descriptive-statistics-on-breast-cancer-data.ipynb

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   "source": [
    "# Introduction\n",
    "Welcome to this detailed analysis where we explore a dataset focused on breast cancer survival rates. The goal is to provide a comprehensive understanding of the data through descriptive statistics, enhancing our knowledge about key factors influencing survival.\n",
    "\n"
   ]
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   "source": [
    "# Objective\n",
    "Our primary objective is to use descriptive statistics to summarize and understand the data's behavior, which will assist in further analyses like predictive modeling or hypotheses testing."
   ]
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   "source": [
    "# Data Description\n",
    "The dataset comes from a .sav file, commonly used in SPSS software, containing a mix of clinical and demographic variables related to breast cancer patients.\n",
    "\n",
    "\n"
   ]
  },
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      "Collecting pyreadstat\r\n",
      "  Downloading pyreadstat-1.2.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.0 kB)\r\n",
      "Requirement already satisfied: pandas>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from pyreadstat) (2.2.2)\r\n",
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      "Downloading pyreadstat-1.2.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB)\r\n",
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      "\u001b[?25hInstalling collected packages: pyreadstat\r\n",
      "Successfully installed pyreadstat-1.2.7\r\n"
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   "source": [
    "!pip install pyreadstat"
   ]
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   "source": [
    "# Load the Dataset\n",
    "Now, we will load the dataset from the SPSS file and inspect the initial few entries to understand the structure."
   ]
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   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import pyreadstat  # To read SPSS files\n",
    "import numpy as np\n",
    "from scipy.stats import skew, kurtosis\n",
    "from scipy import stats\n",
    "from scipy.stats import t\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
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     "text": [
      "    id   age  pathsize  lnpos  histgrad   er   pr  status       time\n",
      "0  1.0  60.0       NaN    0.0       3.0  0.0  0.0     0.0   9.466667\n",
      "1  2.0  79.0       NaN    0.0       NaN  NaN  NaN     0.0   8.600000\n",
      "2  3.0  82.0       NaN    0.0       2.0  NaN  NaN     0.0  19.333333\n",
      "3  4.0  66.0       NaN    0.0       2.0  1.0  1.0     0.0  16.333333\n",
      "4  5.0  52.0       NaN    0.0       3.0  NaN  NaN     0.0   8.500000\n",
      "{'histgrad': {4.0: 'Unknown'}, 'er': {0.0: 'Negative', 1.0: 'Positive', 2.0: 'Unknown'}, 'pr': {0.0: 'Negative', 1.0: 'Positive', 2.0: 'Unknown'}, 'status': {0.0: 'Censored', 1.0: 'Died'}}\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "df, meta = pyreadstat.read_sav('/kaggle/input/breast-cancer-dataset/Breast cancer survival.sav')\n",
    "\n",
    "# Display the first few rows to understand the data\n",
    "print(df.head())\n",
    "\n",
    "# Check metadata for any additional insights into variable labels\n",
    "print(meta.variable_value_labels)"
   ]
  },
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       "      <th>id</th>\n",
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       "      <th>pathsize</th>\n",
       "      <th>lnpos</th>\n",
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       "      <th>er</th>\n",
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       "      <th>time</th>\n",
       "    </tr>\n",
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       "      <th>count</th>\n",
       "      <td>1207.000000</td>\n",
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       "      <td>1121.000000</td>\n",
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       "      <td>1207.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>621.072080</td>\n",
       "      <td>56.387738</td>\n",
       "      <td>1.733488</td>\n",
       "      <td>0.880696</td>\n",
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       "      <td>46.956476</td>\n",
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       "      <th>std</th>\n",
       "      <td>359.623207</td>\n",
       "      <td>13.327627</td>\n",
       "      <td>0.995857</td>\n",
       "      <td>2.535457</td>\n",
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       "      <td>0.498450</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>2.633333</td>\n",
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       "      <th>25%</th>\n",
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       "      <td>22.550000</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>56.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.966667</td>\n",
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       "      <th>75%</th>\n",
       "      <td>931.500000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.583333</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1266.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>133.800000</td>\n",
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       "                id          age     pathsize        lnpos    histgrad  \\\n",
       "count  1207.000000  1207.000000  1121.000000  1207.000000  920.000000   \n",
       "mean    621.072080    56.387738     1.733488     0.880696    2.269565   \n",
       "std     359.623207    13.327627     0.995857     2.535457    0.607487   \n",
       "min       1.000000    22.000000     0.100000     0.000000    1.000000   \n",
       "25%     310.500000    46.000000     1.000000     0.000000    2.000000   \n",
       "50%     619.000000    56.000000     1.500000     0.000000    2.000000   \n",
       "75%     931.500000    66.500000     2.200000     0.000000    3.000000   \n",
       "max    1266.000000    88.000000     7.000000    35.000000    3.000000   \n",
       "\n",
       "               er          pr       status         time  \n",
       "count  869.000000  851.000000  1207.000000  1207.000000  \n",
       "mean     0.611047    0.542891     0.059652    46.956476  \n",
       "std      0.487793    0.498450     0.236939    29.638977  \n",
       "min      0.000000    0.000000     0.000000     2.633333  \n",
       "25%      0.000000    0.000000     0.000000    22.550000  \n",
       "50%      1.000000    1.000000     0.000000    42.966667  \n",
       "75%      1.000000    1.000000     0.000000    65.583333  \n",
       "max      1.000000    1.000000     1.000000   133.800000  "
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   "source": [
    "df.describe()"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1207 entries, 0 to 1206\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   id        1207 non-null   float64\n",
      " 1   age       1207 non-null   float64\n",
      " 2   pathsize  1121 non-null   float64\n",
      " 3   lnpos     1207 non-null   float64\n",
      " 4   histgrad  920 non-null    float64\n",
      " 5   er        869 non-null    float64\n",
      " 6   pr        851 non-null    float64\n",
      " 7   status    1207 non-null   float64\n",
      " 8   time      1207 non-null   float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 85.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
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   },
   "source": [
    "**Identifying Categorical Variables**\n",
    "histgrad\n",
    "Based on the data inspection, we identified four categorical columns: 'histgrad', 'er', 'pr', and 'status'. These columns contain categorical data that need to be converted into a format suitable for analysis and modeling."
   ]
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   "source": [
    "# List of categorical columns\n",
    "categorical_columns = ['histgrad', 'er', 'pr', 'status']\n",
    "\n",
    "# Using a for loop to convert columns to string type\n",
    "for col in categorical_columns:\n",
    "    df[col] = df[col].astype(str).replace('nan',np.nan)\n",
    "    \n",
    "# in the \"histgrad\" column by converting them from 'nan' (string) to np.nan (actual NaN object). \n",
    "# this ensures that these values are treated appropriately in analyses as missing data rather than as a category. "
   ]
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      "id          float64\n",
      "age         float64\n",
      "pathsize    float64\n",
      "lnpos       float64\n",
      "histgrad     object\n",
      "er           object\n",
      "pr           object\n",
      "status       object\n",
      "time        float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Display the updated data types to confirm the changes\n",
    "print(df.dtypes)"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1207 entries, 0 to 1206\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   id        1207 non-null   float64\n",
      " 1   age       1207 non-null   float64\n",
      " 2   pathsize  1121 non-null   float64\n",
      " 3   lnpos     1207 non-null   float64\n",
      " 4   histgrad  920 non-null    object \n",
      " 5   er        869 non-null    object \n",
      " 6   pr        851 non-null    object \n",
      " 7   status    1207 non-null   object \n",
      " 8   time      1207 non-null   float64\n",
      "dtypes: float64(5), object(4)\n",
      "memory usage: 85.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d7442e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:22.607605Z",
     "iopub.status.busy": "2024-07-18T12:36:22.607238Z",
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     "shell.execute_reply": "2024-07-18T12:36:22.611243Z"
    },
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     "exception": false,
     "start_time": "2024-07-18T12:36:22.595671",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "categorical_columns = ['histgrad', 'er', 'pr', 'status']\n",
    "continuous_columns = ['age', 'pathsize', 'lnpos', 'time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5783d6a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:22.637765Z",
     "iopub.status.busy": "2024-07-18T12:36:22.636710Z",
     "iopub.status.idle": "2024-07-18T12:36:22.648940Z",
     "shell.execute_reply": "2024-07-18T12:36:22.647849Z"
    },
    "papermill": {
     "duration": 0.026457,
     "end_time": "2024-07-18T12:36:22.651763",
     "exception": false,
     "start_time": "2024-07-18T12:36:22.625306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency table for histgrad:\n",
