10_sample_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10b52380-c60c-49aa-8561-83298b9bf859",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Sample Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ab97641-e32b-41db-b954-3a1584abe136",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ae452e67-3378-463d-84b6-c94728751271",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "### Imports\n",
    "Put the import statements at the top so it is easy for your readers to know what they must install to use your notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f3f23b6f-d679-41b8-aa26-45ab812e6ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotnine as gg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b982b1e0-a71d-4960-8217-73bc417ab799",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81871437-4501-4126-86df-6380b6bafb08",
   "metadata": {},
   "source": [
    "### Configurations\n",
    "\n",
    "For instance, how to show cell output. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "11b8bda7-9137-4bc6-ae10-f906a0e758e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4950"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example with default 'last'\n",
    "sum(range(10)) #no\n",
    "\n",
    "print(sum(range(10))) #yes\n",
    "\n",
    "sum(range(100)) #yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4f6a25f1-8549-45dd-8499-3b1a3762edee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"  \n",
    "\n",
    "#Options: 'all', 'last', 'last_expr', 'none', 'last_expr_or_assign'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6bcbd14f-3e1c-4202-93de-dc29e1884b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4950"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example v2 with 'all'\n",
    "sum(range(10))        #yes\n",
    "print(sum(range(10))) #yes\n",
    "sum(range(100))       #yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bbc3457-1952-4242-a4d4-af21c7ac6078",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "39d05879-affc-4e63-ab52-ea0386b5ae11",
   "metadata": {},
   "source": [
    "## Working with data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6011dd51-5958-424a-9e25-19e08f8c829b",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0082e023-72f9-4bb8-b9ff-3b3920de2657",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/registration_times.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c6f38446-f2c5-4a1f-8e48-f28f54edce79",
   "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>Registration Time</th>\n",
