{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "488975f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c53635e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| label: load-data\n",
    "df1 = pd.read_csv(\"anonymized_all.csv\")\n",
    "df2 = pd.read_csv(\"anonymized_fy24_only.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "456f0479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique Identifier</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Client Name</th>\n",
       "      <th>Job ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537926</td>\n",
       "      <td>6/14/21</td>\n",
       "      <td>537926 Record</td>\n",
       "      <td>job-188418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>476870</td>\n",
       "      <td>6/18/21</td>\n",
       "      <td>476870 Record</td>\n",
       "      <td>job-188751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>458255</td>\n",
       "      <td>6/14/21</td>\n",
       "      <td>458255 Record</td>\n",
       "      <td>job-189026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>479256</td>\n",
       "      <td>8/4/20</td>\n",
       "      <td>479256 Record</td>\n",
       "      <td>job-181914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>472697</td>\n",
       "      <td>8/7/20</td>\n",
       "      <td>472697 Record</td>\n",
       "      <td>job-181916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unique Identifier Start Date    Client Name      Job ID\n",
       "0             537926    6/14/21  537926 Record  job-188418\n",
       "1             476870    6/18/21  476870 Record  job-188751\n",
       "2             458255    6/14/21  458255 Record  job-189026\n",
       "3             479256     8/4/20  479256 Record  job-181914\n",
       "4             472697     8/7/20  472697 Record  job-181916"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e97fa56",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique Identifier</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Client Name</th>\n",
       "      <th>Job ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>581013</td>\n",
       "      <td>9/25/23</td>\n",
       "      <td>581013 Record</td>\n",
       "      <td>job-193451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>583586</td>\n",
       "      <td>7/17/23</td>\n",
       "      <td>583586 Record</td>\n",
       "      <td>job-198299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>585047</td>\n",
       "      <td>7/11/23</td>\n",
       "      <td>585047 Record</td>\n",
       "      <td>job-198331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>576444</td>\n",
       "      <td>7/9/23</td>\n",
       "      <td>576444 Record</td>\n",
       "      <td>job-198365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>585401</td>\n",
       "      <td>9/18/23</td>\n",
       "      <td>585401 Record</td>\n",
       "      <td>job-198436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unique Identifier Start Date    Client Name      Job ID\n",
       "0             581013    9/25/23  581013 Record  job-193451\n",
       "1             583586    7/17/23  583586 Record  job-198299\n",
       "2             585047    7/11/23  585047 Record  job-198331\n",
       "3             576444     7/9/23  576444 Record  job-198365\n",
       "4             585401    9/18/23  585401 Record  job-198436"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc58cdb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| label: data-clean-fn\n",
    "def clean_df(df, date_col, drop_col):\n",
    "    df[date_col] = pd.to_datetime(df[date_col])\n",
    "    df = df.drop([drop_col], axis = 1)\n",
    "    df.columns = df.columns.str.lower()\n",
    "    \n",
    "    return df\n",
    "\n",
    "cleaned_df1 = clean_df(df1,'Start Date', 'Client Name')\n",
    "cleaned_df2 = clean_df(df2,'Start Date', 'Client Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3cbe8377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>unique identifier</th>\n",
       "      <th>start date</th>\n",
       "      <th>job id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537926</td>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>job-188418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>476870</td>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>job-188751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>458255</td>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>job-189026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>479256</td>\n",
       "      <td>2020-08-04</td>\n",
       "      <td>job-181914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>472697</td>\n",
       "      <td>2020-08-07</td>\n",
       "      <td>job-181916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique identifier start date      job id\n",
       "0             537926 2021-06-14  job-188418\n",
       "1             476870 2021-06-18  job-188751\n",
       "2             458255 2021-06-14  job-189026\n",
       "3             479256 2020-08-04  job-181914\n",
       "4             472697 2020-08-07  job-181916"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1201cecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| label: fiscal-year-function\n",
    "def fiscal_year(date):\n",
    "    if date.month >=7:\n",
    "        return date.year + 1\n",
    "    else:\n",
    "        return date.year\n",
    "    \n",
    "cleaned_df1['fiscal year'] = cleaned_df1['start date'].apply(fiscal_year)\n",
    "cleaned_df2['fiscal year'] = cleaned_df2['start date'].apply(fiscal_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0eae8ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2021, 2022, 2023, 2024])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| label: check-years-in-dataset-1\n",
