From bb1325f96f7e08d82337236e0ec106fc21d77121 Mon Sep 17 00:00:00 2001
From: ChaitanyaSagar89 <106511025+ChaitanyaSagar89@users.noreply.github.com>
Date: Thu, 25 May 2023 15:25:39 +0530
Subject: [PATCH 1/5] Add files via upload

---
 Assignment 1/01-Numpy Exercise.ipynb          | 410 ++++++++++++
 Assignment 1/02-SF Salaries Exercise.ipynb    | 610 ++++++++++++++++++
 .../03-Ecommerce Purchases Exercise .ipynb    | 487 ++++++++++++++
 3 files changed, 1507 insertions(+)
 create mode 100644 Assignment 1/01-Numpy Exercise.ipynb
 create mode 100644 Assignment 1/02-SF Salaries Exercise.ipynb
 create mode 100644 Assignment 1/03-Ecommerce Purchases Exercise .ipynb

diff --git a/Assignment 1/01-Numpy Exercise.ipynb b/Assignment 1/01-Numpy Exercise.ipynb
new file mode 100644
index 0000000..9f856f6
--- /dev/null
+++ b/Assignment 1/01-Numpy Exercise.ipynb	
@@ -0,0 +1,410 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NumPy Exercises \n",
+    "\n",
+    "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Import NumPy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create an array of 10 zeros "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a=np.zeros(10)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create an array of 10 ones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "b=np.ones(10)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create an array of 10 fives"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c=np.full((10),5)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create an array of the integers from 10 to 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr=np.arange(10,41)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create an array of all the even integers from 10 to 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr=np.arange(10,50,2)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a 3x3 matrix with values ranging from 0 to 8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr=np.arange(9).reshape((3,3))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a 3x3 identity matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr=np.identity(3)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Use NumPy to generate a random number between 0 and 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_number = np.random.rand()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_array = np.random.normal(size=25)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create the following matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr = np.arange(0.01, 1.01, 0.01).reshape((10, 10))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create an array of 20 linearly spaced points between 0 and 1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr = np.linspace(0, 1, 20)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numpy Indexing and Selection\n",
+    "\n",
+    "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr = np.arange(1, 26).reshape((5, 5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr = np.array([[12, 13, 14, 15],\n",
+    "                [17, 18, 19, 20],\n",
+    "                [22, 23, 24, 25]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr=20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr = np.array([[2], [7], [12]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr=np.arange(21,26)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr=np.arange(16,26).reshape((2,5))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now do the following"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "mat = array([[ 1,  2,  3,  4,  5],\n",
+    "       [ 6,  7,  8,  9, 10],\n",
+    "       [11, 12, 13, 14, 15],\n",
+    "       [16, 17, 18, 19, 20],\n",
+    "       [21, 22, 23, 24, 25]])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get the sum of all the values in mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat = np.array([[ 1,  2,  3,  4,  5],\n",
+    "       [ 6,  7,  8,  9, 10],\n",
+    "       [11, 12, 13, 14, 15],\n",
+    "       [16, 17, 18, 19, 20],\n",
+    "       [21, 22, 23, 24, 25]])\n",
+    "sum_of_mat = np.sum(mat)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get the standard deviation of the values in mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat = np.array([[1, 2, 3, 4, 5],\n",
+    "                [6, 7, 8, 9, 10],\n",
+    "                [11, 12, 13, 14, 15],\n",
+    "                [16, 17, 18, 19, 20],\n",
+    "                [21, 22, 23, 24, 25]])\n",
+    "\n",
+    "std_of_mat = np.std(mat)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get the sum of all the columns in mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat = np.array([[1, 2, 3, 4, 5],\n",
+    "                [6, 7, 8, 9, 10],\n",
+    "                [11, 12, 13, 14, 15],\n",
+    "                [16, 17, 18, 19, 20],\n",
+    "                [21, 22, 23, 24, 25]])\n",
+    "\n",
+    "sum_of_columns = np.sum(mat, axis=0)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Great Job!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Assignment 1/02-SF Salaries Exercise.ipynb b/Assignment 1/02-SF Salaries Exercise.ipynb
new file mode 100644
index 0000000..012f91f
--- /dev/null
+++ b/Assignment 1/02-SF Salaries Exercise.ipynb	
@@ -0,0 +1,610 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SF Salaries Exercise \n",
+    "\n",
+    "Welcome to a quick exercise for you to practice your pandas skills! We will be using the [SF Salaries Dataset](https://www.kaggle.com/kaggle/sf-salaries) from Kaggle! Just follow along and complete the tasks outlined in bold below. The tasks will get harder and harder as you go along."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Import pandas as pd.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Read Salaries.csv as a dataframe called sal.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sal=pd.read_csv('salaries.csv')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Check the head of the DataFrame. **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Id</th>\n",
+       "      <th>EmployeeName</th>\n",
+       "      <th>JobTitle</th>\n",
+       "      <th>BasePay</th>\n",
+       "      <th>OvertimePay</th>\n",
+       "      <th>OtherPay</th>\n",
+       "      <th>Benefits</th>\n",
+       "      <th>TotalPay</th>\n",
+       "      <th>TotalPayBenefits</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Notes</th>\n",
+       "      <th>Agency</th>\n",
+       "      <th>Status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NATHANIEL FORD</td>\n",
+       "      <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
+       "      <td>167411.18</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>400184.25</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>567595.43</td>\n",
+       "      <td>567595.43</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>GARY JIMENEZ</td>\n",
+       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
+       "      <td>155966.02</td>\n",
+       "      <td>245131.88</td>\n",
+       "      <td>137811.38</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>538909.28</td>\n",
+       "      <td>538909.28</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>ALBERT PARDINI</td>\n",
+       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
+       "      <td>212739.13</td>\n",
+       "      <td>106088.18</td>\n",
+       "      <td>16452.60</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>335279.91</td>\n",
+       "      <td>335279.91</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>CHRISTOPHER CHONG</td>\n",
+       "      <td>WIRE ROPE CABLE MAINTENANCE MECHANIC</td>\n",
+       "      <td>77916.00</td>\n",
+       "      <td>56120.71</td>\n",
+       "      <td>198306.90</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>332343.61</td>\n",
+       "      <td>332343.61</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>PATRICK GARDNER</td>\n",
+       "      <td>DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)</td>\n",
+       "      <td>134401.60</td>\n",
+       "      <td>9737.00</td>\n",
+       "      <td>182234.59</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>326373.19</td>\n",
+       "      <td>326373.19</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Id       EmployeeName                                        JobTitle  \\\n",
+       "0   1     NATHANIEL FORD  GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY   \n",
+       "1   2       GARY JIMENEZ                 CAPTAIN III (POLICE DEPARTMENT)   \n",
+       "2   3     ALBERT PARDINI                 CAPTAIN III (POLICE DEPARTMENT)   \n",
+       "3   4  CHRISTOPHER CHONG            WIRE ROPE CABLE MAINTENANCE MECHANIC   \n",
+       "4   5    PATRICK GARDNER    DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)   \n",
+       "\n",
+       "     BasePay  OvertimePay   OtherPay  Benefits   TotalPay  TotalPayBenefits  \\\n",
+       "0  167411.18         0.00  400184.25       NaN  567595.43         567595.43   \n",
+       "1  155966.02    245131.88  137811.38       NaN  538909.28         538909.28   \n",
+       "2  212739.13    106088.18   16452.60       NaN  335279.91         335279.91   \n",
+       "3   77916.00     56120.71  198306.90       NaN  332343.61         332343.61   \n",
+       "4  134401.60      9737.00  182234.59       NaN  326373.19         326373.19   \n",
+       "\n",
+       "   Year  Notes         Agency  Status  \n",
+       "0  2011    NaN  San Francisco     NaN  \n",
+       "1  2011    NaN  San Francisco     NaN  \n",
+       "2  2011    NaN  San Francisco     NaN  \n",
+       "3  2011    NaN  San Francisco     NaN  \n",
+       "4  2011    NaN  San Francisco     NaN  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sal.head(5)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Use the .info() method to find out how many entries there are.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 148654 entries, 0 to 148653\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column            Non-Null Count   Dtype  \n",
+      "---  ------            --------------   -----  \n",
+      " 0   Id                148654 non-null  int64  \n",
+      " 1   EmployeeName      148654 non-null  object \n",
+      " 2   JobTitle          148654 non-null  object \n",
+      " 3   BasePay           148045 non-null  float64\n",
+      " 4   OvertimePay       148650 non-null  float64\n",
+      " 5   OtherPay          148650 non-null  float64\n",
+      " 6   Benefits          112491 non-null  float64\n",
+      " 7   TotalPay          148654 non-null  float64\n",
+      " 8   TotalPayBenefits  148654 non-null  float64\n",
+      " 9   Year              148654 non-null  int64  \n",
+      " 10  Notes             0 non-null       float64\n",
+      " 11  Agency            148654 non-null  object \n",
+      " 12  Status            0 non-null       float64\n",
+      "dtypes: float64(8), int64(2), object(3)\n",
+      "memory usage: 14.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "sal.info()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**What is the average BasePay ?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66325.44884050643"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sal['BasePay'].mean()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What is the highest amount of OvertimePay in the dataset ? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "245131.88"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sal['OvertimePay'].max()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What is the job title of  JOSEPH DRISCOLL ? Note: Use all caps, otherwise you may get an answer that doesn't match up (there is also a lowercase Joseph Driscoll). **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24    CAPTAIN, FIRE SUPPRESSION\n",
+       "Name: JobTitle, dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sal.loc[sal.EmployeeName == 'JOSEPH DRISCOLL','JobTitle']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How much does JOSEPH DRISCOLL make (including benefits)? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OvertimePay          97868.77\n",
+       "OtherPay             31909.28\n",
+       "TotalPay            270324.91\n",
+       "TotalPayBenefits    270324.91\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sal.loc[sal.EmployeeName=='JOSEPH DRISCOLL',['OvertimePay','OtherPay','TotalPay','TotalPayBenefits']].sum()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What is the name of highest paid person (including benefits)?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "row=sal.loc[:,['OvertimePay','OtherPay','TotalPay','TotalPayBenefits']]\n",
+    "max_sum_element=row.idxmax()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What is the name of lowest paid person (including benefits)? Do you notice something strange about how much he or she is paid?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "row=sal.loc[:,['OvertimePay','OtherPay','TotalPay','TotalPayBenefits']]\n",
+    "max_sum_element=row.idxmin()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What was the average (mean) BasePay of all employees per year? (2011-2014) ? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "2011    63595.956517\n",
+       "2012    65436.406857\n",
+       "2013    69630.030216\n",
+       "2014    66564.421924\n",
+       "Name: BasePay, dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sal.groupby('Year')['BasePay'].mean()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How many unique job titles are there? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2159"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sal['JobTitle'].nunique()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What are the top 5 most common jobs? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a=sal['JobTitle'].value_counts()\n",
+    "b=a.head(5)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How many Job Titles were represented by only one person in 2013? (e.g. Job Titles with only one occurence in 2013?) **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a=sal[sal['Year']==2013]\n",
+    "b=a['JobTitle'].value_counts()\n",
+    "c=(b==1).sum()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How many people have the word Chief in their job title? (This is pretty tricky) **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "chief_count = sal['JobTitle'].str.contains('chief', case=False).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "477"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Bonus: Is there a correlation between length of the Job Title string and Salary? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sal['TitleLength'] = sal['JobTitle'].str.len()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "correlation = sal['TitleLength'].corr(sal['TotalPay'])\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Great Job!"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Assignment 1/03-Ecommerce Purchases Exercise .ipynb b/Assignment 1/03-Ecommerce Purchases Exercise .ipynb
new file mode 100644
index 0000000..4b0f8f3
--- /dev/null
+++ b/Assignment 1/03-Ecommerce Purchases Exercise .ipynb	
@@ -0,0 +1,487 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "___\n",
+    "# Ecommerce Purchases Exercise\n",
+    "\n",
+    "In this Exercise you will be given some Fake Data about some purchases done through Amazon! Just go ahead and follow the directions and try your best to answer the questions and complete the tasks. Feel free to reference the solutions. Most of the tasks can be solved in different ways. For the most part, the questions get progressively harder.\n",
+    "\n",
+    "Please excuse anything that doesn't make \"Real-World\" sense in the dataframe, all the data is fake and made-up.\n",
+    "\n",
+    "Also note that all of these questions can be answered with one line of code.\n",
+    "____\n",
+    "** Import pandas and read in the Ecommerce Purchases csv file and set it to a DataFrame called ecom. **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ecom=pd.read_csv('Ecommerce Purchases')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Check the head of the DataFrame.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Address</th>\n",
+       "      <th>Lot</th>\n",
+       "      <th>AM or PM</th>\n",
+       "      <th>Browser Info</th>\n",
+       "      <th>Company</th>\n",
+       "      <th>Credit Card</th>\n",
+       "      <th>CC Exp Date</th>\n",
+       "      <th>CC Security Code</th>\n",
+       "      <th>CC Provider</th>\n",
+       "      <th>Email</th>\n",
+       "      <th>Job</th>\n",
+       "      <th>IP Address</th>\n",
+       "      <th>Language</th>\n",
+       "      <th>Purchase Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...</td>\n",
+       "      <td>46 in</td>\n",
+       "      <td>PM</td>\n",
+       "      <td>Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...</td>\n",
+       "      <td>Martinez-Herman</td>\n",
+       "      <td>6011929061123406</td>\n",
+       "      <td>02/20</td>\n",
+       "      <td>900</td>\n",
+       "      <td>JCB 16 digit</td>\n",
+       "      <td>pdunlap@yahoo.com</td>\n",
+       "      <td>Scientist, product/process development</td>\n",
+       "      <td>149.146.147.205</td>\n",
+       "      <td>el</td>\n",
+       "      <td>98.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...</td>\n",
+       "      <td>28 rn</td>\n",
+       "      <td>PM</td>\n",
+       "      <td>Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...</td>\n",
+       "      <td>Fletcher, Richards and Whitaker</td>\n",
+       "      <td>3337758169645356</td>\n",
+       "      <td>11/18</td>\n",
+       "      <td>561</td>\n",
+       "      <td>Mastercard</td>\n",
+       "      <td>anthony41@reed.com</td>\n",
+       "      <td>Drilling engineer</td>\n",
+       "      <td>15.160.41.51</td>\n",
+       "      <td>fr</td>\n",
+       "      <td>70.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Unit 0065 Box 5052\\nDPO AP 27450</td>\n",
+       "      <td>94 vE</td>\n",
+       "      <td>PM</td>\n",
+       "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
+       "      <td>Simpson, Williams and Pham</td>\n",
+       "      <td>675957666125</td>\n",
+       "      <td>08/19</td>\n",
+       "      <td>699</td>\n",
+       "      <td>JCB 16 digit</td>\n",
+       "      <td>amymiller@morales-harrison.com</td>\n",
+       "      <td>Customer service manager</td>\n",
+       "      <td>132.207.160.22</td>\n",
+       "      <td>de</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7780 Julia Fords\\nNew Stacy, WA 45798</td>\n",
+       "      <td>36 vm</td>\n",
+       "      <td>PM</td>\n",
+       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...</td>\n",
+       "      <td>Williams, Marshall and Buchanan</td>\n",
+       "      <td>6011578504430710</td>\n",
+       "      <td>02/24</td>\n",
+       "      <td>384</td>\n",
+       "      <td>Discover</td>\n",
+       "      <td>brent16@olson-robinson.info</td>\n",
+       "      <td>Drilling engineer</td>\n",
+       "      <td>30.250.74.19</td>\n",
+       "      <td>es</td>\n",
+       "      <td>78.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...</td>\n",
+       "      <td>20 IE</td>\n",
+       "      <td>AM</td>\n",
+       "      <td>Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...</td>\n",
+       "      <td>Brown, Watson and Andrews</td>\n",
+       "      <td>6011456623207998</td>\n",
+       "      <td>10/25</td>\n",
+       "      <td>678</td>\n",
+       "      <td>Diners Club / Carte Blanche</td>\n",
+       "      <td>christopherwright@gmail.com</td>\n",
+       "      <td>Fine artist</td>\n",
+       "      <td>24.140.33.94</td>\n",
+       "      <td>es</td>\n",
+       "      <td>77.82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             Address    Lot AM or PM  \\\n",
+       "0  16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...  46 in       PM   \n",
+       "1  9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...  28 rn       PM   \n",
+       "2                   Unit 0065 Box 5052\\nDPO AP 27450  94 vE       PM   \n",
+       "3              7780 Julia Fords\\nNew Stacy, WA 45798  36 vm       PM   \n",
+       "4  23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...  20 IE       AM   \n",
+       "\n",
+       "                                        Browser Info  \\\n",
+       "0  Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...   \n",
+       "1  Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...   \n",
+       "2  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
+       "3  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...   \n",
+       "4  Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...   \n",
+       "\n",
+       "                           Company       Credit Card CC Exp Date  \\\n",
+       "0                  Martinez-Herman  6011929061123406       02/20   \n",
+       "1  Fletcher, Richards and Whitaker  3337758169645356       11/18   \n",
+       "2       Simpson, Williams and Pham      675957666125       08/19   \n",
+       "3  Williams, Marshall and Buchanan  6011578504430710       02/24   \n",
+       "4        Brown, Watson and Andrews  6011456623207998       10/25   \n",
+       "\n",
+       "   CC Security Code                  CC Provider  \\\n",
+       "0               900                 JCB 16 digit   \n",
+       "1               561                   Mastercard   \n",
+       "2               699                 JCB 16 digit   \n",
+       "3               384                     Discover   \n",
+       "4               678  Diners Club / Carte Blanche   \n",
+       "\n",
+       "                            Email                                     Job  \\\n",
+       "0               pdunlap@yahoo.com  Scientist, product/process development   \n",
+       "1              anthony41@reed.com                       Drilling engineer   \n",
+       "2  amymiller@morales-harrison.com                Customer service manager   \n",
+       "3     brent16@olson-robinson.info                       Drilling engineer   \n",
+       "4     christopherwright@gmail.com                             Fine artist   \n",
+       "\n",
+       "        IP Address Language  Purchase Price  \n",
+       "0  149.146.147.205       el           98.14  \n",
+       "1     15.160.41.51       fr           70.73  \n",
+       "2   132.207.160.22       de            0.95  \n",
+       "3     30.250.74.19       es           78.04  \n",
+       "4     24.140.33.94       es           77.82  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ecom.head(5)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How many rows and columns are there? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_rows, num_columns = ecom.shape\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What is the average Purchase Price? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "average_purchase_price = ecom['Purchase Price'].mean()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What were the highest and lowest purchase prices? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "highest_purchase_price = ecom['Purchase Price'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lowest_purchase_price = ecom['Purchase Price'].min()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How many people have English 'en' as their Language of choice on the website? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "english_count = ecom[ecom['Language'] == 'en'].shape[0]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How many people have the job title of \"Lawyer\" ? **\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lawyer_count = ecom[ecom['Job'] == 'Lawyer'].shape[0]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How many people made the purchase during the AM and how many people made the purchase during PM ? **\n",