       "      <th>org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-10-19 13:43:15</td>\n",
       "      <td>cu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-10-19 18:53:05</td>\n",
       "      <td>cu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-10-27 09:56:39</td>\n",
       "      <td>cu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-10-25 13:45:37</td>\n",
       "      <td>cu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-10-20 09:37:27</td>\n",
       "      <td>cu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Registration Time org\n",
       "0  2022-10-19 13:43:15  cu\n",
       "1  2022-10-19 18:53:05  cu\n",
       "2  2022-10-27 09:56:39  cu\n",
       "3  2022-10-25 13:45:37  cu\n",
       "4  2022-10-20 09:37:27  cu"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Registration Time    object\n",
       "org                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "df.dtypes  #not the types we want!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8be992a8-e33f-4bd0-89a1-b3d58910e042",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a73ceabb-76db-4e56-b1eb-49f538331265",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Aside: Editor Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7929a49c-e616-4a63-be32-0ba13492800e",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pandas' has no attribute 'to_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#demonstrate completion\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_\u001b[49m   \u001b[38;5;66;03m#(tab)\u001b[39;00m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'to_'"
     ]
    }
   ],
   "source": [
    "#demonstrate completion\n",
    "pd.to_   #(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75960eec-6d5b-42ae-8637-35966e7abebd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function pandas.core.tools.datetimes.to_datetime(arg: 'DatetimeScalarOrArrayConvertible | DictConvertible', errors: 'DateTimeErrorChoices' = 'raise', dayfirst: 'bool' = False, yearfirst: 'bool' = False, utc: 'bool' = False, format: 'str | None' = None, exact: 'bool | lib.NoDefault' = <no_default>, unit: 'str | None' = None, infer_datetime_format: 'lib.NoDefault | bool' = <no_default>, origin: 'str' = 'unix', cache: 'bool' = True) -> 'DatetimeIndex | Series | DatetimeScalar | NaTType | None'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#demonstate help\n",
