Intentional Walk 2023 Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "62740fba",
   "metadata": {},
   "source": [
    "# Data Imputation for 2023 IWALK Contest Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cee033c",
   "metadata": {},
   "source": [
    "This dataset contains daily step data of 301 participants during the 2023 IWalk contest period from May 1st 2023 to Aug 31st 2023 . There are missing data during the baseline period of May 1st to May 31st and during the contest period from June 1st to August 31st that we will impute. This notebook compares the performance of two methods of data imputation by analyzing differences in the distribution of average step data before and after the imputation. Finally, we test for statistical significance between the average daily step count before and after the contest to show if the participants' average daily step count increased after the intervention. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e7b06c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from scipy import stats\n",
    "from scipy.stats import skew, kurtosis\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "\n",
    "#worksheet with deduped, de-ided step data \n",
    "stepsdata_path = (\"/Users/tanvitilloo/Desktop/SF Civic Tech/intentional-walk-analysis/App_DataRaw.xlsx\")  \n",
    "stepdata_raw = pd.read_excel(stepsdata_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77cc358a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 123)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepdata_raw.shape #show number of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74cb0e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-05-01', '2023-05-02', '2023-05-03', '2023-05-04',\n",
       "               '2023-05-05', '2023-05-06', '2023-05-07', '2023-05-08',\n",
       "               '2023-05-09', '2023-05-10',\n",
       "               ...\n",
       "               '2023-08-22', '2023-08-23', '2023-08-24', '2023-08-25',\n",
       "               '2023-08-26', '2023-08-27', '2023-08-28', '2023-08-29',\n",
       "               '2023-08-30', '2023-08-31'],\n",
       "              dtype='datetime64[ns]', length=123, freq=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepdata_raw.columns #show column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d19b3828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2023-05-01', '2023-05-02', '2023-05-03', '2023-05-04', '2023-05-05',\n",
       "       '2023-05-06', '2023-05-07', '2023-05-08', '2023-05-09', '2023-05-10',\n",
       "       ...\n",
       "       '2023-08-22', '2023-08-23', '2023-08-24', '2023-08-25', '2023-08-26',\n",
       "       '2023-08-27', '2023-08-28', '2023-08-29', '2023-08-30', '2023-08-31'],\n",
       "      dtype='object', length=123)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepdata_raw.columns = pd.to_datetime(stepdata_raw.columns).strftime('%Y-%m-%d') #remove time from column names\n",
    "stepdata_raw.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "16a30fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023-05-01    float64\n",
       "2023-05-02    float64\n",
       "2023-05-03    float64\n",
       "2023-05-04    float64\n",
       "2023-05-05    float64\n",
       "               ...   \n",
       "2023-08-27    float64\n",
       "2023-08-28    float64\n",
       "2023-08-29    float64\n",
       "2023-08-30    float64\n",
       "2023-08-31    float64\n",
       "Length: 123, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepdata_raw.dtypes #show data type of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5840ea4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023-05-01     43\n",
       "2023-05-02     43\n",
       "2023-05-03     42\n",
       "2023-05-04     44\n",
       "2023-05-05     43\n",
       "             ... \n",
       "2023-08-27    199\n",
       "2023-08-28    202\n",
       "2023-08-29    209\n",
       "2023-08-30    208\n",
       "2023-08-31    256\n",
       "Length: 123, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepdata_raw.isna().sum() #total number of missing values per column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3809bb9",
   "metadata": {},
   "source": [
    "As we can see, we have missing data for every day of the baseline and contest period. Thus, we first collect the count of missing data per user in a variable. Then we filter the dataset by removing users who have more than 25 percent of missing data during baseline and contest period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "76a91d7a-149b-4b05-96ae-c948e45a359e",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data_count = stepdata_raw.isna().sum(axis = 1) #total number of missing values per user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "80c27917-3016-4e28-accc-43842f1f6ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      100\n",
       "1       24\n",
       "2       49\n",
       "3        0\n",
       "4       64\n",
       "      ... \n",
       "296      1\n",
       "297      0\n",
       "298     71\n",
       "299      1\n",
       "300      0\n",
       "Length: 301, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e06ae9b0-8e0e-44ef-9a24-ae30b5882e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_columns = stepdata_raw.loc[:, \"2023-05-01\" : \"2023-05-31\"].columns\n",
    "contest_columns = stepdata_raw.loc[:, \"2023-06-01\" : \"2023-08-31\"].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "840adf6b-9931-44d7-bf25-d600b3a47917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126, 123)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing users who have more than 25% of data missing during either baseline or contest period\n",
    "stepdata_filtered = stepdata_raw.loc[\n",
    "                         (stepdata_raw[baseline_columns].isna().mean(axis = 1) <= 0.25) &\n",
    "                        (stepdata_raw[contest_columns].isna().mean(axis = 1) <= 0.25)\n",
    "]\n",
    "stepdata_filtered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4d4064c9-95f2-4148-8fae-830b1d2aea26",
   "metadata": {},
   "outputs": [
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       "      <td>9065.0</td>\n",
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       "      <td>9406.0</td>\n",
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       "      <td>8279.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>407.0</td>\n",
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       "      <td>4320.0</td>\n",
       "      <td>7021.0</td>\n",
       "      <td>2313.0</td>\n",
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       "      <td>7620.0</td>\n",
       "      <td>10971.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>5603.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "    2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "3       4930.0     29351.0     30457.0     25895.0      5528.0     17858.0   \n",
       "5       3802.0      2655.0      2653.0      3209.0       247.0        80.0   \n",
       "10      8953.0      4788.0      3025.0         NaN      9065.0       839.0   \n",
       "13      1855.0      2557.0      1634.0      5155.0      3287.0      2439.0   \n",
       "14       942.0       165.0      1239.0      4048.0      2635.0      2653.0   \n",
       "16      2470.0      3745.0      3976.0      3203.0      2349.0      4475.0   \n",
       "17      4168.0      1031.0      2154.0      4458.0      1710.0      4161.0   \n",
       "18      1898.0      2744.0      3286.0      2246.0      3659.0      1687.0   \n",
       "19      7267.0      5774.0      6780.0     10771.0      5660.0      2167.0   \n",
       "22      1647.0      5247.0      7655.0      7222.0      8142.0      4813.0   \n",
       "\n",
       "    2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-08-22  \\\n",
       "3       2879.0      3567.0     26381.0     21659.0  ...     13341.0   \n",
       "5        527.0      1041.0      1581.0      3412.0  ...      4232.0   \n",
       "10      1297.0      9406.0      5441.0      8279.0  ...         NaN   \n",
       "13      4916.0      3626.0      1677.0      2465.0  ...      4557.0   \n",
       "14      2724.0       525.0       617.0      1058.0  ...      1985.0   \n",
       "16      1088.0      2605.0      3085.0      3698.0  ...      4767.0   \n",
       "17      1495.0      4320.0      7021.0      2313.0  ...      7027.0   \n",
       "18      4242.0      6451.0      5168.0      5367.0  ...         NaN   \n",
       "19      5297.0      5666.0     11123.0      5065.0  ...     15504.0   \n",
       "22      2333.0      2013.0      1106.0     10376.0  ...         NaN   \n",
       "\n",
       "    2023-08-23  2023-08-24  2023-08-25  2023-08-26  2023-08-27  2023-08-28  \\\n",
       "3      25484.0     24971.0     21905.0     21560.0      2291.0      2989.0   \n",
       "5       6175.0      3086.0      3121.0      5402.0      2972.0      4501.0   \n",
       "10         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "13      3686.0      5706.0       106.0         NaN         NaN         NaN   \n",
       "14      1671.0      8111.0      3858.0      1429.0      1101.0      2556.0   \n",
       "16      5045.0      3131.0      3372.0     11700.0      2065.0      3211.0   \n",
       "17      9040.0      7620.0     10971.0      8807.0      3665.0      6630.0   \n",
       "18         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "19      2610.0      5327.0      8376.0      1717.0      1050.0      7737.0   \n",
       "22         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "    2023-08-29  2023-08-30  2023-08-31  \n",
       "3        387.0     23299.0     23534.0  \n",
       "5       5652.0       923.0         NaN  \n",
       "10         NaN       407.0         NaN  \n",
       "13         NaN         NaN         NaN  \n",
       "14      1471.0      5606.0         NaN  \n",
       "16      5831.0      3343.0         NaN  \n",
       "17      6852.0      7886.0         NaN  \n",
       "18         NaN         NaN         NaN  \n",
       "19      6543.0      5603.0         NaN  \n",
       "22         NaN         NaN         NaN  \n",
       "\n",
       "[10 rows x 123 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepdata_filtered.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "778532cd-23da-4978-8ed3-bc3a28d066fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       0\n",
       "5       1\n",
       "10     24\n",
       "13      6\n",
       "14      3\n",
       "       ..\n",
       "292     3\n",
       "296     1\n",
       "297     0\n",
       "299     1\n",
       "300     0\n",
       "Length: 126, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepdata_filtered.isna().sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "437fa336-5c43-4d53-8773-0ae3caf1ad7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of removed users: [0, 1, 2, 4, 6, 7, 8, 9, 11, 12, 15, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39, 42, 43, 45, 46, 47, 50, 51, 57, 59, 60, 61, 63, 65, 72, 74, 76, 77, 78, 79, 86, 87, 88, 90, 91, 92, 94, 95, 98, 101, 103, 104, 105, 106, 108, 110, 111, 112, 117, 118, 119, 121, 123, 125, 126, 127, 129, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 144, 146, 147, 148, 149, 152, 154, 157, 160, 161, 163, 164, 165, 166, 167, 169, 170, 171, 172, 173, 174, 176, 178, 179, 183, 184, 185, 186, 187, 189, 192, 193, 196, 198, 199, 200, 201, 202, 203, 205, 209, 214, 216, 220, 221, 222, 223, 224, 227, 228, 229, 231, 235, 239, 241, 242, 244, 245, 247, 249, 250, 252, 253, 255, 257, 259, 260, 261, 263, 264, 265, 266, 267, 268, 269, 271, 272, 275, 278, 279, 280, 282, 286, 287, 289, 290, 291, 293, 294, 295, 298]\n",
      "Number of users removed: 175\n",
      "Percentage of users removed: 58.139534883720934 %\n"
     ]
    }
   ],
   "source": [
    "#information on removed users\n",
    "removed_users = stepdata_raw.index[\n",
    "    (stepdata_raw[baseline_columns].isna().mean(axis=1) > 0.25) |\n",
    "    (stepdata_raw[contest_columns].isna().mean(axis=1) > 0.25)\n",
    "].tolist()\n",
    "num_removed_users = len(removed_users)\n",
    "total_users = len(stepdata_raw)\n",
    "percentage_removed = (num_removed_users / total_users) * 100\n",
    "\n",
    "print(\"List of removed users:\", removed_users)\n",
    "print(\"Number of users removed:\", num_removed_users)\n",
    "print(\"Percentage of users removed:\", percentage_removed, \"%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5431726f-44b5-4e58-ba15-5cee4da62dec",
   "metadata": {},
   "source": [
    "We dropped 175 observations or 58% of the original data to adhere to the 25% missing data rule. We will now perform imputation on this new dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8590144",
   "metadata": {},
   "source": [
    "## Baseline Data Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c869eddc",
   "metadata": {},
   "source": [
    "We will first calculate the number of missing values in the baseline data and visualize them to see if any patterns emerge. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8d89609f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per column:\n",
      " 2023-05-01    1\n",
      "2023-05-02    1\n",
      "2023-05-03    0\n",
      "2023-05-04    1\n",
      "2023-05-05    0\n",
      "2023-05-06    0\n",
      "2023-05-07    0\n",
      "2023-05-08    0\n",
      "2023-05-09    0\n",
      "2023-05-10    0\n",
      "2023-05-11    0\n",
      "2023-05-12    0\n",
      "2023-05-13    0\n",
      "2023-05-14    1\n",
      "2023-05-15    1\n",
      "2023-05-16    1\n",
      "2023-05-17    0\n",
      "2023-05-18    0\n",
      "2023-05-19    0\n",
      "2023-05-20    0\n",
      "2023-05-21    0\n",
      "2023-05-22    0\n",
      "2023-05-23    1\n",
      "2023-05-24    0\n",
      "2023-05-25    0\n",
      "2023-05-26    0\n",
      "2023-05-27    0\n",
      "2023-05-28    0\n",
      "2023-05-29    0\n",
      "2023-05-30    1\n",
      "2023-05-31    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "baseline_df = stepdata_filtered.loc[:, \"2023-05-01\": \"2023-05-31\"] #subset the data by baseline month\n",
    "b_missing_per_column = baseline_df.isna().sum() #calculate missing values per day\n",
    "print(\"Missing values per column:\\n\", b_missing_per_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5abb2400-2c36-4266-8582-a324f0ea2f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum number of missing values in a single day: 1\n"
     ]
    }
   ],
   "source": [
    "# plots those values in a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=b_missing_per_column.index, y=b_missing_per_column.values, color=\"indigo\")\n",
    "\n",
    "plt.title('Distribution of Missing Values per Day after Users Missing >25% Data Removed - Baseline', fontsize=16)\n",
    "plt.xlabel('Days', fontsize=12)\n",
    "plt.ylabel('Number of Missing Values', fontsize=12)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "max_missing_values = b_missing_per_column.max()\n",
    "print(\"Maximum number of missing values in a single day:\", max_missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "82d0ae3e-01c6-4660-94b6-cd43c43fcf82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_missing_data_count = baseline_df.isna().sum(axis = 1) #calculate missing values per user \n",
    "b_missing_data_count.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cde87ee-9c28-4b19-ad0c-18943a62d3aa",
   "metadata": {},
   "source": [
    "As we can see, the maximum number of days a user is missing data is 2. Thus, using a mean imputation for this dataset should not be a cause for concern as we will not be imputing a lot of values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346d5823-d194-4155-b4f3-c4425d58582b",
   "metadata": {},
   "source": [
    "#### Mean Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f883d14-0fa7-4ac7-9b93-b3e272bc0462",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1707d7a8-c0db-47d7-8cee-cfdf90f6f379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      15839.580645\n",
       "5       3416.935484\n",
       "10      6483.600000\n",
       "13      3928.354839\n",
       "14      1979.677419\n",
       "           ...     \n",
       "292     1818.258065\n",
       "296     7501.032258\n",
       "297     6250.193548\n",
       "299     7312.838710\n",
       "300    19098.387097\n",
       "Length: 126, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_row_mean = baseline_df.mean(axis = 1) #calculate row mean on non-imputed data\n",
    "baseline_row_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8abc15a6-4d7c-4104-a893-399d5bd68ddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_mean_impute_df = baseline_df.apply(lambda row: row.fillna(row.mean()), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7d769b8d-6fc8-4e2c-9f80-c84126289825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023-05-01             0\n",
       "2023-05-02             0\n",
       "2023-05-03             0\n",
       "2023-05-04             0\n",
       "2023-05-05             0\n",
       "2023-05-06             0\n",
       "2023-05-07             0\n",
       "2023-05-08             0\n",
       "2023-05-09             0\n",
       "2023-05-10             0\n",
       "2023-05-11             0\n",
       "2023-05-12             0\n",
       "2023-05-13             0\n",
       "2023-05-14             0\n",
       "2023-05-15             0\n",
       "2023-05-16             0\n",
       "2023-05-17             0\n",
       "2023-05-18             0\n",
       "2023-05-19             0\n",
       "2023-05-20             0\n",
       "2023-05-21             0\n",
       "2023-05-22             0\n",
       "2023-05-23             0\n",
       "2023-05-24             0\n",
       "2023-05-25             0\n",
       "2023-05-26             0\n",
       "2023-05-27             0\n",
       "2023-05-28             0\n",
       "2023-05-29             0\n",
       "2023-05-30             0\n",
       "2023-05-31             0\n",
       "Average Daily Steps    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_mean_impute_df.isna().sum() #check that the imputation worked "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a7ca8546-9931-43cc-9339-398bf1c32feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      15839.580645\n",
       "5       3416.935484\n",
       "10      6483.600000\n",
       "13      3928.354839\n",
       "14      1979.677419\n",
       "           ...     \n",
       "292     1818.258065\n",
       "296     7501.032258\n",
       "297     6250.193548\n",
       "299     7312.838710\n",
       "300    19098.387097\n",
       "Name: Average Daily Steps, Length: 126, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_mean_impute_df[\"Average Daily Steps\"] = baseline_mean_impute_df.mean(axis = 1) #calculate row mean on imputed data\n",
    "baseline_mean_impute_df[\"Average Daily Steps\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2cb77db0-217a-4ebc-99b2-1c9bddb6a215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12abc1af0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the distribution of average daily steps before and after the imputation\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "#Baseline average: Before mean imputation\n",
    "sns.histplot(baseline_row_mean, label = \"Not imputed\", kde=True, color=\"skyblue\", ax=ax[0])\n",
    "ax[0].set_title(\"Baseline Distribution of Average Daily Steps Before Mean Imputation\")\n",
    "ax[0].legend()\n",
    "ax[0].set_xlabel(\"Average Daily Steps\")\n",
    "\n",
    "#Baseline average: After mean imputation\n",
    "sns.histplot(baseline_mean_impute_df[\"Average Daily Steps\"], label = \"Imputed with Mean\", kde=True, color=\"salmon\", ax=ax[1])\n",
    "ax[1].set_title(\"Baseline Distribution of Average Daily Steps After Mean Imputation\")\n",
    "ax[1].legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2622c549-79f4-42a1-87cd-12d543cab6d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of Average Steps Before Imputation: 6257.54\n",
      "Mean of Average Steps After Imputation: 6257.54\n",
      "Variance of Average Steps Before Imputation : 14515523.35\n",
      "Variance of Average Steps After Imputation : 14515523.35\n",
      "Standard Deviation of Average Steps Before Imputation: 3809.92\n",
      "Standard Deviation of Average Steps After Imputation: 3809.92\n"
     ]
    }
   ],
   "source": [
    "notimputed_mean = round(baseline_row_mean.mean(), 2)\n",
    "notimputed_variance = round(baseline_row_mean.var(), 2)\n",
    "notimputed_std_dev = round(baseline_row_mean.std(), 2)\n",
    "\n",
    "imputed_mean = round(baseline_mean_impute_df[\"Average Daily Steps\"].mean(), 2)\n",
    "imputed_variance = round(baseline_mean_impute_df[\"Average Daily Steps\"].var(), 2)\n",
    "imputed_std_dev = round(baseline_mean_impute_df[\"Average Daily Steps\"].std(), 2)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Mean of Average Steps Before Imputation: {notimputed_mean}\")\n",
    "print(f\"Mean of Average Steps After Imputation: {imputed_mean}\")\n",
    "print(f\"Variance of Average Steps Before Imputation : {notimputed_variance}\")\n",
    "print(f\"Variance of Average Steps After Imputation : {imputed_variance}\")\n",
    "print(f\"Standard Deviation of Average Steps Before Imputation: {notimputed_std_dev}\")\n",
    "print(f\"Standard Deviation of Average Steps After Imputation: {imputed_std_dev}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc6ee36-3e3f-429c-b678-3fbf9cdb3b4d",
   "metadata": {},
   "source": [
    "As we can see, the mean imputation does not change the distribution of average daily steps. This makes sense as the dataset after the filtering only contained a maximum of 1 missing value per day of the baseline period. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aef2662-a14d-47bb-8174-768ae8112c6f",
   "metadata": {},
   "source": [
    "### Contest Data Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5998dfd4-a110-430b-aaa2-57a200ebdf17",
   "metadata": {},