+    "\n",
+    "**(Hint: Check out [value_counts()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) ) **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "am_count = ecom['AM or PM'].value_counts()['AM']\n",
+    "pm_count = ecom['AM or PM'].value_counts()['PM']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What are the 5 most common Job Titles? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job_title_counts = ecom['Job'].value_counts().head()\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Someone made a purchase that came from Lot: \"90 WT\" , what was the Purchase Price for this transaction? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lot_filter = ecom['Lot'] == '90 WT'\n",
+    "purchase_price = ecom.loc[lot_filter, 'Purchase Price']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** What is the email of the person with the following Credit Card Number: 4926535242672853 **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cc_number_filter = ecom['Credit Card'] == '4926535242672853'\n",
+    "email = ecom.loc[cc_number_filter, 'Email']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** How many people have American Express as their Credit Card Provider *and* made a purchase above $95 ?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cc_provider_filter = ecom['CC Provider'] == 'American Express'\n",
+    "purchase_price_filter = ecom['Purchase Price'] > 95\n",
+    "num_people = len(ecom.loc[cc_provider_filter & purchase_price_filter])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Hard: How many people have a credit card that expires in 2025? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ecom['ExpirationYear'] = pd.to_datetime(ecom['CC Exp Date'], format='%m/%y').dt.year\n",
+    "num_people = len(ecom[ecom['ExpirationYear'] == 2025])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...) **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ecom['EmailDomain'] = ecom['Email'].str.split('@').str[1]\n",
+    "top_email_providers = ecom['EmailDomain'].value_counts().head(5)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Great Job!"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From c9f9de80572f6ff44b1e571f586831328694617b Mon Sep 17 00:00:00 2001
From: ChaitanyaSagar89 <106511025+ChaitanyaSagar89@users.noreply.github.com>
Date: Wed, 7 Jun 2023 14:13:06 +0530
Subject: [PATCH 2/5] Add files via upload

---
 .../02-Matplotlib Exercises(sol).ipynb        | 445 +++++++++++++
 Assignment 2/07-Seaborn Exercises(sol).ipynb  | 601 ++++++++++++++++++
 2 files changed, 1046 insertions(+)
 create mode 100644 Assignment 2/02-Matplotlib Exercises(sol).ipynb
 create mode 100644 Assignment 2/07-Seaborn Exercises(sol).ipynb

diff --git a/Assignment 2/02-Matplotlib Exercises(sol).ipynb b/Assignment 2/02-Matplotlib Exercises(sol).ipynb
new file mode 100644
index 0000000..1a08cab
--- /dev/null
+++ b/Assignment 2/02-Matplotlib Exercises(sol).ipynb	
@@ -0,0 +1,445 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n",
+    "\n",
+    "# Exercises\n",
+    "\n",
+    "Follow the instructions to recreate the plots using this data:\n",
+    "\n",
+    "## Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "x = np.arange(0,100)\n",
+    "y = x*2\n",
+    "z = x**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "** Follow along with these steps: **\n",
+    "* ** Create a figure object called fig using plt.figure() **\n",
+    "* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n",
+    "* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "x = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "y = [2 * value for value in x]\n",
+    "\n",
+    "ax.plot(x, y)\n",
+    "\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_title('Plot of y = 2x')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2\n",
+    "** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.2, 0.2])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Now plot (x,y) on both axes. And call your figure object to show it.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a figure object\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# Add the first axis to the figure canvas\n",
+    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "# Add the second axis to the figure canvas\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.2, 0.2])\n",
+    "\n",
+    "# Define the x values\n",
+    "x = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "# Define the corresponding y values\n",
+    "y = [2 * value for value in x]\n",
+    "\n",
+    "# Plot (x, y) on the first axis\n",
+    "ax1.plot(x, y)\n",
+    "ax1.set_xlabel('x')\n",
+    "ax1.set_ylabel('y')\n",
+    "ax1.set_title('Plot on ax1')\n",
+    "\n",
+    "# Plot (x, y) on the second axis\n",
+    "ax2.plot(x, y)\n",
+    "ax2.set_xlabel('x')\n",
+    "ax2.set_ylabel('y')\n",
+    "ax2.set_title('Plot on ax2')\n",
+    "\n",
+    "# Call the figure object to show the plot\n",
+    "fig\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3\n",
+    "\n",
+    "** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.4])\n",
+    "\n",
+    "# Show the plot\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.4])\n",
+    "\n",
+    "x1 = np.linspace(0, 100, 1000)\n",
+    "y1 = np.exp(x1 * 0.1)\n",
+    "ax1.plot(x1, y1, color='blue')\n",
+    "ax1.set_xlim(0, 100)\n",
+    "ax1.set_ylim(0, 10000)\n",
+    "ax1.set_xlabel('X')\n",
+    "ax1.set_ylabel('Z')\n",
+    "\n",
+    "x2 = np.linspace(20, 22, 1000)\n",
+    "y2 = np.exp(x2 * 0.1)\n",
+    "ax2.plot(x2, y2, color='red')\n",
+    "ax2.set_xlabel('X')\n",
+    "ax2.set_ylabel('Y')\n",
+    "ax2.set_title('zoom')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4\n",
+    "\n",
+    "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(1,2,1)\n",
+    "plt.subplot(1,2,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(1,2,1)\n",
+    "plt.plot(x2, y2,'b--', linewidth=4) # More on color options later\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.plot(x1,y1,'r', linewidth=4);\n",
+    "\n",
+    "x1 = np.linspace(0, 100, 1000)\n",
+    "y1 = np.exp(x1 * 0.1)\n",
+    "ax1.set_xlim(0, 100)\n",
+    "ax1.set_ylim(0, 10000)\n",
+    "\n",
+    "x2 = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "y2= [2 * value for value in x2]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "\n",
+    "ax1 = axes[0]\n",
+    "ax2 = axes[1]\n",
+    "\n",
+    "x1 = np.linspace(0, 100, 1000)\n",
+    "y1 = np.exp(x1 * 0.1)\n",
+    "ax1.plot(x1, y1, 'r', linewidth=4)\n",
+    "ax1.set_xlim(0, 100)\n",
+    "ax1.set_ylim(0, 10000)\n",
+    "ax1.set_xlabel('X')\n",
+    "ax1.set_ylabel('Y')\n",
+    "ax1.set_title('Plot 1')\n",
+    "\n",
+    "x2 = [1, 2, 3, 4, 5]\n",
+    "y2 = [2 * value for value in x2]\n",
+    "ax2.plot(x2, y2, 'b--', linewidth=4)\n",
+    "ax2.set_xlabel('X')\n",
+    "ax2.set_ylabel('Y')\n",
+    "ax2.set_title('Plot 2')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Great Job!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Assignment 2/07-Seaborn Exercises(sol).ipynb b/Assignment 2/07-Seaborn Exercises(sol).ipynb
new file mode 100644
index 0000000..88f6e38
--- /dev/null
+++ b/Assignment 2/07-Seaborn Exercises(sol).ipynb	
@@ -0,0 +1,601 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Seaborn Exercises\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set_style('whitegrid')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = sns.load_dataset('titanic')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survived</th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>class</th>\n",
+       "      <th>who</th>\n",
+       "      <th>adult_male</th>\n",
+       "      <th>deck</th>\n",
+       "      <th>embark_town</th>\n",
+       "      <th>alive</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
+       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
+       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
+       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
+       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
+       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
+       "\n",
+       "     who  adult_male deck  embark_town alive  alone  \n",
+       "0    man        True  NaN  Southampton    no  False  \n",
+       "1  woman       False    C    Cherbourg   yes  False  \n",
+       "2  woman       False  NaN  Southampton   yes   True  \n",
+       "3  woman       False    C  Southampton   yes  False  \n",
+       "4    man        True  NaN  Southampton    no   True  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "** Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints.**\n",
+    "\n",
+    "** *Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!* **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x257abf91e50>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.jointplot(data=titanic, x='fare', y='age', kind='scatter')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x11d0389e8>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d038cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 500.0)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.histplot(data=titanic, x='fare')\n",
+    "plt.ylim(0, 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.boxplot(data=titanic, x='class', y='age')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.stripplot(data=titanic, x='class', y='age', jitter=-0.2)\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.countplot(data=titanic, x='sex')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11f207ef0>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d84cc18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "columns = ['survived', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'adult_male', 'alone']\n",
+    "\n",
+    "# Create the correlation matrix\n",
+    "corr_matrix = titanic[columns].corr()\n",
+    "\n",
+    "# Create the colored square plot\n",
+    "sns.heatmap(corr_matrix, cmap='coolwarm')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x11d72da58>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d8229b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
+    "\n",
+    "# Plot the histogram for males\n",
+    "sns.histplot(data=titanic[titanic['sex'] == 'male'], x='age', ax=ax1, color='blue')\n",
+    "ax1.set_title('sex=male')\n",
+    "ax1.set_xlabel('Age')\n",
+    "ax1.set_ylabel('Count')\n",
+    "\n",
+    "# Plot the histogram for females\n",
+    "sns.histplot(data=titanic[titanic['sex'] == 'female'], x='age', ax=ax2, color='pink')\n",
+    "ax2.set_title('sex=female')\n",
+    "ax2.set_xlabel('Age')\n",
+    "ax2.set_ylabel('Count')\n",
+    "\n",
+    "# Adjust the spacing between subplots\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Great Job!\n",
+    "\n",
+    "### That is it for now! We'll see a lot more of seaborn practice problems in the machine learning section!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 814eca82b335c2f13cf227002f26c054016b009a Mon Sep 17 00:00:00 2001
From: ChaitanyaSagar89 <106511025+ChaitanyaSagar89@users.noreply.github.com>
Date: Wed, 7 Jun 2023 14:14:24 +0530
Subject: [PATCH 3/5] Delete 02-Matplotlib Exercises(sol).ipynb

---
 .../02-Matplotlib Exercises(sol).ipynb        | 445 ------------------
 1 file changed, 445 deletions(-)
 delete mode 100644 Assignment 2/02-Matplotlib Exercises(sol).ipynb

diff --git a/Assignment 2/02-Matplotlib Exercises(sol).ipynb b/Assignment 2/02-Matplotlib Exercises(sol).ipynb
deleted file mode 100644
index 1a08cab..0000000
--- a/Assignment 2/02-Matplotlib Exercises(sol).ipynb	
+++ /dev/null
@@ -1,445 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n",
-    "\n",
-    "# Exercises\n",
-    "\n",
-    "Follow the instructions to recreate the plots using this data:\n",
-    "\n",
-    "## Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "x = np.arange(0,100)\n",
-    "y = x*2\n",
-    "z = x**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 1\n",
-    "\n",
-    "** Follow along with these steps: **\n",
-    "* ** Create a figure object called fig using plt.figure() **\n",
-    "* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n",
-    "* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "\n",
-    "ax = fig.add_axes([0, 0, 1, 1])\n",
-    "\n",
-    "x = [1, 2, 3, 4, 5]\n",
-    "\n",
-    "y = [2 * value for value in x]\n",
-    "\n",
-    "ax.plot(x, y)\n",
-    "\n",
-    "ax.set_xlabel('x')\n",
-    "ax.set_ylabel('y')\n",
-    "ax.set_title('Plot of y = 2x')\n",
-    "\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 2\n",
-    "** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "\n",
-    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
-    "\n",
-    "ax2 = fig.add_axes([0.2, 0.5, 0.2, 0.2])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "** Now plot (x,y) on both axes. And call your figure object to show it.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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AoNEqVyEu6ujR/FsvpgphMxFRjZCkCy64QOvXr9fx48fl8/nUoUMH3XLLLTr//POr3d7tdsvtdod4SgAAEOmoQkQe2wfh01q2bKmWLVvq8OHDeuedd/T4449bPRIAAHCItV8e0tSXt+sIV4WIKLYPwu+8846MMerRo4d27dqle++9V6mpqRo7dqzVowEAgAhHFSKy2T4Ie71eTZ8+Xfv27VObNm1044036pFHHlHz5s2tHg0AAEQwqhCRz/ZflmssriMMAAAaiipEeIuYL8sBAACEClUIZyEIAwAAiCqEExGEAQCA41GFcCaCMAAAcCyqEM5GEAYAAI5EFQIEYQAA4DhUISARhAEAgINQhcCZCMIAAMARqEKgMoIwAACIeFQhUB2CMAAAiFhUIVAbgjAAAIhIVCFQF4IwAACIOFQhUB8EYQAAEDGoQqAhCMIAACAiUIVAQxGEAQBA2KMKgbNBEAYAAGGLKgQagyAMAADCElUINBZBGAAAhB2qEGgKBGEAABA2qEKgKRGEAQBAWKAKgaZGEAYAALZHFQLBQBAGAAC2RRUCwUQQBgAAtkQVAsFGEAYAALZDFQKhQBAGAAC2QRUCoUQQBgAAtkAVAqFGEAYAAJajCgErEIQBAIBlqELASgRhAABgCaoQsBpBGAAAhBxVCNgBQRgAAIQMVQjYCUEYAACEBFUI2A1BGAAABB1VCNgRQRgAAAQNVQjYGUEYAAAEBVUI2B1BGAAANDmqEAgHBGEAANBkqEIgnBCEAQBAk6AKgXBDEAYAAI1GFQLhKMrqAYBIs27dOrlcLq1bt87qUQAg6ErLyjVr1ZcaW1CoIydKdVFHj1bcNZAQjLBAEAbqqaCgQC6XK3CLjY1V9+7dNXnyZB08eLBJPmPlypWaOXNmk7yXXZ04cUILFizQkCFD1KFDB8XFxeniiy/WwoULVVZWZvV4ABqg5MhJjXx2U6APnJWeolcnptMHRtggCAMN9Ic//EGLFy/W/Pnz1b9/fy1cuFDp6ek6ceJEo9975cqVys3NbYIp7evrr7/WlClTZIzRPffco9mzZ6tr166aNGmSxo0bZ/V4AOpp7ZeHdP28D7R1z2HFuZvp6dGXKHd4Gn1ghBU6wkADDRs2TP369ZMk3XHHHWrbtq3mzJmj119/XaNGjbJ4Ovtr3769ioqK1KtXr8BjEyZM0Lhx45Sfn68HHnhAF154oYUTAqgNV4VAJOGIMNBIV199tSSpuLi41u1eeeUVXXrppWrRooXOO+88jRkzRt98803g+dtvv10LFiyQpAoVjLo8/fTT6tWrl9xut5KSkpSdna0jR45U2Oaqq65SWlqavvjiCw0ePFjnnHOOOnbsqMcff7xeP2N+fr6uvvpqJSYmyu12q2fPnlq4cGGFbd5//31FRUXpwQcfrPD4kiVL5HK5Atufd955FULwaT//+c8lSTt27KjXTABCjyoEIo2tg3BZWZkeeOABde3aVS1atNAFF1yghx56SMYYq0cDAnbv/uE/CG3btq1xm4KCAt18882Kjo5WXl6e7rzzTr322msaOHBgILROmDBB1157rSRp8eLFgVttZs6cqezsbCUlJemJJ57QjTfeqEWLFmnIkCEqLS2tsO3hw4d13XXXqU+fPnriiSeUmpqq+++/X6tWrarzZ1y4cKFSUlL0u9/9Tk888YSSk5M1adKkQHCXfvgfgkmTJikvL0/btm2TJO3fv19TpkxRRkaGfv3rX9f6GQcOHJD0Q1AGYD9UIRCRjI098sgjpm3btmbFihWmuLjYvPLKK6ZVq1bmqaeeqvd7eL1eI8l4vd4gTgonyM/PN5LMmjVrzLfffmv27t1rli5datq2bWtatGhh9u3bZ4wxZu3atUaSWbt2rTHGmFOnTpnExESTlpZmTp48GXi/FStWGEnmwQcfDDyWnZ1t6vvX8tChQyYmJsYMGTLElJWVBR6fP3++kWReeOGFwGODBg0yksxLL70UeMzv95v27dubG2+8sc7POnHiRJXHhg4das4///wKjx0/ftxceOGFplevXua7774zmZmZJj4+3uzZs6fW9/f7/aZnz56ma9euprS0tM55AITOqe/LTN7KHSbl/hUm5f4V5r/mfWD+9Z9jVo8F1Kq++c/WR4Q//vhjDR8+XJmZmerSpYt++ctfasiQIfrkk0+sHg0OlpGRoYSEBCUnJ2vkyJFq1aqVli1bpo4dO1a7/ZYtW3To0CFNmjRJsbGxgcczMzOVmpqqt95666zmWLNmjU6dOqWcnBxFRf34V/nOO+9UfHx8lfdt1aqVxowZE7gfExOjyy+/XF9//XWdn9WiRYvAP3u9Xv3nP//RoEGD9PXXX8vr9QaeO+ecc1RQUKAdO3boyiuv1FtvvaW5c+eqc+fOtb7/5MmT9cUXX2j+/Plq1oyvLgB2UXLkpEZRhUAEs/V/cfr3769nn31W//znP9W9e3f9/e9/14cffqg5c+bU+Bq/3y+/3x+47/P5QjEqHGTBggXq3r27mjVrpnbt2qlHjx4Vgmhle/bskST16NGjynOpqan68MMPz2qOmt43JiZG559/fuD50zp16lSlc3zuuefqs88+q/OzPvroI82YMUMbN26scnUMr9crj8cTuD9gwABNnDhRCxYs0NChQ+u8EsQf//hHPffcc3rooYd0/fXX1zkLgNDgF2TACWwdhKdNmyafz6fU1FRFR0errKxMjzzyiEaPHl3ja/Ly8iL+8lOw1uWXXx64akQ4iY6uvsdn6ujc7969W9dcc41SU1M1Z84cJScnKyYmRitXrtTcuXNVXl5eYXu/3x/4ZSK7d+/WiRMndM4551T73gUFBbr//vv161//Wr///e8b/kMBaHKVrwqR1jFeC269hKPAiEi2rka8/PLL+stf/qIlS5Zo27ZtevHFFzV79my9+OKLNb5m+vTp8nq9gdvevXtDODFQVUpKiiRp586dVZ7buXNn4HlJ9bpKRF3ve+rUKRUXF1d438Z488035ff79cYbb2jChAm6/vrrlZGRUaEucaYZM2Zox44dmj17toqLizVt2rRqt3v99dd1xx136Be/+EWFL90BsE51V4X434n9CcGIWLYOwvfee6+mTZumkSNH6qKLLtJtt92mqVOnKi8vr8bXuN1uxcfHV7gBVurXr58SExP1zDPPVKjtrFq1Sjt27FBmZmbgsZYtf/iPTeXLn1UnIyNDMTExmjdvXoWjun/605/k9XorvG9jnD6SfOZneL1e5efnV9l28+bNmj17tnJycvSb3/xG9957r+bPn6/169dX2G7Dhg0aOXKkrrzySv3lL3+ptVoCIDS4KgScyNbViBMnTlT5D2R0dHSVU7GAnTVv3lyPPfaYxo4dq0GDBmnUqFE6ePCgnnrqKXXp0kVTp04NbHvppZdKku666y4NHTpU0dHRGjlyZLXvm5CQoOnTpys3N1fXXXedfvazn2nnzp16+umnddlll1X4YlxjDBkyRDExMbrhhhs0YcIEHTt2TM8995wSExO1f//+wHbfffedsrKy1K1bNz3yyCOSpNzcXL355psaO3asioqK1LJlS+3Zs0c/+9nP5HK59Mtf/lKvvPJKhc/r3bu3evfu3SSzA6gbvyADjhaKS1icraysLNOxY8fA5dNee+01c95555n77ruv3u/B5dPQVE5fPq2wsLDW7SpfPu20v/3tb+biiy82brfbtGnTxowePTpwybXTvv/+ezNlyhSTkJBgXC5XvS6lNn/+fJOammqaN29u2rVrZyZOnGgOHz5cYZtBgwaZXr16VXltVlaWSUlJqfMz3njjDdO7d28TGxtrunTpYh577DHzwgsvGEmmuLjYGGPM1KlTTXR0tNm8eXOF127ZssU0a9bMTJw40Rjz4/6p6TZjxow65wHQNL45fML84umPApdGe3B5kfmu9HurxwIarb75z2WMfX87xdGjR/XAAw9o2bJlOnTokJKSkjRq1Cg9+OCDiomJqdd7+Hw+eTweeb1eahIAAPx/XBUCkay++c/WQbgpEIQBAPgRVQg4QX3zn607wgAAoOmUHDmpKX/9VFv3HJb0w1Uhfpf5E74QB8ciCAMA4ABUIYCqCMIAAEQwqhBAzQjCAABEKKoQQO0IwgAARCCqEEDdCMKIaOXl5SopKVFcXFyDfn0xIoMxRkePHlVSUhK/vQ6OQRUCqD+CMCyzYcMG/fGPf9TWrVu1f/9+LVu2TCNGjAg8b4zRjBkz9Nxzz+nIkSMaMGCAFi5cqG7dutX7M0pKSpScnByE6RFO9u7dq06dOlk9BhB0VCGAhiEIwzLHjx9Xnz59NG7cOP3iF7+o8vzjjz+uefPm6cUXX1TXrl31wAMPaOjQofriiy8UGxtbr8+Ii4uT9EMQ4jrSzuPz+ZScnBxYB0AkowoBNBxBGJYZNmyYhg0bVu1zxhg9+eST+v3vf6/hw4dLkl566SW1a9dOy5cv18iRI+v1GafrEPHx8QThCPZ9Wbn2e79Tcptzqn2eWgwiGVUI4OxRmoMtFRcX68CBA8rIyAg85vF4dMUVV2jjxo01vs7v98vn81W4IbKVHDmpkc9u0shnN+nIiVNWjwOE1On1fzoEZ6Wn6NWJ6YRgoJ4IwrClAwcOSJLatWtX4fF27doFnqtOXl6ePB5P4EY/OLKt/fKQMud9oC17Dst3slT/PHjM6pGAkFn75SFdP+8Dbd1zWHHuZnp69CXKHZ5GHxhoAKoRiCjTp0/XPffcE7h/uiOKyMKpYDgZ6x9oOgRh2FL79u0lSQcPHlSHDj9+2ePgwYPq27dvja9zu91yu93BHg8WKjlyUnf99VNt4VvxcCCuCgE0LYIwbKlr165q37693nvvvUDw9fl82rx5syZOnGjtcLDM2i8P6Z6Xt+sw34qHA3FVCKDpEYRhmWPHjmnXrl2B+8XFxdq+fbvatGmjzp07KycnRw8//LC6desWuHxaUlJShWsNwxk4FQwnY/0DwUMQhmW2bNmiwYMHB+6f7vZmZWWpoKBA9913n44fP65f/epXOnLkiAYOHKi333673tcQRmSgCgEnowoBBJfLGGOsHiKYfD6fPB6PvF4v15F1IP78w1tjqxD8+SOcUYUAzl59//3PEWEAtsOpYDgZ6x8IHYIwAFuhCgEnowoBhBZBGIBtcFUIOBlVCCD0CMIALMepYDgZ6x+wDkEYgKWoQsDJWP+AtQjCACxDFQJOxvoHrEcQBhBynAqGk7H+AfsgCAMIKU4Fw8lY/4C9EIQBhAynguFkrH/AfgjCAIKOU8FwMtY/YF8EYQBBxalgOBnrH7A3gjCAoOFUMJyM9Q/YH0EYQJPjVDCcjPUPhA+CMIAmxalgOBnrHwgvBGEATYZTwXAy1j8QfgjCABqt8qngtI7xWnDrJZwKhiNQhQDCF0EYQKNwKhhOxvoHwhtBGMBZ41QwnIz1D4Q/gjCABuNUMJyM9Q9EDoIwgAbhVDCcjPUPRBaCMIB641QwnIz1D0QegjCAOnEqGE7G+gciF0EYQK04FQwnY/0DkY0gDKBGnAqGk7H+gchHEAZQBaeC4WSsf8A5CMIAKuBUMJyM9Q84S5TVA9SlS5cucrlcVW7Z2dlWjwZEnLVfHlLmvA+0Zc9hxbmb6enRlyh3eBohAI7A+gecx/ZHhAsLC1VWVha4//nnn+vaa6/VTTfdZOFUQGThVDCcjPUPOJftg3BCQkKF+7NmzdIFF1ygQYMGWTQREFk4FQwnY/0Dzmb7IHymU6dO6c9//rPuueceuVyuarfx+/3y+/2B+z6fL1TjAWGHb8XDyVj/AMIqCC9fvlxHjhzR7bffXuM2eXl5ys3NDd1QQBjiVDCcjPUP4DSXMcZYPUR9DR06VDExMXrzzTdr3Ka6I8LJycnyer2Kj48PxZiwEZ/PJ4/Hw5//GZx0Kpg/f1TmpPUPOFl9//0fNkeE9+zZozVr1ui1116rdTu32y232x2iqYDwwqlgOBnrH0BlYROE8/PzlZiYqMzMTKtHAcIOp4LhZKx/ADUJiyBcXl6u/Px8ZWVlqVmzsBg5YmVlZWn8+PG68sorrR4F9cSpYDgZ6x9AbcIiVa5Zs0b//ve/NW7cOKtHcTyv16uMjAylpKRo7NixysrKUseOHa0eCzVYu/OQ7vkbp4LhTFQhANTF9r9ZTpKGDBkiY4y6d+9u9SiOt3z5cn3zzTeaOHGi/va3v6lLly4aNmyYXn31VZWWllo9Hv6/0rJyzVr1pcbmF+rwiVJd1NGjFXcNJATAEQLrv4D1D6B2YRGEYS8JCQm655579Pe//12bN2/WhRdeqNtuu01JSUmaOnWqvvrqK6tHdLSSIyc16tlNgT7kf6en6NWJ6fQh4QiV138W6x9ALcKiGgF72r9/v1avXq3Vq1crOjpa119/vYqKitSzZ089/vjjmjp1qtUjOg6nguFkrH8ADUUQRoOUlpbqjTfeUH5+vt5991317t1bOTk5uvXWWwPX6Vu2bJnGjRtHEA4hvhUPJ2P9AzhbBGE0SIcOHVReXq5Ro0bpk08+Ud++fatsM3jwYLVu3TrkszkV34qHk7H+ATQGQRgNMnfuXN10002KjY2tcZvWrVuruLg4hFM5F6eC4WSsfwCNRRBGg9x2221WjwBxKhjOxvoH0FQIwkCY4VQwnIz1D6Apcfk02NbMmTPlcrkq3FJTU60ey1JrvzykzHkfaMuew4pzN9PToy9R7vA0QgAcgfUPoKlxRBi21qtXL61ZsyZw36m/YrvyqeC0jvFacOslnAqGI1CFABAszkwVCBvNmjVT+/btrR7DUpwKhpOx/gEEE0EYtvbVV18pKSlJsbGxSk9PV15enjp37lzj9n6/X36/P3Df5/OFYsyg4VvxcDLWP4BgIwjDtq644goVFBSoR48e2r9/v3Jzc/XTn/5Un3/+ueLi4qp9TV5ennJzc0M8adOjCgEnowoBIFRcxhhj9RDB5PP55PF45PV6A7/5DOHpyJEjSklJ0Zw5czR+/Phqt6nuiHBycnJY/flzKrjp8Pc//LD+ATSF+v77nyPCCButW7dW9+7dtWvXrhq3cbvdcrvdIZyqaXEqGE7G+gcQagRhhI1jx45p9+7dEflLPahCwMmoQgCwCkEYtvXb3/5WN9xwg1JSUlRSUqIZM2YoOjpao0aNsnq0JsWpYDgZ6x+AlQjCsK19+/Zp1KhR+r//+z8lJCRo4MCB2rRpkxISEqwerclwKhhOxvoHYDWCMGxr6dKlVo8QNFQh4GRUIQDYBUEYCDFOBcPJWP8A7IQgDIQQp4LhZKx/AHZDEAZCgCoEnIwqBAC7IggDQcapYDgZ6x+AnRGEgSDiVDCcjPUPwO4IwkAQUIWAk1GFABAuCMJAE+NUMJyM9Q8gnBCEgSbEqWA4GesfQLghCANNgCoEnIwqBIBwRRAGGolTwXAy1j+AcEYQBhqBU8FwMtY/gHBHEAbOAlUIOBlVCACRgiAMNBCnguFkrH8AkYQgDDQAp4LhZKx/AJGGIAzUA1UIOBlVCACRiiAM1IFTwXAy1j+ASEYQBmrBqWA4GesfQKQjCAPVoAoBJ6MKAcApCMJAJSVHTmrKXz/VVk4Fw4GoQgBwEoIwcIa1Xx7S1Je36winguFAVCEAOA1BGBCnguFsrH8ATkUQhuNRhYCTUYUA4GQEYTgaVQg4GVUIAE5HEIYjcSoYTsb6B4AfEIThOFQh4GRUIQDgRwRhOM6Kz0q0dc9hTgXDcahCAEBFUVYPUJdvvvlGY8aMUdu2bdWiRQtddNFF2rJli9VjIYzdMfB8jR/YVSvuGkgIgCOUlpVr1qovNbagUIdPlOqijh7WPwDoLI4IZ2Vlafz48bryyiuDMU8Fhw8f1oABAzR48GCtWrVKCQkJ+uqrr3TuuecG/bMRuaKiXHrgv3paPQYQElQhAKBmDQ7CXq9XGRkZSklJ0dixY5WVlaWOHTsGYzY99thjSk5OVn5+fuCxrl27BuWzACDSrN15SPf8jSoEANTEZYwxDX3Rt99+q8WLF+vFF1/UF198oYyMDI0fP17Dhw9X8+bNm2y4nj17aujQodq3b5/Wr1+vjh07atKkSbrzzjtrfI3f75ff7w/c9/l8Sk5OltfrVXx8fJPNhvDg9XrVunVr7d27lz9/Bzr99//IkSPyeDxWjxMyXBUCgNP5fD55PJ46899ZBeEzbdu2Tfn5+Xr++efVqlUrjRkzRpMmTVK3bt0a87aSpNjYWEnSPffco5tuukmFhYW6++679cwzzygrK6va18ycOVO5ublVHicIO9O+ffuUnJxs9Riw2N69e9WpUyerxwiJ/d6TmrLkxyrEf6en6H+oQgBwmJAE4f379+ull15Sfn6+9u3bpxtvvFHffPON1q9fr8cff1xTp04927eWJMXExKhfv376+OOPA4/dddddKiws1MaNG6t9DUeEcaby8nKVlJQoLi5OLpfL0llOr8VIPzptp5/TGKOjR48qKSlJUVG2/25wo1GFAIAf1DcIN7gjXFpaqjfeeEP5+fl699131bt3b+Xk5OjWW28NfNCyZcs0bty4RgfhDh06qGfPil9q+slPfqL//d//rfE1brdbbre7UZ+LyBEVFWW7I4Hx8fGWB8RQsMvP6YRKBFUIADg7DQ7CHTp0UHl5uUaNGqVPPvlEffv2rbLN4MGD1bp160YPN2DAAO3cubPCY//85z+VkpLS6PcGgEhQuQrBVSEAoP4aHITnzp2rm266KdDfrU7r1q1VXFzcqMEkaerUqerfv78effRR3Xzzzfrkk0/07LPP6tlnn230ewNAuKMKAQCN0+AgfNtttwVjjmpddtllWrZsmaZPn64//OEP6tq1q5588kmNHj06ZDMATcXtdmvGjBkRX91xys9pJaoQANA0Gn3VCLurb1kaAMIBVQgAqFvQviwHALAGVQgAaFoEYQCwOaoQABAcBGEAsDGqEAAQPARhALApqhAAEFyR/6uWAAvl5eXpsssuU1xcnBITEzVixIgq18aORLNmzZLL5VJOTo7Vo4Sl0rJyzVr1pcbmF+rwiVJd1NGjFXcNJAQDQBMjCANBtH79emVnZ2vTpk1avXq1SktLNWTIEB0/ftzq0YKmsLBQixYtUu/eva0eJSzt957UqGc3BfrAWekpenViOn1gAAgCqhFAEL399tsV7hcUFCgxMVFbt27VlVdeadFUwXPs2DGNHj1azz33nB5++GGrxwk7VCEAILQ4IgyEkNfrlSS1adPG4kmCIzs7W5mZmcrIyLB6lLBCFQIArMERYSBEysvLlZOTowEDBigtLc3qcZrc0qVLtW3bNhUWFlo9SljhqhAAYB2CMBAi2dnZ+vzzz/Xhhx9aPUqT27t3r+6++26tXr1asbGxVo8TNqhCAIC1+BXLQAhMnjxZr7/+ujZs2KCuXbtaPU6TW758uX7+858rOvrHo5hlZWVyuVyKioqS3++v8JzT8QsyACC4+BXLgA0YYzRlyhQtW7ZM69ati8gQLEnXXHONioqKKjw2duxYpaam6v777ycEn4EqBADYB0EYCKLs7GwtWbJEr7/+uuLi4nTgwAFJksfjUYsWLSyerunExcVV6T23bNlSbdu2jcg+9NmiCgEA9kIQBoJo4cKFkqSrrrqqwuP5+fm6/fbbQz8QLEEVAgDsiSAMBFGEV/BrtW7dOqtHsAWqEABgXwRhAAgSqhAAYG8EYQBoYlQhACA8EIQBoAlRhQCA8EEQBoAmQhUCAMILQRgAGokqBACEJ4IwADQCVQgACF8EYQA4S1QhACC8EYQBoIGoQgBAZCAIA0ADUIUAgMhBEAaAeqIKAQCRhSAMAHWgCgEAkSnK6gEANK1vv/1W7du316OPPhp47OOPP1ZMTIzee+89CycLT/u9JzXq2U2BEJyVnqJXJ6YTggEgAnBEGIgwCQkJeuGFFzRixAgNGTJEPXr00G233abJkyfrmmuusXq8sEIVAgAim8sYY6weIph8Pp88Ho+8Xq/i4+OtHgcImezsbK1Zs0b9+vVTUVGRCgsL5Xa7