    "pd.to_datetime #(shift-tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "657e9d0e-241d-4078-ac62-46bd6f6e5a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#contextual help in Help > Show Contextual Help"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d981a717-f166-46b2-8efc-67e6e350dfa5",
   "metadata": {},
   "source": [
    "### Prep Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "84d466ef-a834-4bd5-b4e0-e39e161389be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Registration Time            object\n",
       "org                        category\n",
       "Registration_Time    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Registration_Time'] = pd.to_datetime(df['Registration Time'], utc=False)\n",
    "df['org'] = pd.Categorical(df.org, categories=['wcm', 'cu', 'other'])\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6b4e9cc-b9ea-4171-94e2-55936cb6d634",
   "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>Registration Time</th>\n",
       "      <th>org</th>\n",
       "      <th>Registration_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>266</td>\n",
       "      <td>266</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>266</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2022-10-19 13:43:15</td>\n",
       "      <td>wcm</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-23 23:35:12.714285568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-18 13:04:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-19 13:28:39.750000128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-20 11:08:16.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-27 14:41:17.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-11-07 14:48:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registration Time  org              Registration_Time\n",
       "count                   266  266                            266\n",
       "unique                  266    3                            NaN\n",
       "top     2022-10-19 13:43:15  wcm                            NaN\n",
       "freq                      1  136                            NaN\n",
       "mean                    NaN  NaN  2022-10-23 23:35:12.714285568\n",
       "min                     NaN  NaN            2022-10-18 13:04:49\n",
       "25%                     NaN  NaN  2022-10-19 13:28:39.750000128\n",
       "50%                     NaN  NaN     2022-10-20 11:08:16.500000\n",
       "75%                     NaN  NaN     2022-10-27 14:41:17.500000\n",