   "source": [
    "We will first calculate the number of missing values in the contest data and see if any patterns emerge "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ae272e8d-de12-4955-a685-3b1518d63a40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per column:\n",
      " 2023-06-01     0\n",
      "2023-06-02     0\n",
      "2023-06-03     0\n",
      "2023-06-04     0\n",
      "2023-06-05     0\n",
      "              ..\n",
      "2023-08-27    35\n",
      "2023-08-28    37\n",
      "2023-08-29    44\n",
      "2023-08-30    43\n",
      "2023-08-31    87\n",
      "Length: 92, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "contest_df = stepdata_filtered.loc[:, \"2023-06-01\": \"2023-08-31\"] #subset the data by baseline month\n",
    "c_missing_per_column = contest_df.isna().sum() #calculate missing values per day\n",
    "print(\"Missing values per column:\\n\", c_missing_per_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9b3c7c16-f9d1-4cd3-8551-2b5506229e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum number of missing values in a single day: 87\n"
     ]
    }
   ],
   "source": [
    "# plots those values in a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=c_missing_per_column.index, y=c_missing_per_column.values, color=\"indigo\")\n",
    "\n",
    "plt.title('Distribution of Missing Values per Day after Users Missing >25% Data Removed - Contest', fontsize=16)\n",
    "plt.xlabel('Days', fontsize=12)\n",
    "plt.ylabel('Number of Missing Values', fontsize=12)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "max_missing_values = c_missing_per_column.max()\n",
    "print(\"Maximum number of missing values in a single day:\", max_missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "ab717100-ff05-46da-831d-893d47a91b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       0\n",
       "5       1\n",
       "10     23\n",
       "13      6\n",
       "14      3\n",
       "       ..\n",
       "292     3\n",
       "296     1\n",
       "297     0\n",
       "299     1\n",
       "300     0\n",
       "Length: 126, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_missing_data_count = contest_df.isna().sum(axis = 1) #calculate missing values per user \n",
    "c_missing_data_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a63d7b-70b3-4bae-a553-8c8c2fc9ae27",
   "metadata": {},
   "source": [
    "As we can see, an increasing number of users have missing data towards the end of the contest. This has two implications - 1) Data are not missing at random 2) Using a method like back fill or forward fill is likely to skew the distribution of average daily steps for users with large amounts of missing data toward the end of the contest. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b16f355-2999-4c3d-ab72-337f4d79e58d",
   "metadata": {},
   "source": [
    "We will first try the simplest approach of filling in missing values with the user's average daily steps over the number of days with available values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c907591f-2e08-4187-a43b-3d853801814b",
   "metadata": {},
   "source": [
    "#### Mean Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82b152fa-93f0-4131-a5ac-204d23def165",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2861baab-09a4-4e2c-8f24-647a3e075504",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ea43162c-1e47-4ffb-aa53-e78bea858555",
   "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>2023-06-01</th>\n",
       "      <th>2023-06-02</th>\n",
       "      <th>2023-06-03</th>\n",
       "      <th>2023-06-04</th>\n",
       "      <th>2023-06-05</th>\n",
       "      <th>2023-06-06</th>\n",
       "      <th>2023-06-07</th>\n",
       "      <th>2023-06-08</th>\n",
       "      <th>2023-06-09</th>\n",
       "      <th>2023-06-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-08-22</th>\n",
       "      <th>2023-08-23</th>\n",
       "      <th>2023-08-24</th>\n",
       "      <th>2023-08-25</th>\n",
       "      <th>2023-08-26</th>\n",
       "      <th>2023-08-27</th>\n",
       "      <th>2023-08-28</th>\n",
       "      <th>2023-08-29</th>\n",
       "      <th>2023-08-30</th>\n",
       "      <th>2023-08-31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1628.0</td>\n",
       "      <td>2802.0</td>\n",
       "      <td>21968.0</td>\n",
       "      <td>23583.0</td>\n",
       "      <td>5649.0</td>\n",
       "      <td>26682.0</td>\n",
       "      <td>25961.0</td>\n",
       "      <td>3571.0</td>\n",
       "      <td>4919.0</td>\n",
       "      <td>21482.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13341.0</td>\n",
       "      <td>25484.0</td>\n",
       "      <td>24971.0</td>\n",
       "      <td>21905.0</td>\n",
       "      <td>21560.0</td>\n",
       "      <td>2291.0</td>\n",
       "      <td>2989.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>23299.0</td>\n",
       "      <td>23534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2882.0</td>\n",
       "      <td>7223.0</td>\n",
       "      <td>4818.0</td>\n",
       "      <td>4001.0</td>\n",
       "      <td>7732.0</td>\n",
       "      <td>6731.0</td>\n",
       "      <td>4121.0</td>\n",
       "      <td>1727.0</td>\n",
       "      <td>3308.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4232.0</td>\n",
       "      <td>6175.0</td>\n",
       "      <td>3086.0</td>\n",
       "      <td>3121.0</td>\n",
       "      <td>5402.0</td>\n",
       "      <td>2972.0</td>\n",
       "      <td>4501.0</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8063.0</td>\n",
       "      <td>6442.0</td>\n",
       "      <td>3070.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>4752.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>2828.0</td>\n",
       "      <td>2638.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>407.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4210.0</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>4616.0</td>\n",
       "      <td>4423.0</td>\n",
       "      <td>6208.0</td>\n",
       "      <td>4545.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>4971.0</td>\n",
       "      <td>3226.0</td>\n",
       "      <td>1697.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4557.0</td>\n",
       "      <td>3686.0</td>\n",
       "      <td>5706.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5240.0</td>\n",
       "      <td>5155.0</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>6945.0</td>\n",
       "      <td>3991.0</td>\n",
       "      <td>9201.0</td>\n",
       "      <td>7247.0</td>\n",
       "      <td>3071.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>1671.0</td>\n",
       "      <td>8111.0</td>\n",
       "      <td>3858.0</td>\n",
       "      <td>1429.0</td>\n",
       "      <td>1101.0</td>\n",
       "      <td>2556.0</td>\n",
       "      <td>1471.0</td>\n",
       "      <td>5606.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>463.0</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>3203.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2717.0</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>...</td>\n",
       "      <td>744.0</td>\n",
       "      <td>2313.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>688.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>9119.0</td>\n",
       "      <td>9085.0</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>4159.0</td>\n",
       "      <td>13396.0</td>\n",
       "      <td>7276.0</td>\n",
       "      <td>13186.0</td>\n",
       "      <td>10177.0</td>\n",
       "      <td>11086.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11775.0</td>\n",
       "      <td>8520.0</td>\n",
       "      <td>8455.0</td>\n",
       "      <td>11643.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>3110.0</td>\n",
       "      <td>11227.0</td>\n",
       "      <td>10432.0</td>\n",
       "      <td>9494.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>7746.0</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>11671.0</td>\n",
       "      <td>8195.0</td>\n",
       "      <td>7589.0</td>\n",
       "      <td>8790.0</td>\n",
       "      <td>10151.0</td>\n",
       "      <td>2404.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4246.0</td>\n",
       "      <td>7584.0</td>\n",
       "      <td>9723.0</td>\n",
       "      <td>12112.0</td>\n",
       "      <td>3957.0</td>\n",
       "      <td>4327.0</td>\n",
       "      <td>7347.0</td>\n",
       "      <td>4752.0</td>\n",
       "      <td>6028.0</td>\n",
       "      <td>1657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>14235.0</td>\n",
       "      <td>7576.0</td>\n",
       "      <td>1662.0</td>\n",
       "      <td>11679.0</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>2511.0</td>\n",
       "      <td>5703.0</td>\n",
       "      <td>5926.0</td>\n",
       "      <td>4908.0</td>\n",
       "      <td>10699.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7167.0</td>\n",
       "      <td>5923.0</td>\n",
       "      <td>7251.0</td>\n",
       "      <td>7763.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>6798.0</td>\n",
       "      <td>7533.0</td>\n",
       "      <td>5681.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>22574.0</td>\n",
       "      <td>21309.0</td>\n",
       "      <td>23406.0</td>\n",
       "      <td>3567.0</td>\n",
       "      <td>5055.0</td>\n",
       "      <td>23931.0</td>\n",
       "      <td>19691.0</td>\n",
       "      <td>21014.0</td>\n",
       "      <td>27703.0</td>\n",
       "      <td>21147.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26331.0</td>\n",
       "      <td>29399.0</td>\n",
       "      <td>29934.0</td>\n",
       "      <td>25111.0</td>\n",
       "      <td>27408.0</td>\n",
       "      <td>5676.0</td>\n",
       "      <td>7551.0</td>\n",
       "      <td>19042.0</td>\n",
       "      <td>29407.0</td>\n",
       "      <td>28767.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2023-06-01  2023-06-02  2023-06-03  2023-06-04  2023-06-05  2023-06-06  \\\n",
       "3        1628.0      2802.0     21968.0     23583.0      5649.0     26682.0   \n",
       "5        2882.0      7223.0      4818.0      4001.0      7732.0      6731.0   \n",
       "10       8063.0      6442.0      3070.0      2259.0      1531.0      3439.0   \n",
       "13       4210.0      4284.0      4616.0      4423.0      6208.0      4545.0   \n",
       "14       5240.0      5155.0      5069.0       953.0      5197.0      6945.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "292       463.0      1510.0      1961.0      3203.0      2910.0       322.0   \n",
       "296      9119.0      9085.0      1164.0      4159.0     13396.0      7276.0   \n",
       "297      7725.0      7746.0      2379.0       350.0     11671.0      8195.0   \n",
       "299     14235.0      7576.0      1662.0     11679.0      2206.0      2511.0   \n",
       "300     22574.0     21309.0     23406.0      3567.0      5055.0     23931.0   \n",
       "\n",
       "     2023-06-07  2023-06-08  2023-06-09  2023-06-10  ...  2023-08-22  \\\n",
       "3       25961.0      3571.0      4919.0     21482.0  ...     13341.0   \n",
       "5        4121.0      1727.0      3308.0      6800.0  ...      4232.0   \n",
       "10       4752.0      2087.0      2828.0      2638.0  ...         NaN   \n",
       "13       1401.0      4971.0      3226.0      1697.0  ...      4557.0   \n",
       "14       3991.0      9201.0      7247.0      3071.0  ...      1985.0   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "292      2717.0      1661.0       456.0      1940.0  ...       744.0   \n",
       "296     13186.0     10177.0     11086.0      2827.0  ...     11775.0   \n",
       "297      7589.0      8790.0     10151.0      2404.0  ...      4246.0   \n",
       "299      5703.0      5926.0      4908.0     10699.0  ...      7167.0   \n",
       "300     19691.0     21014.0     27703.0     21147.0  ...     26331.0   \n",
       "\n",
       "     2023-08-23  2023-08-24  2023-08-25  2023-08-26  2023-08-27  2023-08-28  \\\n",
       "3       25484.0     24971.0     21905.0     21560.0      2291.0      2989.0   \n",
       "5        6175.0      3086.0      3121.0      5402.0      2972.0      4501.0   \n",
       "10          NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "13       3686.0      5706.0       106.0         NaN         NaN         NaN   \n",
       "14       1671.0      8111.0      3858.0      1429.0      1101.0      2556.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "292      2313.0       859.0       309.0       415.0      1400.0       688.0   \n",
       "296      8520.0      8455.0     11643.0      2478.0      3110.0     11227.0   \n",
       "297      7584.0      9723.0     12112.0      3957.0      4327.0      7347.0   \n",
       "299      5923.0      7251.0      7763.0       535.0      2382.0      6798.0   \n",
       "300     29399.0     29934.0     25111.0     27408.0      5676.0      7551.0   \n",
       "\n",
       "     2023-08-29  2023-08-30  2023-08-31  \n",
       "3         387.0     23299.0     23534.0  \n",
       "5        5652.0       923.0         NaN  \n",
       "10          NaN       407.0         NaN  \n",
       "13          NaN         NaN         NaN  \n",
       "14       1471.0      5606.0         NaN  \n",
       "..          ...         ...         ...  \n",
       "292         NaN         NaN         NaN  \n",
       "296     10432.0      9494.0         NaN  \n",
       "297      4752.0      6028.0      1657.0  \n",
       "299      7533.0      5681.0         NaN  \n",
       "300     19042.0     29407.0     28767.0  \n",
       "\n",
       "[126 rows x 92 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contest_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "8e40318b-a71e-4e32-b073-30a422bf47df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      16604.641304\n",
       "5       4600.725275\n",
       "10      4556.782609\n",
       "13      3256.023256\n",
       "14      4090.730337\n",
       "           ...     \n",
       "292     1819.146067\n",
       "296     6870.208791\n",
       "297     6467.326087\n",
       "299     5397.483516\n",
       "300    17921.750000\n",
       "Length: 126, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contest_row_mean = contest_df.mean(axis = 1) #calculate row mean on non-imputed data\n",
    "contest_row_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "fc2bc624-6eca-4454-87f0-8f4c7551bace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023-06-01    0\n",
       "2023-06-02    0\n",
       "2023-06-03    0\n",
       "2023-06-04    0\n",
       "2023-06-05    0\n",
       "             ..\n",
       "2023-08-27    0\n",
       "2023-08-28    0\n",
       "2023-08-29    0\n",
       "2023-08-30    0\n",
       "2023-08-31    0\n",
       "Length: 92, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contest_mean_impute_df = contest_df.apply(lambda row: row.fillna(row.mean()), axis=1)\n",
    "contest_mean_impute_df.isna().sum() #check that the imputation worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d9cc4159-e2a1-42a2-ab3f-29ee98f5da20",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2023-06-01</th>\n",
       "      <th>2023-06-02</th>\n",
       "      <th>2023-06-03</th>\n",
       "      <th>2023-06-04</th>\n",
       "      <th>2023-06-05</th>\n",
       "      <th>2023-06-06</th>\n",
       "      <th>2023-06-07</th>\n",
       "      <th>2023-06-08</th>\n",
       "      <th>2023-06-09</th>\n",
       "      <th>2023-06-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-08-22</th>\n",
       "      <th>2023-08-23</th>\n",
       "      <th>2023-08-24</th>\n",
       "      <th>2023-08-25</th>\n",
       "      <th>2023-08-26</th>\n",
       "      <th>2023-08-27</th>\n",
       "      <th>2023-08-28</th>\n",
       "      <th>2023-08-29</th>\n",
       "      <th>2023-08-30</th>\n",
       "      <th>2023-08-31</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1628.0</td>\n",
       "      <td>2802.0</td>\n",
       "      <td>21968.0</td>\n",
       "      <td>23583.0</td>\n",
       "      <td>5649.0</td>\n",
       "      <td>26682.0</td>\n",
       "      <td>25961.0</td>\n",
       "      <td>3571.0</td>\n",
       "      <td>4919.0</td>\n",
       "      <td>21482.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13341.000000</td>\n",
       "      <td>25484.000000</td>\n",
       "      <td>24971.000000</td>\n",
       "      <td>21905.000000</td>\n",
       "      <td>21560.000000</td>\n",
       "      <td>2291.000000</td>\n",
       "      <td>2989.000000</td>\n",
       "      <td>387.000000</td>\n",
       "      <td>23299.000000</td>\n",
       "      <td>23534.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2882.0</td>\n",
       "      <td>7223.0</td>\n",
       "      <td>4818.0</td>\n",
       "      <td>4001.0</td>\n",
       "      <td>7732.0</td>\n",
       "      <td>6731.0</td>\n",
       "      <td>4121.0</td>\n",
       "      <td>1727.0</td>\n",
       "      <td>3308.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4232.000000</td>\n",
       "      <td>6175.000000</td>\n",
       "      <td>3086.000000</td>\n",
       "      <td>3121.000000</td>\n",
       "      <td>5402.000000</td>\n",
       "      <td>2972.000000</td>\n",
       "      <td>4501.000000</td>\n",
       "      <td>5652.000000</td>\n",
       "      <td>923.000000</td>\n",
       "      <td>4600.725275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8063.0</td>\n",
       "      <td>6442.0</td>\n",
       "      <td>3070.0</td>\n",
       "      <td>2259.0</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>4752.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>2828.0</td>\n",
       "      <td>2638.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4556.782609</td>\n",
       "      <td>4556.782609</td>\n",
       "      <td>4556.782609</td>\n",
       "      <td>4556.782609</td>\n",
       "      <td>4556.782609</td>\n",
       "      <td>4556.782609</td>\n",
       "      <td>4556.782609</td>\n",
       "      <td>4556.782609</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>4556.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4210.0</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>4616.0</td>\n",
       "      <td>4423.0</td>\n",
       "      <td>6208.0</td>\n",
       "      <td>4545.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>4971.0</td>\n",
       "      <td>3226.0</td>\n",
       "      <td>1697.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4557.000000</td>\n",
       "      <td>3686.000000</td>\n",
       "      <td>5706.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>3256.023256</td>\n",
       "      <td>3256.023256</td>\n",
       "      <td>3256.023256</td>\n",
       "      <td>3256.023256</td>\n",
       "      <td>3256.023256</td>\n",
       "      <td>3256.023256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5240.0</td>\n",
       "      <td>5155.0</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>6945.0</td>\n",
       "      <td>3991.0</td>\n",
       "      <td>9201.0</td>\n",
       "      <td>7247.0</td>\n",
       "      <td>3071.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>1671.000000</td>\n",
       "      <td>8111.000000</td>\n",
       "      <td>3858.000000</td>\n",
       "      <td>1429.000000</td>\n",
       "      <td>1101.000000</td>\n",
       "      <td>2556.000000</td>\n",
       "      <td>1471.000000</td>\n",
       "      <td>5606.000000</td>\n",
       "      <td>4090.730337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>463.0</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>3203.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2717.0</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>...</td>\n",
       "      <td>744.000000</td>\n",
       "      <td>2313.000000</td>\n",
       "      <td>859.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>688.000000</td>\n",
       "      <td>1819.146067</td>\n",
       "      <td>1819.146067</td>\n",
       "      <td>1819.146067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>9119.0</td>\n",
       "      <td>9085.0</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>4159.0</td>\n",
       "      <td>13396.0</td>\n",
       "      <td>7276.0</td>\n",
       "      <td>13186.0</td>\n",
       "      <td>10177.0</td>\n",
       "      <td>11086.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11775.000000</td>\n",
       "      <td>8520.000000</td>\n",
       "      <td>8455.000000</td>\n",
       "      <td>11643.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>3110.000000</td>\n",
       "      <td>11227.000000</td>\n",
       "      <td>10432.000000</td>\n",
       "      <td>9494.000000</td>\n",
       "      <td>6870.208791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>7746.0</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>11671.0</td>\n",
       "      <td>8195.0</td>\n",
       "      <td>7589.0</td>\n",
       "      <td>8790.0</td>\n",
       "      <td>10151.0</td>\n",
       "      <td>2404.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4246.000000</td>\n",
       "      <td>7584.000000</td>\n",
       "      <td>9723.000000</td>\n",
       "      <td>12112.000000</td>\n",
       "      <td>3957.000000</td>\n",
       "      <td>4327.000000</td>\n",
       "      <td>7347.000000</td>\n",
       "      <td>4752.000000</td>\n",
       "      <td>6028.000000</td>\n",
       "      <td>1657.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>14235.0</td>\n",
       "      <td>7576.0</td>\n",
       "      <td>1662.0</td>\n",
       "      <td>11679.0</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>2511.0</td>\n",
       "      <td>5703.0</td>\n",
       "      <td>5926.0</td>\n",
       "      <td>4908.0</td>\n",
       "      <td>10699.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7167.000000</td>\n",
       "      <td>5923.000000</td>\n",
       "      <td>7251.000000</td>\n",
