwhJ9CpqamqI2ITY8faU9uSWwuPBYXHjiHheW6FOwefPmqE2IDf/XbE9uCSwuPBYXnrjHhSX6FBw4cCBqE2LDpTZ41TgWFx6LC0/c42JWiV5EbhWRVhGZcs5XcfyXiFSIyOMi8oykbdeJSHn4d91cGX68ifsgRenEBq/yWFx4LC48cY+L2XrqNmDfNNufA2wP/24AvgwgIitxc8yeDZwFfEhECo7W2HRSWloatQmx4UobvGociwuPxYUn7nExq0Svqr8HOqbZ5SrgW+r4I7BCRNYDVwL3qGqHqnYC9zD9DSM2bNmyJWoTYsPvmqwuNoHFhcfiwhP3uJhxcvBZshGoS1quD9elWn8YInID7tcAGzdupK+vj6amJjZv3syBAwfYuXMnpaWlbNmyhdraWtasWUNPTw85OTlkZGQwMDDAypUraWxs5Dm3lnLZRuXu+gyuLAr4fZOwd7VS3i2sXaQcGoOhMWHNIrdu72rl3kbh8qQyQaAMjgmVvUJhnjIcwOCosH6xUtIlnLVauadBuKLIl/lDi/D0lUp1r1CQq3z9umeSm5tLd3c3a9eupba2lp07d1JSUsKuXbsoLS1l06ZNtLS0sHz5coaGhlBVFi1aRGdnJ+vXr6empmb83Hfs2EFZWRmbNm2itbWVpUuXMjIywtjYGEuWLKG9vZ2NGzdSXV09XiZDlMs2KPvbhO3LldZBITdTycuElkG3bn+bcOF6fx73NggXrVceaxc2L1U2LVGe6BQWZ0PTgLBrufJwm9snUea+RuGCdcrjHcLJ+UrXkCCiLM2G+n6hoqKC4uJiKioqxs99y5Yt1NTUsHbtWrq7u8nNzUVEGBwcpKCgYNz/iTIlJSVs3bqV2tpaVq9eTW9vL9nZ2WRmZtLf38+qVatoaGhg69atVFRUjGuwefNm6uvredV3/s7qPGV4DAbHhHWLlLKE/xuEy5N8+UCzcOYqpapXWJmrjCn0jQqXrAv4aW0Gz1qj3F0vXDnJ/08rUGr7heU5CgrdI8JJS5QnO4Vz1rp9737NtnHbEr5ctmwZw8PDBEHA4sWL6ejoYMOGDVRVVY3rNZ3/8/PzOXjwIEVFRVRVVY3H2Y4dO6ioqKCoqIiDBw+Sn5/P6Ogou1cGtAwKO5Yrj0zy/32NwoXrvP87hoRMUfKz3TyxpxYoD7UK120P+EpJJlcWBdzfKJy31sXJpnyle9hd18tzoLZPOL1AebBFuGSDP85vm4QDBw5Mey1v2bLlMF/W1dWxevVq+vr6yMrKIisri76+PgoLC6mvr2fbtm2UlZWN65bwf2FhIX19fWRmZpKdnU1vby9r1qzh7V//zYRreV/o/92r/LUcKPSNCBuWKE91Seh/dx5312fwxl1jfP9ABqcVKHX9wvJsBYHuYWHTEuVvncK5a5PLuNj5Y6twygqlcUD44RueeZj/k6/l7du3U15ezkknnURbWxtLly5ldHSU0dFR8vPzp03Qojq7FyoiUgzcqaqnT7HtTuBmVX0gXL4XeA9wMZCnqh8L138AGFTVT013rL179+r+/ftnZddUFN/0i6Mum2DtIqVl8NieWKpvft4x23GsmBYe08JjWnjmixYi8qiq7p1q21y9TWkATkpaLgrXpVofe9YvthYFCUwLj2nhMS08cddirhL9HcA/hq1vngV0q2oTcDdwhYgUhC9hrwjXxZ4BG7BpHNPCY1p4TAtP3LWYVR29iHwfVw1TKCL1uJY02QCqegtwF/BcoAIYAF4TbusQkY8Cj4Rf9RFVne6lbmwYtQGbxjEtPKaFx7TwxF2LWSV6Vb1mhu0KvCXFtluBW4/ctGhZmatU9sbbeenCtPCYFh7TwhN3LazHQwqqYuy0dGNaeEwLj2nhibsWluhTcMaqeL9cSSemhce08JgWnrhrYYk+BdYZxGNaeEwLj2nhibsWluhTcNnGeN+h04lp4TEtPKaFJ+5aWKJPgQ3Y5DEtPKaFx7TwxF2LeFsXITZgk8e08JgWHtPCE3ctLNGn4Pcxr3NLJ6aFx7TwmBaeuGthiT4Fe1fHu84tnZgWHtPCY1p44q6FJfoUlHfH+w6dTkwLj2nhMS08cdfCEn0K1i6K9x06nZgWHtPCY1p44q6FJfoUHBqL2oL4YFp4TAuPaeGJuxaW6FMwNBbvn2LpxLTwmBYe08ITdy0s0adgTcx/iqUT08JjWnhMC0/ctbBEn4K4v1xJJ6aFx7TwmBaeuGthiT4FcW8ulU5MC49p4TEtPHHXwhJ9Cu5tjPcdOp2YFh7TwmNaeOKuhSX6FFwe80GK0olp4TEtPKaFJ+5azCrRi8g+ESkVkQoRuWmK7Z8RkcfCvzIR6UraNpa07Y45tP24EvdBitKJaeExLTymhSfuWsxonYhkAl8EngOcClwjIqcm76Oq/6yqZ6jqGcDngf9N2jyY2KaqL5w7048vcR+kKJ2YFh7TwmNaeOKuxWxuQ2cBFapaqarDwO3AVdPsfw3w/bkwLkoeaI53nVs6MS08poXHtPDEXYvZJPqNQF3Scn247jBE5GRgM3Bf0uo8EdkvIn8UkRelOoiI3BDut7+5uZm+vj7Ky8sZHR2ltLQUgNLSUkZGRjhw4AC9vb00NDTQ1tZGe3s7dXV19Pf3U15eTpbo+B32yqKARZnKBesC1i1Sdq8M2Lk8oDhfOWt1QEGOcvnGgIxJZc5eHXD+2oANi5WnrwzYtSLg5HzlWWsCVuQoV2wMECaWWZqtnLc2YONi5fSCgObmZjo7O6murmZwcHD8PEpKSsbPZ3BwkOrqajo7O2lubqapqYmuri6qqqo4dOjQhHNX1fEyNTU1dHR00NLSQmNjI93d3VRWVjI0NDShTIa48yvIcedbnK/sXB6we6XT44J1Tp/k88gS5bINAatylb2FAc/eELBjWcAZqwLWLlIuWheQN6lMdoZy6YaAwjxlT2HA1qXKtmUBZ64KWJ2nVFRUMDo6OuHcR0ZGqKiooLe3l/r6etra2jh48CB1dXUT/J8oU1JSMu7/np4eGhoaaG1tpb29ndraWvr7+ykrK2NsbGyCBsPDw1RWVrI+4cvlzpdnJ/t/ki+XZCnnh758WkHAqSsCNuUr/1AcsDwnse/h/j93TUDREuW0goDTVrjP56wJWJbt9022LeHLzs5OWlpaaGpqoru7m6qqKoaGhiboNZ3/e3p6qKysZHh4eEKcBUFAWVkZAwMD1NbW0tHRQWtr67j/L5zC/9kZE/2/ZamyPfTlmjzl4vUBuZnKK7f6MjkZyiXrna/3FAZsW+b+9hS6dZesd/skHyc3U2e8lqfyZcL/jY2NtLa20tHRQW1tLQMDA5SVlREEwQTdEv5PlGlpaaGjo4OamhoGBwcPu5b3FQXkZ028lk9dEbBpibv+vf/9k/wrtzofn5Pw/4qA0wrc53PXuNiYWMYtL89x37kpX6f0f/K5B0FAaWkpAwMD4/5vbW0d9/90iOr0LxFE5Gpgn6q+Ply+FjhbVW+cYt/3AEWq+takdRtVtUFEtuBuAJep6oHpjrl3717dv3//tHZNR/FNvzjqsgk2LFYaB47tLl198/OO2Y5jxbTwmBYe08IzX7QQkUdVde9U22bzRN8AnJS0XBSum4pXMKnaRlUbwv+VwG+BM2dxzMgpzIv3W/R0Ylp4TAuPaeGJuxazSfSPANtFZLOI5OCS+WGtZ0RkF1AAPJS0rkBEcsPPhcB5wN/nwvDjzXC8362kFdPCY1p4TAtP3LXImmkHVR0VkRuBu4FM4FZVfVJEPgLsV9VE0n8FcLtOrAs6BfiKiAS4m8rNqnpCJPrB0Xi/XEknpoXHtPCYFp64azFjogdQ1buAuyat++Ck5Q9PUe4PwNOOwb7IWL9YqemLt/PShWnhMS08poUn7lrEu5V/hJR0xddp6ca08JgWHtPCE3ctLNGn4KyYD1KUTkwLj2nhMS08cdfCEn0K7mmI9x06nZgWHtPCY1p44q6FJfoUXFEU7zt0OjEtPKaFx7TwxF0LS/QpiPsgRenEtPCYFh7TwhN3LeJtXYTEfZCidGJaeEwLj2nhibsWluhT8IeWeNe5pRPTwmNaeEwLT9y1sESfgqevjHedWzoxLTymhce08MRdC0v0KajujfcdOp2YFh7TwmNaeOKuhSX6FBTkxvsOnU5MC49p4TEtPHHXwhJ9CoJ4+y2tmBYe08JjWnjiroUl+hT0jsT7p1g6MS08poXHtPDEXQtL9CkoWhLzW3QaMS08poXHtPDEXQtL9Cl4sjPed+h0Ylp4TAuPaeGJuxaW6FNwztp436HTiWnhMS08poUn7lpYok9B3Ls0pxPTwmNaeEwLT9y1iLd1EbIv5l2a04lp4TEtPKaFJ+5azCrRi8g+ESkVkQoRuWmK7deLSJuIPBb+vT5p23UiUh7+XTeXxh9PfhXzO3Q6MS08poXHtPDEXYsZrRORTOCLwHOAU4FrROTUKXb9gaqeEf59PSy7EvgQcDZwFvAhESmYM+uPI3EfpCidmBYe08JjWnjirsVsbkNnARWqWqmqw8DtwFWz/P4rgXtUtUNVO4F7gH1HZ2p6eSjmgxSlE9PCY1p4TAtP3LWYTaLfCNQlLdeH6ybzEhF5XER+JCInHWFZROQGEdkvIvubm5vp6+ujvLyc0dFRSktLASgtLWVkZIQDBw7Q29tLQ0MDbW1ttLe3U1dXR39/P+Xl5WSJjt9hrywKWJSpXLAuYN0iZffKgJ3LA4rzlbNWBxTkKJdvDMiYVObMVQHnrw3YsFh5+sqAXSsCTs5XnrUmYEWOcsXGAGFimaXZynlrAzYuVk4vCGhubqazs5Pq6moGBwfHz6OkpGT8fAYHB6murqazs5Pm5maampro6uqiqqqKQ4cOTTh3VR0vU1NTQ0dHBy0tLTQ2NtLd3U1lZSVDQ0MTymSIO7+CHHe+xfnKzuUBu1c6PS5Y5/RJPo8sUS7bELAqV9lbGHDR+oAdywLOWBWwdpFy0bqAvEllsjOUSzcEFOYpewoDti5Vti1zOq7OUyoqKhgdHZ1w7iMjI1RUVNDb20t9fT1tbW0cPHiQurq6Cf5PlCkpKRn3f09PDw0NDbS2ttLe3k5tbS39/f2UlZUxNjY2QYPh4WEqKytZn/DlcufLs