      "histgrad\n",
      "2.0    514\n",
      "3.0    327\n",
      "1.0     79\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Mode for histgrad: 2.0\n",
      "\n",
      "************************************************************\n",
      "Frequency table for er:\n",
      "er\n",
      "1.0    531\n",
      "0.0    338\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Mode for er: 1.0\n",
      "\n",
      "************************************************************\n",
      "Frequency table for pr:\n",
      "pr\n",
      "1.0    462\n",
      "0.0    389\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Mode for pr: 1.0\n",
      "\n",
      "************************************************************\n",
      "Frequency table for status:\n",
      "status\n",
      "0.0    1135\n",
      "1.0      72\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Mode for status: 0.0\n",
      "\n",
      "************************************************************\n"
     ]
    }
   ],
   "source": [
    "# Generate frequency tables and find the mode for each categorical column\n",
    "for col in ['histgrad', 'er', 'pr', 'status']:\n",
    "    frequency_table = df[col].value_counts()\n",
    "    mode_value = df[col].mode()[0]\n",
    "    print(f\"Frequency table for {col}:\\n{frequency_table}\\n\")\n",
    "    print(f\"Mode for {col}: {mode_value}\\n\")\n",
    "    print('**'*30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3d4404e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:22.674525Z",
     "iopub.status.busy": "2024-07-18T12:36:22.674140Z",
     "iopub.status.idle": "2024-07-18T12:36:22.697917Z",
     "shell.execute_reply": "2024-07-18T12:36:22.696695Z"
    },
    "papermill": {
     "duration": 0.037899,
     "end_time": "2024-07-18T12:36:22.700457",
     "exception": false,
     "start_time": "2024-07-18T12:36:22.662558",
     "status": "completed"
    },
    "tags": []
   },
   "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>age</th>\n",
       "      <th>pathsize</th>\n",
       "      <th>lnpos</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1207.000000</td>\n",
       "      <td>1121.000000</td>\n",
       "      <td>1207.000000</td>\n",
       "      <td>1207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.387738</td>\n",
       "      <td>1.733488</td>\n",
       "      <td>0.880696</td>\n",
       "      <td>46.956476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.327627</td>\n",
       "      <td>0.995857</td>\n",
       "      <td>2.535457</td>\n",
       "      <td>29.638977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.500000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>133.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age     pathsize        lnpos         time\n",
       "count  1207.000000  1121.000000  1207.000000  1207.000000\n",
       "mean     56.387738     1.733488     0.880696    46.956476\n",
       "std      13.327627     0.995857     2.535457    29.638977\n",
       "min      22.000000     0.100000     0.000000     2.633333\n",
       "25%      46.000000     1.000000     0.000000    22.550000\n",
       "50%      56.000000     1.500000     0.000000    42.966667\n",
       "75%      66.500000     2.200000     0.000000    65.583333\n",
       "max      88.000000     7.000000    35.000000   133.800000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[continuous_columns].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d48c31a",
   "metadata": {
    "papermill": {
     "duration": 0.010262,
     "end_time": "2024-07-18T12:36:22.721358",
     "exception": false,
     "start_time": "2024-07-18T12:36:22.711096",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Advanced statistical calculations for continuous_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1c2d4ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:22.744734Z",
     "iopub.status.busy": "2024-07-18T12:36:22.743701Z",
     "iopub.status.idle": "2024-07-18T12:36:22.763050Z",
     "shell.execute_reply": "2024-07-18T12:36:22.761329Z"
    },
    "papermill": {
     "duration": 0.033571,
     "end_time": "2024-07-18T12:36:22.765622",
     "exception": false,
     "start_time": "2024-07-18T12:36:22.732051",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance:\n",
      " age         177.625654\n",
      "pathsize      0.991730\n",
      "lnpos         6.428541\n",
      "time        878.468930\n",
      "dtype: float64\n",
      "************************************************************\n",
      "Skewness:\n",
      " age         0.049489\n",
      "pathsize    1.340715\n",
      "lnpos       5.346459\n",
      "time        0.630557\n",
      "dtype: float64\n",
      "************************************************************\n",
      "Kurtosis:\n",
      " age         -0.765026\n",
      "pathsize     2.659314\n",
      "lnpos       43.047260\n",
      "time        -0.239387\n",
      "dtype: float64\n",
      "************************************************************\n",
      "Interquartile Range (IQR):\n",
      " age         20.500000\n",
      "pathsize     1.200000\n",
      "lnpos        0.000000\n",
      "time        43.033333\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "variance = df[continuous_columns].var()\n",
    "skewness = df[continuous_columns].skew()\n",
    "kurtosis_values = df[continuous_columns].kurt()\n",
    "Q1 = df[continuous_columns].quantile(0.25)\n",
    "Q3 = df[continuous_columns].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Displaying the advanced statistics\n",
    "print(\"Variance:\\n\", variance)\n",
    "print(\"**\"*30)\n",
    "print(\"Skewness:\\n\", skewness)\n",
    "print(\"**\"*30)\n",
    "print(\"Kurtosis:\\n\", kurtosis_values)\n",
    "print(\"**\"*30)\n",
    "print(\"Interquartile Range (IQR):\\n\", IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "81e22ebb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:22.789349Z",
     "iopub.status.busy": "2024-07-18T12:36:22.788978Z",
     "iopub.status.idle": "2024-07-18T12:36:22.814545Z",
     "shell.execute_reply": "2024-07-18T12:36:22.813240Z"
    },
    "papermill": {
     "duration": 0.040707,
     "end_time": "2024-07-18T12:36:22.817052",
     "exception": false,
     "start_time": "2024-07-18T12:36:22.776345",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>IQR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>177.625654</td>\n",
       "      <td>0.049489</td>\n",
       "      <td>-0.765026</td>\n",
       "      <td>46.00</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pathsize</th>\n",
       "      <td>0.991730</td>\n",
       "      <td>1.340715</td>\n",
       "      <td>2.659314</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lnpos</th>\n",
       "      <td>6.428541</td>\n",
       "      <td>5.346459</td>\n",
       "      <td>43.047260</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>878.468930</td>\n",
       "      <td>0.630557</td>\n",
       "      <td>-0.239387</td>\n",
       "      <td>22.55</td>\n",
       "      <td>65.583333</td>\n",
       "      <td>43.033333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Variance  Skewness   Kurtosis     Q1         Q3        IQR\n",
       "age       177.625654  0.049489  -0.765026  46.00  66.500000  20.500000\n",
       "pathsize    0.991730  1.340715   2.659314   1.00   2.200000   1.200000\n",
       "lnpos       6.428541  5.346459  43.047260   0.00   0.000000   0.000000\n",
       "time      878.468930  0.630557  -0.239387  22.55  65.583333  43.033333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate various advanced statistics\n",
    "variance = df[continuous_columns].var().rename(\"Variance\")\n",
    "skewness = df[continuous_columns].skew().rename(\"Skewness\")\n",
    "kurtosis_values = df[continuous_columns].kurt().rename(\"Kurtosis\")\n",
    "Q1 = df[continuous_columns].quantile(0.25).rename(\"Q1\")\n",
    "Q3 = df[continuous_columns].quantile(0.75).rename(\"Q3\")\n",
    "IQR = (Q3 - Q1).rename(\"IQR\")\n",
    "\n",
    "# Combine all statistics into a single DataFrame for easy comparison\n",
    "advanced_stats = pd.concat([variance, skewness, kurtosis_values, Q1, Q3, IQR], axis=1)\n",
    "\n",
    "# Display the results\n",
    "advanced_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b89bb4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:22.842886Z",
     "iopub.status.busy": "2024-07-18T12:36:22.842433Z",
     "iopub.status.idle": "2024-07-18T12:36:22.874191Z",
     "shell.execute_reply": "2024-07-18T12:36:22.873086Z"
    },
    "papermill": {
     "duration": 0.04713,
     "end_time": "2024-07-18T12:36:22.876920",
     "exception": false,
     "start_time": "2024-07-18T12:36:22.829790",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Std</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Min</th>\n",
       "      <th>25%</th>\n",
       "      <th>75%</th>\n",
       "      <th>Max</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>56.387738</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>13.327627</td>\n",
       "      <td>177.625654</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>46.00</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.049489</td>\n",
       "      <td>-0.765026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pathsize</th>\n",
       "      <td>1.733488</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.995857</td>\n",
       "      <td>0.991730</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.340715</td>\n",
       "      <td>2.659314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lnpos</th>\n",
       "      <td>0.880696</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.535457</td>\n",
       "      <td>6.428541</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.346459</td>\n",
       "      <td>43.047260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>46.956476</td>\n",
       "      <td>42.966667</td>\n",
       "      <td>29.638977</td>\n",
       "      <td>878.468930</td>\n",
       "      <td>2.633333</td>\n",
       "      <td>22.55</td>\n",
       "      <td>65.583333</td>\n",
       "      <td>133.8</td>\n",
       "      <td>0.630557</td>\n",
       "      <td>-0.239387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Mean     Median        Std    Variance        Min    25%  \\\n",
       "age       56.387738  56.000000  13.327627  177.625654  22.000000  46.00   \n",
       "pathsize   1.733488   1.500000   0.995857    0.991730   0.100000   1.00   \n",
       "lnpos      0.880696   0.000000   2.535457    6.428541   0.000000   0.00   \n",
       "time      46.956476  42.966667  29.638977  878.468930   2.633333  22.55   \n",