    "cleaned_df1['fiscal year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1962f2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2024])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| label: check-years-in-dataset-2\n",
    "cleaned_df2['fiscal year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8fde6972",
   "metadata": {},
   "outputs": [],
   "source": [
    "# <!-- ```{python}\n",
    "# merged_df = pd.merge(cleaned_all, cleaned_fy24, how = 'outer')\n",
    "# merged_df\n",
    "# ```\n",
    "\n",
    "\n",
    "\n",
    "# ```{python}\n",
    "# merged_df['fiscal year'] = merged_df['start date'].apply(get_fiscal_year)\n",
    "# merged_df.head()\n",
    "# ```\n",
    "# ```{python}\n",
    "# merged_df['fiscal year'].unique()\n",
    "# ```\n",
    "\n",
    "# ```{python}\n",
    "# merged_df = merged_df[merged_df['fiscal year'].isin([2023, 2024])]\n",
    "# merged_df\n",
    "# ```\n",
    "\n",
    "# ```{python}\n",
    "# weekly_performance = (\n",
    "#   merged_df\n",
    "#   .groupby(['fiscal year', pd.Grouper(key='start date', freq='W')]).size()\n",
    "#   .reset_index(name = 'weekly_performance')\n",
    "# )\n",
    "\n",
    "# weekly_performance\n",
    "# ```\n",
    "\n",
    "# ```{python}\n",
    "# weekly_performance['cumulative_performance'] = (\n",
    "#   weekly_performance\n",
    "#   .groupby('fiscal year')['weekly_performance']\n",
    "#   .cumsum()\n",
    "# )\n",
    "\n",
    "# weekly_performance\n",
    "# ```\n",
    "# ```{python}\n",
    "# # Pivot the data\n",
    "# weekly_performance['start date'] = weekly_performance['start date'].dt.strftime('%m-%d')\n",
    "\n",
    "# pivoted_data = weekly_performance.pivot(index='start date', columns='fiscal year', values='cumulative_performance')\n",
    "# pivoted_data.columns = [f'Cumulative Performance FY{year}' for year in pivoted_data.columns]\n",
    "\n",
    "# pivoted_data.reset_index(inplace=True)\n",
    "\n",
    "# pivoted_data\n",
    "# ```\n",
    "# ```{python}\n",
    "# # Plotting\n",
    "# fig, ax = plt.subplots(figsize=(10, 6))\n",
    "# pivoted_data.plot(x='start date', ax=ax)\n",
    "\n",
    "# ax.set_title('Cumulative Weekly Performance by Fiscal Year')\n",
    "# ax.set_xlabel('Date')\n",
    "# ax.set_ylabel('Cumulative Performance')\n",
    "# ax.legend()\n",
    "\n",
    "# plt.show()\n",
    "# ```\n",
    "# ```{python}\n",
    "# weekly_performance.to_csv(\"weekly_performance.csv\", index = False) \n",
    "# ``` -->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f15d442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique identifier</th>\n",
       "      <th>start date</th>\n",
       "      <th>job id</th>\n",
       "      <th>fiscal year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8381</th>\n",
       "      <td>581013</td>\n",
       "      <td>2023-09-25</td>\n",
       "      <td>job-193451</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8382</th>\n",
       "      <td>583586</td>\n",
       "      <td>2023-07-17</td>\n",
       "      <td>job-198299</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8383</th>\n",
       "      <td>585047</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>job-198331</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8384</th>\n",
       "      <td>576444</td>\n",
       "      <td>2023-07-09</td>\n",
       "      <td>job-198365</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8385</th>\n",
       "      <td>585401</td>\n",
       "      <td>2023-09-18</td>\n",
       "      <td>job-198436</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11009</th>\n",
       "      <td>619903</td>\n",
       "      <td>2024-03-13</td>\n",
       "      <td>job-205994</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11010</th>\n",
       "      <td>602062</td>\n",
       "      <td>2024-03-26</td>\n",
       "      <td>job-206010</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11011</th>\n",
       "      <td>598480</td>\n",
       "      <td>2024-03-06</td>\n",
       "      <td>job-206013</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11012</th>\n",
       "      <td>615498</td>\n",
       "      <td>2024-03-03</td>\n",
       "      <td>job-206017</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11013</th>\n",
       "      <td>460343</td>\n",
       "      <td>2024-02-11</td>\n",
       "      <td>job-206027</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2633 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       unique identifier start date      job id  fiscal year\n",
       "8381              581013 2023-09-25  job-193451         2024\n",
       "8382              583586 2023-07-17  job-198299         2024\n",
       "8383              585047 2023-07-11  job-198331         2024\n",
       "8384              576444 2023-07-09  job-198365         2024\n",
       "8385              585401 2023-09-18  job-198436         2024\n",
       "...                  ...        ...         ...          ...\n",
       "11009             619903 2024-03-13  job-205994         2024\n",
       "11010             602062 2024-03-26  job-206010         2024\n",
       "11011             598480 2024-03-06  job-206013         2024\n",
       "11012             615498 2024-03-03  job-206017         2024\n",
       "11013             460343 2024-02-11  job-206027         2024\n",
       "\n",
       "[2633 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| label: filter-fy24-previous\n",
    "cleaned_previous = cleaned_df1[cleaned_df1['fiscal year'].isin([2024])]\n",
    "cleaned_previous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9567589c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fiscal year</th>\n",
       "      <th>start date</th>\n",