rR4rLFCFAIDwVt/8RxAGItTJkyeVlpamvXv3auvWrbrooousHikscFUIAAh/9c1/VCOACLV7926VlJSovLxc//rXvwjC9UAVAgCchSAMRKBTp05pzJgxuuWWW9SjRw/dcccdKioqUmJiotWj2RJVCABwJoIwEIH+53/+R16vV/PmzVOrVq20cuVKjRs3TitWrLB6NNuhCgEAzsXl04AIs27dOj355JNavHix4uPjFRUVpcWLF+uDDz7QwoULrR7PVtbuPKTrn/pAW/YcVpy7mZ4efYlyh6cRggHAITgiDESYq666SqWlpRUe69Kli7xer0UT2Q9VCACARBAG4DBUIQAApxGEATgGV4UAAJyJIAwg4lGFAABUhyAMIKJRhQAA1IQgDCBiUYUAANSGIAwg4lCFAADUB0EYQEShCgEAqC+CMICIQRUCANAQtv7NcjNnzpTL5apwS01NtXosADZTWlauWau+1Nj8Qh0+UaqLOnq04q6BhGAAQK1sf0S4V69eWrNmTeB+s2a2HxlACFGFAACcLdunymbNmql9+/ZWjwHAhqhCAAAaw/ZB+KuvvlJSUpJiY2OVnp6uvLw8de7cucbt/X6//H5/4L7P5wvFmABCiKtCAACagq07wldccYUKCgr09ttva+HChSouLtZPf/pTHT16tMbX5OXlyePxBG7JyckhnBhAsO33ntSoZzcFQnBWeopenZhOCAYANJjLGGOsHqK+jhw5opSUFM2ZM0fjx4+vdpvqjggnJyfL6/UqPj4+VKMCCAKqEACA+vD5fPJ4PHXmP9tXI87UunVrde/eXbt27apxG7fbLbfbHcKpAAQbVQgAQDDYuhpR2bFjx7R792516MARIMApqEIAAILF1keEf/vb3+qGG25QSkqKSkpKNGPGDEVHR2vUqFFWjwYgBKhCAACCydZBeN++fRo1apT+7//+TwkJCRo4cKA2bdqkhIQEq0cDEERUIQAAoWDrILx06VKrRwAQYvyCDABAqNg6CANwFqoQAIBQIggDsBxVCACAFQjCACxFFQIAYBWCMADLUIUAAFiJIAwg5KhCAADsgCAMIKSoQgAA7IIgDCBkqEIAAOyEIAwg6KhCAADsiCAMIKioQgAA7IogDCBoqEIAAOyMIAygyVGFAACEA4IwgCZFFQIAEC4IwgCaDFUIAEA4IQgDaDSqEACAcEQQBtAoVCEAAOGKIAzgrFGFAACEM4IwgAajCgEAiAQEYQANQhUCABApCMIA6o0qBAAgkhCEAdSJKgQAIBIRhAHUiioEACBSEYQB1IgqBAAgkhGEAVRBFQIA4AQEYQAVUIUAADgFQRhAAFUIAICTEIQBUIUAADgSQRhwOKoQAACnIggDDkYVAgDgZARhwIGoQgAAQBAGHIcqBAAAPyAIAw5CFQIAgB8RhAEHoAoBAEBVBGEgwlGFAACgegRhIIJRhQAAoGYEYSACUYUAAKBuBGEgwlCFAACgfgjCQAShCgEAQP0RhIEIQBUCAICGIwgDYY4qBAAAZ4cgDIQxqhAAAJw9gjAQhqhCAADQeARhIMxQhQAAoGkQhIEwQhUCAICmQxAGwgBVCAAAml6U1QM0xKxZs+RyuZSTk2P1KEDI7Pee1KhnNwVCcFZ6il6dmE4IBgCgkcLmiHBhYaEWLVqk3r17Wz0KEDJUIQAACJ6wOCJ87NgxjR49Ws8995zOPfdcq8cBgu77snLNWvWlxuYX6vCJUl3U0aMVdw0kBAMA0ITCIghnZ2crMzNTGRkZdW7r9/vl8/kq3IBwUnLkpEZShQAAIOhsX41YunSptm3bpsLCwnptn5eXp9zc3CBPBQTH2i8P6Z6XqUIAABAKtj4ivHfvXt199936y1/+otjY2Hq9Zvr06fJ6vYHb3r17gzwl0Hilp6sQBVQhAAAIFZcxxlg9RE2WL1+un//854qO/vEXBZSVlcnlcikqKkp+v7/Cc9Xx+XzyeDzyer2Kj48P9shAg5UcOam7/sovyAAAoKnUN//ZuhpxzTXXqKioqMJjY8eOVWpqqu6///46QzBgd1QhAACwjq2DcFxcnNLS0io81rJlS7Vt27bK40A44RdkAABgPVsHYSASUYUAAMAewi4Ir1u3zuoRgLNGFQIAAPsIuyAMhCOqEAAA2A9BGAgyqhAAANgTQRgIIqoQAADYF0EYCAKqEAAA2B9BGGhiVCEAAAgPBGGgCVGFAAAgfBCEgSZAFQIAgPBDEAYaiSoEAADhiSAMNAJVCAAAwhdBGDgLVCEAAAh/BGGggahCAAAQGQjCQANQhQAAIHIQhIF6oAoBAEDkIQgDdaAKAQBAZCIIA7WgCgEAQOQiCAPVoAoBAEDkIwgDlVCFAADAGQjCwBmoQgAA4BwEYUBUIQAAcCKCMByPKgQAAM5EEIajUYUAAMC5CMJwJKoQAACAIAzHoQoBAAAkgjAchioEAAA4jSAMR6AKAQAAKiMII+JRhQAAANUhCCOiUYUAAAA1IQgjIlGFAAAAdSEII+JQhQAAAPVBEEZEoQoBAADqiyCMiEAVAgAANBRBGGGPKgQAADgbBGGENaoQAADgbBGEEZaoQgAAgMYiCCPsUIUAAABNgSCMsEIVAgAANBWCMMICVQgAANDUCMKwPaoQAAAgGAjCsDWqEAAAIFgIwrAlqhAAACDYCMKwHaoQAAAgFAjCsBWqEAAAIFQIwrAFqhAAACDUoqweoDYLFy5U7969FR8fr/j4eKWnp2vVqlVWj4UmVnLkpEY9uykQgrPSU/TqxHRCMAAACCpbHxHu1KmTZs2apW7duskYoxdffFHDhw/Xp59+ql69elk9HpoAVQgAAGAVlzHGWD1EQ7Rp00Z//OMfNX78+Hpt7/P55PF45PV6FR8fH+TpUF9UIQAAQLDUN//Z+ojwmcrKyvTKK6/o+PHjSk9Pr3E7v98vv98fuO/z+UIxHhqAq0IAAAA7sH0QLioqUnp6ur777ju1atVKy5YtU8+ePWvcPi8vT7m5uSGcEA1BFQIAANiF7asRp06d0r///W95vV69+uqrev7557V+/foaw3B1R4STk5OpRliMKgQAAAiV+lYjbB+EK8vIyNAFF1ygRYsW1Wt7OsLWowoBAABCKeI6wqeVl5dXOOILe6MKAQAA7MrWQXj69OkaNmyYOnfurKNHj2rJkiVat26d3nnnHatHQx0qVyHSOsZrwa2XUIUAAAC2YesgfOjQIf33f/+39u/fL4/Ho969e+udd97Rtddea/VoqAVVCAAAEA5sHYT/9Kc/WT0CGogqBAAACBe2DsIIH1QhAABAuCEIo9GoQgAAgHBEEEajUIUAAADhiiCMs0IVAgAAhDuCMBqMKgQAAIgEBGE0CFUIAAAQKQjCqBeqEAAAINIQhFEnqhAAACASEYRRK6oQAAAgUhGEUS2qEAAAINIRhFEFVQgAAOAEBGFUQBUCAAA4BUEYkqhCAAAA5yEIgyoEAABwJIKww1GFAAAATkUQdiiqEAAAwOkIwg5EFQIAAIAg7DhUIQAAAH5AEHYIqhAAAAAVEYQdgCoEAABAVQThCEcVAgAAoHoE4QhFFQIAAKB2BOEIRBUCAACgbgThCEMVAgAAoH4IwhGCKgQAAEDDEIQjAFUIAACAhiMIhzmqEAAAAGeHIBymqEIAAAA0DkE4DFGFAAAAaDyCcJihCgEAANA0CMJhgioEAABA0yIIhwGqEAAAAE2PIGxzVCEAAACCgyBsU1QhAAAAgosgbENUIQAAAIKPIGwzVCEAAABCgyBsE1QhAAAAQosgbANUIQAAAEKPIGwxqhAAAADWIAhbhCoEAACAtQjCFqAKAQAAYD2CcIhRhQAAALAHgnCIUIUAAACwF4JwCFCFAAAAsJ8oqweoTV5eni677DLFxcUpMTFRI0aM0M6dO60eq0HWfnlImfM+0JY9hxXnbqanR1+i3OFphGAAAACL2ToIr1+/XtnZ2dq0aZNWr16t0tJSDRkyRMePH7d6tDqVlpVr1qovNbagUIdPlCqtY7xW3DWQPjAAAIBNuIwxxuoh6uvbb79VYmKi1q9fryuvvLJer/H5fPJ4PPJ6vYqPjw/yhD+gCgEAAGCd+ua/sOoIe71eSVKbNm1q3Mbv98vv9wfu+3y+oM91ps+/8eq2P23mqhAAAAA2Z+tqxJnKy8uVk5OjAQMGKC0trcbt8vLy5PF4Arfk5OQQTildkNBKCXFuqhAAAAA2FzbViIkTJ2rVqlX68MMP1alTpxq3q+6IcHJycsirEW1bxVCFAAAAsEBEVSMmT56sFStWaMOGDbWGYElyu91yu90hmqx6Sa1bWPr5AAAAqJutg7AxRlOmTNGyZcu0bt06de3a1eqRAAAAECFsHYSzs7O1ZMkSvf7664qLi9OBAwckSR6PRy1acNQVAAAAZ8/WHWGXy1Xt4/n5+br99tvr9R5WXD4NAAAA1omIjrCNMzoAAADCXNhcPg0AAABoSgRhAAAAOBJBGAAAAI5EEAYAAIAjEYQBAADgSARhAAAAOBJBGAAAAI5EEAYAAIAjEYQBAADgSARhAAAAOBJBGAAAAI5EEAYAAIAjEYQBAADgSARhAAAAOFIzqwcINmOMJMnn81k8CQAAAELhdO47nQNrEvFB+OjRo5Kk5ORkiycBAABAKB09elQej6fG512mrqgc5srLy1VSUqK4uDi5XK6QfKbP51NycrL27t2r+Pj4kHxmuGEf1Y79Uzf2Ud3YR3VjH9WO/VM39lHdrNhHxhgdPXpUSUlJioqquQkc8UeEo6Ki1KlTJ0s+Oz4+nr8UdWAf1Y79Uzf2Ud3YR3VjH9WO/VM39lHdQr2PajsSfBpflgMAAIAjEYQBAADgSAThIHC73ZoxY4bcbrfVo9gW+6h27J+6sY/qxj6qG/uoduyfurGP6mbnfRTxX5YDAAAAqsMRYQAAADgSQRgAAACORBAGAACAIxGEAQAA4EgE4bOwYcMG3XDDDUpKSpLL5dLy5cvrfM26det0ySWXyO1268ILL1RBQUHQ57RKQ/fPunXr5HK5qtwOHDgQmoEtkJeXp8suu0xxcXFKTEzUiBEjtHPnzjpf98orryg1NVWxsbG66KKLtHLlyhBMG3pns38KCgqqrKHY2NgQTRx6CxcuVO/evQMXqE9PT9eqVatqfY1T1s9pDd1HTltDlc2aNUsul0s5OTm1bue0dXSm+uwjp62jmTNnVvl5U1NTa32NndYQQfgsHD9+XH369NGCBQvqtX1xcbEyMzM1ePBgbd++XTk5Obrjjjv0zjvvBHlSazR0/5y2c+dO7d+/P3BLTEwM0oTWW79+vbKzs7Vp0yatXr1apaWlGjJkiI4fP17jaz7++GONGjVK48eP16effqoRI0ZoxIgR+vzzz0M4eWiczf6RfvitRWeuoT179oRo4tDr1KmTZs2apa1bt2rLli26+uqrNXz4cP3jH/+odnsnrZ/TGrqPJGetoTMVFhZq0aJF6t27d63bOXEdnVbffSQ5bx316tWrws/74Ycf1rit7daQQaNIMsuWLat1m/vuu8/06tWrwmO33HKLGTp0aBAns4f67J+1a9caSebw4cMhmcmODh06ZCSZ9evX17jNzTffbDIzMys8dsUVV5gJEyYEezzL1Wf/5OfnG4/HE7qhbOjcc881zz//fLXPOXn9nKm2feTUNXT06FHTrVs3s3r1ajNo0CBz991317itU9dRQ/aR09bRjBkzTJ8+feq9vd3WEEeEQ2Djxo3KyMio8NjQoUO1ceNGiyayp759+6pDhw669tpr9dFHH1k9Tkh5vV5JUps2bWrcxsnrqD77R5KOHTumlJQUJScn13nkL5KUlZVp6dKlOn78uNLT06vdxsnrR6rfPpKcuYays7OVmZlZZX1Ux6nrqCH7SHLeOvrqq6+UlJSk888/X6NHj9a///3vGre12xpqZsmnOsyBAwfUrl27Co+1a9dOPp9PJ0+eVIsWLSyazB46dOigZ555Rv369ZPf79fzzz+vq666Sps3b9Yll1xi9XhBV15erpycHA0YMEBpaWk1blfTOorkLrVU//3To0cPvfDCC+rdu7e8Xq9mz56t/v376x//+Ic6deoUwolDp6ioSOnp6fruu+/UqlUrLVu2TD179qx2W6eun4bsIyeuoaVLl2rbtm0qLCys1/ZOXEcN3UdOW0dXXHGFCgoK1KNHD+3fv1+5ubn66U9/qs8//1xxcXFVtrfbGiIIw3I9evRQjx49Avf79++v3bt3a+7cuVq8eLGFk4VGdna2Pv/881o7VU5W3/2Tnp5e4Uhf//799ZOf/ESLFi3SQw89FOwxLdGjRw9t375dXq9Xr776qrKysrR+/foag54TNWQfOW0N7d27V3fffbdWr14d0V/maoyz2UdOW0fDhg0L/HPv3r11xRVXKCUlRS+//LLGjx9v4WT1QxAOgfbt2+vgwYMVHjt48KDi4+MdfzS4JpdffrkjguHkyZO1YsUKbdiwoc4jBTWto/bt2wdzREs1ZP9U1rx5c1188cXatWtXkKazXkxMjC688EJJ0qWXXqrCwkI99dRTWrRoUZVtnbh+pIbto8oifQ1t3bpVhw4dqnDmraysTBs2bND8+fPl9/sVHR1d4TVOW0dns48qi/R1VFnr1q3VvXv3Gn9eu60hOsIhkJ6ervfee6/CY6tXr661p+Z027dvV4cOHaweI2iMMZo8ebKWLVum999/X127dq3zNU5aR2ezfyorKytTUVFRRK+jysrLy+X3+6t9zknrpza17aPKIn0NXXPNNSoqKtL27dsDt379+mn06NHavn17tQHPaevobPZRZZG+jio7duyYdu/eXePPa7s1ZMlX9MLc0aNHzaeffmo+/fRTI8nMmTPHfPrpp2bPnj3GGGOmTZtmbrvttsD2X3/9tTnnnHPMvffea3bs2GEWLFhgoqOjzdtvv23VjxBUDd0/c+fONcuXLzdfffWVKSoqMnfffbeJiooya9assepHCLqJEycaj8dj1q1bZ/bv3x+4nThxIrDNbbfdZqZNmxa4/9FHH5lmzZqZ2bNnmx07dpgZM2aY5s2bm6KiIit+hKA6m/2Tm5tr3nnnHbN7926zdetWM3LkSBMbG2v+8Y9/WPEjBN20adPM+vXrTXFxsfnss8/MtGnTjMvlMu+++64xxtnr57SG7iOnraHqVL4iAuuoqrr2kdPW0W9+8xuzbt06U1xcbD766COTkZFhzjvvPHPo0CFjjP3XEEH4LJy+3FflW1ZWljHGmKysLDNo0KAqr+nbt6+JiYkx559/vsnPzw/53KHS0P3z2GOPmQsuuMDExsaaNm3amKuuusq8//771gwfItXtH0kV1sWgQYMC++y0l19+2XTv3t3ExMSYXr16mbfeeiu0g4fI2eyfnJwc07lzZxMTE2PatWtnrr/+erNt27bQDx8i48aNMykpKSYmJsYkJCSYa665JhDwjHH2+jmtofvIaWuoOpVDHuuoqrr2kdPW0S233GI6dOhgYmJiTMeOHc0tt9xidu3aFXje7mvIZYwxoTv+DAAAANgDHWEAAAA4EkEYAAAAjkQQBgAAgCMRhAEAAOBIBGEAAAA4EkEYAAAAjkQQBgAAgCMRhAEAAOBIBGEAAAA4EkEYAAAAjkQQBgAAgCMRhAEggnz77bdq3769Hn300cBjH3/8sWJiYvTee+9ZOBkA2I/LGGOsHgIA0HRWrlypESNG6OOPP1aPHj3Ut29fDR8+XHPmzLF6NACwFYIwAESg7OxsrVmzRv369VNRUZEKCwvldrutHgsAbIUgDAAR6OTJk0pLS9PevXu1detWXXTRRVaPBAC2Q0cYACLQ7t27VVJSovLycv3rX/+yehwAsCWOCANAhDl16pQuv/xy9e3bVz169NCTTz6poqIiJSYmWj0aANgKQRgAIsy9996rV199VX//+9/VqlUrDRo0SB6PRytWrLB6NACwFaoRABBB1q1bpyeffFKLFy9WfHy8oqKitHjxYn3wwQdauHCh1eMBgK1wRBgAAACOxBFhAAAAOBJBGAAAAI5EEAYAAIAjEYQBAADgSARhAAAAOBJBGAAAAI5EEAYAAIAjEYQBAADgSARhAAAAOBJBGAAAAI5EEAYAAIAj/T8MSwQR/9pfwQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Create a figure object\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "# Add the first axis to the figure canvas\n",
-    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
-    "\n",
-    "# Add the second axis to the figure canvas\n",
-    "ax2 = fig.add_axes([0.2, 0.5, 0.2, 0.2])\n",
-    "\n",
-    "# Define the x values\n",
-    "x = [1, 2, 3, 4, 5]\n",
-    "\n",
-    "# Define the corresponding y values\n",
-    "y = [2 * value for value in x]\n",
-    "\n",
-    "# Plot (x, y) on the first axis\n",
-    "ax1.plot(x, y)\n",
-    "ax1.set_xlabel('x')\n",
-    "ax1.set_ylabel('y')\n",
-    "ax1.set_title('Plot on ax1')\n",
-    "\n",
-    "# Plot (x, y) on the second axis\n",
-    "ax2.plot(x, y)\n",
-    "ax2.set_xlabel('x')\n",
-    "ax2.set_ylabel('y')\n",
-    "ax2.set_title('Plot on ax2')\n",
-    "\n",
-    "# Call the figure object to show the plot\n",
-    "fig\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 3\n",
-    "\n",
-    "** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "\n",
-    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
-    "\n",
-    "ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.4])\n",
-    "\n",
-    "# Show the plot\n",
-    "fig"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "\n",
-    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
-    "\n",
-    "ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.4])\n",
-    "\n",
-    "x1 = np.linspace(0, 100, 1000)\n",
-    "y1 = np.exp(x1 * 0.1)\n",
-    "ax1.plot(x1, y1, color='blue')\n",
-    "ax1.set_xlim(0, 100)\n",
-    "ax1.set_ylim(0, 10000)\n",
-    "ax1.set_xlabel('X')\n",
-    "ax1.set_ylabel('Z')\n",
-    "\n",
-    "x2 = np.linspace(20, 22, 1000)\n",
-    "y2 = np.exp(x2 * 0.1)\n",
-    "ax2.plot(x2, y2, color='red')\n",
-    "ax2.set_xlabel('X')\n",
-    "ax2.set_ylabel('Y')\n",
-    "ax2.set_title('zoom')\n",
-    "\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 4\n",
-    "\n",
-    "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi4AAAGiCAYAAADA0E3hAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdU0lEQVR4nO3df2zddb348Vfb0VOItMy7u+6HxWUYRAU23VgtSAimugQz7v4w7oLZ5sKPi+4SXaOyOVhFdJ2IuASGixPEP/RuSsAYtwy1shhkZsm2JngZEBi4aWxh0bVzSMva9/3DL/XbrYWd7vTHe3s8kvNHP3w+Pa8C55Vnz2l7ylJKKQAAMlA+1gMAAJws4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBko+hw+e1vfxsLFiyIadOmRVlZWfzsZz9722t27NgRH/rQh6JQKMR73vOeePjhh4cxKpArewMolaLD5ejRozFr1qzYsGHDSZ3/0ksvxSc+8Ym4+uqro62tLb7whS/EjTfeGI8//njRwwJ5sjeAUik7lTdZLCsri8ceeywWLlw45Dm33XZbbN26Nf7whz/0H/vP//zPOHz4cGzfvn24dw1kyt4ATsWEkb6DnTt3RmNj44Bj8+fPjy984QtDXtPd3R3d3d39H/f19cVf//rX+Ld/+7coKysbqVGBIaSU4siRIzFt2rQoLx/5H42zN+D0MBK7Y8TDpb29PWprawccq62tja6urvjHP/4RZ5999gnXtLS0xJ133jnSowFFOnjwYLzrXe8a8fuxN+D0UsrdMeLhMhyrVq2Kpqam/o87Ozvj/PPPj4MHD0Z1dfUYTgZnpq6urqirq4tzzz13rEcZkr0B489I7I4RD5cpU6ZER0fHgGMdHR1RXV096HdNERGFQiEKhcIJx6urqy0gGEOj9ZKLvQGnl1LujhF/sbqhoSFaW1sHHPvVr34VDQ0NI33XQKbsDWAoRYfL3//+92hra4u2traI+OevLba1tcWBAwci4p9P1y5ZsqT//FtuuSX2798fX/7yl+PZZ5+NBx54IH7yk5/EihUrSvMVAOOevQGUTCrSE088kSLihNvSpUtTSiktXbo0XXXVVSdcM3v27FRZWZlmzpyZfvCDHxR1n52dnSkiUmdnZ7HjAiVwqo9BewPOTCPxODylv+MyWrq6uqKmpiY6Ozu9Vg1jIMfHYI4zw+lmJB6H3qsIAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsDCtcNmzYEDNmzIiqqqqor6+PXbt2veX569evj/e+971x9tlnR11dXaxYsSJef/31YQ0M5MneAEqh6HDZsmVLNDU1RXNzc+zZsydmzZoV8+fPj1deeWXQ83/84x/HypUro7m5Ofbt2xcPPvhgbNmyJb7yla+c8vBAHuwNoFSKDpd77703brrppli2bFm8//3vj40bN8Y555wTDz300KDnP/XUU3HFFVfE9ddfHzNmzIiPf/zjcd11173td1vA6cPeAEqlqHDp6emJ3bt3R2Nj478+QXl5NDY2xs6dOwe95vLLL4/du3f3L5z9+/fHtm3b4pprrhnyfrq7u6Orq2vADciTvQGU0oRiTj506FD09vZGbW3tgOO1tbXx7LPPDnrN9ddfH4cOHYqPfOQjkVKKY8eOxS233PKWT/m2tLTEnXfeWcxowDhlbwClNOK/VbRjx45Yu3ZtPPDAA7Fnz5549NFHY+vWrXHXXXcNec2qVauis7Oz/3bw4MGRHhMYR+wNYChFPeMyadKkqKioiI6OjgHHOzo6YsqUKYNec8cdd8TixYvjxhtvjIiISy65JI4ePRo333xzrF69OsrLT2ynQqEQhUKhmNGAccreAEqpqGdcKisrY86cOdHa2tp/rK+vL1pbW6OhoWHQa1577bUTlkxFRUVERKSUip0XyIy9AZRSUc+4REQ0NTXF0qVLY+7cuTFv3rxYv359HD16NJYtWxYREUuWLInp06dHS0tLREQsWLAg7r333vjgBz8Y9fX18cILL8Qdd9wRCxYs6F9EwOnN3gBKpehwWbRoUbz66quxZs2aaG9vj9mzZ8f27dv7f/DuwIEDA75Tuv3226OsrCxuv/32+POf/xz//u//HgsWLIhvfOMbpfsqgHHN3gBKpSxl8LxrV1dX1NTURGdnZ1RXV4/1OHDGyfExmOPMcLoZiceh9yoCALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwwqXDRs2xIwZM6Kqqirq6+tj165db3n+4cOHY/ny5TF16tQoFApx4YUXxrZt24Y1MJAnewMohQnFXrBly5ZoamqKjRs3Rn19faxfvz7mz58fzz33XEyePPmE83t6euJjH/tYTJ48OR555JGYPn16/PGPf4zzzjuvFPMDGbA3gFIpSymlYi6or6+Pyy67LO6///6IiOjr64u6urq49dZbY+XKlSecv3HjxvjWt74Vzz77bJx11lnDGrKrqytqamqis7Mzqqurh/U5gOE71cegvQFnppF4HBb1UlFPT0/s3r07Ghsb//UJysujsbExdu7cOeg1P//5z6OhoSGWL18etbW1cfHFF8fatWujt7d3yPvp7u6Orq6uATcgT/YGUEpFhcuhQ4eit7c3amtrBxyvra2N9vb2Qa/Zv39/PPLII9Hb2xvbtm2LO+64I7797W/H17/+9SHvp6WlJWpqavpvdXV1xYwJjCP2BlBKI/5bRX19fTF58uT43ve+F3PmzIlFixbF6tWrY+PGjUNes2rVqujs7Oy/HTx4cKTHBMYRewMYSlE/nDtp0qSoqKiIjo6OAcc7OjpiypQpg14zderUOOuss6KioqL/2Pve975ob2+Pnp6eqKysPOGaQqEQhUKhmNGAccreAEqpqGdcKisrY86cOdHa2tp/rK+vL1pbW6OhoWHQa6644op44YUXoq+vr//Y888/H1OnTh10+QCnF3sDKKWiXypqamqKTZs2xQ9/+MPYt29ffPazn42jR4/GsmXLIiJiyZIlsWrVqv7zP/vZz8Zf//rX+PznPx/PP/98bN26NdauXRvLly8v3VcBjGv2BlAqRf8dl0WLFsWrr74aa9asifb29pg9e3Zs3769/wfvDhw4EOXl/+qhurq6ePzxx2PFihVx6aWXxvTp0+Pzn/983HbbbaX7KoBxzd4ASqXov+MyFvw9BhhbOT4Gc5wZTjdj/ndcAADGknABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAwrXDZs2BAzZsyIqqqqqK+vj127dp3UdZs3b46ysrJYuHDhcO4WyJzdAZyqosNly5Yt0dTUFM3NzbFnz56YNWtWzJ8/P1555ZW3vO7ll1+OL37xi3HllVcOe1ggX3YHUApFh8u9994bN910Uyxbtize//73x8aNG+Occ86Jhx56aMhrent749Of/nTceeedMXPmzLe9j+7u7ujq6hpwA/I20rvD3oAzQ1Hh0tPTE7t3747GxsZ/fYLy8mhsbIydO3cOed3Xvva1mDx5ctxwww0ndT8tLS1RU1PTf6urqytmTGCcGY3dYW/AmaGocDl06FD09vZGbW3tgOO1tbXR3t4+6DVPPvlkPPjgg7Fp06aTvp9Vq1ZFZ2dn/+3gwYPFjAmMM6OxO+wNODNMGMlPfuTIkVi8eHFs2rQpJk2adNLXFQqFKBQKIzgZMJ4NZ3fYG3BmKCpcJk2aFBUVFdHR0THgeEdHR0yZMuWE81988cV4+eWXY8GCBf3H+vr6/nnHEybEc889FxdccMFw5gYyYncApVLUS0WVlZUxZ86caG1t7T/W19cXra2t0dDQcML5F110UTz99NPR1tbWf7v22mvj6quvjra2Nq9BwxnC7gBKpeiXipqammLp0qUxd+7cmDdvXqxfvz6OHj0ay5Yti4iIJUuWxPTp06OlpSWqqqri4osvHnD9eeedFxFxwnHg9GZ3AKVQdLgsWrQoXn311VizZk20t7fH7NmzY/v27f0/dHfgwIEoL/cHeYGB7A6gFMpSSmmsh3g7XV1dUVNTE52dnVFdXT3W48AZJ8fHYI4zw+lmJB6Hvr0BALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwgUAyIZwAQCyIVwAgGwIFwAgG8IFAMiGcAEAsiFcAIBsCBcAIBvCBQDIhnABALIhXACAbAgXACAbwwqXDRs2xIwZM6Kqqirq6+tj165dQ567adOmuPLKK2PixIkxceLEaGxsfMvzgdOX3QGcqqLDZcuWLdHU1BTNzc2xZ8+emDVrVsyfPz9eeeWVQc/fsWNHXHfddfHEE0/Ezp07o66uLj7+8Y/Hn//851MeHsiH3QGUQllKKRVzQX19fVx22WVx//33R0REX19f1NXVxa233horV6582+t7e3tj4sSJcf/998eSJUsGPae7uzu6u7v7P+7q6oq6urro7OyM6urqYsYFSqCrqytqampO6TE40rvD3oDxpxS743hFPePS09MTu3fvjsbGxn99gvLyaGxsjJ07d57U53jttdfijTfeiHe+851DntPS0hI1NTX9t7q6umLGBMaZ0dgd9gacGYoKl0OHDkVvb2/U1tYOOF5bWxvt7e0n9Tluu+22mDZt2oAFdrxVq1ZFZ2dn/+3gwYPFjAmMM6OxO+wNODNMGM07W7duXWzevDl27NgRVVVVQ55XKBSiUCiM4mTAeHYyu8PegDNDUeEyadKkqKioiI6OjgHHOzo6YsqUKW957T333BPr1q2LX//613HppZcWPymQLbsDKJWiXiqqrKyMOXPmRGtra/+xvr6+aG1tjYaGhiGvu/vuu+Ouu+6K7du3x9y5c4c/LZAluwMolaJfKmpqaoqlS5fG3LlzY968ebF+/fo4evRoLFu2LCIilixZEtOnT4+WlpaIiPjmN78Za9asiR//+McxY8aM/tez3/GOd8Q73vGOEn4pwHhmdwClUHS4LFq0KF599dVYs2ZNtLe3x+zZs2P79u39P3R34MCBKC//1xM53/3ud6Onpyc++clPDvg8zc3N8dWvfvXUpgeyYXcApVD033EZCyPxe+DAycvxMZjjzHC6GfO/4wIAMJaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJAN4QIAZEO4AADZEC4AQDaECwCQDeECAGRjWOGyYcOGmDFjRlRVVUV9fX3s2rXrLc//6U9/GhdddFFUVVXFJZdcEtu2bRvWsEDe7A7gVBUdLlu2bImmpqZobm6OPXv2xKxZs2L+/PnxyiuvDHr+U089Fdddd13ccMMNsXfv3li4cGEsXLgw/vCHP5zy8EA+7A6gFMpSSqmYC+rr6+Oyyy6L+++/PyIi+vr6oq6uLm699dZYuXLlCecvWrQojh49Gr/4xS/6j334wx+O2bNnx8aNGwe9j+7u7uju7u7/uLOzM84///w4ePBgVFdXFzMuUAJdXV1RV1cXhw8fjpqammF9jpHeHfYGjD+l2B0nSEXo7u5OFRUV6bHHHhtwfMmSJenaa68d9Jq6urr0ne98Z8CxNWvWpEsvvXTI+2lubk4R4ebmNs5uL774YjErY1R3h73h5jZ+b8PdHYOZEEU4dOhQ9Pb2Rm1t7YDjtbW18eyzzw56TXt7+6Dnt7e3D3k/q1atiqampv6PDx8+HO9+97vjwIEDpSu2EfZmZeb03Z6ZR0eOM7/57MU73/nOYV0/GrvD3hgbOc4ckefcOc58qrtjMEWFy2gpFApRKBROOF5TU5PNf6w3VVdXm3kUmHl0lJeP319EtDfGVo4zR+Q5d44zl3J3FPWZJk2aFBUVFdHR0THgeEdHR0yZMmXQa6ZMmVLU+cDpx+4ASqWocKmsrIw5c+ZEa2tr/7G+vr5obW2NhoaGQa9paGgYcH5ExK9+9ashzwdOP3YHUDLF/lDM5s2bU6FQSA8//HB65pln0s0335zOO++81N7enlJKafHixWnlypX95//ud79LEyZMSPfcc0/at29fam5uTmeddVZ6+umnT/o+X3/99dTc3Jxef/31YscdM2YeHWYeHaWYebR3x5n673m05ThzSnnObeZ/KjpcUkrpvvvuS+eff36qrKxM8+bNS7///e/7/9lVV12Vli5dOuD8n/zkJ+nCCy9MlZWV6QMf+EDaunXrKQ0N5MnuAE5V0X/HBQBgrIzfXxEAADiOcAEAsiFcAIBsCBcAIBvjJlxyfLv7YmbetGlTXHnllTFx4sSYOHFiNDY2vu3XOBKK/ff8ps2bN0dZWVksXLhwZAccRLEzHz58OJYvXx5Tp06NQqEQF1544aj//1HszOvXr4/3vve9cfbZZ0ddXV2sWLEiXn/99VGaNuK3v/1tLFiwIKZNmxZlZWXxs5/97G2v2bFjR3zoQx+KQqEQ73nPe+Lhhx8e8TmPZ2+MDntj9OS0O8Zsb4z1rzWl9M+/71BZWZkeeuih9L//+7/ppptuSuedd17q6OgY9Pzf/e53qaKiIt19993pmWeeSbfffnvRfxtmtGe+/vrr04YNG9LevXvTvn370mc+85lUU1OT/vSnP43bmd/00ksvpenTp6crr7wy/cd//MfoDPv/FDtzd3d3mjt3brrmmmvSk08+mV566aW0Y8eO1NbWNm5n/tGPfpQKhUL60Y9+lF566aX0+OOPp6lTp6YVK1aM2szbtm1Lq1evTo8++miKiBPeDPF4+/fvT+ecc05qampKzzzzTLrvvvtSRUVF2r59++gMnOyN8Trzm+yNkZ97rHfHWO2NcREu8+bNS8uXL+//uLe3N02bNi21tLQMev6nPvWp9IlPfGLAsfr6+vRf//VfIzrn/6/YmY937NixdO6556Yf/vCHIzXiCYYz87Fjx9Lll1+evv/976elS5eO+gIqdubvfve7aebMmamnp2e0RjxBsTMvX748ffSjHx1wrKmpKV1xxRUjOudQTmYBffnLX04f+MAHBhxbtGhRmj9//ghONpC9MTrsjdGT8+4Yzb0x5i8V9fT0xO7du6OxsbH/WHl5eTQ2NsbOnTsHvWbnzp0Dzo+ImD9//pDnl9pwZj7ea6+9Fm+88UZJ3zHzrQx35q997WsxefLkuOGGG0ZjzAGGM/PPf/7zaGhoiOXLl0dtbW1cfPHFsXbt2ujt7R23M19++eWxe/fu/qeE9+/fH9u2bYtrrrlmVGYejhwfgznOfDx74+3luDcizozdUarH4Ji/O/RovN19qQ1n5uPddtttMW3atBP+I46U4cz85JNPxoMPPhhtbW2jMOGJhjPz/v374ze/+U18+tOfjm3btsULL7wQn/vc5+KNN96I5ubmcTnz9ddfH4cOHYqPfOQjkVKKY8eOxS233BJf+cpXRnze4RrqMdjV1RX/+Mc/4uyzzx7R+7c37I2h5Lg3Is6M3VGqvTHmz7icidatWxebN2+Oxx57LKqqqsZ6nEEdOXIkFi9eHJs2bYpJkyaN9Tgnra+vLyZPnhzf+973Ys6cObFo0aJYvXp1bNy4caxHG9KOHTti7dq18cADD8SePXvi0Ucfja1bt8Zdd9011qMxjtgbIyfHvRFx5u6OMX/GJce3ux/OzG+65557Yt26dfHrX/86Lr300pEcc4BiZ37xxRfj5ZdfjgULFvQf6+vri4iICRMmxHPPPRcXXHDBuJo5ImLq1Klx1llnRUVFRf+x973vfdHe3h49PT1RWVk57ma+4447YvHixXHjjTdGRMQll1wSR48ejZtvvjlWr14d5eXj7/uLoR6D1dXVI/5sS4S9MVrsjdHZGxFnxu4o1d4Y868qx7e7H87MERF333133HXXXbF9+/aYO3fuaIzar9iZL7roonj66aejra2t/3bttdfG1VdfHW1tbVFXVzfuZo6IuOKKK+KFF17oX5YREc8//3xMnTp1VJbPcGZ+7bXXTlgwby7QNE7fSizHx2COM0fYGyM9c8TY742IM2N3lOwxWNSP8o6Q0X67+7GYed26damysjI98sgj6S9/+Uv/7ciRI+N25uONxW8HFDvzgQMH0rnnnpv++7//Oz333HPpF7/4RZo8eXL6+te/Pm5nbm5uTueee276n//5n7R///70y1/+Ml1wwQXpU5/61KjNfOTIkbR37960d+/eFBHp3nvvTXv37k1//OMfU0oprVy5Mi1evLj//Dd/rfFLX/pS2rdvX9qwYcOY/Dq0vTH+Zj6evTFyc4/17hirvTEuwiWlPN/uvpiZ3/3ud6eIOOHW3Nw8bmc+3lgsoJSKn/mpp55K9fX1qVAopJkzZ6ZvfOMb6dixY+N25jfeeCN99atfTRdccEGqqqpKdXV16XOf+1z629/+NmrzPvHEE4P+//nmnEuXLk1XXXXVCdfMnj07VVZWppkzZ6Yf/OAHozbvm+yN8Tfz8eyN4uS0O8Zqb5SlNA6fTwIAGMSY/4wLAMDJEi4AQDaECwCQDeECAGRDuAAA2RAuAEA2hAsAkA3hAgBkQ7gAANkQLgBANoQLAJCN/wM67QKIu94xvgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplot(1,2,1)\n",
-    "plt.subplot(1,2,2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplot(1,2,1)\n",
-    "plt.plot(x2, y2,'b--', linewidth=4) # More on color options later\n",
-    "plt.subplot(1,2,2)\n",
-    "plt.plot(x1,y1,'r', linewidth=4);\n",
-    "\n",
-    "x1 = np.linspace(0, 100, 1000)\n",
-    "y1 = np.exp(x1 * 0.1)\n",
-    "ax1.set_xlim(0, 100)\n",
-    "ax1.set_ylim(0, 10000)\n",
-    "\n",
-    "x2 = [1, 2, 3, 4, 5]\n",
-    "\n",
-    "y2= [2 * value for value in x2]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
-    "\n",
-    "ax1 = axes[0]\n",
-    "ax2 = axes[1]\n",
-    "\n",
-    "x1 = np.linspace(0, 100, 1000)\n",
-    "y1 = np.exp(x1 * 0.1)\n",
-    "ax1.plot(x1, y1, 'r', linewidth=4)\n",
-    "ax1.set_xlim(0, 100)\n",
-    "ax1.set_ylim(0, 10000)\n",
-    "ax1.set_xlabel('X')\n",
-    "ax1.set_ylabel('Y')\n",
-    "ax1.set_title('Plot 1')\n",
-    "\n",
-    "x2 = [1, 2, 3, 4, 5]\n",
-    "y2 = [2 * value for value in x2]\n",
-    "ax2.plot(x2, y2, 'b--', linewidth=4)\n",
-    "ax2.set_xlabel('X')\n",
-    "ax2.set_ylabel('Y')\n",
-    "ax2.set_title('Plot 2')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Great Job!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From ef9146c09f314593c82964abf01c264bbf676c6e Mon Sep 17 00:00:00 2001
From: ChaitanyaSagar89 <106511025+ChaitanyaSagar89@users.noreply.github.com>
Date: Wed, 7 Jun 2023 14:14:41 +0530
Subject: [PATCH 4/5] Delete 07-Seaborn Exercises(sol).ipynb

---
 Assignment 2/07-Seaborn Exercises(sol).ipynb | 601 -------------------
 1 file changed, 601 deletions(-)
 delete mode 100644 Assignment 2/07-Seaborn Exercises(sol).ipynb

diff --git a/Assignment 2/07-Seaborn Exercises(sol).ipynb b/Assignment 2/07-Seaborn Exercises(sol).ipynb
deleted file mode 100644
index 88f6e38..0000000
--- a/Assignment 2/07-Seaborn Exercises(sol).ipynb	
+++ /dev/null
@@ -1,601 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Seaborn Exercises\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The Data\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.set_style('whitegrid')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "titanic = sns.load_dataset('titanic')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>survived</th>\n",
-       "      <th>pclass</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age</th>\n",
-       "      <th>sibsp</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>embarked</th>\n",
-       "      <th>class</th>\n",
-       "      <th>who</th>\n",
-       "      <th>adult_male</th>\n",
-       "      <th>deck</th>\n",
-       "      <th>embark_town</th>\n",
-       "      <th>alive</th>\n",
-       "      <th>alone</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>male</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>man</td>\n",
-       "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>no</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>female</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.2833</td>\n",
-       "      <td>C</td>\n",
-       "      <td>First</td>\n",
-       "      <td>woman</td>\n",
-       "      <td>False</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Cherbourg</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>female</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.9250</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>woman</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>female</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>53.1000</td>\n",
-       "      <td>S</td>\n",
-       "      <td>First</td>\n",
-       "      <td>woman</td>\n",
-       "      <td>False</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>male</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>man</td>\n",
-       "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>no</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
-       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
-       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
-       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
-       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
-       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
-       "\n",
-       "     who  adult_male deck  embark_town alive  alone  \n",
-       "0    man        True  NaN  Southampton    no  False  \n",
-       "1  woman       False    C    Cherbourg   yes  False  \n",
-       "2  woman       False  NaN  Southampton   yes   True  \n",
-       "3  woman       False    C  Southampton   yes  False  \n",
-       "4    man        True  NaN  Southampton    no   True  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "titanic.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exercises\n",
-    "\n",
-    "** Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints.**\n",
-    "\n",