       "max                     NaN  NaN            2022-11-07 14:48:17"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879e32cd-34bd-456a-8ad9-436abbe3fee8",
   "metadata": {},
   "source": [
    "### Plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "81f31b02-cf37-4297-8eab-1995b3bdf8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_t =  pd.to_datetime('2022-10-18 13:00:00', utc=False)\n",
    "stop_t = pd.to_datetime('2022-10-20 13:00:00', utc=False)\n",
    "\n",
    "start_t =  pd.to_datetime('2022-10-19 1:00:00', utc=False)\n",
    "stop_t = pd.to_datetime('2022-10-20 01:00:00', utc=False)\n",
    "\n",
    "#pandas to manipulate data\n",
    "plot_df = (\n",
    "    df\n",
    "    .sort_values(\"Registration_Time\")\n",
    "    .query(\"(Registration_Time >= @start_t) & (Registration_Time <= @stop_t)\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6159b10a-50b8-40e1-8453-7b35422dd070",
   "metadata": {},
   "outputs": [
    {
     "data": {
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1q3nRsFu3bsrIyFBxcbF27txpbrtXr14KDQ2VpBqHh/z666/NxwwaNEiTJ09W165dqwyh9+ijj3rNa9S7d29ddtll6tGjh0JDQ3X48GEtX75cH330kXkB/sEHH9Q555yj0aNHm4/r37+/JOno0aNKS0uTJIWGhprDf3bs2LHBr+GUKVO8QrUePXpoypQp6tmzp5xOp3bu3Kk5c+aY+/nBBx8oNzdXn332mRyOmr9ybt26Veedd54KCwsVGxurq6++WkOHDlVYWJi2bt2ql156SYcPH5ZUfiH0oYce0v333++1DZfLpUmTJpnDt4WFhemyyy7TySefrMTEROXk5OjHH3/Ue++9p5ycHJWVlWnq1Kn66aefGjwkakJCgvn6HjhwwDymkpKSzAvG3bp183rMvHnzNG3aNPPibXR0tCZNmqRTTjlFrVq1Ulpamr766it9/fXXcrvd2r9/v84++2ytWLFCAwYMqLWeW265RTt37lRISIguueQSjR07Vm3atKnzcS2lvueUupx77rlmMLRlyxYdPny42qHtKocpBw4c0I4dO9SjR48q63oOHThu3DjzfVvh//7v//TUU0+Z9zt27KipU6eqb9++cjgc2rNnj+bNm6effvpJkrRs2TKNGzdOK1asUExMTI374na7NXHiRB09elRxcXGaNm2ahgwZoujoaK95yeoyf/58TZkyRaWlpZKkMWPG6JNPPqny3HfffbfXvo4dO1bnn3++2rdvr5KSEm3dulXvv/++du3aJan8vDNgwABdffXVNT53ZmamJk6caL5Pr7jiCp188skKDQ3Vli1b9Prrr5vDJq5atUqPP/64/vSnP1XZzq5duzR69GjzhxIVnyXjxo1Tu3btdPToUa1Zs0YffPCBCgoKVFxcrOuvv14hISG65ppran19rrnmGv3yyy+y2Wy68MILNWbMGCUmJio1NVVz5841Q8FDhw5p/Pjx2rBhw3EbLvHiiy/W5s2bJUnnnHOOzjvvPCUlJSkjI0PvvfeevvvuO0nl57Lp06drx44dDR4atDHnpoaoz7mmqcee5z7s2LHDDHo996E52mzWrFnm8XDjjTc26jw1efJkPfvss5KkZ555RpMmTap2mFjDMHTrrbcqNzdXkmS323X55Zc3oXpv48ePV0ZGhvm5d/rppys6Olq7du3Sm2++qR07dkgq/+HG3XffrRdeeKHKNrKysnT66adrz5495t9GjRplfifLzc3VTz/9pDlz5igzM1Nut1v33HOPXC6X7r333mbbl/rIzMzU5MmTVVRUpPbt22vatGnq37+/QkNDFRUVJal5vvMBAAAEDQt7LwIAgCZqynCALpfLuPbaa72Gjlq9enW1695yyy3mesnJycby5curXS8zM9NrGK3IyEhj//79tW7P4XAYb7/9drXbW7BggREVFVXjEGkV6hpi9NlnnzWXx8fHG1u2bKn2+Xbs2GF07NjRXPdvf/tblXV++OEHr3o8hxX15DnEaMV/9957r+Fyuapd3zAMr