       "      <td>7763.000000</td>\n",
       "      <td>535.000000</td>\n",
       "      <td>2382.000000</td>\n",
       "      <td>6798.000000</td>\n",
       "      <td>7533.000000</td>\n",
       "      <td>5681.000000</td>\n",
       "      <td>5397.483516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>22574.0</td>\n",
       "      <td>21309.0</td>\n",
       "      <td>23406.0</td>\n",
       "      <td>3567.0</td>\n",
       "      <td>5055.0</td>\n",
       "      <td>23931.0</td>\n",
       "      <td>19691.0</td>\n",
       "      <td>21014.0</td>\n",
       "      <td>27703.0</td>\n",
       "      <td>21147.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26331.000000</td>\n",
       "      <td>29399.000000</td>\n",
       "      <td>29934.000000</td>\n",
       "      <td>25111.000000</td>\n",
       "      <td>27408.000000</td>\n",
       "      <td>5676.000000</td>\n",
       "      <td>7551.000000</td>\n",
       "      <td>19042.000000</td>\n",
       "      <td>29407.000000</td>\n",
       "      <td>28767.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2023-06-01  2023-06-02  2023-06-03  2023-06-04  2023-06-05  2023-06-06  \\\n",
       "3        1628.0      2802.0     21968.0     23583.0      5649.0     26682.0   \n",
       "5        2882.0      7223.0      4818.0      4001.0      7732.0      6731.0   \n",
       "10       8063.0      6442.0      3070.0      2259.0      1531.0      3439.0   \n",
       "13       4210.0      4284.0      4616.0      4423.0      6208.0      4545.0   \n",
       "14       5240.0      5155.0      5069.0       953.0      5197.0      6945.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "292       463.0      1510.0      1961.0      3203.0      2910.0       322.0   \n",
       "296      9119.0      9085.0      1164.0      4159.0     13396.0      7276.0   \n",
       "297      7725.0      7746.0      2379.0       350.0     11671.0      8195.0   \n",
       "299     14235.0      7576.0      1662.0     11679.0      2206.0      2511.0   \n",
       "300     22574.0     21309.0     23406.0      3567.0      5055.0     23931.0   \n",
       "\n",
       "     2023-06-07  2023-06-08  2023-06-09  2023-06-10  ...    2023-08-22  \\\n",
       "3       25961.0      3571.0      4919.0     21482.0  ...  13341.000000   \n",
       "5        4121.0      1727.0      3308.0      6800.0  ...   4232.000000   \n",
       "10       4752.0      2087.0      2828.0      2638.0  ...   4556.782609   \n",
       "13       1401.0      4971.0      3226.0      1697.0  ...   4557.000000   \n",
       "14       3991.0      9201.0      7247.0      3071.0  ...   1985.000000   \n",
       "..          ...         ...         ...         ...  ...           ...   \n",
       "292      2717.0      1661.0       456.0      1940.0  ...    744.000000   \n",
       "296     13186.0     10177.0     11086.0      2827.0  ...  11775.000000   \n",
       "297      7589.0      8790.0     10151.0      2404.0  ...   4246.000000   \n",
       "299      5703.0      5926.0      4908.0     10699.0  ...   7167.000000   \n",
       "300     19691.0     21014.0     27703.0     21147.0  ...  26331.000000   \n",
       "\n",
       "       2023-08-23    2023-08-24    2023-08-25    2023-08-26   2023-08-27  \\\n",
       "3    25484.000000  24971.000000  21905.000000  21560.000000  2291.000000   \n",
       "5     6175.000000   3086.000000   3121.000000   5402.000000  2972.000000   \n",
       "10    4556.782609   4556.782609   4556.782609   4556.782609  4556.782609   \n",
       "13    3686.000000   5706.000000    106.000000   3256.023256  3256.023256   \n",
       "14    1671.000000   8111.000000   3858.000000   1429.000000  1101.000000   \n",
       "..            ...           ...           ...           ...          ...   \n",
       "292   2313.000000    859.000000    309.000000    415.000000  1400.000000   \n",
       "296   8520.000000   8455.000000  11643.000000   2478.000000  3110.000000   \n",
       "297   7584.000000   9723.000000  12112.000000   3957.000000  4327.000000   \n",
       "299   5923.000000   7251.000000   7763.000000    535.000000  2382.000000   \n",
       "300  29399.000000  29934.000000  25111.000000  27408.000000  5676.000000   \n",
       "\n",
       "       2023-08-28    2023-08-29    2023-08-30    2023-08-31  \n",
       "3     2989.000000    387.000000  23299.000000  23534.000000  \n",
       "5     4501.000000   5652.000000    923.000000   4600.725275  \n",
       "10    4556.782609   4556.782609    407.000000   4556.782609  \n",
       "13    3256.023256   3256.023256   3256.023256   3256.023256  \n",
       "14    2556.000000   1471.000000   5606.000000   4090.730337  \n",
       "..            ...           ...           ...           ...  \n",
       "292    688.000000   1819.146067   1819.146067   1819.146067  \n",
       "296  11227.000000  10432.000000   9494.000000   6870.208791  \n",
       "297   7347.000000   4752.000000   6028.000000   1657.000000  \n",
       "299   6798.000000   7533.000000   5681.000000   5397.483516  \n",
       "300   7551.000000  19042.000000  29407.000000  28767.000000  \n",
       "\n",
       "[126 rows x 92 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contest_mean_impute_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d489d3f4-8b9e-40be-8e8a-df96014db922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      16604.641304\n",
       "5       4600.725275\n",
       "10      4556.782609\n",
       "13      3256.023256\n",
       "14      4090.730337\n",
       "           ...     \n",
       "292     1819.146067\n",
       "296     6870.208791\n",
       "297     6467.326087\n",
       "299     5397.483516\n",
       "300    17921.750000\n",
       "Name: Average Daily Steps, Length: 126, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contest_mean_impute_df[\"Average Daily Steps\"] = contest_mean_impute_df.mean(axis = 1) #calculate row mean on imputed data\n",
    "contest_mean_impute_df[\"Average Daily Steps\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "7df262ad-daba-46f5-a927-d4a8b4565f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of Average Steps Before Imputation: 7187.50478\n",
      "Mean of Average Steps After Imputation: 7187.50478\n",
      "Variance of Average Steps Before Imputation : 22907642.51514\n",
      "Variance of Average Steps After Imputation : 22907642.51514\n",
      "Standard Deviation of Average Steps Before Imputation: 4786.1929\n",
      "Standard Deviation of Average Steps After Imputation: 4786.1929\n"
     ]
    },
    {
     "data": {
      "image/png": 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UnGp3NG3OMeIoybJk1n0ns2Or3eEAABAWHa+S004F1n/raNNPA3ITXOqS6JLPSMsLGQUHAACih9npH/2mzE6yrI6xzu++rLQMWX0PkST5vvivzdEAABAeFODaAWOM9uwdAZfWwaaf7mt4ln8U3PLCSnlpxgAAAKLF7r3TTzMybQ7EPo7D9jZj+HKJTBUnWwEA0afjVnPakdIaI4+RnJaU4u64v7K+aTFKcFkq9fi0tqja7nAAAAAOmKkol8rLJFlSesctwFnde0pZ2VJNtXwrl9kdDgAAra7jVnPakd3B0W/ODjktIcDpsDQoM04S01ABAECU2Nt8Qampslxue2OxkWVZcgw/UpLkW7JIhtkOAIAoY2sB7q677tKhhx6q5ORkZWdn67TTTtOaNWtCtjHGaNq0aercubPi4+M1evRoffXVVzZFbI/d1f4CXHoM9dIhewtw60pqgtNyAQBA5CHPaxoT6H6akWVzJPZzDB4huVzSts0yP220OxwAAFqVrRWdhQsX6oorrtBnn32mefPmyePxaMKECSorKwtuc8899+iBBx7QI488osWLFys3N1fjx49XSUmJjZG3nRqfUWmN/wxgegdtwLCvtFineiT7zw4zCg4AgMhFntc44/NKe3b5L1CAkxWfIOvgIZIk39JF9gYDAEArs7UA984772jq1Kk6+OCDNXjwYM2YMUMbN27U0qVLJfnPij744IO68cYbNWnSJB1yyCF69tlnVV5erhdffNHO0NtMYPppostSjLPjTj/d19As/yi4Fbsq5fUxPQEAgEhEntcEe/ZIPp8UEyslJtsdTUQITEM1q/7nXx8PAIAoEVFzGouKiiRJGRkZkqR169Zp69atmjBhQnCb2NhYjRo1Sp9++mmdj1FVVaXi4uKQn/Zsd5VPEqPf9tUrNUZJbofKPUbf0owBAIB2gTyvtsD0U2Vkdeh1fvdldS2QcvIkj0e+FUvtDgcAgFYTMQU4Y4yuueYaHX300TrkkEMkSVu3bpUk5eTkhGybk5MTvG1/d911l1JTU4M/+fn54Q08jIwxwXXO0mIj5ldlO4dlaXBmrCTpf0xDBQAg4pHn1WN3YP23jtv9dH/+ZgxHSaIZAwAgukRMVefKK6/UihUr9NJLL9W6bf8zgsaYes8S3nDDDSoqKgr+bNq0KSzxtoUyj5HHSE5LSnZHzK8qIgzOjJMlaWNpjXZWeuwOBwAANIA8rzZTVSmV710PL40C3L4cg4ZJ7hipcJvMxh/sDgcAgFYREVWdq666Sq+//ro+/PBDde3aNXh9bm6uJNU6C7p9+/ZaZ0sDYmNjlZKSEvLTXgVGv6XGOORgWkKIlBineqbGSKIZAwAAkYw8rx67d/r/TU6R5XbbG0uEsWLjZA0cJsk/Cg4AgGhgawHOGKMrr7xSs2bN0gcffKAePXqE3N6jRw/l5uZq3rx5weuqq6u1cOFCHXXUUW0dbpvbU+1f/y0thvXf6jI009+MYeWuKtXQjAEAgIhCntcwEyjApTP6rS7O4UdIkszqFTJlpTZHAwDAgXPZufMrrrhCL774ol577TUlJycHz4CmpqYqPj5elmXp6quv1vTp09W7d2/17t1b06dPV0JCgiZPnmxn6GHnNUbFewtwqaz/VqceKW6lxDhUXO3Tmj1VOiQjzu6QAADAXuR59TPGBEfAWRTg6mR1zpfVOV9m8yb5li+Wc+QYu0MCAOCA2FqAe/zxxyVJo0ePDrl+xowZmjp1qiTpuuuuU0VFhS6//HLt3r1bhx9+uN577z0lJ0d3q/biap+MpBiHpXgn00/r4rAsDcmM00dbyrW8sJICHAAAEYQ8rwFlJVJNjeRwSilpdkcTsRzDj5B38yb5ln0mx1Gj6RQLAGjXbC3ANaWrkWVZmjZtmqZNmxb+gCLInqq9009jHSQbDRiYGauPt5TrxzKPdlZ6lBln6yENAAD2Is9rQGD6aVq6LAczHepjHTJUevd1aVehzMYfZBX0tDskAABajG/8CLWn2t+AIS2GX1FDkt1O9UzxN2NYsbPK5mgAAAAaZ3bvksT008ZYMbGyDh4iSfIt+9zeYAAAOEBUdyJQtdeo3OM/a5wWSwOGxgzKjJUkrdxVKW8TzrYDAADYxXi90p7d/gsU4BrlGHa4JMl8/aVMZYXN0QAA0HIU4CJQ0d7Rb4kuS24H008b0zM1RokuS+Ueo7VF1XaHAwAAUL/iPZLxSTGxUkKi3dFEPKtLN6lTruTxyLfyf3aHAwBAi1GAi0BFge6nMYx+awqnZWlgpr8Bw4qdlTZHAwAAUD8TGP2WlsE6v01gWVZwFJzvf0xDBQC0XxTgItDPBTh+PU01aG8H1B+Ka1S8dwQhAABAxNmzd/23tHSbA2k/HIOGSU6ntOVHmS0/2h0OAAAtQsvICFPlNar0+tcxS2lHBbjVq1fbtu+srCx169ZN+UkubSr1aOWuKo3MTbAtHgAAgLoYr0cqKfJfSMuwN5hmiIQ8z+p3iMxXX8r3vy/kzOtqWzwAALQUBbgIE1j/LcllydUO1n/buX2rZFk677zzbIshISFBq1ev1uDMbG0qLdWKnZU6KieeaR0AACCyFBVJxkixcVJcvN3RNGrrrl2ypIjI87oOPVzer76Ub8VSOcZPlOV22xYTAAAtQQEuwgSnn7aT7qelexPJK2+/X4MPPbzN979h7RrdceXFKiws1MCu+Zr3Y5mKqn3aUFqj7skxbR4PAABAfcze6adKS28XJwr3lJbJSHro8t/oiMED23z/32zYpAum36PCwkLlDx0ipaZLRbtlVq+QNWh4m8cDAMCBoAAXYYrb6fpvXXr0VN9BQ2yNwe2wdHB6rJYVVurLwkoKcAAAILIUBdZ/az/TTyWpV5c8DevT29YYLMshx9DD5Fvwrnz/+1wOCnAAgHamfVV5olyV1xdc/y3Zza+mJQbt7Yb6bVG1Kjw+m6MBAADw86//Vuy/0M4KcJHCMeRQSZbM+u9ldhXaHQ4AAM1ClSeCBKafJrnbx/pvkSg3waWceKe8RvpqV5Xd4QAAAPgV7fGv/xYXJ6sdrP8WiazUdFm9+kqSfP/7wuZoAABoHgpwESS4/ltM+1j/LVIN3jsK7sudlTLG2BwNAADAPuu/pTL67UA4hvrXHPYt/0LG57U5GgAAmo4CXAQpqmqf679FmgHpsXJZ0o5Kr7aUe+wOBwAAQNrTPtd/izRW3wFSQpJUWiLz3Wq7wwEAoMmo9ESISo9PVT4jS1IK678dkDiXQ33TYiVJK3YyDRUAANjLeDxSSYn/Qlq6vcG0c5bTJcfgEZKYhgoAaF+o9ESIn9d/c8jJ+m8HbFCmvwD39e4qVXuZhgoAAGxUtFuSkeLiWf+tFTiGHSZJMt+ulikpsjkaAACahgJchPh5/Td+Ja2hW5Jb6bEOVfuMvtnDKDgAAGCf4PpvTD9tFVZWjqz87pLxybd8id3hAADQJFR7IoAxhgJcK7MsS4Myfm7GAAAAYJui3ZIki+mnrcYxbG8zhv99TtMtAEC7QLUnAlR6jar3rv+WTAGu1QzMjJMl6acyj3ZV0iULAAC0PeOpkUqK/RcYAddqrAGDpZhYafdOmY3r7A4HAIBGUe2JAMV7R78lux1yWqz/1lqS3A4dlOKWJK3cxSg4AABgg6I9/n/jE2TFxtkaSjSxYmL9RThJvuU0YwAARD5XS+500EEHafHixcrMzAy5fs+ePRo2bJh++OGHVgmuowhMP01h9FuLrV5ddxv6RMVLVict21qipK1rFI7yZlZWlrp16xaGRwYAoO2R57Wun9d/Y/ppS9WX5yUkpKqnJM/K/2lVXg/5XO5W3zd5HgCgtbSoALd+/Xp5vbWn9FVVVemnn3464KA6Ev/6b/7XkvXfmm/n9q2SZem8886r83anO0Z/eXellJahyVf+Sd9++kGrx5CQkKDVq1eTnAEAogJ5XivbW4CzmH7abFt37ZIl1ZvnSdJXV12o3pnpeuA3F+n5L79u9RjI8wAAraVZBbjXX389+P93331Xqampwcter1fz589X9+7dWy24jsC//ptY/62FSouKJGN05e33a/Chh9e9TYylSkmX3vf/lFK8pVX3v2HtGt1x5cUqLCwkMQMAtGvkea3PeGqk0hL/hVQKcM21p7RMRtJDl/9GRwweWOc2SSV7pNLd+sc5p+mqyy9r1f1/s2GTLph+D3keAKBVNKsAd9ppp0nyd5icMmVKyG1ut1vdu3fX/fff32rBdQSB9d+SWP/tgHTp0VN9Bw2p87bSGp++3FmlmrgU9eyWLZeD1xkAgP2R54VByPpvsbaG0p716pKnYX1613mbqayU+fwjJVVXamh+F1nxCW0cHQAATdOsApzP5y8W9ejRQ4sXL1ZWVlZYgupIimtY/y3cEl2WElyWyj1GOyq9ykto0cxrAACiGnle6zOBAlxKmp1hRDUrLk4mPdPfDXXrZlk9etkdEgAAdWpR1WfdunUkZa0kMAIuxU0BLlwsy1JOvL/otr3CY3M0AABENvK8VlS0W5JkpdKAIZys3M7+/2zbLGOMvcEAAFCPFg8Fmj9/vubPn6/t27cHz5gGPPPMMwccWEdQ7TWq9PqTBEbAhVeneKfWl9SotMaovManBAqeAADUizzvwBmfVyop8l9ITbM1lqiXmS05XVJVpb/pRXpm4/cBAKCNtagKceutt2rChAmaP3++CgsLtXv37pAfNE1g+mmCy2JdsjBzOyylx/oP920VtTu7AQAAP/K8VlJSLBkjuWMk1iULK8vplLJzJUlm62abowEAoG4tGgH3xBNPaObMmTr//PNbO54OpbjaXwhi+mnbyI53aVdVtXZUelSQ7JKDphcAANRCntdKAuu/pabJIucIOyu3s8yWH6XCbTKefrJcbrtDAgAgRIsqP9XV1TrqqKNaO5YOJ7j+G9NP20R6rENuh1Tjk/ZU+Rq/AwAAHRB5XuswrP/WtpJTpYREyeeTdmyzOxoAAGppUeXn17/+tV588cXWjqVD8fiMyjys/9aWHJalTnH+QZ/baMYAAECdyPMOnDEmZAQcws+yrGAzBrP1J5ujAQCgthZNQa2srNRTTz2l999/X4MGDZLbHTrE+4EHHmiV4KJZ6d7132IdlmKdFODaSna8U5vLPdpd5VONz8jN2nsAAIQgz2sFZaWS1yM5nFJSst3RdBzZnaUf1krFRTLlZbISEu2OCACAoBYV4FasWKEhQ4ZIklatWhVyG2tcNA3TT+2R6HYo0WWpzGO0o8KrzoktbgQMAEBUIs9rBXunnyolVZZFrtdWrNhYmYxMaVehzNbNsg7qbXdIAAAEtaj68OGHH7Z2HB1OoAMqBbi2lxPv0g8lNdpe4aEABwDAfsjzDpzZO/2U9d/anpXbRWZXobRts0yPXhSNAQARg+qPDXzGqCQwAo4OqG0uK94pS1KZxwSnAgMAALQG//pve0fAsf5b28vsJLncUnWVtHun3dEAABDUouE/Y8aMafBs0gcffNDigDqCshojnySXJcW7OCvX1twOSxlxTu2s9Gp7hUdJ7hi7QwIAIGKQ5x2gqkp/8ceypJRUu6PpcCyHQyYnT/ppo8zWn2RlZNkdEgAAklpYgAusCxJQU1Oj5cuXa9WqVZoyZUprxBXVimu8kqTkGAfD4m2SE+8vwO2o8Kp7spGD3wMAAJLI8w5YYPRbUrIsJ0td2MHK6Szz00apcLtMTY2s/RqJAABghxZlBX//+9/rvH7atGkqLS09oIA6Ahow2C8txqEYh1Ttk3ZV+ZQV57Q7JAAAIgJ53oEJrP8m1n+zT1KylJjk70a7fYvUpZvdEQEA0LprwJ133nl65plnWvMho44x5ucCHOu/2cayLHWK99eft1d4bI4GAIDIR57XRHtHwNGAwT6WZcnK7SJJMts22xwNAAB+rVoBWrRokeLi4lrzIaNOhdfIY/wvfBIFOFtlx/tHve2u8qnaa2yOBgCAyEae1zhTUy2Vl/kvpKTZGkuHl53nX4evpFimrMTuaAAAaNkU1EmTJoVcNsZoy5YtWrJkiW666aZWCSxaBUa/JbkdrDtmswSXQ8luh0pqfNpe6VHXRNYHAQCAPO8ABKafJiTKiqHJk52smBiZjE7Szu0yWzfL6tnX7pAAAB1ciwpwqamhHZ0cDof69u2r2267TRMmTGiVwKIV679Flux4p78AV+5VlwQXTTEAAB0eeV7LmUADhtQ0W+OAn5XbWWbndmnbFpkevWU5yL8BAPZpUQFuxowZrR1Hh1FcQwEukmTFObWuuEYVXqPSGqPkGApwAICOjTzvAOwdAWelsP5bRMjIktwxUk21tKtQysq2OyIAQAd2QL3Rly5dqtWrV8uyLA0YMEBDhw5trbiiUrXXqGrvWmPJrP8WEVwOS5lxTu2o9Gp7hUfJTBcBAEASeV5zGZ9XKi32X2AEXESwHA6ZnDzpxw0y2zbLogAHALBRiwpw27dv17nnnqsFCxYoLS1NxhgVFRVpzJgxevnll9WpU6fWjjMqlOwd/ZbgsuRyMNIqUmTH+wtwOyq96p5i5GQaKgCgAyPPa6GSYskY/4iruHi7o8FeVm5nmR83SDt3yFRXszYfAMA2LRqGddVVV6m4uFhfffWVdu3apd27d2vVqlUqLi7W7373u9aOMWoE1n9j9FtkSY1xKNZhyWukXZVeu8MBAMBW5HktVLzH/29KGmvKRhArMVlKTvEXR7dvsTscAEAH1qIRcO+8847ef/999e/fP3jdgAED9Oijj7I4bwNKWP8tIlmWpU7xTv1Y5tH2Cq86xR/QzGwAANo18ryWMcVFkiRrvyYWsJ+V01mmpNjfDbVrgd3hAAA6qBZVgnw+n9xud63r3W63fD7fAQcVjXzGqLSGEXCRKifeKUnaU+1TlZdjGADQcZHnNZ8xJtiAQSlpdoaCumTnSZYllZXIlBTbHQ0AoINqUSVo7Nix+v3vf6/NmzcHr/vpp5/0hz/8QePGjWu14KJJaY1PRpLbIcU5mZYQaeJcDqXsLYxur2AaKgCg4yLPa4HKCn+nTcvyT3dERLHc7mAHVLNtcyNbAwAQHi0qwD3yyCMqKSlR9+7d1bNnT/Xq1Us9evRQSUmJHn744daOMSqU7DP6jXVBIlP23lFw2yu8/jPZAAB0QOR5LbB3+qmSUmQ5nPbGgjpZOZ39/9m2RYaRnAAAG7Rosav8/HwtW7ZM8+bN0zfffCNjjAYMGKBf/OIXrR1f1CihAUPEy4pz6oeSGlV6jUpqfEqJIYEGAHQ85HnNZ/ZpwIAIlZElxcRK1VXSzh1Spxy7IwIAdDDNqgZ98MEHGjBggIqL/WsnjB8/XldddZV+97vf6dBDD9XBBx+sjz/+OCyBtmfGGBUHRsDRgCFiOR2WsuL8RbdtTEMFAHQw5HkHYG8BjgYMkcuyLGnvKDiz9SebowEAdETNqgY9+OCDuuSSS5SSUntti9TUVF166aV64IEHWi24aFHlNarxSZakJEbARbTANNTCSq+8PqahAgA6DvK8ljEej1Ra4r/ACLiIZuXunYa6a6dMdZW9wQAAOpxmVYO+/PJLHX/88fXePmHCBC1duvSAg4o2gfXfEt2WnKz/FtFS3A7FOS35jLSzilFwAICOgzyvhUr2rv8WGycrNs7eWNAgKyFRSkmVZKRtW+wOBwDQwTSrALdt27Y629IHuFwu7dix44CDijb7NmBAZLMsK6QZAwAAHQV5XgsFGjAw+q1dsHK6SPJPQ6XpFgCgLTWrItSlSxetXLmy3ttXrFihvLy8Aw4q2hTvbcCQQgGuXQgU4Iqqfar00CULANAxkOe1TKABg5XC+m/tQnaO5HBI5WVSSbHd0QAAOpBmVYROPPFE3XzzzaqsrKx1W0VFhW655RadfPLJrRZcNPD6jMo8/rNrNGBoH2KdDqXu/V1tr2QUHACgYyDPaz5jTLABg1LT7AwFTWS53FJWtiTJbKMZAwCg7bias/Ff//pXzZo1S3369NGVV16pvn37yrIsrV69Wo8++qi8Xq9uvPHGcMXaLpXunX4a47AU66QA115kxztVVO3T9gqv8hNd/s5ZAABEMfK8Figvkzwe/4iqxGS7o0ETWbldZLZvlbZvlenZV5bDaXdIAIAOoFkFuJycHH366ae67LLLdMMNNwTXTbAsS8cdd5wee+wx5eTkhCXQ9qo4sP4bo9/alcw4p34orlGV16i42qfUWBIzAEB0I89rgcDot+RUWQ5yvXYjLUOKjZOqKqXCHVJ2rt0RAQA6gGZnCgUFBXrrrbdUWFiozz//XJ999pkKCwv11ltvqXv37s16rI8++kgTJ05U586dZVmW5syZE3L71KlTZVlWyM8RRxzR3JBtFWjAwPpv7YvTspQVRzMGAEDHQp7XPIYGDO2SZVlSTmdJ/mYMAAC0hWaNgNtXenq6Dj300APaeVlZmQYPHqwLL7xQZ5xxRp3bHH/88ZoxY0bwckxMzAHtsy0ZY1RSTQfU9io73qltFV4VVnnVw2fkcjANFQDQMZDnNRENGNotK7ezzMYfpN07ZaoqZcXG2R0SACDKtbgA1xpOOOEEnXDCCQ1uExsbq9zcpg8Lr6qqUlVVVfBycbF93Y0qvEYe4x9mmOimeNPeJLsdinNaqvQa7az0KifB1rcLAADtSrTneaamxr8GnEQDhnbIik+QSU2TivZI27ZI3XrYHRIAIMpF/LCsBQsWKDs7W3369NEll1yi7du3N7j9XXfdpdTU1OBPfn5+G0VaW2D0W5LbIQeL+Lc7lmUpJ55pqAAAhEt7zvOC67/FJ8hyt7ORe5AkWTldJPmnoQbWPAQAIFwiugB3wgkn6IUXXtAHH3yg+++/X4sXL9bYsWNDznzu74YbblBRUVHwZ9OmTW0YcagSGjC0e53i/aPeimt8qvD4bI4GAIDo0d7zPNZ/iwKdcvwdbCvKpcDvEwCAMInoOXXnnHNO8P+HHHKIRowYoYKCAs2dO1eTJk2q8z6xsbGKjY1tqxAbFCzAsf5buxXrtJQW49Ceap+2V3hVkMzvEgCA1tDe87yf139LszUMtJzlcsl0ypG2bZHZ9pMsphIDAMKoXVUT8vLyVFBQoO+++87uUBrl8RmVe/xD2SnAtW/Z+0xDZXoCAADh0Z7yPGN8P4+YogFDu2bl+qehavs2GS9LjgAAwqddVYZ27typTZs2KS8vz+5QGhUY/RbntBTjZP239iwzzimnJVX7jIqqmYYKAEA4tKc8T6Wlks8rOV1SYpLd0eBApKZLcXGS1yMVNrwGIQAAB8LWAlxpaamWL1+u5cuXS5LWrVun5cuXa+PGjSotLdW1116rRYsWaf369VqwYIEmTpyorKwsnX766XaG3SSBBgyMfmv/HJalTnE0YwAAoDmiOc8LNmBISZVFo612zbKskGYMAACEi61rwC1ZskRjxowJXr7mmmskSVOmTNHjjz+ulStX6rnnntOePXuUl5enMWPG6JVXXlFycrJdITdZMQ0Yokp2vEtbK7zaWemVx2fkcpBsAwDQkGjO8wzTT6NLTp604Xtpzy6ZygpZcfF2RwQAiEK2FuBGjx7d4Jpa7777bhtG03qMMcEpqCmMgIsKSW5L8S5LFR6jwkqvchMiun8JAAC2i9Y8TxINGKKMFZ8gk5Yu7dktbdsiFRxkd0gAgChEdSgMyj1GPiM5LSnBxUipaGBZVkgzBgAA0DGZ6iqpssJ/gRFwUWPfaag03QIAhAMFuDAITD9NcjtYFySKZMf5R72V1PhU7qEZAwAAHVJg/bfEJFkut62hoBV1ypacTn9xtWiP3dEAAKIQBbgwCDRgYPppdIlxWkqP9f9OGQUHAEDHZALFGaafRhXL6ZI65UqSzDaaMQAAWh8VojAooQFD1MqO94+C21HhYXoCAAAd0d4GDBbTT6OOldvZ/5/t22S8HnuDAQBEHSpErazaa1Tp9RdmkhkBF3UyYh1yWVK1T9pTzTRUAAA6EuPzSSXF/guMgIs+KWlSfILk80o7ttsdDQAgylAhamWB0W8JLksuB+u/RRuHZakTzRgAAOiYSosl45Pcbn+hBlHFsixZOf5RcGYr01ABAK2LAlwrC04/ZfRb1ApMQ91Z6ZXP4vcMAECHsc/6bzTailJ7C3Aq2q0YT429sQAAogrVg1YWaMBAAS56JbosJbgsGUlVcSl2hwMAANqICa7/lmZvIAgbKy5OSs+UJGVUlNocDQAgmlAlakU+Y1RKA4aoZ1lWcBQcBTgAADoGY4xUvMd/gQYMUS0wDTW9olQMdAQAtBaqRK2orMbIJ8llSfFOvq2jWac4pyxJHne8snv0sTscAAAQblWVUnWVZFlSMgW4qJaVLTldivV6dGxBvt3RAACiBAW4VlSyz+g31gWJbjFOS+mx/rfPsFPOtTkaAAAQdoHRb0nJspxOW0NBeFlOp5SdK0m6YMgAm6MBAEQLCnCtqJgGDB1KYBrq0BPPls/mWAAAQHgF1n8T6791CFaufxrqpAF95KAZAwCgFVApakWBBgwpFOA6hPRYhyyfRymdcrRTcXaHAwAAwmlvB1SL9d86huRUVTrdSoxxK3XzRrujAQBEASpFrcTrcKnaZyRJSRTgOgSHZSm2sliS9JOSbI4GAACEi8Pnk0pL/BcYAdchWJalXQn+/C590/c2RwMAiAZUilqJxx0vSUp0WXI6WP+to4ir8BfgdiheFR4mogIAEI3ia6okGSkmVopl1HtHsSs+SV6fT4m7dsjsKrQ7HABAO0cBrpXUuP3JWEoML2lH4vJW6afVX8pYlr7aXWV3OAAAIAwSa/Z+x6ek0WirA/E4XXrv+w2SJN/yxTZHAwBo76gWtZLACDgaMHQ8S19/WZK0cmelzZEAAIBwSKz2f8dbqWn2BoI29/zyryRJvi8Xy/iY7QAAaDmqRa3AFRsnj8s/Ai6ZEXAdzpfv/EeWMdpW4dX2Co/d4QAAgFaWWB0YAUcDho7mjTXfy+OOkYqLZNZ9Z3c4AIB2jGpRK+g6YIhkWXI7pFjWf+twyot2q5MqJEkrGAUHAEBU6Z2ZJpfxSZZDSkqxOxy0sSqPV0VdCiRJvuVf2BwNAKA9owDXCroNOlSSlOJ2sC5IB9VFpZKkVbuq5NnbDRcAALR/R+R39v8nOUWWg9S5I9qV31OSZFavkqkotzkaAEB7RRbRCgoG+wtwTD/tuLJUqWS3Q5Veo++Kqu0OBwAAtJIjAwW4lDRb44B9KlMzpJzOktcj34qldocDAGinqBgdICOp26ARkqQUt9PeYGAbS9LAjFhJTEMFACCaHNHVX4CjAUMHZllyDD1MkuT73xcyhtkOAIDmowB3gCrkUlJGJ8n4lOhm+mlHNijT34hjXUmNiqq9NkcDAAAOlKOmWgOyM/0XaMDQoTkGDZecLmnbZmnLj3aHAwBohyjAHaA98o96ctVUycH6bx1aWqxT3ZLckqSVO6tsjgYAAByohN2FcliWqpwuWTGxdocDG1nxCbL6HyLJPwoOAIDmogB3gPYoRpLkrqmwORJEgsGZe6eh7qpkegIAAO1cwu5CSVIZxTdIcgw9XJLkW7lMpqbG5mgAAO0NBbgDFBwB56EAB6lPWqxinZaKq31aX0JiBgBAe5awa4ckqcwdZ3MkiARWj15SWoZUVSmzeoXd4QAA2hkKcAegyutTqfxTDhkBB0lyOywdnE4zBgAA2jvj8ylhj38EXDkj4CDJshxyDDlUkuT73+c2RwMAaG8owB0AS5YO0U4teOYfcvhYdB9+gWYM3xZVq8LjszkaAADQIsbop0GH68FFS1XhirE7GkQIfwHOkln/vcyuQrvDAQC0IxTgDkCM01JnlevdR+6wOxREkNwEl7LjnfIa6atdNGMAAKA9spxOFXXpruveXSjRaAt7Wanpsnr2kUQzBgBA81CAA8Jg8N5RcF/upBkDAABANAk2Y/hysQyzYAAATUQBDgiDg9Nj5bSkHZVeba3w2B0OAAAAWonV92ApPkEqKZZZu8bucAAA7QQFOCAM4lwO9U0LNGNgGioAAEC0sFwuOQaNkMQ0VABA01GAA8JkUKa/APf1rirV+JiGCgAAEC0cww6TJJlvv5IpLbE5GgBAe0ABDgiTgiS3UmMcqvIZrdnDKDgAAIBoYWXnyerSTfL55FuxxO5wAADtAAU4IEwsy9KgfZoxAAAAIHo4hvpHwfn+9wVNtwAAjaIAB4TRwAz/NNRNpR7trqJLFgAAQLSwDhkquWOkwu0yP663OxwAQISjAAeEUUqMUwcluyVJKxgFBwAAEDWs2DhZAwZJknzLaMYAAGgYBTggzAZl+aehrthZKS/TEwAAAKKGY9jhkiTz1XKZKk62AgDqRwEOCLPeqTFKdFkq8xitLaq2OxwAAAC0Eiu/h5SVI9VUy7dimd3hAAAiGAU4IMyclqWBe5sxLC/kzCgAAEC0sCxLjuFHSJJ8SxfRjAEAUC8KcEAbGLK3ALeupEZ7aMYAAAAQNRyDR0hOl7Rts8zmTXaHAwCIUBTggDaQFutUj73NGL6kGQMAAEDUsOITZB08WJLkW7LI5mgAAJGKAhzQRgbTjAEAACAqBaahmq+Wy1RW2BwNACASUYAD2si+zRi+oxkDAABA1LDye0id9jZjWEkzBgBAbRTggDbitCwNohkDAABA1LEsS45hNGMAANSPAhzQhgbvLcCtpxkDAABAVHEMHiG5XNK2LTI/bbQ7HABAhKEAB7ShfZsxLKcZAwAAQNTwN2MYIsk/Cg4AgH1RgAPa2JB9mzH4mJ4AAAAQLQLTUM0qmjEAAEJRgAPaWK+9zRjKacYAAAAQVaz87lKnXMlTI9+KpXaHAwCIIBTggDbmtKzgWnBMQwUAAIgelmXJMTzQjOEzmjEAAIIowAE2GLRPM4bdNGMAAACIGo5Bw/3NGLZvkflxg93hAAAiBAU4wAZpsU4dtLcZw5eFjIIDAACIFiHNGJZ9Zm8wAICIQQEOsMngQDOGXTRjAAAAiCaBaag0YwAABFCAA2zSKzVGSS6Hyj1G39KMAQAAIGpYXbtL2XubMXy5xO5wAAARgAIcYBOnZWlQZqwkaTnTUAEAAKKGvxnDkZJoxgAA8KMAB9goMA11Q2mNdlZ6bI4GAAAArcXfjMEt7dgqs3Gd3eEAAGxGAQ6wUWqMU71SYiRJyxgFBwAAEDWsuHhZA4dJknyLP7E5GgCA3WwtwH300UeaOHGiOnfuLMuyNGfOnJDbjTGaNm2aOnfurPj4eI0ePVpfffWVPcECYTK8k38U3KqdVar2Mj0BABAdyPMAyXnYSEmSWb1SpqTY5mgAAHaytQBXVlamwYMH65FHHqnz9nvuuUcPPPCAHnnkES1evFi5ubkaP368SkpK2jhSIHy6J7uVHutQlc/oq92MggMARAfyPECycrvIyu8u+XzyLV1kdzgAABvZWoA74YQTdMcdd2jSpEm1bjPG6MEHH9SNN96oSZMm6ZBDDtGzzz6r8vJyvfjiizZEC4SHZVkalhUvSVq6o5JFegEAUYE8D/BzHOofBedb+pmM12tzNAAAu0TsGnDr1q3T1q1bNWHChOB1sbGxGjVqlD799NN671dVVaXi4uKQHyDSDcyIldshFVZ6tamUZgwAgOhGnoeOxBowSEpMlkqLZb5ZaXc4AACbRGwBbuvWrZKknJyckOtzcnKCt9XlrrvuUmpqavAnPz8/rHECrSHO5dDB6f614JYVVtgcDQAA4UWeh47EcrrkGH6EJMm3+L82RwMAsEvEFuACLMsKuWyMqXXdvm644QYVFRUFfzZt2hTuEIFWMWxvM4Y1e6pVUs30BABA9CPPQ0fhGH6kZDlkNvwgs22L3eEAAGwQsQW43NxcSap1FnT79u21zpbuKzY2VikpKSE/QHuQHe9S10SXjKTlO2nGAACIXuR56GislFRZ/Q+RxCg4AOioIrYA16NHD+Xm5mrevHnB66qrq7Vw4UIdddRRNkYGhM/wTv5mDMsLK+X10YwBABCdyPPQEQWbMaxYKlPJkiMA0NG47Nx5aWmp1q5dG7y8bt06LV++XBkZGerWrZuuvvpqTZ8+Xb1791bv3r01ffp0JSQkaPLkyTZGDYRPn9QYJboslXmMvi2qVv/0WLtDAgCgRcjzgFBWQU+pU660Y6t8Xy6R8/Bj7A4JANCGbC3ALVmyRGPGjAlevuaaayRJU6ZM0cyZM3XdddepoqJCl19+uXbv3q3DDz9c7733npKTk+0KGQgrp8PSkKw4/XdrhZbsqKAABwBot8jzgFCWZclx6Ej53vqPfF98IsdhI2VZETshCQDQymwtwI0ePVrG1D/NzrIsTZs2TdOmTWu7oACbDc2K16JtFfqpzKMtZTXKS3TbHRIAAM1GngfU5hg8XL75c6VdhTLffSOrzwC7QwIAtBFOuQARJsntUP80/8i3xTtoxgAAABAtrJhYOYYdIUnyffaRzdEAANoSBTggAh2a7W/G8M3uKpVUe22OBgAAAK3FcdhIybJk1n0ns22L3eEAANoIBTggAuUmuJSf5JJP0rJCRsEBAABECystQ1b/QZIkL6PgAKDDoAAHRKgRnfyj4P5XWKkaX/1r6AAAAKB9cRzh74BqVi6TKSuxORoAQFugAAdEqN6pMUqNcajSa7RqF6PgAAAAooXVtbusLt0kr0e+JYvsDgcA0AYowAERymFZwVFwS7ZXNthJDgAAAO2HZVlyHO4fBedb/KmMx2NzRACAcHPZHQAQDVavXh2WxzWy5FQX7ayS5v1vtbJUeyRcVlaWunXrFpb9AwAAdHThyvPk86lfXLzcZSVaN3eO9uQfVGsT8jwAiB4U4IADsHP7VsmydN5554VtHyf98XYd/avf6uVFqzTjynNq3Z6QkKDVq1eTnAEAALSirbt2yZLCmuf96ehDdecvjtHOt1/TYU/+q9bt5HkAED0owAEHoLSoSDJGV95+vwYfenhY9uF1uLXbGPU5aqwen/eFXN7q4G0b1q7RHVderMLCQhIzAACAVrSntExG0kOX/0ZHDB4Yln04fV75tm3SkLxsff3QPSqNjQ/e9s2GTbpg+j3keQAQJSjAAa2gS4+e6jtoSNgef/XuKu2q8immay/1To0J234AAAAQqleXPA3r0ztsj+9z+KQtP6qXPHKEcT8AAHvRhAFoB7ok+mvlOyq8qvLSjAEAACBaWF0L/P/ZVShTVmpvMACAsKEAB7QDKTFOJbsdMpK2lNMlCwAAIFpYCYlSVrYkyWxaZ3M0AIBwoQAHtBNd946C21rukcfHKDgAAIBoYeX38P9n+1aZyor/396dx0lV3fn/f51bW1fv3TS9sTRrsy8CCpgIEnEdtyROnAwqZmKMo2bMmH38JppMJiYzY2I0k5mvifqLiUn8zmgyMRpUBBFlEQFlkVWWbqCh6X2vrqp7fn8UlLbgAnT3re5+Px+P+6jqurfu+dRpy/7wueee420wIiLSI1SAE+kj8kIO6X5D3CaKcCIiIiLSP5jsHMjNA2uxB/Z7HY6IiPQAFeBE+ghjTHIuuENtMVyrUXAiIiIi/UVyFFzVQWw06m0wIiLS7VSAE+lDCtJ8BB1D1IXq9rjX4YiIiIhId8kbBJlZ4MbhUIXX0YiISDdTAU6kD3HeNQruYGsMjYETERER