-    "** *Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!* **"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.JointGrid at 0x257abf91e50>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# CODE HERE\n",
-    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
-    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
-    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
-    "sns.jointplot(data=titanic, x='fare', y='age', kind='scatter')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.JointGrid at 0x11d0389e8>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d038cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 500.0)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# CODE HERE\n",
-    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
-    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
-    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
-    "sns.histplot(data=titanic, x='fare')\n",
-    "plt.ylim(0, 500)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# CODE HERE\n",
-    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
-    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
-    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
-    "sns.boxplot(data=titanic, x='class', y='age')\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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o1P43120Cs2/fPpx99tl45513sHz5cnR0dGD8+PGYMmUKGhsbkZeX57N9eno6ampqNB9n+/bt4RpyVH1T3yz9+sFmVFVVhbz/LABzhqUASOl6wXEQVVUHQ9rXzaub0e4MfBTJf8yR/m4U3xhfFIxwXhMpOnSbwLS2tmLv3r1YtWoVFi1ahMbGRsyZMwcpKSloa2uDzWbz2d5ms8HhcGg+TkFBgWw7bqMaWLkZ2w4E9tAY2KcHioqKoj8gFdSO2YzfjcyD8UVkbk6nU9XNB90mMImJiTh+/Dj+9Kc/4eyzzwYA/PDDD3jttdfQr1+/gMmKw+FAcnKy5uNYrVZTTmDKRuVh8stbfGqrWCxA2cg8w34ftWM243cj8wglvtjckeQwPoxFtyTezMxMJCUleSYvAHDuuefi4MGDyM7ORlNTk8/2TU1NyMrKivYwdWPGGhVqx2zG70bmEWx8MfmX5DA+jEe3OzCFhYVob2/Hd999h3PPPRcAsGfPHpx99tkoLCzEs88+C5fLBYvFApfLhS+//BKTJ0/Wa7i6MGONCrVjNuN3I/MIJr7kipkxVonxYTy63YE577zzUFJSghkzZmDXrl347LPPsHLlStx0000YPXo0mpubsXDhQuzevRsLFy5EW1sbxowZo9dwiSjGsZgZyWF8GI+ulXj/+Mc/Yv78+bjpppuQkpKCW265BRMmTIDFYsGKFSswd+5cvP766xg4cCBWrlwJu92u53BjhtI6rtT7AHxeG56bgcrapqDXgrmWTEYzIDsVW/cHJv+ymBkBjA8jsrhcUi34zM/pdKKqqgpFRUVMDPXiXsf1ZrEAy28tRumQ3tLvo6tztRzvfYQ6BiI9VFTXSyb/Mj+LAMZHNKn9/c1mjnFGqWmZ5Psq9qul8Rkbp5ERMbmc5DA+jEfXJSSKPqV1XNH7aqhdC+ZaMhkVk8tJDuPDWDiBiTNK67ii99VQuxbMteT4Jsp/Yl4UEWnBJaQ4o9S0TPJ9FfvV0viMjdPil6iWxuK1u1hjg4g04QQmziit40q+P6EYKyb4vjalJDfotWCuJccvUf7T85vrJF9nXhQRifApJCKKmvzZ69DWob67r91mxY55oyM4IiIyGrW/v5kDYxLe+QHZPZIAAA3N7RHPFQg2L4H5DCRFlP+UYrOizRE4sWFeFBGJcAnJBPzzBuoOt6LucGvEcwWC7f3BniEkIsp/umNYDvOiiEgTTmBMQCpvwFukcgWCrdfCOi8kIsp/mj5mEPOiyHAqqusxbukm5M9eh3FLN/GPMIPhEpIJqKnNEokaKsHWa2GdF5IjqqXBGhtkJP4Vw913klkx3Dh4B8YEBmSnKm4TiVwB0XGVjhXM5/iXTnzh+Saj451k4+MExgSk8ga8RSpXINh6LVo/x5yZ+MLzTWbAO8nGxwmMCfjnDeSk25GTbo94rkCw9Vq0fo5/6cQXnm8yg2DvQFP0MAfGJPTKDwj2uFo+x7904gvPN5nB1JF5kt2n+WSccfAODOmOf+nEF55vMgNWDDc+3oGJc3o21nMfY+dB6b+8h+emh/V4ZAyh/GXrHTPWBAucnS7k90kLiE+12xHJ4ZNxxsZWAnHM/zFBoOsXyeSf5mLZxtqA18P5+KDUsf2F+5hkHBXV9SjfUIuahhb0z05DWUmu4l+2cjHjHStqtyMiY2IrAVIUTGO9cF34lYrzReKYZBzB/GUrFzPesaJ2OyIyN+bAxDFRMqVUTxogvEmWaorzhfuYZG5KMeOOFbXbEZG58Q5MHItmYz3/nJrsHkmoO9yq+DkmdpKbKF7d3LGidjsiMjfegYlj0WqsJ1W4bK+KyQsfWSRvcgUdvWNF7XZEZG6cwMSxaDXWk8y1AZCTbvc5xpSSXD6ySELe8ZqUmAC7zQpbYkJArKjdjojMjU8hUcTlz16Hto7AJSm7zYod80brMCIiIjIqPoVEAMR1XsKxr+G5GVj79UF8f6QVLgD9etkxY2x+wP5FOQnMRSCj8Y7x7B5JAICG5vaI1UIiouDxDkwME9V5CaYOhpq6LW4rJhQHFBWTKlzG2/lkJEoxzhoyRNGh9vc3c2BiWDib5qmp2+LZ1m//LMlNZqAU42w4SWQsXEKKYeFsmqe2boto/yzJTUanJsZZQ4bIODiBiWFqck/U5sgo1dYQ7Z/ILNTEeP/stKj0CaPQGP0chTI+o3+3aOISUgwT1Xlx18GQqs8y+eUtqKiuD9jX8NwM1cdlnQ0yI7n6MUDXz87w3HTVPzOkDy3XNT2EMj6jf7do4wQmhinlnmjJkamsbVI8Xk5Gd6ycwNwWMif/n5ecdDty0u0+PzuVuwN/DpgbYyzhzP2LhFDGZ/TvFm26LiF99NFHuOeee3xeKy0txVNPPYUdO3Zg7ty5+Pbbb5GXl4dHHnkE559/vk4jNS+53BMtOTJK+QF2mxUbfl+ieXxERqKUq/WbVVWSrzM3xjjCmfsXCaGMz+jfLdp0vQOze/dujBw5Eps2bfL8b8GCBWhtbcXEiRNx0UUXYfXq1Rg6dCgmTZqE1lbl8vOk3oDsVMnXpXJYRNvKfYYo1mj5mSF9GP0chTI+o3+3aNP1DkxtbS0GDBiAzMxMn9fffPNNJCUlYdq0abBYLJg1axY+/fRTrFu3DuPHj9dptNEXyWStiup6HGvrCHhd1Ctm6si8gFou3p8ZnpuOkiXrA4raAWDCWYyJVFzqkZyo5phSxe28sb+SsUhdq6J5jpRiKpTx6f3djEbXQnbXXnstJkyYEDApmT17Ntrb2/H44497XnvwwQdhs9kwb948Vfs2eyG7cBahU7NvoKs30cyx+cIclorqepRvqMWug82wJlhwstOF/D49MDw3HctUrsGyGJi5RSouIxnvoRxTchsA/dLtONTSjv7ZaSgryWXel8G4r1U1DS1RPUdq4ziU8en13aLJ8K0EXC4XvvvuO2zatAkrVqyA0+nE6NGjcd9996GxsRF5eXk+26enp6OmpkbzcZzOwB48ZvDMPwTJWut344pBmRKfCG3fAHBGSjdcPihT+N/sikGZkse+tnyz6mOH6zuQPiIVl5GM91COKbkNun5WPnngp57XzHqdiVVS16ponCO1cRzK+PT6btGk9vvoNoH54Ycf0NbWBpvNhieffBL79+/HggULcOLECc/r3mw2GxwOh+bjbN++PVxDjqpv6pulXz/YjKqqKkPtW7Q/4fZh+A6kj0jFZSTjPZRj6jEuMi/GS3TpNoE5++yz8a9//QtnnHEGLBYL8vPz0dnZiT/84Q+4+OKLAyYrDocDycnJmo9TUFBgyiWkgZWbse1AYFGtgX16oKioKOL7/nBHA5Zt2IOaQ8eRmmTFsbaTcDg7kdLNituG/QjTSgcq7k94fJnv4H3c/lmpmFJyHq4anK3p+1HkRCouIxnvoRxTj3GReYU7XuL1euh0OlXdfNA1ibdnz54+/56bm4v29nZkZmaiqcm33kJTUxOysrI0H8NqtZpyAlM2SpCsNTIv5O+jtO+K6npMeeUrz3ttHU6ff17x6XdIsCRg+phBp/f30haoSaaS+w7+x9124BimvvoVc2YMJFJxGcl4D+WYeoyLzCuc8cLroTLdHqP+7LPPcMkll6Ctrc3z2s6dO9GzZ08UFxfjq6++gju/2OVy4csvv0RhYaFew426SDZADKbAnb8XNtf57m9CMWyJ0uFkt1lVfQcWaTK+SMWlHg0/1RyTjUhJi3DGC6+HynR7Cun48eMYO3YsfvzjH6OsrAz79u3DQw89hNtuuw033XQTrrzySlx99dW48cYbsWrVKqxbtw4ffvgh7Ha7qv2b/SkkPeXPXudz10Wk7rGrVX3ObrNix7zRQR9X7eeJiGJFPF8PDf8UUmpqKv72t7/h0UcfxXXXXYfu3bvjxhtvxN133w2LxYIVK1Zg7ty5eP311zFw4ECsXLlS9eQl1oSjPoZULYuG5vaA/antqWG3BQaVmuaRct9H7ecpPoQa92x6R5ESjdgK9XoaD3StAxNJsXIHJhz1MUR1X/z3B0B2O29TS3IxbfSggONIrf9630KV+z4AFD9P8SHUuNejrgzFh2jFVqjXUzPHudrf32zmaHDhWAdVymlx70+0XWKCxZPfYrdZJScvgLr1X7nvw3wDcgs17pk/QJESrdgK9XoaD3R9ComUhaN5l1IjRvf+RPfibIkJqtdclZrhKX0fpc9TfAg17tn0jiIlmrEV6vU01nECY3DZPZJQdziwiaVUXojW3BIfLqD9pHTirtocFP/jD8/NQGVtk894mOdCaoQaJ1p+boi05JFE6xpWUV2PRWt2BvSX8x5XvF9PuYRkYBXV9ZIXYSCweZd7LXTr/mNo63Bi6/5jmPzyFlRU12PqyDxYLPLHau1wolPQqFFNozCp4y/bWBswnuG5GQFjiedmZCRNKma1xKLanxsiuWunlFBiU+uY6g63otPVtSxUd7gVk17yHVc0xmJknMAYmCgnJSfdHpAXoiW3JCfdjpx0e1d9lm7SCVIJFmjKQVFTO8blAir3HGaeCykKJR9Ky88NkdY8kmjk6sldT73HFe95g1xCMjDR+uahlnbV2yrlluTPXif5ueRuVrxbdqnaoarKs3GPh3kupEawcaLl54YomDySSF/D5K6n/uOK5+spJzAGpmV9M9iaAaHmCrj3d0KQPxPsfonUCndMU3wxYh6JXN4i4/g0LiEZmJb1TTXbSq317pW40AeT96KmmlA8rc1SdEjFdN3hVvinfDH2SMSIeSRTR+YFxLAb4/g0TmAMTMv6ZtA1A9CVGxDOXAN3/syUEblxuzZL0SGKwX5BxjTFHyPmkbj7y+Wk25Fg6bqm5mR0x8oJjGNvXEIyOC3rm8HWDPj+SCuS3G0FSnJV/4CI9qc1f4YoWHL5Lhv+MDLKoyGzMlIeif+S6DKTV9WNJN6BiSMDslMlX+90QdXjg2r3xzVaihbGIMUSrY90xzvegYlRUoWZpo7MC+it4c/70WslUvuTyrsRFYhyF2rae6QrZ+FHEoWayHyi2VxOTQxGQjw30DO7aJ87Ldc5pXIYwe43VrGZYwxSapi4aM1OYaEvQFu79orqepRvqEVNQwv6Z6ehzGsJSmkcUo0jLQCWT+AtU7PSo7mcXAxG6nix2EAvHkT73Ika6Yquc/mz16GtI/CJTv9rstb9mo3a39+8AxOD5Gbx75Zdqlh0Tsvtd7m1Y9kCUYJ5swvq7wCR8Wj5CzJcop2/oMd3pPCI9rkTXWtF1zm1j3Rr3W+sYg5MDFIqzCRXJCmct9/lxqGlUBOZRzw0l4uH7xiron3utF7n1D7SzetnF05gDKyiuh7jlm5C/ux1GLd0U9iSa0Xv221WTB6Ri2fW79Z8TKmxi/TPThOOwXucZD5GTaoN9mdJilG/Y7zScm7Dfe6Ujq31Oqf2kW5eP7twAmNQoWSjK83iRe/fPjwHyzYENmDUerF3j11qLdc9jqkj8yQ/awELNZnZ8NwMwevpUR7JaeF+ssOIhc/ilZ6NGNUcO5jrXOmQ3ni37FLsmDca75ZdKpnP1eeMFOG44ikOOYExKK0NxrwpzeJF71fubgr6mEpjB7ru8LjHUTqkN1b4F2pKt2MFCzWZWmVtYAx1vX44yiM5LZSfJSlGLHwWr/RsxKjm2JG6zm38tlHydVtiQlzFIZN4DSrUtVqlxEap93+zqiqkY7rJrc96/3AZqXgUhYcR80MiMSbGrjHo2YhR7bEjEStSd7cBwHGyM6zHMTpOYAwq1AZjwdQ6CFdTMyM2R6PoMOK5D+eYWP/FWPSMN9Gxs9KSMG7pJp8YARB03EjFXEo3q/Bx63jCJSSDCmWtNtg1/3CtDzNHIH4Z8dyHa0yskmo8esab1LEBoO5wq0+MTHppS9BxI4q5EQMyJbe/Y3hOiN/KXDiBMahQ1mqDXfMP1/owcwTilxHPfbjGFO5cGgqdnvEmdeycdLuqz6qNG1HMHWw+gSkjcj13XOw2K6aW5GLa6EHavoTJsRJvDFJbzZGI1OPPFSkRxYgUNXETrzHHSrwxbPHaXXi+sg5tHU6kdLPijuE5mD7m9Mw7GuvCkcoFYI6Buag5X97bZPdIAgA0uLufh/H8qo2dYGPMiPk9pEypH1swsSD6nChGpKiJG9H+TnQ4MW7ppri/PvIOjMksXrsLyzYG3nqcMiLXM4mpqK6XbHAXrlurkeonwh4z5qLmfIl6toi2j+RYtGwnOkYkf64o/LT2Y1MTC0r7lGqYa0FXmX/v7dXEjVTMaR2vGan9/R10DkxLSwteeeUVLFiwAEeOHMH69evx/fffB7s7Uun5yjrJ11/YfPr1SK8LRyoXgDkG5qLmfCn13QrX+VUbO5Gsr0TGI3e+g40FpX5K/jGyckIxlk8ILm6895cgkTAc79fHoJaQvv32W9x+++3o06eP558//PBDrFu3DitWrMDFF18c7nHSKaL11VaH7+uRrFMRqVofRqwhQmJqzpdcTSCp7SM5Fi3bibD+i7nInW/RXQ2lWFCKIVGMBBs37v2J8mHi+foY1B2YBQsW4KabbsLq1avRrVs3AMCiRYtw88034/HHHw/rAMlXSjfp22nRfP4/Ur1g2GPGXNScL7meLVLbR3IsWraj2CB3voONBb1iiLEbKKgJzPbt23HNNdcEvH7jjTdi9275W8YUGtFz/mqe/w9XQ7tI1V4wYg0RElNzvkS1Mrwda3WEXEtFbewwxmKb/zVueG6G8HxLxgK64tH9+cVrdwVcM0X9jSIdQ4zdQEFNYHr16oXvvvsu4PUvv/wS6en6NW2LB9PHDArq+f9wFuGKVC4AcwzMRc358t8mJ92OrDSbz37qDreGXBBObewwxmKX1DVu+ae1mPzTXMnzLRWbLnTFo/vzyzYGNret+v5owLH9k3QjgbEbKKinkFatWoVnnnkGkydPxpIlSzBr1iwcPHgQL7zwAu6//37ceuutmgcyceJE9OrVC4899hgAYMeOHZg7dy6+/fZb5OXl4ZFHHsH555+ven+x+hRSsMYt3ST5OF5h3554t+xSHUZE8YqxSJEgiqucdDvOSOmm+Ki06PP+7N2saJXIRdEzfmOt/EREn0K68cYb8cgjj2DNmjVITk7G448/jk2bNmH+/PlBTV4++OADbNy40fPvra2tmDhxIi666CKsXr0aQ4cOxaRJk9Da2hrMcAlMkCXjYCxSJIjiyr+0v+hun5qEcwCSkxdAv/iN5xYXQReyGzVqFEaNGhXyAI4ePYrHH38cBQUFntfWrFmDpKQkTJs2DRaLBbNmzcKnn36KdevWYfz48SEfMxZonXGLCiK1qSiIFEohslj7y4BCl90jCXWHA/8YkWqCx1ghtdQWkfN+5DmYz4vuwMhdS8NxHRRdh6WIvmOsCWoJacaMGdI7s1jQrVs3ZGZm4qqrrsKAAQNU7SszMxOHDh0CADz22GOYPXs22tvbfZ5oevDBB2Gz2TBv3jxVY4zlJaRginEFWxAplEJkLExH/pTiyRtjhbRQusZ5kyrFr+bzFgsweUQulm+sVX0tDcd1UMvPjZuZ2w1EtJVA9+7d8corr6CwsBBFRUVwuVyorq7G//3f/+GKK65AfX09nn32WTz55JMYOXKkcD+bN2/G//3f/+G9997Dww8/7Hm9sbEReXm+md7p6emoqanRPFanU11fCjN55h+CQkrrd+OKQdJdSq8YlInym4di+cY92H7gGDpd6j4vdSy1xw1mnBTblOLJG2OFtPC+xtUcOo7+Wak42tqBvUcC7/b1z0oN+N3g/vwDr2+T7j/UzYo//+oCXDk4Gxec3UP1tTQc10EtPzduUt/RLNSOO6gJzN69ezFlyhTcd999Pq8vX74cVVVVWLFiBd544w385S9/EU5g2tvbMXfuXMyZMwfJyck+77W1tcFm831SwWazweFwaB7r9u3bNX/G6L6pb5Z+/WAzqqqqhJ/LAjBnWApuXt2Mdmfgnw9SnxcdS81xgx0nxS418eSzPWOFNHBf44AUAMC/DliwpLLVt4w/gNE/skjGVRaAzs5OyX13dnYi03EQVVUHNV1Lw3Ed1PpzI/cdY0lQE5gvvvgCs2bNCnh99OjRKC8vBwBceumlWLBggXAfS5cuxfnnn4+f/OQnAe8lJSUFTFYcDkfAREeNgoKCmFtCGli5GdsOBK7VOjpdeKSyDVNKzsNVg7M1fz7N3g2PVLZ5/nqZUnIeevdsxl6JfAWf/fXpgaKiItXHEW0v5cMdDVi2YY/PmOS+G0WH+7zsqm+BNcECZ6cLg3qnCc+Pe3uH/5+rCrTEChEAPF7xDV7c/L2n2e2Vg7PQ0Nx++hoy4jxcGcT1USoWe3/yqeQdnt49UzzbivbXu2dKwPVWdG0T7cObvZsVsEDVdzQ6p9Op6uZDUBOYvn37oqKiApMmTfJ5/aOPPkKfPn0AAHV1dejVq5dwHx988AGampowdOhQAPBMWCoqKvCzn/0MTU1NPts3NTUhKytL81itVmvMTWDKRuVJrtV2uoBtB45h6qtfya6vlo3Kk1xPbWxxoLGl6zxsO3AMU175SnEsFgtQNjJP8r+x1DjltvdXUV3vMwY1340iz/+84NTdXtH5CdheQKrhndpYIQK6mt2u+PR0jbK2Dic+3HEIU0bk4t17LlO1D7XXrYrqesnJC9D15NPHuxpROqS39P5ObeOmdG27NC9DdgJjsQBP3lgUdzVhgnqMevr06XjmmWcwYcIELF68GIsXL8aECRPw1FNPYcaMGdi5cyfuv/9+3HjjjcJ9vPTSS3jvvffwzjvv4J133vE81fTOO++gsLAQX331Fdz5xS6XC19++SUKCwuD+5YxJtQGX6VDeiMn3R708W2JCaoKKYVaeInNHY1JrkGj2gaKbgkWICeje0gN74jc1DS7VaL2uqXUqNT9cyC1v34S11+5a1tlbZPk6wkWxPXPSlB3YC677DJ88MEHePPNN/HNN98gMTERxcXFePzxx5GRkYG6ujo8+uijuPzyy4X7OPvss33+vXv37gCAfv36IT09HX/605+wcOFC3HjjjVi1ahXa2towZsyYYIYbk0Jt8CV6/E6NxASL6uz2UJrfsV6IMSnVy1DbQFHqKQneWaNQqG12q0TNdUvLz4H//vJnr1P8jJpjJXezxnXxx6AmME1NTXjxxRexe/duOJ1OuFwubNmyBZ9//jlqa2vxxRdfoH///kEPKjU1FStWrMDcuXPx+uuvY+DAgVi5ciXs9uDvGpiRmtoBotoFahqSqal5IMW9b9H4tNY8EG0f7HejyFKKHf/4OHFS+pcHzyOFW0o3q+QkJsHSFY/+S5uh1GZR+jk4IagLI1dgTvQzwWuhtKCWkGbOnInPPvsMBQUF+PLLL1FUVIT09HRs27YN9957b1ADeeyxxzxtBADgggsuwNtvv41t27bhjTfewODBg4Par1mpra4YbIOv4bkZQY3LvW/R+Bav3aWpKqTc92TzMmOSa9AoFR9S9TJ4HikSRE1tO13wuQ6Fo3qtUqPSThcC9us+rtQkS+5ngtdCaUFNYL744gssWrQIDzzwAAYOHIiSkhI8+eST+O1vf4tPP/003GOMS2rzP4LNMxGtqdptVsm8Gvd77n2Lxve8xFqz3Nqu3Pdk8zJj8j4vSafyoWyJCT7nR5QfEO9r9hRZ7ma3SrmB4civk/o5kLp0Kh0X8L22Kh2L18LTglpCcrlcyM7uekQrLy8PO3bsQHFxMcaMGYO//e1vYR1gvNKS/xFMnonc+m1SovRtWACeHxjR59sEa81a13bd24eSQ0ORo3ReuGZPepk+ZhCer6yTzQ0MV36dVG5LMMcFoDgZ4bUwUFB3YAYPHox3330XAJCfn49//vOfAID9+/eHb2RxbkB2quTr7nXVUBt1ifbfPztN9j2lz4tuqcqt7cptX1Fdj3FLNyF/9rqwfG+KDjUxZAaMP3NSir9wx2dFdT1KlqwX/uEXqePGu6AmML/73e/w3HPP4fnnn8e4cePw9ddf4+c//znuuecejB07NtxjjEui9VWpddVw7d+9pqpmvVU0Pq35DnLHiucuq2YXC2v2jD/zUoq/cManO06kGpRG8rgUZDNHADh+/DhOnDiBjIwMNDQ04OOPP0bPnj0xZswYJCQENS8Kq1ho5lhRXY/yDbXYvv9oQL8NoCuXIJTb8e791zS0oH92GspKcj23MeXe8/78b/+3SnLZyG7r+m8u+qyacYxbukky8z7U703RoSaGjIzxZ25K8Reu+BTFCdB1HXzyhqKIHDeWqf39HfQExuhiYQLjJlpXNUK30UiOzcjfmwKF+liq0TD+SA1RnACMlWCp/f2t/60SUmTkddNIjs3I35t8xeJyC+OP1BDFCcBYibSgnkKi6Kmorsexto6A17Wsm3r/ZZzdIwlAVyVe0T8PyE7F8NwMVNY2Sf41LbU/ubH5/2Uut29vU0cKepJwvdhwlB6HB4KPAxH3/nYePN1QMr9Pmqr9qLlbJBV/ALDzh2YMmLVW9bEotk0dKd1bDgg+p0bqei2K08Vrd3meurJYAKvFgiFn9YiL2OQSkoG5/6r1l5Nux8yx+arWTUX70MpiAZbfWgwAAfuzAOiXbsehlnbJXBql47v3LfXDxvVic1Babgk1DvzJ7U9pP1KfFX2moroei9bslE3QZHPR+CaKxSkjcjF9zKCw7MvNP94Wr92FZRula9eYOTbV/v7mHRgDExU9OsNuC6khYjA8xZgk5ruuU2Pa8IeRQR3f/y91b6x9YA5Kpc5DjQN/ahpKivaj5m6RW6lMUT6tY6bYJIqPyj2Hw7YvN/94EzWvlNo2FjEHxsDCUWxJqeGYFjUNLZrHpPb4bNBobkqPh4Y7DrQ2lFTz2WBjmLEb38LZdFbNz4n3fkXJw6GMwUx4B8bAsnskSd66FiWGea+dpiZbcaz1JBzOzrCNp392Go61OjSNSXXTSNfpJmex9CRLvHCXOhct96mNA/9GkKL8FqX9ZaUF5ma5aW2Mp7Z5JRmL2lwSuXyoUBvqKn3e/33RNd9/v26i5pVS28Yi5sAYlGgt1AJgxYTAHhjhynWRM3pIb6yTeKpENCb3uKQSIaVY0LUc5fOaiddx6TQ1cWCxACtuLYYLgXlW3tu4c7GU9rdigjivSio5XNRbRm7scp8j/ajNJQEkcvpUvOc/CZGKp8k/zQ3IT/H+vNw1XhTW/vGmlANj1tjkY9QmJ1oL7ZfRXTIgg811sZ1qQpaTbkdORnfYbVak2KQD5tNvGzWNCZBuQjZlRC7s3QKPIfVDq7XBGhmTZByU5Eo2p1Obc+Len6ghsChutDbG897e/fOS5Ne8koxFbS6JXD5UqA11pRrmqmns2C/d7tmX93XZvV8X4GlvUVnbhNFDensKhyZYgMQES9zEJpeQDEq0Fnqo+YSm7ZUkJlgCCi3lz14nuW2r4FalaExuUom4csln/mJ9HTdeqE3IVptz4t6fUgO9UMYS7PakL7W5JKI7eErv+ZOKj9+sqpL9vPAa39Iu+UAEEHhnaev+Y9h24Fjc3qXmBMaghOvup3JF1K7DKpFaIxXty97NKjmJCWadVct4Y30d1+yCqcDrfjx575FWWAD8qJcdY87vg8raJpw4KZ+Y6B0PcoXy1MaN2noyUmOeMTY/Ln9xGIlU/Km5vvTPTgNcLnE+lNx7KsYid9xg41bLE3TxgEtIBiVqltja4ZSscCraXo6oKJzoiZLbL80JWyMyyWNoGCMZQzAVeL2b37lcXQ1K6w63YtnGWmzdf0wxT8YdD+79iJIYh5+Xrmn8Dmcn2jqccDg7A76HaMyTXzJ3tWGzE8Xf8NwM2c+paVyrtfGi/1ik4tJi6YpLUdwqXe/C+cRTLOAExqDc66qSuSIq1mEz02ywJXadXrvNiqkluVgxQd26v2hNd/roQZpyB9R8P599TShWPUYyBrV5AkqfEbGgK35tEjknSvtRU4dDTb6N3HYuMEdLT6L4q9xzGDnpdsnP2G1WTxzJ5UNpzZUSxYjdZlXMjfEflwjbW/jiEpKBlQ7pLcxGV7sOK7WN2mNHurCc3DHIHIL5i1BLvlaKTDO8cNRnUbsPue3i9a9fI5CLP7k7ed6TBLlrmpbrnVyMeMewKDfGf1xS2F7FFycwBqe1ZoWIlnX+YPIZWLslPqmNTzW5AVLkah6F8lm1Y3HvQy6nIl7/+jUCuVxBUU2VrLQkjFu6SfJaCARfh0rtz0Iw13Tv/Cu4up4etQAY1KdHXLdX4RKSwWldh5WidZ0/mHyGWOpCTOqpiU81uQFSRHGulPui5rNqxuK9D/cvt4BtEL9//RqBXK5g3eHWgLw6C7pyl6SuhZNe2hLStUzttbrPGSmSn+/TI1nydf/8KxcAx8lOOE52YmocT14ATmAMT+s6rJRQ1vmDyWdg7Zb4IRefFdX1GLd0Eya/LF1QzJaYgARLV+2KnHQ7poyQrgvjTy6eLejal9bPuvNsRDVeSof0xooJxchJt/uMWVTAkaJDLlcQ8K2pUti3J/oJ8mJEtFzL1F6rNwrqaX1aI/0686/EuIRkAqHmnYS6zh/OvjIUe6TiU01l6MQEC75dcLXm48nFswvA3iOtwtwxuc9+u2CM7HFZC8aY5HIF/WuqiGpcydFyLVMTI6K7fq0O6deZfyXGOzBxQJS57ua9zi/3vpZ9My8gvql50ijYGFGKZ7m/mhmvsUnteVWKHTX7CFWK4G6RXVABXW7M8R63vAMTB6Qy170Nz00XbqeUb6P0GSb4xielu37uGFETH/5N+f7rOKl4/J0HmzFu6aaA/fIpjtgkusb9cLQV+bPXeWJA6VooZfv+ozh3xgdwubomH3cMz8H0MYM870vFMAAsWrMT35+6G9jPq+jhHcNzJPsXOU52YtzSTQE/A8NzMySTfr3zr+L1OstmjnGioroe5RtqUX3gGE52+p5y/wZjoo7CSvv2/4zUMgKbM8aHcUs3SV50EyxAwTk9UVaSK9m00T8+wtWkNNQYJ+OTa2wI+DZpLN9Qi10Hmz0Jsf4SEywB10lvU0bkYvqYQZrj091gdPHaXXhhc53kspGaho+ZaTYsvKYgZq+zan9/cwITZ0S/WAr79sS7ZZea9lhkLGo6PquJD9E2wWDcxTY1seIfA6LPpNisaBPkpABdyz075o3WHJ9qj+/eLpSfETPHO7tRk6RoJt0ywTd+qXkiQ018qKnV4q7S6z6OzSp9WWPcxTa1DRzVfEZu8gKcTrjVWtdI7fG1PFgRz9dZ5sCYnNzap5YmZ+FKBnMXXPr+SCtEd2DlipzF0/qt2S1euwvPV9ahrcMpmRsgejpJTbM7NzVN+fL79FD1V20occcYNT7VDRxVfEZpCSnBEtwTTSc6nD55LqJie1oaPkb6mm5kvANjYnJF5OSanIWrIaNoPHWHxZMXpSJnLIRnDu58A/cjoW0dTizbWIvFa3cJP6O22Z13fCg1KZWK3WCK68nFHWPUHNQ0tPVv8Cn6jNzkBehq5qmlKKP359zxs3jtLsnJi3ucahs+hqPYqVnpOoHZu3cv7rrrLgwdOhQlJSX461//6nlv3759uOOOO1BUVISxY8di06ZNOo7UmOSKyMk1OQtXQ0Y143FLsEDyWCyEZ07PV9ZJvv7CZunXAfXN7vz71HjHa066HTkZ3WVjV83ylZa4Y4yag/d5TxBMZPwbfErFimgJUo53DK881ZTWXfRQissFPC/4WclKS9LU8DEcxU7NSrclpM7OTkycOBEFBQV4++23sXfvXjzwwAPIzs7Gz372M5SVlWHAgAF466238PHHH+Oee+7BmjVrcNZZZ+k1ZMMJppFZTUNLxApyyS0LJHezSiaUxfP6rZlpLcYFqG925y+YeFX6jJa4Y4yah/u8589eJxmjaprg5jz4QVDH9o9h9z5FYxHl2RxqacfR1g7hcaQmJvFaZFG3CUxTUxPy8/Px8MMPIzU1FTk5ORg2bBi2bNmCjIwM7Nu3D6tWrYLdbkdubi42b96Mt956C/fee69eQzYc2bVPl0vyPRfgUxch2DV/Lfk1ouOqXeMl47FZE+BwBj5+KirGBcg33hPFpHcTOwuA9FQbutsS0dDcHlIuipa8AVGegty4SV9ysVZRXS97LRPFthwXAvfr3reI3HE6OqVfT01KFDaijMf4020JKSsrC08++SRSU1PhcrmwZcsWfPHFF7j44ouxdetWDB48GHb76b4VxcXFqKqq0mu4hiS39ila221zOENe85fLrxHxP+7itbtUr/GSsVRU1wsvvHcMzxF+Tq7xnlycuZvYdbqAxhYH6g63hpyLIopVd1FHt4rqemGegmjcpD+5WFO6lmmdvABd1zf/GFBqOip3HNEd9EMt7YpNeeOJIZ5CGjVqFH744QeMHDkSpaWlePTRR5GVleWzTXp6OurrtZ8gp1NbkpWZXDEoE+U3D8XyjXtQc+g4+melYsqI83D5oEwA8HnP5Qq87e9yda3vX3Fqe7dn/iFY8z+1rej9ytom9Otl72r5LsPlEudQ2LtZ8edfXYDLB2XG9LkzM6nzD3St3f/uyv7C8+Yfr0oxKTqOaHst/rlbOsegcneTz/jVjCGUcVBkuGPtgde3BRVjmWk2/PeEE60dTti7WeFwdiom9vrHgGjf9m5WZPZIwl7BxDgYsRZ/aq/9hpjAPPXUU2hqasLDDz+MRYsWoa2tDTabzWcbm80Gh8Ohed/bt28P1zANKQvAnGEpAE61aHccRFXVwYD3bl7dIPn5bw42B9zZ+qa+WXZbuffVVkUU/VXS2dmJTK/vQMYjOv/NrQ7Fu6RaYlJ0HNH2WijFuNJ24RoHRU4Wuq4nUpRirKW1A6+Mz/b8+w1vqfvj2TsGRPvu7OxE/dE2VfvTIh7jzxATmIKCAgBAe3s7fv/73+O6665DW5vvCXY4HEhOTg5q3/FQiffDHQ1YtsHrTkzJebhq8OkfwIGVm7HtQOCa8MA+PVBUVOT7msK2vT/5VPIuy8A+PeByQfKz/lK6WaWXjxIScOs7jZLfgYxBTSwpxeOHOxqQkNAISPyl5d5Pj3X/QGOL8h8t7Z0uPFLZpile1P48iLaT3KfEzxLpwx1/DsFdkzR7N9z6TiPandLv+5/LwSrjYGCfHjhk6yN7bC3XSS1OOF144JNmPDhmoOmvm06nU9XNB12TeKuqqnDFFVd4XsvLy0NHRwcyMzOxZ8+egO39l5XUsFqtMT+Bqaiux5RXvvL8+7YDxzD11a98emGUjRI0sRuZF/DfR27bj3c1CpeIykbmwQUoNkuzWIA7Ls3B8o21Adu5JzVS34GMQSmWlOLR/31v7v388cMaVZMXAJ5fBlriRe3Pg9R2cuOO9WuNGcjFl5tSbPU5I0VzHFgswPC8DNlju+NEzXUS6GrYqKXXz94jrZjyyleenkuxTrck3v379+Oee+5BQ8Pp28hff/01evXqheLiYlRXV+PEiROe97Zs2YLCwkI9hmp4aupUaKkVILetqJZHTrodV516lM//s1NG5Absa/roQT7bpUg8vcJaG8akFEtK8ShXD8a9H1GOFABhfQ0t8aL250FNbRGp2hykH1F8uWtRqanz8uGOep+kWLXXtUpBbpV/HSzv/Ylq7+Wk27F8gt8xS3Jh76Y8SY6X66Zud2AKCgowZMgQzJw5EzNmzMCBAwewZMkSTJ48GRdffDH69OmDGTNmYOrUqVi/fj22bduGRYsW6TVcQ1Nbp0JLrQDRtqJjHWpp13wc7+1EZblZa8OY5M5xsP1dgNM1LuQqnG74w0hNdT5EtMap6JiAdG0O0ocovty1qNTUeel0dd0h8b6jpyZefrOqSvbY3pTi6lBLu+Qxn/9nneL44+W6qdsExmq1ory8HPPnz8cNN9yAlJQUTJgwAbfddhssFgvKy8sxa9YsjB8/Hv369cMzzzzDInYCofTC0FrvRa53hxbe+83ukQSnYL2Y9WDMRxSP7lpAonOdlZbk+WdRjlSCpSt21Ma8fx2ZH/WyY8bYfM231+UeUfUeN+lPKTZEseXP5QLKXvkSz9xyoep6WanJgn27gAGz1sKaYEH7SSdcp/af0s2KtOREyc+4+yFpqbfl/11jncXlUlqFMye17bhjQUV1veR6vtJtbXedAm8WC3xyFQLeR+CarJpjKR1Xitb9kjG4+yRpZQGw/NTavdw+LBZg8k9zsfzTWtmYF8WZ93HUUIpXrfujyFK6HgYTn1I5JWqvY8EaPaQ31vlNnC0WoHRw4Ov+Vk4w93VT7e9vNnOMAcH2wggmV8GFrrXZUPpuyPVMcmNegXmJ+rgoceF07E0fMwhTRuQKc13U9PQSxZn3cdRQilet+6PIUroeumNLrmq0P7U9ssLp028bA15zuaRf9+bOR4wHhniMmkIXTC+MYHMVDrW0Y8MfRmoboIrj+ouXH8JYo/b8SvFeu58+ZhCer6wT5roE2+/I/zhK1HyfeMk5MAul2Jg+ZhCmjxnk+XeluylaemSFS6uo35jM8pfFAswcmx+pIRkO78DEsQHZqZKvu9dPld4P93HDeQzSj5rzK+J/3kOJQblxaIkvxmvsKx3SW/bpJKnzG0qcqyF62kj4ehzeteYEJo5UVNdj3NJNyJ+9DiVL1qOu6b8B23j3IZLrtSS1z3FLN6nqxyHqUyI6BpmL0vkVsaCrF9G4pZswYNZa5M9eh+ofpKuZDj8vXfJ1/3GIjuOOLzXxy3iNPVLn/a7LzhVuL3V+g41zNSwW4PZLcyTfu/3SHMnr8pM3FMXV5AVgEm/cUJtwNmVEbsCt1fINtahpaEH/7DSUleTKJkl6JwErjce9X/dTHIda2gOOQebkHzfDz0tH5Z7DnvPd6jiJQyoL1UnREmeL1uzE96eKL/6olx0zx+bjKlGSumC/osTPnPTT+yNzEJ33yT/NlTzHU0pyMX30oIDXAe0J67bEBJx0dsLlki5Ql5SYgEF9eqCsJBcuQPIhiuUTigFAeF2OBWp/fzMHJk6oTTir3HPY59/l1pLlkoCVfrEEk7ND5qF0fsct3RTSBCYccaYlfkWJyWfYbTH1iyMeiM77C4LiiZW1hyVf73pPW8J6fp8eeLfsUoxbuknyUehBp94Hun5GAsaJrvh8t+xSXj/BJaS4oTbhLBzJjUxoJCXhSIAMNc60xC9jPXaIzqUoOVbuHGuNY6UHJLyPxZhTxjswcUJN8SNAe3Kj1D6z0pIwbukmT/Gl4bkZqKxtEhbLo/ijNh7lhCOZXG0ByHAVcKTIUFuQc+fBFnQIOlTbEhPgOBn4nlRxxIfe2a66V5fUvoTx7+oq9ihXNBSurjFI1aWR+28Qi3gHJk6oSTjzTm4Mdp8WAHWHW7F1/zG0dTixdf8xLNtY6/Pvk1/eoirZl2JXqAmQ4UicVZOkDnT9YpD6RaL154Uiw53TIrrGeL/vOJV/IkVq8iL10MKkl7YENXlRekAC6LoL5P4OdYdbJfsktXY4A66hSv8NYhUnMHHCv7hTTrodmWk2JFi6yrPnpNuxQmP1RqmCUf3S7YqfY5NGkoqdlROKkSOIn8w0W0jFE9WOQWq/ovyxfhndmf9iAME2D5WiVKRTzb4SEyxIsHRNcG2JCbAlJgTsyz/2RI9G90u3S77nfw1V09A3FnEJKY5EInHWf5+ipoz+uI5LUvEoaob333ZnQDO8SI3Bn7CgY/OJsI+HtAuleag/pSKdavZlS0zAjnmjFbdT08z2UEu75NNKAPNlAE5gSAXR2mqwjcaA8DSaJHPyb+QJAA3N7bLr/q0OJ3688CN0tyV6tg1nPMjFmlxzSqlcBIoupXwSTbyaLnY4O9HNmgBnpwv5fdI0N1JUc/1yb9N+UjqB2AUI3/O+hobS0NfMWAeGZGmpmSBqsucvHI0myZzUNEZUe0EKVzyoaWrq3xww3GOg4Mmdn3DS0khRsoaLX6yE0gxSqnFpMA19jYrNHCksRGurz2+uk3xdqsnelBG5YW80SeakpjFiTrodKSoa7YUrHpRizZ2vIDUmxqT+Sof0xuSf5npyRSJVHVepkeIZyYmeLtBqrl/BNINMsEDyGhpsQ1+z4xISyRKtrbY5xDUTwpFrE69rurFOTQ7BoZZ21bdhwhEPamKtdEhv4ZgYk/qqqK73uRscyTsxoloxdpsVWx8u9fx7KHVe5CR3swpzweKxOCgnMCahNh9EbrvFa3d5uvvarAk4w56I4yecsvsTra2m2KySk5i2DifGLd0UVH6C99hFYn1N16zUxqeaHAIXxHVX/EnFg1yOjVR9EBHvekbZPZLQ4ZSuH8KY1FcwdzKCZbFIT5Cy0pJQsmQ9vj/SChcg+fgzoC5vRU5bhxMlS9YD8I1pAHGZL8gcGBNQmw8it13V90dle3aI1vJFa6tKuS5acwPUrAebeU03lmnJV1Kbr6AmF0YqHhRzbPxyW0Tbqs3FsQCayw9QeOXPXoc2wZ0Ro/HuqxTJ3B2z52YxByaGqM0HkdvueUGfD7n9AeK11eljBnleT5D4c0NrboDoryi7zRpXa7pmpCVfyTue5LhzYXLS7Z5aRZlpSchJtwdVs0VqXHIxp6aeEcBaMEYwIDtV8nXva0dWmk3zfi2A5LXNf9+i2kVSvPsqSdXmsiWG51dyvORmcQnJBNTmg8htp+YvFNFavmht1f266C+gcPRVAqCqpgLpR2u+kjtuch78QHa/h1raNZ97NXkFauqDNDS3qzoea8Hob+rIPMm7xE/eUOSZXGp+nBpdy+QuF4TXTndsatm3/89EsHW0gjlWLOIdGBMQ/YXhv/Yut12KoNKj3P7UUju+SO+D9BHsuVOKyWDOvWgsUvuVG7ea/Xjvi/Sj5gkctefTm1wc+OeyaNmnnGDGGeyxYgEnMCagtmeL3HZ3DM+RPYZ7u4rqeoxbugn5s9dh3NJNqnppiPp67Pyh2bMPpf2q/Y5kPKLzv23fUeQ8+AEGzFqLAbPWBpz3EQMyhfvUGo/u7XYelP+rU00/muHnpavu1cT4NIbSIb3xbtml2DFvNN4tuzRgWS+Y3lvHWh3CePKOTaWY8/+cnGB7hPl/JF6unUziNYmK6nqUb6hFTUML+menoawkV3LtXW67xWt34YXNdWh1OGFLTEDPlG443n7Ss52a4ktK49t5sDmgKZpUQqQoCVnNdyTjcZ+7XQeb0S7RFM/Nfd6BwFgDAGuCBeeffYameBQl49oSE3DWGcmAxYJDzSckY2rx2l2SBRndY3THY1ZaElodJ9F0vKuJ34/Su2PmmEGMTxPxvr5kpSUBFgu+P/xfdGr8DWgBMHlEYCFP93sWC5CWlIj/Opw4eWrnWWk2LLimQFW8SMYkuvoi/XDshOT1dfKIXFTuORwz1061v785gSGPcUs3ST7WV9i3p+o+NKJ9SNGyXzIHNee/sG9PwOVSjDW18RhK3IYj5sm8gn0SyN7NKlkTJhxxIxeTan5uYgGfQiLNwlE8TktxpnhIMos3apNoQyn0pSV5XQkLJsY3qfwZm1X516KooF2kCysyXn3xKaQYE2wDRLlcF6ViYd7HUVuADOhaVsqfvS6uCi/FOtXN7gR/SQbToE5uO3ec7jzYAmuCxacxX+mQ3nHbBC9WSZ3vs3omAxAXM/R/EkjLXWR/rQ5nwNN1dpsVJ50uOJydSOlmxYgBmTh4rE2yOe6iNTuFTz3J/dxkpSUFNV6z4x2YGOLOBdi6/xjaOpzYuv8YJr+8RTER1/05qR8cqWQw0XEWr92levICdLUj0DJOMr6pI/OEVUiB0/GkJmk71OT14eele+LU4exEW4cTDmenT7wxeTx2eF+XvM933eFW1B1uVX2tCTaRVqTV0TUOoOuR7HXV9ZLXzkkvbZG9fg7PTRf+fNUdbo3L6ycnMDEk2AaIcgW91BYLEzV4BICcjO5YMeH0bVo2xYtdpUN6Y/mEYk8BOgu6kmmTEhN8Hm9V8+ir2gZ1ou0qa5uE43THW7w2wYtFalsKKF1rpArMueM5EuSund4qaw+jdEhvYZHFeLx+cgkphgS7PiqXtyB1Idfa4PFQ8wmf27SiYk3xuo4ba9Q2lVOzXSj7+s2qKtnPuOMtHpvgxaJw5t9JxUQkWxaIrp3e3GMWFVmMx+snJzAmJMo/EeYfuLo+I7pIa80D0NrgUW3OQiiNIEk/weZdqdlHKPtWysfRkttFxqelOeLJTpfnmih3ztU0mI0W95hFeYbxmAfDx6hNRq5xHgDhI4Fy9VxEDRtFt9K1NHgUNdyTe3TR7I3I4omWRo5a9zH5p4G1NrTsWy7O1DaCZCyaRzCPRE+RqOeiVKvIqCwAlk+IjVjlY9QxSi7Pxb12a5co0S637qs1D0BNg0e1OQvhaARJ+gk270rNPl6QaECqZd/ecZaUmAC7zQqbXy6OmnEwFs3B+3yrTVeRyj1xn3NRTk2i10XLlpiA0UN6a2roGCkuxF+s6rqE1NDQgIULF+Lzzz9HUlISxo4diwceeABJSUnYt28fZs+ejaqqKpx11lmYOXMmLrvsMj2HawhKeS6lQ3oHVL3130aK1jwApQaPaj8fjkaQpJ9I1g4KR60NLXHNGhvmp3Rd8SfKPalpaBHeybElJmC3RJPRSObIqBVvsarbHRiXy4X77rsPbW1teOWVV/DEE09g/fr1ePLJJ+FyuVBWVoaMjAy89dZbGDduHO655x788MMPeg3XMEJpLmbE2hZmGisFimQjT6k7iVr3rQVj0bz8e2Zl9wgxH8QFnDgpPRlx13r58cKPfB5dDmcjxmDFWx6MbhOYPXv2oKqqCosWLUL//v1x0UUX4b777sP777+Pzz//HPv27cO8efOQm5uLSZMmoaioCG+99ZZewzWMcNbPMAIzjZUCheP8ifZx+6U5UY0NxqI5SdWl2quhHpWU1g6nYi5NY4sDk146XVMm3PVjgrE3zurB6LaElJmZib/+9a/IyMjwef348ePYunUrBg8eDLv99LpicXExqqqqNB/H6dT3ll64XTEoE+U3D8XyjXtQc+g4+melYsqI83D5oEzPd1WzjVGYaawUKBznT7SPKwdn44Kze0QtNhiL5vTMPyRylwDkpNtxRko31Bw67tOMU2vzRiXl63fjikGZAfHTdUwnmo53Pfb8o152PDh6IFwA5vy/ajS2OII6ns2a4CmM58/lNR4zU/vzZpinkDo7O3HzzTfjzDPPxFlnnYUjR47giSee8Lz/6quv4pVXXsEHH3wgs5fT3FnMREQUu25e3YB2Z+CvsWSrBa+Mz1a9fbBEx1ES7nGEOh4jUnoKyTB1YJYsWYIdO3bgzTffxPPPPw+bzebzvs1mg8OhfcZaUFAQU49Ra/XhjgYs2+D1F2XJebhqcLbw9WiMZVf96T4lg3qnSR5bj/GROqGcG6nPAoj6/hhfsWNg5WZsOxBY/8WSkIBb32nsyguxAAePnoA1wRL2SUN7pws3rT4UcC2Ti7EPdzQgIaERCOLOXko3q2yy8MA+PVBUVBTUdzEKp9OJ7du3K25niDswS5Yswd///nc88cQTKC0txSOPPIKjR48G3IF57bXX8N5776naZ6zWgdEiUvU1wjUW0bFZj8O4Qjk3cjEQrv1ZgICn8BhfsS2Y+i+RIldDJlz1ZaaMCKy35X2MWGiFYZo6MPPnz8ff//53LFmyBKWlpQCA7OxsNDX59jFpampCVlaWHkM0rUjV1wjXWETHZj0O4wrl3ISrV43sWFTsj/EVW/zrUomeXtNC1PdIKUdXroaMUn0ZJVlpSVg5wbfelu1UfSP/XmPxQtclpKVLl2LVqlX485//jNGjTz9XX1hYiJUrV+LEiRNITu5qhb5lyxYUFxfrNVRTimR9jXCNRerYrMdhXKGcm3D2qgllf4yv2KOm15oWokTfFJsVLhdkl3DkasjIvSdit1mxw6/uDPt3ddHtDkxtbS3Ky8vxP//zPyguLkZjY6PnfxdffDH69OmDGTNmoKamBitXrsS2bdvwy1/+Uq/hmpJR6mvIjUXq2KzHYVyhnBstdTIiuT/GV2wLRz0WuWukmmuZXIxpHR/jUky3OzCffPIJnE4nli1bhmXLlvm8980336C8vByzZs3C+PHj0a9fPzzzzDM466yzdBqtMahpNLd47S48X1mHtg4nbNbA+am7vsbyjYE9iyJZ72LqyDzZvjRlJbme77fzYOBfwqzHYQyi87jzh2bZRpwV1fU41tYhuU//vBW151oupnz2L1EnSaqXl/8x/X/ehudmYO3XB/H9kVa4APTrZceMsfn8S9hg1MaFiOgaCQBb9x1V/GyfHsn4eGeD5Ps7f