       "\n",
       "                75%    Max  Skewness   Kurtosis  \n",
       "age       66.500000   88.0  0.049489  -0.765026  \n",
       "pathsize   2.200000    7.0  1.340715   2.659314  \n",
       "lnpos      0.000000   35.0  5.346459  43.047260  \n",
       "time      65.583333  133.8  0.630557  -0.239387  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the continuous columns\n",
    "continuous_columns = ['age', 'pathsize', 'lnpos', 'time']\n",
    "\n",
    "# Initialize a dictionary to hold the results\n",
    "results = {}\n",
    "\n",
    "# Loop over each continuous variable to calculate statistics\n",
    "for col in continuous_columns:\n",
    "    # Collecting all statistics for the column\n",
    "    stats = {\n",
    "        'Mean': df[col].mean(),\n",
    "        'Median': df[col].median(),\n",
    "        'Std': df[col].std(),\n",
    "        'Variance': df[col].var(),\n",
    "        'Min': df[col].min(),\n",
    "        '25%': df[col].quantile(0.25),\n",
    "        '75%': df[col].quantile(0.75),\n",
    "        'Max': df[col].max(),\n",
    "        'Skewness': df[col].skew(),\n",
    "        'Kurtosis': df[col].kurt()\n",
    "    }\n",
    "    results[col] = stats\n",
    "\n",
    "# Convert the dictionary to a DataFrame\n",
    "stats_df = pd.DataFrame(results)\n",
    "\n",
    "# Transpose the DataFrame to get variables as columns and statistics as rows\n",
    "stats_df = stats_df.T\n",
    "\n",
    "# Print the resulting DataFrame\n",
    "stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f242c4",
   "metadata": {
    "papermill": {
     "duration": 0.010668,
     "end_time": "2024-07-18T12:36:22.898668",
     "exception": false,
     "start_time": "2024-07-18T12:36:22.888000",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Distribution Analysis of Continuous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce2920f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:22.922371Z",
     "iopub.status.busy": "2024-07-18T12:36:22.921978Z",
     "iopub.status.idle": "2024-07-18T12:36:24.604969Z",
     "shell.execute_reply": "2024-07-18T12:36:24.603724Z"
    },
    "papermill": {
     "duration": 1.697714,
     "end_time": "2024-07-18T12:36:24.607448",
     "exception": false,
     "start_time": "2024-07-18T12:36:22.909734",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare to plot histograms for continuous variables\n",
    "plt.figure(figsize=(20, 5))  # Adjust figure size for clarity\n",
    "\n",
    "# Create a histogram for each continuous variable\n",
    "for i, col in enumerate(continuous_columns):\n",
    "    plt.subplot(1, len(continuous_columns), i+1)  # Arrange subplots in a single row\n",
    "    sns.histplot(df[col], kde=True, color=\"blue\")  # Adding KDE to smooth histogram\n",
    "    plt.title(f'Histogram of {col}')  # Title for each histogram\n",
    "    plt.xlabel(col)  # x-axis label\n",
    "    plt.ylabel('Frequency')  # y-axis label\n",
    "\n",
    "# Display the histograms\n",
    "plt.tight_layout()  # Ensure there is no overlap of plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa632374",
   "metadata": {
    "papermill": {
     "duration": 0.013068,
     "end_time": "2024-07-18T12:36:24.634597",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.621529",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Discretization of the 'lnpos' Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0add5e74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:24.665001Z",
     "iopub.status.busy": "2024-07-18T12:36:24.664546Z",
     "iopub.status.idle": "2024-07-18T12:36:24.688889Z",
     "shell.execute_reply": "2024-07-18T12:36:24.687293Z"
    },
    "papermill": {
     "duration": 0.042914,
     "end_time": "2024-07-18T12:36:24.692112",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.649198",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   lnpos lnpos_YN\n",
      "0    0.0       No\n",
      "1    0.0       No\n",
      "2    0.0       No\n",
      "3    0.0       No\n",
      "4    0.0       No\n",
      "      lnpos lnpos_YN\n",
      "1202    5.0      Yes\n",
      "1203    0.0       No\n",
      "1204    7.0      Yes\n",
      "1205   11.0      Yes\n",
      "1206    0.0       No\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Check the unique values\n",
    "unique_values = df['lnpos'].unique()\n",
    "\n",
    "if len(unique_values) > 1:\n",
    "    # Define the bins by directly dividing the range into two\n",
    "    min_value = df['lnpos'].min()\n",
    "    max_value = df['lnpos'].max()\n",
    "    median_value = df['lnpos'].median()\n",
    "\n",
    "    # Adjust bin edges if necessary to ensure they are unique\n",
    "    bins = [min_value - 0.1, median_value, max_value]\n",
    "\n",
    "    # Use pd.cut to discretize 'lnpos'\n",
    "    df['lnpos_YN'] = pd.cut(df['lnpos'], bins=bins, labels=['No', 'Yes'], include_lowest=True)\n",
    "\n",
    "# If only one unique value, handle separately\n",
    "else:\n",
    "    df['lnpos_YN'] = 'Yes'  # or 'No', depending on your analysis needs\n",
    "\n",
    "# Verify the discretization by checking the first few rows\n",
    "print(df[['lnpos', 'lnpos_YN']].head())\n",
    "print(df[['lnpos', 'lnpos_YN']].tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b482637",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:24.721711Z",
     "iopub.status.busy": "2024-07-18T12:36:24.721278Z",
     "iopub.status.idle": "2024-07-18T12:36:24.732371Z",
     "shell.execute_reply": "2024-07-18T12:36:24.731131Z"
    },
    "papermill": {
     "duration": 0.029119,
     "end_time": "2024-07-18T12:36:24.735276",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.706157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lnpos_YN\n",
       "No     929\n",
       "Yes    278\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the frequency table for 'lnpos_YN'\n",
    "lnpos_YN_frequency = df['lnpos_YN'].value_counts()\n",
    "lnpos_YN_frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab2091f",
   "metadata": {
    "papermill": {
     "duration": 0.013691,
     "end_time": "2024-07-18T12:36:24.763243",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.749552",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Discretization of the 'pathsize' Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb65e7ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:24.793371Z",
     "iopub.status.busy": "2024-07-18T12:36:24.792958Z",
     "iopub.status.idle": "2024-07-18T12:36:24.810141Z",
     "shell.execute_reply": "2024-07-18T12:36:24.808127Z"
    },
    "papermill": {
     "duration": 0.036531,
     "end_time": "2024-07-18T12:36:24.813696",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.777165",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pathsize pathsize_category\n",
      "0       NaN               NaN\n",
      "1       NaN               NaN\n",
      "2       NaN               NaN\n",
      "3       NaN               NaN\n",
      "4       NaN               NaN\n",
      "      pathsize pathsize_category\n",
      "1202       5.5                >5\n",
      "1203       6.0                >5\n",
      "1204       6.5                >5\n",
      "1205       5.5                >5\n",
      "1206       7.0                >5\n"
     ]
    }
   ],
   "source": [
    "# Define the bins and labels according to your specification\n",
    "bins = [-float('inf'), 2, 5, float('inf')]  # Extending bins to negative infinity\n",
    "labels = ['<2', '2 <= pathsize <= 5', '>5']\n",
    "\n",
    "# Use pd.cut to discretize 'pathsize'\n",
    "df['pathsize_category'] = pd.cut(df['pathsize'], bins=bins, labels=labels, include_lowest=True)\n",
    "\n",
    "# Verify the discretization by checking the first few rows\n",
    "print(df[['pathsize', 'pathsize_category']].head())\n",
    "print(df[['pathsize', 'pathsize_category']].tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e50623c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:24.849195Z",
     "iopub.status.busy": "2024-07-18T12:36:24.848560Z",
     "iopub.status.idle": "2024-07-18T12:36:24.860786Z",
     "shell.execute_reply": "2024-07-18T12:36:24.859251Z"
    },
    "papermill": {
     "duration": 0.032522,
     "end_time": "2024-07-18T12:36:24.864264",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.831742",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pathsize_category\n",
       "<2                    826\n",
       "2 <= pathsize <= 5    283\n",
       ">5                     12\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the frequency table for 'pathsize_category'\n",
    "pathsize_category_frequency = df['pathsize_category'].value_counts()\n",
    "pathsize_category_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5c167e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:24.901486Z",
     "iopub.status.busy": "2024-07-18T12:36:24.901049Z",
     "iopub.status.idle": "2024-07-18T12:36:24.924672Z",
     "shell.execute_reply": "2024-07-18T12:36:24.922324Z"
    },
    "papermill": {
     "duration": 0.04797,
     "end_time": "2024-07-18T12:36:24.928078",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.880108",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1207 entries, 0 to 1206\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   id                 1207 non-null   float64 \n",
      " 1   age                1207 non-null   float64 \n",
      " 2   pathsize           1121 non-null   float64 \n",
      " 3   lnpos              1207 non-null   float64 \n",
      " 4   histgrad           920 non-null    object  \n",
      " 5   er                 869 non-null    object  \n",
      " 6   pr                 851 non-null    object  \n",
      " 7   status             1207 non-null   object  \n",
      " 8   time               1207 non-null   float64 \n",
      " 9   lnpos_YN           1207 non-null   category\n",
      " 10  pathsize_category  1121 non-null   category\n",
      "dtypes: category(2), float64(5), object(4)\n",
      "memory usage: 87.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1758726",
   "metadata": {
    "papermill": {
     "duration": 0.01599,
     "end_time": "2024-07-18T12:36:24.959410",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.943420",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# \"Can involved lymph nodes (lnpos_YN) and (pathsize_category) affect their status?\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff1043a",
   "metadata": {
    "papermill": {
     "duration": 0.015629,
     "end_time": "2024-07-18T12:36:24.990142",
     "exception": false,