6Fbr7jiCsPpdFa73k8//WTExcWZ69Y0BOOjjz5ar+HS6hpi9MUXX/Tajz/+8Y9GaWlplfVcLpfxl7/8xWvdBx54oMp61Q0jN3LkyGqHuNu3b5+RlJRU6358+umn5vLQ0FBj2bJl1e7n4cOHjYEDB5rrXnvttTW+JvXhOSxaTcPeHTx40GjdurW5Xv/+/Y1du3ZVu+7SpUu99rV3797VHgOe7SXJaNOmjbFq1aom7UuF43VOqUtOTo4RGhpqbmvu3LlV1ikpKalyfpBkvPTSS9Vu86yzzjLXefXVV72WffHFF17bmDx5spGfn1/tdp599lmvofyuv/76KutUHjJUkjF06NAah/mra4jROXPmeA2TN27cOKOwsLDKellZWV6v28MPP1zt85WVlRlXXnmluV63bt2qrFPd+/Scc84xDh8+XGXdjIwMo0ePHuZ6PXv2rLKO2+02TjvtNHOd2NjYGod5TU1N9RrmNTw83NixY0eV9SrXFx0dbSxevLjabf7zn//0WnfKlCnVrlcfDR1iVCofQvjLL7+ssp7b7TamTZvmtW5N+1Bf9Tk3NXSI0brONc157BmGYfTv37/OfWiMxYsXm+/fE044wet9Xt8hRg3DMNLT070+syMiIoxZs2YZmzdvNkpLS43c3Fzjm2++McaNG+f1Gv75z39ucv3VnVuq+1wpLCw0Ro4c6fX+qO6z+/LLLzfXcTgcxrvvvlvtc2dmZhrjx4/3OsdX93nbkkOMVvw3YcKEGoczbs7vfAAAAIGOgBAAAD/W0Iv5JSUlxp49e4w333yzyhxbf//736t9zM6dO82LaQ6Hw1i3bl2tz1FYWGh069bN3O6dd97ptfzw4cNe80PV9LwV3njjDa86GxMQTpkyxVw+derUWp/v5ZdfNtc99dRTqyxvbEA4adKkWp93z549XhfD65qH7vbbbzfXr5jHr7LmCAhLS0u95oObMGGC4Xa7a63N82J3QkKCkZub67W8cvDQo0ePGsMYwzCMhx56yGv9ygHF3XffXWubeVq4cKG5bnJycq3r1qU+F+E92ykqKsrYuXNnrdv86quvvPb1rbfeqrJO5Yv29Zn/qr6Oxzmlvs444wxzW7fcckuV5Z7zD3qec6644ooq6xYVFZnzjdlsNuPQoUNey4cOHWo+ftCgQXXOmzhr1iyvC+qVL85XDvzi4uKq/bFETet7euedd4yQkBBz2XnnnVdjfZ9//rm5XkhIiFFQUFDjc2ZnZ3sFrJXnjq38Pu3fv3+1oWSFV155xWv9jIwMr+ULFizwWj579uwat2UY5eG651xo1QWxlUODN998s9ZtXn311V6vz549e2pdvyaNCQg///zzGrd34MABr3n9/vWvfzWqrgotFRDWdq5pzmPPMFomIMzOzjZSUlLMGlesWOG1vCEBoWEYxt69e41Ro0ZVaevq/nM4HE0+JxpG1XZr165dtW1X4ZtvvvFaf+3atV7LN27c6LW8pmC3Qn5+vtGrVy9z/bPOOqvKOi0dEHbt2rXWuU6b8zsfAABAoGMOQgAAAsTSpUtls9lq/S88PFxdu3bV1Vdfbc6x5XA49OCDD+qvf/1rtdt95plnzKERJ0+erMGDB9daR2RkpP7whz+Y9z/99FOv5e+++645bFh8fLzuuOOOWrd39dVXN3gYyMo853yqaz7Diy66SP/85z/1z3/+s84h7RrigQceqHV5YWGhBg0apEGDBunSSy9V69ata10/JSXFvF3TnHvN4cMPPzSHDpSkRx55RDabrdbHeA45lpWVpS+++KLW9R988EFFR0fXuPzss8/2ul95DkzP9s3MzKx1Lr7Ro0eb7ft///d/tdbVVKWlpXrllVfM+zfddJO6d+9e62PGjRunU0891bz/v//9r9b1TznlFJ133nlNK7QGLXVOqS/PYf+qm+PLc3jR++67z7xd3TyE3333nfk+GTp0qJKTk81lq1ev1tq1a837//jHP+qcN/Guu+4y13E6nXr//fdrXX/GjBmNGtr3jTfe0FVXXSWXyyWpfKjKefPm1TpXbAWXy1XrnKdxcXF69tlnzfdDxfB8NbnzzjsVGRlZ4/LKwytWDPlb4bnnnjNvDx06VFOmTKn1+dq3b6/f/va35v25c+ear0N1evXqpWnTptW6zfvvv988f7lcLn388ce1rt9cJkyYoPHjx9e4vEOHDurbt695v6Z5fq1U17mmJY+95nLTTTcpNTVVkvSnP/1JI0aMaNL2UlJSdNttt9X5eS1JJ5xwgsaNG9ek56vOTTfdpMTExBqXV35fVux/Bc/3ZYcOHXTbbbfV+nzR0dGaOXOmef+bb74xhwE/Xu6+++5ah3X2he98AAAA/oKAEACAIDdo0CBNnDixxtDHM9zxnOeuNp7rbd682Zx7R5KWL19u3j7nnHPqdWHwoosuqtfz1qRnz57m7a+//loPPPCAOY9XZUlJSfrTn/6kP/3pT5oxY0aTnrdC165d1adPn1rX6du3r9avX6/169frnXfeqXObP/zwQ7PUVhfP+aSGDBmiE088sc7H9OrVS4MGDTLvVxfueJowYUKtyz3DHKl8DktPnu27bds23XzzzcrPz692W2FhYWb73nXXXbU+b1OtWbPG60JlfS8+egYny5cvrzUUqeu1s0Jd55T68gwjNm3apKNHj3otrwgIQ0NDNWnSJHMutEOHDnnNbyqVX8SubruS9zGelJRUr9e0VatWXoFPU4/x6vz3v//V9OnTzR9oTJkyRXPmzKlxrlPJ+70gSZdddpn27t1b4/rXXnut+X6oaz7EykF9ZZVDCs/3oNPp9HqNGvNeyM3NNefBrc65554ru732f9526dJFAwcONO9///339aqjqerT/p7nucrnOF9Q1z605LHXHGbPnm3+4GLw4MFePypojMLCQl188cWaNGmSMjMzzb/HxsaqZ8+eSklJ8Toef/nlF5122mn6+9//3qTnrayu92VMTIzXDwoqfzZ6nv+mTp1a548jJGnSpElec2QuW7asvuU2i4Yci1Z85wMAAPAnBIQAAASIqKgo9e/fv9b/evfuraSkJK/HrV27VieddJLmzp1bZZvZ2dnaunWref/BBx/UgAED6vyvci+OAwcOmLfXr19v3vYMkWrjeUG3MW6++WZFRESY9++9916lpKTohhtu0HvvvedVX0vo1KlTkx6flZWl9evXa/78+Xrqqac0ceJEvf32281UXe1Wr15t3h4zZky9H9evXz/z9s6dO2tcLyEhQbGxsbVuq3KvJafT6XX/6quv9gonXnjhBXXu3FlXXXWV3njjDe3atavedTcnz9eudevW9QpXJe/XLjc3t9YeEE09tmrTEueUhujbt6+6desmSTIMQ99++625rKyszAx3hg8frujoaK8L5ZV7EXpeBK8cEHq206hRo7wufNemvse41PB2eu6553TDDTd49Ya9/PLL5XA4an3c0KFDvd6nK1asUM+ePXXeeefpqaee0o8//lhr4FyT6OjoOntAVq7N8326adMmFRYWmvfrey7xfI2l2l/n0047rV7bPOWUU8zbtQVYzalLly51ruN5nqt8jvMFdR3DLXXsNYd9+/bppptukiRFRETorbfeUmhoaJO2OXXqVM2fP9+8P2bMGC1btkzZ2dnatm2b9u7dq6ysLL3yyitq3769pPLz2N/+9jc988wzTXpuT717965zHc/3puexlZWVpe3bt3vtQ320adNGbdu2Ne/Xdf5rbnUdi1Z/5wMAAPAntf8LEwAA+I3hw4d7DblXm0OHDunxxx/X448/Lqn8YvvVV1+tUaNGmReyKtbzvEC9f//+Rg195tmD0LMXUH2H3GvTpk2Dn9NT37599eGHH2ratGnmL/3T09P18ssv6+WXX5Yk9ejRQ2eddZYuuOACnXPOOU2+eOipvoGDVH4B8dNPP9X8+fO1du1a7dy5U3l5ec1WS0N5Di9anwuRFdq1a2fe9uxdUVltQ4vWV1JSkj7//HNdeuml5vBp2dnZevvtt80gtVOnTjrrrLN03nnn6bzzzjsuQ9p5vnYNGSbX87WTyl+/yn+r0JBjq6Fa4pzSUBMmTDCHwFu6dKkmTpwoqTzUqwiczjzzTEnlPWmefPJJSeW9C2+88UZJ5cfCunXrJElt27bVsGHDqtReoSWOcanh7fT73/++2r+NHTtWcXFxtT527ty5mjx5stljr6ysTJ999pk+++wzSeXDO44ePVrnnnuuJk6cWKWHbnXqes66eL7GUv1f57CwMMXHx5s9cWt7nTt06FCvbXoG2nW1W3NpjvOc1epzDLfEsddUhmHommuuMY+hhx56SP3792/SNj/66CMtWLDAvH/FFVforbfeqtKDtVWrVrruuut0/vnn6/TTTzd7Nt9zzz266qqrmvy+kpr23kxLS/O639DzX8X7+ni9jyrUdSxa/Z0PAADAn9CDEACAINS+fXs99thj5i/qpfJ57D755BOv9WqbQ6ghKuYclLzDwtrms2pu48eP17Zt2/TAAw94zfVUYceOHXrxxRd1/vnnq1OnTnriiSdqncuuJWzYsEGDBg3SBRdcoFdeeUXr16+vEg5GR0fr/PPP14UXXnhcavIcIrMhFyI9A7jaeo00V8A1bNgwbd68WU8++aSGDh1aZfn+/fv1xhtv6LLLLlP79u01a9YslZWVNctz16Q5Xjup9tfPV9T3nNJQnr39PIeo9AwuKwLCMWPGmBd5PZcvXrzYfA3PPffcKkOftvQx3hSeQ+kdOHBAd955Z52PSUpK0uLFi/X+++9r/PjxVYYkzcnJ0YIFC3TTTTepc+fOmjp1apWgoLKmXjz3fI3Dw8PrNYxhhfq+zgkJCfXanufcZU0dBre+WjLI9yUtcew11QsvvGD2KD7jjDPqnGOvPl599VXzdkJCgp5//vlah7dt27atnn/+efN+Tk5OlfmZG6sp703P96Vk3fmvYhjl5uQP3/kAAAB8AQEhAABB7I9//KPXfc/hRCVVuYj75ZdfyjCMBv/nOSeh50Ulz+CwNunp6Q3dtWq1