2jGqEFZ2HbgmOrxecdlRXU9Fq3Zib1HWmFB16PZ8RyDhkjijYRYS+JVk3i4eO2ugORcoGvt9Hj7SfTPTkNZSS6ucndg3VCLmoYWn9cj/R3KN9Ri58FmJLqfQurTA2UluXBBOrHNlpiA/FPbxNParpF5n0fHSd96FFLJsKLk3Zx0O2aOzff0cAkmFr3jOCtNukvvlBG5mD5mkPBzUsdUm3AMACtipIFeLHH/opeKB3+jh/TGweYTAbHgjpFdB5vRflK67grQ1RvJmmDBoD490KdHMtapKCTnPub2/Udl69JMKcnF9NGDPN9JKiZjqYmjm9rf34Z5jJrkKTVxBIDnJZJzAeB4+0lDrKHKHXPc0k2Sr+f36YF3yy6N5LBII/d5HLd0E7bu93181T8mAXHy7hl2m2fSEGwseseUKIYq9xyW/ZwULYmW/t+X9Fc6pLfqc3iw+YTkNUYuzr3ZEhM811e1+Tef1jRix7zRivuurD0du6Lv4/4DIB5jUPenkEgdNYmHosSyVkHDMiNhYqX5qD1n0Tq34TxOuBOOKfrUnkOl86e0H+/rq9pmju7PhOPhBv/t4gnvwJjEgOxUyZm6d4KXqMS03Xb6FpyaPJpQBXMMNd+PjEXtOYvWuQ3ncdKSE1X/MmKMGpMoHvx5nz/vPDx3sU2r6JnqUxIsXXdeBmSnypb592axAANmrUVHp/y2/snnou+TmpTo6Yad0s2KO4bnBCybxiLegTEJpUZzFdX1wh+cO4bneLbxb3o2+eUtYW3+Fewx2EjPfNSes2id23AdZ/HaXTjU0q56e8aoMU0dmae4jf811H3tcjg70dbh9Py/nM5Tj1W7P6eGywU4nJ2yibxSyecih1raPeNs63Bi2cZaLF67S9VYzIwTGJPwL9bkX7RItD6alZaEaaeSwKJRwCvYYyh9PzIetecsWuc2XMcR5ZIlJliQk25HgqXrr+6cjO5YOYExalSlQ3pjxYRin3OWlZaEnIzumq6hbmobTWem2WBLPP2rNaVbgufflbpVJ1i6cmqkYlfq++Sk2yWfNgWAFzbXqRyxeXEJyUTkEg9F66PH208qbhPO9dNQjsHiTOaj9pxF69yG4ziiv7hPdrqw4Q8jQ9o3RZeWeFDKR1H7uO5/2534dsEYyffcyzwiyd0Ci9Z5k/o+OQ9KNzg2Q+5jqDiBiRFq1v9F22SlJWHc0k2KOStqcluUxhGNHByKPCOdx3CPRdQszzuXjMxFTYxk95B+DN/NlpgQUDZASvckq/B6qpSX09bhxOK1u1D0o55YtGanqppD8RyvXEKKEWrW/6W2AYC6w62KOStqc1vkxhGNHByKPCOdx0iMxZ0zpvZ1MjY1MVJRXa9YM0bN5AUAGlscwmOJrsFuLhewbGMtJr20BXWHW9Hp6nqt7nArJr0kHdfxHK+cwMQINev/UtvkpNsD9iWVs6I2t0VuHGyiFxuMdB4jMZbpYwZhyohcz1+wdpsVU0tyPblkZC5qYiTYBotqeB/L+/ronSejllRcx3O8cgkphqhZ7/XfRlR4KZRaHqJxsNZLbDDSeYzUWKaPGRQXj6HGAzUxoqXuTzC8j+V9fRTlr6jZj7d4jVfegYlzosZiWWlJqrbTUgODTfRig5HOo5HGQsYkd40bt3RTWLpXKxHFY4qgaaTcfiqq6z3jHrd0U1wvwXMCE+emjsyTfDyw7nCrzw9GOGpssNZLbDDSeTTSWMiY1OT+qS1aGAy5eNSapzL8vHTD5J8ZAScwca50SG/0k8iDAXzXW8NRY4O1XmKDkc6jkcZC+pO6O6E29w/oyh9xbzOlJFfVZ+TYbVbZeBTlrwTUezlVc6iytilgH/GcR8hu1CSsTWC3ydckICIyCqluzVLd0YHgrnlyn3G5pOsHhfsaGi/XarW/v3kHhphHQESmp+WJtGCueXKfidY1lNdqX3wKiTB1ZB4mv7zFpy+HEfIIjFQsjcyP8RTbtDyRFsw1T+4zLiDgPQA41urwLGNp4R+rw3MzUFnbhJ0HpZ9CGn5euqb9xwouIRGAUz8wG2pR09CC/tlpKCvJ1TWPQMvtYCIljKfYN27pJskqt4V9e+LdsksDXg/mmif3mYrqeixaszOgIJ7WOJOKVSWxFstqf3/zDgwBMF4fIrnbwUYaJ5kD4yn2ab2rEsw1T+4zpQrFOtUeK5iievEay8yBIUMyUrE0Mj/GU+wzwhNp4YizYIvqxWMs8w4MGZKa5pQUG6KRm8J4ig963kmWq8WiteCnXMPHcBwjVvAODBkSC5TFh2g1hmQ8USS541jqEedwFPxUEq+xzAkMGZIRbgdT5EWrMSTjiSJJlLeiVMhOilSsThmRK/vv8RrLXEIiwzJaYjGFXzRzUxhPFClyeSvBTCwYq+rwDgwR6YaFuSgc9G5wyDjWBycwRKQb5qZQqKKVRyWHcawPTmCISDfMTaFQRSuPSg7jWB/MgSEiXXG9n0JhlBo/jOPo4wSGdMG+NEQUDnrW+InEdcy9z50HW2BNsMDZ6UJ+nzReIyVwCYmizghr1kQUG/TKP4nEdcx7nw5nJ9o6nHA4O3mNFOAEhqLOCGvWRBQb9Mo/icR1TK4PEq+RgQyxhORwODB+/HjMnj0bl1xyCQBg3759mD17NqqqqnDWWWdh5syZuOyyy3QeKYWDUdasiSg26JF/EonrmFIfJF4jfel+B6a9vR0PPPAAampqPK+5XC6UlZUhIyMDb731FsaNG4d77rkHP/zwg44jpXBhzQQiMiIt9WQicR0T7VNp33rXwdGLrhOY3bt341e/+hW+//57n9c///xz7Nu3D/PmzUNubi4mTZqEoqIivPXWWzqNlMKJNROIyGi05rRE4jom1wdJtO94zinUdQnp3//+Ny655BLcf//9KCoq8ry+detWDB48GHa73fNacXExqqqqNB/D6QxsrkX6umJQJspvHorlG/eg5tBx9M9KxZQR5+HyQZk8X0Ski2f+IchpWb8bVwzKDHgvEtcx733uqm+B1WLBSZcL+b3ThPvWOm4zUPvfT9cJzM033yz5emNjI7KysnxeS09PR3299hnl9u3bgxobRVYWgDnDUgCkdL3gOIiqqoN6DomI4tg39c3Srx9sFv7xHInrWMA+3QT7DmbcscIQSbz+2traYLPZfF6z2WxwOBya91VQUACr1RquoRERUQwaWLkZ2w4E1pMZ2KeHzwqB0Zh13HKcTqeqmw+GnMAkJSXh6NGjPq85HA4kJydr3pfVauUEhogoDvgXlhuem4HK2ibJQnP+216al4HtPxyDy3V6fxYLUDYyL2y/Q7QWvquorseiNTux93ArXAAsAPql2zFjbD5Kh/RGRXU9mk90BHwu3OM2KkNOYLKzs7F7t++6XlNTU8CyEhEREXA6mdVt6/5jPhV63cmty28tBoCAbbcdOIbJP81F5Z7DqGloQf/sNJSV5IatnozU+NzjkZrE+G8PAC4AdYdbMemlLZgyIhfLNgbWhclJt2Pm2Py46MNkyAlMYWEhVq5ciRMnTnjuumzZsgXFxcU6j4yIiIxIrgicm6cYnPdtFq/3Kvccxrtll0ZieLKF76QmMErf54XKOsnXz7Db4mLyAhigDoyUiy++GH369MGMGTNQU1ODlStXYtu2bfjlL3+p99CIiMiAlIrAudU0tOhSTFPrMZW+T2uH9JM68VTszpB3YKxWK8rLyzFr1iyMHz8e/fr1wzPPPIOzzjpL76ERUQzQkovAxqPmIGrq6K9/dhrgcsk2gIzEOdfadFLp+9isCXA4O1XvLxZZXC6Je2kxwOl0oqqqCkVFRTGfyERE6knlFlgskMxF0LIt6auiuh6TX94itTrkYbEAK24thgsI2Nb7vUicc6nxuY8pteQjFXtqrJwQ+T5Qkab297chl5CIiCJFSxM+Nh41D6mmjlNKciWbPMo1gIzUOdfadLJ0SG+smFCMnHQ73MV5LQByMrojJ90u+ZmcdLvpJy9aGHIJiYgoUrTkIrDxqLloaeoo2jaS51xr00nR9vmz10luf6ilPeixmRHvwBBRWBm9sZyWJnxsPBpf5GLVSOeccdmFExgiChszNJbT0oSPjUfjhzt22ySe7jHaOR+emyF4PT3KI9EXJzBEFDZmyBnRkougNW+BzEtUd8VusxrunFfWNglePxzlkeiLOTBEFDZmyRkJR64ExRa5uitGmrwA5vk5izROYIgobLTWuggn1muhYIUj9yWa8afnz5mRcAmJiMJGr5wRM+TekDGFI/cl2vHH3KwunMAQUdjolTNihtwbMqZw5L5EO/6Ym9WFS0hEFFZ65IwwJ4CCFY7cFz3ij7lZvANDRDGAdTEoWOGIHcafPjiBISLTU5sTYPQiexReas53OPJJpPYBADt/aFaMM8Zk8NjMkYhiQkV1Pco31KKmoQX9s9NQVpLrswTAxozxRWvTTrnYUXu88g212HWwGe0nfbtEs1moNmp/fzMHhohiglJOgFyiZTz/sohVWs53OPJJ3PsYt3RTwCPOouMyJkPDJSQiigtM9I0vep1vNguNHt6BoaCwaBgFI5i4CVessfhXfBGd77YOJ8Yt3eQTR+GIMfc+TpwMrCcDiJuFSo3RdWp/ojG4j7XzYAusCRY4O13I75MWd9dh3oEhzVg0jIIRTNyEM9ZY/Cu+iBJrXS74xFE4Ysx7H6KsUqlGi6IxtjmcwjF4H8vh7ERbhxMOZ2dcXoc5gSHNWDSMghFM3IQz1lj8K754n+8EwUSmfENtWGJMVAzPm1SjRfcYU2yBiaqiMcgdK96uw1xCIs24bkvBCCZuwh1rLP4VX9znO3/2OslWATUNLcI7JlpiTK4YntL+Sof07lozUvkZpWPF03WYExjSjLkEpJVSszxRDkKoscZcLQIUrlkul+R7WWlJKFmyHt8faYULQL9edswYmy8ZP6L9BxzLTzjzZuQ+E6u4hESaMZeAtFBqljc8N12YgxBKrDFXi9zk4kjyPQB1h1tRd7gVna6upZm6w62Y9JJ0/IhyWfyP5U0pb0YU53LHirfrMCcwpBlzCUgLpWZ5lbubAt7zroURbKwxV4vc5OJI6r1+6XbhvqTix38fOel25GR0l41Z0c9FggWyce59rKTEBNhtVtgSE+LyOswlJAoKcwlILaVmeb9ZVSX5nnstP9hYY64WeZOLI//38mevE+5HLpdFS5yK4jO5mxXvll0q+1lef7twAmNgXL+nWKCUx6I2z0Xu50HqPeZqUbDk8kzc8eMdc9k9kgAADc3tstdq7/otHZ2dAe97719ORXU9Fq3Zib2Hu/JzLAD6pYtzdGIVl5AMiuv3FCuU8ljU5LnI/TyI3huem8FcLQrK1JF5EKW0lJXkBsScO19G7lrtX79FS96L1H7qTk1egK6HmORydGIVJzAGxfV7ihVKeSxq8lzkfh5E71XuOcxcLQpK6ZDeWD6hGDnpdiRYuvJScjK6Y+WErvhRqvsida1W+ow7J0wpPpX2E0+/I7iEZFBcv6dYorRmr/S+3M+DXB0P5gpQsORiJ5i6L2o+o2ZyzTowp3ECY1Bcv6dYpTW3S6mGjKiOB39WSC2tMRlM3Rc19VvUjENpP1lpSbLjiiVcQjKoYOtfVFTXY9zSTcifvQ7jlm6Kq/VQMj6tuV1qasgca+uQ/CxzXUiNYPINg6n7olS/Ra4ekv9+5NQdbo2b6z4nMAYVTP0LJv6S0WnN7ZKrITN5RC6WbahF3eHWgPctEFZnDwr/MDC2UM5PMPmGwdR98f6MLTEBtsQEWNA1eenXy4612w+qGkfpkN7IkalTA8RPHoyhl5Da29vxyCOP4MMPP0RycjLuvPNO3HnnnXoPK2q0rt/L/SAyD4CMQGtul9x6v1QBPDcXwhf37j8M3Nx/GCy/tZg/VwYQ6vkJNt8wmPwq92d8xnyqyq/IzoPNAa81NLfLHide8mAMfQfm8ccfx9dff40XXngBc+fOxdKlS7FunbjAULxj4i8Z3YDsVMnXRfkqcttHK5mRTwQaW6jnR2tMhoOa7tVujpOdAXeURGN2i5f8L8NOYFpbW/HGG29g1qxZGDJkCK688krcfffdeOWVV/QemmHp8YNIpIXW3C657aN1EecfBsYW6vnRo7ebmieSvPlPxuTq1FgQP/lfhp3A7Nq1CydPnsTQoUM9rxUXF2Pr1q3oFFQwjHdsskhGpzW3S277aDW14x8Gxhbq+dGjt5tozKJJif9kzLtOjfszFgA56XasmBA/tY4MmwPT2NiIM888EzabzfNaRkYG2tvbcfToUfTq1UvVfpxO6TblseiKQZkov3kolm/cg5pDx9E/KxVTRpyHywdlxtV/BzK2KwZl4opBmT6vycWnaHvveN9Z34JEiwVOlwuDeqeFNe4njzgPU1/9yqfejMUCTPnpufy5MoBwnB+tMRkq0Zh/1MuOvRL5MP2zUgPGIzVmN7PHpdrxG3YC09bW5jN5AeD5d4fDoXo/27dvD+u4jC4LwJxhKQBSul5wHERVVWB2O1EsCIh3tzDGfRaAPwzribd3/Rf7jp1E3zMSMX5Qd2TyZ8sQzHh+RGN2AVhS2erzBJ0FwOgfWVBVVaXLWI3MsBOYpKSkgImK+9+Tk5NV76egoABWqzWsYyOi+FJUBEy6Wu9RkIgZz49ozOee2xBwF/3KwdlRH5+enE6nqpsPhp3AZGdn4z//+Q9OnjyJxMSuYTY2NiI5ORk9evRQvR+r1coJDBERmcKYgrMwpuAsvYdhCoZN4s3Pz0diYqLPbbMtW7agoKAACQmGHTYRERFFgWFnAikpKbjmmmvw8MMPY9u2bfj444/x3HPP4bbbbtN7aERERKQzwy4hAcCMGTPw8MMP4/bbb0dqairuvfdeXHXVVXoPi4iIiHRmcblEzejNzel0oqqqCkVFRcyBISIiMgm1v78Nu4REREREJMIJDBEREZkOJzBERERkOpzAEBERkelwAkNERESmwwkMERERmQ4nMERERGQ6hi5kFwp3eRuztxUnIiKKJ+7f20pl6mJ2AtPZ2QkAqjpaEhERkbG4f4+LxGwl3s7OTpw8eRIJCQmwWCx6D4eIiIhUcLlc6OzsRGJiomzz5pidwBAREVHsYhIvERERmQ4nMERERGQ6nMAQERGR6XACQ0RERKbDCQwRERGZDicwREREZDqcwBAREZHpxGwlXhIbNWoUDhw4EPD6hRdeiMTERFx88cW49957Ne93586daGtrw4UXXhiOYVKM6OjowPLly/HOO++goaEBGRkZKC0txb333ovU1FS9h+exf/9+XH755fjkk09wzjnn6D0c0smDDz6It99+W/j+2WefjYsvvhiPPfaY5PujRo3CPffcg/Hjx6s63tNPP41///vfeOmll4IabzzjBCZOzZw5E2PHjvV5rVu3bj7/r1VZWRnuueceTmDIxx//+EdUVlZiwYIF6Nu3L/bt24eFCxdi7969WL58ud7DI/Ixa9Ys/O53vwMArFmzBs899xzefPNNz/uiiYvbm2++CbvdHtExUhdOYOJUWloaMjMz9R4GxYG3334bjz76KIYNGwYAOOecc/Dwww/jlltuwaFDh5CVlaXzCIlOS0tLQ1pamuefrVarz7VS6Q+8Xr16RXR8dBpzYMjHhAkT8PTTTwPoupX64IMP4he/+AWGDRuGuro6rFmzBqWlpSgoKMDYsWPx8ccfez534MABzJgxAw8++KCeX4EMxmKx4PPPP/dpzDZ06FB88MEHOPPMM+FwOLBgwQJccskluOSSS/D73/8eR48e9Wy7d+9e3HXXXRg6dChKSkrw4osvet6rra3FXXfdhQsvvBA/+clPsHTpUs9xnn76afzud7/D3LlzceGFF2LYsGF49tlnPZ/t6OjA/PnzcdFFF+GnP/0pNm7cGPn/GBQTjh8/jvvvvx+FhYUoKSnBe++953lv1KhRWL16NYCu6+L8+fNx+eWXo6SkBMePH8fu3btx0003obCwELfddhv+85//6PU1TI8TGJL17rvv4re//S1WrFiBtLQ0TJs2DZMmTcK6detw3XXX4YEHHsDRo0fx9NNPo3fv3pg5cyZmzZql97DJQG677Ta89NJLGDVqFObOnYuKigqcOHECeXl56NatG/785z/j66+/xrPPPosXX3wRx48fx29+8xsAQHt7O+688050794dr7/+OubMmYMnnngC69evx5EjR3DzzTcjKysLb7zxBubOnYuXX37ZZ4JTUVGBpKQkvP3227jrrrvwxz/+Ed999x2ArgnO+vXrsWzZMvzlL3/x+RyRnI8++ghDhgzB+++/jzFjxmDmzJloaWmR3Hb16tVYsmQJli5dCpvNhokTJ6Jv375YvXo1SktL8b//+79RHn3s4BJSnJo7dy7mz5/v89o///nPgO0KCgowatQoAMCOHTvQ0dGB3r174+yzz8add96JgQMHIikpCSkpKbBarT63X4mArtyovn374tVXX8Xrr7+OVatWoXv37pg1axbGjh2Ll19+GW+99RYGDhwIAHj88cdxySWX4JtvvsH+/ftx5MgRPProo0hNTUX//v3x0EMPISEhAe+//z5SUlIwf/58JCYmIjc3F42NjXjmmWdwxx13AAB69uyJ6dOnw2q14u6778azzz6Lr7/+Gjk5OXjjjTcwffp0/PjHPwbQlRc2ceJEvf4zkYkMHToUd999NwBg6tSpeO6557Bnzx4UFhYGbFtSUuLJC9ywYQOOHj2Khx9+GHa7Hbm5ufj3v/+NI0eORHX8sYITmDh133334aqrrvJ5LSUlJWC7s88+2/PP+fn5KCkpwa9//Wuce+65uPzyy3H99ddLfo7I2y9+8Qv84he/wH/+8x9s2rQJL7/8MmbNmoW+ffuio6MDN954o8/2nZ2dqKurw759+3Duuef6PK103XXXAeiahA8ZMgSJiacvY0OHDkVjYyOam5sBdOXbWK1Wz/vdu3fHyZMn8Z///AdHjhxBfn6+572CgoKIfHeKPX379vX8s/sPtvb2dsltva+hu3fvRk5Ojk+Sb0FBAZcvg8QJTJxKT09Hv379FLdLSkry/LPFYsGKFSuwbds2fPLJJ/joo4/w6quv4tVXX/X5RUDktmvXLrzzzjuevKgzzzwTP//5z1FaWoqrrroK27ZtAwC8+uqrAU9upKen+zz94c87Nt3c+S9OpxOAdMKly+WS/Odgn76j+OM9KXbzjiVv/nHqvx3jLnjMgSHVamtrsXjxYlxwwQW4//778cEHH6BPnz747LPP9B4aGZTT6cTf//537Nixw+d1m82G5ORkJCUlwWq14ujRo+jXrx/69euH1NRULFq0CIcPH0ZOTg727t2LtrY2z2cXL16MBQsW4Nxzz0V1dTU6Ojo873311Vfo1asXevbsKTuuM888ExkZGdi+fbvnNf8xEoVb//79UVdX55Mvs3PnTh1HZG6cwJBqPXr0wGuvvYby8nLs27cPGzZswIEDBzB48GAAgN1ux549e3yeIKH4NmTIEJSUlGDq1Kl47733sH//flRVVWHu3LlwOBy49tprcf311+Phhx/Gv/71L+zevRvTpk3D3r17cc455+Cyyy5DRkYG5syZg9raWnzyySdYtWoVLrvsMvz85z+Hw+HwvPfxxx/j6aefxk033QSLxSI7LovFgltuuQVPPfUUKisrsX37dixatChK/1UoXg0fPhx9+vTBrFmzUFtbi9WrV2PNmjV6D8u0OIEh1TIzM/H000+joqICV199NebNm4cHHngAl112GQDgpptuwiuvvIKHHnpI55GSkTz55JMYN24cli5dijFjxmDSpEk4fvw4Xn75ZaSmpuLBBx/EsGHDcN999+FXv/oVEhMTsXLlSlitViQmJqK8vByHDh3Ctddei4ULF2LatGkoKSlBamoq/vrXv+L777/HNddcg/nz5+P222/HPffco2pckydPxjXXXIP7778fkyZNwvXXXx/h/xIU77p164YVK1bg2LFjuPbaa/Haa6/hlltu0XtYpmVxiRbuiIiIiAyKd2CIiIjIdDiBISIiItPhBIaIiIhMhxMYIiIiMh1OYIiIiMh0OIEhIiIi0+EEhoiIiEyHExgiIiIyHU5giMhw/vWvf2HgwIF6D4OIDIwTGCIiIjIdTmCIiIjIdDiBISJd7d27F3fddReGDh2KkpISvPjiiwHbbNmyBTfddBMKCwtRVFSE//mf/8GhQ4cAAB0dHXjooYdwySWXYOjQoZg8eTIaGhoAAM3Nzbj33ntx0UUX4cc//jF+//vf4/jx41H9fkQUGZzAEJFu2tvbceedd6J79+54/fXXMWfOHDzxxBNobW31bNPS0oJJkybh0ksvxfvvv4+//e1v+P7777Fy5UoAwCuvvIIvvvgCzz33HN58803897//xaOPPgoAeOqpp9DY2IjXXnsNL774Inbt2oXy8nJdvisRhVei3gMgovi1adMmHDlyBI8++ihSU1PRv39/PPTQQ0hIOP231YkTJzB16lT8+te/hsViQd++fXHVVVdh27ZtAID9+/cjKSkJZ599Nnr27InHHnsMR48eBQAcOHAA3bt3xznnnIOUlBT85S9/0eNrElEE8A4MEenmu+++w7nnnovU1FTPa9dddx2Sk5M9/56ZmYlrrrkGzz//PKZNm4bx48fjueeeQ2dnJwDghhtuQGNjIy677DLceeed2LhxI3JzcwEAt912G7788ksMGzYMU6ZMwfbt25GTkxPV70hEkcEJDBHpJjFR+SZwQ0MDfvGLX+Dzzz/HkCFDMHPmTPz617/2vN+/f3/84x//wJIlS5CZmYk///nPuPPOO+FyuTBs2DBs3LgRc+fOhc1mw5w5czB9+vRIfiUiihIuIRGRbnJycrB37160tbUhJSUFALB48WJs2rTJs81HH32EM844AytWrPC89tJLL8HlcgEA3nnnHdhsNowdOxZjxoxBVVUVbrjhBhw+fBjvv/8+Bg4ciGuvvRbXXnstPvjgA8yYMSO6X5KIIoJ3YIhIN5dddhkyMjIwZ84c1NbW4pNPPsGqVavwu9/9zrNNz5498cMPP2Dz5s3Yt28fVq5ciQ8//BAOhwNAV5LvwoULPe+/99576N27N84880zU19dj3rx5qKqqQl1dHSoqKjB48GC9vi4RhRHvwBCRbhITE1FeXo558+bh2muvRUZGBqZNm+a5GwMAY8aMwRdffIH77rsPFosFBQUFmD59Op5++mk4HA7ccsstqK+vxx/+8AccO3YM559/PpYtWwar1Yrf/OY3aGlpwZQpU9Da2oof//jHWLJkiY7fmIjCxeJy34clIiIiMgkuIREREZHpcAJDREREpsMJDBEREZkOJzBERERkOpzAEBERkelwAkNERESmwwkMERERmQ4nMERERGQ6nMAQERGR6XACQ0RERKbDCQwRERGZzv8HUr1EjcJvpX4AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# CODE HERE\n",
-    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
-    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
-    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
-    "sns.stripplot(data=titanic, x='class', y='age', jitter=-0.2)\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# CODE HERE\n",
-    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
-    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
-    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
-    "sns.countplot(data=titanic, x='sex')\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11f207ef0>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d84cc18>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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IiESk0lGJtsmZbMKPvr6+5udXoUWnmMN3UVFR8PHxQU5ODtzd3bFx40bUrl1b8/7YsWNx5MgRjBkzBqmpqRg+fDiioqIAAPPnz8e+ffswePBgXL16FYMHD8ZPP/0k4pkRERGVEx0d8TYZk0318fHxmp/j4uJQq1YtNGzYEAkJCZr2ly9fws3NDbGxsVr7fvvtt5g4cSICAgLg5eUFMzMzPHr0CIIgICcnB4sWLYKBgQFGjBiBLVu2YPDgwTh06BAyMjKwYMEC2NjYYPz48dixYwfat2+PmJiYcjtvIiIiEpdswk9QUBB++eUXnDlzBmvWrMGQIUMwdOhQ7NmzBzt37sSdO3ewZMkSCIKgNRUGAGZmZjh79ixu3bqFuLg4TJs2Dbm5uVCr1TA0NMSlS5ewcOFC3Lx5E7/88gsuXryIFi1awMTEBEeOHMHixYuRkpKCCxcuICEhAS1atJDoWyAiInp7nPYqIJsFz7169YKvry/y8/Ph7e2NsWPHQkdHB/PmzcP69euRkZGBli1bIiQkBEZGRlr7BgQEICAgAH379oW5uTk8PDxgbGysGU1avXo1AgMDMXDgQOjp6aFnz56YMGECDAwMsGHDBixevBjvv/8+qlatioEDB2LQoEFSfAVERESlolLJZsyjTMkm/Hh6esLX17dQ+8CBAzFw4MBC7X9/rEXjxo2xbdu21x7bxsYGERERRb7XqlUr3tuHiIioEpFN+CEiIqJSkvl0lVgYfoiIiBSCNzksUOHDj7W1NRITE6Uug4iIiCqJCh9+iIiISBxyv0pLLAw/RERESsGrvQAw/BARESkGR34KMAISERGRonDkh4iISCl4tRcAhh8iIiLFUKk47QVw2ouIiIgUhiM/RERESsFpLwAMP0RERIrBq70KMAISERGRojD8EBERKYVKR7ytBHJychAQEAAXFxe4uroiMjLytX0TExPh7e2NVq1aoU+fPvj5559Le9aFMPwQEREphY5KvK0Eli9fjri4OGzatAnz5s1DcHAwDh48WKjf06dPMXLkSDRp0gR79+5Fjx49MGnSJDx69EisbwAAww8RERGVoaysLERFRWH27Nmws7NDjx49MHr0aGzdurVQ3507d6JKlSqYP38+bGxsMGXKFNjY2CAuLk7UmrjgmYiISCFUEjzbKyEhAXl5eXByctK0OTs7IyQkBPn5+dD52xVo58+fR7du3aCrq6tp27Fjh+g1MfyUsY5ta0hdQqntWnBG6hJE4TWvo9QliGK76SWpSyi1unUNpC5BFMsCm0tdQqnZCElSlyCKpzo1pS5BHkS82kutVkOtVmu1GRgYwMBA+893RkYGzMzMtNotLCyQk5ODP/74AzVr/vXfLjU1Fa1atcJnn32GmJgY1KtXDzNnzoSzs7NodQOc9iIiIlIMlY6OaFtoaCicnZ21ttDQ0EKf+eLFi0KB6NXrf4anrKwshIWFwdLSEl999RXatGmDUaNG4ffffxf1e+DIDxEREZWYr68vRowYodX2z5ADAIaGhoVCzqvXRkZGWu26urqwtbXFlClTAAAtWrTA6dOnsXv3bowbN0602hl+iIiIlELEZ3sVNcVVFCsrKzx58gR5eXnQ0yuIHRkZGTAyMkKNGtpLQywtLdGoUSOttgYNGog+8sNpLyIiIqXQ0RFvKyZbW1vo6enhypUrmrbY2FjY29trLXYGAEdHRyQmJmq13bx5E/Xq1SvVaf8Tww8RERGVGWNjY3h5eWH+/Pm4du0ajh49isjISAwbNgxAwShQdnY2AODDDz9EYmIi1q1bhzt37mDNmjVITU1F3759Ra2J4YeIiEgpVCrxthLw9/eHnZ0dhg8fjgULFmDy5Mlwd3cHALi6uuLAgQMAgHr16iE8PBw//vgjevfujR9//BFhYWGwsrIS9Wvgmh8iIiKFUEn0VHdjY2MsW7YMy5YtK/TeP6e5nJ2dER0dXab1cOSHiIiIFIUjP0REREohwR2eKyKGHyIiIqUQ8Q7PcsYISERERIrCkR8iIiKFkOLBphURww8REZFScNoLAMMPERGRcnDkBwDX/BAREZHCcOSHiIhIKUR8sKmcMfwQEREphUR3eK5o+C0QERGRonDkh4iISCm44BmAAkZ+YmNj4e3tDQcHBzg6OmLMmDF48OABAODUqVPo06cPWrVqhdGjR2PhwoWYNWuWZt/vvvsObm5ucHJywtChQws9fI2IiEhWdFTibTJWqcPP06dP4evri06dOmHfvn2IiIhASkoKwsLCkJqaivHjx8PDwwO7du2Cvb09tm7dqtk3JiYGwcHB+Oyzz7Bz5044Oztj2LBhyMzMlPCMiIiIqLQqdfjJzs7GhAkTMHHiRNSvXx/Ozs5wd3fHjRs3EBUVhVatWmHChAlo1KgRPv74Yzg4OGj2DQ8Ph6+vL7p27YoGDRpg6tSpqFevHvbs2SPhGREREZWCSke8TcYq9ZofS0tLeHl5YePGjYiPj0dSUhISExPRunVrJCYmwt7eXqu/o6OjZmQnOTkZK1aswOeff655PycnB7dv3y7PUyAiIhIPL3UHUMnDT3p6OgYMGAA7Ozt07NgRgwcPxvHjx3H16lXo6upCEASt/n9//fLlSwQEBKBDhw5afapVq1YutRMREYmOl7oDqOTh58iRIzAxMUFoaKimbcuWLRAEAU2bNkVsbKxW/19//RX169cHADRs2BD379+HjY2N5n1/f390794d3bp1K58TICIiItFV6ghoamqKtLQ0nD17FqmpqQgLC8Phw4ehVqsxePBgXLlyBWFhYbh16xZCQkJw8eJFqP7/kOCIESOwadMm7Nq1CykpKVixYgV++OEHNG7cWOKzIiIieksqlXibjFXqkR8PDw9cuHABU6ZMgUqlgr29PWbOnIl169bB0tISa9euxbJly7B27Vp06tQJ3bp1g76+PgCgV69eePjwIdauXYuHDx+iSZMm+PLLL9GgQQNpT4qIiOhtyXyhslgqdfjR1dXFggULsGDBAq12Hx8f/Pbbb6hduzYOHTqkaR87diwsLS01r4cNG4Zhw4aVW71ERERU9hQbAVNSUjBixAicPn0a9+7dQ1RUFM6ePYsePXpIXRoREVHZ0NERb5OxSj3y82+6d++OGzduYPbs2Xj06BEaNmyI1atXo3nz5lKXRkREVDZkvlZHLIoNPwAwfvx4jB8/XuoyiIiIqBwpOvwQEREpChc8A2D4ISIiUg5OewFQ8IJnIiIiUiaO/BARESmFzK/SEgvDDxERkUIInPYCwPBDRESkHFzwDIBrfoiIiEhhOPJDRESkFBz5AcDwQ0REpBhc81OAEZCIiIgUhSM/RERESsFpLwAc+SEiIlIOlUq8rQRycnIQEBAAFxcXuLq6IjIy8o373L17F05OTjh37tzbnu1rceSHiIiIytTy5csRFxeHTZs2IS0tDTNnzkTdunXRs2fP1+4zf/58ZGVllUk9DD9ERERKIcEdnrOyshAVFYWvvvoKdnZ2sLOzw40bN7B169bXhp89e/bg+fPnZVYTw08ZMzbIl7qEUhvZT1/qEkSx3fSS1CWIwu3j1lKXUGpdYxZJXYIobut1krqEUttwppXUJYjiWNQZqUsQxam9Dcv0+FJc7ZWQkIC8vDw4OTlp2pydnRESEoL8/Hzo/COQPXnyBCtWrEBkZCR69+5dJjUx/BAREVGJqdVqqNVqrTYDAwMYGBhotWVkZMDMzEyr3cLCAjk5Ofjjjz9Qs2ZNrf5Lly5Fv3790LRp0zKrneGHiIhIKUS82is0NBTBwcFabZMmTcLkyZO12l68eFEoEL16/c/wdObMGcTGxmLfvn2i1VkUhh8iIiKFEEQMP76+vhgxYoRW2z9DDgAYGhoWCjmvXhsZGWnasrOzMXfuXMybN0+rvSww/BARESmFiGt+ipriKoqVlRWePHmCvLw86OkVxI6MjAwYGRmhRo0amn7Xrl1DamoqpkyZorX/mDFj4OXlhcDAQNFqZ/ghIiKiMmNraws9PT1cuXIFLi4uAIDY2FjY29trLXZu1aoVDh8+rLWvu7s7Fi1ahE6dxL24gOGHiIhIIcSc9iouY2NjeHl5Yf78+Vi8eDEePHiAyMhILFmyBEDBKFD16tVhZGQEGxubQvtbWVnB3Nxc1Jp4h2ciIiKlkOgOz/7+/rCzs8Pw4cOxYMECTJ48Ge7u7gAAV1dXHDhwoCzO9rU48kNERERlytjYGMuWLcOyZcsKvZeYmPja/f7tvdJg+CEiIlIKPtgUAMMPERGRYkhxh+eKiBGQiIiIFIUjP0RERErBaS8ADD9ERESKIYDTXgCnvYiIiEhhOPJDRESkEFLc5LAiYvghIiJSCoYfAJVw2uvcuXNo1qwZAODu3bto1qwZ7t69K3FVRERE0hNUKtE2Oat04cfJyQmnTp2SugwiIiKqoCrdtJeBgQEsLS2lLoOIiKjC4ZqfArL+FjZv3oyuXbvC3t4e/fv3x8WLF7WmvV45ePAg/vvf/6J169aYO3cu1Go1ACA3Nxdz5sxBu3bt4OTkhHHjxiE9PR0AsG7dOkybNg3+/v5wcHDAe++9h2PHjpX7ORIREYlGogebVjSyDT/Xr1/H8uXLMW/ePPzwww9wcXHB1KlTkZ+fX6jv999/j9WrVyMkJAQnTpxAaGgoAGDr1q24cOECIiMjsX37djx//hyLFy/W7HfkyBEIgoDo6GgMGDAAU6ZMQVJSUrmdIxEREYlPttNe9+7dg0qlQt26dWFtbY2pU6eia9euEAShUN+AgAA4OzsDAD7++GOsXLkSkydPxt27d2FoaIh69erB1NQUS5cuxR9//KHZz8TEBIGBgTAwMEDjxo1x4sQJ7NixAzNnziyv0yQiIhINp70KyPZbcHV1xTvvvIM+ffqgX79+iIyMRKNGjaCrq1uob6tWrTQ/t2jRAg8fPkRmZiY++OADZGRkwNXVFSNHjsRPP/2Exo0ba/q2bNkSBgYGWq+Tk5PL9sSIiIjKiACVaJucyTb8GBsbIyoqCps2bULbtm0RHR2N/v37a9bs/J2Ozl+n+WpkSF9fH02bNkVMTAxWrFgBS0tLfP755xg5cqSmj56e9sDYy5cvtY5FRERE8iPbaa/Lly/j559/xvjx49G+fXt8+umn6NixY6HAAgC//fYb2rZtCwC4du0aateujSpVqmDXrl0wMDBAr1694OHhgStXruCDDz7Ao0ePAACJiYnIz8/XBJ64uDjNcYiIiOSG014FZBt+jIyMsH79elhYWKBDhw64cOECsrKytNbsvLJw4UIsWrQIz549w9q1azFq1CgAwNOnTxESEgIzMzNYW1tj7969qF27NszMzAAAqampWLFiBQYNGoRDhw7h119/xfLly8vzNImIiMQj86u0xCLb8GNra4ugoCBs2LABgYGBqFu3LlasWAELC4tCfb29vTF+/Hjk5uZi8ODBGD58OABgyJAhuH//PmbMmIHMzEy0bNkSX375pWbdkIODAx4/fgwvLy80aNAAYWFhqF+/frmeJxEREYlLtuEHAPr27Yu+ffsWak9MTAQAWFtba37+6KOPCvXT0dHBjBkzMGPGjCKPb2hoiGXLlmHZsmUiVk1ERCQNQb5LfUUl6/BDRERExSf3Z3KJheGHiIhIIbjguQDDz2tMnjxZ6hKIiIioDDD8EBERKYTcb04oFoYfIiIiheC0VwF+C0RERKQoHPkhIiJSCF7tVYDhh4iISCG45qcAp72IiIhIUTjyQ0REpBBc8FyA4YeIiEghOO1VgBGQiIiIFIUjP0RERArBaa8CDD9EREQKwWmvAgw/RERECsGRnwL8FoiIiKhM5eTkICAgAC4uLnB1dUVkZORr+x4/fhx9+/aFk5MT+vTpg2PHjoleD0d+iIiIFEKqaa/ly5cjLi4OmzZtQlpaGmbOnIm6deuiZ8+eWv0SEhIwadIk+Pn54d1338WpU6fw8ccfY/v27WjevLlo9TD8lLHa1Z9LXUKpPVMbSl2CKOrWNZC6BFF0jVkkdQml9qPbHKlLEEWLhP1Sl1BqD9Ll/3cUFZ8Uj7fIyspCVFQUvvrqK9jZ2cHOzg43btzA1q1bC4Wfffv2oX379hg2bBgAwMbGBjExMfjhhx8YfoiIiEhaarUaarVaq83AwAAGBtq/aCYkJCAvLw9OTk6aNmdnZ4SEhCA/Px86On+twOnXrx9yc3MLfdbTp09FrZ1rfoiIiBRCEFSibaGhoXB2dtbaQkNDC31mRkYGzMzMtEKRhYUFcnJy8Mcff2j1bdy4sdYIz40bN3D27Fl06NBB1O+BIz9EREQKIYg45uHr64sRI0Zotf1z1AcAXrx4Uaj91et/jhz93ePHjzF58mS0bt0a3bp1E6HivzD8EBERUYkVNcVVFENDw0Ih59VrIyOjIvd5+PAhRowYAUEQsHbtWq2pMTEw/BARESmEFFd7WVlZ4cmTJ8jLy4OeXkHsyMjIgJGREWrUqFGof3p6umbB8+bNm1GzZk3Ra+KaHyIiIoUQoBJtKy5bW1vo6enhypUrmrbY2FjY29sXGtHJysrC6NGjoaOjg2+++QZWVlZinboWhh8iIiIqM8bGxvDy8sL8+fNx7do1HD16FJGRkZrRnYyMDGRnZwMAQkNDkZKSgmXLlmney8jIEP1qL057ERERKYRUNzn09/fH/PnzMXz4cFSrVg2TJ0+Gu7s7AMDV1RVLlixB//79cejQIWRnZ2PQoEFa+/fr1w9Lly4VrR6GHyIiIoWQKvwYGxtj2bJlmhGdv0tMTNT8fPDgwXKph+GHiIhIIQSBT3UHuOaHiIiIFIYjP0RERAoh1bRXRcPwQ0REpBAMPwU47UVERESKwpEfIiIiheDITwGGHyIiIoXg1V4FOO31GufOnUOzZs2kLoOIiIhExpEfIiIihcjntBcAhh8iIiLF4JqfArKb9rp79y6aNWuGvXv3onPnznBxccGiRYuQl5cHQRAQEhICNzc3tGzZEq6urggODtbsO3ToUCxcuBDdunVDly5d8OzZM9y5cwejRo2Ck5MTunTpgs2bN2t93rfffovOnTvDyckJ/v7+UKvV5X3KREREJCLZjvwEBwdj9erVyMvLg5+fH6pWrYoGDRpg06ZN+Pzzz1G/fn2cPHkS8+fPR9euXWFnZwcAiI6ORkREBAwMDKCvr4+RI0fCzs4O33//PVJTU/Hpp5+ifv36qFKlCgDg0KFDiIiIQEZGBiZNmoRWrVrB29tbylMnIiJ6K1zwXEB2Iz+vzJgxAy4uLmjfvj0+/vhjfP/996hTpw6WLFmCDh06wNraGt7e3rC0tMSNGzc0+3Xp0gWtW7dGy5YtcerUKTx+/BiLFy9G06ZN4ebmhjlz5kBH56+vZd68eXjnnXfQqVMndOzYEQkJCVKcLhERUakJUIm2yZlsR35at26t+blly5Z4/Pgx3nnnHaSmpmLVqlVITk5GfHw8MjIykJ+fr+lbr149zc+3bt1Cw4YNUa1aNU3bgAEDABRc7QUA//nPfzTvVa9endNeREQkWxz5KSDbkR99fX3Nz6/Czfbt2+Hj44OcnBy4u7tj48aNqF27ttZ+hoaGmp/19N6c/XR1dbVeC4JQmrKJiIhIYrId+YmPj0fbtm0BAHFxcahVqxYOHjyIiRMnYvTo0QCAP//8E48ePXptYGnQoAHu3LmDFy9ewNjYGACwbNky5ObmokePHuVzIkREROVE7tNVYpHtyE9QUBB++eUXnDlzBmvWrMGQIUNgZmaGs2fP4tatW4iLi8O0adOQm5v72qkqV1dXWFhYYO7cuUhOTsaxY8fw3XffwdXVtZzPhoiIqOwJgkq0Tc5kO/LTq1cv+Pr6Ij8/H97e3hg7dix69OiBgIAA9O3bF+bm5vDw8ICxsTHi4+OLPIaenh42bNiAwMBA9OvXDxYWFvDz80OXLl00a36IiIioclEJMlvEcvfuXXTr1g3Hjh2DtbW11OW80dn4P6UuodSeqQ3f3EkGrt0ykLoEUYw33y51CaX2o9scqUsQRYuE/VKXUGrLoy2kLkEUv5y4KnUJoji1990yPf7PCZmiHat9cxPRjlXeZDvyQ0RERCUj9+kqsch2zQ8RERHR25DdyI+1tTUSExOlLoOIiEh2eLVXAdmFHyIiIno7nPYqwGkvIiIiUhSO/BARESkEp70KMPwQEREpRL6sbm5Tdhh+iIiIFIIjPwW45oeIiIgUhSM/RERECsGrvQow/BARESmEvB5oVXY47UVERESKwpEfIiIihcjngmcADD9ERESKwTU/BTjtRURERIrC8ENERKQQgiDeVhI5OTkICAiAi4sLXF1dERkZ+dq+169fx6BBg+Dg4IABAwYgLi6ulGddGMMPERGRQghQibaVxPLlyxEXF4dNmzZh3rx5CA4OxsGDBwv1y8rKwtixY+Hi4oLo6Gg4OTnB19cXWVlZYn0FABh+iIiIqAxlZWUhKioKs2fPhp2dHXr06IHRo0dj69athfoeOHAAhoaG8PPzQ+PGjTF79mxUrVq1yKBUGlzwXMZsdG5LXUKpDQl6IXUJolgW2FzqEkRxW6+T1CWUWouE/VKXIIrrzT2lLqHUmu1IkLoEUdQ0by91CbIgxbO9EhISkJeXBycnJ02bs7MzQkJCkJ+fDx2dv8Zhrl69CmdnZ6hUBSNLKpUKrVu3xpUrV9C/f3/RamL4ISIiUggxr/ZSq9VQq9VabQYGBjAwMNBqy8jIgJmZmVa7hYUFcnJy8Mcff6BmzZpafZs0aaK1v7m5OW7cuCFa3QCnvYiIiBRDzAXPoaGhcHZ21tpCQ0MLfeaLFy8KBaJXr/8Znl7X95/9SosjP0RERFRivr6+GDFihFbbP4MLABgaGhYKL69eGxkZFavvP/uVFsMPERGRQoh5h2cDA/0iw84/WVlZ4cmTJ8jLy4OeXkHsyMjIgJGREWrUqFGo78OHD7XaHj58iFq1aolWN8BpLyIiIsWQ4j4/tra20NPTw5UrVzRtsbGxsLe311rsDAAODg64fPkyhP//AYIg4NKlS3BwcBDj9DUYfoiIiKjMGBsbw8vLC/Pnz8e1a9dw9OhRREZGYtiwYQAKRoGys7MBAD179sSff/6JoKAgJCUlISgoCC9evICHh4eoNTH8EBERKYQgqETbSsLf3x92dnYYPnw4FixYgMmTJ8Pd3R0A4OrqigMHDgAAqlWrhtDQUMTGxqJ///64evUqwsLCUKVKFVG/B675ISIiUggp7vMDFIz+LFu2DMuWLSv0XmJiotbrVq1aYefOnWVaD0d+iIiISFE48kNERKQQJX0gaWXF8ENERKQQJX0gaWXFaS8iIiJSFI78EBERKYRUC54rGoYfIiIiheCanwIMP0RERArB8FOAa36IiIhIUSp1+Dl27Bj++9//wsHBASdPnpS6HCIiIknlCyrRNjmr1NNea9euhaurKyZOnAhzc3OpyyEiIpIUp70KVOrw8/TpUzg7O6NevXpSl0JEREQVRKWd9nJzc8O9e/cQEBAANzc3xMbGwtvbGw4ODnB0dMSYMWPw4MEDAEB0dDQ+/PBDTJw4Ec7OztizZw8EQcD69evh6uoKFxcXjBs3DmlpaRKfFRER0dsTBPE2Oau04Wf79u2oXbs2AgICsGXLFvj6+qJTp07Yt28fIiIikJKSgrCwME3/y5cvo0mTJvj+++/h6uqKb775Bnv37sWqVauwbds2mJubY+TIkcjNzZXwrIiIiN5eviDeJmeVdtqrZs2a0NXVRfXq1WFgYIAJEyZgxIgRUKlUqF+/Ptzd3XHt2jVNf5VKhfHjx8PIyAgAEB4ejnnz5qFdu3YAgMDAQLi6uuLkyZNwc3OT5JyIiIio9Cpt+Pk7S0tLeHl5YePGjYiPj0dSUhISExPRunVrTR9zc3NN8Hn+/Dnu37+PadOmQUfnr8Gx7Oxs3L59u7zLJyIiEoUg86u0xKKI8JOeno4BAwbAzs4OHTt2xODBg3H8+HFcvXpV08fQ0FDz88uXLwEAa9asQcOGDbWOZWJiUj5FExERiUzua3XEUmnX/PzdkSNHYGJigtDQUAwfPhwuLi5ITU2F8Jr/C2rUqAFzc3NkZGTAxsYGNjY2qFOnDlasWIFbt26Vc/VEREQkJkWEH1NTU6SlpeHs2bNITU1FWFgYDh8+DLVa/dp9fHx88MUXXyAmJga3b9/GnDlzcOnSJTRq1KgcKyciIhIPFzwXUMS0l4eHBy5cuIApU6ZApVLB3t4eM2fOxLp1614bgEaNGoXnz59j7ty5ePbsGVq2bImIiAhOexERkWxx2quASnjd3A+JIi3x2ps7VXBDPnshdQmiWBbYXOoSRFFN77nUJZSasSpL6hJEcb25p9QllNqNHQlSlyCKjIevH8mXk6CRhm/uVAqRMeIda6SML3xWxLQXERER0SuKmPYiIiIi+a/VEQvDDxERkUJwoUsBTnsRERGRonDkh4iISCHy86WuoGJg+CEiIlIITnsV4LQXERERKQpHfoiIiBSCIz8FGH6IiIgUgpe6F+C0FxERESkKR36IiIgUQtwnWqlEPFb5YvghIiJSCK75KcDwQ0REpBC8z08BrvkhIiIiReHIDxERkUJw2qsAR36IiIgUIl8QbxOLIAhYuXIl2rdvj7Zt22L58uXI/5f5uStXruDDDz+Ek5MT3nvvPURFRZX4MznyQ0RERJL5+uuvsW/fPgQHByMvLw8zZsyAubk5Ro0aVahvRkYGxowZA29vbyxduhS//vor/P39YWlpiS5duhT7Mxl+ythHs59LXUKpLV/UQuoSRGEjJEldgig2nGkldQml9iBd/n8uAKDZjgSpSyi1pgOaS12CKMb8tETqEkTSv0yPXhGnvTZv3owpU6bAxcUFADB9+nSsWbOmyPBz9OhRWFhY4JNPPgEANGjQAOfOncPevXsZfoiIiKgwQcT5KrU6F2q1WqvNwMAABgYGxT5Geno6fv/9d7Rp00bT5uzsjHv37uHBgweoVauWVv/OnTvD1ta20HGePXtWotq55oeIiIhKLDQ0FM7OzlpbaGhoiY6RkZEBAFohx8LCAgBw//79Qv2tra3h6Oioef3o0SPs378fHTp0KNHncuSHiIhIIcRcqOzr64sRI0ZotRU16pOdnY309PQij5GVlVVov1c//3NUqajjTp48GRYWFvjggw9KVDvDDxERkUKIueanuFNcV69exbBhw4p8b8aMGQAKgo6hoaHmZwAwNjZ+7TGfP3+OCRMm4Pbt2/i///u/f+1bFIYfIiIiKjPt2rVDYmJike+lp6djxYoVyMjIgLW1NYC/psIsLS2L3OfZs2cYPXo0UlJSsGnTJjRo0KDENTH8EBERKUS+mPNeIrCyskLdunURGxurCT+xsbGoW7