     "start_time": "2024-07-18T12:36:24.974513",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Analyzing the Impact of 'lnpos_YN' and 'pathsize_category' Involvement on Patient Status Through Cross-Tabulation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4d002e67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:25.096528Z",
     "iopub.status.busy": "2024-07-18T12:36:25.095932Z",
     "iopub.status.idle": "2024-07-18T12:36:25.132001Z",
     "shell.execute_reply": "2024-07-18T12:36:25.130842Z"
    },
    "papermill": {
     "duration": 0.127899,
     "end_time": "2024-07-18T12:36:25.134739",
     "exception": false,
     "start_time": "2024-07-18T12:36:25.006840",
     "status": "completed"
    },
    "tags": []
   },
   "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>status</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lnpos_YN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.954790</td>\n",
       "      <td>0.045210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.892086</td>\n",
       "      <td>0.107914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "status         0.0       1.0\n",
       "lnpos_YN                    \n",
       "No        0.954790  0.045210\n",
       "Yes       0.892086  0.107914"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a cross-tabulation of the 'lnpos_YN' and 'status' columns\n",
    "cross_tab = pd.crosstab(df['lnpos_YN'], df['status'] , normalize='index')\n",
    "cross_tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce0d189a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:25.167739Z",
     "iopub.status.busy": "2024-07-18T12:36:25.167195Z",
     "iopub.status.idle": "2024-07-18T12:36:25.199199Z",
     "shell.execute_reply": "2024-07-18T12:36:25.197012Z"
    },
    "papermill": {
     "duration": 0.052202,
     "end_time": "2024-07-18T12:36:25.202109",
     "exception": false,
     "start_time": "2024-07-18T12:36:25.149907",
     "status": "completed"
    },
    "tags": []
   },
   "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>status</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pathsize_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;2</th>\n",
       "      <td>0.962470</td>\n",
       "      <td>0.037530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 &lt;= pathsize &lt;= 5</th>\n",
       "      <td>0.883392</td>\n",
       "      <td>0.116608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;5</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "status                   0.0       1.0\n",
       "pathsize_category                     \n",
       "<2                  0.962470  0.037530\n",
       "2 <= pathsize <= 5  0.883392  0.116608\n",
       ">5                  0.833333  0.166667"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_tab = pd.crosstab(df['pathsize_category'], df['status'], normalize='index')\n",
    "cross_tab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d1b6be",
   "metadata": {
    "papermill": {
     "duration": 0.015664,
     "end_time": "2024-07-18T12:36:25.234686",
     "exception": false,
     "start_time": "2024-07-18T12:36:25.219022",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Comparing some categorical and continuous variables using Pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f581b3c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:25.266567Z",
     "iopub.status.busy": "2024-07-18T12:36:25.266051Z",
     "iopub.status.idle": "2024-07-18T12:36:25.293621Z",
     "shell.execute_reply": "2024-07-18T12:36:25.292122Z"
    },
    "papermill": {
     "duration": 0.047621,
     "end_time": "2024-07-18T12:36:25.296585",
     "exception": false,
     "start_time": "2024-07-18T12:36:25.248964",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/515169679.py:2: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
      "  pivot_table = df.pivot_table(values='time', index='pathsize_category', aggfunc='mean')\n"
     ]
    },
    {
     "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>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pathsize_category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;2</th>\n",
       "      <td>47.652542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 &lt;= pathsize &lt;= 5</th>\n",
       "      <td>43.195995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;5</th>\n",
       "      <td>34.347222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time\n",
       "pathsize_category            \n",
       "<2                  47.652542\n",
       "2 <= pathsize <= 5  43.195995\n",
       ">5                  34.347222"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pivot table that calculates the mean of 'time' for each category in 'pathsize_category'\n",
    "pivot_table = df.pivot_table(values='time', index='pathsize_category', aggfunc='mean')\n",
    "#index => categorical\n",
    "#value => continous \n",
    "# Print the pivot table\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b1d0675c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:25.331703Z",
     "iopub.status.busy": "2024-07-18T12:36:25.331201Z",
     "iopub.status.idle": "2024-07-18T12:36:25.364509Z",
     "shell.execute_reply": "2024-07-18T12:36:25.363169Z"
    },
    "papermill": {
     "duration": 0.053416,
     "end_time": "2024-07-18T12:36:25.367056",
     "exception": false,
     "start_time": "2024-07-18T12:36:25.313640",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/1864406513.py:2: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
      "  pivot_table = df.pivot_table(values=['time','age'], index='lnpos_YN', aggfunc={'time':['mean','std'],\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lnpos_YN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>47.385181</td>\n",
       "      <td>29.378292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>82.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>45.523861</td>\n",
       "      <td>30.504200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age             time           \n",
       "           max   min       mean        std\n",
       "lnpos_YN                                  \n",
       "No        88.0  22.0  47.385181  29.378292\n",
       "Yes       82.0  24.0  45.523861  30.504200"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pivot table that calculates the mean of 'time' and 'age' for each category in 'lnpos_YN'\n",
    "pivot_table = df.pivot_table(values=['time','age'], index='lnpos_YN', aggfunc={'time':['mean','std'],\n",
    "                                                                              'age': ['min','max']})\n",
    "\n",
    "# Print the pivot table\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2074287f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:25.399655Z",
     "iopub.status.busy": "2024-07-18T12:36:25.398832Z",
     "iopub.status.idle": "2024-07-18T12:36:25.432651Z",
     "shell.execute_reply": "2024-07-18T12:36:25.431117Z"
    },
    "papermill": {
     "duration": 0.052876,
     "end_time": "2024-07-18T12:36:25.436170",
     "exception": false,
     "start_time": "2024-07-18T12:36:25.383294",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/1437472375.py:2: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
      "  pivot_table = df.pivot_table(values='time', index=['pathsize_category','lnpos_YN'], aggfunc=['mean','std'])\n",
      "/tmp/ipykernel_18/1437472375.py:2: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
      "  pivot_table = df.pivot_table(values='time', index=['pathsize_category','lnpos_YN'], aggfunc=['mean','std'])\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pathsize_category</th>\n",
       "      <th>lnpos_YN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">&lt;2</th>\n",
       "      <th>No</th>\n",
       "      <td>47.057558</td>\n",
       "      <td>28.835447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>50.129167</td>\n",
       "      <td>31.580492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2 &lt;= pathsize &lt;= 5</th>\n",
       "      <th>No</th>\n",
       "      <td>46.315263</td>\n",
       "      <td>29.784837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>36.823297</td>\n",
       "      <td>26.825617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">&gt;5</th>\n",
       "      <th>No</th>\n",
       "      <td>31.808333</td>\n",
       "      <td>16.465663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>35.616667</td>\n",
       "      <td>22.173851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  mean        std\n",
       "                                  time       time\n",
       "pathsize_category  lnpos_YN                      \n",
       "<2                 No        47.057558  28.835447\n",
       "                   Yes       50.129167  31.580492\n",
       "2 <= pathsize <= 5 No        46.315263  29.784837\n",
       "                   Yes       36.823297  26.825617\n",
       ">5                 No        31.808333  16.465663\n",
       "                   Yes       35.616667  22.173851"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pivot table that calculates the mean of 'time' for each category in 'pathsize_category' and 'lnpos_YN'\n",
    "pivot_table = df.pivot_table(values='time', index=['pathsize_category','lnpos_YN'], aggfunc=['mean','std'])\n",
    "\n",
    "# Print the pivot table\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea2a1d2",
   "metadata": {
    "papermill": {
     "duration": 0.015956,
     "end_time": "2024-07-18T12:36:25.466666",
     "exception": false,
     "start_time": "2024-07-18T12:36:25.450710",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Visualization Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58483588",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:25.499258Z",
     "iopub.status.busy": "2024-07-18T12:36:25.498476Z",
     "iopub.status.idle": "2024-07-18T12:36:26.000153Z",
     "shell.execute_reply": "2024-07-18T12:36:25.999032Z"
    },
    "papermill": {
     "duration": 0.521537,
     "end_time": "2024-07-18T12:36:26.002651",
     "exception": false,
     "start_time": "2024-07-18T12:36:25.481114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the aesthetic style of the plots\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create a histogram combined with a kernel density estimate\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['age'], kde=True, color=\"blue\",edgecolor=\"black\", binwidth=3, binrange=(df['age'].min(), df['age'].max()))\n",
    "\n",
    "# Adding more details to the plot\n",
    "plt.title('Distribution of Age with KDE')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b057bb4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:26.035287Z",
     "iopub.status.busy": "2024-07-18T12:36:26.034916Z",
     "iopub.status.idle": "2024-07-18T12:36:26.455370Z",