6R+MMZhhIwCwBysw1vU2IBER6VYqwIn0MUVhH34DHXFLZyjT63BEREREpLsMLoK0MESjDGpr8ToaERHpRirAifQxPsdQnJ4YBdeePsjjaERERESkuxjjYIaWATC4tRGfYzyOSEREuosKcCJ9UGmGHweIBdIYfc48r8MRERERke5SPAQCAULxGJ+ZNM7raEREpJuoACfSBwUcQ1G6D4ALbv6q5oITERER6SeMz4cZkhgF9615s0FzwYmI9AsqwIn0UUMyAmBdRs6YSx0hr8MRERERke4yZDgx4zB+8CBytCKqiEi/oAKcSB8V8hnS2hsBeJscrNU4OBEREZH+wPj9HM3IAaBw52asq1FwIiJ9nQpwIn1YuK2OWGeEBpPG/pao1+GIiIiISDc5mpFNXXsHaS1N2K1veB2OiIicIRXgRPownxvjtad+DcArVW0aBSciIiLST7iOw/2rXgcg/vILGgUnItLHqQAn0setePSnONZyoDWmUXAiIiIi/ch/vPYGsUAQaqo1Ck5EpI9TAU6kj2s6epihtAAaBSciIiLSnzRHOqkZNQHQKDgRkb5OBTiRfmAETfgMGgUnIiIi0s/UjhwHaWGNghMR6eNUgBPpB9KIM70gDdAoOBEREZH+xA0EcOaeD2gUnIhIX6YCnEg/MacwnBwFt69Zo+BERERE+gtn9sffGQW3eYPX4YiIyGlQAU6kn8gK+jjr2Ci45YdaNQpOREREpJ8woTScjy0AIL58CTami60iIn2NCnAi/cjHitMJ+QzV7XG21ke8DkdEREREuokzex5k50BjPe5rr3odjoiInCIV4ET6kbDfYU5hGICXD7URczUKTkRERKQ/MIEAvvMvAcBduRTb3uZxRCIicipUgBPpZ2YVhskKODRFXdYfbfc6HBERERHpJmbaLCgsho523Fde9DocERE5BSrAifQzAcdwXkk6AKuPtNMR00pZIiIiIv2BcRx8Cy8HwF37Crax3uOIRETko1IBTqQfmpwfYnCaj464ZfURjYITERER6S/MmPGYEaMhHiO+fInX4YiIyEekApxIP+QYw/zSDABeP9pOY2fc44hEREREpDsYY3COjYKzb67HHj7kcUQiIvJRqAAn0k+Nzg4wPDNA3MLKKk3SKyIiItJfOEOGYyZNByzxpX/2OhwREfkIVIAT6aeMMSwoTcwFt6UuQlVr1OOIRERERKS7+D5xKTg+7Ns7cHdt8zocERH5ECrAifRjJRkBJuWFAHjuQCuutR5HJCIiIiLdweQX4Mw+D4D4X/6Ajeliq4hIKlMBTqSfWzAkg5BjONwWY1NtxOtwRERERKSbOPMvhKxsqK/FffUlr8MREZEPoAKcSD+XGXA4ryRxK+pLh1ppi7keRyQiIiIi3cGE0vBddCUA7itLsfW1HkckIiLvRwU4kQFgxuA0Bqf56IhbVhxq9TocEREREekmZtJ0zMgxEIsRf+5/vQ5HRETehwpwIgOAYwwXDcsE4M3aCIe0IIOIiIhIv2CMwXfpp8BxsDu24u58y+uQRETkJFSAExkghmUGmJyfWJDh+UotyCAiIiLSX5jBRThz5gPHFmSI6mKriEiqUQFOZABZUJpByGc43B7jzdoOr8MRERERkW6SWJAhBxrqcF9d5nU4IiLyHirAiQwgGQGHeckFGdpojsY9jkhEREREuoMJhvBdfBUA7isvYo8e8TgiERF5NxXgRAaYswrSKE73E4lbnq9sxepWVBEREZF+wUycihkzHuJx4n96Auu6XockIiLHqAAnMsA4xnDZ8EwcA7saO9ne0Ol1SCIiIiLSDYwx+C6/BoIh7IH9uK+94nVIIiJyjApwIgNQYdjPuUWJW1GfP9BCa1RXR0VERET6A5OTh3PhFQC4Lz6LravxOCIREQEV4EQGrLlFYQan+WiPWZZUtuhWVBEREZF+wpk5GzNiDMSixP/4O92KKiKSAlSAExmgfI7h8rKs5K2om+oiXockIiIiIt3AGAffVdcmbkWt3If76nKvQxIRGfBUgBMZwIrS/clVUV880EpDRKuiioiIiPQHJjcf36WfBMB9aQm26oDHEYmIDGwqwIkMcOcUhhma4afTtfxpXzNx3YoqIiIi0i+YabMwE6aC6xJ78jfYTt3xICLiFRXgRAY4xyRuRQ35DIfaYrx8qM3rkERERESkGyRWRf00ZOVA7VHizzypeX9FRDyiApyIkBvycdnwTADWVrfzdmOnxxGJiIiISHcw6Zn4Pr0IjMFuWo99c53XIYmIDEgqwIkIAONyQ8wcnAbAn/c309ip+eBERERE+gOnbDTO+ZcAEH/mKeyRKo8jEhEZeFSAE5GkBaUZFIf9tMctT+1pIurqFgURERGR/sD5+Ccwo8ohFiX2xKPYdk07IiLSm1K6AHfPPfdgjOmyFRcXex2WSL/ldwyfHJVF2G840h5nSUWL5gkREZEeo1xPpPcYx8H36esgNx/qa4k/+Wus63odlojIgJHSBTiASZMmUVVVldw2b97sdUgi/VpO0MfVI7IwwNb6CK9Vt3sdkoiI9GPK9UR6j0nPwH/t58AfwL69E3fpM16HJCIyYPi9DuDD+P1+XQkV6WVlWUEuGJLB0oOtLD/URk7Ix/jckNdhiYhIP6RcT6R3meJSfFddS/zJ3+CufgnyB+Gbda7XYYmI9HspX4DbtWsXpaWlhEIhZs+ezQ9+8ANGjRr1vsdHIhEikUjy56ampt4IU8RT27Zt6/ZzGmAYeVSaLP60p5FDVJPLiaujFhQUMHz48G5vX0REBoZTyfWU58lA1BN5HkDhuKkU7dhE/JmnePtoLS1FQ044RnmeiEj3SekC3OzZs3nssccoLy/nyJEjfP/73+fcc89l69atDBo06KTvuffee/nud7/by5GKeKO2+jAYw3XXXdcj53d8Pq7/8WOMP+8iltX7eejzn+Tovt1djklPT2fbtm1KzkRE5JSdaq6nPE8GksN1dRjosTwP4JdXX8wN0ycx+JXnue5X/8O6g4e77FeeJyLSfVK6AHfppZcmn0+ZMoW5c+cyevRofvWrX3HnnXee9D3f+ta3uuxrampi2LBhPR6riBdaGhvBWm7/5/uYdvbsHmnDGkNjtIPMvAK++t8vk1Nfgc+NAbB/9w6+f/vnqampUWImIiKn7FRzPeV5MpA0tLRigQduvZk506b0SBvGWprrDpMFrLj5OnYPKqEjEARg+/5KbvjBvyrPExHpJildgHuvjIwMpkyZwq5du973mFAoRCikuapkYBkycjTjpk7vsfNHXcvm2gjtBGgvGsOU/BBBn+mx9kREZGD6sFxPeZ4MRGOGlDCjfGyPnd/GR2HfXI+/uZHxTTWY6Wdjwuk91p6IyECV8qugvlskEmHbtm2UlJR4HYrIgBJwDJPyQ4R8ho64ZXNdhEjceh2WiIj0M8r1RHqf8fkxU2ZARiZ0RrBvrMO2tXodlohIv5PSBbivfvWrrFixgr1797J27VquueYampqaWLx4sdehiQw4IZ9hcl6QkJMowm2pixB3+tQgWhERSTHK9URSgwkEMFNnQnpGogj35jpC0RMX3xIRkdOX0v96PnDgAJ/97Gepqalh8ODBzJkzhzVr1lBWVuZ1aCIDUprfYfKgIFvqOumIWzrzhlM4stzrsEREpI9SrieSOkwwBNPOxm56HVpbGFtbxdlDir0OS0Sk30jpAtzvf/97r0MQkfdI8zlMyQ+yta6TdgJ88ZE/U4+ukIqIyKlTrieSWkwwCNNmYTdvwN/cxAs3/jXVRw4AM7wOTUSkz0vpW1BFJDWFfA5TBoXwR9tJz8ljPYVsqevwOiwREREROUMmEMRMm0VTKEx6IEDZupeJr3kZazX/r4jImVABTkROS8Ax5NRXsnX5s7jG8Of9Lbx4oAVXyZmIiIhIn2Z8fvbkFfHohi0Ya3Gf+1/i//t7bCzqdWgiIn2WCnAictoMlse/eiOjbCMA64528LvdjTR1xj2OTERERETOiDF88U/Pc2jSTDAO9s3XiT38ILb2qNeRiYj0SSrAicgZsdYyhkauHpFF0DFUtsR4ZHsDOxsiXocmIiIiImeodtR4fNd9IbFC6uGDxP7vj3HfeE23pIqInCIV4ESkW4zPC3HjuFyKw3464pan9jbz9L5m2mOu16GJiIiIyBlwRpXj/+JXMCNGQ7ST+P8+Qfz3j2CbGr0OTUSkz1ABTkS6TX6aj+vLc5hdGMYAW+sj/GJbPZtrO3SVVERERKQPM9k5+K6/BecTl4LPh935FrGf/yvxtSuxrqYfERH5MH6vAxCR/sXnGBYMyWBcbpBnK1qo6YjzTEULG2s6uGBoBkMyAt3aXkVFBTU1Nd16zlMRiUQIhUIDtv2CggKGDx/uWfsiIiLSe4zj4DtvIc64ycT/9AT2YAXukj/irl+D7+IrMaPKMcZ0W3vK85TnifQnKsCJSI8ozQjwuXG5vH60nVcPt3OoLcavdzYyOjvAvJIMitLP/H8/FRUVTJgwgba2tm6I+PQYYzwd3ed1++np6Wzbtk3JmYiIyABiCovx/d2XcDeswV32Fzh6mPhvHsKMGI2z4FKc4SPPuI3UyPPAy5s4vG5feZ5I91IBTkR6jM8xzC5KZ1J+Gi8famVzXYS3m6K83dTAqKwA5xSFKcsMnPaV0pqaGtra2vg/P3uYsjHjujn6D7dm+fM8/KPvcfs/38e0s2cPuPb3797B92//PDU1NUrMREREBhjjOPhmnYszaTruiudxX1+F3fc28Ud/hjt8FM6552PKJ2DM6c16dDzPe+yfvs74smHdHP2HW7J2Hd955DEeuPVm5kybMuDa376/kht+8K/K80S6kQpwItLjMgMOl5VlMbsozCtVbWxv6GRPc5Q9zVEK0nxMG5TG5PwQYf/pJWhlY8Yxbur07g36I9i/awcAQ0aOHpDti4iIiJhwOr5LrsaZO5/4ihewb76OrdhDvGIP5A3CmTEbZ/rZmMzs0zr/+LJhzCgf281Rf7jtFZUAjBlSMiDbF5HupwKciPSaQWl+rhqZzbxInHXV7Wyu66CmI86LB1tZfrCVsqwA5blBxuaEyAxojRgRERGRvsLk5OG/8jPY8y/GfW0l7uurob4W98VncZf9BTN8FGbCFJzxkzE5eV6HKyLS61SAE5FelxfycdGwTOaVpvNWXYQ3ajuobo+ztznK3uYoz1W2MjTDz+jsIEMzA5Sk+/E73Tehr4iIiIj0DJOdg2/h5TjzLsS+9Sbu+jXYA/ux+9/G7n8bd8kfMaXDMOUTE0W5IcMwQe8WGhAR6S0qwImIZ9J8DjMGh5kxOExdR5ydjRF2NHRS1RbjQGtiA/AZKEn3MzQzQHHYT0Gaj7w0n8fRi4iIiMj7McEQZvo5ONPPwTbU4W7fgt22GVuxF3uoEnsocYsljoMpGYoZNhJTOhRTWAyDCr0NXkSkB6gAJyIpIT/Nx5y0dOYUpdPYGWdXYycVzVEOtEZpi9kuBTkABwhTzGd/+EtaMwqoao0R8hlCPkPQZ/AbTntxBxERERHpPiY3H9+ceTBnHralGbtjC+6+3dj9e6G5EXuwAnuw4l1vMJSnZ/L/rr2C0qZa7IEgpIUhlAZpaeA//UW8RES8ogKciKScnKCPWYPDzBocxlpLfcTlQGuiGHe0PU5tR5xO19Jqgky96CragT3N0RPO4zfgdwwBx+B3IGAMfsfgM+AYcMzJn/uMOfZa4nVD4rlBRT0RERGRM2EyszAz5+LMnIu1FhrrE6PiKvZiq6uw1Ych0kGotZmrJ4yF1ibs200nnMf6AxAIwPHH4899fozPBz4fOM6xR9+7Hp3Eo+Mc2449N0Z5noj0KBXgRCSlGWPIT/ORn+Zj6qA0AKy1NEVd1mzZwfd/8jM+fcs/kp43iEjcEolbYjbx3piFWNzSEbfdFo/DsWKcgfDMC/jy/7wCJUPYVNuBMSa53zlexCNxrPOefcZw4rEG/CZRLPQdKwgqERQREZH+yhgDufmY3HyYOhNI5Hk0N7Fr1Ss8eM+3+eY1V1EYToNIB3R0QLQz8eZYNLGdxOlmfjZZlHO43B9j022LGRJtxd2w9l0Fu/cW8N7ZzPvtM8cKgX5/okjo9ycKhcrzRAYUFeBEpM8xxpAT9DGYDl75zX+y+LpFjBtVmtzvWkvMhahrib37uZsozsWtxbUQt4lj3Xe99t7n703gXBKvY8FJy6Bo1DgAmqPHXuxmx0fx+R0IOIbgsS3gM/jyixk66SzwB7HWKokTERGRPs8YA9k5tBSW8LO1G7nh7/6O4vKxyf3WdROFt+ixLRZNFOViUWw0CvE4uHGIuxCPgeu+67X4u352wbpdG3fdxAZkGhg/eBDgQnPjR4r9VDNBmyzIBSAYhGAIgiFMMMgQ43LO0BLSrIt13URxT0T6NBXgRKTfcYwh6IOg78wLUtZaLCQLcq61ySLcmmVL+fWD/87N3/4B46edhfs+x9p3Ffpc3tln3/Nz/FjxL+a+k8AdH8VHHN6b1qVNOIfbfv08AKuPdBDyGcI+Q5rfkOZzSEs+NzgqzomIiEg/YBwnWag6Yd8pnstamyjCuSduz619jR/9+nf8202LmTl+3Hv2x5PP7fFC3kn2nfCeWBSisXcKf7FYYqO9a1zAeX545abPQqwVu/JFbFoahNMhHMakpSefk5aeuOVWRFKeCnAiIh/AvGsOuGOvJPe5LfXs3bAKWhsY1I2rstpjhbuYC/Fjo/iix0bxdbqWzrgl6lqO1tTS3NpGblEJ1jh0HL/dthOOVeySEYf9hnS/Q8axx8yA0y0FShEREZG+yhgD5tjccO9RZw0r9x+gzvFjCt5/VdbTyaZsPH6s+BZ957EzAp0RbGcEIp3UHK2mpamR4bk5OFjoaE9s9SeOtLPhdMjMwmRkQkYWZGZBKE13R4ikGBXgRERSjDEGH4mpQviAIlnlspX86Euf597f/JFZ8z9Be8wmi3AdMTf5PG6hLWZpi8Wpedf7g44hK2DIDDhkBRwygw4+JWoiIiIiPSq5SETo/UfxLV26jBvuf5hnv/8dLpw1A9rboKMd294G7e2Jn9vbErfZHntujx5550TBIDYrB5OVA9k5kJWD8euf/yJe0jdQRKQfCPkcQicZhGdtYtRca8zSFnUTjzGXtlji9dqIpTaSuA3CAJkBh5ygQ3bQITvg4HNUkBMRERHxjDGYUBqEEouRvTszs9ZCZye0NkNrC7a1GVpaoK0l8XrtUWzt0XeOz8qGnDxMTh7k5mH8gV7+MCIDmwpwIiL9mDGGkM8Q8kH+uyp0cdfSEnNpjrq0dCYeO11ojiae05pI8LICDnkhh7yQj3S/0a0MIiIiIinCGJMYRRcKQX5Bsjhn43FoaYbmRmxTIzQ3JFaQbW6C5ibsgf2J47JyMPkFkF8AWdnK80R6mApwIiIDkM9JrCSbE/RBRuIKaiRuaex0aep0aex0ibiWpqhLU9Rlf0uMoAO5IR95IR+5Qa3EJSIiIpKKjM8HObmQk/tOUa6jAxrrsA310FifuG21uRHb3Aj734ZAAJtXcKwgN8jL8EX6LRXgREQEY46tmOp3KEpPvNYRc6mPuNR3xmmMJEbIVbfHqW5PLPAQyB3K7GtuJIKKcSIiIiKpzKSlQVoppqgUABvpgLoabF0N1NdCNArVVdjqKgDGBEJ8ac5ZBNpavQxbpF9RAU5ERE4qze9Q4ncoyfDj2sTouPpInIaIS3vcEg1mcPU//RsrrOXtnQ2Myw0xLjdIdrD7VoQVERERke5nQmlQMhRTMhTrutDUkCjG1dVAawuZ0Qj3XbIAXvwjsW3rMROm4kycmhghJyKnRQU4ERH5UI4x5B27/RSgPeayfV8l2/dWMHzKLA60xjjQGuPFg62UpPsZnxtkYl6ILBXjRERERFKacRzIzcfk5sOocmykg8q3trBn0ybOGzEUDlViD1XivvgMFJXgTJyGM2UGJk+3qoqcChXgRETklIX9Dult9fzn4kt5df0bBIeWs6MxQmVLjKq2xLb8UBvDMwNMyg8xLidIml+3qoqIiIikOhNKoyYjh4X/3//jzVWvMCngYLdtwu7dDUeqcI9U4S5fghlahpkyA2fSdExGptdhi6Q8FeBEROSMpBFnRmGYWYVhWqMuOxsjbK2LcKA1RkVLlIqWKM8bGJ0dZFJ+iNHZQfyOVtkSERERSXWxUBjfjBkway62vQ27Ywvulo3YPbuwB/ZjD+zHXfK/mNHliVFx4ydjgiGvwxZJSSrAiYhIt8kIOJxVEOasgjCNnXHeqovwVn2Eox1xdjZ2srOxk5DPMC43yKS8EMMzA1ryXkRERKQPMOF0zPRzcKafg21pwt3yBnbzBuyhSuzu7cR3b4dAEDN+Es6UmZhR5YkVWUUEUAFORER6SE7Qx9zidOYWp1PdHmPrsWJcc9RlU22ETbURsgIOk/JDTM4LURDWnyQRERGRvsBkZuObMw/mzMPWVONu3oC7eQPU12I3byS+eSNkZOJMPgtn6szEgg+66CoDnP61IyIiPa4w7KdwiJ/zS9OpaInyVn2E7Q2dNEdd1hxpZ82RdorCPibnpzExL0RGQPPFiYiIiPQFpqAQ34JLcM6/GHuwArt5A+6WN6C1BXftSty1K2FwEc7UmYnbVHPyvA5ZxBMqwImISK8xxlCWFaQsK8iFQy27mzrZUhdhT2MnR9rjHDnYyrKDrYzKDjApP42xOUECmi9OREREJOUZYzBDy2BoGc5FV2Lf3oG7aT12+xY4egT3xWdxX/wLZsRonGkzMROmYkJpXoct0mtUgBMREU/4HcP43BDjc0O0xVy21UfYUhehqi3G201R3m6KEnKOzReXr/niRERERPoK4/NhyifilE/EdrRj39qUKMbtfxu7bzfxfbvhmacw4yfjTJuFGTUW42i+OOnfVIATkTO2bdu2AdWudL90v8PMwWFmDg5T25GYL25LfYSmTpdNdRE21UXIPj5fXH6IQWn68yUiItIblOfJmTJpYcyM2TgzZmMb6nA3bcDd9DrUHsVu2Uh8y0bIyMKZchbOtFlQVKqLrtIv6V8wInLaaqsPgzFcd911nsbR0tLiafvSvQal+ZlX6ue8knQqW2Nsretge30nTVGX1UfaWX2knZJ0P5PyQ0zMDZGu+eJERES63eG6Ogwoz5NuZXLz8c1biHPeBYnVU998HXfrG9DajLvmZdw1L0Nh8bH54mZisnO8Dlmk26gAJyKnraWxEazl9n++j2lnz+719tcsf56Hf/Q9Ojo6er1t6XnGGIZnBhieGWDhUMvuxk621HWwpylKVVuMqrYYyw60Mio7yOT8EGNygvg1X5yIiEi3aGhpxQIP3Hozc6ZN6fX2l6xdx3ceeUx5Xj9ljMEMGQ5DhuNcfBV293bcTa9jd7wF1Ydxlz6Du/RZzKgxOFNnYSZMwQRDXoctckZUgBORMzZk5GjGTZ3e6+3u37Wj19sUbwQcw4S8EBPyQrRGj80XVx/hcFuM3U2d7G7qJOQzjM8NMjk/jaEZft26ICIi0g3GDClhRvnYXm93e0Vlr7cp3jA+H2bcJJxxkxLzxW19M1GMq9iL3bOL+J5d8MyTmAlTcKbOxIwci3F0B4T0PSrAiYhIn5IRcJhVGGZWYZiaY/PFba2L0BR1ebM2wpu1EXKCDpPyQkzSfHEiIiIifYZJC2NmzsGZOQdbX/vOfHF1NdhN64lvWg9Z2TiTZ+BMnaH54qRP0b9KRESkzypI8zO/1M+8knQqWqJsqYuwo6GTxk6XVUfaWXWkncFpPibkJVZbzU/T6loiIiIifYHJG4Rv/oU48xZiD1a8M19ccxPu6pdwV78EgwbjTJyGM2k6FBarGCcpTQU4ERHp84wxlGUFKcsKctEwy67GTrbWdbC3KcrRjjhHq9p4uaqNorCP8bmJW1lzQyrGiYiIiKQ6YwxmaBkMLcO55Crsrm24mzZgd70FtUdxVy7FXbkUCopwJiWKcWZwkddhi5xABTgREelXAo5hYl6IiXkh2mMuuxo72VYfYV9zlCPtcY60t7Giqo3idD8TcoOMzwuRE1QxTkRERCTVGZ8fM34Kzvgp2EgHdudbuFvfwO7eDjVHcFc8j7vi+cRKqhOn40yejhk02OuwRQAV4EREpB8L+x2mDkpj6qA02mIuOxs62d4QYX9zlMNtMQ63xVh+qI3SdD/j80KMyw2qGCciIiLSB5hQGmbKDJwpMxKLN+zYivvWm9jdOxIrqVYvwX1pCRSVJkbGTZymYpx4SgU4EREZENL9DtML0phekEZr1GVnY4Rt9Z1UtEQ51BbjUFuMZQdbKQ77GZsbpDwnSEGaT3OJiIiIiKQ4kxbGTJuFM20Wtr0Nu2ML7tY3sXt2wpFDuEcO4S77Cwwuxhk/GWfCFCgeojxPepUKcCIiMuBkBBzOKghzVkGYlqjLjoYI2xsiVLbEONye2FZWtZEXcijPCVGeG6Q03a8kTURERCTFmXA6Zvo5ONPPwba1YrdvSdymum83HD2Me/RwYs64nDyc8ZMx4ydjho/EOLoLQnqWCnAiIjKgZQYcZg4OM3NwmNaoy+7GTnY2JuaMq4+4rK1uZ211O5kBh7E5iZFxwzMD+BwV40RERERSmUnPwMyYjTNjdmJk3K5tuNs3J25TbazHXbsS1q6E9AxM+USc8VMwo8oxgYDXoUs/pAKciIjIMRkBh2kFaUwrSCMSd9nTFGVnQ4S3m6K0RF021nSwsaaDoGMYmR1gdHaQ0dlBMgKO16GLiIiIyAcw4XTM1Jk4U2dio53Yt3cminE734K2Vuwb64i/sQ78AcyosZixE3DGTsDk5HkduvQTKsCJiIicRMjnMCEvxIS8EDHXsr85ys7GCLsbO2mNWXY0dLKjoROAknQ/o7ODjMkJUhTWvHEiIiIiqcwEgpjxk3HGT8a6cez+Pdhtm3F3bIWmBuzOtxIrrAIUleCMnYgpn4gZMhzj6MKrnB4V4ERE5Ixs27bNs7YjkQihUKjX2isCCoEmghwlzFEbotlJo6otRlVbjFcOtxGyMQrooIB28ukggO2xeAoKChg+fHiPnf/DVFRUUFNT41n7Xn9+ERGR/m4g5XkUj4CiMkLNDWQfOUjm4QNkNNRhjlThHqmCV14kFgzRXFhKc2EpLQXFxENpPRaO13mO13keeN8H3U0FOBEROS211YfBGK677jrPYjDGYG3PFbg+SvuZgwoZ97GFjDtvIWPnLID0DA6SyUEyceNxDry1kd1rX2b3mhVUbH6deLSz29pPT09n27ZtniQmFRUVTJgwgba2tl5v+zgvP7+IiEh/driuDgMe53ngYZqHMZAfTuPiMSO5dOxILhozgjwg78Be8g7sBWBj1RGW7angxT0VvLL/IB2xWLe1P9DzPOh/uZ4KcCIiclpaGhvBWm7/5/uYdvbsXm9/zfLnefhH3/O8/cV3fD3Zvm05SDQSpjOUSTSYDv4Qw6fMYviUWXzipjvBugQ62wh2thHobMMXj3C6N6vu372D79/+eWpqajxJSmpqamhra+P//OxhysaM6/X2vf78IiIi/VlDSysWeODWm5kzbUqvt79k7Tq+88hjnrd/9403JNvfby01nR1kR9rJjrQTjnVyVkkRZ5UU8ZWPnY2LoTUYojkUpjkYpj0QTFTxTsP2/ZXc8IN/9TzPe+yfvs74smG93j543wc9QQU4ERE5I0NGjmbc1Om93u7+XTtSvv1I3KUh4tLQ6dLYGSfqOkRDmURDmQAEHMgO+sgOOGQHHTL8ps/NH1c2Zpwn/S8iIiI9b8yQEmaUj+31drdXVKZ8+7YzAvV12IZaqK/FiUTI6uwgq7MDqAd/AHLyMDm5kJMLmdl9bv648WXDPOn//koFOBERkR4S8jkUpTsUpYO1lraYpaEzTkPEpSnqEnWhtiNObUccAJ+BrIBDVtAhO+CQFXDwOX2rICciIiIyEJhgCIpKMEUliSlR2tugvhZbXwsN9RCLQm01trY68QbHwWblvFOUy87B+AOefgbpXSrAiYiI9AJjDBkBQ0bAYUgGuNbSHHVp6kxszVGXuIWGzsSIuePCPkNmwCEj4JAZMGT4HfwqyomIiIikDGMMpGdAegZmyHCsdaGpCRrrsU0N0NiQKMg11ideO/Y+G05PjIzLyoLM7MTzgIpy/ZUKcCIiIh5wjCEn6CMn6APeGSHX1JkYHdfU6dLpWtrjlvZ4nKPHRslBoigXzy7hgpu/ymHSOdIWIz/NR0CFORERERHPGeMkbjvNycWQyPNoa4XGBmxTfaIg19GeGDXX3oY9ejj5XpsWZgSGuxecS+6BvdjiwVBQmBhxJ32aCnAiIiIp4N0j5EqOvdYZt7REXVpjLi1Rl5aoTRblSMtm4S3fYBOwaUcDANlBh0EhH/lpx+eV8yVvac0MOPj62PxyIiIiIv2BMQYyMiEjE1M6FADb2QktTdDShG1uTjzvaIeOdnKBu+bPgY2riG1clThJdg5mUCFm0GDIzcdk50B27rHHHIxP5Z1Up9+QiIhIigr6DPk+H/n4kq91xi2tMZd9lQdZsfR5LrzqGjr8aXTEbfJ21r3N0ZOeL9OfKMal+w1pPoeQz5DmM6T5ncSjzxB0DD7H4DPgMwa/k3j0mcSoveO3THTgI7uwhLjjpyPucnxH8pYKwNp3P9qTvHb8uT3hNZLP7QnvAWjNGMTCW75OPboaLCIiIn2PCQYhvwDyCzh+idRGo9DSxIG9e3l2+Ut89qKFZEbaoLUFmhqxTY3YvbtOfsKMrEQxLiMT0sKYtDCE0xPPw2FIS4dQCHx+8Pkwfn/i+fFHn5NMuPwdbQzJziQQj2E72o8FZ995POnmfsC+D99vk/sBYyhurufC0WU9/FvoXSrAiYiI9CFBnyHo81HdVsdT3/syd111HjOmzqAt5lLbEaeuI05dJH5sfrnEY3PUxbXQEnNpibkf3shHYYbwrSWbqAfWH410zzlPRUYBF9z8NRpsfe+3LSIiItIDTCAAeYM4erSO2/68lDnf/REzZszAtrdha49CzRFszdHEvHJNjclH4jFobca2NifPZd+3lQ83Adh7581QXYmtrjzDT3V6ioGFKsCJiIhIqkn3O6RnOgzLPHHi3uT8ctE4zZ0u7XFLR8wlErd0JDeXjpgl6lriFmLWEj/2PG4tsXcGuSWu0lpLLBbD5/dhjJN4zbyz3wDGHH9u3nlu3rs/8cIJ7zHmJOdJPAeor6lh+Z//wMy/vrxH+lNEREQkVZhwOmZoGQw9sSCVnF+uqQHb1AhtrYlRa8fmmEs+72jHdkYgFoN4/Nhj7J1H9/hFWoM1JPI8nw/HOMkcL5G0fdjmgGM+4FjnnefOSfYlPhRHGxpYU1nFwl7o397SJwpwP//5z/m3f/s3qqqqmDRpEvfffz/nnXee12GJiIj0CV3ml0s//fNYaxNzmAAbNmxg5uyZ/GLJK4ybOr17Aj0FO/ZV86cffoO7/7o/pWUDk/I8ERGR09dlfrmSoad9nhPyvJkzee3/PsiM8rHdFeopObhzF3/Ytov/40nrPcPxOoAP88QTT/DlL3+Zu+66i40bN3Leeedx6aWXUlFR4XVoIiIiA8rxpEykuyjPExERSQ3K83peyhfgfvzjH/P5z3+em266iQkTJnD//fczbNgw/vM//9Pr0ERERETkDCjPExERkYEipW9B7ezsZP369Xzzm9/s8vpFF13EqlWrTvqeSCRCJPLOZNCNjY0ANDU19UiMLS0tAOzc/Abtra090sYH2f/2TgD2bttKRjis9tW+2lf7an+AtF+5J7EC1vr165N/i3rTjh07AO/+/h3//C0tLT3yN/74Oa09kymM5YP0pTxv487dtLS390gbH2T7/sRIwM1795OWkaH21b7aV/tqf4C0v6vyIOB9nufV3z94pw/6Va5nU9jBgwctYF999dUur//Lv/yLLS8vP+l77r77bsuxxXO1adOmTZs2bdrOZKusrOyNlGdAUp6nTZs2bdq0afN6681cL6VHwB333nuR7bsmB3yvb33rW9x5553Jn13Xpa6ujkGDBume5m7S1NTEsGHDqKysJDs72+twBiT9Dryl/veW+t9bA6X/rbU0NzdTWlrqdSj9nvK81DJQvuOpTL8Db6n/vaX+99ZA6n8vcr2ULsAVFBTg8/k4fPhwl9erq6spKio66XtCoRChUKjLa7m5uT0V4oCWnZ3d77+UqU6/A2+p/72l/vfWQOj/nJwcr0Po15TnpbaB8B1PdfodeEv97y31v7cGSv/3dq6X0oswBINBZs6cyQsvvNDl9RdeeIFzzz3Xo6hERERE5EwpzxMREZGBJKVHwAHceeedXH/99cyaNYu5c+fy0EMPUVFRwS233OJ1aCIiIiJyBpTniYiIyECR8gW4a6+9ltraWr73ve9RVVXF5MmTefbZZykrK/M6tAErFApx9913n3ALiPQe/Q68pf73lvrfW+p/6U7K81KPvuPe0+/AW+p/b6n/vaX+71nG2t5cc1VERERERERERGRgSek54ERERERERERERPo6FeBERERERERERER6kApwIiIiIiIiIiIiPUgFOBERERERERERkR6kAtwAdc8992CM6bIVFxcn91trueeeeygtLSUcDnP++eezdevWLueIRCJ86UtfoqCggIyMDK688koOHDjQ5Zj6+nquv/56cnJyyMnJ4frrr6ehoaE3PmJKefnll7niiisoLS3FGMMf//jHLvt7s78rKiq44ooryMjIoKCggH/4h3+gs7OzJz52yviw/r/xxhtP+D7MmTOnyzHq/9N37733cvbZZ5OVlUVhYSFXX301O3bs6HKMvgM956P0v74DIv2L8rzepTzPW8rzvKU8z1vK8/oYKwPS3XffbSdNmmSrqqqSW3V1dXL/D3/4Q5uVlWWffPJJu3nzZnvttdfakpIS29TUlDzmlltusUOGDLEvvPCC3bBhg12wYIGdNm2ajcViyWMuueQSO3nyZLtq1Sq7atUqO3nyZHv55Zf36mdNBc8++6y966677JNPPmkB+4c//KHL/t7q71gsZidPnmwXLFhgN2zYYF944QVbWlpqb7/99h7vAy99WP8vXrzYXnLJJV2+D7W1tV2OUf+fvosvvtg++uijdsuWLfaNN96wf/VXf2WHDx9uW1paksfoO9BzPkr/6zsg0r8oz+tdyvO8pTzPW8rzvKU8r29RAW6Auvvuu+20adNOus91XVtcXGx/+MMfJl/r6OiwOTk59r/+67+stdY2NDTYQCBgf//73yePOXjwoHUcxy5ZssRaa+1bb71lAbtmzZrkMatXr7aA3b59ew98qr7hvYlBb/b3s88+ax3HsQcPHkwe87vf/c6GQiHb2NjYI5831bxfYnbVVVe973vU/92rurraAnbFihXWWn0Hett7+99afQdE+hvled5Rnuct5XneU57nLeV5qU23oA5gu3btorS0lJEjR/I3f/M37NmzB4C9e/dy+PBhLrroouSxoVCI+fPns2rVKgDWr19PNBrtckxpaSmTJ09OHrN69WpycnKYPXt28pg5c+aQk5OTPEZ6t79Xr17N5MmTKS0tTR5z8cUXE4lEWL9+fY9+zlT30ksvUVhYSHl5OV/4wheorq5O7lP/d6/GxkYA8vPzAX0Hett7+/84fQdE+hflealBf+NSg/7G9R7led5SnpfaVIAboGbPns1jjz3Gc889xy9+8QsOHz7MueeeS21tLYcPHwagqKioy3uKioqS+w4fPkwwGCQvL+8DjyksLDyh7cLCwuQxQq/29+HDh09oJy8vj2AwOKB/J5deeimPP/44y5Yt47777mPdunV84hOfIBKJAOr/7mSt5c477+TjH/84kydPBvQd6E0n63/Qd0Ckv1Gelzr0N857+hvXe5TneUt5Xurzex2AeOPSSy9NPp8yZQpz585l9OjR/OpXv0pOyGiM6fIea+0Jr73Xe4852fEf5TwDUW/1t34nJ7r22muTzydPnsysWbMoKyvjmWee4VOf+tT7vk/9f+puv/12Nm3axCuvvHLCPn0Het779b++AyL9i/K81KO/cd7R37jeozzPW8rzUp9GwAkAGRkZTJkyhV27diVXyXpvlbq6ujpZ0S4uLqazs5P6+voPPObIkSMntHX06NETKuMDWW/2d3Fx8Qnt1NfXE41G9Tt5l5KSEsrKyti1axeg/u8uX/rSl/jTn/7E8uXLGTp0aPJ1fQd6x/v1/8noOyDSvyjP847+xqUe/Y3rGcrzvKU8r29QAU6AxLLD27Zto6SkhJEjR1JcXMwLL7yQ3N/Z2cmKFSs499xzAZg5cyaBQKDLMVVVVWzZsiV5zNy5c2lsbOS1115LHrN27VoaGxuTxwi92t9z585ly5YtVFVVJY95/vnnCYVCzJw5s0c/Z19SW1tLZWUlJSUlgPr/TFlruf3223nqqadYtmwZI0eO7LJf34Ge9WH9fzL6Doj0L8rzvKO/calHf+O6l/I8bynP62N6anUHSW1f+cpX7EsvvWT37Nlj16xZYy+//HKblZVl9+3bZ61NLBWdk5Njn3rqKbt582b72c9+9qRLRQ8dOtQuXbrUbtiwwX7iE5846VLFU6dOtatXr7arV6+2U6ZMGZDL0zc3N9uNGzfajRs3WsD++Mc/ths3brT79++31vZefx9fGvqCCy6wGzZssEuXLrVDhw7t90tDf1D/Nzc326985St21apVdu/evXb58uV27ty5dsiQIer/bvL3f//3Nicnx7700ktdlj9va2tLHqPvQM/5sP7Xd0Ck/1Ge17uU53lLeZ63lOd5S3le36IC3AB17bXX2pKSEhsIBGxpaan91Kc+Zbdu3Zrc77quvfvuu21xcbENhUJ23rx5dvPmzV3O0d7ebm+//Xabn59vw+Gwvfzyy21FRUWXY2pra+2iRYtsVlaWzcrKsosWLbL19fW98RFTyvLlyy1wwrZ48WJrbe/29/79++1f/dVf2XA4bPPz8+3tt99uOzo6evLje+6D+r+trc1edNFFdvDgwTYQCNjhw4fbxYsXn9C36v/Td7K+B+yjjz6aPEbfgZ7zYf2v74BI/6M8r3cpz/OW8jxvKc/zlvK8vsVYa233j6sTERERERERERER0BxwIiIiIiIiIiIiPUoFOBERERERERERkR6kApyIiIiIiIiIiEgPUgFORERERERERESkB6kAJyIiIiIiIiIi0oNUgBMREREREREREelBKsCJiIiIiIiIiIj0IBXgREREREREREREepAKcCIip2DEiBHcf//9yZ+NMfzxj3/0LB4RERER6R7K80SkJ6kAJzIArVq1Cp/PxyWXXOJ1KD1u3759GGOSW1ZWFpMmTeK2225j165dp3y+devWcfPNN3dbfPF4nHvvvZfx48cTDofJz89nzpw5PProo8ljzj//fL785S93W5siIiLSfynPU54nIqnJ73UAItL7HnnkEb70pS/xy1/+koqKCoYPH95jbcXjcYwxOI639f6lS5cyadIk2tra2Lx5Mz/96U+ZNm0aTz/9NBdccMFHPs/gwYO7Na577rmHhx56iJ/97GfMmjWLpqYmXn/9derr67u1HRERERkYlOcpzxORFGVFZEBpaWmxWVlZdvv27fbaa6+13/3ud5P75syZY7/xjW90Ob66utr6/X67bNkya621kUjEfu1rX7OlpaU2PT3dnnPOOXb58uXJ4x999FGbk5Njn376aTthwgTr8/nsnj177GuvvWYXLlxoBw0aZLOzs+28efPs+vXru7S1bds2+7GPfcyGQiE7YcIE+8ILL1jA/uEPf0gec+DAAfuZz3zG5ubm2vz8fHvllVfavXv3vu/n3bt3rwXsxo0bu7wej8ft+eefb8vKymwsFrPWWrt792575ZVX2sLCQpuRkWFnzZplX3jhhS7vKysrsz/5yU+SP787vgULFtjbbruty/E1NTU2GAzaF1988aTxTZs2zd5zzz3vG//ixYst0GU7/nm3bt1qL730UpuRkWELCwvtddddZ48ePZp87/z58+1tt91mb7vtNpuTk2Pz8/PtXXfdZV3XTR7zH//xH3bMmDE2FArZwsJC++lPf/p9YxEREZHUpjwvQXlegvI8kdSiW1BFBpgnnniCcePGMW7cOK677joeffRRrLUALFq0iN/97nfJn48fX1RUxPz58wH43Oc+x6uvvsrvf/97Nm3axF//9V9zySWXdBnm39bWxr333ssvf/lLtm7dSmFhIc3NzSxevJiVK1eyZs0axo4dy2WXXUZzczMAruty9dVXk56eztq1a3nooYe46667usTe1tbGggULyMzM5OWXX+aVV14hMzOTSy65hM7OzlPqB8dxuOOOO9i/fz/r168HoKWlhcsuu4ylS5eyceNGLr74Yq644goqKio+0jlvuukmfvvb3xKJRJKvPf7445SWlrJgwYKTvqe4uJhly5Zx9OjRk+7/6U9/yty5c/nCF75AVVUVVVVVDBs2jKqqKubPn8/06dN5/fXXWbJkCUeOHOEzn/lMl/f/6le/wu/3s3btWh544AF+8pOf8Mtf/hKA119/nX/4h3/ge9/7Hjt27GDJkiXMmzfvI31WERERST3K8xKU5ynPE0lJ3tb/RKS3nXvuufb++++31lobjUZtQUFB8urf8augL7/8cvL4uXPn2q997WvW2sSVQ2OMPXjwYJdzXnDBBfZb3/qWtTZxZRSwb7zxxgfGEYvFbFZWln366aettdb+5S9/sX6/31ZVVSWPee+V0YcfftiOGzeuy5W9SCRiw+Gwfe65507azvtdGbU2cSUWsE888cT7xjlx4kT74IMPJn/+oCujHR0dNj8/v8v5pk+f/oFXPrdu3WonTJhgHcexU6ZMsV/84hfts88+2+WY+fPn2zvuuKPLa9/+9rftRRdd1OW1yspKC9gdO3Yk3zdhwoQu/fWNb3zDTpgwwVpr7ZNPPmmzs7NtU1PT+8YnIiIifYfyvHcoz1OeJ5JqNAJOZADZsWMHr732Gn/zN38DgN/v59prr+WRRx4BEvNeXHjhhTz++OMA7N27l9WrV7No0SIANmzYgLWW8vJyMjMzk9uKFSt4++23k+0Eg0GmTp3ape3q6mpuueUWysvLycnJIScnh5aWluRVxx07djBs2DCKi4uT7znnnHO6nGP9+vXs3r2brKysZNv5+fl0dHR0af+jsseuABtjAGhtbeXrX/86EydOJDc3l8zMTLZv3/6Rr4yGQiGuu+66ZH++8cYbvPnmm9x4443v+56JEyeyZcsW1qxZw+c+9zmOHDnCFVdcwU033fSBba1fv57ly5d3+T2MHz8eoEtfzJkzJ/n5AObOncuuXbuIx+NceOGFlJWVMWrUKK6//noef/xx2traPtJnFRERkdSiPK8r5XnK80RSjRZhEBlAHn74YWKxGEOGDEm+Zq0lEAhQX19PXl4eixYt4o477uDBBx/kt7/9LZMmTWLatGlA4vYBn8/H+vXr8fl8Xc6dmZmZfB4Oh7skAwA33ngjR48e5f7776esrIxQKMTcuXOTtxRYa094z3u5rsvMmTOTieO7nc6kudu2bQNg5MiRAHzta1/jueee49///d8ZM2YM4XCYa6655pRue7jpppuYPn06Bw4c4JFHHuGCCy6grKzsA9/jOA5nn302Z599Nv/4j//Ib37zG66//nruuuuuZGzv5bouV1xxBT/60Y9O2FdSUvKRYs3KymLDhg289NJLPP/883znO9/hnnvuYd26deTm5n6kc4iIiEhqUJ7XlfI85XkiqUYFOJEBIhaL8dhjj3Hfffdx0UUXddn36U9/mscff5zbb7+dq6++mi9+8YssWbKE3/72t1x//fXJ48466yzi8TjV1dWcd955p9T+ypUr+fnPf85ll10GQGVlJTU1Ncn948ePp6KigiNHjlBUVAQkloJ/txkzZvDEE09QWFhIdnb2KbX/Xq7r8sADDzBy5EjOOuusZIw33ngjn/zkJ4HEXCH79u07pfNOmTKFWbNm8Ytf/ILf/va3PPjgg6cc28SJE4HElVpIXGmOx+NdjpkxYwZPPvkkI0aMwO9///+Vr1mz5oSfx44dm0ys/X4/CxcuZOHChdx9993k5uaybNkyPvWpT51y3CIiIuIN5XldKc9TnieSinQLqsgA8ec//5n6+no+//nPM3ny5C7bNddcw8MPPwxARkYGV111Fd/+9rfZtm0bf/u3f5s8R3l5OYsWLeKGG27gqaeeYu/evaxbt44f/ehHPPvssx/Y/pgxY/j1r3/Ntm3bWLt2LYsWLSIcDif3X3jhhYwePZrFixezadMmXn311eTkvMevmC5atIiCggKuuuoqVq5cyd69e1mxYgV33HEHBw4c+MD2a2trOXz4MHv27OFPf/oTCxcu5LXXXuPhhx9OJiljxozhqaeeSt5S8Ld/+7e4rnvKfX3TTTfxwx/+kHg8nkzy3s8111zDT37yE9auXcv+/ft56aWXuO222ygvL0/eajBixAjWrl3Lvn37qKmpwXVdbrvtNurq6vjsZz/La6+9xp49e3j++ef5u7/7uy5JXGVlJXfeeSc7duzgd7/7HQ8++CB33HEHkPhv4oEHHuCNN95g//79PPbYY7iuy7hx4075M4uIiIh3lOcpz1OeJ9IHeDf9nIj0pssvv9xedtllJ923fv16CySXi3/mmWcsYOfNm3fCsZ2dnfY73/mOHTFihA0EAra4uNh+8pOftJs2bbLWvrM8/Xtt2LDBzpo1y4ZCITt27Fj73//93ydMdHt8efpgMGjHjx9vn376aQvYJUuWJI+pqqqyN9xwgy0oKLChUMiOGjXKfuELX7CNjY0n/WzHJ+c9vqWnp9sJEybYW2+91e7ateuEYxcsWGDD4bAdNmyY/dnPfnbCxLgfNDnvcc3NzTY9Pd3eeuutJ43p3R566CG7YMECO3jwYBsMBu3w4cPtjTfeaPft25c8ZseOHXbOnDk2HA53WZ5+586d9pOf/KTNzc214XDYjh8/3n75y19OTsY7f/58e+utt9pbbrnFZmdn27y8PPvNb34zuX/lypV2/vz5Ni8vz4bDYTt16tQPnKhYREREUpPyPOV5yvNEUp+x9l3rUIuIpJBXX32Vj3/84+zevZvRo0d7Hc5HVllZyYgRI1i3bh0zZszwLI7zzz+f6dOnc//993sWg4iIiMjJKM87M8rzRPoezQEnIinjD3/4A5mZmYwdO5bdu3dzxx138LGPfazPJGXRaJSqqiq++c1vMmfOHE+TMhEREZFUojxPRAY6FeBEJGU0Nzfz9a9/ncrKSgoKCli4cCH33Xef12F9ZK+++ioLFiygvLyc//mf//E6HBEREZGUoTxPRAY63YIqIiIiIiIiIiLSg7QKqoiIiIiIiIiISA9SAU5ERERERERERKQHqQAnIiIiIiIiIiLSg1SAExERERERERER6UEqwImIiIiIiIiIiPQgFeBERERERERERER6kApwIiIiIiIiIiIiPUgFOBERERERERERkR70/wOwMpeQgvtW6AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the distribution of average daily steps before and after the imputation\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "#Contest average: Before mean imputation\n",
    "sns.histplot(contest_row_mean, label = \"Not imputed\", kde=True, color=\"skyblue\", ax=ax[0])\n",
    "ax[0].set_title(\"Distribution of Average Daily Steps Before Mean Imputation - Contest \")\n",
    "ax[0].legend()\n",
    "ax[0].set_xlabel(\"Average Daily Steps\")\n",
    "#ax[0].text(\"\n",
    "\n",
    "#Contest average: After mean imputation\n",
    "sns.histplot(contest_mean_impute_df[\"Average Daily Steps\"], label = \"Imputed with Mean\", kde=True, color=\"salmon\", ax=ax[1])\n",
    "ax[1].set_title(\"Distribution of Average Daily Steps After Mean Imputation - Contest\")\n",