duocXOAJCfn49Jkybh7t272LJlCxo3bvxWn8vwQ0REpBAV8VJ3b29vrFy5ErVr1wYArFq1CiNHjtS8//jxYxgaGqJq1arYvn07zp07hy+//BI1atTQjBLp6+vD1NS02J/J8ENERKQQFTH8jBo1Co8ePcKkSZOgq6uLgQMHwsfHR/P+wIED0a9fP0yePBmHDh1Cfn4+fH19tY7Rtm1bbNmypdifyfBDREREktHV1YW/vz/8/f2LfD8mJkbzc0REhCifyfBDRESkEPkVcehHAgw/RERECiG8/nmhisI7PBMREZGicOSHiIhIIQROewFg+CEiIlKMfE57AeC0FxERESkMR36IiIgUgtNeBcps5Gfo0KFYt25dsfq6ubkhOjoaQMEzO3bt2lVWZRW7DiIiosomXxBvk7MKN+21ceNG7NixQ+oyiIiIqJKqcNNeHJIjIiIqG4Lch2xEUqKRn9jYWHh7e8PBwQGOjo4YM2YMHjx4AAA4cuQI3nvvPTg6OiIwMBAvX77U7Ddr1izMmjVL61jNmjXDuXPntNqio6MRHByM8+fPo1mzZm+s5+7du2jWrBmOHz8ONzc3ODk5YdGiRfjtt9/Qv39/ODo6wtfXF8+ePQMAqNVqLFmyBJ07d4adnR3c3Nywbdu2Io8tCALWr18PV1dXuLi4YNy4cUhLSyvJ10VERFShCIJ4m5wVO/w8ffoUvr6+6NSpE/bt24eIiAikpKQgLCwMSUlJmDp1Kry9vbFjxw7k5eUhNja2xMX06tULI0eOhJOTE06dOlXs/cLCwrBhwwYsXLgQW7ZswaRJk/Dpp58iIiICV65cwfbt2zX9jh8/jnXr1uHgwYPw8vLCwoUL8fDhw0LH/Oabb7B3716sWrUK27Ztg7m5OUaOHInc3NwSnxcREVFFkJ8viLbJWbHDT3Z2NiZMmICJEyeifv36cHZ2hru7O27cuIEdO3bAxcUFPj4+aNy4MT777DPUqlWrxMUYGRmhSpUq0NfXh6WlZbH3mzBhApo3b47evXvD3Nwcnp6e6NSpE5ydndGhQwfcvHkTANC8eXMEBQXB0dER9evXx7hx45Cbm4vbt28XOmZ4eDj8/PzQrl07NG7cGIGBgcjMzMTJkydLfF5ERERUcRR7zY+lpSW8vLywceNGxMfHIykpCYmJiWjdujWSk5Nha2ur6auvr6/1uqzVr19f87ORkRHq1aun9VqtVgMAunfvjtOnT2Pp0qW4efMmrl+/DgBaU3QA8Pz5c9y/fx/Tpk2Djs5f+TA7O7vIoERERCQHXFdboNjhJz09HQMGDICdnR06duyIwYMH4/jx47h69SqAwl+ovr6+5meVSqX1fl5eXmnr1qKrq6v1+u+B5e9Wr16NqKgo9O/fH15eXpg3bx7c3NwK9XsVhtasWYOGDRtqvWdiYiJS1UREROWLDzYtUOzwc+TIEZiYmCA0NFTTtmXLFgiCgKZNm+Ly5cua9vz8fCQkJKB58+YACoLQkydPNO+npqa+9nNUKlWJTqAkvvvuO8yfPx8eHh4AgKSkJACFg1uNGjVgbm6OjIwMdOnSBUDBYulPPvkEo0aNgpOTU5nVSERERGWr2Gt+TE1NkZaWhrNnzyI1NRVhYWE4fPgw1Go1Bg8ejLi4OHz55Ze4efMmli1bpnVllL29PU6fPo2zZ8/it99+Q2BgoNbI0N8ZGxvjwYMHuHv3bunProhz+PHHH5GamoqLFy/Cz88PADTTYn/n4+ODL774AjExMbh9+zbmzJmDS5cuoVGjRqLXRUREVB7yBUG0Tc6KHX48PDzw/vvvY8qUKRgwYADOnTuHmTNnIjk5GXXq1MGXX36J/fv3w8vLCxkZGXj33Xc1+/bt2xfvvfceJkyYgNGjR6N3796vXRDdo0cP5Ofnw9PTE48ePSr9Gf7N4sWLER8fD09PT/j7+6Nnz55o1aoV4uPjC/UdNWoUBg4ciLlz58LLywtpaWmIiIjgtBcREcmWIAiibXKmEuR+BhVcl4FnpS6h1JYvaiF1CaKwEZKkLkEUGy60krqEUnuQ/lzqEkTRrJn8fxlqOqC51CWIostPS6QuQRRVO/Yv0+N/ukG8P3urJlQV7VjlrcLd4ZmIiIjKhtzvzyOWCh1+2rVrV+R6nFf279+PunXrlmNFRERE8sW5ngIVOvxs374d+fmvvy7vbW6kSERERMpWocPP329eSERERKXDB5sWqNDhh4iIiMQj90vUxcLwQ0REpBAc+SlQ7Pv8EBEREVUGHPkhIiJSCI78FGD4ISIiUghmnwKc9iIiIiJF4cgPERGRQnDaqwDDDxERkULwcZ4FOO1FREREisKRHyIiIoXgg00LMPwQEREpBKe9CnDai4iIiBSF4YeIiEghhHxBtE20mgQBK1euRPv27dG2bVssX74c+fn5b9zv6dOn6Ny5M6Kjo0v8mZz2KmO9vNtJXUKpGek+lLoEUTzVqSl1CaI4FnVG6hLo/6tp3l7qEkptzE9LpC5BFMff9Ze6BFF45vYv0+NXxEvdv/76a+zbtw/BwcHIy8vDjBkzYG5ujlGjRv3rfitWrMCDBw/e6jM58kNERKQQ+YIg2iaWzZs3Y8qUKXBxcUH79u0xffp0bN269V/3uXjxIn7++WdYWlq+1Wcy/BAREVGJqdVqPHv2TGtTq9UlOkZ6ejp+//13tGnTRtPm7OyMe/fuvXZUR61W47PPPsPcuXNhYGDwVrUz/BARESmEmGt+QkND4ezsrLWFhoaWqJ6MjAwAQK1atTRtFhYWAID79+8XuU9ISAhatGgBV1fXt/wWuOaHiIhIMcS81N3X1xcjRozQaitqJCY7Oxvp6elFHiMrK6vQfq9+LmoUKSkpCd999x327Nnz1nUDDD9ERET0FgwMDIo17XT16lUMGzasyPdmzJgBoCDoGBoaan4GAGNjY62+giBgzpw5mDJlimZ06G0x/BARESmEFHd4bteuHRITE4t8Lz09HStWrEBGRgasra0B/DUV9s/FzGlpabh8+TISExOxbNkyAMCLFy8wb948HDhwAOHh4cWuieGHiIhIISrape5WVlaoW7cuYmNjNeEnNjYWdevW1VoH9Krv4cOHtdqGDh2KoUOH4v333y/R5zL8EBERkWS8vb2xcuVK1K5dGwCwatUqjBw5UvP+48ePYWhoiKpVq8LGxkZrXz09PZibm8PKyqpEn8nwQ0REpBAV8dleo0aNwqNHjzBp0iTo6upi4MCB8PHx0bw/cOBA9OvXD5MnTxbtMxl+iIiIFEIoxmMjypuuri78/f3h71/0XbpjYmJeu++/vfdveJ8fIiIiUhSO/BARESmEFFd7VUQMP0RERApREdf8SIHhh4iISCEq2qXuUuGaHyIiIlIUjvwQEREpBEd+ClSqkZ/o6Gi4ublJXQYREVGFlC/ki7bJWaUKP0RERERvwmkvIiIiheC0VwFZhp/Y2FisXLkS169fh0qlQps2bRAUFFSoX3JyMhYvXozLly+jatWq+OCDDzBhwgTo6Ohg3bp1uH37NqpVq4a9e/fC0NAQI0eOxJgxYwAUXA64YcMGfPvtt8jOzoaLiwvmzp2LunXrlvfpEhERiYLhp4Dspr2ePn0KX19fdOrUCfv27UNERARSUlIQFham1e/x48f46KOPUKtWLURFRWHevHn45ptvsHnzZk2fQ4cOwdDQEDt37sSoUaOwcuVK3Lp1CwDwzTffYO/evVi1ahW2bdsGc3NzjBw5Erm5ueV6vkRERCQu2YWf7OxsTJgwARMnTkT9+vXh7OwMd3d33LhxQ6vfvn37YGxsjIULF6Jx48bo3r07Pv74Y4SHh2v6mJqaYubMmbCxscHo0aNhamqKuLg4AEB4eDj8/PzQrl07NG7cGIGBgcjMzMTJkyfL9XyJiIjEIgiCaJucyW7ay9LSEl5eXti4cSPi4+ORlJSExMREtG7dWqtfcnIy7OzsoKf31yk6OTkhIyMDf/75JwDA2toaurq6mverVq2KvLw8PH/+HPfv38e0adOgo/NXPszOzsbt27fL9gSJiIjKSH4FfLCpFGQXftLT0zFgwADY2dmhY8eOGDx4MI4fP46rV69q9TM0NCy076v/6C9fvgQA6OvrF+ojCILm/TVr1qBhw4Za75uYmIhyHkREROWNa34KyG7a68iRIzAxMUFoaCiGDx8OFxcXpKamFhqCa9iwIX799VetNTqXL19GzZo1YWpq+q+fUaNGDZibmyMjIwM2NjawsbFBnTp1sGLFCs2aICIiIpIn2YUfU1NTpKWl4ezZs0hNTUVYWBgOHz4MtVqt1a9Pnz5Qq9WYO3cukpOTcfToUaxbtw7e3t5QqVRv/BwfHx988cUXiImJwe3btzFnzhxcunQJjRo1KqtTIyIiKlOCkC/aJmeym/by8PDAhQsXMGXKFKhUKtjb22PmzJlYt26dVgCqVq0awsPDERQUBC8vL9SsWRPDhw+Hr69vsT5n1KhReP78OebOnYtnz56hZcuWiIiI4LQXERHJFqe9CqgEuS/ZruCW75B3OgaAnq0eSl2CKKroPJe6BFH4TE2RugT6/97t117qEkotoPl+qUsQxfF3/aUuQRSeuYllevxeI38R7VgHIu1FO1Z5k93IDxEREb0djvwUYPghIiJSCLk/kFQsslvwTERERFQaHPkhIiJSCE57FWD4ISIiUgiBd3gGwGkvIiIiUhiO/BARESkEp70KMPwQEREphNzvzCwWhh8iIiKFyOfIDwCu+SEiIiKF4cgPERGRQvBqrwIMP0RERArBBc8FOO1FREREisKRHyIiIoXg1V4FGH6IiIgUgtNeBTjtRURERIrCkR8iIiKF4NVeBVSCIHAMjIiIiBSD015ERESkKAw/REREpCgMP0RERKQoDD9ERESkKAw/REREpCgMP0RERKQoDD9ERESkKAw/REREpCgMP0RERKQoDD9ERESkKAw/REREpCgMP0RERKQofKo7SSY5ORm1atVC9erVcfLkScTExKBFixYYNGiQ1KUV29OnT7Fnzx7cunULEyZMwNWrV9G4cWP85z//kbq0EklOTsaOHTtw8+ZNqFQqNGvWDAMHDoS1tbXUpRVbbm4uoqOjkZCQgJycHPzzmc1LliyRqLKSefnyJU6ePInbt2+jf//+uHXrFho1aoTq1atLXVqJVIbzqAznQEVj+JEBNzc3qFSqYvU9duxYGVcjjm3btiEwMBBff/01qlWrhvHjx6N9+/Y4cuQI0tLS8PHHH0td4hv99ttvGD58OOrUqaP5+fDhwzh48CBCQ0PRtm1bqUsslpiYGEyZMgVOTk5o2bIlXr58ifPnz2Pjxo346quv0KZNG6lLLJa5c+fi4MGD6NSpk2z/cfr9998xatQo/PHHH8jMzES3bt0QHh6Oy5cvIyIiAs2aNZO6xGKpDOdRGc6B/oVAFV50dLRm++KLLwQnJydh+fLlwg8//CAcPXpUWLt2rdC2bVth/fr1UpdabN27dxf27t0rCIIgBAYGCgMHDhQEQRDOnz8vdOrUScrSim3o0KHCmjVrBEEQBEdHRyElJUUQBEFYvny5MGDAAClLK5GePXsKYWFhhdo3bNgg9O3bt/wLekuOjo7CmTNnpC6jVMaNGyfMmTNHyMvL0/w/pVarhRkzZgj/+9//pC6v2CrDeVSGc6DXY/iRmX79+gkHDhwo1H7kyBHB09NTgorejr29vZCWliYIgiB07dpVCAkJEQRBEFJSUgRHR0cpSys2R0dH4c6dO5qfX4WflJQUwcHBQcLKSsbBwUFITk4u1H7z5k2hVatWElT0djp37iwkJSVJXUapuLi4CDdv3hQEQfv/qVu3bsnmz4UgVI7zqAznQK/HBc8yc+vWLbzzzjuF2uvXr4979+5JUNHbadSoEfbu3Yvt27cjLS0N3bt3R25uLiIjI9G8eXOpyyuWmjVr4tatW4XaL126BHNzcwkqejseHh4IDw9Hbm6uVntUVBR69eolUVUlN2HCBAQFBSElJaXQeh+5MDIywqNHjwq137p1C9WqVZOgordTGc6jMpwDvR7X/MiMs7MzFi9ejMWLF8PKygoAkJqaikWLFqFz584SV1d8M2fOxNSpU5GZmYmPPvoIjRs3RmBgII4cOYKQkBCpyyuWMWPGYM6cORg3bhwEQcDPP/+MnTt3YtOmTZg2bZrU5RVbTk4ODh8+jBMnTqBly5bQ19dHYmIiUlNT4eDggGHDhmn6bt68WcJKC2vevLnWejhBEPDee+8V2Tc+Pr68ynprH374IebOnQs/Pz8ABf/Qnj9/HqtXr5bVhQCV4TwqwznQ66kEuf6KpFAPHjzAlClTcPXqVZiYmEAQBPz555/o0KEDVq9eDRMTE6lLLLb8/Hw8ffpUU/PDhw9hYmICfX19iSsrvpiYGERERCA5ORkvX75Ew4YN4ePjI6sRk+Dg4GL3nTRpUhlWUnLnz58vdl+5LEDfsmULIiIicP/+fQCAubk5fHx8MGrUKOjoyGewvjKcR2U4Byoaw49MJSUlISkpCQDQtGlTNG7cWOKKSu7EiROws7ODubk5tm/fjsOHD6NFixaYMGECDAwMpC5P0R4/fgwzM7NiX2VYUfz000/Q0dHRjIIGBQWhc+fO+O9//ytxZcWzb98+uLq6wtTUFFlZWXj58qVsr1x7pTKcR2U4B9LGaS8ZevnyJe7evYv79+9r7j3x9OlTWf3BXL9+PcLDw7Fx40YkJydj7ty5GDRoEI4cOYLMzEzMmzdP6hLfyN/fv8h2lUoFfX19WFpawt3dvcg1WhVJeno6li5dirFjx6JRo0YYNWoUYmNjUadOHWzYsEE2a7C2bNmC1atX47PPPtO06enpYerUqZg1axYGDx4sYXXFs2DBAmzbtg2mpqaoUqWK1OWUyK5du4rd18vLq8zqENOdO3cQFxdXaD0cIJ9zoKJx5Edmfv/9d4wcORKZmZnIzMzEwYMHsXz5ctnde+Ldd99FUFAQXF1dMXv2bNy9exebNm3CL7/8gtGjR+PcuXNSl/hGixYtwtatW+Hg4ABHR0cIgoBff/0VFy9e1Czg/vnnn/HFF1+ga9euUpf7WuPHj0dWVhaWLl2K48ePY/Xq1fjqq6+wZ88eJCQkYOvWrVKXWCxubm747LPPCn3Xx44dw5IlS3D06FGJKiu+SZMm4Z133sG4ceNkN/rp5uZWrH4qlUoW9yMLDw/HypUrYWJigqpVq2q9J5dzoNfjyI/MBAYGwsXFBfPnz4eLiwsA4PPPP8fs2bOxaNEibNmyReIKiyczMxONGjWCIAg4fvw4xowZAwCoVq0aXr58KXF1xXPnzh2MHz8eU6ZM0WoPCQnBlStXEBoaiqioKKxZs6ZCh5+ff/4Z0dHRqFOnDo4ePYpu3brBwcEBNWvWRO/evaUur9iePHlS5J21GzZsiIcPH0pQUck9evQIGzZsQEhICGrWrAlDQ0Ot9yvyP7gxMTFSlyCqyMhIzJgxA6NGjZK6FCoDDD8yc/HiRXz//ffQ1dXVtOnr62PChAno16+fhJWVTPPmzREREQFTU1M8fvwYPXr0QHp6Oj7//HM4OjpKXV6xXLhwAbNnzy7U3rNnT2zYsAEA0KlTJyxatKi8SysRQ0ND5OTkIDMzE+fOncOqVasAAHfv3pXVAvrWrVtj3bp1WLJkCYyNjQEUXMkWEhICJycniasrnsGDB8tieq44Hj9+jFu3biE/Px9AwZV4arUa169fx9ixYyWu7s1ycnLg7u4udRlURhh+ZObVvScaNmyo1S63e0/Mnz8fM2fOxL179/Dpp5+iXr16CAoKwr1797BmzRqpyyuW+vXr49ChQ/D19dVqP3LkCOrUqQMAuH37NmrWrClFecXWvXt3TJ06FUZGRjAxMUGXLl1w4MABLF68WFaBeu7cuRg1ahRcXV3RoEEDAEBKSgosLCw0YbSi+7fvu6h1JxXV999/j8DAQOTl5UGlUmnuu6RSqdCqVStZhJ8+ffrg//7v/+Dn5ye7hf/0ZlzzIzPr16/H/v374efnh2nTpmHNmjXIyMjQ3HtCDs/Eeh21Wi2rdQ6nTp3ChAkT4ODggJYtWwIA4uLicOXKFaxbtw5WVlbw8fHByJEjCwWkiiQvLw/ffPMN7t27hw8++ABNmjTBrl278OzZMwwZMkQ2f/FnZWVBT09P8yBKPT09NGjQAK6urlojpRXZw4cPERoaiqSkJM30ryAIyM3NRXJyMi5cuCBxhcXj5uaG/v37Y+zYsXBzc0NUVBSeP38OPz8/9OrVC6NHj5a6xDeaPn06Dh48CDMzM1hbWxe6BUdFu+cVlQzDjwxVlntPxMfH48aNG0UOiy9YsEDi6oonNTUV27dvR2JiIvT09NCkSRN88MEHsLCwwO3bt5GSkoJu3bpJXWaxPX36FPr6+jAyMpK6lBJzc3NDcHAwWrRoIXUpb23s2LFISUmBu7s7IiMjMWLECKSkpODIkSOYNWuW1g0nK7KWLVvi4MGDsLa2hq+vL7y8vODh4YGLFy9i9uzZOHTokNQlvtGb7n9V0e55RSXDaS+ZUavVGDp0KIYOHSrre08EBwcjODgYFhYWePToEaysrPDw4UO8fPkSPXr0kLq8Ynn48CE2b96s+S1dEATExsbi559/1vyW3rRpU6nLfKPc3FyEhobiu+++09zOv3bt2vDx8cHw4cMlrq74dHR0ZDU1VJQLFy4gMjISTk5OOH36NLp06QJnZ2eEhYXhxIkTsgk/NWvWxOPHj2FtbY1GjRohPj4eHh4esLKyQnp6utTlFcvfw82zZ8/w8uVLWa2Bo3/H8CMzHTp0QLdu3eDp6YlOnTpBT0+e/wm3bduGBQsW4IMPPoCbmxs2bdoEExMTTJs2rcgrdiqigICAQr+lp6am4vDhw5g1a5bU5RXbwoULcfLkSUyfPh0tWrRAfn4+rl27hrVr1+LRo0f45JNPpC6xWLp06YIRI0aga9euqFevXqEpVDn8pi4IguaxNU2aNMH169fh7OwMDw8PRERESFxd8Xl4eGDmzJmam0z6+fnBzs4OP/74I2xsbKQur9g2bdqE8PBwzdWCNWvWhLe3tyz+X6J/J89/ORVszZo1OHz4MAICApCXl4fu3bvD09MT7du3l9WU15MnTzR34bW1tcXly5fx/vvvY9q0aZgyZQqmT58ucYVvVll+S9+/fz9CQ0M1t04ACq7Gq1evHj755BPZhJ/ExETY2dnhwYMHePDggdZ7clm31KJFC+zevRvjx4+Hra0tTp8+jaFDh+Lu3btSl1Yi06dPR/Xq1fHkyRN069YNAwYMwLx582BqaorFixdLXV6xrF+/Ht988w0+/vhjODk5IT8/H5cuXUJwcDAMDAxksWibXo/hR2ZcXV3h6uqK+fPn48KFCzhy5Ahmz54NtVqN9957D3PnzpW6xGKxsrJCamoq6tati8aNG+P69et4//33Ua1aNTx+/Fjq8oqlsvyWXq1atSJHEKtXry6rkUW53OPqn06fPo02bdrAwMAA06dPh6+vL4yNjdG3b1+Eh4ejT58+SEtLQ58+faQutdj09fW1RkemTZsmq4f9AgVXrAUFBWndvNHW1hZWVlYICgpi+JE5+fzNRlp0dHTg7OyM7OxsvHz5Ert378aJEyekLqvYBg0ahE8++QSLFy9G9+7d4ePjg1q1auHMmTOyeZyCnH9LT0tL0/w8bNgwzJw5E7Nnz4a9vT10dXXx22+/ITAwEJMnT5awypKT4yL6SZMm4YcffkDt2rUxY8YM7NmzB/r6+jAzM8OOHTtw9OhRmJqayuphuVlZWYiKisLNmzehVqsLvb9kyRIJqiqZZ8+eaW6Z8HcNGzaUzS9o9HoMPzKTk5ODEydO4NChQ/jpp59QpUoV9OzZE19//TUcHBykLq/Yxo0bh9q1a8PY2BitWrWCv78/vvvuO1kNi3/66acYN25ckb+lv//++1KX96/c3Nw0U0GvLvgcO3ZsobYFCxbgww8/lKbIEpLrIvoaNWpg/fr1aN26Ne7du4djx45p3bOratWqyM3Nxe7du2XzPKlPPvkEly9fRseOHWV55SAAODk5ITIyEoGBgZolBS9fvkRkZCRatWolcXVUWrzUXWYcHR1RpUoV9OjRA56enmjTpo1s1jNURs+ePUN2djYsLCyQnp6u+S3dw8OjQq/BunfvXrH71qtXrwwrEU/nzp0xadKkIhfR29raVth1ZMeOHcO6devw9OlTpKWlwcrKqsj/d+T0PKlXwUEud9YuSnJyMoYMGYIqVarAzs4OAPDrr79CrVYjPDxcNiPUVDSGH5k5efIkOnbsKJubtv3d656CXhQ5DItTxdKyZUscPnwYdevWxcSJE/Hee+/h/fffR1xcHKZMmSKLZ0+5ublhx44dMDMzk7qUUhkwYACmT5+ODh06SF1KqTx58gR79+7FzZs3YWhoiIYNG6JPnz6FHnRK8sNpLxnYtWsXevXqBQMDAzx69Ah79+59bV+5DIuTtGxtbXHq1CmYm5ujefPm/zp6GB8fX46Vvb3KsIheDgGtOJYuXYpJkyahT58+qFu3bqGRLLn8PWVmZiabqzapZDjyIwN//23w71ce/JOchsWBgscqZGZmwtzcHABw+fJl2NnZyeoRF3J1/vx5tG7dGnp6ejh//jzy8/MhCAJ0dXWRnp4OXV1dZGZmonHjxmjbtq3U5RZLSEgItmzZgsWLF8PMzAw+Pj6YNGkSTp8+jefPn+O7776TukTFCAoKwpYtW2Bubl7oyfQV+e+pv6+Fe5OKeg5UPAw/JIn4+HiMGzcOnp6e8PPzAwDNYyBCQ0PRpEkTKctTlNjYWEydOhUrVqxAo0aN0L9/f+Tk5ODFixdYsWIFPDw8pC6x2Hbt2oW6deuiadOm2L59Ow4dOgRTU1MEBASgUaNGUpenGE5OTggKCpLVFWoAsHPnzmL1y83NxeDBg8u4GipLnPaSmffeew+enp7o1auXrANCYGAgevTooXXvjyNHjmDx4sVYsGCBbO/ZIkdLliyBp6cnHBwcEBERAUNDQ8TExGD//v1Yu3atbMJPfn4+bt26heXLl+PJkyea+zC5u7sz+JQzMzMzWf791K9fP83PGRkZCAsLe+1DZhl+5K3iXo5CRRo5ciSuXr0KLy8v9OnTBxs2bMCdO3ekLqvE4uPjMXz4cK0nJevo6GDYsGGIi4uTsDLl+e233zBs2DAYGxsjJiYG7u7uMDAwQNu2bbXuB1TRLVmyBLt27cL06dOxe/du7Nq1C5MmTcKWLVve+JBKEte8efMQGBiIs2fPIjU1FWlpaVqbHMyePRsnT56Evb09Ll26BAcHB5ibm+PatWuyu/8VFcaRH5n54IMP8MEHHyAzMxPHjh3D4cOHERYWhkaNGsHT0xOjRo2SusRiqVOnDs6ePYv69etrtV+6dAkWFhYSVaVMFhYWSEpKQlZWFq5fv655LtmZM2dQp04diasrvt27dyM4OFhrjdKrx3RMnz6dz2MqR76+vgCAESNGaK2hEQQBKpVKFovoK8vja6hoDD8yZWJigv79+6NVq1b44Ycf8PXXXyM4OFg24WfcuHGYPXs2Ll++jJYtWwIoGA3as2cP5s+fL21xCuPj44OJEydCR0cH9vb2aNu2LUJCQhAcHCyrWw4YGRlpjSS+UqNGDd4Lq5wVdzHw/fv3UatWrQp5T6zK8vgaKhoXPMvQ9evXcejQIRw5cgT37t1D586d0atXL3Tt2hXGxsZSl1dsp06dwnfffYc7d+5ApVKhUaNG+N///qf1gE0qH/Hx8bh37x5cXV1hZGSEK1euwMjISFY3ctu3bx/Wr18PPz8/ODk5QU9PDwkJCQgKCoKHhwd69+6t6Vu3bl0JK6VXWrdujd27dxcaAa4IPvroI3Tu3Bnjx4/Hxo0b8fPPPyMkJARnz57F1KlTce7cOalLpFJg+JEZNzc3PHjwAO3bt4enpyd69OihdSt8ucjPz8fatWvx/fffa+7BYmVlhSFDhvCBgfRW/h7U/vmYjldtcpp2UQInJyfs2bOnQoaf2NhYjBs3DhMnTkTfvn3Rp08fmJmZaR5fM2/ePKlLpFLgtJfM9O/fHwMHDkTt2rWlLqVUlixZgsOHD2P69Olo2bIl8vPz8csvv2Dt2rVQq9Vcn0ElxvuukJicnZ3x448/Ijs7u9BDZuVyBSS9Hkd+ZKZdu3bYtGmTrKYjitK2bdtCi1OBgkW206dPx5kzZySqjIjKS0Ue+aHKreKtMqN/1bRpU1y7dk3qMkqNi1OJiEgqnPaSGRMTE8ybNw9r166FtbV1oUdBbN68WaLKSsbPzw8BAQFFLk4dPny41r1AuDiViIjExGkvmXnTzdrkslaGi1OJqCJf7UWVG0d+ZEYu4eZNuDiVSJkeP36MmjVrAgAMDAw4zU2S4MiPzPj7+//r+3K6KR0RVU62trY4ffq0JuS8cu/ePfTu3RuXL1+WqDKiAhz5kbm8vDykpqYiPj4e//vf/6Quh4gUateuXYiOjgZQMIU9ceLEQhc1PHjwAJaWllKUR6SF4UdmXjeyEx4ejt9++62cqyEiKtCjRw/cvXsXAHD+/Hk4OjqiatWqWn2qVKmCHj16SFEekRZOe1USd+/eRZ8+fTicTESS27lzJzw9PQtdjUpUUXDkpxLIysrCtm3bYGZmJnUpRKRQu3bt0vysUqlw4MCB1/b18vIq+4KI/gXDj8w0b968yKsjDA0NsWjRIgkqIiIC1q5dW6x+KpWK4Yckx2kvmTl//jzy8/MhCAJ0dXWhUqlw7tw59OjRA82aNZO6PCIiogqP4UdmYmNjMXXqVKxYsQKNGjVC//79kZOTgxcvXmDFihV84B4RSe7ChQv/+n6bNm3KqRKionHaS2aWLFkCT09PODg4ICIiAoaGhoiJicH+/fuxdu1ahh8iktzQoUOLbDcwMIClpSVvckqSY/iRmd9++w1r166FsbExYmJi4O7uDgMDA7Rt2xbz58+XujwiIiQkJGi9fvnyJVJSUrBw4UL06dNHoqqI/sKnusuMhYUFkpKSkJSUhOvXr6Nr164AgDNnzqBOnToSV0dEVJiuri4aNmyIWbNmYc2aNVKXQ8SRH7nx8fHBxIkToaOjA3t7e7Rt2xYhISEIDg7moy2IqEJ79OgR/vzzT6nLIOKCZzmKj4/HvXv34OrqCiMjI1y5cgVGRkZaT0onIpJKUc8gfP78Oc6cOQN3d3csXrxYgqqI/sLwQ0REoioq/Ojr68Pe3h59+/blnZ9Jcgw/REREpChc80NERKUWHBxc7L6TJk0qw0qI3ozhh4iISu3cuXOan/Pz8xEbG4tatWrB1tYW+vr6SEhIwO+//47//ve/ElZJVIDTXkREJKqFCxciNzcXc+fOhZ5ewe/YgiBg6dKlePjwIVatWiVxhaR0DD9ERCQqJycnREdHo2HDhlrtt27dQr9+/XDlyhVpCiP6/3iTQyIiElWtWrVw8uTJQu2HDx9G/fr1JaiISBtHfoiISFRHjhzBtGnT0KZNG839x65du4a4uDiEhISgQ4cOEldISsfwQ0REoktOTsaOHTuQnJwMlUqFpk2bYvDgwahduzb09fWlLo8UjuGHiIhE9fDhQ4SGhiIpKQkvX77Eq39mcnNzkZycjAsXLkhcISkd1/wQEZGoAgICcPLkSdjb2+PSpUtwdHSEubk5rl27hsmTJ0tdHhHv80NEROK6cOECIiMj4eTkhNOnT6NLly5wdnZGWFgYTpw4gWHDhkldIikcR36IiEhUgiDAysoKANCkSRNcv34dAODh4YFffvlFytKIADD8EBGRyFq0aIHdu3cDAGxtbXH69GkAwN27d6Usi0iD015ERCSqTz/9FOPGjYOxsTH69u2L8PBw9OnTB2lpaXj//felLo+IV3sREZH4nj17huzsbFhYWCA9PR1Hjx6FqakpPDw8oKPDSQeSFsMPERERKQrjNxERESkKww8REREpCsMPERERKQrDDxERESkKww8REREpCsMPERERKQrDDxERESnK/wM216j+15FbNwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# CODE HERE\n",
-    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
-    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
-    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
-    "columns = ['survived', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'adult_male', 'alone']\n",
-    "\n",
-    "# Create the correlation matrix\n",
-    "corr_matrix = titanic[columns].corr()\n",
-    "\n",
-    "# Create the colored square plot\n",
-    "sns.heatmap(corr_matrix, cmap='coolwarm')\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x11d72da58>"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d8229b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# CODE HERE\n",
-    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
-    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
-    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
-    "\n",
-    "# Plot the histogram for males\n",
-    "sns.histplot(data=titanic[titanic['sex'] == 'male'], x='age', ax=ax1, color='blue')\n",
-    "ax1.set_title('sex=male')\n",
-    "ax1.set_xlabel('Age')\n",
-    "ax1.set_ylabel('Count')\n",
-    "\n",
-    "# Plot the histogram for females\n",
-    "sns.histplot(data=titanic[titanic['sex'] == 'female'], x='age', ax=ax2, color='pink')\n",
-    "ax2.set_title('sex=female')\n",
-    "ax2.set_xlabel('Age')\n",
-    "ax2.set_ylabel('Count')\n",
-    "\n",
-    "# Adjust the spacing between subplots\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Great Job!\n",
-    "\n",
-    "### That is it for now! We'll see a lot more of seaborn practice problems in the machine learning section!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 277c76db5ede971f3f7b9d6612f71bf8e595f0e2 Mon Sep 17 00:00:00 2001
From: ChaitanyaSagar89 <106511025+ChaitanyaSagar89@users.noreply.github.com>
Date: Wed, 7 Jun 2023 14:16:51 +0530
Subject: [PATCH 5/5] Add files via upload

---
 .../210276_Chaitanya_matplotlib.ipynb         | 445 +++++++++++++
 Assignment 2/210276_Chaitanya_seaborn.ipynb   | 601 ++++++++++++++++++
 2 files changed, 1046 insertions(+)
 create mode 100644 Assignment 2/210276_Chaitanya_matplotlib.ipynb
 create mode 100644 Assignment 2/210276_Chaitanya_seaborn.ipynb

diff --git a/Assignment 2/210276_Chaitanya_matplotlib.ipynb b/Assignment 2/210276_Chaitanya_matplotlib.ipynb
new file mode 100644
index 0000000..1a08cab
--- /dev/null
+++ b/Assignment 2/210276_Chaitanya_matplotlib.ipynb	
@@ -0,0 +1,445 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n",
+    "\n",
+    "# Exercises\n",
+    "\n",
+    "Follow the instructions to recreate the plots using this data:\n",
+    "\n",
+    "## Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "x = np.arange(0,100)\n",
+    "y = x*2\n",
+    "z = x**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "** Follow along with these steps: **\n",
+    "* ** Create a figure object called fig using plt.figure() **\n",
+    "* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n",
+    "* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "x = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "y = [2 * value for value in x]\n",
+    "\n",
+    "ax.plot(x, y)\n",
+    "\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_title('Plot of y = 2x')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2\n",
+    "** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.2, 0.2])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Now plot (x,y) on both axes. And call your figure object to show it.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a figure object\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# Add the first axis to the figure canvas\n",
+    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "# Add the second axis to the figure canvas\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.2, 0.2])\n",
+    "\n",
+    "# Define the x values\n",
+    "x = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "# Define the corresponding y values\n",
+    "y = [2 * value for value in x]\n",
+    "\n",
+    "# Plot (x, y) on the first axis\n",
+    "ax1.plot(x, y)\n",
+    "ax1.set_xlabel('x')\n",
+    "ax1.set_ylabel('y')\n",
+    "ax1.set_title('Plot on ax1')\n",
+    "\n",
+    "# Plot (x, y) on the second axis\n",
+    "ax2.plot(x, y)\n",
+    "ax2.set_xlabel('x')\n",
+    "ax2.set_ylabel('y')\n",
+    "ax2.set_title('Plot on ax2')\n",
+    "\n",
+    "# Call the figure object to show the plot\n",
+    "fig\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3\n",
+    "\n",
+    "** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.4])\n",
+    "\n",
+    "# Show the plot\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.4])\n",
+    "\n",
+    "x1 = np.linspace(0, 100, 1000)\n",
+    "y1 = np.exp(x1 * 0.1)\n",
+    "ax1.plot(x1, y1, color='blue')\n",
+    "ax1.set_xlim(0, 100)\n",
+    "ax1.set_ylim(0, 10000)\n",
+    "ax1.set_xlabel('X')\n",
+    "ax1.set_ylabel('Z')\n",
+    "\n",
+    "x2 = np.linspace(20, 22, 1000)\n",
+    "y2 = np.exp(x2 * 0.1)\n",
+    "ax2.plot(x2, y2, color='red')\n",
+    "ax2.set_xlabel('X')\n",
+    "ax2.set_ylabel('Y')\n",
+    "ax2.set_title('zoom')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4\n",
+    "\n",
+    "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(1,2,1)\n",
+    "plt.subplot(1,2,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(1,2,1)\n",
+    "plt.plot(x2, y2,'b--', linewidth=4) # More on color options later\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.plot(x1,y1,'r', linewidth=4);\n",
+    "\n",
+    "x1 = np.linspace(0, 100, 1000)\n",
+    "y1 = np.exp(x1 * 0.1)\n",
+    "ax1.set_xlim(0, 100)\n",
+    "ax1.set_ylim(0, 10000)\n",
+    "\n",
+    "x2 = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "y2= [2 * value for value in x2]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "\n",
+    "ax1 = axes[0]\n",
+    "ax2 = axes[1]\n",
+    "\n",
+    "x1 = np.linspace(0, 100, 1000)\n",
+    "y1 = np.exp(x1 * 0.1)\n",
+    "ax1.plot(x1, y1, 'r', linewidth=4)\n",
+    "ax1.set_xlim(0, 100)\n",
+    "ax1.set_ylim(0, 10000)\n",
+    "ax1.set_xlabel('X')\n",
+    "ax1.set_ylabel('Y')\n",
+    "ax1.set_title('Plot 1')\n",
+    "\n",
+    "x2 = [1, 2, 3, 4, 5]\n",
+    "y2 = [2 * value for value in x2]\n",
+    "ax2.plot(x2, y2, 'b--', linewidth=4)\n",
+    "ax2.set_xlabel('X')\n",
+    "ax2.set_ylabel('Y')\n",
+    "ax2.set_title('Plot 2')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Great Job!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Assignment 2/210276_Chaitanya_seaborn.ipynb b/Assignment 2/210276_Chaitanya_seaborn.ipynb
new file mode 100644
index 0000000..88f6e38
--- /dev/null
+++ b/Assignment 2/210276_Chaitanya_seaborn.ipynb	
@@ -0,0 +1,601 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Seaborn Exercises\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set_style('whitegrid')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = sns.load_dataset('titanic')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survived</th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>class</th>\n",
+       "      <th>who</th>\n",
+       "      <th>adult_male</th>\n",
+       "      <th>deck</th>\n",
+       "      <th>embark_town</th>\n",
+       "      <th>alive</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
+       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
+       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
+       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
+       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
+       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
+       "\n",
+       "     who  adult_male deck  embark_town alive  alone  \n",
+       "0    man        True  NaN  Southampton    no  False  \n",
+       "1  woman       False    C    Cherbourg   yes  False  \n",
+       "2  woman       False  NaN  Southampton   yes   True  \n",
+       "3  woman       False    C  Southampton   yes  False  \n",
+       "4    man        True  NaN  Southampton    no   True  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "** Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints.**\n",
+    "\n",