     "shell.execute_reply": "2024-07-18T12:36:26.454146Z"
    },
    "papermill": {
     "duration": 0.439666,
     "end_time": "2024-07-18T12:36:26.458148",
     "exception": false,
     "start_time": "2024-07-18T12:36:26.018482",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Create a scatter plot for 'age' and 'time'\n",
    "sns.scatterplot(x='age', y='time', data=df)\n",
    "plt.title('Scatter Plot of Age vs. Time')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Time')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "79bf8fe7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:26.494626Z",
     "iopub.status.busy": "2024-07-18T12:36:26.494044Z",
     "iopub.status.idle": "2024-07-18T12:36:27.543931Z",
     "shell.execute_reply": "2024-07-18T12:36:27.542699Z"
    },
    "papermill": {
     "duration": 1.071863,
     "end_time": "2024-07-18T12:36:27.546583",
     "exception": false,
     "start_time": "2024-07-18T12:36:26.474720",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the aesthetic style of the plots\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create a figure to hold the subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Histogram for 'age'\n",
    "sns.histplot(df['age'], kde=True, color=\"purple\", ax=axes[0])\n",
    "axes[0].set_title('Histogram of Age')\n",
    "axes[0].set_xlabel('Age')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "# Histogram for 'time'\n",
    "sns.histplot(df['time'], kde=True, color=\"green\", ax=axes[1])\n",
    "axes[1].set_title('Histogram of Time')\n",
    "axes[1].set_xlabel('Time')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bcaf7a28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T12:36:27.584916Z",
     "iopub.status.busy": "2024-07-18T12:36:27.584479Z",
     "iopub.status.idle": "2024-07-18T12:36:27.762011Z",
     "shell.execute_reply": "2024-07-18T12:36:27.760541Z"
    },
    "papermill": {
     "duration": 0.200349,
     "end_time": "2024-07-18T12:36:27.765389",
     "exception": false,
     "start_time": "2024-07-18T12:36:27.565040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yMuzevRtSymHXGZrMMxrvvvturMV1wYIFseOHz4YFPnoN9+/fP6zFOBwO4+DBg2hubj7u4yxatAgGgwEvv/wyLr/88hHrev3113H55Zfj7rvvjh0LBoNHtO4d67UZ+jnn5eUd9+c79LyO9prt379/2PdlZWVYvXo1vF7vsFa+oW7vY03KOJ7c3FxkZmZCCDGq92FGRgYuvvhiXHzxxQiFQrjjjjvwpz/9CbfeemtS/2FEBHBZFqJjuuiii6BpGv7whz8ccVkkEhnWzXo0X//61yGlxHe/+92jdsVt3rwZL7zwwphqOlbLyl//+tcx3c/Q+nEjPQcAOO2002AymfD4448Pax167rnn4PF4Jr0lbbQ0TTsiwOTl5aGwsBChUCh2LD09/ajdmGeccQa6u7uxbNmy2LFgMIhnn3121DUMDQk4/HUJhUJ46qmnhl1v1qxZyM3Nxd///vdhtb3wwguj+hmUlJTgyiuvxL///W88/vjjR1wuhMCjjz6Krq4uAEd/nzz++ONHLBUz9D74+Otz5plnIisrCw8++CDC4fAR9zXUTVpUVITGxka8+OKLw97j77777hGt24sWLYKmabElgYY89thjUBQFixYtOubzPxaDwYALLrgAr7/++lFb04fqBHDEskVmsxl1dXWQUh71ORIlG7bwER3DySefjKuvvhoPPvggtm3bhtNPPx0mkwn79u3Da6+9hnvvvRcXXnjhMW9/4okn4r777sOPf/xjXHTRRcN22nj33Xfx5ptv4lvf+taYasrKysKCBQvw5z//GeFwGEVFRXjnnXeOaFEaycyZMwEAP/3pT3HGGWfAYDDgkksuOep1c3Nzceutt+KBBx7AV77yFZx77rnYu3cvnnrqKcyePXvUS4VMtsHBQZx11lm44IIL0NzcjIyMDKxatQqbNm0a1rI1c+ZMvPrqq/j5z3+O2bNnIyMjA+eeey6uvvpqPPHEE/jOd76DG264AQUFBXj55ZdjLT2jaQU94YQTYLfbcffdd+P666+Hoij45z//eUS3qclkwre+9S3cd999uPHGG3HxxRfj4MGDeP7550c1hg+IrhPZ1taGn/70p3jjjTdwzjnnwGazobOzE6+99hpaW1tjP8Ozzz4b//znP5GVlYX6+nps2LABq1atOmIJopaWFhgMBjz88MPweDwwm8049dRTkZeXhx/96Ef47ne/iyuuuAIXX3wxcnNz0dHRgeXLl8fe2wBw55134mtf+xq+8IUv4IorroDb7caTTz6JxsbGYSHw3HPPxSmnnILf/va3sXUR33nnHSxbtgw33njjMcdOjuQ73/kO1q5di6uuugpXXnkl6uvr4XK5sGXLFqxevRrvvvsuAOCmm25Cfn4+TjzxROTl5aG1tRVPPPEEzjrrrKOuk0mUbBj4iI7jP//zPzFr1iz8/e9/x29/+1sYDAaUlZXhsssuw4knnjji7T//+c9j9uzZePTRR/Hiiy/C6XTG9tL9+c9/Pq6w9Otf/xo/+clP8NRTT0FKidNPPx0PP/wwzjzzzFHfxyc/+Ulcf/31+Ne//oWXXnoJUspjBj4AuOOOO5Cbm4snnngCP//5z2G323HVVVfh29/+9qTvUDJaaWlp+MIXvoB33nkHb7zxBqSUqKysxA9/+ENcc801setdc8012LZtG55//nk89thjKCsrw7nnnovMzEz89a9/xU9/+lP87W9/Q0ZGBi6//HKccMIJuOOOO0bVxZeTk4M//elP+K//+i/8z//8D2w2Gy677DIsXLgQN91007DrXn311dA0DY888gh++ctforGxEX/84x/xv//7v6N6vunp6Xj44Yfx/PPP48UXX8Qf/vAHBAKB2F66v/rVr2Izzu+9916oqoqXX34ZwWAQJ554Iv7yl7/gK1/5yrD7LCgowI9//GM8+OCDuPfee6FpGv72t78hLy8Pn/rUp1BYWIiHHnoIjzzyCEKhEIqKijB//vxhs5XPPfdc/OY3v8H999+PX//616iursbPf/5zvPjii8MWaVZVFX/84x/xu9/9Dq+++iqef/55lJWV4bvf/S6+/OUvj+o1OJr8/Hz84x//wO9//3ssWbIETz/9NLKzs1FfX4+77rpr2Ov/8ssv4y9/+Qt8Ph+Ki4tx/fXX42tf+9q4H5sokShyNCN4iYhSxGOPPYaf//znWLFixTGX7KGRffrTn0Zubi7+8pe/6F0KEYFj+IgohQUCgWHfB4NBPPPMM6iurmbYG6VwOIxIJDLs2Nq1a7F9+3acfPLJOlVFRB/HLl0iSlm33347SktL0dzcDK/Xi5deegmtra3HXIqHjtTd3Y0vfelLuOyyy1BYWIjW1lb8/e9/R0FBwYQX/SaiycPAR0Qp64wzzsBzzz2Hl19+GZqmob6+Hr/97W9x8cUX611awrDb7Zg5cyb+8Y9/wOFwICMjA2eddRbuuusu5OTk6F0eER3CMXxERERESY5j+IiIiIiSHAMfERERUZJj4CMiIiJKcgx8REREREmOgY+IiIgoyTHwERERESU5Bj4iIiKiJMfAR0RERJTkGPiIiIiIkhwDHxEREVGSY+AjIiIiSnIMfERERERJjoGPiIiIKMkx8BERERElOQY+IiIioiTHwEdERESU5Bj4iIiIiJIcAx8RERFRkmPgIyIiIkpyDHxERERESY6Bj4iIiCjJMfARERERJTkGPiIiIqIkx8BHRERElOQY+IiIiIiSHAMfERERUZJj4CMiIiJKcgx8REREREmOgY+IiIgoyTHwERERESU5Bj4iIiKiJMfAR0RERJTkGPiIiIiIkhwDHxEREVGSY+AjIiIiSnIMfERERERJjoGPiIiIKMkx8BERERElOQY+IiIioiTHwEdERESU5Bj4iIiIiJIcAx8RERFRkmPgIyIiIkpyDHxERERESY6Bj4iIiCjJMfARERERJTkGPiIiIqIkx8BHRERElOQY+IiIiIiSHAMfERERUZJj4CMiIiJKcgx8REREREmOgY+IiIgoyTHwERERESU5Bj4iIiKiJMfAR0RERJTkGPiIiIiIkhwDHxEREVGSY+AjIiIiSnIMfERERERJjoGPiIiIKMkx8BERERElOQY+IiIioiTHwEdERESU5Ix6F0BENFFSSkj50feKAiiKMqrbxb4+4ouPUQB1FPdJRBSPGPiIKG4MBbdjBTYpJSJCQtMkwkIgogmEIwJhTUIT0f8jmkBEHPpfk9CkhILo/SlKtFtj6GtFUaAgGuRi3yuIXV9Vhv5XYDaqsJhUpJkMMBtVmAzqETUeHjxHGzqJiKYDAx8RTZtjBbpwRMAfimAwGIEvpMEfiv4fDGuIaBJhLRruxLFa33RiMkRDoMVoiAVC89DXxmg4tBwKiEbDRyNoxKFUyBZDIpouijy8T4OIaIKklJAYHmZChwU6f0iD79D/Q8FOi7ckNwVUBci0GJGVZkJWWvR/a7oRmRYTDGr0tRqphZOIaLwY+Iho3MRh3aUA4AtGMOALweULw+MPwxeKBrtUCHQTkWYyxEJgVpoR1kP/W0yG2HWEkAyCRDRuDHxENCqHBw4pJQaDEQwMhuDyh+HyheD2hRFhsJtURlVBVpoRmYcFwZwsMyzGaBBkCCSi0WLgI6IjHB4khJQYDETgHAzB5YsGPI8/zFY7HaWZDMjJNCM704zcLDPs6WaoqnLU7nQiIoCBj4gwvGvWF4yg1xOAazAMlz8Ejz8cd5MlaDgFgC3ddCgAWpBvtcBiMjAAElEMAx9RCjo8CIQ1gT53AL3uIHo9AfhDmt7l0STIMBuQm2WJBcAMS3RRBiElAyBRCmLgI0oRQkioarSL1ukNoccdQJ8nAJcvrHdpNA0sJjUW/ort6bCYDEdMuiGi5MXAR5SkDm/J8fjDsYDn8IY4/o5gzzChyJ6Okux0WNNNsV1HGP6IkhMDH1ESGQp5wbB2KOAF0ecOIBgRepdGcSzdbECRPQ3F9nTkWi1QD03WYdcvUfJg4CNKcEMfzL5gBO1OHzqdfrj97Kal8TGqCgpsaSjKjgZAo0Fl+CNKAgx8RAmIIY+mgwIgN8sSbf3LTkeGxciuX6IExcBHlCCkFFCU6H6s/Z4Athx0MeTRtMpKM6LIno6KvAxkpZnY8keUQBj4iOLYUMiLiDAcwT70B3tRY21AJGzE21u79S6PUlh2hhkVeRkoy81gty9RAjDqXQARDSelPLTDhcBAsB/9wV64wy4A0b/NHIFelGRUwKgCnItBehnwhTDgC2HLQRdKctJRmZeJPKuFwY8oTrGFj8Zl//79eOSRR7Bx40bs2rULtbW1eOWVV0a8nZQSDz/8MJ566ik4HA60tLTgnnvuwbx586a+6Dg31JrnCbvRF+jGQKgfQh6Z6CyGNMzKORG7u9zY3uHWoVKio8swG1Cel4nKvEykmQ0Mf0RxRNW7AEpMu3btwvLly1FVVYW6urpR3+7hhx/G7373O3zxi1/Egw8+iIKCAnz5y19GW1vbFFYbv6SUkFJCSIG+QA+2ODdgp2szHMHeo4Y9AAhqAfgigyjLzZjmaomOzxfSsLPTjaWbO7FmVy+6nH6IQ+9xti0Q6YstfDQuQgioavTvhbvvvhubN28esYUvGAzitNNOw7XXXotvf/vbAIBQKIQLL7wQixYtwo9+9KOpLjtuDLXmBbUAevyd6A/2QJOj39KsKL0UZRlVWLKpEyH261IcMxkUlOVmoDI/E7Z0M1v9iHTCMXw0LkNhbyzWrVsHr9eLiy66KHbMbDbjE5/4BJYsWTKZ5cWtofF57rALPf5OuMMD47ofR7AP5ZnVaCyxYXPb+O6DaDqENYl9vYPY1zsIW7oJFXmZqMzPgKooXNqFaBqxS5emTWtrKwCgtrZ22PG6ujp0dHQgEAjoUdaUG+rO0qSGnkAnNjvWYbd727jDHgCERQjesAcl2WmTVyjRFHP7w9hycABLN3ViR6cboYhgdy/RNGELH00bt9sNs9kMi8Uy7LjNZoOUEi6XC2lpyRNgDu+27Q50wBHohcDkdb86gr2oyKxBhtkAX2j03cFEegtrEru7PGjt9qAiLxN1xVZkmI2xFnAimnwMfESTTEoBQIEr5ER3oBPe8NTMpHUG+1GRWYPGEhs27HdOyWMQTSUhgf19gzjQN4jinHQ0FFs5zo9oijDw0bSx2WwIhUIIBoPDWvncbjcURYHdbtexuokbCnr9wV50+g4iJIJT+ngRGYYn7EKh3Tqlj0M01SSATqcfnU4/8q0W1BdbkW9NY/AjmkQMfDRthsbu7d27F83NzbHjra2tKC0tTdju3KHxR/3BPnT5DiIopm8soiPYh6osO2zpRrj9kWl7XKKp0ucJos8ThD3dhLpiK0qy0yEBBj+iCeKkDZo2J554IrKysrB48eLYsXA4jDfeeAOLFi3SsbLxkTI64NwR7MUW53rs9+6e1rAHAAOhfgBAQ7FtWh+XaKq5/GGs2+vAW1u7cKBvEEJwcgfRRLCFj8bF7/dj+fLlAID29nZ4vV689tprAICTTz4Zubm5uPHGG9HR0RFbcsViseDWW2/F/fffj9zcXDQ2NuLpp5/GwMAAbrrpJt2ey1gNfeg4g/3o8B9EUPPrVosmNbhCTuTbErs7nOhYfEENm9sGsLPTjZqCLNQUZsGgckkXorFi4KNx6e/vxze/+c1hx4a+/9vf/oZTTjkFQgho2vDZozfffDOklHj00UdjW6s98sgjqKiomLbax2to1q0z1I9OXxsCOga9wzmCvci25CI30wzHYEjvcoimRCgisKPTjdYeLxqKraguzALArl6i0eJOG0QjiAW9YD86fG0IaD69SxpGhYq5eSejzx3Cu3v69C6HaFpkmA1oLrOjNCeDkzuIRoEtfETHMBT0XCEnOnxt8MdZ0BsiIDAQ6keuNU/vUoimjS+kYd1eB1p7vJhZZkdOloXr+BEdB1v4iD4mtv1ZaAAHB/fDrw3qXdKI7OYc1Nta8O6ePvS4knPHEqLjKc5Ow4yybKSbDQDA4Ef0MWzhIzqMlBJhEcKBwb1whRx6lzNq7tAANBFBXaGVgY9SUtdAAN2uLlTlZ6KpxA6jgaGP6HAMfEQ4tN8tJLp8B9Hl74CcxC3QpoOEhDPUj5zMAr1LIdKNlMC+3kEc7PehvtiK2kIroHBiBxHALl1KcUPdtwNBB9oG90757hhTyWqyo9E+E+v39aPdER8ziIn0lGYyoLnUhvK8TE7soJTHwEcpS0qJkAjigLcV7vCA3uVMirm5C+D1S/x7R4/epRDFDVu6CTPK7ci3pnFiB6UsdulSyol23wp0+NrQ4++ERPL8zeMI9iE/o0jvMojiitsfxppdfSjOTsPsihyYjSpDH6Ucbq1GKePwHTI2O9ej29+RVGEPiAY+VVFRXZCpdylEcadrIIC3t3ahrT+6xJJgBxelEHbpUtIbeosHND8OeFvhjbh1rmhqzc45CYGQihXbuvUuhShu5VstmFuVgzSTga19lBLYwkdJTUoJAYG2wb3YOrAh6cMeEG3ly0oz8Jeb6Dj6PEG8vbUbrT3e6HmCbR+U5PiZQElpqFXPHXZis2MdegNdOlc0fYa6dWuLs/QuhSiuaUJiW7sL7+zowWAgAnZ4UTJj4KOkM9Sqt9ezC7vd2xGRYb1LmlZ+bRABzY/yXI7jIxqNAV8YK7Z3Y2enG4KtfZSkGPgoaXzUqjeALc71cAR7da5IP45gHzItRhj5G040KlICu7o8WLGtG25f+NAxBj9KHvw4oKQgpYCAwD7Pbux2b0NYhPQuSVeOYB8URUF9sU3vUogSijcQwb939GBz2wCE5ExeSh4MfJQUvGEPtjjXoz/IBYcBIKj54Y8MojQnQ+9SiBLSvl4v3t7ahX5PdPcdtvZRomPgo4QlpYSUAm3evdjp3pLyrXof1x/sRbrZADP7dYnGxR/SsHZ3Hzbud7C1jxIePwkoIUkpEdD82DrwIXoCnXqXE5ecwX4oioLGYqvepRAltLZ+H1Zu74YvyJm8lLgY+CihDJ1sewKd2DawEQHNp3NF8SskgvCGPSjJSde7FKKE5w1EsHJ7T2yXDgY/SjQMfJQwpJSIyDB2urbg4OC+pNsWbSo4gr0wGw3IMPNXnWiiNCHx4QEnNuxL7C7e5cuX47rrrsOpp56KWbNm4bzzzsPPf/5zeDyeEW/7j3/8AxdccAFmz56Nyy67DG+99dY0VEyTgVurUUKQUsIdHsBezy5oMqJ3OQnDqJgwJ3c+2h0+bNjv1LscoqSRlWbE/No8ZFqMCbc12z//+U/s2LEDc+fORXZ2Nnbt2oX7778fM2fOxKOPPnrM2/3rX//Cd77zHdx222049dRT8eqrr+L//u//8OSTT2LevHnT9wRoXBj4KK5JKaEoCjp8bej0teldTkJqsM1AmmrFGx9yrCPRZDKoCmaWZ6MyPzN2rkpUzz77LH7wgx9gxYoVKCoqOup1LrjgAsyaNQu//vWvY8c+//nPw2q14uGHH56uUmmc2M9DcWtox4zd7m0MexPgCPbBZFBhTTPqXQpRUkmWLl4AyM7OBgCEw0ffmaitrQ379u3DRRddNOz4xRdfjNWrVyMU4ioJ8Y6Bj+KSlBJBLYBtzo1whdgVOREDoX4AQGMJF2EmmgoHHYk5i1fTNASDQWzZsgW///3vce6556K8vPyo121tbQUA1NTUDDteV1eHcDiMtjb+UR7v+Cc/xaWBkAP7vLsgpNC7lISnSQ2ukBP5NrvepRAlraFZvInUxXvOOeegu7sbAHDmmWcO66r9OJfLBQCw2Yb/4Tj0/dDlFL/YwkdxI7qQssTBwf1o9exg2JtE0W5dA3IyzXqXQpS0Eq2L96GHHsLf//53/PSnP0Vraytuu+02aJqmd1k0RRj4KC5IKaBJDbvcW9Htb9e7nKTjCjkgpEAD99YlmnKHd/HGc+hrbm7GCSecgCuvvBJ/+MMfsHbtWixZsuSo17Xboz0EH1+6xe12D7uc4hcDH+luaNeMbQMb4QmzW2AqCAgMhBzItbKFj2g6eAMRvLOjB05vKCHG9TU1NcFkMuHAgQNHvby2thbAR2P5hrS2tsJkMqGiomLKa6SJYeAj3TmCvdg2sAkhEdS7lKTmCPbCqKootFn0LoUoJYQ1iTW7e9HuiP8dgTZu3IhwOHzMSRsVFRWorq7Ga6+9Nuz4q6++ioULF8Js5h+T8Y6TNkgXQ3/xtg3uRW+gS+dqUoM7NABNaqgrsqHH3at3OUQpQUpgw34nBoMRNJXa42Iyx+23345Zs2ahqakJaWlp2L59Ox555BE0NTXh/PPPBwB8//vfx4svvoitW7fGbnfHHXfgrrvuQmVlJU455RS8+uqr+PDDD/HEE0/o9VRoDBj4aNpJKaFJDXvc2+GNuPUuJ2VISDiDfcjJLNC7FKKUs6vLA19Iw9yqHEDn0Ddnzhy8+uqreOihhyClRFlZGa688krcdNNNsZY6IcQREzguvfRS+P1+PPzww3jooYdQU1ODBx54ACeccIIeT4PGiDtt0LSSUiAsovvhBkVA73JSjtVkR6N9Jtbv7Ue70693OUQpJy/LggV1eVBVBWqcL9tCyYVj+GjaRCdnBLDd9SHDnk48YRciIoyaQqvepRClpH5vEP/e0YNgWIvrGbyUfBj4aFpIKTEY8WCHaxPC4uhb99D0cAT7YMvgaA4ivQwt0uzxhxNiBi8lBwY