    "ax[1].legend()\n",
    "\n",
    "c_notimputed_mean = round(contest_row_mean.mean(), 5)\n",
    "c_notimputed_variance = round(contest_row_mean.var(), 5)\n",
    "c_notimputed_std_dev = round(contest_row_mean.std(), 5)\n",
    "\n",
    "c_mn_imputed_mean = round(contest_mean_impute_df[\"Average Daily Steps\"].mean(), 5)\n",
    "c_mn_imputed_variance = round(contest_mean_impute_df[\"Average Daily Steps\"].var(), 5)\n",
    "c_mn_imputed_std_dev = round(contest_mean_impute_df[\"Average Daily Steps\"].std(), 5)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Mean of Average Steps Before Imputation: {c_notimputed_mean}\")\n",
    "print(f\"Mean of Average Steps After Imputation: {c_mn_imputed_mean}\")\n",
    "print(f\"Variance of Average Steps Before Imputation : {c_notimputed_variance}\")\n",
    "print(f\"Variance of Average Steps After Imputation : {c_mn_imputed_variance}\")\n",
    "print(f\"Standard Deviation of Average Steps Before Imputation: {c_notimputed_std_dev}\")\n",
    "print(f\"Standard Deviation of Average Steps After Imputation: {c_mn_imputed_std_dev}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e180c47a-bf14-46c1-8c9d-5e93e682a597",
   "metadata": {},
   "source": [
    "The mean of the distribution remains the same after imputation. But we get lesser variation in the dataset by imputing missing values with the user's average daily steps. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29d6bd5-2bed-4558-a6df-5552c6049e5e",
   "metadata": {},
   "source": [
    "Next, we will try a median imputation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36656f51-dc85-465d-850b-432a1711d811",
   "metadata": {},
   "source": [
    "#### Median Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "357492e1-41bd-4a50-947c-f1ec4bd2a623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023-06-01    0\n",
       "2023-06-02    0\n",
       "2023-06-03    0\n",
       "2023-06-04    0\n",
       "2023-06-05    0\n",
       "             ..\n",
       "2023-08-27    0\n",
       "2023-08-28    0\n",
       "2023-08-29    0\n",
       "2023-08-30    0\n",
       "2023-08-31    0\n",
       "Length: 92, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contest_median_impute_df = contest_df.apply(lambda row: row.fillna(row.median()), axis=1)\n",
    "contest_median_impute_df.isna().sum() #check that the imputation worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "7a5aba69-1340-42e8-8ace-021083742677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      16604.641304\n",
       "5       4597.728261\n",
       "10      4430.836957\n",
       "13      3234.076087\n",
       "14      4089.956522\n",
       "           ...     \n",
       "292     1819.532609\n",
       "296     6873.380435\n",
       "297     6467.326087\n",
       "299     5392.163043\n",
       "300    17921.750000\n",
       "Name: Average Daily Steps, Length: 126, dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contest_median_impute_df[\"Average Daily Steps\"] = contest_median_impute_df.mean(axis = 1) #calculate row mean on imputed data\n",
    "contest_median_impute_df[\"Average Daily Steps\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d6fe1fe6-cf87-4d74-9383-a66dbdce9fc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of Average Steps Before Imputation: 7187.5\n",
      "Mean of Average Steps After Imputation: 7187.5\n",
      "Variance of Average Steps Before Imputation : 22907642.52\n",
      "Variance of Average Steps After Imputation : 14515523.35\n",
      "Standard Deviation of Average Steps Before Imputation: 4786.19\n",
      "Standard Deviation of Average Steps After Imputation: 3809.92\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the distribution of average daily steps before and after the imputation\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "#Contest average: Before mean imputation\n",
    "sns.histplot(contest_row_mean, label = \"Not imputed\", kde=True, color=\"skyblue\", ax=ax[0])\n",
    "ax[0].set_title(\"Distribution of Average Daily Steps Before Median Imputation - Contest\")\n",
    "ax[0].legend()\n",
    "ax[0].set_xlabel(\"Average Daily Steps\")\n",
    "#ax[0].text(\"\n",
    "\n",
    "#Contest average: After mean imputation\n",
    "sns.histplot(contest_median_impute_df[\"Average Daily Steps\"], label = \"Imputed with Median\", kde=True, color=\"salmon\", ax=ax[1])\n",
    "ax[1].set_title(\"Distribution of Average Daily Steps After Median Imputation - Contest\")\n",
    "ax[1].legend()\n",
    "\n",
    "c_notimputed_mean = round(contest_row_mean.mean(), 2)\n",
    "c_notimputed_variance = round(contest_row_mean.var(), 2)\n",
    "c_notimputed_std_dev = round(contest_row_mean.std(), 2)\n",
    "\n",
    "c_md_imputed_mean = round(contest_mean_impute_df[\"Average Daily Steps\"].mean(), 2)\n",
    "c_md_imputed_variance = round(baseline_mean_impute_df[\"Average Daily Steps\"].var(), 2)\n",
    "c_md_imputed_std_dev = round(baseline_mean_impute_df[\"Average Daily Steps\"].std(), 2)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Mean of Average Steps Before Imputation: {c_notimputed_mean}\")\n",
    "print(f\"Mean of Average Steps After Imputation: {c_md_imputed_mean}\")\n",
    "print(f\"Variance of Average Steps Before Imputation : {c_notimputed_variance}\")\n",
    "print(f\"Variance of Average Steps After Imputation : {c_md_imputed_variance}\")\n",
    "print(f\"Standard Deviation of Average Steps Before Imputation: {c_notimputed_std_dev}\")\n",
    "print(f\"Standard Deviation of Average Steps After Imputation: {c_md_imputed_std_dev}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a835c324-cf4f-457e-8e02-eccd9b3878b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65850da0-7cd5-4aa5-a582-8612fad112a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "8f0321bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b_notmissing_per_column = baseline_df.notna().sum() #calculate total available values per day\n",
    "#print(\"Available values per column:\\n\", b_notmissing_per_column)\n",
    "\n",
    "b_missing_df = pd.DataFrame({      #create a df with count of missing and available values\n",
    "    'Missing': b_missing_per_column,\n",
    "    'Available': b_notmissing_per_column\n",
    "})\n",
    "\n",
    "b_missing_df.plot(kind='bar', stacked=True, figsize=(10, 6), color=['indigo', 'green'], alpha=0.7) \n",
    "\n",
    "\n",
    "plt.title('Comparison of Available and Missing Data at Baseline', fontsize=16)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(['Missing', 'Available'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "06d73b83-cd4b-4846-8828-cca0f4ff6a01",
   "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>2023-06-01</th>\n",
       "      <th>2023-06-02</th>\n",
       "      <th>2023-06-03</th>\n",
       "      <th>2023-06-04</th>\n",
       "      <th>2023-06-05</th>\n",
       "      <th>2023-06-06</th>\n",
       "      <th>2023-06-07</th>\n",
       "      <th>2023-06-08</th>\n",
       "      <th>2023-06-09</th>\n",
       "      <th>2023-06-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-08-22</th>\n",
       "      <th>2023-08-23</th>\n",
       "      <th>2023-08-24</th>\n",
       "      <th>2023-08-25</th>\n",
       "      <th>2023-08-26</th>\n",
       "      <th>2023-08-27</th>\n",
       "      <th>2023-08-28</th>\n",
       "      <th>2023-08-29</th>\n",
       "      <th>2023-08-30</th>\n",
       "      <th>2023-08-31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2023-06-01  2023-06-02  2023-06-03  2023-06-04  2023-06-05  2023-06-06  \\\n",
       "3         False       False       False       False       False       False   \n",
       "5         False       False       False       False       False       False   \n",
       "10        False       False       False       False       False       False   \n",
       "13        False       False       False       False       False       False   \n",
       "14        False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "292       False       False       False       False       False       False   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-06-07  2023-06-08  2023-06-09  2023-06-10  ...  2023-08-22  \\\n",
       "3         False       False       False       False  ...       False   \n",
       "5         False       False       False       False  ...       False   \n",
       "10        False       False       False       False  ...        True   \n",
       "13        False       False       False       False  ...       False   \n",
       "14        False       False       False       False  ...       False   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "292       False       False       False       False  ...       False   \n",
       "296       False       False       False       False  ...       False   \n",
       "297       False       False       False       False  ...       False   \n",
       "299       False       False       False       False  ...       False   \n",
       "300       False       False       False       False  ...       False   \n",
       "\n",
       "     2023-08-23  2023-08-24  2023-08-25  2023-08-26  2023-08-27  2023-08-28  \\\n",
       "3         False       False       False       False       False       False   \n",
       "5         False       False       False       False       False       False   \n",
       "10         True        True        True        True        True        True   \n",
       "13        False       False       False        True        True        True   \n",
       "14        False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "292       False       False       False       False       False       False   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-08-29  2023-08-30  2023-08-31  \n",
       "3         False       False       False  \n",
       "5         False       False        True  \n",
       "10         True       False        True  \n",
       "13         True        True        True  \n",
       "14        False       False        True  \n",
       "..          ...         ...         ...  \n",
       "292        True        True        True  \n",
       "296       False       False        True  \n",
       "297       False       False       False  \n",
       "299       False       False        True  \n",
       "300       False       False       False  \n",
       "\n",
       "[126 rows x 92 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contest_df.isna().sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a70e2c",
   "metadata": {},
   "source": [
    "From the graphs we can see that every day of the baseline month has missing values and that there are fewer missing values at the end of the month than at the start of the month. We can also see that the proportion of missing values to available values stays roughly the same for all days except the last few. This allows us to proceed with imputing missing values since we have more available data for each individual than missing. The assumption here is that based on the observed daily steps we should be able to infer the number of daily steps for days that do not have data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485fce32",
   "metadata": {},
   "source": [
    "#### Baseline Average Daily Steps with Available Values "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f16618",
   "metadata": {},
   "source": [
    "We will now calculate the average number of daily steps by dividing the sum of available values in the row observations with the number of days in the baseline period. We then visualize the distribution of averages and get the moments of distribution.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "107b51e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute row average including na observations \n",
    "baseline_df[\"Mean with values\"] = (baseline_df.sum(axis=1) /baseline_df.shape[1]).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "6714a5d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x139eb97f0>"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(baseline_df[\"Mean with values\"], density=True)\n",
    "num_bins = 30\n",
    "plt.hist(baseline_df[\"Mean with values\"], num_bins, density=False, label = \"Not imputed\",\n",
    "                                edgecolor='k')\n",
    "                            \n",
    "plt.title('Distribution of Average Daily Steps during Baseline', fontsize=20)\n",
    "plt.xlabel(r'Average Daily Steps')\n",
    "plt.ylabel(r'Frequency of Average ')\n",
    "plt.xlim([0, 20000])\n",
    "plt.legend(loc = \"upper right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "92da7908",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean, variance, and standard error of this distribution are:\n",
      "Mean: 4912.855\n",
      "Variance: 12521843.19\n",
      "Standard Deviation: 3538.622\n"
     ]
    }
   ],
   "source": [
    "mean_with_values = round(baseline_df[\"Mean with values\"].mean(), 3)\n",
    "var_with_values = round(baseline_df[\"Mean with values\"].var(), 3)\n",
    "sd_with_values = round(baseline_df[\"Mean with values\"].std(),3)\n",
    "#mode_with_values = baseline_df[\"Mean with values\"].mode(axis = 'columns')\n",
    "#median_with_values = round(baseline_df[\"Median with values\"].median(), 3)\n",
    "print(\"Mean, variance, and standard error of this distribution are:\")\n",
    "print(\"Mean: \" + str(mean_with_values))\n",
    "print(\"Variance: \" + str(var_with_values))\n",
    "print(\"Standard Deviation: \" + str(sd_with_values))\n",
    "#print(\"Mode: \" + str(mode_with_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "508122f7",
   "metadata": {},
   "source": [
    "As we can see from this graph, there is high variance in the average number of daily steps with the mean being 4913 steps a day. Majority of the averages lie below 10,000 steps a day and there are a few outlier observations that have 15,000 to 20,000 average daily steps. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93fd4343",
   "metadata": {},
   "source": [
    "The goal of our imputation will be to retain this distribution of average daily steps after the imputation. Hence, we will use moments of distribution like mean, variance, and standard variance to compare the new dataset with imputed daily steps to the original baseline datasee with missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54bd0a2e",
   "metadata": {},
   "source": [
    "#### Imputing Missing Data with Mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542312e2",
   "metadata": {},
   "source": [
    "This method uses the row mean to fill in the missing values for each observation. In other words, we use the individual's baseline average daily number of steps to fill the values for days with missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "a09aaf06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2023-05-01', '2023-05-02', '2023-05-03', '2023-05-04', '2023-05-05',\n",
       "       '2023-05-06', '2023-05-07', '2023-05-08', '2023-05-09', '2023-05-10',\n",
       "       '2023-05-11', '2023-05-12', '2023-05-13', '2023-05-14', '2023-05-15',\n",
       "       '2023-05-16', '2023-05-17', '2023-05-18', '2023-05-19', '2023-05-20',\n",
       "       '2023-05-21', '2023-05-22', '2023-05-23', '2023-05-24', '2023-05-25',\n",
       "       '2023-05-26', '2023-05-27', '2023-05-28', '2023-05-29', '2023-05-30',\n",
       "       '2023-05-31', 'Mean with values'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "7758f4b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>2023-05-01</th>\n",
       "      <th>2023-05-02</th>\n",
       "      <th>2023-05-03</th>\n",
       "      <th>2023-05-04</th>\n",
       "      <th>2023-05-05</th>\n",
       "      <th>2023-05-06</th>\n",
       "      <th>2023-05-07</th>\n",
       "      <th>2023-05-08</th>\n",
       "      <th>2023-05-09</th>\n",
       "      <th>2023-05-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-05-23</th>\n",
       "      <th>2023-05-24</th>\n",
       "      <th>2023-05-25</th>\n",
       "      <th>2023-05-26</th>\n",
       "      <th>2023-05-27</th>\n",
       "      <th>2023-05-28</th>\n",
       "      <th>2023-05-29</th>\n",
       "      <th>2023-05-30</th>\n",
       "      <th>2023-05-31</th>\n",
       "      <th>Mean with values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0          True        True        True        True        True        True   \n",
       "1          True        True        True        True        True        True   \n",
       "2          True        True        True        True        True        True   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-23  \\\n",
       "0          True        True        True        True  ...       False   \n",
       "1          True        True        True        True  ...        True   \n",
       "2          True        True        True        True  ...        True   \n",
       "3         False       False       False       False  ...       False   \n",
       "4         False       False       False       False  ...       False   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "296       False       False       False       False  ...       False   \n",
       "297       False       False       False       False  ...       False   \n",
       "298       False       False       False       False  ...       False   \n",
       "299       False       False       False       False  ...       False   \n",
       "300       False       False       False       False  ...       False   \n",
       "\n",
       "     2023-05-24  2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  \\\n",
       "0         False       False       False       False       False       False   \n",
       "1          True       False       False       False       False       False   \n",
       "2          True        True        True       False       False       False   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-30  2023-05-31  Mean with values  \n",
       "0         False       False             False  \n",
       "1         False       False             False  \n",
       "2         False       False             False  \n",
       "3         False       False             False  \n",
       "4         False       False             False  \n",
       "..          ...         ...               ...  \n",
       "296       False       False             False  \n",
       "297       False       False             False  \n",
       "298       False       False             False  \n",
       "299       False       False             False  \n",
       "300       False       False             False  \n",
       "\n",
       "[301 rows x 32 columns]"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "7ee72e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "b_mean_impute_df = baseline_df.copy() #copy the baseline data\n",
    "\n",
    "#Imputing with row means\n",
    "for col in b_mean_impute_df.columns[:-1]: #for all columns except the mean column\n",
    "    b_mean_impute_df[col] =  b_mean_impute_df[col].fillna(b_mean_impute_df['Mean with values']) \n",
    "    #fill missing values with the row means "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "ab1aaa28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023-05-01          0\n",
       "2023-05-02          0\n",
       "2023-05-03          0\n",
       "2023-05-04          0\n",
       "2023-05-05          0\n",
       "2023-05-06          0\n",
       "2023-05-07          0\n",
       "2023-05-08          0\n",
       "2023-05-09          0\n",
       "2023-05-10          0\n",
       "2023-05-11          0\n",
       "2023-05-12          0\n",
       "2023-05-13          0\n",
       "2023-05-14          0\n",
       "2023-05-15          0\n",
       "2023-05-16          0\n",
       "2023-05-17          0\n",
       "2023-05-18          0\n",
       "2023-05-19          0\n",
       "2023-05-20          0\n",
       "2023-05-21          0\n",
       "2023-05-22          0\n",
       "2023-05-23          0\n",
       "2023-05-24          0\n",
       "2023-05-25          0\n",
       "2023-05-26          0\n",
       "2023-05-27          0\n",
       "2023-05-28          0\n",
       "2023-05-29          0\n",
       "2023-05-30          0\n",
       "2023-05-31          0\n",
       "Mean with values    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_mean_impute_df.isna().sum() #check if there are still missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "08781e0d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0          True        True        True        True        True        True   \n",
       "1          True        True        True        True        True        True   \n",
       "2          True        True        True        True        True        True   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-23  \\\n",
       "0          True        True        True        True  ...       False   \n",
       "1          True        True        True        True  ...        True   \n",
       "2          True        True        True        True  ...        True   \n",
       "3         False       False       False       False  ...       False   \n",
       "4         False       False       False       False  ...       False   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "296       False       False       False       False  ...       False   \n",
       "297       False       False       False       False  ...       False   \n",
       "298       False       False       False       False  ...       False   \n",
       "299       False       False       False       False  ...       False   \n",
       "300       False       False       False       False  ...       False   \n",
       "\n",
       "     2023-05-24  2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  \\\n",
       "0         False       False       False       False       False       False   \n",
       "1          True       False       False       False       False       False   \n",