5P9P8mXS7KU80NfPq0g4NQVAZvylRdsCliek9j3cP+fuyagaIlyWkHAaSvc53PWBCzL9vsm25bwZWdnJy0tLTQ1NdHd3U1VVRVDQ0MT9JrO/z09PVRWVjI8PDwhzoIgoKysjIGBAWpra+no6KC1tXXc/xdO4f/sjIn+37JU2R76ck2ecvH6gNxM5WVbfJmcDOWS9c7XewoDti1zf3sK3bpL1rt9ko+Tm6kzXstT+TLh/8bGRlpbW+no6KC2tpaBgQHKysoIgmCCbgn/J8q0tLTQ0dFBTU0Ng4ODh13L+4oC8rMmXsunrgjYtMRd/97//kn+ZVucj89J+H9FwGkF7vO5a1xsTCzjlpfnuO/clK9T+j/53IMgoLS0lIGBgXH/t7a2jvt/OkR1+mZBInI1sE9VXx8uXwucrao3Ju2zCuhT1SEReSPwclW9VETeCeSp6sfC/T4ADKrqp6Y75t69e3X//v3T2jUdxTf94qjLJjhpiVLXf2x36eqbn3fMdhwrpoXHtPCYFp75ooWIPKqqe6faNpsn+gbgpKTlonDdOKrarqpD4eLXgT2zLRtXlmbHu11sOjEtPKaFx7TwxF2L2ST6R4DtIrJZRHKAVwB3JO8gIuuTFl8IPBV+vhu4QkQKwpewV4TrYk9GvKvc0opp4TEtPKaFJ+5aZM20g6qOisiNuASdCdyqqk+KyEeA/ap6B/A2EXkhMAp0ANeHZTtE5KO4mwXAR1S14zicx5zTORRzz6UR08JjWnhMC0/ctZgx0QOo6l3AXZPWfTDp83uB96Yoeytw6zHYGAnFS5WGgXg7L12YFh7TwmNaeOKuRbxb+UfI4x3xdVq6MS08poXHtPDEXQtL9Ck4N+aDFKUT08JjWnhMC0/ctbBEn4K4D1KUTkwLj2nhMS08cdci3tZFSNy7NKcT08JjWnhMC0/ctbBEn4Jf18e7zi2dmBYe08JjWnjiroUl+hRcvjHedW7pxLTwmBYe08ITdy0s0afgT23xvkOnE9PCY1p4TAtP3LWwRJ+CXSvifYdOJ6aFx7TwmBaeuGthiT4FTTHu/JBuTAuPaeExLTxx18ISfQoWZcX7Dp1OTAuPaeExLTxx18ISfQpyTJlxTAuPaeExLTxx1yLm5kXHwUPx/imWTkwLj2nhMS08cdfCEn0KtiyN90+xdGJaeEwLj2nhibsWluhT8Jf2eN+h04lp4TEtPKaFJ+5aWKJPwfnr4n2HTiemhce08JgWnrhrYYk+BXEfpCidmBYe08JjWnjirkW8rYuQuA9SlE5MC49p4TEtPHHXYlaJXkT2iUipiFSIyE1TbP8XEfm7iDwuIveKyMlJ28ZE5LHw747JZePKPQ3xrnNLJ6aFx7TwmBaeuGsxY6IXkUzgi8BzgFOBa0Tk1Em7/QXYq6pPB34EfCJp26CqnhH+vXCO7D7uXLYh3nVu6cS08JgWHtPCE3ctZvNEfxZQoaqVqjoM3A5clbyDqt6vqgPh4h+Bork1M/3sj/kgRenEtPCYFh7TwhN3LWaT6DcCdUnL9eG6VLwO+GXScp6I7BeRP4rIi1IVEpEbwv32Nzc309fXR3l5OaOjo5SWlgJQWlrKyMgIBw4coLe3l4aGBtra2mhvb6euro7+/n7Ky8vJEh2vM7uyKGBRpnLBuoB1i5TdKwN2Lg8ozlfOWh1QkKNcvjEgY1KZ0wsCzl8bsGGx8vSVAbtWBJycrzxrTcCKHOWKjQHCxDJLs5Xz1gZsXKycXhDQ3NxMZ2cn1dXVDA4Ojp9HSUnJ+PkMDg5SXV1NZ2cnzc3NNDU10dXVRVVVFYcOHZpw7qo6XqampoaOjg5aWlpobGyku7ubyspKhoaGJpTJEHd+BTnufIvzlZ3LA3avdHpcsM7pk3weWaJctiFgVa6ytzDg3LUBO5YFnLEqYO0i5aJ1AXmTymRnKJduCCjMU/YUBmxdqmxbFnDmqoDVeUpFRQWjo6MTzn1kZISKigp6e3upr6+nra2NgwcPUldXN8H/iTIlJSXj/u/p6aGhoYHW1lba29upra2lv7+fsrIyxsbGJmgwPDxMZWUl6xO+XO58eXay/yf5ckmWcn7oy6cVBJy6ImBTvrLvpIDlOYl9D/f/uWsCipYopxUEnLbCfT5nTcCybL9vsm0JX3Z2dtLS0kJTUxPd3d1UVVUxNDQ0Qa/p/N/T00NlZSXDw8MT4iwIAsrKyhgYGKC2tpaOjg5aW1vH/X/hFP7Pzpjo/y1Lle2hL9fkKRevD8jNVF5c7MvkZCiXrHe+3lMYsG2Z+9tT6NZdst7tk3yc3Eyd8VqeypcJ/zc2NtLa2kpHRwe1tbUMDAxQVlZGEAQTdEv4P1GmpaWFjo4OampqGBwcPOxa3lcUkJ818Vo+dUXApiXu+vf+93XzLy52Pj4n4f8VAacVuM/nrnGxMbGMW16e475zU75O6f/kcw+CgNLSUgYGBsb939raOu7/6RDV6X9yiMjVwD5VfX24fC1wtqreOMW+rwZuBC5S1aFw3UZVbRCRLcB9wGWqemC6Y+7du1f3798/rV3TUXzTL4667Ph35CvVfcd2l66++XnHbMexYlp4TAuPaeGZL1qIyKOquneqbbN5om8ATkpaLgrXTT7Is4H3Ay9MJHkAVW0I/1cCvwXOnLXlEZKbGe86t3RiWnhMC49p4Ym7FrNJ9I8A20Vks4jkAK8AJrSeEZEzga/gknxr0voCEckNPxcC5wF/nyvjjyd5mVFbEB9MC49p4TEtPHHXImumHVR1VERuBO4GMoFbVfVJEfkIsF9V7wA+CeQDPxQRgNqwhc0pwFdEJMDdVG5W1RMi0bcMxvvlSjoxLTymhce08MRdixkTPYCq3gXcNWndB5M+PztFuT8ATzsWA6Ni+3KlOebOSxemhce08JgWnrhrYT1jUxD35lLpxLTwmBYe08ITdy0s0afgwvXxfrmSTkwLj2nhMS08cdfCEn0K4j5IUToxLTymhce08MRdi3hbFyFxH6QonZgWHtPCY1p44q6FJfoU3BvzQYrSiWnhMS08poUn7lpYok/BRTGvc0snpoXHtPCYFp64a2GJPgWPxXxqsHRiWnhMC49p4Ym7FpboU7A55pP9phPTwmNaeEwLT9y1sESfgo6heN+h04lp4TEtPKaFJ+5aWKJPQZbE+w6dTkwLj2nhMS08cdfCEn0KFmdHbUF8MC08poXHtPDEXQtL9CloGoj3T7F0Ylp4TAuPaeGJuxaW6FOwa3m8f4qlE9PCY1p4TAtP3LWwRJ+Ch2M+SFE6MS08poXHtPDEXQtL9CmIeweIdGJaeEwLj2nhibsWluhTEPdBitKJaeExLTymhSfuWsTbugiJ+yBF6cS08JgWHtPCE3ctZpXoRWSfiJSKSIWI3DTF9lwR+UG4/WERKU7a9t5wfamIXDmHth9X7muMd51bOjEtPKaFx7TwxF2LGRO9iGQCXwSeA5wKXCMip07a7XVAp6puAz4D/EdY9lTcZOKnAfuAL4XfF3suWBfvOrd0Ylp4TAuPaeGJuxazeaI/C6hQ1UpVHQZuB66atM9VwDfDzz8CLhM3S/hVwO2qOqSqVUBF+H2x5/GOeN+h04lp4TEtPKaFJ+5azGZy8I1AXdJyPXB2qn1UdVREuoFV4fo/Tiq7caqDiMgNwA3hYp+IlM7CtuNGDRQCB4/lO+Q/5siYiDEtPKaFx7TwxESLk1NtmE2iTwuq+lXgq1HbkUBE9qvq3qjtiAOmhce08JgWnrhrMZuqmwbgpKTlonDdlPuISBawHGifZVnDMAzjODKbRP8IsF1ENotIDu7l6h2T9rkDuC78fDVwn6pquP4VYauczcB24E9zY7phGIYxG2asugnr3G8E7gYygVtV9UkR+QiwX1XvAP4b+LaIVAAduJsB4X7/A/wdGAXeoqpjx+lc5prYVCPFANPCY1p4TAtPrLUQ9+BtGIZhzFesZ6xhGMY8xxK9YRhGTBGROcnRlujTzFw5bj4QdqozMC2SsWvEo6pzMoiOCZomwmanc+a4ExkRWRp+PCGGwziemBYeu0YcInKViHxQRO4RkZeKyNpjvfnZy9g0ICIvAy7E9Sj+OvBX4O+q2hOpYREgIq8Cngvswg2XUQX8TlWbIjUsAkwLj10jDhFZhBsq5k3AauCVQCPwHVX99VF/ryX640vouBrghcAG4Nm4X1IPAz9S1V4REV0Ajgi1qAUuBtYAz8Bp0onTosS0WLBa2DUCiMjzgDep6guS1r0ZeCPwK1U9bPTgWX3vAtAuUkTkWcC/qurzk9a9BNfXoFlV3xqZcWlGRHYAn1DVFyWtuwB4PrACePNC+dluWnjsGvGIyApcm/w7gJ+rane4fhPwOeDdqlp+pN9rdfTHn78BwyLyIRE5GUBVfwy8GdgmIudEal16qcFp8Q0R2Q2gqv8HfBT3JPfCKI1LM6aFx66REFXtwnVAfR5wtYgUi8gKVa0FsoGj0sIS/XFGVfuBd+J+nr9cRC4SkfWqehAYBJ4WqYFpRFWHgFfj6qKvEZHrRGSLqvYBA0wcF2leY1p47BqBMKHvFpGrgf8D/h1XhfVB4L0icgturLBvH9X3W9XN8SH8aV4EnIl7ubQeeA2QD6zE3WS3AXvne92jiJwOPB03F8GXgCXAubhk9iygB1gLPMu0WFBa2DUSIiIPASXh4hXAJ1X1syJyPi42FgGPqeqfj+r757l+kSEijwP34MapvhL4T+AruHH61+LG5f+rqlZEZmSaEJFy3IVcDOwFfhb+teF+ju4AnlTV5qhsTBemhceuEYeIvA54qaruC5d34XRYDrxOVR895oOoqv3N8R/wcuDupOWtwI+BMuCKqO1Lsxb7cK0FEssn4aabfBC4Jmr7TIvItLBrxJ/7ucAtQM6k9W/A/erLPNZjWB398eEJoFNEThORTFU9oKovwdW3vVFECiK2L508BaiIvFBEFqtqnar+M/Bx4C0isiVi+9KJaeGxa8Tzd2AZ8NVEB7qwOenXgM24/gXHhCX640Mp7snk7bg6RkQkS1Vvx2l+QYS2pRVVrQG+j6t3PFdECkUkR1XvBCpx9dILAtNiAnaNhKhrafN6IABqROS9wGYRuQz3S+dvx3oMq6M/TohIHvCvwIuAH+AmXFkB/Btwjqq2RWZcmgknrHkNTotHgFZAgXfgtGiJzrr0Ylp47BqB8NfMWNLyGbgmtgPAIeBhVf3SMR/HEv3cIiIZqhqIyBJV7ReRU4AbcROv5AAPqeq3orUyPSRpsQY3cfLJwMsAwb1we0BVfxaljenCtPDYNXI4IvJJ4HOqWh8ur53Lm74l+uNEOAPXN1X1QLi8WFUHIjYrEkTkJuAOVf171LZEjWnhWcjXiIisB7qBbFXtFpG9qro/rL4aTdwE5+x4lujnhrBJVB+wWFXLRGSzqlYlOW6Tut5t856wS/sorj3074GlYTAntHiGqv55IYxfYlp47BpxiMh7cD1cTwH+FziAG8yuPNz+QlycfHfOjjnPYystiMj7cG/GVwAP4Xrz3aGqfwy3XwqcrKrfiMzINCEiH8AN1NWJaxteAHxbVX8Rbj8bOENVvxKZkWnCtPDYNeIQkULgSdxL1rW4+NgJDAM/VNW/ish2YEBVG+bsuJbojw0RWYVz3GZcE6mn4UYiLMa1E/6ZiJwEDKlqa2SGpoGwSdzjwOnACK7DywW4zjAPAV/EXegjYUuDeYtp4bFrxBM2of0v4JUaDsEc9pZ+KU6T61S1Y66PmzXXX7gAUeABYFX4IqVFRJ7CjVPxGhH5s6rWRWph+ugH7sY9mT0OlItII+6n6RuBe1T1ySgNTCOmhceukRBVrRSRA8AXReS/VfW3qvoE8ISI3IrT5H/m+rjWjv4YCe++ZcB3ReTl4boGVf0mboTCq6O0L52o6jBuvI4fi8jbw3X9qvq7cP11IgtjyjzTwmPXyERU9e24X3vPE5G3he9xwLXEWnE8jmmJ/hgQkUwAVX0frqvyeSLyWRHZF+5yGjAUlX3pREQWA6jqp3CjMp4mIneIyMtFJBu4CKib7y8cwbRIxq4Rh4g8W0TeJSI/C99H/BLXEWoVcLOI/A7oVdWvHpfjL4BYOy6ImwnmYtzb8y8DDUAerh7yNbimU+2q+oqobEwXIvIi4CrclHg/xg29W4Wrg31P+LlfVV8XkYlpw7Tw2DXiEDcXbhXO/2uBfwD+AnwPN2XiIVwLnDpV7T0uNliiP3JCx9UD/4h7yXY+bnjZh3EvlzrFzQjTpKoj0Vl6/Am1aMRNlLEG94S2Ctcp6Fuq2igiq1S1PUIz04Jp4bFrxBPe8N6s4Qxa4a+6f8LNB/sjVf23426DJfojR9yMN+9PclwWbtqzFwEHVPU9EZqXVkRkJ/AfGk6JJ262+ktxrUsWqeqNEZqXVkwLj10jnrDV0VdxE8D/Iqm1zanAJ4G363Eeitnq6I+OJ4BARP5VRDaq6qiqfgc3S85ZSS9XFgI1uBEZvyYiu1Q1UNXf4AJ4Z9j5Y6FgWnjsGmF8FMp24HbcDf/5IlIkIkvD3tHDuGGKjyuW6I+CsB7tQ8Bq4GUicm74k7waaAZ2R2lfOlHVQ7hxs5uA60XkFSKyIWwP3YSrj10QmBYeu0YciRfuqvpD4Ie48Y1uAt4pIv+Jm2jm9uNth1XdHAMisgfXqgLc1GcBbtagvermBF0wiEgR7iXTSbiOH+24ID7btFjQWizYayTRfFZVVdzAbXW4878ayMR1HvuTqj503G2xRD97JjnuLNyTSSvuRVOAu7D3L4SOMJO0uAyXzJ7EjS2uuG7df1kgY5eYFiF2jUyNiLwb9+K1MorjW8/YI2BSu+fdQGf4c/03EZkUKUl6FAANYeuJp8J1JVOXmp+YFg67RsbnHHgBbjC7PFwv6E+E2yS8CV6KG8hsbJqvmjub7Il+ZsRNkHBduLgc+I6qNobbEmNrX4MblGg0KjvTQdgZ6J9wE1mvAT6lqlXhtoQW/wR8cQE0mzMtQuwa8YjIl4ENuIHsOnFt5H8OfDVM8ucDWzSNY+7by9jZ8WncG/PluBnr7xaRDyUF8OlA1nwP4JBP4p7U/oobcvbPIvKFJC2KgIPzPbGFmBYeu0YYH2f+clW9SlVfD3wWN/H3XuB9IpILPIqbUSt9dtkT/fSIyGrgt8AFqtoR1kHuBt6F67r9XqAD96t1vgdxAW6grmtVtTRctw43EmMhbrCuUjjsJ/y8w7Tw2DXiCYd8+CrwE3VzARMm9zOAdwOfVtUHE1U46bLLnuhnQN28lT/B1bmhjsdwc3wOAXvCJ7a01LVFiap24rptvyBpXbOqvgS4H9eSQud7YgPTIhm7Rjxhnfs9wMdF5JMislpVh1T1Ydzw1C8N90trXFiinx33Ax8Wke+LyAZwFzXwZ+DacHneX9Ah9wJXi8gvElqE/B3fjG6hYFp4Fvw1IiKrw6qq23FNKIeAH4jIB8RNJvJqXLVN2rFWN9MgIptx43P8FjgV19HhARH5CfAr4C3ApyIzMI2IyG5cK4JeVX2WiHwY+JuIfAtXRXEDrp523mNaeOwacYjIW3EDuJ0qIj8CqoE7gDtxVTYnAz9W1W9HYt88v8keNSLyz8AVuE4ef8K9Qf8f3IQSH8IN2NSpqp+LzMg0ISLvAp6Du6A7cT/BPw50Af8Pp80hVb0tIhPThmnhsWvEEb6vKcXNJrYWN5PYybjpEr+tqgdEJC9sZhqNjZboD0dEVuAcdwpuUudTgT3AJtwohA+KSGa62sBGiYgsw7UHPxM3Lsd64DLgclwHkEieUKLAtPDYNeIRka3AZ4AXJ8437BH8D7jhqq9T1b4ITbSqmxRkAb8HUNebsVZESnAjEf6LiFSoakuUBqaRYdwkCUtV9QDQJSL1uHro14vIw6paFqmF6cO08Ng14qnEDU99m4h8SVUfUtVHgUdF5HvAPtzIlZFhL2OnQFUP4kYi/F8RSbQkqFbVW3E/T18epX3pJPy5WQX8UkReE67rVdX7cPOfvibR7X2+Y1p47BrxhC+Z340b9uIlIvKm8D0OuH4FayIzLsQSfQpU9Z3AbcALROQ/wt5sAEXAgriYE6ibGOFG4EoR+R8ReU646Uxch6AFU/9nWnjsGvGoG2P+u7hkXwR8TkTuxXUS+1KkxmF19FOSNB7FctwF/EzgGtyTypCqLphxxZM7doQtLC7HJboSINB5Pg1cMqaFx64Rj4hM6PEbNi/txQ1qV6WqXVHZlsAS/RRM7rUmIjmqOhy+dGmI8u15XBCRNbgn2CBqW6JmIWoxRXJbcNeIiBSGVViJ5Yy4xoBV3YSIyGZxU36R9NSW0GckXH9ggQTws0Xk5EnrMsP/GQCq2hrXoJ5LROTNSVUSk7dlwoLSYqOIXACQSPIJDVhA10iow+3Ap0Vkv4i8GEDdmD6ZcXxPY4keEJG1wGO4lhPnhU3HSFy8C6neVdxAXB/BtYUeR1XHwgBeSFqcBLwN10Z+fKz1pJvdvG86mCDU4nbgMyJSLSKF4DVYSNcI8Anci+i3Ap8DPiAiPxORrao6FkctLNE7DuJGINwLvAr35nyXiFwvbmzphcS/44aSPSgixSLyfBH5uoicEY5hErsgPo68Hdeb8QkROQ34oIjcCbxO3EBeC4kPA3eo6l7cyIv/ICI/EJHnRWtWegn7UiwF7lPVblX9tqo+A5c/7hORfdFaODWW6Bl/Kvkq7qn+TtxIc98D3ozrDLIgEJEduGZxveGqT+B6+Q3jmhS+LSrbIuKPuIsa4D9xLxp/jusk9a6ojEo3Yc/PjbjrA+DFQDHwN+BDIvKRaCxLP2HrmttwLY3WJK3/IK6J5TMiMm1aFnyiT6pPuws35dsTuDGkNwINwLtFZFM01qWdg8D1wF4ReQrYqKrvUdX/h+vltz3pvcVC4H5grYj8C64r/5dU9SvAm4CdIrIxWvPSg7qROn+I+yXzPWBAVd+nqh/DDd61K7wZLBR+hZvz9XYReaGIJB4GFXhudGalZsH3jE1URagbR/s7uLE7zgK+jhuM6VpdAHN9gtMA+L6I/B9QDjyctHkDcPpCeOmYQFXbReQbhF3ZReQdwLdwY5qcpKoNkRqYXn6MG1O+BbhORM5U1b8A24Hi8GYwbxGRLNwMWkuAp1T1zSLyj8B7gJeJyBCumelNEZqZkgXbvDJ03ItwVTP9uJHmlgA/w7V/3aQLY2aghBYvxmnRhdMiC9c2fCTc51HgPao6r+f+nBQXXar6U3HjlrwAV43zNFzV1hdU9f7IDE0Dk+KiCTf36ZiIvAFXpdeIG+DtX1T13ugsPf6IyMdx4/qUAitw/SdGw74E5+Ce5sdU9ZHorEzNQn6i/xjuoj2A68VXq6oPi8hFwCmqOiIRjziXRpK1yAAa1U2UkLjYnwP8Zr4n+ZBkLTJFpCmMiz8D63AjEi5ZIE/zyVrsxY3W+TDu124G7gHpvgWQ5LfgroE94Y3uQeA7QK+IZAPvi3s8LMgn+rBTxy9UdVfYDvhduLGkXxX+XF8Qo+7BtFq8XFW7w30EyFHVoegsPf6k0OIS4Jqwai+t079FyTRxcc18r6aZjIi8Fndz/7yInA18E/dLJ8C1zLpfVX8YpY0zsZBerCVzIfBrcC1uVPVm4BBuiFWAt4jI3qiMSzOptNgCICL/BJwx35N8yFRaDOLGFgd4m4icFZVxaSZVXBSDi4uFooW6gdq+HC4GwOtU9Sl1cwU/BsS+iemCfKKH8bFKqoGM8OfYv+Fa2dwJ/C9w9gJ6qjctQkwLj2lx+HAoSeszwp6wd+D6WnwzAvNmzYJ7ok/q1Vg1yYH3AlcB3wa+ON8DGEyLZEwLj2nhST7/5KENwiT/LtzcBLFO8rCAn+inQkT+hpsH9NyobYka08JjWngWkhaJp/kwwWeq6mhYpft0Vb1VRM4FelT1iYhNnZEF1epmGsftVtX/Bt6H6/047zEtPKaFx7SYQK6IDId9RxIjdX4c+CKAqv4hMsuOkAX1RC8ieUDCcYl19wBfUtWfRGdZ+jEtPKaFx7QAEdkOvAP34rUZuEVVW0XkmcA/q+orIzXwKFgQT/STHSciyY5rWygBDKZFMqaFx7SYwOeBB3FzwZ4LPB+4VVUfCZtaHjYef9xZEE/0IvIrJjru0bDJFIlOUSea444W08JjWnhMC4e40Sc/oKrnhcuXAu8HXqmqLeIGMutV1cEo7TxS5n2rm9BxS1X1o6r6XdyYHa8SNwY9wDIRWTTfAxhMi2RMC49pMYFR4EfiyFY38XsvkJh85j+BUyOz7iiZ94meeeq4o8S08JgWHtMiRN0wH19QR2Ksq18Cm0XkhcAaVX00OguPjoVSdZOd5DRE5I24AarKgLeo6pWRGZdmTAuPaeFZ6Fokd4ya1F5eRWQbbnatxcArVPXxiMw8aubty9hkxwGjCeeF6+4lyXERmZg2TAuPaeExLSbwPhFR3NN8D7iOY6EkNbjJd6pOxCQP8zjRM88dd4SYFh7TwmNaAOKGGX4n7sb2TRH5kap+N6mJ6YiIXAhkR2bkMTIvq25Cx92Fc9w64EfhS6bkfbKA7BPt7fmRYlp4TAuPaeERkdfh3kF8Ang2bpCyUeDrqvp7cePv/1RVT9iOYvM10c97x80W08JjWnhMi4mIyFJVTYwvvwXXdn4vsBrYpqrFUdp3rMzLRA/z33FHgmnhMS08poUfhXKK9Ytx0yT+HjdPxZ1pN24OmXd19AnHqWovQNiSoFRE6khyXJQ2pgvTwmNaeEwLz1RJPlw/ICLnA/tP9CQP87Ad/XSOw7ULnheOmw2mhce08JgWhxO+j0BEni4iZ4Sr7wCujcyoOWQ+V91kqRt57+m4iRMeE5GTcBP4NkZtXzoxLTymhWchayEiubhJvrs1aV5oEXkceK2q7o/KtuPBvHmiF5FcEVkrbvQ91HfX/g5hFZWq1s33AAbTIhnTwmNaOMIWRz/GvYh+btL683C/ZuZVkod5UkcfOu79QDvwM9w0Z/PacakwLTymhce0mMDncHPACvASEekG1uCGfXgDpH5Je6IyLxI9C9Bx02BaeEwLj2kBiJtEpUNVvxEuvy/c1ISryqkE/j7fdDjhE/1CddxUmBYe08JjWkygCVgqIj8GBoEKVX2ViGwE3o1rYvr3KA08HpzwiZ4F6rgUmBYe08JjWoSoaoOIvAp4E9CI6xWcWN8CbIzSvuPFCZ/oF6rjpsK08JgWHtMCxI2t/1IgS1U/C9wkIsuAz4jIV4AngOuBeTlK5wnbvHIKx5FwHK4r9xPAW4ErVbUqKjvTgWnhMS08poVHRH6Jq6I6H/gd8CFV7RSRS3C9gU8GfqOqP43OyuPHiZzoF7TjkjEtPKaFx7RwiMgFwMdV9fxw+TfAENAN/FxVvx+lfenghKy6CR23VFXfEi7/BvhO2JLg56r6yUgNTCOmhce08JgWE8gBWsMmphfgqq72Ai/DDdXcrqq/FpkwPv+84oRM9JjjkjEtPKaFx7Tw3A+cB7wHWAJ8JuwN+62wJ/AzgF/PZx1O1ES/4B2XhGnhMS08poUnV1U/EvYIfiZu4vOngFrcO4wbI7UuDZyQdfTiZqQfTHYc8C2c4+4EblTVB6K0MV2YFh7TwmNagIi8B8gHtgF1wIfVjUr5YWAXsBL4q6q+Kzor08MJlejNcR7TwmNaeEwLh4icCfw38GagB/gBrifwTap6m4icjmtq2qeqw9FZmiZU9YT4A84E/gycDZwCPA40A9eH20/HBXFO1LaaFqaFaRG5Fv+Ku8kllq/GDQPxG+CVSeslalvT8Xci1dE/D7hDVR8GEJGP4F4yvVpEhlX1e+F6idDGdGFaeEwLj2nhuR/4sIhsBaqBVwLfxo3U+UYR+aGqjmiY7ec7J9IwxfcD54nIVhHJxDnut8B7gUvFTYfGAnGcaeExLTymBeODsz0I7AceAr4B9KvqT1T1EWBr+LdgOCESvTnOY1p4TAuPaeHRcHA2VX0v7r3ETcBrAETkbUC2qpZEZ2H6OaFexgKIyEogD2hVNzvO24CXadjrbSFhWnhMC89C1UJE1uAmE/lieHNLrM9Q1UBEVuPG5P+aqj4ZlZ1REOsnehFZIyK3icgzE+tUtQNoDgN4NW72+jdGZmSaMC08poXHtJjAZ3H9A74gIreE5578hN8GfHChJXmI+RO9iHwP11JgEPgL8IHQWcn7LFPVnijsSyemhce08JgWDhEpwCX6m4E+XHXNBcD3VPVmEVkCvERVvxWdldER2yf60HEjwMtxTaPGgHtF5KZw+xIR+cf5HsBgWiRjWnhMC4+qdgJvARrVzXv7lnD5HBG5F3gSKIzSxiiJ+xN9PpCpqt3h8gXAO3EdQrYC/6Wqn47QxLRhWnhMC49pMTMicgtwgaqeFrUtURHrRJ8Kc5zHtPCYFh7TwiMivwM+pao/j9qWqDiROkwlcwquDs4wLZIxLTwLXouwY5gA/6aqv47anig5oZ7okxz37AXvONNiHNPCY1oYU3FCJXrDMAzjyIltqxvDMAxjbrBEbxiGMc+xRG8YhjHPsURvGIYxz7FEbxiGMc+xRG8YhjHP+f8KxgNPQiG+JAAAAABJRU5ErkJggg==\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": [
    "from IPython.display import display\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'item_id':[\n",
    "        '273','231', '133', '555', '123','234','342'\n",