+mnJSSrhCTux0bYUmuain3hzBPqiKiqr8TL1LIUpZoYjAOzt60e0KMPTRtGDgoynXH+zBHs92SHDnjHgwGPEgpIVQVcDAR6QnISXeb+3H3h6v3qVQCmDgoynV6WvDfu8evcugj3EEe5GVZuQJgCgObG13YXObM7a9JNFU4PmeJt3QSeuAtxUdvja9y6GjGOrWrS3K0rsUIgKwr3cQ77X2J8QevJSYGPhoUg39dbrXs5MLKscxvzaIoBZAeR67dYniRY8rgDW7eiGEZOijScfAR5NGSgkJgV3urXCG+vUuh0bQH+xFpsUII88CRHHDORjCml19DH006Xiqp0kxtHvGDtdmeMIuvcuhUXAG+6AoCuqLbHqXQkSHGfCFsGpnLzSNoY8mDwMfTZiUEmERwvaBD+GLDOpdDo1SQPPDH/GhNDdD71KI6GPc/jBW7exBhKGPJgkDH02IlBIREcYO12bunpGA+oO9SDcbYGa/LlHc8QQiWLWzB+GIYOijCeNZnsZNSomIjGCHazNCIqh3OTQOQ926DcXcao0oHnkDEaza2YsQQx9NEAMfjcvQmL2dri1s2UtgIRHEYNiDkpx0vUshomMYDLKljyaOgY/GTEoJAYGdri0IaD69y6EJ6g/2wmI0IMPM0wFRvPIFNaza2cvQR+PGMzyNSWzpFdcW+DVO0EgGzmB0CZ2GErvOlRDR8QwGI1i9q5cTOWhcGPho1KJhT2KXaxsGI9z7MVlEZBjesBtF9jS9SyGiEXgDEaze2cMlW2jMGPhoVKI7aEjscW+HN+LWuxyaZP3BXpgMKqxpRr1LIaIReALRlj6NizPTGDDw0YiGtkvb49kBd3hA32JoSgyEhrp1uQgzUSJw+8NYszO6DZtk6KNRYOCj4zp8b1xXyKlzNTRVNKnBFXKiwGbRuxQiGiWXP4y1u/sgJRj6aEQMfHRciqJgn3c398ZNAY5QH0wGA3IyzXqXQkSj5BwMYf0+BxRF0bsUinMMfHRc+7174Aj26l0GTQNX0AEhBRdhJkownQN+bD04oHcZFOcY+OiY2rx70Rfo1rsMmiYCAgMhB3Kt7NYlSjStPV7s6/Wya5eOiYGPjiClRLe/Az2BTr1LoWnmCPbBqKoo5Fg+ooSzpW0APe4AQx8dFQMfDSOlhCvkxMHBfXqXQjpwh5zQpIa6Is7WJUo0EsC6vQ64/WEu10JHYOCjGCkl/JoPez079S6FdCIh4Qz2IzvTpHcpRDQOmpB4d3cfgmGNoY+GYeAjANGwFxZh7HJthYDQuxzSkSPYC4OqojQnXe9SiGgcghGBtbv7oHGNPjoMAx9BSgkBgV3urYjIsN7lkM48YRciIozaQs7WJUpU3kAE7+3pgwTX6KMoBj6Coijo8rUjoPn0LoXihCPYB1sGt1kjSmQObwgbuEYfHcLAR5CRMEozKpBvKdK7FIoTzmAfVEVFZX6m3qUQ0QR0OP3Y3u7SuwyKAwx8KUxKCbF/L7Qlr0J6XKiy1qE8s1rvsigOeCMehLUQqgsY+IgS3e5uD/b3cY2+VMfAl6KkEICzH2LLRkAIiJVvQXQcRGFaCeptLVAVg94lks76g73ISjPyJEGUBDYfGECvJ8iZuymM5/IUJIUAggFo768FDvvlF+vfg9ixFTZTNlrsc2BWufhuKhvq1q0pytK7FCKaIAlgXWs/AiEu15KqGPhSjJQSkALae6uBcOjIy/fshPbeKlhUM1qy5yLTyJmaqcqnDSKoBVCRx25domQQERLvt/YDkjN3UxEDX4pRFAViwweAx33sK/X2QFu+DAZNoMk+E7mWgukrkOJKf7AXmRYjjDxTECUFtz+MzQcHOHM3BfE0nkKklBB7d0N2dYx8Zd8gtKWLIQcHUWNtQGlG5dQXSHHHGeyDoijcao0oiRzoG0S7w8dWvhTDwJcipBCA2wWxfcvobyQExPKlEF2dKE4vQ521CSrfMikloPnhj/hQlpuhdylENIk+PODEYDDC8XwphJ/eKUBKCQgB7YO1gBj7tmnigzUQu3bAbs5FU/ZsmFTzFFRJ8ao/2It0swFm9usSJQ1NSHzQ2g/J8Xwpg2fwFKAoCsTGDwD/+HfSkLu2QVv3LtLVdLRkz0WGkTM3U8VQt25DMSfwECUTTyCCTQecHM+XIhj4ktyYxu2NpKsD2so3YRRAk30Wcsx5E79PinshEcRg2IOSnHS9SyGiSXbQ4UNb/yBb+VIAA18SG9e4vZF4PdCWvQb4/ai1NaEkvXzy7pviVn+wFxajAelmnjKIks2mAwPwBjieL9nx7J2kJjpu77giEYi33oDo7UFpZiVqshqh8K2U1JzBfgBAYwln6xIlGyGj6/NJKdnSl8T4KZ2kJmPc3kjEu+9A27MLOZY8NNlnwaiYpuyxSF8RGYY37EaRnd26RMloMBjBxv0cz5fMGPiSUHTc3p7JGbc30mNt3wxt4wfIMGSgJXsu0g1cviNZ9Qd7YTKosKYZ9S6FiKZAh9OP/b1etvIlKQa+JPPRuL3N0/eg7W3Q3nkbJqhozp4Nuzln+h6bps1AyAEAaGC3LlHS2nJwAB6O50tKDHxJR0Jb/97kj9sbidsF7c3XoQRDqLM2oyi9dHofn6acJiNwh50osFn0LoWIpoiQwPutfRzPl4QY+JKIlDI6I3fQq08BoVB0Bq/TgfLMalRl1UEBx4MkE0ewDyaDATmZXHybKFn5ghq2HnRxPF+SYeBLElIIYMAJuXeP3qVAW70CYn8r8iyFaLTPhEHhmK9kMRB0QEjBRZiJktz+vkE4vEF27SYRBr5kISW0De/rXUWM2LwR2uYNyDRmoSV7DtIMnN2ZDAQEBkIO5FrZrUuU7DbudwLMe0mDgS9JiO1bAN+g3mUMd2AftNUrYYIRzdlzYDNl610RTQJnsA9GVUUBQx9RUhsMRrC9w8WxfEmCgS/BSSEgnQ7Iffp35R6V0wHx1hIooTDqbS0oSCvWuyKaIFfICU1qqC/mbF2iZLe3xwu3P8yu3STAwJfo4qwr96iCAYiliwGXC5VZtajIrAE4mSNhSUg4g/3IzuRC20TJTgLYsM+hdxk0CRj4ElxcduUeg/bOWxAHD6AgrRgNthYYFIPeJdE4OYN9MKgqSrI5NpMo2XkCEezu8rBrN8Ex8CWoaFduf/x25R6D2PgBxLZNsJpsaM6eA4uapndJNA7usAsREUFtEWfrEqWCXV1ueLkgc0Jj4EtUUkLb8IHeVYyL3LsH2tpVMCtmNGfPQZaJY8ESj4Qj2Ad7OpfcIUoFUgIb9zs4GCeBMfAlqETqyj2q/l6It5ZCjWhotM1EnqVQ74pojJzBXqiqisr8TL1LIaJpMOALo7WHe+0mKga+BCOFgBxwJlxX7lEFfBDLXoP0ulFtrUd5ZrXeFdEYeCMehEUI1Qx8RCljR4cb/pDG0JeAGPgSjaJA27xB7yomjxAQK96E6GxHYVoJ6m3NUBW+LRNFf6AXWelGnkiIUoSQEhv2O7jtWgJSJGN6wpBCQB48ALFpvd6lTAmlvhFq4wwEND92u7chJIJ6lzQhnW1d+MfDL2Dnh7vgdQ8irygXp3/iVFxyzYWwpI28aPHqpWux+NklaNvTBoPRgLLqUlx1yxWYedIMAEA4FMaTDzyD1cvWwmQy4bzLz8ZnvnjZsPvo73Hgrmu+j7t/8x00zWmY9OeYYcxES/ZcbGsfwJ5unfZwJqJpN6siG1X5mQx+CYQjrhOElBIQGsSOLXqXMmXk7p3Q3G5YTjoZLdlzsNu9HYMRj95ljUt/dz9+cPNPkJGZjk9+9jxk2jKxa/MePPfIi9i7Yx++81/fPO7tn3vkRbzwl5dw8jnzcdbFpyMS0XCwtR2O3oHYdV55ajFWvrYKl994KfyDATz/l5dQVFaI0z5xauw6T/3+WZx0xrwpCXsA4IsMIqgFUJGXycBHlEK2t7tQbE+HxaQy9CUIBr4EInZsBUIhvcuYWj1dECvehOH0s9Fon4n93t1wBPv0rmrMVr62Gj6PDz/6w/dRXlsGADjv02dDCoGVr62C1z2ILNvRx77t2rwHL/zlJVx7+9W4+PMXHPMx1r+zEZd8/gJ86tqLAQCOHgc++PeGWODbvnEn1r2zAb966meT/OyG6w/2oiS9HEYViIgpfSgiihMRIbHl4ABOqs3TuxQaJQ69SQBSCMDrgdy/V+9SpsegF9qbrwE+H2qsjSjNqNC7ojHz+/wAAFvu8CVnsvOzoagKjKZj/621+Nk3YM+148KrPgEpJQK+wFGvFwqFkWn9KDRm2jIRCkS7wYUQ+Nv/PoVPXXMR8gpzJ/p0jssZ7IOiKKgr4vI6RKmkc8APpzfItfkSBANfAlBUNTpRI5V+qSIRiLeXQHR3oTi9HLXWJigJ9HadcUIzAODhnz+KfTsPoL+7H6uXrsXSF97ChZ/7BNLSjz2Gb8sHW1HXUo3X/7EUt17yDXz5E1/F1y77Fl5/bumw69U2V+PNl97GgT1t2Ll5N1YvXYu6GbUAgLdfWQnvgBeXXnvR1D3JQwKaH/6ID2W5GVP+WEQUX7YcdEFll25C4KSNOCeFgOzqgFj/nt6l6EZpmgG1rhF+zYfd7m0Ii8To1n7hsZfwz7/9C6HgR/VefuOluOqWzx7zNl73IG656HZk2bMQCUVwxZcvQ35RHpa/+m9sXLMJN/3HDTjv8nMARCdk/Ne3f4ODe9sBAM1zG/HdX90JIQS+ffXduPHOa7Hw/FOm9kkeUpxehtKMSizZ1IkQ+3WJUsqJNbkozk5n8ItzHMMX76SE2LZJ7yp0JXdsheZ2IW3e/OhkDtc2+LT4X3S6oCQfzfMacfLZ85Fly8SG1R/in3/7F+y5dlzwufOPepugP9p963V5ccePb4sFtpPPmY/vXf8DvPDXl2OBL68wFz977Ec42NoBo8mA0qoSqKqKx3/3NEoqi7Hw/FOwfeNOPPnAM3D2ObFg0Um49varj9udPF6OYB/KMqvQUGzFloOuSb9/Iopf29pdKOa+2nEvcfrIUpCUEmLXdiBw9DFcKaWzHeLfb8EgFDRlz0a2eWrHpU3UqqVr8ef/+ituvvtLOPeys3Dy2fNxyz1fxpkXnY6///Ef8LiOPqPVZDEDAAxGA045Z0HsuKqqWHjeyXD0ONHX1R87bjQaUd1YifKaMqiqivb9nVj6wpu44VvXwOv24r//438w/8wT8M2ffB2b3tuCF//68pQ835AIYjDsRQlP+kQpxx/SsJc7cMQ9Br44JaUA/D7Ivbv1LiV+eNwQy14D/H7U2ZpRnF6md0XHtPT5N1HdWHnEhImTzpiHYCCEfTv3H/V2WbZMmMwmWO1ZUA3Dfz1tOdFJEYOeY7duPvG7p3H6Jxeipqka61d9iCxbJj59w6VomFWHS6+9CO+8sWaCz+zYHMFeWEwGpJt5WiFKNbu73IhokqEvjvHMHKcURYXYshEQHA81TCQC8dYbEH29KMusQnVWA5Q43M7b5XBDaEf+7LSIBgBHvQyItuRVNVTCPeBBJBwZdpmzbwAAYMu2HvW2697ZgJ2bduPq2z536PpOZOfZY5fn5GfD0esc83MZraHlcxqLOVuXKNWENYmdnW69y6DjYOCLQ1IIiP4+yJ5uvUuJW2LtvyH27kauJR+N9lkwKia9SxqmuLII+3YdQOeBrmHHVy1dC0VVUFlXDgDo6+pH+/7OYddZeN7JEJrAisXvxI6FgmG888ZqlFWXIqcg54jHi4QjeOL+v+PyGz8F+6GWQHuOHd0He2Ihs2Nf57AAONkiMgxv2I0idusSpaR9fV7usxvHOGkjDimqCi2Jd9SYLGLrJsDlQubcE9CSPQe73NsQ0Hx6lwUAuPSai7BxzSb8+Gs/xyc/ex6s9iyse2cDNq7ZhHM+tSgW2v7404exbf0OPPXOX2K3Pe/ys/HWyyvwl18/js4DXcgvysPK11ehr7sfdx1jh47Fzy4BAFx01Sdix+adNgeP/eZxPPDjB9E4qx4vPPYSzvnUoil81tFu3cosG6xpRngCkZFvQERJQ0pg68EBzK/L17sUOgouyxJnpBCQfT0Q763Wu5TEYc+BYeEZkKqCVvdOuMJT1205Fru3tuL/HnkR+3cdgMflRWFJAc686HR86tqLYDAaAAA/uf0XRwQ+AHA53Xj6989i3TsbEAwEUVVfic9+5XLMPWX2EY/jcrjw7avvxtd+eCtOOmPesMs2rtmEx3/3NAYcLpx0+jx8+T9uGNU+vuNlUIyYm7sAnQN+rNvrmLLHIaL4dVpjAbIzzVymJc4w8MWhyMo3ATeXthgTsxnqmedCsaTh4OA+9AQ6R74NTYl6WwsyDDa8vpE/A6JUlJ1hwhnNRXqXQR/DMXxxRAoB0dnOsDceoVB0Bu+AExVZNajMqovLyRypwBHshclgQE6mWe9SiEgHA74w2h0+brkWZxj44omiQOzcpncVCU1btRziwD7kWwrRYJsBg8JhqtNtIOSAkAL1xUefTUxEyW97hwtg3osrDHxxQgoB2d4GeD16l5LwxKb10DZvRJbJipbsObAYOGt0OgkpMBByIM86dWMFiSi++UMaWrkYc1xh80e8UMDWvcl0YC80jxumU05Di30O9ni2wxNmV/l0cQb7kGvJR77Vgj5PUO9yaBJ1te3D6//3VxzcuwueAQdMZguKy6tw9qVXYeZJp8Wu9/Qf/wvvr3jjiNsXlFbg7l8/NuLj/PSOa+DsO3JpqoXnXYrPfeXOj+o5uA/P/fm36Ni/BwUlFfjMF29HdePMYbdZ/q9/YO1bi/Gd/3oYBoNhDM+WJmJPtwc1hVkwcHRNXGDgiwNSCMi2/YA/PpYUSRrOfoi3l0A98xw02GbgwGAr+gJc23A6uEJOaFJDfbGVgS/JOPu6EfT7Mf/MT8Kek4dQKIBN767Eo7/6AT73lTux8LxLY9c1mky46ubvDLt9WkbmqB+rtKoeZ1/yuWHHCkoqYl8LoeGx3/4IGZlWXHrNrdiybhX+8uv7cM9v/xZ7HI/LiTeefxw3fPM+hr1pFtYE9vd6UV2YxRm7cYCBL06I3dv1LiE5BQIQy16H4bSzUGWvQ7ohA22De/WuKulJSAwE+5GTyfW4kk3LCaeg5YRThh0744LL8dvvfxXLX31uWOBTVQNOOvMTH7+LUbPn5h339r2d7ejtaMP/u/8p5OQXYf6iT+C+W67Avl1b0Tw3uhf1q888gtrmOWiaM3/cddD4tfZEAx/pj2P4dCaFgNzXCgQCepeSvISA9u+3INoPoCCtGPW2GVAV/qU/1RzBPhhUFSXceSPpqaoB2XkFCAx6j7hMCA0B37H3fx5JJBJGMOA/6mXhULT1OD0zOkHIbEmDyWyOHT+4dyfW/XsZPn39V8f9+DQxgbCGg/2csRsP2MKnNykh9uzQu4qUIDZ8AMXlgq1lJlrsc7DLvRUhwe7GqeIOuxAREdQWZqFz4Ogf2JS4ggE/wqEQAv5BbPlgFbZveBdzF54z7DrhUBD3fvkyhIIBpGdaccJp5+DSa26BJW10fwTs3rIB99x4MYQQyMkvwqKLP4tFF302dnlBSTnSMjLxxnN/xRkXXoGNa95GwO9DWXUDAOCFv/4eZ1xwOfKLyybvidOY7en2oCIvQ+8yUh4Dn46kEJAH9gKhkN6lpAy5dzc0jxvmBaeiJXsu9ri3wxvhht9TQ8IZ7ENeRqHehdAUePmJP2H1slcAAIqiYvbJZ+CKL94Ru9yWnYezP3U1yqsbIKXA9o3vYdWSl9BxoBVf+8FvRhxPV1JZi5qmWSgsrcCgx433V7yOf/7tD3A7+3HpNbcAACxp6fjsl7+FZx/6FZa/+hxUVcUlX7gZuQVFWPfOMvR3tePm7/5s6l4EGpXBYARdLj+K7Okcy6cj7rShIykltLfe4GQNPaRnQD3jHCgmE/Z796A/2KN3RUkpy2hDU/YsfLjfgQP9fJ8nk+72A3A5euF29mPDmrdhNJrw2S9/E9bs3GPeZumLT2LxM4/iujvuxQmnnTumx5NS4uFf3INdW9bh3v99Etl5BbHLfF4PejvbkFtQDGt2LkLBAH7x7S/igs/dgAVnXYglzz+B91e+AYslDRdc+UXMXnDGuJ83jQ9339Afx/DpRAoB2dnOsKcXvw9i2WLIQQ+qrfUoy6jSu6Kk5I24ERYhVBVw0HayKSqrROPskzB/0Sfxle/+DMGAH4/86v8dd921sy7+HBRFxc7N68b8eIqiYNHFn4XQNOzZumHYZRlZVlQ1zIiFzWX/fBpWezYWnHUh3n17MVYvexlX3fwdnHnRZ/H4//4EfV3tY358mpgBXxh9ngDH8umIgU8niqpCtO7Wu4zUJgTE8mUQnR0oSi9FnbUZKn8lJp0j2AdrupGvbJKbc8oitO3Zgd7Og8e8jslsQabVBv84F5gfatXzDR779o7eLiz/1z/w6Ru+DlVVsX7VW1h43qVomHUCTjnnIlQ1zMD6VW+N6/FpYnZ3edilqyOeg3UghYB09AMup96lEACxbi3Eru2wm3PQnD0bJpV7wE4mR7APqqJyaYYkNzQz9ngzcgN+HwY9LmTa7ON6DEdPJwAg05p9zOu8/MSfMPOkhahtng0AcDv7YcvJi11uy8mDy9k3rsenienzBOHyhdjKpxMGPh0oqgqxZ6feZdBh5K7t0N5fgzQ1HS3Zc5FhZDiZLL6IF0EtgMr80S+4S/HLc5Q/VLVIBB+sXAKT2YKi8qpDs3ePHK6y5PnHIaVE89yTh922u/0A3M7+2DGf1w0htCMeY9lLf4fBaEL9zHlHrW33lvXYtuHd2KQOALDac9DTcSD2fU/HAVjtxx5nSFOLrXz64SzdaSalBHw+yJ4uvUuhj+vpgrZiGYynn40m+yzs8+yCM9Q/8u1oRI5gL4rTy2FUgYjQuxqaiOf+/FsE/D7UNs+BPTcPngEn1r2zDD0dB3DZdbfBkpYOR28XfnPPrThh4bkoLIvujLFj4/vYtmEtmucuGLYFm8vZh1/e9SXMX/RJfOGr3wMAbP5gFZa+8CTmnLIIeQXF8Hk9WLfqTXS17cXFV98E21Emhgih4cW//QFnX3oVcvI/mhww55RFeOWph5BlzYazrxudB/bi2q9/f4pfJTqWzgE/BoMRZJgNUBj8phUDnw5EK1v34tagF9qbr0E981zU2prQ4WtDp69N76oSniPYh5KMCtQV2bCjk8vgJLJ5C8/G2rcWY/XSlzDodcOSloHymgZc8oWbMWt+NMilZ2RhxgmnYufmD/D+yjcghIb8ojJcfPVNOPvSq6Cqx+9cKqmoRVFZFdb9eym8bheMRiNKq+pwwzfvw9xTzzrqbVYvfQU+rwfnXvb5YccXnv8pOHo6sfzV52C2pOHzt/0HiiuqJ+W1oPHZ3eXGnMocvctIOVyWZZrJcAja0sWAYDNHvFMXnAa1sAiOYB/2eXZDgj+ziZiRPQ9SM+PNLWzdJkplqgKcN6sEZqPKVr5pxDF800hKCbl3D8NeghDvrYK2ZydyzHloyp4Fo2LSu6SE5gj2It1sgNnI0w5RKhMyuvsGTS+eeaeTlBD7W/WugsZAbt8Cbf17yDBkYEbOXKQbOPFgvBzBPiiKgoZiq96lEJHO9vcNQhPsYJxODHzTRAoB2baf26glos52aCvfhkGoaM6ejWwzZ/iNR0gEMRj2oiR7dPuoElHy0oREu9PHJVqmEQPfNFFUla17iczjglj2OhAIoM7WjKJ0bsY+Ho5gLywmA9JMPPUQpbq2Ph+XaJlGPOtOAyklpGsA8HB2YkKLhCDefB2yvw/lmVWozqqHAp6sxsIZii5421hi07kSItLbgC8EbyB83O34aPIw8E0TcWCv3iXQJNHWrITYtwe5lgI02mfBqHB1o9EKizC8YTeK7OzWJaLoWD6aHgx800EIyI5j7y9JiUds+RDapg3INGaiJXsu0gwMMKPlCPbBbFSRlcagTJTq2h0+sH1vejDwTTE5FPYiEb1LocnWtg/a6hUwwoDm7DmwmbL1righDO1e0lDMbl2iVBeKCHQP+Dl5Yxow8E0xRVUh2vbrXQZNFacT4s03oITCqLe1oDCtRO+K4p4mI3CHB1Bot+hdChHFgQP9nLwxHRj4ppCUEnJwEHByP9akFgpCLF0MDDhRkVWDysxagJM5jssR7IXJYIA9g4tZE6W6XncAgbCmdxlJj4FvinGyRurQVi2HaNuP/LQiNNpmwKAY9C4pbg2EHBBSoJHdukQE4EDfIGfrTjEGvikm2w/oXQJNI/HhOmhbP0SWyYqW7LmwqGl6lxSXhBRwhZzIs5n1LoWI4sDB/kHuqzvFGPimiBQCsqcLCAb1LoWm275WaGvegVkxoSV7DqwmtmIdjSPYC6NqQL6VY/mIUp0vpKHPE2Ar3xRi4JsiiqpCHtindxmkF0cftLeWQIloaLDNRL6lSO+K4o4rNABNaqgv4t66RBTt1mUr39Rh4JsiMhiE7O3WuwzSU8APsew1SI8LVdY6lGdW611RXJEQGAj2IyeL3bpEBHQN+BHRhN5lJC0GvikghYA8uB9g0zQJAbHyLYiOgyhMK0G9rQUqJ3PEOIJ9MKgqSrK5cDVRqhMSONjv45p8U4SBbwooqgrBnTXoMGL9exA7tsJmykazfTbMKsetAYA77EJERFBbmKV3KUQUBw70D3JNvinCwDcFpN8PuF16l0FxRu7ZCe391UhTLWjJnotMI8euARLOYB/smVyPj4gAtz8Mjz/MyRtTgIFvkkkhIDvZukfH0NMNbfkyGDSBJvtM5FoK9K5Id45gH1RFRWVeht6lEFEc6Bzwc3/dKcDAN8kUVYXo7NC7DIpnvkFoSxdDDg6ixtqA