       "2          True        True        True       False       False       False   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-30  2023-05-31  Mean with values  \n",
       "0         False       False             False  \n",
       "1         False       False             False  \n",
       "2         False       False             False  \n",
       "3         False       False             False  \n",
       "4         False       False             False  \n",
       "..          ...         ...               ...  \n",
       "296       False       False             False  \n",
       "297       False       False             False  \n",
       "298       False       False             False  \n",
       "299       False       False             False  \n",
       "300       False       False             False  \n",
       "\n",
       "[301 rows x 32 columns]"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_df.isna()#check if original baseline data still has missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "d14e9f51",
   "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>2023-05-01</th>\n",
       "      <th>2023-05-02</th>\n",
       "      <th>2023-05-03</th>\n",
       "      <th>2023-05-04</th>\n",
       "      <th>2023-05-05</th>\n",
       "      <th>2023-05-06</th>\n",
       "      <th>2023-05-07</th>\n",
       "      <th>2023-05-08</th>\n",
       "      <th>2023-05-09</th>\n",
       "      <th>2023-05-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-05-23</th>\n",
       "      <th>2023-05-24</th>\n",
       "      <th>2023-05-25</th>\n",
       "      <th>2023-05-26</th>\n",
       "      <th>2023-05-27</th>\n",
       "      <th>2023-05-28</th>\n",
       "      <th>2023-05-29</th>\n",
       "      <th>2023-05-30</th>\n",
       "      <th>2023-05-31</th>\n",
       "      <th>Mean with values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>...</td>\n",
       "      <td>45.000</td>\n",
       "      <td>3099.000</td>\n",
       "      <td>1339.000</td>\n",
       "      <td>4193.000</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>6415.0</td>\n",
       "      <td>3911.0</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>3570</td>\n",
       "      <td>1038.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>...</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>3170.000</td>\n",
       "      <td>5205.000</td>\n",
       "      <td>3098.0</td>\n",
       "      <td>4481.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>7019</td>\n",
       "      <td>950.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>...</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>9965.0</td>\n",
       "      <td>24058.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>524</td>\n",
       "      <td>1212.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4930.000</td>\n",
       "      <td>29351.000</td>\n",
       "      <td>30457.000</td>\n",
       "      <td>25895.000</td>\n",
       "      <td>5528.000</td>\n",
       "      <td>17858.000</td>\n",
       "      <td>2879.000</td>\n",
       "      <td>3567.000</td>\n",
       "      <td>26381.000</td>\n",
       "      <td>21659.000</td>\n",
       "      <td>...</td>\n",
       "      <td>27183.000</td>\n",
       "      <td>21784.000</td>\n",
       "      <td>1323.000</td>\n",
       "      <td>22493.000</td>\n",
       "      <td>32316.0</td>\n",
       "      <td>17272.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>25778.0</td>\n",
       "      <td>25065</td>\n",
       "      <td>15839.581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3222.000</td>\n",
       "      <td>5197.000</td>\n",
       "      <td>4490.000</td>\n",
       "      <td>4124.000</td>\n",
       "      <td>2107.000</td>\n",
       "      <td>7063.000</td>\n",
       "      <td>3905.000</td>\n",
       "      <td>3479.000</td>\n",
       "      <td>4723.000</td>\n",
       "      <td>4692.000</td>\n",
       "      <td>...</td>\n",
       "      <td>7182.000</td>\n",
       "      <td>2738.000</td>\n",
       "      <td>5379.000</td>\n",
       "      <td>13257.000</td>\n",
       "      <td>3564.0</td>\n",
       "      <td>5404.0</td>\n",
       "      <td>2841.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>4630</td>\n",
       "      <td>4946.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3802.000</td>\n",
       "      <td>2655.000</td>\n",
       "      <td>2653.000</td>\n",
       "      <td>3209.000</td>\n",
       "      <td>247.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>527.000</td>\n",
       "      <td>1041.000</td>\n",
       "      <td>1581.000</td>\n",
       "      <td>3412.000</td>\n",
       "      <td>...</td>\n",
       "      <td>6171.000</td>\n",
       "      <td>4034.000</td>\n",
       "      <td>3145.000</td>\n",
       "      <td>8659.000</td>\n",
       "      <td>7306.0</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>5044.0</td>\n",
       "      <td>1532.0</td>\n",
       "      <td>6771</td>\n",
       "      <td>3416.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1322.000</td>\n",
       "      <td>1136.000</td>\n",
       "      <td>1119.000</td>\n",
       "      <td>2452.000</td>\n",
       "      <td>1354.000</td>\n",
       "      <td>3163.000</td>\n",
       "      <td>1097.000</td>\n",
       "      <td>1039.000</td>\n",
       "      <td>955.000</td>\n",
       "      <td>1399.000</td>\n",
       "      <td>...</td>\n",
       "      <td>973.000</td>\n",
       "      <td>1335.000</td>\n",
       "      <td>1211.000</td>\n",
       "      <td>1076.000</td>\n",
       "      <td>4049.0</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>1212</td>\n",
       "      <td>1721.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>392.000</td>\n",
       "      <td>191.000</td>\n",
       "      <td>1126.000</td>\n",
       "      <td>3447.000</td>\n",
       "      <td>2129.000</td>\n",
       "      <td>741.000</td>\n",
       "      <td>2747.000</td>\n",
       "      <td>376.000</td>\n",
       "      <td>3142.000</td>\n",
       "      <td>489.000</td>\n",
       "      <td>...</td>\n",
       "      <td>2952.000</td>\n",
       "      <td>2893.000</td>\n",
       "      <td>3674.000</td>\n",
       "      <td>4352.000</td>\n",
       "      <td>4058.0</td>\n",
       "      <td>2579.0</td>\n",
       "      <td>1171.0</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>1540</td>\n",
       "      <td>2377.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3205.000</td>\n",
       "      <td>5516.000</td>\n",
       "      <td>3696.000</td>\n",
       "      <td>6920.000</td>\n",
       "      <td>7326.000</td>\n",
       "      <td>3799.000</td>\n",
       "      <td>6821.000</td>\n",
       "      <td>7552.000</td>\n",
       "      <td>1678.000</td>\n",
       "      <td>6647.000</td>\n",
       "      <td>...</td>\n",
       "      <td>4833.000</td>\n",
       "      <td>4894.000</td>\n",
       "      <td>4442.000</td>\n",
       "      <td>5959.000</td>\n",
       "      <td>2999.0</td>\n",
       "      <td>9329.0</td>\n",
       "      <td>6049.0</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>5053</td>\n",
       "      <td>5305.419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4691.000</td>\n",
       "      <td>4516.000</td>\n",
       "      <td>6834.000</td>\n",
       "      <td>5044.000</td>\n",
       "      <td>4602.000</td>\n",
       "      <td>8291.000</td>\n",
       "      <td>6263.000</td>\n",
       "      <td>5622.000</td>\n",
       "      <td>7203.000</td>\n",
       "      <td>3721.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5948.000</td>\n",
       "      <td>7878.000</td>\n",
       "      <td>2248.000</td>\n",
       "      <td>628.000</td>\n",
       "      <td>161.0</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>2361.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>6471</td>\n",
       "      <td>4982.677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0    1038.613    1038.613    1038.613    1038.613    1038.613    1038.613   \n",
       "1     950.032     950.032     950.032     950.032     950.032     950.032   \n",
       "2    1212.226    1212.226    1212.226    1212.226    1212.226    1212.226   \n",
       "3    4930.000   29351.000   30457.000   25895.000    5528.000   17858.000   \n",
       "4    3222.000    5197.000    4490.000    4124.000    2107.000    7063.000   \n",
       "5    3802.000    2655.000    2653.000    3209.000     247.000      80.000   \n",
       "6    1322.000    1136.000    1119.000    2452.000    1354.000    3163.000   \n",
       "7     392.000     191.000    1126.000    3447.000    2129.000     741.000   \n",
       "8    3205.000    5516.000    3696.000    6920.000    7326.000    3799.000   \n",
       "9    4691.000    4516.000    6834.000    5044.000    4602.000    8291.000   \n",
       "\n",
       "   2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-23  \\\n",
       "0    1038.613    1038.613    1038.613    1038.613  ...      45.000   \n",
       "1     950.032     950.032     950.032     950.032  ...     950.032   \n",
       "2    1212.226    1212.226    1212.226    1212.226  ...    1212.226   \n",
       "3    2879.000    3567.000   26381.000   21659.000  ...   27183.000   \n",
       "4    3905.000    3479.000    4723.000    4692.000  ...    7182.000   \n",
       "5     527.000    1041.000    1581.000    3412.000  ...    6171.000   \n",
       "6    1097.000    1039.000     955.000    1399.000  ...     973.000   \n",
       "7    2747.000     376.000    3142.000     489.000  ...    2952.000   \n",
       "8    6821.000    7552.000    1678.000    6647.000  ...    4833.000   \n",
       "9    6263.000    5622.000    7203.000    3721.000  ...    5948.000   \n",
       "\n",
       "   2023-05-24  2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  \\\n",
       "0    3099.000    1339.000    4193.000      6350.0      6415.0      3911.0   \n",
       "1     950.032    3170.000    5205.000      3098.0      4481.0      2910.0   \n",
       "2    1212.226    1212.226    1212.226      9965.0     24058.0      1967.0   \n",
       "3   21784.000    1323.000   22493.000     32316.0     17272.0      2427.0   \n",
       "4    2738.000    5379.000   13257.000      3564.0      5404.0      2841.0   \n",
       "5    4034.000    3145.000    8659.000      7306.0      2682.0      5044.0   \n",
       "6    1335.000    1211.000    1076.000      4049.0      3147.0      1307.0   \n",
       "7    2893.000    3674.000    4352.000      4058.0      2579.0      1171.0   \n",
       "8    4894.000    4442.000    5959.000      2999.0      9329.0      6049.0   \n",
       "9    7878.000    2248.000     628.000       161.0      1599.0      2361.0   \n",
       "\n",
       "   2023-05-30  2023-05-31  Mean with values  \n",
       "0      3275.0        3570          1038.613  \n",
       "1      3568.0        7019           950.032  \n",
       "2      1065.0         524          1212.226  \n",
       "3     25778.0       25065         15839.581  \n",
       "4       286.0        4630          4946.194  \n",
       "5      1532.0        6771          3416.935  \n",
       "6      1082.0        1212          1721.387  \n",
       "7      1914.0        1540          2377.194  \n",
       "8      2711.0        5053          5305.419  \n",
       "9      2607.0        6471          4982.677  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_mean_impute_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "98bb5c1e",
   "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>2023-05-01</th>\n",
       "      <th>2023-05-02</th>\n",
       "      <th>2023-05-03</th>\n",
       "      <th>2023-05-04</th>\n",
       "      <th>2023-05-05</th>\n",
       "      <th>2023-05-06</th>\n",
       "      <th>2023-05-07</th>\n",
       "      <th>2023-05-08</th>\n",
       "      <th>2023-05-09</th>\n",
       "      <th>2023-05-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-05-23</th>\n",
       "      <th>2023-05-24</th>\n",
       "      <th>2023-05-25</th>\n",
       "      <th>2023-05-26</th>\n",
       "      <th>2023-05-27</th>\n",
       "      <th>2023-05-28</th>\n",
       "      <th>2023-05-29</th>\n",
       "      <th>2023-05-30</th>\n",
       "      <th>2023-05-31</th>\n",
       "      <th>Baseline Average Steps Per Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>...</td>\n",
       "      <td>45.000</td>\n",
       "      <td>3099.000</td>\n",
       "      <td>1339.000</td>\n",
       "      <td>4193.000</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>6415.0</td>\n",
       "      <td>3911.0</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>3570</td>\n",
       "      <td>1775.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>...</td>\n",
       "      <td>950.032</td>\n",
       "      <td>950.032</td>\n",
       "      <td>3170.000</td>\n",
       "      <td>5205.000</td>\n",
       "      <td>3098.0</td>\n",
       "      <td>4481.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>7019</td>\n",
       "      <td>1685.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>...</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>9965.0</td>\n",
       "      <td>24058.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>524</td>\n",
       "      <td>2228.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4930.000</td>\n",
       "      <td>29351.000</td>\n",
       "      <td>30457.000</td>\n",
       "      <td>25895.000</td>\n",
       "      <td>5528.000</td>\n",
       "      <td>17858.000</td>\n",
       "      <td>2879.000</td>\n",
       "      <td>3567.000</td>\n",
       "      <td>26381.000</td>\n",
       "      <td>21659.000</td>\n",
       "      <td>...</td>\n",
       "      <td>27183.000</td>\n",
       "      <td>21784.000</td>\n",
       "      <td>1323.000</td>\n",
       "      <td>22493.000</td>\n",
       "      <td>32316.0</td>\n",
       "      <td>17272.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>25778.0</td>\n",
       "      <td>25065</td>\n",
       "      <td>15839.581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3222.000</td>\n",
       "      <td>5197.000</td>\n",
       "      <td>4490.000</td>\n",
       "      <td>4124.000</td>\n",
       "      <td>2107.000</td>\n",
       "      <td>7063.000</td>\n",
       "      <td>3905.000</td>\n",
       "      <td>3479.000</td>\n",
       "      <td>4723.000</td>\n",
       "      <td>4692.000</td>\n",
       "      <td>...</td>\n",
       "      <td>7182.000</td>\n",
       "      <td>2738.000</td>\n",
       "      <td>5379.000</td>\n",
       "      <td>13257.000</td>\n",
       "      <td>3564.0</td>\n",
       "      <td>5404.0</td>\n",
       "      <td>2841.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>4630</td>\n",
       "      <td>4946.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3802.000</td>\n",
       "      <td>2655.000</td>\n",
       "      <td>2653.000</td>\n",
       "      <td>3209.000</td>\n",
       "      <td>247.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>527.000</td>\n",
       "      <td>1041.000</td>\n",
       "      <td>1581.000</td>\n",
       "      <td>3412.000</td>\n",
       "      <td>...</td>\n",
       "      <td>6171.000</td>\n",
       "      <td>4034.000</td>\n",
       "      <td>3145.000</td>\n",
       "      <td>8659.000</td>\n",
       "      <td>7306.0</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>5044.0</td>\n",
       "      <td>1532.0</td>\n",
       "      <td>6771</td>\n",
       "      <td>3416.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1322.000</td>\n",
       "      <td>1136.000</td>\n",
       "      <td>1119.000</td>\n",
       "      <td>2452.000</td>\n",
       "      <td>1354.000</td>\n",
       "      <td>3163.000</td>\n",
       "      <td>1097.000</td>\n",
       "      <td>1039.000</td>\n",
       "      <td>955.000</td>\n",
       "      <td>1399.000</td>\n",
       "      <td>...</td>\n",
       "      <td>973.000</td>\n",
       "      <td>1335.000</td>\n",
       "      <td>1211.000</td>\n",
       "      <td>1076.000</td>\n",
       "      <td>4049.0</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>1212</td>\n",
       "      <td>1721.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>392.000</td>\n",
       "      <td>191.000</td>\n",
       "      <td>1126.000</td>\n",
       "      <td>3447.000</td>\n",
       "      <td>2129.000</td>\n",
       "      <td>741.000</td>\n",
       "      <td>2747.000</td>\n",
       "      <td>376.000</td>\n",
       "      <td>3142.000</td>\n",
       "      <td>489.000</td>\n",
       "      <td>...</td>\n",
       "      <td>2952.000</td>\n",
       "      <td>2893.000</td>\n",
       "      <td>3674.000</td>\n",
       "      <td>4352.000</td>\n",
       "      <td>4058.0</td>\n",
       "      <td>2579.0</td>\n",
       "      <td>1171.0</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>1540</td>\n",
       "      <td>2377.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3205.000</td>\n",
       "      <td>5516.000</td>\n",
       "      <td>3696.000</td>\n",
       "      <td>6920.000</td>\n",
       "      <td>7326.000</td>\n",
       "      <td>3799.000</td>\n",
       "      <td>6821.000</td>\n",
       "      <td>7552.000</td>\n",
       "      <td>1678.000</td>\n",
       "      <td>6647.000</td>\n",
       "      <td>...</td>\n",
       "      <td>4833.000</td>\n",
       "      <td>4894.000</td>\n",
       "      <td>4442.000</td>\n",
       "      <td>5959.000</td>\n",
       "      <td>2999.0</td>\n",
       "      <td>9329.0</td>\n",
       "      <td>6049.0</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>5053</td>\n",
       "      <td>5305.419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4691.000</td>\n",
       "      <td>4516.000</td>\n",
       "      <td>6834.000</td>\n",
       "      <td>5044.000</td>\n",
       "      <td>4602.000</td>\n",
       "      <td>8291.000</td>\n",
       "      <td>6263.000</td>\n",
       "      <td>5622.000</td>\n",
       "      <td>7203.000</td>\n",
       "      <td>3721.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5948.000</td>\n",
       "      <td>7878.000</td>\n",
       "      <td>2248.000</td>\n",
       "      <td>628.000</td>\n",
       "      <td>161.0</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>2361.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>6471</td>\n",
       "      <td>4982.677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0    1038.613    1038.613    1038.613    1038.613    1038.613    1038.613   \n",
       "1     950.032     950.032     950.032     950.032     950.032     950.032   \n",
       "2    1212.226    1212.226    1212.226    1212.226    1212.226    1212.226   \n",
       "3    4930.000   29351.000   30457.000   25895.000    5528.000   17858.000   \n",
       "4    3222.000    5197.000    4490.000    4124.000    2107.000    7063.000   \n",
       "5    3802.000    2655.000    2653.000    3209.000     247.000      80.000   \n",
       "6    1322.000    1136.000    1119.000    2452.000    1354.000    3163.000   \n",
       "7     392.000     191.000    1126.000    3447.000    2129.000     741.000   \n",
       "8    3205.000    5516.000    3696.000    6920.000    7326.000    3799.000   \n",
       "9    4691.000    4516.000    6834.000    5044.000    4602.000    8291.000   \n",
       "\n",
       "   2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-23  \\\n",
       "0    1038.613    1038.613    1038.613    1038.613  ...      45.000   \n",
       "1     950.032     950.032     950.032     950.032  ...     950.032   \n",
       "2    1212.226    1212.226    1212.226    1212.226  ...    1212.226   \n",
       "3    2879.000    3567.000   26381.000   21659.000  ...   27183.000   \n",
       "4    3905.000    3479.000    4723.000    4692.000  ...    7182.000   \n",
       "5     527.000    1041.000    1581.000    3412.000  ...    6171.000   \n",
       "6    1097.000    1039.000     955.000    1399.000  ...     973.000   \n",
       "7    2747.000     376.000    3142.000     489.000  ...    2952.000   \n",
       "8    6821.000    7552.000    1678.000    6647.000  ...    4833.000   \n",
       "9    6263.000    5622.000    7203.000    3721.000  ...    5948.000   \n",
       "\n",
       "   2023-05-24  2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  \\\n",
       "0    3099.000    1339.000    4193.000      6350.0      6415.0      3911.0   \n",
       "1     950.032    3170.000    5205.000      3098.0      4481.0      2910.0   \n",
       "2    1212.226    1212.226    1212.226      9965.0     24058.0      1967.0   \n",
       "3   21784.000    1323.000   22493.000     32316.0     17272.0      2427.0   \n",
       "4    2738.000    5379.000   13257.000      3564.0      5404.0      2841.0   \n",
       "5    4034.000    3145.000    8659.000      7306.0      2682.0      5044.0   \n",
       "6    1335.000    1211.000    1076.000      4049.0      3147.0      1307.0   \n",
       "7    2893.000    3674.000    4352.000      4058.0      2579.0      1171.0   \n",
       "8    4894.000    4442.000    5959.000      2999.0      9329.0      6049.0   \n",
       "9    7878.000    2248.000     628.000       161.0      1599.0      2361.0   \n",
       "\n",
       "   2023-05-30  2023-05-31  Baseline Average Steps Per Day  \n",
       "0      3275.0        3570                        1775.693  \n",
       "1      3568.0        7019                        1685.541  \n",
       "2      1065.0         524                        2228.931  \n",
       "3     25778.0       25065                       15839.581  \n",
       "4       286.0        4630                        4946.194  \n",
       "5      1532.0        6771                        3416.935  \n",
       "6      1082.0        1212                        1721.387  \n",
       "7      1914.0        1540                        2377.194  \n",
       "8      2711.0        5053                        5305.419  \n",