    "    ],\n",
    "    'purchase_date':[\n",
    "        '1988-12-28', '1988-12-24', '1988-12-25', '1988-12-25', '1988-12-26', '1988-12-28' , '1988-12-30',\n",
    "    ],\n",
    "})\n",
    "\n",
    "display(df)\n",
    "\n",
    "grouped_df = df.groupby('purchase_date').size().to_frame(\"count\").reset_index()\n",
    "\n",
    "display(grouped_df)\n",
    "\n",
    "datetime_series = pd.to_datetime(grouped_df['purchase_date'])\n",
    "\n",
    "datetime_index = pd.DatetimeIndex(datetime_series.values)\n",
    "\n",
    "grouped_df.set_index(datetime_index, inplace=True)\n",
    "\n",
    "grouped_df.sort_index(inplace=True)\n",
    "\n",
    "grouped_df.plot(kind='bar', y='count', rot=60, title='number of purchases per day')\n",
    "\n",
    "add_grid()\n",
    "\n",
    "grouped_df_filled_missing = grouped_df.asfreq('D')\n",
    "\n",
    "grouped_df_filled_missing.plot(kind='bar', y='count', rot=60, title='number of purchases per day')\n",
    "\n",
    "add_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae81730f",
   "metadata": {},
   "source": [
    "## stacked bar plot count per date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c7fbcf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import date,datetime,timedelta\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# create a dummy dataset\n",
    "df = pd.DataFrame(\n",
    "    index=pd.to_datetime(\n",
    "        [datetime(2019,1,1)+timedelta(hours=h+10*np.random.uniform()) for h in range(0,1000)]\n",
    "    ),\n",
    "    data={\n",
    "        'browser': np.random.choice([\n",
    "            'chrome',\n",
    "            'firefox',\n",
    "            'safari',\n",
    "            'edge'\n",
    "        ],size=1000,replace=True)\n",
    "    },\n",
    "    \n",
    ")\n",
    "\n",
    "print(type(df.index))\n",
    "# 'pandas.core.indexes.datetimes.DatetimeIndex'\n",
    "\n",
    "# just to make it more intuitive\n",
    "df.index.name= 'login_datetime'\n",
    "\n",
    "# you can pass a lambda function to the groupby function\n",
    "# so that it groups by the day (or anything else you want)\n",
    "df.groupby(by=[lambda dt: dt.strftime('%Y-%m-%d'),'browser']).size().groupby(level=0).apply(\n",
    "    lambda x: 100 * x / x.sum()\n",
    ").unstack().plot(kind='bar',stacked=True,legend='reverse',width=0.8)\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "plt.gcf().set_size_inches(16,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c450110b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}