0oxKvSvSlTfiRliEUFXAbl0iik7eYLfu5GPgm2QyGAAGHHqXQfFOCIjlSyG6O1GcXoY6axPUFP51dAT7YE03pvArQERD3P4wAiFutTbZeH6dRFIIyI52vcugBCLeXwOxawfs5lw0Zc+GSU3NJUqGunWrOXmDiBBt5eNs3cnFwDeJFFWF6GLgo7GRu7ZBW/cu0tV0tGTPRYYx9UKPL+JFUAugMj9T71KIKA50uditO9kY+CaRDIUAR7/eZVAi6uqAtvJNGAXQZJ+FbHOe3hVNO0ewF5kWIww8KxGlvH5vkIswTzKeWidJdHYuW/doArweaMteA/x+1NmaUJxerndF08oR7IeiKKgr4t7DRKlOSqDbFWC37iRi4JskiqpCsjuXJioSgXjrDYjeHpRlVqImqxFKivyaBjQf/BEfynO5PAsRAd3s1p1UqfFJMg1kOAzZ36d3GZQkxLvvQNuzCzmWPDTZZ8GopMbCxI5gL9LNBpiNPDURpboetvBNKp5VJ4EUArKrg3vn0qSS2zdD2/gBMgwZaMmei3RD8rd8OYJ9UBQF9cXchYQo1UWERL8nyF03JgkD3ySIdudysWWaAu1t0FYthwkqmrNnw27O0buiKRUSQQyGvSjNTte7FCKKA10Dfr1LSBoMfJNACgHZ36t3GZSsXAPQ3nwdSjCEOmszitJL9a5oSjmCvbCYDEgz8fRElOq6XQEoHMc3KXhGnSApJaTTAWhcFZymUCgUncHrdKA8sxpVWXVQkJwnQWcourRRYwln6xKlukBYg9sXYrfuJGDgmzAJ2dutdxGUIrTVKyD2tyLPUogG+0wYFKPeJU26sAjBG/GgyM5uXSICOgf8YNybOAa+CVIUlYGPppXYvBHa5g3IMmahJXsO0gzJF4wcwV6YjSqyLMkXaIlobLpcAS7PMgkY+CZIhkKA26V3GZRqDuyDtnolTDCiOXsOrCa73hVNKmcw2q3bwG5dopTn8YfhD3HY1EQx8E2AFIKte6QfpwPirSVQQmE02GagIK1Y74omjSYjcIcHUGiz6F0KEcWBXncAQrBjdyIY+CZCUSD7evSuglJZMACxdDHgcqEyqxYVmTV6VzRpHME+mIwG2DNSY9FpIjq2fm8Qqspu3Ylg4JsARVG4uwbFBe2dtyAOHkBBWjEabDNgUAx6lzRhAyEHhBRoLGa3LlGq6/cE9S4h4THwTYD0+wC/T+8yiAAAYuMHENs2wWqyoTl7Dixqmt4lTYiQGlwhJ/KsZr1LISKdBcIa/KGI3mUkNAa+cZJCsDuX4o7cuwfa2lUwK2Y0Z89BlimxW8ccwV4YDQbkWzmWjyjV9XmC3Ft3Ahj4xklRVcg+7q5Bcai/F+KtpVAjGhptM5FnKdS7onFzhQagSQ31RdxblyjV9XuCSbrc/PRg4JsAjt+juBXwQSx7DdLrRrW1HmUZVXpXNC4SAgPBfuRksVuXKNU5vEFuszYBDHzjJH2DQDCgdxlExyYExIo3ITrbUZReinpbM1Ql8X7lHcE+GFQVxdnJt8A0EY2eL6QhGOZ6fOOVeGf/OCCFgHT0610G0aiIde9C7NwGmykHzfY5MKuJNR7OHXYhIiKoK8zSuxQi0hnH8Y0fA994KAqky6l3FUSjJnfvgPb+GlhUC1qy5yDTmEjhScIZ7IM9k+vxEaU652CI4/jGiYFvHBRFgRwY0LsMorHp6YJY8SYMGtBon4VcS77eFY2aI9QHVVFRkZehdylEpKOBQY7jGy8GvnGQQgDuAb3LIBq7QS+0N18DfD7UWBtRmlGhd0Wj4g27ERYhVBckUsskEU02lz/MLdbGiYFvPAa9gBB6V0E0PpEIxNtLILq7UJxejlprE5QEOBU4gn2wphv1LoOIdCQl4PKHIDmOb8zi/ywfZ6QQkE5O2KDEJ95fDbFnJ7LNuWjOng2TGt9LnziC0W7dGk7eIEppDm8IzHtjx8A3Vhy/R0lE7tgKbf17SFPT0ZI9BxmGTL1LOiZfxIugFkRVfvzWSERTb2AwBFXlOL6xYuAbI4UzdCnZdLZD/PstGISCpuzZyDbn6l3RMTmCvciwGKDyzEWUspyDQb1LSEg8bY6RFBrgcetdBtHk8rghlr0GBAKoszWjOL1M74qOaqhbl1utEaWuQFhwAeZxYOAbAykl4HKBgwcoKUUiEG++Dtnfi7LMKlRn1UOJsxWvApoP/ogP5bns1iVKZW5/mBM3xoiBbyykhBxw6F0F0ZTS1vwbYu9u5FoK0GifBaMSXwseO4K9SDcbYGS/LlHK8vjDbHsZI54xx0BRVUjXgN5lEE05sXUTtA/XIdOYiZbsOUgzxM+Cx85gPxRFQWMJu3WJUpU3EAHXXx4bBr4xkgOcsEEp4uABaO+sgAkGtGTPhs2Uo3dFAICgCGAw4kVpTrrepRCRTryBMHfcGCMGvjGQQkQXXSZKFS4ntDdfB4Ih1NuaUZhWondFAABHoBcWkwFpRp7CiFKRJxDRu4SEw7PlWPh9eldANP1CoegM3gEnKrJqUJlVq/tkDmcouvh5Y6lN1zqISB9hTSAc4Y5XY8HAN0pSSkgux0IpTFu1HOLAPuRbitBgmwGDot82Z2ERgjfiQZGd3bpEqcoTCOtdQkJh4BstKdmdSylPbFoPbfNGZJmsaMmeA4shTbdaHMFemI0qMi3cX5coFXn8YQjBqbqjxcA3WooC6WXgI8KBvdBW/xsmGNFinwOrya5LGc7goW5dztYlSkmcqTs2DHyjpCgKJFv4iKKc/RBvL4ESiaDBNgP5aUXTXoImI3CHB1Bo06+VkYj04+FM3TFh4BuLQY/eFRDFj0AAYtnrgNuFqqw6VGTWTHsJjmAfTEYD7BnxtTg0EU09L2fqjgkD3yjJSBgIhfQugyi+CAHt329BtB9AQVox6m0zoCqGaXv4gZADQgo0FHO2LlGqCYQ1aIIzdUeLgW+0OH6P6JjEhg8gtm2GzWRDi30OzKpleh5XanCFnMi3mqfl8YgovrCVb/QY+EZBCgHpZXcu0fHIvbuhvbsaZtWMluy5yDJOz2QKR7APRoMB+dbpCZlEFD88/jAEN9UdFQa+UZIcv0c0sr4eiLeXQo0INNpnIc9SMOUP6Qo5IaSG+iLO1iVKNdxxY/QY+EZBUVV26RKNlt8HsWwx5KAH1dYGlGVUTenDSQg4g/3IyWK3LlGq8QbCUDlTd1QY+EaJS7IQjYEQEMuXQXR2oCi9FHXWZqhTeLpxBPthUFUUZ3PnDaJUwjF8o8fAN1rcR5dozMS6tRC7tsNuzkFz9myY1KlphfOEBxAREdQVZk3J/RNRfAqGNb1LSBgMfKMgNQ2I8K8IovGQu7ZDe38N0tR0tGTPRYZx8kOZhIQz2Mf1+IhSTERIbq82Sgx8oxEK6l0BUWLr6YK2YhmMGtBkn4Ucc96kP4Qj1AdVVVGRmzHp901E8SsU4Vp8o8HANxqBgN4VECW+QS+0N18D/H7U2ppQklExqXfvDbsRFmFUs1uXKKUEI+zWHQ0GvhFIKSEDfr3LIEoOkQjEW29A9HajNKMCNdZGKJN4GnIEe2FNN07a/RFR/POHNEiuxTciBr6RSAkE2cJHNJnEu6ug7dmJHHMemrJnwahMztg7R7APqqKipoCtfESpIhTRwLw3Mga+UZAMfESTTm7fAm39e8gwZGBG9lykGzInfJ++iBchLYiqgonfFxElhiDH8I0KA98IFFUFgpy0QTQlOtuhrXwbBqmiOXs27ObcCd9lf7AXGRYjVJ7diFJCMCzAtZdHxlPiaLCFj2jqeFwQy16HEgigztqEovSyCd1dtFtXQV0ht1ojSgXBiAaFiW9EDHyjIDlLl2hqRULQ3nwdcPSjPLMKVVn1UDC+E3hA8yEQ8aM8j8uzEKWCUJhduqPBwDca7NIlmhbampUQ+/Ygz1KARvtMGJXxzbjtD/Yiw2yEkf26REmPy7KMDs+GI5BScuFlomkktnwIbdMGZBqz0JI9F2mGse+P6wz2QVEUNJSwW5co2XF7tdFh4BtJOAzO9yaaZm37oK1eASMMaM6eA5spe0w3D4oAfBEvynLGHhaJKLGENQnBz+kRMfCNhK17RPpwOiHefANKKIx6WwsK0orHdPP+QC8sJgPSjDzNESW7MJdmGRHPhCOJRPSugCh1hYIQSxcDrgFUZtWiMrMWGOVkDmeoHwDQUGKbwgKJKB5wLb6RMfCNQGoMfER60955G6JtP/LTitBomwGDYhjxNmERgjfiQXE2u3WJkl0gzO3VRsLANxK28BHFBfHhOmhbP0SWyYqW7LmwqGkj3sYR7IXZqCLTwv11iZJZMKyBce/4GPiOQ0oJaJz9QxQ39rVCW/sOTIoJLdlzYDUdv7t2IBjt1m3kbF2ipCaEBBPf8THwHQ8DH1H86e+DeGsJlIiGBttM5FuKjnnViIzAHXah0DZyayARJS7BsDciBr6RcAwfUfwJ+CGWvQbpcaPKWofyzOpjXtUR7IXRoMKebpq++ohoWnH83sgY+EbCFj6i+CQExMo3IToOojCtBPW2FqhHmcwxEHJAQnK2LlESYwvfyBj4RsLARxTXxPr3IHZshc2UjWb7bJhVy/DLpQZ3yIl8q1mnColoqrGFb2QMfCOQDHxEcU/u2Qnt/dVIUy1oyZ6DTOPwSRr9wT4YDQbkWy3HuAciSmTcaWNkDHzHoygcw0eUKHq6oS1fBoMm0WSfiVxLQewiV8gJITXUFXG2LlEykhKjXZM9ZTHwHYeiKOzSJUokvkFoSxdDDg6ixtqA0oxKAICEgDPoQG4Wu3WJkhFb+EbGwDcSBj6ixCIExPKlEN2dKE4vQ621CSpUOIJ9MKgqd94gSkJSsoFvJAx8I2HgI0pI4v01ELt3INuci6bs2QhoPmgigtrCLL1LI6JJxha+kTHwjYhvIqJEJXdug7buXaSr6WjOno2AFkB2BtfjI0o2zHsjY+AbicKXiCihdXVA+/ebMAgFmaYsqKqK8twMvasiokkkpIyOu6djYpoZCd9ARInP44FY9hqEbxAA0FBiQ3YmJ3AQJQs28I2MgW8kDHxEySESgXjrDQinA+kmFWc0FeL8WcVoKbNz2zWiBMcxfCMz6l1A3GPgI0oqYtXy6P/VtbBUVqOmwIa6Iit8wQjaHT50OH3wBLj+JlEi4U4bI2PgGwkDH1Fy2tcKbV8roKqQ1XVIr6hCXbEVDSU2eAPhQ+HPj8Egwx9RvONeuiNj4BsJJ20QJTchIFt3QWvdFQ1/dY3IKKtEQ4kNTaV2uP0htDv86HD64A9xmSaieMQWvpEpkq/ScWkfrods26d3GUQ03YxGKPVNUErLoaSlQ1EUDAyG0O7woXPAh0BY6F0hER1SZE/Dgrp8vcuIawx8xyGlhNi8EfLAXr1LISI9mc1QG5qB4lIoljQAgDMW/vwIRRj+iPRUlpuBE6pz9S4jrjHwHYeUEmLrh5D7WvUuhYjiRVo61PqmaPgzR5d26fcG0e7woWvAj7DGUyrRdKvKz8SsimyuxXccDHzHIaWE2LYJcu8evUshoniUkRlt+SsshmIyQQLocwfQ7vSje8CPCEeSE02LuiIrmkptUBn4jomTNo5HSk7aIKJj8w1CbPwg+rXVCrW+GfkFRSi050IIiR53AO0OH3rcAWgMf0RTxqgq0dWXmfeOiYFvJPxrgYhGw+OBWP8eAEDYc6DWN6EwvwDF2XnQhETXQHSmb687wCUkiCaZ0cDP6pEw8I1AUVVu2UJEY+NyQnywBgAgcvOh1jWiJDcfZbkZiGgCnQN+dDj96HMHeH4hmgQGVWXr3ggY+I5LAiZuuUREE+Dog3D0AQBEYREMtQ0oy85FRV4mwppAhzO6wHO/J6hzoUSJy2hQmPdGwMB3XApg4gbrRDRJerqh9XQDAERxKYw19ajIzUFVfhZCEQ0dDj/anT44B0M6F0qUWIwGlTN0R8DAdzyKwhY+IpoaXR3QujoAAKKsAqbqOlTm21FdmIVAWIuu8ef0YcAX1rlQovhn4hi+ETHwHYeiKIDZoncZRJTs2tugtbcBAERlDSxVNagpsKGuyAp/KIJ2hw/tTj88foY/oqMxqlxRYyQMfCMxs0uXiKbRgb3QDu3uI2vqkFZZjdpCK+qLbRgMRNDu9KHD6YM3ENG5UKL4wVm6I2PgGwnH8BGRTuTePdD27gFUFbK2AelllagvtqKxxAaPP4x2RzT8+UKa3qUS6YotfCPjThsjkEJAW/xPvcsgIooyGqHUNUIpLYeSngFFUeDyhQ6FPz8CYYY/Sj0XzyuDqrKV73gY+EYh8tpLgMaTKBHFGbMZal0TUFIGJS0NiqLA6Q2i3elDp9OPYEToXSHRlFMAXHJiud5lxD0GvlGILHsNCPj1LoOI6NgsaVAbmoDiUiiHJps5vKFY+AtrDH+UnEwGBRfMLdO7jLjHwDcKkZVvAm6X3mUQEY1OegbUhmagqBjKoXHIfZ4g2h0+dLn8iGg87VPySDMZcP7sEr3LiHuctDEKisnE7Y+IKHH4fRAfrot+nZkFtaEZeYXFyK/KwRzkoNcVQLvTh25XABo39qUEl2Yy6F1CQmDgGw3O1CWiRDXohdjwfvRrmx1qfTMK8gtRlJ0HTUj0uPxod/rR4/KD2Y8SUbqZgW80GPhGIKVk4COi5OB2QaxbCwAQOblQ65pQlJePkpwMaEKgc8CPDqcffe4Awx8ljHSzAVJKbq02Aga+kUgJJS2NXbpElFycDoj3VwMARH4h1LoGlObkoTw3ExFNoMPpR4fTh35PkOc/imvpZiOkjO6GSsfGwDcaGZl6V0BENHX6eiD6egAAorAYhtoGlOfkoDI/E6GIQMehmb793qDOhRIdKcNsYNgbBQa+ESiqysBHRKmjpwtaTxcAQJSUwVRTj8q8bFQXZCEY1tDhjO7rOzAY0rlQoqgMi5HduaPAwDcaGRl6V0BENP0626F1tgMAREUVzFW1qMq3oabQikAognanHx0OH1z+sM6FUirjpI3RYeAbDUtadHAAlywkolTVth9a234AgKiuhaWyBjUFVtQVWeELRmL7+noCEZ0LpVRiVBUYDdxHdzS48PIoRd58HfD79C6DiCh+qCqUmjqo5VWQmVlQFQXeQDi2r+9gkOGPppY1zYizZhTrXUZCYOAbJW3NSsj+Pr3LICKKT6oKpa4RSlklkJEBVVHg9ofQ7ojO9vWHuB85Tb5CWxpOrs/Xu4yEwMA3StrGDyAPHtC7DCKi+Gc0QqlvglJaDiUtHYqiYGAwhHaHD50DPgTC3NeXJkdVfiZmVWRz0sYoMPCNghQCcvcOiF3b9S6FiCixmM3RfX2LS6FY0gAAzlj48yMUYfij8Wsps6OmIAuqysA3Ega+UZBCQHYchNj4gd6lEBElrrT0aPgrKoFiju5g1O8Not3hQ9eAH2GNH0c0NidU56IkJx0qW/hGxMA3StLRD231Cr3LICJKDhmZ0fBXWAzFZIIE0OcOoN3pR/eAHxHu7UajcEZTIbIzuf3paHBZltHiWnxERJPHN/hRr4nVBrW+GfkFhSi050IIiR53AO0OH3rcAWgMf3QMXINv9Bj4Rotr8RERTQ2PG2L9uwAAYc+BWt+EwvwCFGfnQRMSXQPRmb697gCY/WiIqgAWEwPfaDHwjZKiKNEt1ga9epdCRJS8XE6ID9YAAERuPtT6RpTk5KMsNwMRTaBzwI8Opx997gCY/VJbGlv3xoSBbwyULCskAx8R0fRw9EG8G13/VBQWwVDbgLLsXFTkZSKsCXQ4ows893uCOhdKerCmmfQuIaEw8I2SFAKw2oDuTr1LISJKPT3d0Hq6AQCiuBTG2npU5OagKj8LoYiGDocf7U4fnIMhnQul6WLPMENIyRm6o8TANwaK1cYuBCIivXV1QOvqAACIskqYqmtRmW9HdWEWAmEtusaf04cBX1jnQmkq2dJNYNQbPQa+UVJUFbBl610GEREdrv0AtPboLkiisgaWqhrUFNhQV2SFPxRBu8OHdqcfHj/DX7LJzjRzh40xYOAbi8xMQFUBwZXhiYjizoG90A7sBVQVsqoWaZVVqC20or7YhsFABO1OHzqcPngDEb0rpQkyGVSkcYbumHDh5TGKrFgGeNx6l0FERKOhqlBqG6CUV0YXe1YUePxhtDui4c8X0vSukMYh32rBqQ0FepeRUBj4xkjb8D5ke5veZRAR0VgZjVDqGqGUlkNJz4CiKHD5QofCnx+BMMNfoqgtzEJLmZ1dumPAwDcGUgjIfa0Q2zbpXQoREU2E2Qy1rgkoLYNiSYOiKHB6g2h3+tDp9CMY4dCdeMY9dMeOgW8MpJSQjn6INSv1LoWIiCaLJQ1qQxNQXArFbAEAOLzRlr/OAT/CGsNfvDlnRjEy0zgNYSwY+MZIRsLQXn9F7zKIiGgqpGdAbWgGikqgmKIL+/Z6guhw+NDl8iOi8SNTbwZVwYVzS9mdO0YMfOMQefN1wO/TuwwiIppKWdZoy19BMRSjERJAryuAdqcP3a4ANG7sq4vsTDPOaCrUu4yEw/bQcVBsdkgGPiKi5Ob1QKx/P/q1zQ61vhkF+YUoys6DJiS6XdF9fXtcfjD7TR97uglSSrbwjRFb+MZICgG5ZyfEzm16l0JERHrIyYNa3wjk5kM1GqEJgc4BPzocfvR6AuCn6tSaU5mD8twMqCoD31iwhW+sFAXIzde7CiIi0ouzH+K91QAAkV8Ita4BpTl5KM/NREQT6HD60eH0od8T5HacUyA7w8SwNw4MfGOkKAqQkxMNfvwzjogotfX1QPT1AABEYTEMdQ0oz8lBZX4mQhGBjkO7ezi8IZ0LTQ4KgKx0k95lJCQGvnFQVANgzwEGHHqXQkRE8aKnC1pPFwBAlJbDVF2HyrxsVBdkIRjWDm3t5sfAIMPfeGWlm7j23jhxDN84SCkhtm+BbN2ldylERBTvKqphqKqBtNqgqir8oQg6nH60O3xw+8N6V5dQynMzMK86V+8yEhID3zhIKSF7u2NjOIiIiEZDqa6DWlkNmWmFqirwBSM46PCh0+mDJxDRu7y4N7cqB2W5GWzlGwcGvnHiAsxERDRuqgqlph5qeRVkZiZURYE3EI7t6zsYZPg7mk/MLoHFZNC7jITEwDcBkRXLAI9b7zKIiCiRGY1QahqglFUAGdHWK7c/FAt//pCmd4VxIdNixDkzi/UuI2Ex8I2TlBJiy0bI/Xv1LoWIiJKF0QilvglqaTmQlg5FUTAwOLSvrw+BcOru61tdkImZ5dlccHmcGPjGSQoB2dUBsf49vUshIqJkZLZEt3YrLoViSQMAOGPhz49QJLXC3/zaPBTZ0xj4xomBbwJkMAht6at6l0FERMkuLR1qQzNQVALFbAYA9HuCaHf60DXgR1hL7o9yBcAFc0thNKh6l5KwGPgmKPLWG4BvUO8yiIgoVWRkQm1sBgqKoZhMkAD63AG0O/3oHvAjkoQb+2ZnmHFGc6HeZSQ0Lrw8AVJKKLl5kAx8REQ0XXyDEBs+iH5ttUFtaEZ+fiEK7bkQQqLb5UeH049uVwAiSdp08m0WCCm5HMsEsIVvAqQQkO1tEB+u07sUIiJKdfac6Ji/vAKoRiM0IdE1EN3Xt9cdQCI3/C1sKEBulpnj9yaALXwToKgqUMgp4kREFAdcToj31wAARF4+1LpGlOTkoyw3AxFNoHMg2vLX5w4gkbKfQVWQw7A3YQx8E6RYLIAtG3AP6F0KERFRVH8fRH8fAEAUFsNQW4+y7FxU5GUirAl0HNrXt98T1LnQkeVmmtmVOwnYpTtBUgiI3Tsgd23XuxQiIqLjKymDoaYO0p4DVVURimjocPjR7vTBORjSu7qjaimzo6Ywi6Fvghj4JkhKCXhc0Fa+pXcpREREo1dWCUN1HaTNBlVVEQhr0TX+nD4M+MJ6VxezqKUItnST3mUkPAa+SRJZuhgIBvQug4iIaOwqa2CoqoHMskFVFfhDEbQ7fGh3+uHx6xf+zEYVn5xTqtvjJxMGvkkgpYTYtB6ybb/epRAREY2fqkKpqoVaWQ2ZkQVVVTAYCKPdGZ3t6w1EprWckpx0nFSTN62PmawY+CaBFAKypxvigzV6l0JERDQ5VBVKbQOU8sroYs+KAo8/jHaHDx1OH3whbcpLmFOZjfLcTKgqx+9NFAPfJJGaBu2NVwCRWnsbEhFRCjAaodQ1QimtgJKeDkVR4PKFDoU/PwLhqQl/588qQZrZMCX3nWoY+CaR9u4qyN5uvcsgIiKaOmYz1PomoKQMiiUNiqLA6Y3u69vp9CMYmZyGj+wME85oLpqU+yIGvkkjhYA8sA9iy0a9SyEiIpoelrTo7h7FpVDMFgCAwxtt+esc8COsjT/8cTmWycXAN4lkwA9t2Wt6l0FERDT90jOgNjYDhSVQTNFlVHo9QXQ4fOhy+RHRxhY3zptVjHQz94eYLAx8kyyyYhngcetdBhERkX6yrNGWv4JiKEYjJIAeVwAdTh+6XQFoI2zsa0834cwWdudOJga+SSSlgNi5HXL3Dr1LISIiig/27OiYv7xCqCYjNCHR7Yru69vj8uNo2a+51IbaIiu7cycRA98kklICg4PQli/RuxQiIqL4k5MHtb4RyM2HajRCEwKdA350OPzo9QQwlEjOnVmMDAu7cycTA98UiKx8E3C79C6DiIgofuUXQq1riIZAgwERTaDD6ceAL4Q5lTl6V5d0GPgmmRQCct8eiG2b9S6FiIgoMRSVwFBbD2nPhWpQIaRkd+4kY+CbAjIYhLb0Vb3LICIiSjiG8y+GYrHoXUbSUfUuIBkpFguUvAK9yyAiIkos2TkMe1OEgW8KSCGglFXoXQYREVFCUUsrILlF6ZRg4JsCiqpCKSkDVO7/R0RENCqKAqWsAorKaDIV+KpOEcVohFJUrHcZRERECUHJK4BiNutdRtJi4JsiUggo5ZV6l0FERJQQlNJydudOIQa+KaKoKpT8QsDEv1aIiIiOy2iEUlrO7twpxFd2KikKlNIyvasgIiKKa0pZBcCwN6X46k4xtYzdukRERMejVtXqXULSY+CbQoqiQMnJBTKz9C6FiIgoPuXkQrHaoHBnjSnFwDfFpBD8y4WIiOgY1MoaTtaYBgx8U0xRVSgVVYDBqHcpRERE8cVk4mSNacJXeDoYDFDKufMGERHR4ZSySoBdudOCgW+aqNX1epdAREQUV9RqDnmaLgx800BRFChZWVDy8vUuhYiIKD7k5kHJzOJkjWnCwDdNpBBQquv0LoOIiCguqFW1nKwxjRj4pomiqlCKSoC0dL1LISIi0pfZDKW4lJM1phFf6ekkJdSqGr2rICIi0pVSXsXJGtOMgW8aKaoKpaqG28cQEVHqUhRO1tABk8c0U0xmKCXcX5eIiFKTUlwKJT2DkzWmGQPfNJNSQq3h5A0iIkpNakMTpJR6l5FyGPimmaIoUOw5QHaO3qUQERFNK6WgCIrVztY9HTDw6UAKAbW+Se8yiIiIppVa38SlWHTCwKcDRVWhFpUAVpvepRAREU2PnFwouXlcikUnfNV1IoWA2tCsdxlERETTQq1rZOuejhj4dKKoKpTiUiDLqncpREREUyvLCrWohK17OuIrrycpOZaPiIiSHlv39MfApyNFVaGUlgMZmXqXQkRENDXS06GUVbB1T2d89fXGVj4iIkpiak0DwHX3dMfApzNFVaGUVwDpGXqXQkRENLnMZiiV1WzdiwP8CcQDCah1DXpXQURENKnUmnruHx8n+FOIA4qqQqmoBixpepdCREQ0OSwWKDX13FUjTjDwxRG28hERUbJQG5oBhr24wcAXJxRVhVJVA1gsepdCREQ0MZlZUCprOHYvjvAnEVcUqA0tehdBREQ0IWrTDM7MjTMMfHFEUVUoldXcfYOIiBKXPQdqSRlb9+IMfxrxRkqoLbP1roKIiGhc1JZZ3FUjDjHwxRlFVaEWFkHJL9C7FCIiojFRCgqh5uWzdS8O8ScSh6QQUGfM0bsMIiKiMVFbZkNKtu7FIwa+OKSoKhSrDUp5ld6lEBERjYpSVhH97FIYLeIRfypxSkoJtXkmYDDoXQoREdHxqSrUppmQnJkbt4x6F0BHpygKpNkMpbYBctd2vcuhSfLu7j340h8ePuplT33ja5hbXQkA+OLvH8R7e/YecZ3Tmxrx0K1fHvFx+jwe/PaV17Bi23YMBoKoLSrEzeedjQvmDR8qsG7vPvzs+Zewv68PM8vLcN/nPoPaosJh1/nZ8y9hb28vHr71ptE+TSJKMUplDZCWxl014hgDXxxTFAVqXSO0A/uAYEDvcmgSXXfmaZhVUTHsWGV+3rDvi7Pt+NbFFw47VmAfeckebyCA6+//E/o9Xly36HTkW614fcOH+PbfnsJ/aQKXnjQPAODxB3DHo3/D3KpKXLnwZLz47gf41mNP4IX/+BYMhwZc7+7qxnNr3sWz375jAs+WiJKayQS1sVnvKmgEDHzxTlGgNrZAbFqvdyU0iU6srcEFc4+//E5WWho+Nf+EMd/3s6vW4kBfPx756ldwakM9AODzp52CL/zvH/DfL/0Ln5w7C2ajERv27UcwHMZvb7wWFpMJZzQ34pM//SUO9PWjpjA6S/wXL76Mz516MuqLi8b+JIkoJahNMwGDka17cY5j+OKcoqpQKqoAq03vUmiSDQaCiGjaca8T0TQMBoNjut8P9u5DblZmLOwBgKqquHDeHPR5PHj/UFdxMByG2WiCxWQCANgzMgAAgVAIALBs0xZsa+/A1y88f0yPT0QpJDsHSmU1l2FJAGzhSwRSQp0xB2Ltv/WuhCbJ//v7P+ALhmBQVZxYU427LrsYsyrKh11nX28f5t99H8KahjxrFj536sn46ifPg2mEiTyhSCQW4g6XZo4e23KwHac1NaClvBTeQACPvb0Cn5wzG39b8W9Y09JQXViAUCSC/37pX/j6BZ+IBUEiomEUBYbZJ0S3UGPrXtxj4EsAiqpCyS+ALKuAbG/TuxyaAJPBiE/MmYVFLU3IzszEnu4ePPb2Ctxw/5/w5De+ipbyMgBARV4eTq6vQ0NJMfyhEN7YuAkPLnkT+3v78OsbrjnuY9QUFGDNzt3ocDhRmpsTO/5B6z4AQI/LBQAoy83FnZdciN+88hr++6VXkWYy4T+v/izSzWY8tPQtpJlMuPq0U6bmhSCihKdU1QBWG7tyE4QiOYc6IUgpgUgY2ltLgHBI73JoEu3v7cMVv/pfnFRbc9wZuD989nk8t+bdYbN5j2ZHRyeu+u0DaCkrxfc+fSnyrFl4fcOH+MMbyxCKRPDZU+bjP6/+XOz6fR4P2vudqC7Mhz0jAz0uNy75xa9w/5duwIm11fjvl/6FNzdvRb7Viu9dfilOrKmezKdPRInIYoHh7E8CBgMDX4Jgp3uCUBQFMBihtszSuxSaZFUF+Thn1gy8u3sPtOPsP/nFs88EAKzeteu499dUWoJfXvd5tPX347r7/4iLfvbfeGLlKtx9+aUAgHSzZdj1861WzK2ujHXd/uaVxTi1oR6nNtbjj28sw5pde/DrG67BebNn4msPPwa33z+Rp0tESUCdMQdQVYa9BMIu3QQyNIFDth+A7O/TuxyaRMXZdoQ1Df5QCFlpace8DgC4fCMHrgvmzsY5M1uwo6MTQki0lJfivT2tAIDqwvxj3m7jvgN4Y+MmvPjdOwEAi9dvxK2fOBfzqqswr7oKz65ei+Vbto9r9jARJQclrwBqafnIV6S4wha+BCOlhDrnRIAzopLKwX4HLEYjMszm414HAHIzM0d1n2ajEbMrKzC3uhJmoxGrd+4GACw8bPbu4aSU+NkLL+G6RafH1gTscblRaP9ohnihzYbuQ2MAiSgFqSrU2fO4o0YCYmpIMIqiAOkZUOub9C6FxsHh9R5xbHt7B97asg2nNTVAVVV4AwGEIpFh15FS4sElbwIATm9ujB33h0Jo7e6B0zt43Mfd39uHZ1etxVkzmlF9aI29j3vxvQ/QNeDCreefGzuWZ83C3u5eAEBY03Cgrx/5tpEXfyai5KTUNgAZmezKTUDs0k1AiqIA9Y1Ax0HA69G7HBqD7/ztKaSZTJhXXYXcrCzs6Y7uZJFuMuHOSy8CAGw92I7/ePzvuPjEuajMz0MwHMbSTVuwfu9+XHnqyZhxaCYvAGw60IYv/eFhfO2T5+HrF34idvxT//UbXDB3Nkqys3HQ4cAzq9bCnpGOH37uM0etazAQxP/863V865ILkJn20Ri/T86djT8uWQYhJdbv249gJIxFLfxjgyglpWdAbWhi2EtQDHyJSgKGOSdCW7Vc70poDM6bNROvrNuAvy7/NwYDAeRkZeL82bPw1U+eh6qC6Ni60pwcnFRbjWWbtqDP7YGqKqgtLMQPP/cZXLnw5FE9TlNpCV549330e7zIyczEhfNm4+sXfAJ51qyjXv+PS5ahKNuOyxecNOz41y/4BBzeQfzxjaXIt1rx2xuvQ27W0e+DiJKbOmsuAIa9RMVlWRKctmkD5IG9epdBRERJTCmrgGHefL3LoAngGL4EJqWMLtNiOfqsTiIioglLS4c6ixM1Eh0DXwJTFCU6Y2rWXL1LISKiJKXOO4lr7iUBBr4Ep6gq1OJSKFwTiYiIJplSXQc1rwAKlwJLePwJJgEpJdTZ84C0dL1LISKiZJFlhdoyU+8qaJIw8CWBaNeuIdrsTkRENFGKcmiSBrtxkwUDX5JQVDXa7F5dp3cpRESU4NT6JsBmZ1duEuFPMsmoLbOALO6EQERE42TPgdLQzEkaSYaBLwkZTlzAvXaJiGjsVAMMJ8wHuARL0mEqSDKKqgJZNqjNHGhLRERjozbPjO6Vy0aDpMOfaBJSFAVqTT2UwiK9SyEiogSh5BVAraljV26SYuBLUlJKqHPnAxaL3qUQEVG8s1ignrAAUgq9K6EpwsCXpBRFAYxGqPMW6F0KERHFM0WB4cRTAJMJisJYkKz4k01iiqpCycuHUteodylERBSn1KYZQE4ux+0lOf50k5yiKFCbZkDJy9e7FCIiijNKcSnUukaO20sBDHwpQUI98RRuvUZERB/JzII69yRILsGSEhj4UoCiqIDRCMP8U7k+HxERAQYDDCdFPxPYupca+OmfIhRVBWx2qLNP0LsUIiLSmTr7BCAri+P2Ugh/0ilEURSo5ZVQqmv1LoWIiHSiVFZDLatgy16KYeBLQeqMOUBunt5lEBHRdLPnQJ05l+P2UhADXyqSMjp2g5M4iIhSh8kMw/xTAICteymIgS8FKSoncRARpRRFgXrCAsBs4bi9FMWfeoqKTeKYNU/vUoiIaIqpM+dAyS9g2Eth/MmnMEVRoFZUQams0bsUIiKaIkpNPdSqWnbjpjgGPoI6aw6UXO7EQUSUbJTiUhhmzNa7DIoDDHwEAFAXLASyrHqXQUREkyU7B+oJ8zkjlwAw8BEO7cShqjCcegZn7hIRJYP0DBgWnAZAYVcuAWDgo0MUVY1O2T/ldMBo0rscIiIaL5Pp0LncyEkaFMN3AsUoqgpkZMKwYCGXayEiSkSqGl1yKz2DYY+G4buBhlFUFcjJhTpvvt6lEBHRGKlzTgRy8hj26Ah8R9ARFEWBUlwKlTO7iIgShtrQzD1y6ZgY+OioFEWBWlMPpbZe71KIiGgESnkl1MYWvcugOMbAR8dlaJkNpbRc7zKIiOgYlKISqHNO5PIrdFwMfHRcUkqoc0+CklegdylERPQxSn4B1BNPjn7Nrlw6DgY+Oi5FUaKbbs8/FbBl610OERENycmDOn8hoHCtPRoZAx+NSFGUjxZmttn1LoeIiGzZMJx8GsMejRoDH42KoqqAwQDDqWcy9BER6SnLCsOppwOqyuVXaNT4TqFRY+gjItJZZhYMC88EDNxFg8aG7xYaE4Y+IiKdZGbBsHARYDQx7NGY8R1DYzYs9FltepdDRJT8MjKjLXsmhj0aH75raFxioW8hQx8R0ZRKz4i27JnMDHs0bnzn0LhFQ5+RoY+IaKqkZ8Bw2iLAzLBHE6NILs1NEySFALQItNUrAY9b73KIiJKD1QbDKWewG5cmBQMfTQqGPiKiSZSTG11nTzUw7NGkYOCjSRMLfe+uAgacepdDRJSQlPzC6O5GqspFlWnSMPDRpJJSAEJCrFsL2dOtdzlERAlFKSmDOm8+d9CgScfAR5Nu6C0lPlwHefCAztUQESUGpaIa6ux50a8Z9miSMfDRlJBSQlEUaNu3QO7ZqXc5RERxTalrhKF5ZuzcSTTZGPhoyom9eyC2fqh3GUREcUltngW1rkHvMijJMfDRlJNSQnZ1QGx4HxBC73KIiOKDokCdPQ9qRbXelVAKYOCjaSGlhHT0Q7y/GohE9C6HiEhfqgr1hAVQikrYhUvTgoGPpo0UAhj0QFv7DhAM6l0OEZE+LBYY5i8E7NkMezRtGPhoWkkhgGAgGvoGvXqXQ0Q0vWzZMJy8kPvi0rRj4KNpF12gWYP4YC1kf6/e5RARTQuluPSjNfYY9miaMfCRLmJr9W3bDLl3t87VEBFNLaWhGYbGFi67Qrph4CPdifY2iA/XcQYvESUf1QB17olQS8v1roRSHAMf6U5KCXhc0N5bAwT8epdDRDQ50tJgWLAQsNrZqke6Y+CjuCCFACIRaB+sARz9epdDRDQx9pxo2DOZOF6P4gIDH8UNKQSgAGLLh5D79+pdDhHRuCil5VDnnhT9mmGP4gQDH8Ul0bYPYvNGjusjosShKFCbZkCta+TkDIo7DHwUl6SUgGsA2vtrgGBA73KIiI4vPR2GE0/hYsoUtxj4KG5JIYBwGNq6tRzXR0RxSykqgTrvJEA1sAuX4hYDH8W1oben3L0DYtd2gG9XIooXqgq1eSbUmnp24VLcY+CjhBDr4l33LuD36V0OEaW6jEwYTjqZS65QwmDgo4QhhQCEgNi0HrLjoN7lEFGKUkrKoM45EVBVduFSwmDgo4Qy1G0iDh6A2LIRiET0LomIUoVqgDpzNtTKGnbhUsJh4KOEJKUAAgFoH7wLuJx6l0NEyS7LGp2Fm5XFoEcJiYGPElZsoeYd2yD37NS7HCJKUkplDdQZswFFYRcuJSwGPkp4UkpIRx/EhveBANfsI6JJkp4Bde5JUPPy2YVLCY+Bj5KCFALQNIjNGzihg4gmTKmuhdo8i616lDQY+ChpxCZ09HZDbFoP+P16l0REiSYzC4a5J0HJyWWrHiUVBj5KOlIIQEqI7Vsg9+3RuxwiShBKbT3UphkA2KpHyYeBj5JS7G3tdkHb+AHgcetbEBHFrywrDHNP4j64lNQY+CipDc3klXt2RbdmE0LvkogoXigKlNoGqI0t0W/ZqkdJjIGPUoKUEvANQvtwHeDo17scItKb1RZt1bNxazRKDQx8lDKkEFBUFeLAXohtW4BIWO+SiGi6mcxQG1ugVNUAUrJVj1IGAx+lHCkFEApDbNkI2dmudzlENB0UBUplNdSmmYDBwKBHKYeBj1LS0HIL0tEPbctGwO3SuyQimiJKbj7UWXOBLGv0e3bhUgpi4KOUFp3UoUC27YPYsRUIhfQuiYgmS3o61JbZUEvKYkM6iFIVAx8RDgU/ISB2bouu3cdfC6LEpapQ6hqh1jeCa+oRRTHwER0S+1Xw+yC2bYbs6tC3ICIaM6W4FOrMOYAljV23RIdh4CP6mNj4PqcD2tZNwIBD75KIaCT2bKgts6DmFXBLNKKjYOAjOobYMi6d7RDbtwC+Qb1LIqKPs9qgNs2AWlTCcXpEx8HARzQCeWh3DnlgH8SenUDAr3NFRIQsK9TGFk7IIBolBj6iUYoFv7b9EHt2AH4GP6Jpl5EJtaEZSlkFF04mGgMGPqIxiu3P23YAYvcOwO/TuySi5JeeAbWhCUp5FYMe0Tgw8BGNU2wNv4OHgh/H+BFNPktaNOhVVAMAgx7RODHwEU1QLPi1t0WD36BX75KIEl9aGtSaBijVtQAY9IgmioGPaJLEgl/HwWjw83r0Loko8djsUGvroZSWA5JBj2iyMPARTbJY8OvuhNi7G3D0610SUdxTCouh1jVAyc3nrFuiKcDARzRFhj60pMcNsXc3ZPtBQGh6l0UUPwwGKOWVUGsboGRkMugRTSEGPqIpFvsVi0QgD+yF2N/KJV0otVnSoFbXQqmqBYxGAODOGERTjIGPaBod3t0r9+6BdPTpXRLR9LHZodYcGp+nAIrC1jyi6cLAR6SDWHev1wPRuhuyow3Q2N1LSUg1QCkphVpZAyU3j922RDph4CPSUezXT4tEt25r28/ZvZQcbHaoFdVQyiuhGI0MekQ6Y+CjuLB48WK89NJL2LJlC9xuN6qqqnD99dfjs5/97HHH9kgp8fDDD+Opp56Cw+FAS0sL7rnnHsybN2/6ip8ksVY/twvi4H7IjoNAMKh3WUSjZzRBKS2HWlkNxZ7NkEcURxj4KC5cffXVKCsrw/nnn4+cnBysWrUKf/7zn/H1r38dt99++zFv99BDD+F3v/sd7rrrLjQ1NeHJJ5/EqlWr8M9//hMVFRXT+Awmz+G/krKvB/LgAciuTs7wpfikKFAKiqIteUUlwKE/0DgJgyi+MPBRXHA4HMjNzR127Ac/+AFeffVVvPfee1CP0koQDAZx2mmn4dprr8W3v/1tAEAoFMKFF16IRYsW4Uc/+tF0lD6lYq1+kQhkZztk+wHIfk70oDhgtUEtr4oGPbOZrXlEcc6odwFEAI4IewDQ0tKCZ599Fj6fD1lZWUdcvm7dOni9Xlx00UWxY2azGZ/4xCewZMmSKa13ugx9gCpGI1BWAbWiCjIQiO7f236A4/1oetmzoRaXQikpg5KZNSzkMewRxTcGPopbH3zwAYqKio4a9gCgtbUVAFBbWzvseF1dHf76178iEAggLS1tyuucLrEP1rQ0oLYexvpGSI8bsqsDorsTcA3oWyAlH0WBkpMHpbgkGvLS0hnyiBIUAx/Fpffffx+vvvoqvve97x3zOm63G2azGRaLZdhxm80GKSVcLldSBb7DxT5os6xQ6hphbGiGDAYhuzsgu7sg+3oAIfQtkhKTqkLJK4BSXAqlpBSKycyQR5QEGPgo7nR1deHOO+/EKaecghtuuEHvcuKaoigfDZK3WIDyKqiVNZCaFp3w0d0J2dPF2b50fAZDdOJFcSmUopIjllFhyCNKfAx8FFfcbjduvvlmZGdn4/777z/qZI0hNpsNoVAIwWBwWCuf2+2Goiiw2+3TUXJciX1AGwxAQRGUwmIoigI54IQ41PoHj1vnKkl3igLYs6HkF0DJL4x226oqQx5REmPgo7gRCARw6623wuPx4JlnnoHVaj3u9YfG7u3duxfNzc2x462trSgtLU3a7tzRGvaBbc+GardDaZoJGQxA9vVCOvogHf2c+JEqrLaPAl5ufqwVD4oSW0KFIY8oeTHwUVyIRCL41re+hdbWVjz55JMoKioa8TYnnngisrKysHjx4ljgC4fDeOONN7Bo0aKpLjmhRD/Qh7p+04CSMiil5dHWv1AIsv9QAOzvYwtgssjIjAa8vAIoBYXRsXhSApCxPWwZ8IhSBwMfxYUf//jHeOutt3D33XfD6/Viw4YNsctmzJgBs9mMG2+8ER0dHbElVywWC2699Vbcf//9yM3NRWNjI55++mkMDAzgpptu0umZJIbDP+gVsxkoKomO31IUyEgYsj8a/qSjD3C7AC7XGd9UNdqCZ8+Gkp0bHY+XlhYNeFJ+1E17WPAnotTCwEdx4Z133gEA/OIXvzjismXLlqG8vBxCCGja8N0mbr75Zkgp8eijj8a2VnvkkUcSdpcNvQwLgEbT8PF/WgTS6QRcA5AeF6RrABj0MgTqRVEOhbucaMDLyY3O1lbVowc87nhBROBOG0Q0Ch8PElIIwOuBHHBCul2QbhfgcQGRiM6VJhlFiYa57JxY6x2stqOGOyKi42HgI6Jx+/igf+n3xUIgPG7IQS/g9wEa9wE+LrMZyMyCcugfMjKhWG1AZiYU1cBwR0QTxsBHRJNKHlrw+fBwIkMhwDcYDYC+QUi/L/q/zwcE/KnRPWw0HQp1mdFQl5kFJcsa/d/40eiaaIhGbGIFEdFkYOAjomkzFAaHtQoKAQQDkIODgM8LBAKQoWB0sehQKPp1KPp13FFVwGIBzGnRha8P/VPMadH/09IASxpgtkAxmWI3O1ooJiKaSgx8RBQXhrotAQwLhMMuj0SASBgIhw8FwRAQDkePSwEIASkFIGTs+9jXUka/j/0vAEWNhjaDIfpPjf6vxL5Whx2HwRCd1GKxREOc0Tim50BE9P+3c8coAIQwAAT//+urPBAU7MRl5gEpwxYhtwg+4FlTYK2Mh8IH4fWvwt08AQc8TPABAMQ5IAEAiBN8AABxgg8AIE7wAQDECT4AgDjBBwAQJ/gAAOIEHwBAnOADAIgTfAAAcYIPACBO8AEAxAk+AIA4wQcAECf4AADiBB8AQJzgAwCIE3wAAHGCDwAgTvABAMQJPgCAOMEHABAn+AAA4gQfAECc4AMAiBN8AABxgg8AIE7wAQDECT4AgDjBBwAQJ/gAAOIEHwBAnOADAIgTfAAAcYIPACBO8AEAxAk+AIA4wQcAECf4AADiBB8AQJzgAwCIE3wAAHGCDwAgTvABAMQJPgCAOMEHABAn+AAA4gQfAECc4AMAiBN8AABxgg8AIE7wAQDECT4AgDjBBwAQJ/gAAOIEHwBAnOADAIgTfAAAcYIPACBO8AEAxAk+AIA4wQcAECf4AADiBB8AQJzgAwCIE3wAAHGCDwAgTvABAMQJPgCAOMEHABD3AYLpCxHfiwh/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each category in 'histgrad'\n",
    "histgrad_counts = df['histgrad'].value_counts()\n",
    "\n",
    "# Create a pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(histgrad_counts, labels=histgrad_counts.index, autopct='%1.1f%%', startangle=140, colors=plt.cm.Pastel1.colors)\n",
    "plt.title('Pie Chart of Histgrad Categories')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad34f417",
   "metadata": {
    "papermill": {
     "duration": 0.0181,
     "end_time": "2024-07-18T12:36:27.814454",
     "exception": false,
     "start_time": "2024-07-18T12:36:27.796354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 5068127,
     "sourceId": 8494116,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30698,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
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