adNG99xzjzZv3qy9e/fqtdde0zXXXKOuXbt6rXf48GHNnDmzWS4k1tfGjRs1evRobdy40fzbiSeeqBkzZuiZZ57RggULtHXrVuXm5mrBggUaPHjwcanLc2iymubwqY5nuFzfC/dNFR0drT/+8Y9as2aN0tPTNXv2bN14441V5n/Mzc3VQw89pMmTJ7doPc3x2knH7/VrDnWdUxrqjDPOMH9IsGHDBvOCdkUAGBERoZEjR0oqD34qho9MS0szn7u2+Qcl3z3GZ82apc8++8xrvr6XX35ZixYtqvOxNptNl156qT7//HNlZmbqiy++0N13361TTz21ynCD7733nkaMGNFsPwipjudzlpWVNehCvGeIUdvrXN+wz/NHF61bt653HagfXzv2fvnlF/P24sWLZbfbZbPZqv1v+vTp5rp79+71WlbRe1mSVq1aZd4eP368WrVqVWcdZ5xxhtewnLXNp3m8VB4W2Krzn+fww83Jl7/zAQAA+AoCQgAAglivXr285mmpfJGu8tCetc2FVl+eF5Lq23OgYliu5pSSkqJrr71Wr7/+unbv3q2dO3fqiSeeMOc8k6Snn366RZ67On/4wx/M3pVt27bVkiVL9NNPP+n555/X73//e51//vnq3bt3rb0UWoLnMXDw4MF6P87zomxLzpNXk7Zt22rKlCl64YUXtGXLFh08eFAvvfSS13yWH330kRYuXNhiNTTHaxcWFlZljj9fVtc5paEiIyM1duxYSeW9TJYvX66ysjKtWLFCkjRy5EivHzJUNw9hxfyDoaGhOuecc6o8hy8e488884z+8Y9/SJIee+wxrxp/97vfNeiCenR0tH7zm9/o4Ycf1vfff6+srCx9+OGHOv/888119uzZo8cee6z5dqASz/rdbne9f/Sxb98+r32t7XU+cuRIvbZZMQxx5brQ/Hzh2GsJnkOlVw6bamKz2by+X+Tk5DR3WQ1W+fiv7/nP6XR6zTvY1PNfS/cilXzvOx8AAICvICAEACCI2Ww2r8Cu8kXnzp07KzY21ry/YcOGem03PT3dnP9tzpw5Xss852JbvXp1vbb30Ucf1Wu96hQUFGj9+vVav369tmzZUuN63bt312233aa1a9eac1kZhlGv3jpNdeTIEa/hE5988kmNGTOm1sccjwtqkrx6361cubJejykpKfHqYXHyySc3e10VysrKzPat7fhs3769fve732n16tUaNGiQ+XfP3mXNzfO12759e73nafI8Fk466aQqw/T5srrOKY1ReZjR6uYfrOAZEC5ZskQHDhwwg7xRo0ZV29OnMcd4RS0VmvsY95yDMDExUY8++qh5f9euXZo1a1a1j9uyZYv5figoKKh2nZiYGE2cOFELFizQjBkzzL8fr/eCVP/X2fM1ttvt1Q4fXGHTpk312uaaNWvM257nAjSNrx577du3V//+/ev1n+e8yKGhoV7LunTpYi7zPI/UtK/V8RzW2hd6r6akpHiN6lDf9+XKlSu9ehtWPv959uata/hQt9utn3/+uV7PWx/+8J0PAADAlxAQAgAQ5DwDwKKiIq9lISEhGjVqlHl//vz59drmk08+qauuukpXXXWVnnzySa9lI0aMMG9/9tlndYYmixYt0ubNm+v1vNX55ZdfNHjwYA0ePFhDhgyR0+msdf2EhASdfvrp5v3Kc/S0hN27d3sNuef5/NUxDMPrwnlL8mz/L7/8sl4h14IFC8yLpjabTePGjWux+vLy8sz2Pemkk7R///5a1w8PD9f48ePN+y3Zvp6vndvt1nvvvVfnY4qLi73eZ9X1ePN1tZ1TGqNyQOg5v+AZZ5zhte4pp5xiPv+SJUvM3oOVt+PJs502bNhQr/PNmjVrvHrQtHQ7TZ8+3etHA//5z3+qvZg/ZcoU8/3w4Ycf1rndiy66yLzdku+FDh06ePXU+d///levx82ePdu8PXLkSK/5Ayurz3yXP/74o9ewt5WPHzSerx57d999tzZt2lSv/yp67Urlx6znsqeeespc5tlrsL6fxSUlJV7H3oABA5q+c03kcDjMYZmlxr0vU1JSqvwAwPMzwLO3ZXWWLVvWrO3vD9/5AAAAfAkBIQAAQc7zQk51vX2uu+468/a2bdv07rvv1rq9HTt26Omnn6728ZJ0+eWXm78uLyws1B/+8Icat5WWlqYbb7yx9h2oQ/fu3c1hOYuLi/X555/X+ZgdO3aYtz17FFSnrl/H10dISIjX/bqGcn3uuecaPLdbY+u88sorvV6/v/3tb7Wu73Q69dBDD5n3x44dqx49ejTquesjISHBa5i0+lyYbkj7NkVKSopXqPPggw+aw8jW5LnnnjMvqNpsNl1//fUtVl9Lqeuc0lBdu3Y1L0CvW7dOCxYskFTeG6lyzxWHw2G+5ocPH9YzzzxjLqspILz44ou9egT9+c9/rrOm+++/37zdvXv3Kj0ZW8ILL7xg9iZ1u926/vrrq8wZ1rNnT/O2L70XJOnqq682b7///vtePfmqs2bNGn322Wfm/d/