       "      <th>previous_weekly_performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-02</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-09</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-16</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-23</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fiscal year start date  previous_weekly_performance\n",
       "0         2024 2023-07-02                           15\n",
       "1         2024 2023-07-09                           77\n",
       "2         2024 2023-07-16                           99\n",
       "3         2024 2023-07-23                           71\n",
       "4         2024 2023-07-30                           72"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| label: calc-previous-weekly-performance\n",
    "previous_weekly_performance = (\n",
    "  cleaned_previous\n",
    "  .groupby(['fiscal year', pd.Grouper(key='start date', freq='W')]).size()\n",
    "  .reset_index(name = 'previous_weekly_performance')\n",
    ")\n",
    "previous_weekly_performance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07c2db8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fiscal year</th>\n",
       "      <th>start date</th>\n",
       "      <th>previous_weekly_performance</th>\n",
       "      <th>previous_cumulative_performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-02</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-09</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-16</td>\n",
       "      <td>99</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-23</td>\n",
       "      <td>71</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>72</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fiscal year start date  previous_weekly_performance  \\\n",
       "0         2024 2023-07-02                           15   \n",
       "1         2024 2023-07-09                           77   \n",
       "2         2024 2023-07-16                           99   \n",
       "3         2024 2023-07-23                           71   \n",
       "4         2024 2023-07-30                           72   \n",
       "\n",
       "   previous_cumulative_performance  \n",
       "0                               15  \n",
       "1                               92  \n",
       "2                              191  \n",
       "3                              262  \n",
       "4                              334  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| label: calc-previous-week-over-week-performance\n",
    "previous_weekly_performance['previous_cumulative_performance'] = (\n",
    "  previous_weekly_performance\n",
    "  .groupby('fiscal year')['previous_weekly_performance']\n",
    "  .cumsum()\n",
    ")\n",
    "previous_weekly_performance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80ae3a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fiscal year</th>\n",
       "      <th>start date</th>\n",
       "      <th>current_weekly_performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-02</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-09</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-16</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-23</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fiscal year start date  current_weekly_performance\n",
       "0         2024 2023-07-02                          15\n",
       "1         2024 2023-07-09                          77\n",
       "2         2024 2023-07-16                          98\n",
       "3         2024 2023-07-23                          72\n",
       "4         2024 2023-07-30                          72"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_weekly_performance = (\n",
    "  cleaned_df2\n",
    "  .groupby(['fiscal year', pd.Grouper(key='start date', freq='W')]).size()\n",
    "  .reset_index(name = 'current_weekly_performance')\n",
    ")\n",
    "current_weekly_performance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f00f505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fiscal year</th>\n",
       "      <th>start date</th>\n",
       "      <th>current_weekly_performance</th>\n",
       "      <th>current_cumulative_performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024</td>\n",
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       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-09</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024</td>\n",
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       "      <td>98</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
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       "      <td>72</td>\n",
       "      <td>262</td>\n",
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       "      <th>4</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>72</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fiscal year start date  current_weekly_performance  \\\n",
       "0         2024 2023-07-02                          15   \n",
       "1         2024 2023-07-09                          77   \n",
       "2         2024 2023-07-16                          98   \n",
       "3         2024 2023-07-23                          72   \n",
       "4         2024 2023-07-30                          72   \n",
       "\n",
       "   current_cumulative_performance  \n",
       "0                              15  \n",
       "1                              92  \n",
       "2                             190  \n",
       "3                             262  \n",
       "4                             334  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_weekly_performance['current_cumulative_performance']= (\n",
    "  current_weekly_performance\n",
    "  .groupby('fiscal year')['current_weekly_performance']\n",
    "  .cumsum()\n",
    ")\n",
    "current_weekly_performance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1046db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fiscal year</th>\n",