       "9      2607.0        6471                        4982.677  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_mean_impute_df.drop(\"Mean with values\", axis = 1, inplace = True )\n",
    "\n",
    "b_mean_impute_df[\"Baseline Average Steps Per Day\"] = (b_mean_impute_df.sum(axis=1) /b_mean_impute_df.shape[1]).round(3)\n",
    "#compute row averages on imputed data\n",
    "\n",
    "b_mean_impute_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "119ee49a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc2654d5c70>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#plt.hist(baseline_df[\"Mean with values\"], density=True)\n",
    "num_bins = 30\n",
    "plt.hist(b_mean_impute_df[\"Baseline Average Steps Per Day\"], num_bins, density=False, label = \"Imputed with Mean\", color = \"orange\",\n",
    "                                edgecolor='k')\n",
    "                            \n",
    "plt.title('Distribution of Imputed Average Daily Steps during Baseline', fontsize=20)\n",
    "plt.xlabel(r'Average Daily Steps')\n",
    "plt.ylabel(r'Frequency of Average ')\n",
    "plt.xlim([0, 20000]) \n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "c8634876",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean, variance, and standard error of this distribution are:\n",
      "Mean: 5014.369\n",
      "Variance: 12044123.696\n",
      "Standard Deviation: 3470.464\n"
     ]
    }
   ],
   "source": [
    "mean_with_meanimpute = round(b_mean_impute_df[\"Baseline Average Steps Per Day\"].mean(), 3)\n",
    "var_with_meanimpute = round(b_mean_impute_df[\"Baseline Average Steps Per Day\"].var(), 3)\n",
    "sd_with_meanimpute = round(b_mean_impute_df[\"Baseline Average Steps Per Day\"].std(),3)\n",
    "#median_with_values = round(baseline_df[\"Median with values\"].median(), 3)\n",
    "print(\"Mean, variance, and standard error of this distribution are:\")\n",
    "print(\"Mean: \" + str(mean_with_meanimpute))\n",
    "print(\"Variance: \" + str(var_with_meanimpute))\n",
    "print(\"Standard Deviation: \" + str(sd_with_meanimpute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "fbfeed93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absolute difference in mean: 101.51400000000012\n",
      "Absolute difference in variance: 477719.493999999\n",
      "Absolute difference in standard deviation: 68.1579999999999\n"
     ]
    }
   ],
   "source": [
    "print(\"Absolute difference in mean: \" + str(np.abs(mean_with_meanimpute - mean_with_values)))\n",
    "print(\"Absolute difference in variance: \" + str(np.abs(var_with_meanimpute - var_with_values)))\n",
    "print(\"Absolute difference in standard deviation: \" + str(np.abs(sd_with_meanimpute - sd_with_values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94036065",
   "metadata": {},
   "source": [
    "From the graph, we can see that imputing with the mean of an individual's daily steps retains the shape of the original distribution. The absolute differences in the mean of the imputed dataset and the original dataset is 101 steps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c212d7cf",
   "metadata": {},
   "source": [
    "#### Imputing Missing Data with Median"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f3466f",
   "metadata": {},
   "source": [
    "This method uses the median of the row observation to fill in missing values. In other words, we use the middle point of an individual's total number of steps walked during the baseline period to fill in values for days with missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "cabb3ad7",
   "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>2023-05-01</th>\n",
       "      <th>2023-05-02</th>\n",
       "      <th>2023-05-03</th>\n",
       "      <th>2023-05-04</th>\n",
       "      <th>2023-05-05</th>\n",
       "      <th>2023-05-06</th>\n",
       "      <th>2023-05-07</th>\n",
       "      <th>2023-05-08</th>\n",
       "      <th>2023-05-09</th>\n",
       "      <th>2023-05-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-05-23</th>\n",
       "      <th>2023-05-24</th>\n",
       "      <th>2023-05-25</th>\n",
       "      <th>2023-05-26</th>\n",
       "      <th>2023-05-27</th>\n",
       "      <th>2023-05-28</th>\n",
       "      <th>2023-05-29</th>\n",
       "      <th>2023-05-30</th>\n",
       "      <th>2023-05-31</th>\n",
       "      <th>Mean with values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0          True        True        True        True        True        True   \n",
       "1          True        True        True        True        True        True   \n",
       "2          True        True        True        True        True        True   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-23  \\\n",
       "0          True        True        True        True  ...       False   \n",
       "1          True        True        True        True  ...        True   \n",
       "2          True        True        True        True  ...        True   \n",
       "3         False       False       False       False  ...       False   \n",
       "4         False       False       False       False  ...       False   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "296       False       False       False       False  ...       False   \n",
       "297       False       False       False       False  ...       False   \n",
       "298       False       False       False       False  ...       False   \n",
       "299       False       False       False       False  ...       False   \n",
       "300       False       False       False       False  ...       False   \n",
       "\n",
       "     2023-05-24  2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  \\\n",
       "0         False       False       False       False       False       False   \n",
       "1          True       False       False       False       False       False   \n",
       "2          True        True        True       False       False       False   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-30  2023-05-31  Mean with values  \n",
       "0         False       False             False  \n",
       "1         False       False             False  \n",
       "2         False       False             False  \n",
       "3         False       False             False  \n",
       "4         False       False             False  \n",
       "..          ...         ...               ...  \n",
       "296       False       False             False  \n",
       "297       False       False             False  \n",
       "298       False       False             False  \n",
       "299       False       False             False  \n",
       "300       False       False             False  \n",
       "\n",
       "[301 rows x 32 columns]"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_median_impute_df = baseline_df.copy()\n",
    "b_median_impute_df.columns\n",
    "b_median_impute_df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "42c29f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#baseline_df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "b2393d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2023-05-01', '2023-05-02', '2023-05-03', '2023-05-04', '2023-05-05',\n",
       "       '2023-05-06', '2023-05-07', '2023-05-08', '2023-05-09', '2023-05-10',\n",
       "       '2023-05-11', '2023-05-12', '2023-05-13', '2023-05-14', '2023-05-15',\n",
       "       '2023-05-16', '2023-05-17', '2023-05-18', '2023-05-19', '2023-05-20',\n",
       "       '2023-05-21', '2023-05-22', '2023-05-23', '2023-05-24', '2023-05-25',\n",
       "       '2023-05-26', '2023-05-27', '2023-05-28', '2023-05-29', '2023-05-30',\n",
       "       '2023-05-31', 'Mean with values'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_median_impute_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "2e83215e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2023-05-01</th>\n",
       "      <th>2023-05-02</th>\n",
       "      <th>2023-05-03</th>\n",
       "      <th>2023-05-04</th>\n",
       "      <th>2023-05-05</th>\n",
       "      <th>2023-05-06</th>\n",
       "      <th>2023-05-07</th>\n",
       "      <th>2023-05-08</th>\n",
       "      <th>2023-05-09</th>\n",
       "      <th>2023-05-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-05-24</th>\n",
       "      <th>2023-05-25</th>\n",
       "      <th>2023-05-26</th>\n",
       "      <th>2023-05-27</th>\n",
       "      <th>2023-05-28</th>\n",
       "      <th>2023-05-29</th>\n",
       "      <th>2023-05-30</th>\n",
       "      <th>2023-05-31</th>\n",
       "      <th>Mean with values</th>\n",
       "      <th>Median with values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3099.0</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>4193.0</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>6415.0</td>\n",
       "      <td>3911.0</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>3570</td>\n",
       "      <td>1038.613</td>\n",
       "      <td>3570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3170.0</td>\n",
       "      <td>5205.0</td>\n",
       "      <td>3098.0</td>\n",
       "      <td>4481.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>7019</td>\n",
       "      <td>950.032</td>\n",
       "      <td>3568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9965.0</td>\n",
       "      <td>24058.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>524</td>\n",
       "      <td>1212.226</td>\n",
       "      <td>1967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4930.0</td>\n",
       "      <td>29351.0</td>\n",
       "      <td>30457.0</td>\n",
       "      <td>25895.0</td>\n",
       "      <td>5528.0</td>\n",
       "      <td>17858.0</td>\n",
       "      <td>2879.0</td>\n",
       "      <td>3567.0</td>\n",
       "      <td>26381.0</td>\n",
       "      <td>21659.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21784.0</td>\n",
       "      <td>1323.0</td>\n",
       "      <td>22493.0</td>\n",
       "      <td>32316.0</td>\n",
       "      <td>17272.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>25778.0</td>\n",
       "      <td>25065</td>\n",
       "      <td>15839.581</td>\n",
       "      <td>19012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3222.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>4124.0</td>\n",
       "      <td>2107.0</td>\n",
       "      <td>7063.0</td>\n",
       "      <td>3905.0</td>\n",
       "      <td>3479.0</td>\n",
       "      <td>4723.0</td>\n",
       "      <td>4692.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2738.0</td>\n",
       "      <td>5379.0</td>\n",
       "      <td>13257.0</td>\n",
       "      <td>3564.0</td>\n",
       "      <td>5404.0</td>\n",
       "      <td>2841.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>4630</td>\n",
       "      <td>4946.194</td>\n",
       "      <td>4490.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3      4930.0     29351.0     30457.0     25895.0      5528.0     17858.0   \n",
       "4      3222.0      5197.0      4490.0      4124.0      2107.0      7063.0   \n",
       "\n",
       "   2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-24  \\\n",
       "0         NaN         NaN         NaN         NaN  ...      3099.0   \n",
       "1         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "2         NaN         NaN         NaN         NaN  ...         NaN   \n",
       "3      2879.0      3567.0     26381.0     21659.0  ...     21784.0   \n",
       "4      3905.0      3479.0      4723.0      4692.0  ...      2738.0   \n",
       "\n",
       "   2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  2023-05-30  \\\n",
       "0      1339.0      4193.0      6350.0      6415.0      3911.0      3275.0   \n",
       "1      3170.0      5205.0      3098.0      4481.0      2910.0      3568.0   \n",
       "2         NaN         NaN      9965.0     24058.0      1967.0      1065.0   \n",
       "3      1323.0     22493.0     32316.0     17272.0      2427.0     25778.0   \n",
       "4      5379.0     13257.0      3564.0      5404.0      2841.0       286.0   \n",
       "\n",
       "   2023-05-31  Mean with values  Median with values  \n",
       "0        3570          1038.613              3570.0  \n",
       "1        7019           950.032              3568.0  \n",
       "2         524          1212.226              1967.0  \n",
       "3       25065         15839.581             19012.0  \n",
       "4        4630          4946.194              4490.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute row median for all columns except mean column\n",
    "b_median_impute_df[\"Median with values\"] = b_median_impute_df.iloc[:, :-1].median(axis=1) \n",
    "b_median_impute_df.head(5)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49fc607d",
   "metadata": {},
   "source": [
    "Here we have the median for each row with missing values included in the median calculation. We will now use this median to impute missing data in each row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "445505a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputing with row median\n",
    "for col in b_median_impute_df.columns[:-2]: #for all columns except the mean & median column\n",
    "    b_median_impute_df[col] =  b_median_impute_df[col].fillna(b_median_impute_df['Median with values'])#fill missing values with the row median \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "7cbbb39c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>2023-05-01</th>\n",
       "      <th>2023-05-02</th>\n",
       "      <th>2023-05-03</th>\n",
       "      <th>2023-05-04</th>\n",
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       "      <th>2023-05-06</th>\n",
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       "      <th>...</th>\n",
       "      <th>2023-05-24</th>\n",
       "      <th>2023-05-25</th>\n",
       "      <th>2023-05-26</th>\n",
       "      <th>2023-05-27</th>\n",
       "      <th>2023-05-28</th>\n",
       "      <th>2023-05-29</th>\n",
       "      <th>2023-05-30</th>\n",
       "      <th>2023-05-31</th>\n",
       "      <th>Mean with values</th>\n",
       "      <th>Median with values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0         False       False       False       False       False       False   \n",
       "1         False       False       False       False       False       False   \n",
       "2         False       False       False       False       False       False   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-24  \\\n",
       "0         False       False       False       False  ...       False   \n",
       "1         False       False       False       False  ...       False   \n",
       "2         False       False       False       False  ...       False   \n",
       "3         False       False       False       False  ...       False   \n",
       "4         False       False       False       False  ...       False   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "296       False       False       False       False  ...       False   \n",
       "297       False       False       False       False  ...       False   \n",
       "298       False       False       False       False  ...       False   \n",
       "299       False       False       False       False  ...       False   \n",
       "300       False       False       False       False  ...       False   \n",
       "\n",
       "     2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  2023-05-30  \\\n",
       "0         False       False       False       False       False       False   \n",
       "1         False       False       False       False       False       False   \n",
       "2         False       False       False       False       False       False   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-31  Mean with values  Median with values  \n",
       "0         False             False               False  \n",
       "1         False             False               False  \n",
       "2         False             False               False  \n",
       "3         False             False               False  \n",
       "4         False             False               False  \n",
       "..          ...               ...                 ...  \n",
       "296       False             False               False  \n",
       "297       False             False               False  \n",
       "298       False             False               False  \n",
       "299       False             False               False  \n",
       "300       False             False               False  \n",
       "\n",
       "[301 rows x 33 columns]"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_median_impute_df.isna()#check if there are still missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "ca672d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2023-05-01</th>\n",
       "      <th>2023-05-02</th>\n",
       "      <th>2023-05-03</th>\n",
       "      <th>2023-05-04</th>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0          True        True        True        True        True        True   \n",
       "1          True        True        True        True        True        True   \n",
       "2          True        True        True        True        True        True   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-23  \\\n",
       "0          True        True        True        True  ...       False   \n",
       "1          True        True        True        True  ...        True   \n",
       "2          True        True        True        True  ...        True   \n",
       "3         False       False       False       False  ...       False   \n",
       "4         False       False       False       False  ...       False   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "296       False       False       False       False  ...       False   \n",
       "297       False       False       False       False  ...       False   \n",
       "298       False       False       False       False  ...       False   \n",
       "299       False       False       False       False  ...       False   \n",
       "300       False       False       False       False  ...       False   \n",
       "\n",
       "     2023-05-24  2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  \\\n",
       "0         False       False       False       False       False       False   \n",
       "1          True       False       False       False       False       False   \n",
       "2          True        True        True       False       False       False   \n",
       "3         False       False       False       False       False       False   \n",
       "4         False       False       False       False       False       False   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "296       False       False       False       False       False       False   \n",
       "297       False       False       False       False       False       False   \n",
       "298       False       False       False       False       False       False   \n",
       "299       False       False       False       False       False       False   \n",
       "300       False       False       False       False       False       False   \n",
       "\n",
       "     2023-05-30  2023-05-31  Mean with values  \n",
       "0         False       False             False  \n",
       "1         False       False             False  \n",
       "2         False       False             False  \n",
       "3         False       False             False  \n",
       "4         False       False             False  \n",
       "..          ...         ...               ...  \n",
       "296       False       False             False  \n",
       "297       False       False             False  \n",
       "298       False       False             False  \n",
       "299       False       False             False  \n",
       "300       False       False             False  \n",
       "\n",
       "[301 rows x 32 columns]"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_df.isna() #check if original baseline data still has missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "52a98635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2023-05-01', '2023-05-02', '2023-05-03', '2023-05-04', '2023-05-05',\n",
       "       '2023-05-06', '2023-05-07', '2023-05-08', '2023-05-09', '2023-05-10',\n",
       "       '2023-05-11', '2023-05-12', '2023-05-13', '2023-05-14', '2023-05-15',\n",
       "       '2023-05-16', '2023-05-17', '2023-05-18', '2023-05-19', '2023-05-20',\n",
       "       '2023-05-21', '2023-05-22', '2023-05-23', '2023-05-24', '2023-05-25',\n",
       "       '2023-05-26', '2023-05-27', '2023-05-28', '2023-05-29', '2023-05-30',\n",
       "       '2023-05-31', 'Mean with values', 'Median with values'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_median_impute_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "6c20175a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the columns with row mean and median\n",
    "b_median_impute_df = b_median_impute_df.drop(columns=[\"Mean with values\", \"Median with values\"]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "ab2b969e",
   "metadata": {},
   "outputs": [],
   "source": [
    "b_median_impute_df[\"Baseline Average Steps Per Day\"] = (b_median_impute_df.sum(axis=1) /b_median_impute_df.shape[1]).round(3)\n",
    "#compute row averages on imputed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "a55fbf9e",
   "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>2023-05-01</th>\n",
       "      <th>2023-05-02</th>\n",
       "      <th>2023-05-03</th>\n",
       "      <th>2023-05-04</th>\n",
       "      <th>2023-05-05</th>\n",
       "      <th>2023-05-06</th>\n",
       "      <th>2023-05-07</th>\n",
       "      <th>2023-05-08</th>\n",
       "      <th>2023-05-09</th>\n",
       "      <th>2023-05-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-05-23</th>\n",