97ne1rv/555/X2gPKMAzddddd5v127dqZ81ei6Vry2GuOz9nmNGHCBPP2hg0b6vXjqRdeeME8H4aFhXn9UMVKnu/L1atXa968ebWun5qaqtdff928f/3111eZ/7Ndu3bm7e+++67WOUf/+te/NrDi2rX0dz4AAIBAQ0AIAECQ87yYX92cOJdccolOPPFE8/6MGTO0bNmyare1ZcsWjR8/3uw91rt3b6+LT5LUt29fXXLJJeb9d955R1OmTNHu3bvNv5WVlel///ufTj31VK8LN40RHx/v9evwW2+9tcZwzTAMPfzww1q7dq2k6ucsi4uL87rfHEOj9e7d22setXvvvddrKLIKhYWFuueee/THP/7R6+/VrVu5Vs+hFhuie/fumjp1qnn/6aef1hNPPFHtBb/S0lLdeOON+vHHHyWVB1yeQUpLsNlsXvNZ3Xfffeb8dNV5++23vS5eez62JfzlL38xbx84cEAXX3xxjQHwJ5984jV05G9/+9t6z2/lS+o6pzRGRbjncrnM4WtPP/10ORyOKut6DjNaEUJ17969Si8Xz3pvueUW8/7HH3+sO+64o9qeJ4Zh6N577zVDSkl64IEHqlwgbwl9+vTxCrg2b96sBx54wGudCy64wLw9b948Pf/88zVub/369V6Pb+n3ws0336z4+HhJ5YHPxIkTtXHjxmrX/eWXXzR16lTzPNOvXz9dfvnltW7fMAxddtll1fYArTg3efYo/fOf/+x13kXTNPex5/n5tXTp0jp7gh1PM2bMUHR0tHl/+vTptX4XeP3113XnnXd6PT4xMbFFa6yvyy+/3Kt37/Tp0716aXs6dOiQJk2aZH7Ha9u2rW699dYq6w0bNsy8feDAAa8fDVUoKyvT7373Oy1fvryJe+Ctub/zAQAABLqq/6IGAABBxfMi3K5du6ost9vtmjt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     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_t =  pd.to_datetime('2022-10-18 13:00:00', utc=False)\n",
    "stop_t = pd.to_datetime('2022-10-20 13:00:00', utc=False)\n",
    "\n",
    "#pandas to manipulate data\n",
    "plot_df = (\n",
    "    df\n",
    "    .sort_values(\"Registration_Time\")\n",
    "    .query(\"(Registration_Time >= @start_t) & (Registration_Time <= @stop_t)\")\n",
    ")\n",
    "# Make a new variable with the cumulative count of registrations by group.\n",
    "plot_df['count_group_reg'] = plot_df.groupby('org', observed=True).cumcount()\n",
    "\n",
    "# plotnine to plot (using grammar of graphics like ggplot2)\n",
    "plot = (\n",
    "    gg.ggplot(plot_df, gg.aes(x='Registration_Time', y='count_group_reg', color='org'))\n",
    "    + gg.geom_line()\n",
    "    + gg.scale_x_datetime(\n",
    "        breaks='12 hours', \n",
    "        date_labels='%m-%d %H:%M', \n",
    "    )\n",
    "    + gg.facet_wrap('org')\n",
    "    + gg.theme_bw()\n",
    "    + gg.theme(figure_size=(9,6))\n",
    "    + gg.theme(axis_text_x=gg.element_text(angle=30, hjust=1))\n",
    "    + gg.xlab(\"Time\")\n",
    "    + gg.ylab(\"Registrations\") \n",
    "    + gg.ggtitle(\"Registrations for R workshop in first 48 hours\")\n",
    ")\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "08700f27-d61b-46f4-9654-52fe2dacab93",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Show save, download, export"
   ]
  }
 ],
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