       "      <th>start date</th>\n",
       "      <th>previous_weekly_performance</th>\n",
       "      <th>previous_cumulative_performance</th>\n",
       "      <th>current_weekly_performance</th>\n",
       "      <th>current_cumulative_performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-02</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
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       "      <th>1</th>\n",
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       "      <td>77</td>\n",
       "      <td>92</td>\n",
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       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-16</td>\n",
       "      <td>99</td>\n",
       "      <td>191</td>\n",
       "      <td>98</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-23</td>\n",
       "      <td>71</td>\n",
       "      <td>262</td>\n",
       "      <td>72</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024</td>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>72</td>\n",
       "      <td>334</td>\n",
       "      <td>72</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fiscal year start date  previous_weekly_performance  \\\n",
       "0         2024 2023-07-02                           15   \n",
       "1         2024 2023-07-09                           77   \n",
       "2         2024 2023-07-16                           99   \n",
       "3         2024 2023-07-23                           71   \n",
       "4         2024 2023-07-30                           72   \n",
       "\n",
       "   previous_cumulative_performance  current_weekly_performance  \\\n",
       "0                               15                          15   \n",
       "1                               92                          77   \n",
       "2                              191                          98   \n",
       "3                              262                          72   \n",
       "4                              334                          72   \n",
       "\n",
       "   current_cumulative_performance  \n",
       "0                              15  \n",
       "1                              92  \n",
       "2                             190  \n",
       "3                             262  \n",
       "4                             334  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| label: merge-dfs\n",
    "merged_df = pd.merge(previous_weekly_performance, current_weekly_performance, how = 'outer')\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "358b7176",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df.to_csv(\"weekly_performance.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9278bb88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| label: fig-line-chart\n",
    "#| fig-cap: \"Initial stages of the data visualization workflow, creating basic plots to explore and refine key trends\"\n",
    "#| fig-subcap: \n",
    "#|   - \"The bar chart shows weekly performance trends for previous and current periods, highlighting fluctuations and potential seasonal patterns. This dense representation helps identify key variations but can be visually complex.\"\n",
    "#|   - \"The line chart offers a clearer view of performance over time by smoothing out weekly noise and emphasizing long-term trends.\"\n",
    "#| layout-ncol: 2\n",
    "\n",
    "\n",
    "# Plotting 1\n",
    "# Set 'start date' as the index for better plotting\n",
    "merged_df.set_index('start date', inplace=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "# Width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Positions of the bars\n",
    "index = range(len(merged_df))\n",
    "\n",
    "# Plotting 'previous_weekly_performance'\n",
    "bars1 = ax.bar(index, merged_df['previous_weekly_performance'], width=bar_width, label='Previous Weekly Performance', color='b')\n",
    "\n",
    "# Plotting 'current_weekly_performance' shifted right by bar_width to place next to the previous\n",
    "bars2 = ax.bar([p + bar_width for p in index], merged_df['current_weekly_performance'], width=bar_width, label='Current Weekly Performance', color='r')\n",
    "\n",
    "# Adding labels, title, and legend\n",
    "ax.set_xlabel('Start Date')\n",
    "ax.set_ylabel('Weekly Performance')\n",
    "ax.set_title('Comparison of Previous and Current Weekly Performance')\n",
    "ax.legend()\n",
    "\n",
    "# Set x-ticks to be in the middle of the two bars for each date\n",
    "ax.set_xticks([p + bar_width / 2 for p in index])\n",
    "ax.set_xticklabels(merged_df.index.strftime('%Y-%m-%d'), rotation=45)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Plotting 2\n",
    "# Resetting the index to put 'start date' back as a regular column\n",
    "merged_df.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plotting the 'previous_cumulative_performance'\n",
    "plt.plot(merged_df['start date'], merged_df['previous_cumulative_performance'], label='Previous Cumulative Performance', marker='o')\n",
    "\n",
    "# Plotting the 'current_cumulative_performance'\n",
    "plt.plot(merged_df['start date'], merged_df['current_cumulative_performance'], label='Current Cumulative Performance', marker='o')\n",
    " \n",
    "plt.title('Comparison of Previous and Current Cumulative Performance Over Time')\n",
    "plt.xlabel('Start Date')\n",
    "plt.ylabel('Cumulative Performance')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)  # Rotates the dates on the x-axis for better visibility\n",
    "plt.tight_layout()  # Adjusts plot parameters to give some padding and prevent overlap\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a249c489",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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