       "      <th>2023-05-24</th>\n",
       "      <th>2023-05-25</th>\n",
       "      <th>2023-05-26</th>\n",
       "      <th>2023-05-27</th>\n",
       "      <th>2023-05-28</th>\n",
       "      <th>2023-05-29</th>\n",
       "      <th>2023-05-30</th>\n",
       "      <th>2023-05-31</th>\n",
       "      <th>Baseline Average Steps Per Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3099.0</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>4193.0</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>6415.0</td>\n",
       "      <td>3911.0</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>3570</td>\n",
       "      <td>3572.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>3170.0</td>\n",
       "      <td>5205.0</td>\n",
       "      <td>3098.0</td>\n",
       "      <td>4481.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>7019</td>\n",
       "      <td>3712.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>9965.0</td>\n",
       "      <td>24058.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>524</td>\n",
       "      <td>2861.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4930.0</td>\n",
       "      <td>29351.0</td>\n",
       "      <td>30457.0</td>\n",
       "      <td>25895.0</td>\n",
       "      <td>5528.0</td>\n",
       "      <td>17858.0</td>\n",
       "      <td>2879.0</td>\n",
       "      <td>3567.0</td>\n",
       "      <td>26381.0</td>\n",
       "      <td>21659.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27183.0</td>\n",
       "      <td>21784.0</td>\n",
       "      <td>1323.0</td>\n",
       "      <td>22493.0</td>\n",
       "      <td>32316.0</td>\n",
       "      <td>17272.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>25778.0</td>\n",
       "      <td>25065</td>\n",
       "      <td>15839.581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3222.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>4124.0</td>\n",
       "      <td>2107.0</td>\n",
       "      <td>7063.0</td>\n",
       "      <td>3905.0</td>\n",
       "      <td>3479.0</td>\n",
       "      <td>4723.0</td>\n",
       "      <td>4692.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7182.0</td>\n",
       "      <td>2738.0</td>\n",
       "      <td>5379.0</td>\n",
       "      <td>13257.0</td>\n",
       "      <td>3564.0</td>\n",
       "      <td>5404.0</td>\n",
       "      <td>2841.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>4630</td>\n",
       "      <td>4946.194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2023-05-01  2023-05-02  2023-05-03  2023-05-04  2023-05-05  2023-05-06  \\\n",
       "0      3570.0      3570.0      3570.0      3570.0      3570.0      3570.0   \n",
       "1      3568.0      3568.0      3568.0      3568.0      3568.0      3568.0   \n",
       "2      1967.0      1967.0      1967.0      1967.0      1967.0      1967.0   \n",
       "3      4930.0     29351.0     30457.0     25895.0      5528.0     17858.0   \n",
       "4      3222.0      5197.0      4490.0      4124.0      2107.0      7063.0   \n",
       "\n",
       "   2023-05-07  2023-05-08  2023-05-09  2023-05-10  ...  2023-05-23  \\\n",
       "0      3570.0      3570.0      3570.0      3570.0  ...        45.0   \n",
       "1      3568.0      3568.0      3568.0      3568.0  ...      3568.0   \n",
       "2      1967.0      1967.0      1967.0      1967.0  ...      1967.0   \n",
       "3      2879.0      3567.0     26381.0     21659.0  ...     27183.0   \n",
       "4      3905.0      3479.0      4723.0      4692.0  ...      7182.0   \n",
       "\n",
       "   2023-05-24  2023-05-25  2023-05-26  2023-05-27  2023-05-28  2023-05-29  \\\n",
       "0      3099.0      1339.0      4193.0      6350.0      6415.0      3911.0   \n",
       "1      3568.0      3170.0      5205.0      3098.0      4481.0      2910.0   \n",
       "2      1967.0      1967.0      1967.0      9965.0     24058.0      1967.0   \n",
       "3     21784.0      1323.0     22493.0     32316.0     17272.0      2427.0   \n",
       "4      2738.0      5379.0     13257.0      3564.0      5404.0      2841.0   \n",
       "\n",
       "   2023-05-30  2023-05-31  Baseline Average Steps Per Day  \n",
       "0      3275.0        3570                        3572.161  \n",
       "1      3568.0        7019                        3712.355  \n",
       "2      1065.0         524                        2861.968  \n",
       "3     25778.0       25065                       15839.581  \n",
       "4       286.0        4630                        4946.194  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_median_impute_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "d59fd4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc2640539a0>"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#plt.hist(baseline_df[\"Mean with values\"], density=True)\n",
    "num_bins = 30\n",
    "plt.hist(b_median_impute_df[\"Baseline Average Steps Per Day\"], num_bins, density=False, label = \"Imputed with Median\", color = \"green\",\n",
    "                                edgecolor='k')\n",
    "                            \n",
    "plt.title('Distribution of Imputed Average Daily Steps during Baseline', fontsize=20)\n",
    "plt.xlabel(r'Average Daily Steps')\n",
    "plt.ylabel(r'Frequency of Average ')\n",
    "plt.xlim([0, 20000]) \n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "2004cd17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean, variance, and standard error of this distribution are:\n",
      "Mean: 5326.199\n",
      "Variance: 11094438.261\n",
      "Standard Deviation: 3330.831\n"
     ]
    }
   ],
   "source": [
    "mean_with_medianimpute = round(b_median_impute_df[\"Baseline Average Steps Per Day\"].mean(), 3)\n",
    "var_with_medianimpute = round(b_median_impute_df[\"Baseline Average Steps Per Day\"].var(), 3)\n",
    "sd_with_medianimpute = round(b_median_impute_df[\"Baseline Average Steps Per Day\"].std(),3)\n",
    "#median_with_values = round(baseline_df[\"Median with values\"].median(), 3)\n",
    "print(\"Mean, variance, and standard error of this distribution are:\")\n",
    "print(\"Mean: \" + str(mean_with_medianimpute))\n",
    "print(\"Variance: \" + str(var_with_medianimpute))\n",
    "print(\"Standard Deviation: \" + str(sd_with_medianimpute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "b3d08aca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absolute difference in mean: 413.34400000000005\n",
      "Absolute difference in variance: 1427404.9289999995\n",
      "Absolute difference in standard deviation: 207.7909999999997\n"
     ]
    }
   ],
   "source": [
    "print(\"Absolute difference in mean: \" + str(np.abs(mean_with_medianimpute - mean_with_values)))\n",
    "print(\"Absolute difference in variance: \" + str(np.abs(var_with_medianimpute - var_with_values)))\n",
    "print(\"Absolute difference in standard deviation: \" + str(np.abs(sd_with_medianimpute - sd_with_values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6836e5",
   "metadata": {},
   "source": [
    "## Contest Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63be9486",
   "metadata": {},
   "source": [
    "As before, we will first calculate the number of missing values in contest data and visualize them to see if any patterns emerge. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "f8831013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per column:\n",
      " 2023-06-01      1\n",
      "2023-06-02     10\n",
      "2023-06-03     15\n",
      "2023-06-04     18\n",
      "2023-06-05     25\n",
      "             ... \n",
      "2023-08-27    199\n",
      "2023-08-28    202\n",
      "2023-08-29    209\n",
      "2023-08-30    208\n",
      "2023-08-31    256\n",
      "Length: 92, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "contest_df = step_dataraw.loc[:, \"6/1/23\" : \"8/31/23\"]#subset the data by contest months\n",
    "c_missing_per_column = contest_df.isna().sum()\n",
    "print(\"Missing values per column:\\n\", c_missing_per_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "5b1a5fc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))  # Set the figure size\n",
    "sns.barplot(x=c_missing_per_column.index, y=c_missing_per_column.values, color='teal')\n",
    "\n",
    "# Add titles and labels\n",
    "plt.title('Distribution of Missing Values per Day during Contest', fontsize=16)\n",
    "plt.xlabel('Days', fontsize=12)\n",
    "plt.ylabel('Number of Missing Values', fontsize=12)\n",
    "plt.xticks([], [])\n",
    "\n",
    "\n",
    "# Rotate x-axis labels for better readability\n",
    "#plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "07dbed9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c_notmissing_per_column = contest_df.notna().sum() #calculate total available values per day\n",
    "#print(\"Available values per column:\\n\", b_notmissing_per_column)\n",
    "\n",
    "c_missing_df = pd.DataFrame({      #create a df with count of missing and available values\n",
    "    'Missing': c_missing_per_column,\n",
    "    'Available': c_notmissing_per_column\n",
    "})\n",
    "\n",
    "c_missing_df.plot(kind='bar', stacked=True, figsize=(10, 6), color=['indigo', 'green'], alpha=0.7) \n",
    "\n",
    "\n",
    "plt.title('Comparison of Available and Missing Data during Contest', fontsize=16)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xticks([], [])\n",
    "plt.legend(['Missing', 'Available'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e4c0d2",
   "metadata": {},
   "source": [
    "Every date in the contest period has missing values\n",
    "The number of missing values steadily increase from the start of the contest to the end. \n",
    "The proportion of missing to available is greater than 50% toward the end of the contest\n",
    "The days with the highest number of missing values fall in the last month of contest \n",
    "Imputation can result in “inventing” data and skew analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ea6769",
   "metadata": {},
   "source": [
    "#### Contest Row Averages of Daily Steps with Available Values "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5ecace",
   "metadata": {},
   "source": [
    "We will now calculate the average number of daily steps by dividing the sum of available values in the row observations with the number of days in the contest period. We then visualize the distribution of averages and get the moments of distribution.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "3136b478",
   "metadata": {},
   "outputs": [],
   "source": [
    "contest_df[\"Mean with values\"] = (contest_df.sum(axis=1) /contest_df.shape[1]).round(3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "64cc6098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc2674471f0>"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#plt.hist(baseline_df[\"Mean with values\"], density=True)\n",
    "num_bins = 30\n",
    "count, bins, ignored = plt.hist(contest_df[\"Mean with values\"], num_bins, density=True, label = \"Not imputed\",\n",
    "                                edgecolor='k')\n",
    "                            \n",
    "plt.title('Distribution of Average Daily Steps during Contest', fontsize=20)\n",
    "plt.xlabel(r'Average Daily Steps')\n",
    "plt.ylabel(r'Frequency of Average ')\n",
    "plt.xlim([0, 20000])\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "a7888c44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 4037.0\n",
      "Variance: 18876209.1\n",
      "Standard Deviation: 4060.841\n"
     ]
    }
   ],
   "source": [
    "mean_with_values = round(contest_df[\"Mean with values\"].mean(), 1)\n",
    "var_with_values = round(contest_df[\"Mean with values\"].var(), 1)\n",
    "sd_with_values = round(contest_df[\"Mean with values\"].std(),1)\n",
    "\n",
    "print(\"Mean: \" + str(mean_with_values))\n",
    "print(\"Variance: \" + str(var_with_values))\n",
    "print(\"Standard Deviation: \" + str(std_error_with_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed49e18c",
   "metadata": {},
   "source": [
    "#### Imputing Missing Data with Mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a4fe7a",
   "metadata": {},
   "source": [
    "This method uses the row mean to fill in the missing values for each observation. In other words, we use the individual's average daily number of steps during the contest period to fill the values for days with missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "79004b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([2023-06-01 00:00:00, 2023-06-02 00:00:00, 2023-06-03 00:00:00,\n",
       "       2023-06-04 00:00:00, 2023-06-05 00:00:00, 2023-06-06 00:00:00,\n",
       "       2023-06-07 00:00:00, 2023-06-08 00:00:00, 2023-06-09 00:00:00,\n",
       "       2023-06-10 00:00:00, 2023-06-11 00:00:00, 2023-06-12 00:00:00,\n",
       "       2023-06-13 00:00:00, 2023-06-14 00:00:00, 2023-06-15 00:00:00,\n",
       "       2023-06-16 00:00:00, 2023-06-17 00:00:00, 2023-06-18 00:00:00,\n",
       "       2023-06-19 00:00:00, 2023-06-20 00:00:00, 2023-06-21 00:00:00,\n",
       "       2023-06-22 00:00:00, 2023-06-23 00:00:00, 2023-06-24 00:00:00,\n",
       "       2023-06-25 00:00:00, 2023-06-26 00:00:00, 2023-06-27 00:00:00,\n",
       "       2023-06-28 00:00:00, 2023-06-29 00:00:00, 2023-06-30 00:00:00,\n",
       "       2023-07-01 00:00:00, 2023-07-02 00:00:00, 2023-07-03 00:00:00,\n",
       "       2023-07-04 00:00:00, 2023-07-05 00:00:00, 2023-07-06 00:00:00,\n",
       "       2023-07-07 00:00:00, 2023-07-08 00:00:00, 2023-07-09 00:00:00,\n",
       "       2023-07-10 00:00:00, 2023-07-11 00:00:00, 2023-07-12 00:00:00,\n",
       "       2023-07-13 00:00:00, 2023-07-14 00:00:00, 2023-07-15 00:00:00,\n",
       "       2023-07-16 00:00:00, 2023-07-17 00:00:00, 2023-07-18 00:00:00,\n",
       "       2023-07-19 00:00:00, 2023-07-20 00:00:00, 2023-07-21 00:00:00,\n",
       "       2023-07-22 00:00:00, 2023-07-23 00:00:00, 2023-07-24 00:00:00,\n",
       "       2023-07-25 00:00:00, 2023-07-26 00:00:00, 2023-07-27 00:00:00,\n",
       "       2023-07-28 00:00:00, 2023-07-29 00:00:00, 2023-07-30 00:00:00,\n",
       "       2023-07-31 00:00:00, 2023-08-01 00:00:00, 2023-08-02 00:00:00,\n",
       "       2023-08-03 00:00:00, 2023-08-04 00:00:00, 2023-08-05 00:00:00,\n",
       "       2023-08-06 00:00:00, 2023-08-07 00:00:00, 2023-08-08 00:00:00,\n",
       "       2023-08-09 00:00:00, 2023-08-10 00:00:00, 2023-08-11 00:00:00,\n",
       "       2023-08-12 00:00:00, 2023-08-13 00:00:00, 2023-08-14 00:00:00,\n",
       "       2023-08-15 00:00:00, 2023-08-16 00:00:00, 2023-08-17 00:00:00,\n",
       "       2023-08-18 00:00:00, 2023-08-19 00:00:00, 2023-08-20 00:00:00,\n",
       "       2023-08-21 00:00:00, 2023-08-22 00:00:00, 2023-08-23 00:00:00,\n",
       "       2023-08-24 00:00:00, 2023-08-25 00:00:00, 2023-08-26 00:00:00,\n",
       "       2023-08-27 00:00:00, 2023-08-28 00:00:00, 2023-08-29 00:00:00,\n",
       "       2023-08-30 00:00:00, 2023-08-31 00:00:00,  'Mean with values'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_mean_impute_df = contest_df.copy()\n",
    "c_mean_impute_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "9e3a70e4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>296</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>297</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>298</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>301 rows × 93 columns</p>\n",
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      "text/plain": [
       "     2023-06-01 00:00:00  2023-06-02 00:00:00  2023-06-03 00:00:00  \\\n",
       "0                  False                False                False   \n",
       "1                  False                False                False   \n",
       "2                  False                False                False   \n",
       "3                  False                False                False   \n",
       "4                  False                False                False   \n",
       "..                   ...                  ...                  ...   \n",
       "296                False                False                False   \n",
       "297                False                False                False   \n",
       "298                False                False                False   \n",
       "299                False                False                False   \n",
       "300                False                False                False   \n",
       "\n",
       "     2023-06-04 00:00:00  2023-06-05 00:00:00  2023-06-06 00:00:00  \\\n",
       "0                  False                False                False   \n",
       "1                  False                False                False   \n",
       "2                  False                False                False   \n",
       "3                  False                False                False   \n",
       "4                  False                False                False   \n",
       "..                   ...                  ...                  ...   \n",
       "296                False                False                False   \n",
       "297                False                False                False   \n",
       "298                False                False                False   \n",
       "299                False                False                False   \n",
       "300                False                False                False   \n",
       "\n",
       "     2023-06-07 00:00:00  2023-06-08 00:00:00  2023-06-09 00:00:00  \\\n",
       "0                  False                False                False   \n",
       "1                  False                False                False   \n",
       "2                  False                False                False   \n",
       "3                  False                False                False   \n",
       "4                  False                False                False   \n",
       "..                   ...                  ...                  ...   \n",
       "296                False                False                False   \n",
       "297                False                False                False   \n",
       "298                False                False                False   \n",
       "299                False                False                False   \n",
       "300                False                False                False   \n",
       "\n",
       "     2023-06-10 00:00:00  ...  2023-08-23 00:00:00  2023-08-24 00:00:00  \\\n",
       "0                  False  ...                 True                 True   \n",
       "1                  False  ...                False                False   \n",
       "2                  False  ...                 True                 True   \n",
       "3                  False  ...                False                False   \n",
       "4                  False  ...                 True                 True   \n",
       "..                   ...  ...                  ...                  ...   \n",
       "296                False  ...                False                False   \n",
       "297                False  ...                False                False   \n",
       "298                False  ...                 True                 True   \n",
       "299                False  ...                False                False   \n",
       "300                False  ...                False                False   \n",
       "\n",
       "     2023-08-25 00:00:00  2023-08-26 00:00:00  2023-08-27 00:00:00  \\\n",
       "0                   True                 True                 True   \n",
       "1                  False                False                False   \n",
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       "4                   True                 True                 True   \n",
       "..                   ...                  ...                  ...   \n",
       "296                False                False                False   \n",
       "297                False                False                False   \n",
       "298                 True                 True                 True   \n",
       "299                False                False                False   \n",
       "300                False                False                False   \n",
       "\n",
       "     2023-08-28 00:00:00  2023-08-29 00:00:00  2023-08-30 00:00:00  \\\n",
       "0                   True                 True                 True   \n",
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       "2                   True                 True                 True   \n",
       "3                  False                False                False   \n",
       "4                   True                 True                 True   \n",
       "..                   ...                  ...                  ...   \n",
       "296                False                False                False   \n",
       "297                False                False                False   \n",
       "298                 True                 True                 True   \n",
       "299                False                False                False   \n",
       "300                False                False                False   \n",
       "\n",
       "     2023-08-31 00:00:00  Mean with values  \n",
       "0                   True             False  \n",
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       "4                   True             False  \n",
       "..                   ...               ...  \n",
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       "298                 True             False  \n",
       "299                 True             False  \n",
       "300                False             False  \n",
       "\n",
       "[301 rows x 93 columns]"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_mean_impute_df.isna()"
   ]
  }
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