+    "** *Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!* **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x257abf91e50>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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tALgyhoVc392x+4rl/PcXnDNpOF/Scc6ki/ecFRYWdmt/q9WKDnfnfx8+fBhWqzUGo+pbIp3jhBdKO3bswNSpU5GRkeFdNmjQIJjN/lc8zGZzwO24SOTn53e7Mne5XKiurvY7lisl9BN4A/Ra5OZO7tZ5e0ptQ8+PPdicUWicM2k4X9JxzqTrrXOm1WqhPNdwcvTo0QkeTe+W8EJp3759mDJlit+ySZMm4fnnn4cgCJDJZBAEAbt378btt98u+fhyuTxmP9y+xxqUrg4Zqh6Ypk7a/1MNTIvf2GM5//0F50wazpd0nDPpevOc9dZxJ4uEh7lrampgNBr9ll155ZVoaWnBww8/DJPJhIcffhhWqxVXXXVVgkYZKBah6kTpzWMnIiKKp4RfUTKbzdDr9X7L0tLS8Nxzz2HFihV45ZVXMHr0aKxfvx46nS5BowxuSKYWa+YUwNzmQKutA+kaJbLTekcvot48diIionhJeKG0b9++oMsnTpyIrVu3xnk00mXoem9x0ZvHTkREFA8Jv/VGRERElKxYKBERERGJYKFEREREJCLhGSUiIiKKrSGZWrggS/Qw+gQWSkRERH3MgktHQi6Xw+0WkJLCgqk7WCgRERH1MVt21cGNFMyeNjzRQ+n1mFEiIiLqY/Z904w9dU2JHkafwEKJiIiISAQLJSIiIiIRLJSIiIiIRDDM3U3NFgfMbQ602Dqg1yqRncrXghAREfUVLJS64USTFfds2YcdNWbvsul52Xhs1kQMydQmcGREREQUC7z1FqVmiyOgSAKA7TVm3LtlH5otjgSNjIiIiGKFhVKUzG2OgCLJY3uNGeY2FkpERES9HQulKLXYOkKubw2znoiIiJIfM0pRGqBTYcOtU2F3uqFRyrH7eCPKK2phcbgAAOkapei+8QyAM2xOREQUPRZKUTjRZMV9r+/HDtP5W29FRgNWzynAks1VmDoiC9lpwYuReAbAGTYnIiLqHt56k8gb4jb555MqTQ3YWFmL+68Zh8dnTQx61SaeAXCGzYmIiLqPhZJEoULclaYGTB2RhcEiV2viGQBn2JyIiKj7WChJFC7E3W53Rr1vLAPgDJsTERF1HzNKEulDhLSB0CHuDK0Si0uNKBiWGTQEHmrfeI6TSCo+NEBEfRULJYmy01SYnpeN7UFua03PyxYNcQOASp6CquONWLvN5F3mCYG//PnxkPvGc5xEUvChASLqy3jrTaIMnQqPzZqI6XnZfsun52WLhriBzn9xL91ajUpTg9/ySlMDNlXWYuV142P6L/Box0kkBR8aIEpOQzK1GJrFf6jEAq8oRWFIphZr5hTA3OZAq60D6RolstNC32oIFa6uMDXA1uFOinESSRHJQwP8eSOKvwWXjoRcLofbLSAlRea3LtgyEsdCKUoZOmkFR6LC1VLHSSQFHxogSk5bdtXhVGsH2u1ONFvP//9waJYWd5TmJXBkvQ8LpW6KNMTaW8LVDOWSFL3l55qov9n3TTNMZkuih9EnsFDqBikh1t4QrmYol6TqDT/XRETdwTB3lKSGWJM9XM1QLkUj2X+uiYi6i1eUohRNiDWZw9UM5VK0kvnnmoiou1goRSnaEGuyhqsZyqXuSNafayKi7mKhFCW9RgmdSo6y4tygnbZDhVhPt9jQ2O5Ai80JvVaBLJ0Kg/SaOI4+kG8oN9jnyuIvQSIi6odYKEUpO02F8rkXYc22moBO2+VzLxINsR5vaA9oPFlsNOCRG/Mx3JDa4+MW4wnlfvF1I1bPKcDGylq/z8VQNxER9UcMc3fDs9tMQTttP/uhKej2p1tsQbtzV5gasGxrNU632HpsrOF4Qrn3XzMOGytrA8bIUDcREfVHLJSiZG5zYIcpePh5x7nwc1eN7Y6AAsSjwtSAxvbEFiFDMrWYOiJLdIzbRT4XERFRX8VCKUrRhJ9bbM4wxwy9Ph7a7KHHwFA3ERH1J8woRaHZ4oBWKQ+5TbAwt14TON2+wWkZgIMnmpGqViBTq0zIU0TstExERHQeCyWJPN2rJw3LRJHREPQ2lVhH4qxUFYqNBlSc20enkgcNThcZDbijNA8jBugwOM7haXZaJiIiOo+33iRosTm93avLK2oxrygXRUaD3zahOhIP0mvwyI35KD63T1lxbtDgdKWpAWu21eCjI/VxD0+z0zIREdF5Cb2i5HA48Oijj+Ktt96CUqnED3/4Q9x5552QyWQ4ePAgVqxYgSNHjsBoNOLBBx/EhAkTEjlcNLSf715tcbiwZHMVyopzUVaUC7vTjQuzUzE4QxOymBhuSMXTP56MxnYHXILgdyXJV6WpAWVFuQnpiM1Oy0RERJ0SWig99NBD+Oyzz7Bhwwa0t7fjzjvvxJAhQ3Dddddh/vz5uPbaa/HYY49h8+bNWLBgAd5//33odLqEjbe1S9ja4nD5FTqvL7wkomJikF6DQXoNqo43htzO7nQnLDzNTstEREQJLJSampqwZcsWbNy4ERMnTgQAlJWVYe/evVAoFFCr1bj77rshk8mwfPlybN++He+88w5uuummRA0Z6UHC2P7rIws6N1scMLc54HQLKJ97kbebt8XhAnA+4D1sgBa2DjeO1rchO7VnCxfPmNrsHcjUqeBwutFmd0KvVfb4uYmIiJJVwgqlXbt2IS0tDdOmTfMumz9/PgDg/vvvR2FhIWQyGQBAJpNhypQp2LNnT0ILJUNq94POnjC47wtoi4wGrJ5TgCWbqwAg7p2xPWPada4r9xPvHvbLTbErNxER9VcJC3PX1dVh6NCheP3113HllVfi+9//Pp599lm43W7U19cjJyfHb3uDwYBTp04laLSd9BpFt4LOzRZHQJEEdOaRNlbWduadRALePdUZ23dM8T43ERH1jCGZWlxgSA34MzSL/+CVKmFXlCwWC77++mu89NJLePTRR1FfX48HHngAWq0WVqsVKpV/0aFSqeBwSP9F7XK5uj1WzzFcLhcGpavwx9mT0dDuQKvNiXSNAoZUFfQaRdhz1bfZA4okj0pTA5ZdPRYpMplowHt7jRn1bXakqUP3cJLCd0wFwzJjdm7fOaPIcM6k4XxJxzmTLlFzJpd37+/5BZeOFD2G2y0AAATB3a1z9HaRznHCCiWFQoG2tjY8/fTTGDp0KADgxIkT2Lx5M0aMGBFQFDkcDmg0Gsnnqa6ujsl4xY7VfO5PJFwZw0Kub7fYwx7jbIsVzXVHIjxjeL5jsjtD/58mmnPHcv77C86ZNJwv6Thn0sV7zgoLC7u1/9/+cxQnRd4fqteocPv3x+DgwcOwWq3dOk9vFukcJ6xQGjhwINRqtbdIAoDc3FycPHkS06ZNg9nsf+XFbDYH3I6LRH5+frcrc5fLherqar9jtdic3qtKeo0SA1I7g9xdl/l2465tsIQ8zwB9+EuiA/Ra5OZOjv7DdOE7JrUi9J1YKecONmcUGudMGs6XdJwz6XrrnB2ut6KmPvjvnNxz6ZHRo0fHcUS9V8IKpUmTJsFut6O2tha5ubkAgGPHjmHo0KGYNGkSnn/+eQiCAJlMBkEQsHv3btx+++2SzyOXy2P2w+05VtdAtk4lR/nci/DsNpPfi3K7hqAHpqlDhsEHpqm9/x1qm1j+n9V3TFV1TSG7jUdz7ljOf3/BOZOG8yUd50y63jhn556HEtXbPk+iJCzMfeGFF+J73/seli5dikOHDmHHjh1Yv3495syZgyuvvBItLS14+OGHYTKZ8PDDD8NqteKqq65K1HC9ggWyy4pzsWZbjV+RBASGoCPpeh3vzti+54um2zgREVFfltCGk0899RRWrVqFOXPmQKvV4pZbbsH/+3//DzKZDM899xxWrFiBV155BaNHj8b69esT2mzSw9zmCAhkhwtB+3bXjqTrdbw7Y/uer93egUduyIfD5Ua73cmu3ERE1K8ltFBKT0/HE088EXTdxIkTsXXr1jiPKLyWIJ2yw4Wgu3bXjqTrdbw7Y7MTNxERUaCEFkq9kT5I9+1wIeiuHbubLQ40tHd25nYLAix2JzJ0qn7XAdvTDbzF1tGjHcCDnQdAXM5NRES9GwslibLTArtzhwtB+3bsPtFkxQP/2I/Z04YHNHfsTx2wg3Uo74nPH23wnoiICEhgmLu3Cha2Lq+oxR2leSgJE8D2BMHHDNb36w7YYh3KY/35uxO8JyIiAnhFKSpiYeu1YQLYniD43EsuiDj83RcFC8R7xPLzdzd4T0RExEIpSmLh51C/ZD1BcKnh774mWCDeV6w+fyyC90RE1L+xUOomT1C42eqATq1AikwGRYoMhiDhYE8QXGr4u68JFoj3FavPH4vgPRER9W/MKHXDiSYrFm+uwvd//zFu+tMnuPKZHXjwzQM4Zm7Hb/++Fyea/N+h4wmCe8LfwXQNf/dFnnkIJpafP9h5+vvcExGRNCyUoiQWSK40NWBjZS1GD9YHhIM9QfDDJ1v6dQfseHUf707wnoiICOCtt6iFCiRXmhpQVpSLtdtMAeHgIZlaPPWjSWhod2DltePhcguwOFzI0PavDtjx6j4ebfCeiIgIYKEUtXCBZE9oOFg4mF2wO8VrHqIJ3hMREQEslKIWLpDsCQ0HCwf7dopOUyugkqegyepAukaJVLUCbTZn0nSMjlf3bCIiomTEQilKwTp0exQZDaiqawoaDg7WkbrIaMDPiy9EQ7sDGyqSp1t3vLpnExERJSuGuaOUoVPhkRvzA0LBRUYD5hXl4vDJloBwcKgA+MlmK8orkqdbd7y6ZxMRUewNydTiAkNq0D9Ds/gPXSl4RSlKJ5qsWPnmAUwalol5RblQyGUw6FRQyGVQylPw1I8mBdyiChUAH6TXoCLIu+KAxHSMjlf3bCIiir0Fl46EXC4XXe92C0hJkXn/l8SxUIqC79WWD74847duel421swpCFpEhAqAJ1vH6Hh1zyYiotjbsqsOp1rF/55utzuRplHgjtK8OI6qd2KhFIVwV1vOtNqDBqC7BsB1KjnKinNRMCwTA9PVIc8Z747R8eqeTUREsbfvm2aYzJaQ21xgSI3TaHo3ZpSiEO5qy/GzFnz/9x/jxnX/wfef/hh3bK7CiSarX6donUqO1XMKUHW8Ebe98AXeO3g6qTpGx6t7NhERUTJjoRSFcFdbuvIEoAF4O0WXFediY+X58HZ5RW1SdeuOV/dsIiKiZMZbb1GIpDVAV54A9MicNKyZU4CTzTas3Wbyrrc4XFiyuQplxbkoK8pFhlaJLJ0qoR2j49U9m4iIKFnxilIUxK62lORlY15RLsoraoPu5wlAZ+hUsHa4AtZbHC6s3WbCbS98AUWKDCNz0hJelGToVBiZk4bJw7OSYjxERETxxCtKUep6tUWvVUKZIkNNfTue+tEkaJRy7D7e6C2ayopzoVHJsevrs0hVKaBVyvHCvGnY+fVZlFfUwuLwL5wiDUuLdc6OtqP26RYbGtsdaLE5odcqkKVTYZBeI32CiIiI+gAWSt3g+w6xE01W3PPqPuww+XfcfvbmKRAgoLyi1u9Wm6cx5cETzVg9pwBLNld5i6VIw9LBOmf/YGwO7r9mHJa/vl9yR+3jDe1YurXar+llsdGAR27Mx3A+HUFERP0Qb73FgLevkil4x+2NFbUBzSQrTQ3YWFmLcUMysLGyFmXFuQAiD0uLdc4ePViPpVurJXfUPt1iCyiSAKDC1IBlW6txusUWcjxERER9EQulGAjXcXuHSMftSlMDCoZlotLUgKsmfAv//vWlWDOnAIMjeI+a2Dk9xwvGEygPprHdIbpfhakBje18ZQkREfU/vPUWA93puO1Z3253YfyQjG6fM9oO3y02Z5jzhV5PRETUF7FQioFQfZXUitAX7Tzr9Rpp3wq9RunX2dvudEOjlCM7TQWdSh4QDvcQC4mHO7/U8REREfUFvPUWA6G6WJ9ptYuu8/RcKjYakJUq7bH77DQVyude5O3svfDF3SjbtBNPvnMIG26dCp0q8GWIoULiWakqFIt0Bo9mfERERH0BC6UYCNXFesaogUHXeZ56+/JEMx65MT+qR/Cf3WYKyBXtMDVg3UdHcf814wLGEiokPkivwSM35gcUS56n3tgigIiI+iPeT4mRcF2sPeuarA7oVArIAMhkwCM3TYyqCDG3OQKesvPYUWPGA9eMw79/famkjtrDDal4+seTz/dR0iiQlco+SkRE1H+xUIoh375KUtZFI9yLedvtTkweniX5uIP0GhZGRERE57BQipFmiwNnWu1osnYgVSVHqlqBTK0yon5I0XTQDvdi3kg7ewcbj5TPEe34iYiIegMWSjEg1pX7jtI8jBigE+2LFKyzdiQdtIHQL+aNtLN3dz9HqPEPSmexREREvR/D3N3UbHEEFBdAZzPJNdtq8NGR+qDdsMU6a4froO0RKkAeSWfv7n6OcONn3yUiosQZkqnFBYbUkH+GZoVvbky8otRtoULVlaYGlBXlwtzmCChcQnXz9nTQDlfshAuQ9+TnCDf+BnbyJiJKmAWXjoRcHtgmpiu3W0BKiixm2/VFLJS6KVyo2u50B+2GHW4/sQ7aXcUqJC71c4QfP68oERElypZddTjVGv73SLvdiWZr6O2GZmlxR2lerIbW67BQitLpFhsa2x1QRdB5u2uwutnigFYpx7pbpkCjlGP38UaUV9T6ddOONozd9TyRBq3FOn17xtb1c4QPkyvQ3O1PQERE0dj3TTNMZkuih9EnsFCKwvGGdizdWo1KUwMWlxpRYjQEffFtkdGAM612TB1x/jH9YAHoIqMBq+cUYMnmKlgcrqjD2L6kBsU9nb7XbKvB2m0mv7FtuHUqvmm0+n2OcGFyQ6qKhRIREfV6DHNLdLrF5i2SAKC8ohbzinNRYgzsvL14Rh6mG7O9V3HEAtCVpgZsrKxFWXFu1GFsX9EGxYN1+q40NWDdh0dRNNLgN6ZwYXK+G46IiPqChP42e//997F48WK/ZVdccQVWr16NgwcPYsWKFThy5AiMRiMefPBBTJgwIUEjPa+x3eFXTFgcLiz+WxXmT78Q/3NZHgQB0KnkcAkCPjx8BoP0Q7zbhgpAV5oacP/McfhFcW63M0fRBMVDdvo2mWHtcAcsDxUmd7mCv5SXiIioN0looWQymTBjxgysWrXKu0ytVsNisWD+/Pm49tpr8dhjj2Hz5s1YsGAB3n//feh0ugSOGEEfe7c4XHjmgxo880ENNv/iYnx91oKFL+4GAFyaN9Bn39CBOVuHKy7B7FiGy2PdcZyIiCiZJLRQOnr0KEaNGoWBAwf6LX/11VehVqtx9913QyaTYfny5di+fTveeecd3HTTTQkabadgt5R8Q9CKFBlGDNBhcakR5RW1EgPQ3Qtwe8LbTreA8rkXBQ2Ji50nXmOLZQdvdgXvOZxbIqJOCS+ULrnkkoDle/fuRWFhIWSyzp4NMpkMU6ZMwZ49exJeKGWlqlBsNKDi3O03nUqO1XMKsLGyNiAEXT73Ir9QtkaZ4revr2KjARpl9JGxSELigHjX7p7o9B1qbJF2II/nMakT55aI6LyEhbkFQUBtbS0qKipwxRVX4LLLLsNTTz0Fh8OB+vp65OTk+G1vMBhw6tSpBI32vEF6DR6+MR8lRgMAoKw4Fxsra4OGoJ/98Hzh1GxxYMUbBzC3KBdF5/b1KDYacNcVY7DyjQNhO3IHE0lIHAjdtTvWnb49WmzObnUgD6a7Xc1JHOeWiMhfwq4onThxAlarFSqVCs888wy++eYbPPTQQ7DZbN7lvlQqFRwO6X9JxyJU7DmG53+dbjfuunIM5rbaMTBd7XclydeOGjPq2+xIU8tR32bHB1+ewX+ONqCsOBdlRbmwO91QK1JQVdeExnYH3v/yjHd7Kerb7CFD4suvHosbJg+BIVUFvUYhOieD0lX44+zJaGh3oNXmRLpGEXYfMZ7tG9pDB8tj/XmjPWYy6Ppzlgi9aW6TYb56G86ZdImas0i6aocjCDEYiI++9nMT6RwnrFAaOnQoPvvsM2RkZEAmk2Hs2LFwu9246667MG3atICiyOFwQKPRSD5PdXV1rIbsPZYrYxjMVhcWvrgb626ZEnKfsy1WNNcdgStjGIDO4HewwspzHM/2UniOLabNYoe8uU5yX6Pmc3+642yLNez6WH/eaI6ZTGL5MytVb5zbRM5Xb8U5ky7ec1ZYWNit/a1WK9ra2mIyFouuMwZz+PBhWK2h/07vTSKd44RmlDIzM/2+HjlyJOx2OwYOHAiz2f9ftWazOeB2XCTy8/O7VZm32JxoaHfgbIsVA/RaGFJVOGtxoLWjs+OpOkxn7gF6LXJzJ6O2wRK287Xv9lLUNnSORez4hgwtLojgmJ7P2mpzQq9RYkCqMmh4PZLtXC4XqqurMUDvn2npOkZ9mgaDBk+Q1HfJ83nFRDOHycAzZ939me2O3jS3yTBfvQ3nTLreOmdarRZpabG5pOR52nz06NExOV5vk7BCaceOHfjtb3+Ljz76CFpt5y/TL7/8EpmZmSgsLMTzzz8PQRAgk8kgCAJ2796N22+/XfJ55HJ51D/cYqHWR27Mx9l2B4qMBlTVNaHIaAjIKHm2HZimhlwux8A0dcjO159/ddZveykGpqnxg7E5+Mm04QGh8mKjAbOnDgt7zEgDvFKDvobU8yFxseC71KDwwDR1yOB5NHOYTLrzM9tdvXFuEzlfvRXnTLreOGeyGL/Dtrd9/lhJWJi7oKAAarUa9913H44dO4aPP/4YTzzxBH7+85/jyiuvREtLCx5++GGYTCY8/PDDsFqtuOqqq+I2vlCh1mVbq3HJSAPuKM3DwRPNmBckoB0sBB2q8/WQDG3UoekMnQorrxsfNFReYWrAsq3VIUO4kQZ4own66jUKb0hcLPguNSjcU8Fz4twSEXWVsCtKaWlp2LBhAx555BHMmjULqampmD17Nn7+859DJpPhueeew4oVK/DKK69g9OjRWL9+fVybTYbrbm3rcGPst9LxwDXjYO1w4f6Z4yAAsDicyNSqvB2q/Y4XovP1A9eOw+BuPHpt63AHvarlGW+wbtx+Y4ugk3c0Hb+B8x28TzbbRIPv4cYodsxgXcGpezi3RETnJTSjlJeXh40bNwZdN3HiRGzdujXOIzovkk7VI3PSIv7lEe547fbAjt9SRNtZW8q+3TlHhk6FY+b2qPcXOyZ/efcMzi0RUSe+uVREqE7VOpUcWToVjp5p83YuTlMr0G53otkavJOxXqMMGeZO1ygDuiGHOyYQWTfuYOP1PVYkYws3J0D47t093f2biIgo1lgoiRDrVK1TyVE+9yLc9/p+v1tpxUYD5hblertgdw0oZ6epQoa51XIZFm+u8ru1Fe6YkXTjFhuv77FCjc23u3h3u3f3ZPdvIiKinpCwMHeyEwu13n/NODy7zRSQN6ro0gU7WEBZNMz90VH851hDQP4n1DEj7cYtNt6u4xMbm2938e4GfRkUJiKKjyGZWlxgSI3Jn6FZ/fvVRbyiFIIn1FrfZvf2URIEYOlrwRuPVZoaUFaU6/06IAgtFuauMWPuJRdIOqZnP7F9ll89FjdOHgq3IIiO1+9YIcbmG7LubtCXQWEiop634NKRMX2c3+0WkJIiE/26L2OhFEaGToU0tRzNdUeQmzsZ+/7bEnJ7u9Pt93WkQeiu+4U7Zrg2Yg6nG+OGZKDqeGPI7SI5VteQdXeDvgwKExH1rC276nCqVdoDMqF48rIAMDRLiztK82J27GTHQkkiTyBZLPz8Lb0aG26d6l2WqYssCB2qw3fXdb6hZ7Fx6LWxCWBHug0RESWPfd80w2QO3WmfIsNCSaLsNBUuG5uD2SJdsK+e8C3csflz71NnJUYDHr4xH9lpKpTkZQe9XVaSl40zLbag5/N0//bwDT2HGsfsqcO8440kQM2QNRERUSCGuaPwm8tHi3bBfvSfX3qD1ACw41xnbIvDhUUzjAEdvIuMBiyeYcT4oRkB64qNBswrykV5Ra3360duzPfetgo1Dk837kgC1AxZExERBccrShKZ2xw42WwT7YK9w9SAuT7ha6CzcGmydqBs006UFeeirCgXdqcbakUKquqaMG/TTqy7eQoKhmd512mUcgiCgG9lqPHUjyZ5t3W43BGNwzdIHkmAmiFrIiKiQCyUJGqxdcDW4Qq5TbBgdou1AxaHS/QVHpaO4OvW3TIFC1/c7f36sjE5EY/DN4QdSYCaIWsiIiJ/LJQk0muUONse+uWtwYLZnnC1lH2CLfftkh1uHKlqhWg37lC6dgiPdD8iIqK+hoWSRNlpKnz+1VkUGQ1Bb3uVdAlfA53ZogytUjTMXRxkHyB0kDvsOPKy8cXXjX49lLp29g4mWLfvSPYjIiLqixjmlihDp8L3Rg3EHaV5AeHrkrxsLC7N84avgfMB7FSVXDTMvfK6CTh00r8/U9cgd9dgddhxzDBi1VsH/ZYH6xbuS6zbd7j9iIiI+ipeUYpS3VkL7rlyDADA4nBBo5QjJ00FhTwFr/3yErTYnNBrFMhKVWGQXoOjZ9pEw9yz13+C/y2bhruuGA2rw4V0jRJpms4X4v7t598RDVYPztRCp5LjkRvy0e5wwuJwIUOrhEqRgqv+uMPvxbge27t02vZlbnOIdvsOtR8REVFfxUJJomaLA3cHueoCdF71WTOnAGMG6wPWtdhCh7m/arDgpc+PY82cAknFSLAAdtXxxqBFkkfXTtu+YwxFbD8iIqK+ioWSROY2B3Z93Yg7f5CHGaM7n0CzOFzQKlOQIpPhRLMNh0+3Qa9VIEvXeTUJON8hOztNhcdnTUSOXo02mwvpGgVOt9igSknBF183osnS0e0gdbTduMPtp1HJvb2Z+gOG2omIiIWSRG32Djx78xRolCl4/J1DqDQ1IDtNhRd/fjF+99YBv2C1J5803JCK7DQVrsn/Fu74/qig2/3u+gn48y2FuO/1auzwWRdNkDrSbtxS9isyGvDWvpPYV9fUL4LdDLUTERHAMLdkGVolTjZbsfZDk7fYeXzWxIDiBzjfIft0iw0ZOhXuvWqs6HYP/GM/VEqZX5EERBekjrbTtth+RT7B8v4Q7GaonYiIPHhFSSKb041Beo1fsZOjV4t2yK4wNaCx3YFBeg1a7c6Q291z1Zig66IJUkfbaduz38lmG46Z272B8yWbq7y5p74e7GaonYiIPFgoSdRidQZ03m6zhe6Q3WJznts3dBg61HGiCVJH22k7Q6fCMXO7X0fwWIynt2ConYiIPFgoSaTXKGCxO3H3laPwg3HfQodTgCAIYfcBgAydEotLjSgYlul9n9vu440or6iFxeFCmkYuegyxAHYwsQghiwW7dSo5yopzoVHKUXW8sU+GnKMNwxMRUd/DQkmiAakqQCbg2wO+hRVvdOaNFpcaRTtkFxsNaLJ24FSTFdpzxYVvi4AiowGr5xRg82dfo90e/IpSqAB2V7EKIQcLdutUcqyeU4CNlbV+n6GvhZyjDcMTEVHfw0JJIrUiBU2WDqzZdj7MXV5Ri9VzCgAg4Gm2B64dj5uf/xRrb56CtdtqAoqpSlMDZAAevjEf9a22gF/Q4QLYvsKFkKX0aPIEu+/dss87nrLiXGysrA34DNEcP5kF++yAtO8FEVEiDcnUwinIeuTYQ7P6xj+KI8VCSSJzmwOpaoVfsWBxuLBkc5W367Zeo4RGlYIzLXbc/Pyn5/aRoyJEkLvV5sTPynfiX0tK4HQLkgLYvmOLZQi5ayBco5SLNszsayHnaMPwRETJYMGlIyGXi8c5usvtFpCS0jOFWLJhoSRRi60Dtg53wHLfrtubf3Exrl1T6bc+XOC71db5CpJGiwOTh2dFPbbQ5+heILzqeGPMj5/Mog3DExEl2pZddTjV2jN/J+ekqzF72vAeOXYyYqEkUYZWiXSNgA23Tg0ayAYQNJQdKqgNAOnnAt/hgsKhgto9HUJmyDlx2CWciKTY900zTGZLjxz7AkMqCyUSp5KnYOlr+/waQ3oC2Us2V6FweBbMrfaA/drtLhQbDUFvvxUbDVArUvCDsTkhg8Lhgto9HUJmyDkx2CWciChx2JlbghabE0u3Vgd0z640NWBjZS3umzkWi0qNaLT4X+6cnpeN3AE6PHxjPoqNBr91RUYD5hbl4rF/fYmV140XvUoQSbfoaDtyR6qnj0+B2CWciCixeEVJgoZ28bB0pakBd142Cj8r/xz/WFSEf//60oAQ8LH6NkwenoV5RbmwO90BXa+XXR2YffKINKjd0yFkhpzji13CiYgSi4WSBK3nOmyLOdNqh8XhQrvdGTSQ3WztEH1qrPP44sE7KUHtng4hM+QcP+wSTkSUWCyUJPAErj3dqT0dtlNVCjjdne+AW3fLFKiVcpxosgbkRzK0Stz5gzzMGJ0DoPNJOaU8BTtq6rF++7GQYehwQWq1Uo6dX51FplaJnHQ1C5k+ojsBegbAiYi6j4WSBIZUFX4wNgc/mTbc253at1t112aTD9+YjxGGVO8ytTwF0y4YgMffOeS3bYkxGxvnXhQyDB0qSF1sNODt6pPeq1Ul5zJDDPr2ftEG6BkAJyKKDYa5JdBrFFhx7Xi/okisW3WFqQHLt1bjRJMVQOe/7rfXmLH2Q1PAtjtMncttTvGMkliQuvhcGLy8ovb88Rj07TOiCdAzAE5EFDtRXVFqbW3FG2+8gdraWixcuBB79+7FyJEjMXx43++r0OZw+hU6BcMyRXNHFaYGNFs7MCRTC3ObAzl6ddD3wQGdxU1juwOD9BrRc/sGqRstDqiVKXj3wGlvGNwXg759h9QAPQPgRESxI/mK0pEjR3D55Zdjy5YteOmll9De3o733nsP119/PT7//POeGGNSabH6B7rtIa4CAefDti22jrDbtoQJiwOdVxhG5qRBniJD3Vkr1m4zBRRJXc9NvZ/n+z55eBZG5qSFLHQYACciih3JhdJDDz2EOXPm4LXXXoNS2RkkffTRR3HzzTfjiSeeiPkAk41e438RTq0IPYXpGiV2fnUWGqU87LZdj91sceDomTZUHW/E0fo2v1smeo0yonP3daHmqL9iB3UiotiRXChVV1fjhhtuCFg+e/ZsmEzij773FVmpKpQYz+dFquqaUNSliaRHsdEARYoMt5Z/jrerT+JMi0102xKjAVmp568SnGiyYvHmKnz/9x/jxnX/wfef/hh3bK7yZp6y01Q402oXPV5/6JQdbo76K08APJj+8HNBRBRLkgulAQMGoLa2NmD57t27YTAE/6Xdl2gUKVg0w+gtUMoranHXFWNEO24/+q8vUVbcGbb+VoYWi3329SgxZuPhG/O9+aRIu3B/b9RA3FGaF3i8ftApm4FlceygTkQUO5LD3L/4xS9w33334fbbb4cgCPj000+xdetWvPDCC7jzzjujHsj8+fMxYMAAPPbYYwCAgwcPYsWKFThy5AiMRiMefPBBTJgwIerjx4q5zYGyF3airDgXZec6bCvlCNlx+5bvjIDF4cKiv+3GgksvxIPXjYfd6YbF7kK6VoEsncovxB1pGHdwphY6lRyP3JCPdocTFocLGf2kjxIDy6GxgzoRUWxILpRmz56NnJwcbNiwARqNBk888QRyc3OxatUqXH311VEN4u2338bHH3+MG2+8EQBgsVgwf/58XHvttXjsscewefNmLFiwAO+//z50Ol1U54iVZqsDFofL70m3dbdMCdlx2xPitjhc+MP7NcjLSceQDA0uyh0QdPtk6sKdrBhYDq+//mwQEcVSVO0BSktLUVpaGpMBNDU14YknnkB+fr532T//+U+o1WrcfffdkMlkWL58ObZv34533nkHN910U0zOG6lmiwP1bXa4MobhqwYrdKrAKQsXqu66XqeUI1OnxKFTLWi1OZGuViBFBijkKTCkqpChVWJxqdHb+VujlGP38UaUV9TC4nAxjIvuB5bZtZqIiCIhuVBaunRp0OUymQxKpRIDBw7E5ZdfjlGjRkV0vMcffxzXX389zpw54122d+9eFBYWQiaTeY89ZcoU7NmzJ66FUrDuxm/fUYySvGy/ZZ5Ad7AeSUVGA6rqmrxfl44ZiBy9Gve/vh87fLYvMhowrygXv3/vMO66cgyqjjf6XaUqMhqwek4BXv78OMO46Awsd/0+eJSECSyzazUREUVKcqGUmpqKF198EZMmTcLkyZMhCAIOHDiAL774ApdddhlOnTqF559/Hs888wxmzJgR8liffPIJvvjiC7z55ptYuXKld3l9fT2MRqPftgaDATU1NVKHGzWxsLBalYKF3xsJtyB4C6PyilpsuHUqUiDDDtP57YuNBtx1xRjMef5TAJ3Fzl1XjMEjbx/0K5IAnO/0XZSL5VurA4quSlMDZADDuD4WzTD6fR+AzjleNMMouk+4EPiaOQWcXyLq9YZkauEUZD1y7KFZ/esflJILpa+//hq//OUvsWTJEr/lf/7zn7Fnzx4899xz+Pvf/44//vGPIQslu92OFStW4IEHHoBG49+N2mq1QqXy/2WlUqngcEh/ksnlCt6MMZz6NnvQqxX2Djdue+ELvzC3WpGCz786i8ILsrBs5ljUmtu9gW6VQobyuRchXaNAqkoOm9MdUCR5VJoacO9VY1Ahsr7C1ABrhyvqzxRPnjH21Fjr2+wo27Qz4PtQVdeEsk078cbiIqSp5UH3CxUCr2+zB90vHnp6zvoazpd0nDPpEjVncnn3/h5acOnIbh8jFLdbQEpK8ELM7RYgCKEbLCeDSOdHcqG0c+dOLF++PGD5lVdeiXXr1gEAioqK8NBDD4U8ztq1azFhwgSUlJQErFOr1QFFkcPhCCioIlFdXS15HwBwZQwLurzF6gwIc/v6Tq4BC1/c7f163GA9Fr64G6/Mvxi2kyY4sy4Ied42W+j/M55tsaK57kjowSeRaOc/HFfGsJDfB7F5Evu+htsvnnpqzvoqzpd0nDPp4j1nhYWF3dr/b/85ipMtthiNJlC7zYnGIG1Yhg1IxV0z83Hw4GFYrcnd0y7SOZZcKA0bNgzvvvsuFixY4Lf8/fffx+DBgwEAX331FQYMCP5El8fbb78Ns9mMgoICAPAWRu+++y6uueYamM3+/+o3m83IycmROlzk5+dHVVXXNligU8lRVpzrF6oemK6CTiX3e21IdpoKj8+aiBy9Gha7C2/dUYzTLTbcs2WfN8idoVMiZ9AEnG6146VfXIx0jRxqpQK2Dhfa7E6kquQAZFDIQ18qHaDXIjd3ctB1LTYnGtodaLU5odcoMSBVGdDtOxKe47TbncjUqmB3udFul3ZMl8uF6urqoPMfi3HWNlhCrhebp2j3i4dQc0aBOF/Scc6k661zdrjeipr60H/f9QSdTgAAjB49Ou7n7imSf4vec889WLhwISoqKrx9jfbv3489e/ZgzZo1+PLLL3HnnXeirKws5HH+7//+D07n+XebPfXUUwCA3/72t9i5cyeef/55CIIAmUwGQRCwe/du3H777VKHC7lcHtUP98A0NcrnXoQ122r8rlqUGLOx4dapuO2FL2BxuJCdpsKLP78Yv3vrgF9WpthowEvzL8Z7B0/h+2MGQqtSYMnmPdhhMkOnkmP1nAJsrDwUkK9ZfvVYXDYmBx8cOoOupudlY2CaOujniVVA2XOcXV83YvWcAjzx7mG/MUo9Ztf5j9U4B6apMT0vG9uD3EYLNU/R7hdP0f7M9lecL+k4Z9L1xjmT9UxEKSK9ba5CkdyZu7i4GG+//TamTJmC2tpa1NXVobCwEO+99x6KioqgUCjwyCOPBFxx6mro0KEYMWKE909qaipSU1MxYsQIXHnllWhpacHDDz8Mk8mEhx9+GFarFVdddVXUHzQaz24zBYSqd5jMWPfRUdw/cyyAznB11yIJ6MwTrXzjAKaOGICV147H8teqvUHvsuJcbKysDRrYfvifX+I3l49GiYSuyrHqUu17HLExdqfzdSy7aUfbfZpdq4mISArJV5TMZjP+93//FyaTCS6XC4IgYNeuXfj0009x9OhR7Ny5E3l5ed0aVFpaGp577jmsWLECr7zyCkaPHo3169fHtdmkuc3h9wSbrx01Ztx95Wi8eUcRFCkpQdsCAJ3F0n0zx8HhEvyOVTAsUzRbU2lqwMkWG3533Xi4BUTUVTlWXap9jxNqjNF2vo51N+1ou0+zazUREUVKcqG0bNkyHD9+HJdffjnKy8sxb9481NXV4b333sO9994b9UA8ry7xmDhxIrZu3Rr18borXOfnurNWLHxxNzb/4uIwx3FC2SV35OnULcbW4UJ9mwPTRDp3Sx1rpF2qfY8TbozRdL7uiW7a0XafZtdqIiKKRFRPvZWXl6OgoACVlZX43ve+h8LCQqxfvx7bt2/Hz372s54YZ9zpNcqgYW5Ph2xPSDtNE/o+rF6jgFLuf4czkk7eUsLN0Xap7tqdOk19/pzhxqhRylF1vFFSV+vudtOOpf7Qmbs/fEYiop4muVASBAGDBg0CABiNRhw8eBCFhYW46qqrsGHDhpgPMFGy01RBw9xFRgM23DoVn391FgBwpsWOYqMhaO+jYqMBWakqaBQpfgHicJ28z7TYMWFohqSxhgooB+tSHSxU/ehN+d5u16HGWGw04K3qk955iTSMHc04e0J/6MzdHz4jEVE8SA5zjxs3Dv/4xz8AAGPHjkVlZSUA4JtvvontyJJAsDB3pakB6z46isH6zp5O92zZh/uvGY9io8Fvu2KjASuvmwCNIsUbIPYEtMsrajGvKDdgnyKjAYtn5GFkTio0Ya7o+JIaUBYLVa966yCWXjUGxUaDd4xFQT7X3KJclFfUepdFGsZOhiB1LAPlyao/fEYioniRfEXpN7/5DW6//XZotVpcf/31+Mtf/oJrr70WJ06cwHXXXdcTY0yIcGHu//l+HjbcOhV2pxtnmm24+8oxuEcmQ5vNiQydEiebrJi9/hO8PP+7yNCpMCRTi7VzCnCm1Y5mawdS1XKsvG4CbB0utNud0Ko6b+F9ePgMnvv4GN5cXNxjwWaxULXF4cKZFjsmD8/CvKJcON0C7rlyDBxON+rb7Ph2lhbvHjiNJZur/PpIAZGHsRMdpI51oDwZ9YfPSEQUL5ILpcLCQnz44Yew2WzIysrCli1b8MEHHyAzMzPuj+/3pHDB4zOtdr8O3L7W3TLFu843oOwbIK463oir/lghevyeDDaH+myWDvFu1+tumSK6Doh8zIkMUvdEoDzZ9IfPSEQUL9LbNqPz8f20tDQAwKBBg3DLLbfEdFDJIFzwWK1IEQ1765TnA95dA8qegK3TLWDLLy+BUi7DyWYblPIUb1Dc4nBJCjafbrGhsd2BFpsTeq0CWToVBunFX/cS6rOFCnGHC3jHM4wdrWQKlPeU/vAZiYjiJapCqT8IFTwuMRrQ0GY/11271u8qS7HRgMvGDoJOJcfUEVl+AeVgAdsiowHzinKx+G+7UTA8E6vnFODlz49HHGw+3tCOpVurA7qCP3JjPoYbUiV/tjOt9qjWxTOM3R3JEijvSf3hMxIRxYvkMHd/ESp4vOqGfEz8dkbQztUVpgY8+e4h3H/NOL+AsljAttLUgI2VtSgrzkWlqQGbKmux8rrxEd2aOt1iCyiSPGNYtrUap0VeiBjqs80YNTCqdb2lq3UyBMp7Wn/4jERE8cIrSiF4gsf1bXacbbFigF4LZUoKfrrhM/zpp4WiHbkrTQ1Yee14DPZ5DDtUwLbS1ICyolwAnUWOrSN0s0ePxnZHyK7gje0O0Vtw4ULV0a7rDRIdKI+H/vAZiYjigYWSBDLIYHe5z/03vE+9ebJJL31+HLOnDceU4VlosnTg0KkWpMhkUKTI0GJzYHGpMWjzSovD5c08WRwub9g2XMPAFpsz2DAjXu8heD7QOeHO25NhbKl5q2ibKvaHztz94TMSJRobu/Z9LJRCCJYpmpn/LfzvbdPwwOv7scPnak6R0YAXf34xHn/ny4AGlbcV52LEAB2qjjcGrFs9pwBLNlchO02NP99SiNtf3AW9VhlRw8Bw3btDrQ92/B+MzcH914zD8tf3J6RRodS8FZsqElEi8e+g/oGFkgixTNFNU76N+17fH7QR5aq3DmDy8CxsO1TvtzwFwNX5g4PuAwD3zRyLquONyB2YivuvGYdUtQK//fte0YaBa+YUIEOnQlaqKmxXcCmfbfRgfdDMU9fz9oRweaunfzzZ78pSuKaKPTlWIqJk/ztoSKYWTkEWfsMYG5rV9wpEFkoixDJFOXp1yFzQvHNZI187TA2YG2Q50Fks3XvVGPzkuU/x8oKLMWV4JtpszogaBg7Sa/DIjflYtrXar1jyXIURu2Ul9tkKhmWK9knq6UaFUvNWbKpIRImU7H8HLbh0JOTy0O8i7Slut4CUlNgVabE+nlQslESINe1rs7mCLvewO4MHscWWA8A3jVZYHC602VzQKgUICH0O34aBww2pePrHk8/nejQKZKWGzvWIfbZQY+x63liTmrdiU0UiSqRk/ztoy646nGpNzBja7U40W2Nz7qFZWtxRmheTY0WLhZIIsaZ9aRq5t9HklOFZUKTIMCBVhQ6XGy1WJwakqryhbF+eZo3ZaSo8PmsicvRqtNlcSNcooFamIDtN1XlsZfhviW/DwGaLAy3WDrTanUhTy6FVycO+J07ss4VrKKlRyb3vCYt1eFFq3opNFYkokfQapWjT4fKK2oT/HbTvm2aYzJaEjqGvYKEkQqxpX1N7B8rnXoQ122oCgtnzinLxx38fwdqbC7D4b+ffh1ZiNOB0iw3ZaSq8+POL8bu3DgQEll+afzGaLA4Mz9IBQEQNA080WXHPq/v83klXZDTgjtI8jBig82tPEMlnq6prEs08FRkNeGf/KVx8oQHPbjP5nTMW4UWpeavsNBVK8rKDXvouYVNFIuph2Wkq0d8F5XMv4t9BfQgbTooQa9qXo1fj2W01QYPZGytrMWawHhsrOhtIAucKqOJcjByYirU3TwkokoDODM7KNw5gcIbW+0h3uIaBzRZHQJHkGceabTX46Ei96FvixY5/+GQLHrkxP2C5pwiUyYA122oCzhmLt9J78lbFRoPf8lB5q0UzjCjqsn2R0YBFM4xRj4OIKBI2p1v0d8Gz20ywhYkyUO/BK0ohBGs42eES/NoC+PI0jly7zYR7rx6LK8d/Cx0uN5otHfi09ix+MHZQyMByq08OJ1zDQHObI6Bg6TqOUGHCUMdfM6cAJ5ttOGZuh1qRgqq6JizZXIU1cwrwh/drgh4vFuFFKXkrc5sDZZt2oqw4F2VFubA73d6xlm3aiTcXFzPMTUQ9prHdIfq7YIfJHLLhL/UuLJTCyNCpkKaWo7nuCHJzJ2N3XXPI7T2B6Ha7Cz9+7hO/ddMuMATbxatrYDlUw8BwQUK70x02TCh2/AydCsfM7Vj44u6AY4YSi/DiIL0mor9cWmwdsDhcok/pJTpISUR9W6wa/lLyY6EkQYvNiY4wxYInEJ2hVWDZ1aPxf58cx8rrxiNHr4YiJfSdznSNAkfr2yIKR4cLM6sVKd0KEwY7friwdzzDiwxzE1EidafhL/UuzChJ0NDuwH+ONQTkYjxKjNmoqmtCkdGA3V83onTMt/DX26bhxc++xrVrKnGiyRqQwfEoNhpwstmK7z/9Me7YXIUTTdaQY/EEsoMpMhpwptXerTBhsON7Plsw8X4rfajPH++xEFH/43kAJZhQDX+p92GhJEGztQPlFbWYV5QbNER83zVjcfBEM+YV5WLV219i5Rv7cbLFhnFDMgAA92zZh/uvGR80sLzyugk4dKoFQGThaE8guyRI8PqO0jzMGDWwWxmdYIHv8opa3FGaF3DORLyVPpLAOxFRT4nmARTqnXhtUIJUlQIWhwtLNlcFDRE73W6MG5KBJZs7WwNUmBpwz1VjUDAsE0BnAPmWv3yKx2dNxD1XjYHF7oJOLceZFjtmr/8Ej8+a6D1XJOHoIZlarJ1TgDOtdjRbO6BTyZGqUiBTp4xJoSAW+F6bJG+lDxd4JyLqSdE0/KXeh4WSBCkpMhQZDag0NQSEiD1XmLoub7O5/ELQ5jYHbnvhCwDAulum4Cfrzwemu4alIwkk9/Qb4kMFvpNBT39+IqJQIn0AhXovFkoSKFKAe64cA4vdCbVSDoU8BY3tDijlKUhVy1G2aWfAPmkaOdI0Wqy7ZQq0SjncggC5TAZLhwu52anYuvAStFid2Pn1WeiU/u/liTSQ3GxxxLxTdqjzNFsd0KkVSJHJoEiRwdBD5yMiIko0FkoRSklJgVIuxx8/+BJzvjMCaz40+fVEKjIa8Nisid7bbkDnvep2uxMVPlegio0GzC3KxW//vhcWh8vbzPHLE824bOwg7+tPIg0kn2iyBrzBOhadsiM5j2fsj/7zSzx4/YSYno+IiCgZMMwdIV1WNpZtrcbYIRnYWFkr2pnb05G75FxAu8MpoLyi1rtdRZftPPuNHZKBJ989hLLi3IgDyc0WR0DxAsSmU3Yk5/GMffRgfUzPR0RElCxYKEXIKddhR40ZBcMyRbtrV5oacPm4Qdhw61T87voJ2HboFBb8dVfAC3IrTQ3egLfv15WmBlyTPxhr5hSIvqfNl7nNEfRdZ8D5MHgshDqPZ+yxPB8REVGyYKEUoXZ7Z7ETrjv1N41W3PbCFzjVYscj/zwcUCR5dD2Ot6O3wxlx3idcd+5YdaeOpAt4LM9HRESULJhRilCqujNorVakQKeSo6w4FwXDMmF3uqFRyrH7eCPKK2qhORfI1msUKJ97kXd514Lp21lavDBvGpxuN2QyGXRKOcrnXgStUo5mS2TvTOvanTrYuE632MI+kREupB1JF3CA3bCJiKjvYaEUIYXLgul52dh/ohkbbp2KtR+a/FoBFBkN2HDrVKRrFCgdMxD/OnAKa7eZUGQ0YPWcAr+Qd5HRgA8Pn8H4IHmnEqMBq27Ij6hQ8nSn3l5jhk4lx+o5BdhYWes3Lk/zs+GG1KDHiCSk7XueroqMBlTVNbEbNhER9UkslCJkaTTjsVkTUWkyY12XJ96AzqxOCmS4dtJg/OqyUZi9/lPvcgAoK871Fk7zinKx/7/NQUPhO0wNuP8f+7F2TkHYYsnTnfreLfswcVhm0ONVmBqwbGs1nv7x5IArS6FC2gBQMDwL927ZhzVzCrzn2R6koHr58+Pshk1ElESGZGrhFGSJHka3Dc1K/NPULJQi5Ha7MShdhfxvZ+CuV/cF3WaHyYy5RRfA3Obwu9VWaWrAsqvGomBYJqrqmrBkcxXWzCnAMx/UBD9OBF25PTzdqU802wKaXXpUmBrQ2O4IKJTChbTLijqLO3ObAyNz0rxdsD1dwOUpMshTZHjqR5NYJBERJZEFl46EXC4Pv2Ev4HYLSEkRL/rCre8uFkoSNVsiCzZ31WJzejtyh9rOQ0owOkOnwuHTbSG3abE5gyyTFtJmF2wiot5hy646nGrtGw/YtNudaLYG/yxDs7S4ozSvR8/PQkkivTZ0YFmjlGNguhqLS41+Ie6Mc/tlp6nw+KyJGJypxeZfXIx0jQKnW2y4Z8s+mNsc3kC2RilH1fHGkJ22my0OnGm1o8nagXSNIuCcfuPWBH6rEx3SjldH8Wgl+/iIiMTs+6YZJrMl0cPoE1goSaTXKFCSlx30llWJMRtDMtR49+BpVB1v9Ia4C4Zn4mSzFVdNGIRfXTYav3vrgF+WqNhowIs/vxg/f2EnVlw3PiCQHazT9okmK+55dR92mMx+x+kaHPeMKys18Bd8IkPa8eooHq1kHx8REcUH+yhJJAOw6HsjvS/B9SgyGrBwhhHV/22BIJzvWn3fzLGYV5SLe7bsw68vDyySgM4M0aq3DuDZW6YEDWR37bTdbHEEFEme42zy6frtGdeiUiM0isBvtScMPj0vO+CzzCvKxeGTLT0S0o5XR/FoJfv4iIgofnhFSaJmmxNlL3yB+dMvxJ2XjYLTLUCnkkMGGbYdPo1JQzMx4NzVm0pTA+68bBR+Vv45LA4XbB1u0a7eFaYGLJeniK7f7hPwNrc5Aook3+Pcc9UYjBush1qRgqq6JpRt2ok3FxcHLXg8YfB4hrQj6SieyFtcyT4+IiKKn4QWSl9//TV+97vfYffu3cjIyMBPf/pT/PznPwcA1NXV4f7778eePXswZMgQLFu2DMXFxYkcLgCg9VygbMLQDPzhgyMBL8a9cvy38FXD+fvCZ1rt3ttgbUEC1b5aRMJq3nOfC1WHC2HXnbVi4Yu7g+4bTLxD2vHqKB6tZB8fERHFT8IKJbfbjfnz5yM/Px9bt27F119/jV//+tcYNGgQrrnmGixatAijRo3Cli1b8MEHH2Dx4sX45z//iSFDhiRqyAAAvU6JsuJc0RfjPvTWl1hx3XhvsFqnlGNxqRFTh2chUxe6k7ZeqwwZyPaEqiMNYQfbtzuChZsBRBR49t1Xq5JH9DkTJdz8Jnp8REQUPwkrlMxmM8aOHYuVK1ciLS0NF1xwAb773e9i165dyM7ORl1dHV566SXodDqMHDkSn3zyCbZs2YI77rgjUUMG0FmEXHKhQbRn0Q6TGU0WB6qON2Lj3IugU8lx8EQzJg/LRNXxRhQZDag0NYTspB0skO0bqo4khO2rJAaB7K7hZp2q85Urz24z+d0G9ASeB6WrRPeN9HMmSqj5TYbxERFR/CQszJ2Tk4NnnnkGaWlpEAQBu3btws6dOzFt2jTs3bsX48aNg06n825fWFiIPXv2JGq4Xh0uN+RhGlvVt9lRaWrA2g9N2P/fZow796qSh97+EvOKclFkNIhelQoWyJ6el+0XqvaEsEu6hLBLjNlYPCMP5RW13mVFRgMWzTB26zMHCzeXFedizbaagKyUJ/Ds6dvUYnMGDUZH8jkTRSzknizjIyKi+EmKMHdpaSlOnDiBGTNm4IorrsAjjzyCnJwcv20MBgNOnTol+dguV+CtnWiP4XK54HID7UFuF/lSyTvrzx01Zsy95ALk6DXeq0ZLNlehrDgXV4wfFLKT9n0zx+H7Y3KQrlHAkKqCXqPw+yyD0lVYPXsy6n1C2IIbMLfZsfbmKbB1uPzC3G8sLkKaOrourfVt9oBCp2BYpuj4t9eY0dDe+WRYQ7t4MDqSz5kog9JV+OPsyWhod6DV5ozL+Hx/zig8zpd0nDPpEjVnseiqLQgxGEgvEc33J9I5TopCafXq1TCbzVi5ciUeffRRWK1WqFT+/2pXqVRwOKQ/ll1dXR2rYaK6uhruAbnY7XMLrasSY7bfra+uHbgtDhfWbjNh3GB9yHO1tNsgb65DM4DmMONKAdCeMQw//stO0W3OtljRXHckzJGCc2UMC1gWrrP42RYr5Of+NxQpnzOR4jm+WP7M9gecL+k4Z9LFe84KCwu7tb/VakVbW+g3NvQFFl3nHZ7Dhw/Dag39+6arSOc4KQql/Px8AIDdbsdvf/tbzJo1K+ADOxwOaDSaYLuHPXZ3K3OXy4Xq6mrk5+fjaIMN5RW1WD2nAAACnnq7/9pxuOHZSu+yYMHqUMs9Bui1yM2dHPEYaxtCd2A1ZGgxYPAE7xUSvUaJAanKoB27Izl2JONvbu7833DbSfmcfZnvz1nXn9kWmzOq711fFmq+KDjOmXS9dc60Wi3S0vr+JSVPRGf06NE9do6Ehrn37NmDyy67zLvMaDSio6MDAwcOxLFjxwK273o7LhJyuTxmP9xyuRx6jQJThmd6b6GVFeXC7nRDrUjB6RYb/rX/pDecXGI04EyrHf9tsgZcgaqqaxK9KjU9LxsD09SSxj0wTS0aQP7B2Byo5XIseWlPVJ2mgx073PgNqSo0AzCkhg5GS/2c/UHXn1l2CQ8tlv8f7y84Z9L1xjmT9dx7YpNOT35vEhbm/uabb7B48WKcPn3au2z//v0YMGAACgsLceDAAdhsNu+6Xbt2YdKkSYkYqh8ZgJXXTUDh8M6Mzm0vfIGFL+7Gxspa5Og1eO7jzgKvxGjAQzfm48JsHQ6eaPaGuD3KK2qxeIYRJUb/wHCx0YBHbsyXHBgOFUBeed14LN1aHXWn6WDHLq+oxR2leQGBck/g2XO1Q69RMBjdDewSTkSUWAm7opSfn4/x48dj2bJlWLp0Kf773//iySefxO23345p06Zh8ODBWLp0KRYuXIgPP/wQ+/btw6OPPpqo4Xo125z4fxs+w4s//w6+abTC7nRDo5RDEATIZTI8/eNJGJimxoBUFU632GBxuHHLd0ZAlZKC314+GvdeJYPV4UKrzYnPvzqLwguyMLfoAu9Vqaq6JjhcofM/Yny7bLfaOpCuUSI7TRWTTtNix14bZFmGTuUXrBPbl0VSeOwSTkSUWAkrlORyOdatW4dVq1bhJz/5CbRaLf7f//t/+NnPfgaZTIZ169Zh+fLluOmmmzBixAg8++yzCW82CXR2zza3OXC0vj2g+7WnN1Kwjt3zinKx4C+fweJwYd0tUwL29XXZGOm3GD2Cddk+Zm4PuU+knabFOnhH8os63t2/+wp2CSciSqyEpkEHDRqEtWvXBl03YsQI/PWvf43ziMLTa5XQqeT4dpYWm+ZehAydEgp5ChrbHUhTK3DoVAuqjjf57VNpaoBakYI15wLgqSoFyudehN3HG709j3w7dGuUchw53QpFigyGIJ2ug3XI9mwTbF20naZDnYfig13CiYgSq38/NhMFvUqODbdOxR8/OII53xmBp947HHD1qGvHaZ1Kjpu/MwKbKmuxo8u2a28ugAwy/KXimF9fIs9VqEf/+SUevH6CN7QrFux9fNZECEDQdY/elC+50zQDxMmBXcKJiBKLhVIU1n1owqThWaLvewM6rxB5Cp9Q74ZLAXBV/mDR4xQMz8K9W/Z5r0aJBXs/OlKPf+47GbRT9so3DuCRG/OxbGu13y9csUB1uADxmjkFvLIUJ54g/b1b9kX0vSMiAjqzoU6h7z/2NjSr5//hzkJJohaHCztMDZhblCvambrS1ICyovOv5gjVxdpzrFDHWbvNBHNb59NNYsHenHR1QJHk8f6XZ7D06rERB6oZIE4uDMMTkVQLLh3Z69oZRMvt7uwXlRLm9WLRYqEkUYu1MzwbrjO173op24qta7V1IFTrsHDnaLF24MKBaRH9cmWAOPkwDE9EUmzZVYdTrf3j7+p0jQLzRC44xAILJYk8Ye5BerV3mU4lDwhjjxigw11XjMKzHx5FpjZ04DZUl2vPOrHQrufcwwZose6WKdAo5d6QuMXnnXSRhH494W2nW/ALm1u6vNuOAeLEYLieiCK175tmmMyh39jQV1xgSO3R47NQkkivUWDj3KnQKeUoNhqw+3gTVs8pwMbKWr/bayVGAxaV5uGfSwbD7nSHeDecAadbbAHLgc5Ad1Vdk19o1zfY62lH0PXcXQPlkYR+g4W3gwXTGSBODIbriYgSI2GduXsrGYBj9e148r3DmFuUi/tnjg0a1N5hasCabTX45GgD1PIULL96LIp9OnMDnV245xXnYnCG1q9rN9BZQM0rysXhky3e0G7XDtmhQuIbK2tRVpwbUehXLLztexyAAeJEYXduIqLE4RUliZptTuToNdh2qB6fHjuLV2+/BEu37g+6rSeM3d7hgsPpwuThWZh37t1wwwZo8e6B01j8tyoACHhvXK4hFZABT/1okl9h4hvstTtdIQPl988ch18U54YtbEKFtytNDVh+9VjcOHkoA8QJwnA9EVHisFCSqMXa4Q1OWxwuNFtDh+XsTjdarE40Whx+Rc26W6b4fd214Hl94SWYPDwr6DE9V5eqjjeGPLetwxWT8LbD6ca4IRlhj0M9g+F6IqLEYaEkkV6rhK3DjcWlRhQMy/S+/FWMWpGCTJ0SmTr/ALROKfcewxMA9w1PRxKYDte1WaOSo9kS/mpDuOOkqvljkkjszk1ElDjMKEmUpVFg+AAtqo434rYXvsC/DpwKyBd5FBkNONNiQ7PVAYfT7X2Hm04lR2aqynuMhS/uRtmmnag63ojVcwrwg7E5EQWmPV2bxc791r6TuGNzFU40Wbt1nC++bgx7DOo5ob4/DNcTEfUsFkoSOQHc/4/93gB1eUUt5hXlBhRLRUYD7piRh6m5A7D4b1V46t1D+M3lo1BkNKCsOBdPvnsoaAh7U2UtVl43PuIXzfqGu33PPa8oF+UVtREFfsMdZ9VbBxkaTiCx7w/D9UREPY/3VCRqtTlR4VPgWBwuLNlc5Q1j6zVK6NRy6JRy1J21YM76TzvDuKYGnGyxo2B4Fq4YP0g0hF1haoCtI3TzSF+ecPfJZhuOmduhVqSgqq7J75H+SAK/QzK1WHX9BJjq27yBct/jMDScWOzOTUSUGCyUJGoJEt62OM4/ffbK/Isxc3UF1t0yBQtf3O23na2jc7txg/UhzyE1nJuhU+GYuT3gfFKPedbiwG0vfBGzcVFssTs3EVH8sVAKo9niQH2bHa6MYfiqwertzN21E7cniJ2uVaJ87kXITlNBp5L7d8dWK7Dh1qkYHKZBoG84N9JuzLEI/Eo5Rm/uEt2bx05ERPHFQimEYN2QK+76HjbcOhVrPzQFdMPecOtUyCCgbNNOlHTpan31hEEYNkCH5a9Xo2B4lninbp9wrpRuzBplCoqNBr/bgr77SAmHbw/Ss2d6lONKNr157EREFH8Mc4sQ64YMAOs+NAUNYq/76CjSVJ215w6frtZFRgPuvHw0lr9ejUpTQ8gA+KIZxpDnDxbObrY4sOKNA5gb5JjFRgMeuTG/W+Fw39Bwb+4S3ZvHTkREicErSiLEuiG3OlzYEeSqDQDsqDGj1edWW6WpAfdeNQYAYOtwe4urrgFwu9ONC7JT8c/qkyjbtBNvLi72Hi+YrsFqc5sDH3x5Bv852hDQ4buqrgkOl/RwuFhouDd3ie7NYyciosRgoSRCrBtysDC3r1Zrh182qe6sFWu3mVA00v8qjW8AHAA2/+Ji79ettg4IYcbnG6z2jLXrMT08/ZsiFSo03Ju7RPfmsRMRUWKwUBIhFmzWa8N0sdYo8MqC7wIC4BLcUCnkeGXBd5EepoN3ms/6cMFrnUqOLJ0KR8+0ocXWAa2qs8u3p6t3sDH7BpgztEpolHK02DrQZnPCkKpCh0tAu8MJvVaJNLUC7XYnWm0dyNSp4HC60WY/vy6UZO4SzQ7XkZMaeGdAnoj6KhZKIsSCzWkquWhoushowL/2n8LabSaUGLOxcMZIzHn+M1gcLiwuNYruV2w0QKNMgU4lx9QRWd7QdLDz61RylM+9CPe9vh87TGa/Y/iGxz1+MDYHKnkKFm+uwo4aM3QqOVbPKcDGylpUHW/C6jkF+MMHR/wyV8XnmmLKIMMT7x72W/foTfkoycsOegsr2btERxpW7++kBt4ZkCeivoyFkghPsPneLfv8frE63S48dEM+7nu92q/o8XSxXrK5CgCww2SGGwLKinOxdpsJ5RW1WD2nAAD89is2GvDAtePx+/cO4/5rxuF7owZ6/yUe7Pz3XzMOz24z+RVJvsf0nA/o/GW18rrxuPe1au8vsbLiXGysrEWlqQGLS43e/+56LBmAq/IHB6xb9dZBlM+9CDLAb1y9oUu02Pe0N4w9XsIF3tfMKfCbJ6nbE1F8DMnUwinIEj2MuBia1bP/IGOhFIIn2FzfZsfZFisG6LWwOQVYrBY8emM+2hwutNmcaLF1BHTDBjrD3GVFuQD8A9zLZo5Ds6UDaRo5zrTYcfPznd27f/2D0X49loIFq92CgKWvVQcdb4WpAffNHIfLxuR4Q9hdA8wFwzK9hZTvf3e1w9SAuefG7svicKFs0078a0kJnG6h13WJZofr0KQG3hmQJ0pOCy4dCblcnuhhxI3bLSAlJXxhGOl2vlgohZGhUyFNLUdz3RHk5k7GF183YYfpLAqGuXHbC18E7cDty+48/8SZJ2w9brA+6D7tdmfQ8/v+oqk63hhyvLYOFyYPz/J+fczcLjoe3/8ON3ZfFocLjRaH33l6E3a4Fic18M6APFFy2rKrDqda+8///9rtTjSHedhqaJYWd5TmST42CyWJ9Folth8+gx8Xfhtv3lGEFISuTNWKwFZVwZYBse+eHWx733OLjSOS9bEIPjMAnHy6+/MVbnsiio993zTDZLYkehh9AhtOSpShkmP1nClYurUa166pxL8OnApo8uhRZDSgqq7Jb1lxkGWA9O7ZwQQ7hqdjt0dVXZN3vL7/3VWJ0YDTLbaIzyPViSYrFm+uwvd//zFuXPcffP/pj3HH5iqcaLJ267jUPVJ/vrLTVCgR2b6EAXki6gNYKEnkArwdtgGIdtkuMWZj8Yw8lFfUepd5gtsHTzT7byshTBxJ92yPYB27PeMtNhpEx15sNGBecS4GZ2gD1sUi+MwO2clLys+Xx6IZxpBd5omIejPeepOozeHyexKsa5dtvUaJlBRALZfDJbix5ZeXoN3uRJpaAb1WCWuHE/dcOQZ3XTEG7XYnMrRK5KSrJRUekQaSxTp2K1NScPeVY6CUp6Dd7sRD109Ah0uAxeFEukaJNE1nH6U2WwceuSEfDpcb7XZnzILPDAAnNymBd3ObA2WbdgbtCO/pMs/vJRH1ZiyUJArWmdu3I/bL8y/Gj/70qXfdy/MvRkO7A4oUGcYM1sdsHJEEksN17H594SWYesGAmI0pUgwAJ79IA+8ttg7Rny+A30si6v1YKEnk25lbp5KjrDgXBcMyYXe6oVHKMTBd7fcKk3StEm3nrsZ0FUmYWWybYMsB+C0boFN5xxJsrFkJ+pd+LAPA0QTCGSKPHYa5iaivY6EkkV6jQLHRgN3nulpvrKz1+9d0SV62t0P2lOGZkEHAmVY7po7wf5Q+km7GYts8dMME/O6tg/jgyzN+5100w4iyTTu9Rdr0vGyUz70Ii/+2G4/Nmhgw1kR1T45Vh+xoOkKzi3Rssds5EfV1DHNLJAfw0A35uH/m2KBdrXfUmLGxshb3zxyLh27Ih9XhxAyfbttAZGHmUNss21odcBtvR40Za7bVoKw412/bZz80Ye3NU4KONVHh6WgCw11FEwhniDz2YvG9JCJKZryiJFGjzYmmNjsKhmdh6db9QbepNDXggWvGQQFg5MD0oCHrcGFmAKLbVJgaMC9I12zfTuAeO2rMWH712IAiqev54v0LrbsdsqMJhDNE3jPY7ZyI+jIWShK1WDvQaHOi0RbYRdtXs7UDo3ODh7cjCTMLYcYh1jU72PKWMGNNVOC2Ox2yowmEM0Tec9jtnIj6KhZKEum1SlgdLr9QdzDpGiWqjjcGDQsHC8D6hq073ALSNaG/NWJds4Mt14c5lk6lwKGTLWizO2FIVcHucqPF6oReq0CWToVBek3I/RMhmhAxg8c9hwF5IuqrWChJpNcoIJOpoVHIUWw0oCLILa1iowEdLjdu+ctnsDhcAWHhrgFYnUoeEAxfXGoMefxg3b2DdQKfnpeNrFTxwG1JXja++PosHnr7S6yeU4A/fHDE7zZdsdGAR27Mx3BDasRzFA/RhIgZPO4ZDMgTUV/GMHcUmiwd+N1bB/HbK8b4vR4E6CxW5hbl4pkPjniD1V3Dwl0DsGXFuQFh6/KKWsw910Hb1/S8bDxyYz4On2zxW16Sl407Sv07gXsCtYP0mqCB25K8bCz8nhEPvf1l0DEAnXmoZVurRV9nkijRhIgZPI49BuSJqK/jFSWJWmxOpKoV2HaoHj+7+AJMHp6FeV06Ei/ZXAWLw4VbvjPCu1/XsLBvANbuDGzY59vx+76Z42DrcPmFZJ/60aSA8CwAvLm4OGigNljgNkUGzFxTAYvDhYJhmaJNAytMDWhsdyTdLbhoQsQMHscWA/JE1NcltFA6ffo0Hn74YXz66adQq9W4+uqr8etf/xpqtRp1dXW4//77sWfPHgwZMgTLli1DcXFxIocLoDPM7T6XtLZ0iHckBgKD1V3Dwp4AbNXxxqD7ezoeXzYmB5OHZwXdt6tQv5S67vN57VlvzyWxcLhHuEB4okQTImbwOHYYkCeivi5hhZIgCFiyZAn0ej1efPFFNDc3Y9myZUhJScHdd9+NRYsWYdSoUdiyZQs++OADLF68GP/85z8xZMiQhIw3JSUFLTYn9FolBEHA4lIjRhi0WFxq9Ot2vft4I8oramFxuAKC1WJh4USFjH1D3mLh8GDbEnkwIE9EfV3CMkrHjh3Dnj178OijjyIvLw9Tp07FkiVL8NZbb+HTTz9FXV0dfve732HkyJFYsGABJk+ejC1btiRquNB/awSWvLQHGSo5UtUKHDzRjA4XUHW8Ebe98AUWvrgbZZt2oup4I1bPKUDpmIF+wepQYWFPyDiYngwZZ6WqvBmoqrqmgDfAexQbDchK5RUYCpSon10ionhJ2GWCgQMH4i9/+Quys/3/km1ra8PevXsxbtw46HQ67/LCwkLs2bMnzqPs1GJzYuXbR7DD1ACbS8ADb+zH5OFZePLdQwHh50pTA2QA7r5yDGav73w5rufJMbHbPZ6Q8b1b9vk9kdXTIeNBeg0euTEfy7ZWo7yiFqvnFHg/g4dn7MmWT6LkkKifXSIKbUimFk5BluhhJJWhWdE9hZuwQkmv16OkpMT7tdvtxl//+ldcfPHFqK+vR05Ojt/2BoMBp06dknwel8vV7bE2tDuw41zxYHe5vZ2xQ4Wf75XJ8NSPJnkD3naXK+RYBqWr8MfZk9HQ7kCrzYl0jQKGVBX0GkVMPoOYoZkaPP2jSWi0dKDN7sRD108430dJo0CWrjPsLHUMnu17cux9TW+ds0T97PbW+Uokzpl0iZozuVzerf0XXDqy28foi9xuAYLQmcmNdH6SJnjy5JNP4uDBg3j11VexadMmqFT+/xJVqVRwOKQ/alxdXd3tsbkyhnn/u8XaGU4NF34+ftaChS/u9n49/cIsNB0/EvE5m8/9iScZgLPnLibJAbSf+/NNN44Zi/nvb3r7nMX7Z7e3z1cicM6ki/ecFRYWdmv/v/3nKE4mWVuXWBmk1+LWkjwcO3YMNpu0z+h0OtHR0fl7PNI5TopC6cknn8QLL7yAP/zhDxg1ahTUajWampr8tnE4HNBopN/+yc/P71ZV3WJz4nSL3fu1XquETiXHt7O02HDr1KAhbiAwHD1Ar0Vu7mSY2xznrt50YECqGh0uN9rsTmRqVRiQqoReo0CLzen917leo/QuDzVGKdvHQrhzulwuVFdXd3v++5PePGeJ+BnszfOVKJwz6XrrnB2ut6Km3pLoYfSI3HOJnREjRoTeMEYSXiitWrUKmzdvxpNPPokrrrgCADBo0CCYTP63tcxmc8DtuEjI5fKof7g9HYcnDctEkdGASlMDNIoUbLh1Kp5455Bf1+wiowGr5xRgyeYqFI7IQvV/z/+buiQvGwPT1Phvkw1Lt1aj6njTuS7YNX55oOl52Xjohgn43VsH8cGXZ/yWi3U5TkRXZCnn7M7891e9bc4S3Zm7t81XMuCcSdcb50zWxyNK8fp+JLQz99q1a/HSSy/h97//PWbOnOldPmnSJBw4cMDvktquXbswadKkuI3Nt+NweUUt5hXloshogDxFhnUfmgJeLVJpasDGylrcf804LJ5h9P6AFhkNWDTDCIvDhaVbq1FpahDtgr29xoxlW6sxZrA+YHmwLseJ6IrMTszkiz8PRNTXJaxQOnr0KNatW4df/OIXKCwsRH19vffPtGnTMHjwYCxduhQ1NTVYv3499u3bhx/+8IdxG59vx2FPl+yC4Vlod7i8we6uKk0NmPztTMzbtBMzRudgw61TUTA8C2WbdqLJ2uEtjAqGZQYUSR4VpgYUDMsMWO7pciw2xki2j4VEnJOSF38eiKivS9itt3//+99wuVz405/+hD/96U9+6w4fPox169Zh+fLluOmmmzBixAg8++yzcW022bXjsKdLdokxeM8YjyZrBywOF75ptPqFuX07FIcLgout79rlOBFdkdmJmXzx54GI+rqEFUrz58/H/PnzRdePGDECf/3rX+M4In9iHYf12tCdhrNSlfjHokuglKdgcanR+5La7DS1N/w9bIAu5DHEumR37XIcq67IzRYHzG0OtNg6kKFVIlWtQJvNiRZbB/RaJbJTz7/yo7vn9D1X12NTbMVjrvWazocbyopzg3aoZ2duIurtEh7mTlaejsO+TfR0Kjn0KjlK8rKD3m4oMhpQ9XUTLhyYirPtHTh4ohlrby5AqkqOFf/Y771lt7jUiGKjISDnBHQ2ePTt6O0RrMtxsDGG2j4Y3yCuTiXH6jkFAfkp32Bud86Z6NBvfxKvuc5OU6F87kVYs63Gr69YkdGA8rkXsTM3EfV6CQ1zJzNPx2Hf1zOUFedCALD0qjEo6fK6jyKjAfOKcrHq7YNYu60GJ5utGDckAxsranGsvt0v11ReUYu5Rbne14d4TM/LxiM35uPwyZaA5cG6HAcbY6jtu+oaxA0VMvcEc6M9J0O/8RPvuX52myloh/pnPxR/YTQRUW/BK0ohDMnUYs2cAtS32XG2xQp9qgatDhfOtNhx15VjMLfVDrvT7e2+vWRzFSznwt5zi3IxSK/B2m0mzC3K9TuuJxxeVpyL5TPHwepwIUPb2QE7Q6fCUz+aBHObA622DqRrzi8PNcZIt/fVNYhbMCxTtNu4J5iboVNFdc5IQr+8BRcb8Zxrc5sDO0zBz7WD31ci6gNYKIWRoVMhTS1Hc90RWFUXwuF0w9LhgqVLWLsr30B2sHC2Jxx+2ZgcTBmRFXBOKb9cpG7v0TWIGy5k7hvMlXpOhn7jJ55zze8rEfV1LJQkSNco0OES0GZ3Qhamk5dvIFssnN15zNiGXaUEeLsGs0ONE+jeWD3nEgv+hgvJU+RiFfJPtnMRESUCCyUJDKkqtNmcONtmx4ShGaKB7BKjAadbbDjRbPP+dzCRBq4jJTXA2zWYXVXX5O1AHuuxZqepcNnYHMyeNhwbK2v9bvEVGw2YPXVYiL1JiliE/JPxXEREicAwtwR6jQJKmQwXX2jA0+8dxtxz3bp9FRsNWHr1WAzO0OLQyRYsLs3D4AxtwHaRBq4jFU2At2sw29OBPFjIPBZj/c3lo4OGxStMDVi2tZqB7hjpbsg/Wc9FRJQIvKIkUaPdiTOtNnxwqB7/OXYWZcW5KCvK9Qt1p6TIkKFVoGB4JlLVcmTolHjgmnEQhM5skm9wO1aiDfB2DWbrtUo8/ePJaLM5JYfDw43vZLNNtCM5A92x1Z2QfzKfi4go3lgoSdRi7UC73QXgfCC7q6KR2Zjz/KcAgNzsNAzJ0GDy8KyA7WI6rm6EaoMFswfpRTaOUoutA7YOV8htGPyNrWhD/sl+LiKieGKhJJFeq0S7I/Qv/HRN57TqVHLkDUqD0yVg99dnkalThex67RvEztQqoVbK0WLtQIvNCb1WgSydChpFStCwdjxCtd3p9KzXKHG2PfStNQZ/iYgo2bBQkihDowAEdcjO2mplCrLTVFgzpwAPvnEAFaaGsF2vZQDuPpcxyk5T4cWfX4z7/rHXb9tiowErr5uA2es/8b5s1LN/qFBtsdEAjbJ7cbTudnrOTlPh86/O9lhYnIiIqCewUJJIJpPh6fcO47dXjAFwyK9YKjIaMLcoF4//80usvbkAz24zedeH63p9Vf5gbxHy+KyJ+N1bB4KGnle+sR+Pz5qI2174wm//NXMK8MiN+bj3tX1Bx7TyjQN46keToro9Ei4ovmZOQdjjZuhU+N6ogcjNTgUAv89WwuAvEVFMDcnUwimEbmPTWw3Niu8rr1goSdRk7cAHh+rx04svwOThWZjXJcjt6c595+Wj/V5bEq7r9a2XXOD9OkevFg09V5gacM9VYwL2N7c5IJMh5JiiDUvHqtPz4EwtdCo5HrkhH+0OpzfYnpOuZpFERBRDCy4dCblcnuhh9Bi3W0BKSnwKQRZKErVYOwPHlo7gQe7z2zn9vg7X9dp3fZstdAYq2PpWWwcEIOSYog1Lx7L7MkO/REQ9b8uuOpxqjd0DMjnpasyeNjxmx+uueBVJAAslyfRaJXQqOb6dpcWGW6f6dZcur6iF5VzQO1OnxOJSo3dZuK7XvuvTNKH/FRBsfSRB6GjD0uy+nHjdCdITUf+z75tmmMyWmB3vAkNqUhVK8cRCSaJMrRLlcy/CE+8E5pNWzynAks1VKBieiberT2LP8UbvsnBdr8+02r1fn2mxhwyLn2mx+y3zDUL3RJdkdl9OrO4G6YmIKHrszC1RSooMz26rCShiKk0N2FhZi/tmjsW8olyUV9SiwtSATZW1KCvODdv1+nujBnq7G9+zZR/uv2Z8wLaep97u2bIvYH/PLa2e6JLM7suJE03HdSIiih1eUZKosd3hF9L2VWlqwJ2XjcLPyj/33oKrMDVg+cxxKB2dg0xd6K7Xvt2NVXIZnvjhpPN9lDQKZKV29lF6ef53RTsg91SXZHZfToxYBemJiCg6LJQkarE5Q64/02r3Fkke9g4Xpow435lbrOt1sKBzsFsrkTyK3xO/PBnEjr9YBumJiEg6FkoS6TWhpyxYaDtVHXyfcAHdYOsB9OlQL0PL/hikJyJKLBZKEmWlqkSD1kVGA6rqmgKWqeSBxVO4gG6w9SV52Vg0w4iyTTu9V636UqiXoeVADNITESUWw9wSaRQpWHndhICgdYkxG3fMyEN5Ra13WZHRgHlFuWi2+gduwwV0T7fYgq7fUWPGmm01KCvODdint4d6GVoOjkF6IqLE4hUlicxtDsxe/wme+OFELJ85Dk2WDmTolDjdbMWu42exZk5BQFfsNxcXBxwjVEC3sV18faWpAWVFuQH79PZQL0PL4hikJyJKHBZKErXYOmBuc6Bs0xdYd8sULHxxNxaXGlF1vDHil72GC+iGC4wH6/Ld20O9DC2HxiA9EVFisFCSSK/p7MxdVpyLYQO0WHfLFGiVclw+bhCe+eAIth2qBwDoVHLcf804TBmeiWPmdui1Dm8wOVxAN5rAeG8P9TK0TEREyYiFkkTZaSqUz70Ia7bV+L1XrchowF1XjMHPLr4AdpcbI7NT8eCbB7H0tWrvNp5gcriAblaq+PpggfG+EOplaJmIiJIRw9xReHabKeA2W6WpAU++ewhfHG/E2XYHHnzzIHaYggeTAYQM6A7Sa4KuL8nLxh2l/oHxvhLqZWiZiIiSEa8oSWRucwQUQB6VpgbcP3Mc5CkyvytJvjzB5JE5aSEDumIBXgB4c3Fxnwz1MrRMRETJhoWSROFCx7YOF4Qwx/AEk8MFdMXW9+XCgaFlIiJKJiyUJAoXOtao5JDLZCG3YTCZiIiod2ChJFGo0HGR0YC39p3E0EwtSvKyg/YFYjCZiIh62pBMLZxC6H+0SzE0q3++HQFgoSSZJ3R875Z9fsWSpwv3ks1VAIDyuRdBBvhtw2AyERHFw4JLR0Iul8f0mG63gJSU2BVfvQULpSh4Qscnm204Zm7368LteQdb2aad+NeSEjjdAoPJREQUV1t21eFUa+hMbU66GrOnDY/4mP2xSAJYKEUtQ6fCMXM7Fr64O+h6i8OFRosDk4dnxXlkRETU3+37phkmsyXkNhcYUiUVSv0VC6VuCBXs1qnkyNKpcPRMG1psHdBrld7O3L6aLQ6Y2xwht+nO9r1NX/98RETUu7BQ6gaxYLdOJUf53Itw3+v7/XoueTpzD8nsDMWdaLLini37/ELfXbfxJXX73qavfz4iIup92Jm7G8S6Sd9/zTg8u80k2pm72eJAs8URUBR03caX1O17m77++YiIqHfiFaVuCtZN2i0IYTtzAwjaPsB3G99bTuY2h6Tte5u+/vmIiKh3SoorSg6HA9dccw0+++wz77K6ujrMnTsXkydPxtVXX42KiooEjjC0DJ0KI3PSMHl4FkbmpKHN7gy5fautI2yH79Yu66Vu39v09c9HRES9U8KvKNntdvzmN79BTU2Nd5kgCFi0aBFGjRqFLVu24IMPPsDixYvxz3/+E0OGDEngaCMTrnu3WpkCjUKODbdOhd3phkYpx+7jjSivqPW2F+javTvcMXt7t+++/vmIiKh3SmihZDKZ8Jvf/AaC4P92tE8//RR1dXV46aWXoNPpMHLkSHzyySfYsmUL7rjjjgSNNnKhuneXjhkIu1PAw2/vR4Wpwbu8yGjA6jkFWLK5ClNHZAV07w51zL7Q7buvfz4iIuqdEnrr7fPPP8d3vvMdvPzyy37L9+7di3HjxkGn03mXFRYWYs+ePXEeYXTEQt5FRgN+ddkoPPnuIb8iCQAqTQ3YWFmL+68ZF7R7t9gx+0q3777++YiIqHdK6BWlm2++Oejy+vp65OTk+C0zGAw4deqU5HO4XK6oxhbsGFKONShdhT/OnoyGdgcaLR1osXagqq4J5jYHKrsUSR6VpgasuHY8ctJVQc/le8xWmxPpGgUMqSroNYqYfM5Y6u6cJfvn6wnRzFl/xvmSjnMmXaLmLBavH+lys0ZUf/15iHSOE55RCsZqtUKl8r+CoFKp4HBIf0S8ujr402fRiPZYQsYw3PbCFwCAdbdMCbltU6sVe745EtFxm8/9SWbdmf/e8Pl6Qix/ZvsDzpd0nDPp4j1nhYWF3drfarWira0t5DYWXecrSQ4fPgyr1dqt8/VGkc5xUhZKarUaTU1NfsscDgc0Go3kY+Xn53e7Mne5XKiuro7oWC02J+rbHGi2diBVJYdOJQeEziaUFocLakXou50ZaRo4NBdCp1JiQKoSeo3/t6jF5vRecdFrgm+TDKTMGXXinEnD+ZKOcyZdb50zrVaLtLTQl5Q88ZbRo0fHY0i9VvL9hgUwaNAgmEwmv2Vmszngdlwk5HJ5zH64wx3rRJMV97y6z6/RZJHRgDtmGLFx7lTM2/QFqv/bjBJjdkAzSgAoMWZjb10TJg3Lwk/Wf4Jxg/Xd6uSdDGI5//0F50wazpd0nDPpeuOcySJ8h21v+1zxlhR9lLqaNGkSDhw4AJvN5l22a9cuTJo0KYGjCq3Z4ggokoDO3NGaD004Vm/BfTPHQiYDFs4YiSKjwW+7IqMBC2cYcbLFhlVvHcDjsyZ2q5M3ERERdV9SFkrTpk3D4MGDsXTpUtTU1GD9+vXYt28ffvjDHyZ6aKLMbY6gV4mAzmIpR6/GqEHpKB2Tg9te+AIFw7Ow4dapWHfLFGy4dSoKhmfhthd2YsKQDFSc2x4435U6ks7VREREFFtJeetNLpdj3bp1WL58OW666SaMGDECzz77bFI3mwzXWdrudONMqx0AYHG4sHabSXQ7AGiznX8KodXWgXAPL7BzNRERUewlTaF0+PBhv69HjBiBv/71rwkajXRhu3GfC3HLzt001qnkKCvORcGwTL/u3KkqBRaXGpGhU2LdLVOgUcqRpVPBFeY5T3auJiIiir2kKZR6u1CdpYuMBpxuseFMqx2lY3Lw/TEDMec7I7CxstbvylKx0YCbCobihU+a/ZZPz8vGIzfm4wdjc/D+l2cCjs/O1URE5GtIphZOIXSae2hWcj4ElGxYKMWIp7N018B1kdGAxTPyYOtwob7Njmc+OIJfXTYKj79zKKDxZIWpAQ/8Yz8mD8/CtkP13uXba8xYtrUaj96UD7vT7VeMsXM1ERF1teDSkRE9zeZ2C0hJifDxuH6KhVIMDcnUYu2cApxptaPZ2gGtSg5FigwdLjdabB0oHZODP7xfg59efIFod+4KUwPmFeUGLN9eY4atw401cwpgbnOg1daBdI0S2WkqFklERORny646nGoVz67mpKsxe9pwFkkRYKEUYxm684VL1fFGzFz3H+86T1duW0fodvGeQHdXrbYOjMxJY2FEREQh7fumGSazRXT9BYZUzJ42PI4j6r2Ssj1AX9E14K1WpECnkiMnXR1yP7Hu3alq1rVERETxxEKpB3kC3h77TzRjw61TceR0a0DDSY9iowFVdU0By4uMBnzxdSNONPW/9/EQERElCgulHuQJeHuKJUEA1n1owkNvf4l5RbkBxVKJ0YCV103AoZMtfsuLjAbMK8rFqrcOsgs3ERFRHPFeTg8bkqn1BrDtThee+aAGALBkcxXKinNRVpQLu9MNtSIFA9PVmL3+E/x9wXcxZ9pw7/KquiYs2VwFi8Pl7cLNnBIREVHPY6EUB56Ad9XxRu+yYN25190yBeY2B+rbHLjthS9Ej8cu3ERERPHBQilGmi2d72NrsXVAr1UiOzXwsf2u4W5Pd+4pw7OgSJFhYLoaWxdeAp1KjsWlRpRX1MLiCHxCjl24iYiI4oOFUgycaLIGNJqcnpeNx2ZNxJDM851Pfbt361RyrJ5TENCd25NH+vJEM1bPKfDecvM9LrtwExERxQfD3N3UbHEEFElAZ4PIrsFr33B3WXEuNlbWBjSerDQ1YGNlLcYOycCmylqUFZ9vPsku3ERERPHFK0rdZG5zBBRJHsGC155w98lmW0BGyaPS1ICyolys3WbCfTPH4bIxOezCTURElAAslLqpJUywOljwOkOnwjFze8j9PN25bR0uTB6eFf0AiYiIKGoslLpJr1F6Q9kFwzJhd7qhUcqx+3gjyitqoVHKUXW8MSDgnRamy7anOzeD20RERInDQqmbstNUKJ97EdZsqwkIZW+4dSrePXgKf3i/s3eSJ+AtA7CnrgklxmzsMAXetis6152bwW0iIqLEYpg7Bp7dZgoayl734VEIwvllnoD3R0fqcaLZioUzRgZ05/Y89Xb4ZAuD20RERAnGK0rdZG5zBL0qBAA7TGbMLbrAb9n2GjNuveQC5KSrcdsLX6CsOBe3FV8IhVyGLJ0STpeAZksH7ps5DoN9WgsQERFR/LFQ6qZwYW5PKDvYsmDduT1eX3gJRiC1+wMkIiKiqLFQ6qYMrRKLS41Bg9wWhwsapTwg7H2BQQdFSgqy01SYPW14wL4vfX4cWToVjp5pC9npu6dF0m2ciIioL2Oh1E0qeQqqjjcGBLlXzynA5s++xsA0NdbeXIANFf4duC8bm4OX5l+MFW8cCNj3pfnfxaq3DuKDQ2e8y4N1+u5JkXYbJyKi5DMkUwunIBNdPzSLf49HioVSNzRbHFi6tTpokFsG4O4rx+DLk814Y++JgG3GDNZjxRsHgu678o39mDw8y69Q8gTB18wp6PGrOuG6jcdjDEREFL0Fl46EXC4PuY3bLSAlRbyYkiKWx0o2LJS6IVRX7gpTA8raHDCkqVHRpRgCgIJhmaL5pApTA+YV5QYsD9bpuydI7TZORETJZcuuOpxqDZ2hbbc70WwNvU0khmZpcUdpXrePk6xYKHVDuCC3rcMlui5YyDuS9cE6fcdaNN3GiYgoeez7phkmsyXRw+gTWCh1gz5M12xPd22p60KtTw3T0TsWwn0udgsnIqL+gg0nuyE7TYXpedlB13m6a59usaGkS1NJAKiqa0JxkOUAUHxu32DH/OLrRpxosnZr3OGE+lzsFk5ERP0JC6VuyNCp8NisiQFFhae79sETzRicocW84tyADtxfnmjGqusnBO3MvfK6CTh0siXoMVe9dRD3btmHZoujZz4UxD/X9LxsdgsnIqJ+hbfeumlIphZr5hTA3OZAo8UBtTIFipQUnG62YdyQDCz6224AQFlxLsqKcmF3ujFsgBbvHjiNb85aUTA8y7tcrUhBVV0TZq//BH9f8F3MmTbcb/mSzVWwOFxxCVT7fq5WWwfSNUpkp7GPEhER9S8slGIgQ9dZQFQdb8S1ayqx7pYpWPjibr9tfJ9wW3fLFKzdZsK4c/8bTH2bA7e98IXoOeMRqPZ8LiIiov6KhVI3NVscaGh3wOkWoFHKse6WKRg+QIfFpUZvd24PT4fuEQYtti68BBqlHC/94mLYnS7s/LrRb/sMrSJkx28GqqPHjuNERBQpFkrdcKLJigf+sR+zpw3Hxspav+aRxee6c3tul+lUcqyeU4C/ffY1Jg/LDNi+yGf7S0YaoFUpRDt+v/z5cQaqo8SO40REJAULpSh5uldPClL0APA2mSwrzsXabSaUFediY2UtCoZnBd3e8/V9M8eiYHgWlr8m3vGbgerosOM4ERFJxafeouTpXl0wLDOgoPGoMDVgZv5gvDT/YszMH4xKU0PI7StNDRg1KB0nm23YYRLv+G3rCN2skoKLpOM4ERGRLxZKUfJ0rw7XYbvW3I7Z6z9FkyWy7c+02kN29AbYGTta7DhORERS8dZblDzdq0N12Nap5Bg5MBXlcy+CPEWG8rkXITtNBZ1K7hfy9ttHKYchTY0Nt04NGuIGAI1SjqrjjQwiS6TXKL2B+mAheQbkiYioKxZKUfJ0r66qa0KR0RBwO21YlhYvzJuGUy027xWi3ccbcehEMzbcOhW3vfBFQLFUOmYgslJVeOKdL7FDJOg9ZXgm3qo+6Q15M4gcuew0FcrnXoQ122oCQvKeIpaIiMgXC6UoZehUeOTGfKx4Yz/mFeUiBcCu400oK87FRSMGYPgALe77x/6AJ9vmFeViw45a3DdzLJZt3e+37leXjcKT7xzyK5KA80Hv+2eOxUC9Bks2V3nXMYgszbPbTEFD8ikyGdbOKUjQqIiIKFkldaFkt9vx4IMP4r333oNGo0FZWRnKysoSPSwvlyDgl98zwukWsPyacVDLU/Dw2wcBAH/Z0Sj6ZFvB8CwUDM/CP/+nBG02J3QqOTpcbqTIZAFFku++d142Cj8r/zzgSlQ8OnX3BeY2h2hIfgfnkIiIgkjqQumJJ57A/v378cILL+DEiRO45557MGTIEFx55ZWJHhpONFlx39Zqv8Km2GjA3KLczqsTIh23K00NKCvKRa25PaB797pbpoQ855lWu2i2iUHk8BjmJiIiqZK2ULJYLPj73/+O559/HuPHj8f48eNRU1ODF198MeGFkrcfT5DeSQKABdNHhtzf8/62rkIFw8OtZxA5PH2YOeIcEhFRV0lbKB06dAhOpxMFBedzI4WFhfjzn/8Mt9uNlJTEdTYI1Y+n0tSAe68aE3L/TK0S/zkWeIutqq4JJUZD0NtvJUYDzrTYgh5vel42g8gR8ATwtwf53nEOiagvGZKphVOQxeVcQ7P69sNESVso1dfXIysrCyrV+V9e2dnZsNvtaGpqwoABAxI2tnC3cJwuIeiTcEDn7blhWVocPtkSsK68ohYb514EyGR+hViR0YDFpXnITlejJC874PUb7NQdmQydCo/Nmoh7t+zzK5Y4h0TU1yy4dCTkcnnczud2C0hJiU9hFm9JWyhZrVa/IgmA92uHI/IOyi5X6OaNUo7h+d90Tehpa7Z0YF5RLgD4FUslxmw8dOMEDMnU4MHrJ8Du9P+FPXVEFoYP0GH17Mmob3Og2doBnUoOrUIOeQqg1yqxevZkNLQ70GpzIl2jgCFVBb1GEZPPGUtd5yxZDEpX4Y9JOofJOmfJivMlHedMukTNWXeLnER8j3vbj1WkcywTBEHo4bFE5V//+hceeughVFZWepcdPXoUV199NT777DNkZmaG3N/lcmHPnj09MrY0Qw4e+uC/QW+RFRsNmDw8C+UVtSgrzsUlFxqgVKQgTa1AqgJoPvU1HA4HUlJSoMvKhlOuQ7vdhVS1HAqXBZZGM9xuvqKEiKg/KywsjGq/nvzd19dEOsdJe0Vp0KBBaGxshNPphELROcz6+npoNBro9fqIj5Ofnx+Tyry6utrvWI//MDvoLZyHb8yHw+XC98fk+F2t8Bg6YFyIM2UCw4Z0a6zJIticUWicM2k4X9JxzqTrrXPW28abzJK2UBo7diwUCgX27NmDqVOnAgB27dqF/Px8SUFuuVwesx8W32MNydRizZwCmNscaLV1IF2jRHYaXyfSVSznv7/gnEnD+ZKOcyZdb5uz3jbeZJa0hZJWq8UNN9yAlStX4pFHHsGZM2dQXl6ORx99NNFD88rQsTAiIiLqy5K2UAKApUuXYuXKlbj11luRlpaGO+64A5dffnmih0VERET9RFIXSlqtFo8//jgef/zxRA+FiIiI+qHEdW0kIiIiSnIslIiIiIhEsFAiIiIiEsFCiYiIiEgECyUiIiIiESyUiIiIiESwUCIiIiISwUKJiIiISAQLJSIiIiIRLJSIiIiIRLBQIiIiIhKR1O966w5BEAAALper28fyHCMWx+ovOGfScc6k4XxJxzmTLpFzlpKSAplMFvfzkj+Z4Kko+hiHw4Hq6upED4OIiCgqkydPhlwul7SPy+XCnj17otqXguuzhZLb7YbT6WRFTkREvVI0v78EQYDb7ebvvhjqs4USERERUXcxzE1EREQkgoUSERERkQgWSkREREQiWCgRERERiWChRERERCSChRIRERGRCBZKYdjtdixbtgxTp05FcXExysvLEz2kpOFwOHDNNdfgs88+8y6rq6vD3LlzMXnyZFx99dWoqKjw2+c///kPrrnmGkyaNAk/+9nPUFdXF+9hJ8Tp06exZMkSTJs2DSUlJXj00Udht9sBcM6C+frrr3HbbbehoKAA3/ve9/CXv/zFu47zFd78+fNx7733er8+ePAgfvSjH2HSpEmYNWsW9u/f77f9W2+9hcsuuwyTJk3CokWLcPbs2XgPOSHef/99jB492u/PkiVLAHDO6DwWSmE88cQT2L9/P1544QWsWLECa9euxTvvvJPoYSWc3W7Hr3/9a9TU1HiXCYKARYsWITs7G1u2bMH111+PxYsX48SJEwCAEydOYNGiRbjpppvw6quvYsCAAVi4cCH6eisvQRCwZMkSWK1WvPjii/jDH/6ADz/8EM888wznLAi324358+cjKysLW7duxYMPPog//elPePPNNzlfEXj77bfx8ccfe7+2WCyYP38+pk6ditdeew0FBQVYsGABLBYLAGDfvn1Yvnw5Fi9ejJdffhktLS1YunRpooYfVyaTCTNmzEBFRYX3z0MPPcQ5I38CiWpvbxfy8/OFTz/91Lvs2WefFX76058mcFSJV1NTI1x33XXCtddeK4waNco7P//5z3+EyZMnC+3t7d5tb731VmH16tWCIAjCM8884zd3FotFKCgo8JvfvshkMgmjRo0S6uvrvcvefPNNobi4mHMWxOnTp4X/+Z//EVpbW73LFi1aJKxYsYLzFUZjY6Mwffp0YdasWcI999wjCIIg/P3vfxdKS0sFt9stCIIguN1u4Qc/+IGwZcsWQRAE4a677vJuKwiCcOLECWH06NHC8ePH4/8B4uw3v/mN8PTTTwcs55yRL15RCuHQoUNwOp0oKCjwLissLMTevXvhdrsTOLLE+vzzz/Gd73wHL7/8st/yvXv3Yty4cdDpdN5lhYWF2LNnj3f91KlTveu0Wi3Gjx/vXd9XDRw4EH/5y1+QnZ3tt7ytrY1zFkROTg6eeeYZpKWlQRAE7Nq1Czt37sS0adM4X2E8/vjjuP7662E0Gr3L9u7di8LCQu/rLGQyGaZMmSI6Z4MHD8aQIUOwd+/euI49EY4ePYoLLrggYDnnjHyxUAqhvr4eWVlZUKlU3mXZ2dmw2+1oampK3MAS7Oabb8ayZcug1Wr9ltfX1yMnJ8dvmcFgwKlTpyJa31fp9XqUlJR4v3a73fjrX/+Kiy++mHMWRmlpKW6++WYUFBTgiiuu4HyF8Mknn+CLL77AwoUL/ZaHm5MzZ870yzkTBAG1tbWoqKjAFVdcgcsuuwxPPfUUHA4H54z8KBI9gGRmtVr9iiQA3q8dDkcihpTUxObLM1fh1vcXTz75JA4ePIhXX30VmzZt4pyFsHr1apjNZqxcuRKPPvoof8ZE2O12rFixAg888AA0Go3funBzYrPZ+uWcnThxwjs3zzzzDL755hs89NBDsNlsnDPyw0IpBLVaHfCD7/m6619G1DlfXa+0ORwO71yJzader4/XEBPuySefxAsvvIA//OEPGDVqFOcsjPz8fACdhcBvf/tbzJo1C1ar1W8bzhewdu1aTJgwwe/KpYfYnISbs65XjPuaoUOH4rPPPkNGRgZkMhnGjh0Lt9uNu+66C9OmTeOckRcLpRAGDRqExsZGOJ1OKBSdU1VfXw+NRtPn/+KNxqBBg2AymfyWmc1m7yXqQYMGwWw2B6wfO3Zs3MaYSKtWrcLmzZvx5JNP4oorrgDAOQvGbDZjz549uOyyy7zLjEYjOjo6MHDgQBw7dixg+/48X0Dnk25ms9mbp/T8En/33XdxzTXXBJ2TcHM2cODAOIw8sTIzM/2+HjlyJOx2OwYOHMg5Iy9mlEIYO3YsFAqFXxB0165dyM/PR0oKp66rSZMm4cCBA7DZbN5lu3btwqRJk7zrd+3a5V1ntVpx8OBB7/q+bO3atXjppZfw+9//HjNnzvQu55wF+uabb7B48WKcPn3au2z//v0YMGAACgsLOV9B/N///R/efPNNvP7663j99ddRWlqK0tJSvP7665g0aRKqqqq8LRIEQcDu3btF5+zkyZM4efJkn5+zHTt24Dvf+Y7fFcovv/wSmZmZKCws5JzReYl85K43uP/++4WZM2cKe/fuFd5//31hypQpwrvvvpvoYSUN3/YATqdTuPrqq4Vf/epXwpEjR4TnnntOmDx5svDf//5XEARBqKurE/Lz84XnnntOOHLkiPA///M/wrXXXut9BLevMplMwtixY4U//OEPwpkzZ/z+cM4COZ1O4aabbhLKysqEmpoa4aOPPhIuueQSYdOmTZyvCN1zzz3ex9dbW1uFiy++WFi1apVQU1MjrFq1SigqKvK2WNi9e7cwfvx44ZVXXhG+/PJL4ac//amwYMGCRA4/LlpbW4WSkhLh17/+tXD06FHho48+EoqLi4X169dzzsgPC6UwLBaLcPfddwuTJ08WiouLhY0bNyZ6SEnFt1ASBEH46quvhFtuuUWYMGGCMHPmTKGystJv+48++ki4/PLLhYkTJwq33nprv+g78txzzwmjRo0K+kcQOGfBnDp1Sli0aJEwZcoUoaioSPjTn/7kLXY4X+H5FkqCIAh79+4VbrjhBiE/P1/44Q9/KBw4cMBv+y1btgiXXnqpMHnyZGHRokXC2bNn4z3khDhy5Igwd+5cYfLkyUJRUZGwZs0a788Z54w8ZILQj1rWEhEREUnAoA0RERGRCBZKRERERCJYKBERERGJYKFEREREJIKFEhEREZEIFkpEREREIlgoEREREYlgoUREREQkgoUSEXn9+9//xvTp0zFp0iTs2LEj0cMhIko4duYmIq/rr78e48ePx6JFi2AwGKDRaBI9JCKihFIkegBElDxaW1tRWFiIoUOHJnooRERJgbfeiAgAUFpaiv/+979YtmwZSktLsWvXLsyZMweTJk3C5MmT8Ytf/AJnzpwBALz22muYPXs2Fi1ahMLCQrzxxhsQBAHPPvssiouLMXXqVNx+++04ceJEgj8VEVH3sFAiIgDAq6++im9961tYtmwZ/u///g8LFixAUVER3nrrLWzYsAHHjx/H+vXrvdtXVVXBaDTilVdeQXFxMf7617/izTffxNNPP42XX34ZBoMBZWVl6OjoSOCnIiLqHt56IyIAwIABAyCXy5Geng6VSoWFCxdi3rx5kMlkGDZsGC6//HLs27fPu71MJsMvf/lLb47pL3/5C1asWIHvfOc7AIDf/e53KC4uxo4dO1BaWpqQz0RE1F0slIgowMCBA3HDDTdg06ZN+PLLL2EymXD48GFMmTLFu41v2Lu9vR2nTp3CnXfeiZSU8xeqbTYbvvrqq3gPn4goZlgoEVGA06dPY9asWRg/fjwuueQS/PjHP8ZHH32EvXv3erdRq9Xe/3a5XACAP/7xj8jNzfU7VkZGRnwGTUTUA5hRIqIA77//PjIyMvDcc8/h1ltvxdSpU1FXVwexbiJ6vR4GgwH19fUYMWIERowYgcGDB+PJJ59EbW1tnEdPRBQ7LJSIKEBmZiZOnDiBTz75BHV1dVi/fj3ee+89OBwO0X3mzp2LZ555Btu2bcNXX32F++67D7t378aFF14Yx5ETEcUWb70RUYCrrroKO3fuxJIlSyCTyZCfn4977rkHa9asES2WbrvtNrS3t+OBBx5AW1sbJkyYgA0bNvDWGxH1auzMTURERCSCt96IiIiIRLBQIiIiIhLBQomIiIhIBAslIiIiIhEslIiIiIhEsFAiIiIiEsFCiYiIiEgECyUiIiIiESyUiIiIiESwUCIiIiISwUKJiIiISAQLJSIiIiIR/x88Uy00kNKn/gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.jointplot(data=titanic, x='fare', y='age', kind='scatter')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x11d0389e8>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d038cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 500.0)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.histplot(data=titanic, x='fare')\n",
+    "plt.ylim(0, 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.boxplot(data=titanic, x='class', y='age')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.stripplot(data=titanic, x='class', y='age', jitter=-0.2)\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "sns.countplot(data=titanic, x='sex')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11f207ef0>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d84cc18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "columns = ['survived', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'adult_male', 'alone']\n",
+    "\n",
+    "# Create the correlation matrix\n",
+    "corr_matrix = titanic[columns].corr()\n",
+    "\n",
+    "# Create the colored square plot\n",
+    "sns.heatmap(corr_matrix, cmap='coolwarm')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x11d72da58>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d8229b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CODE HERE\n",
+    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
+    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
+    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
+    "\n",
+    "# Plot the histogram for males\n",
+    "sns.histplot(data=titanic[titanic['sex'] == 'male'], x='age', ax=ax1, color='blue')\n",
+    "ax1.set_title('sex=male')\n",
+    "ax1.set_xlabel('Age')\n",
+    "ax1.set_ylabel('Count')\n",
+    "\n",
+    "# Plot the histogram for females\n",
+    "sns.histplot(data=titanic[titanic['sex'] == 'female'], x='age', ax=ax2, color='pink')\n",
+    "ax2.set_title('sex=female')\n",
+    "ax2.set_xlabel('Age')\n",
+    "ax2.set_ylabel('Count')\n",
+    "\n",
+    "# Adjust the spacing between subplots\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Great Job!\n",
+    "\n",
+    "### That is it for now! We'll see a lot more of seaborn practice problems in the machine learning section!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}