Practice_with_Math_in_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab7c11a5",
   "metadata": {},
   "source": [
    "## Lesson Plan: Introduction to NumPy, Plotting,  and Pandas\n",
    "### Objectives:\n",
    "- Understand basic mathematical operations using `NumPy`.\n",
    "- Learn how to create and manipulate arrays using `NumPy`.\n",
    "- Explore more complex functions and dynamics using `NumPy`.\n",
    "- Get introduced to data manipulation and analysis using `Pandas`.\n",
    "\n",
    "### Outline:\n",
    "1. Simple Math with NumPy\n",
    "2. Arrays in NumPy\n",
    "3. Complex Functions and Dynamics in NumPy\n",
    "4. Introduction to Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a75d245",
   "metadata": {},
   "source": [
    "## 1. Simple Math with `NumPy`\n",
    "Introducing basic mathematical operations using `NumPy`. `NumPy` or Numerical Python, allows users to perfom math functions and apply them to arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "daf02fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can always change our imports to represent anything we want, including 'np'\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b66ce9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple math operations\n",
    "a = 10\n",
    "b = 5\n",
    "\n",
    "# take the sum\n",
    "sum_ab = np.add(a, b)\n",
    "\n",
    "# subtract two digits \n",
    "diff_ab = np.subtract(a, b)\n",
    "\n",
    "# multiply\n",
    "prod_ab = np.multiply(a, b)\n",
    "\n",
    "# divide \n",
    "div_ab = np.divide(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "52e103c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using method #0 ... \n",
      "Sum 15\n",
      "Using method #1 ... \n",
      "Sum: 15\n",
      "Difference: 5\n",
      "Using method #2 ... \n",
      "Product: 50\n",
      "Division: 2.0\n"
     ]
    }
   ],
   "source": [
    "# lets show the results, in the cell above we have only *stored* them\n",
    "# side note: there are three ways to print values\n",
    "\n",
    "# 0. this one is doing the calculation in-line\n",
    "print('Using method #0 ... ')\n",
    "print('Sum', (np.add(a, b))) # and so on... \n",
    "\n",
    "# 1. calling the value directly\n",
    "print('Using method #1 ... ')\n",
    "print('Sum:',sum_ab)\n",
    "print('Difference:', diff_ab)\n",
    "\n",
    "# 2. using 'f-strings'\n",
    "print('Using method #2 ... ')\n",
    "print(f'Product: {prod_ab}')\n",
    "print(f'Division: {div_ab}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa705eea",
   "metadata": {},
   "source": [
    "### Dabbling with pseudocode and boolean logic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20509a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# i want a code that will calculate some value from two input values\n",
    "# i want my calcultor to interpret my inputs and the input math calculation selected \n",
    "# how would i do that? \n",
    "\n",
    "\n",
    "# step 1: create a ... function\n",
    "# step 2: create a ... condition\n",
    "# step 3: create a(n) ... expression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "c0e9af9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# try this on your own ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "3dadb58b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Create a function to perform the calculation\n",
    "def calculate(input1, input2, operation):\n",
    "    # Step 2: Create a condition to check the operation selected\n",
    "    if operation == 'add':\n",
    "        # Step 3: Create an expression for addition\n",
    "        result = input1 + input2\n",
    "    elif operation == 'subtract':\n",
    "        # Step 3: Create an expression for subtraction\n",
    "        result = input1 - input2\n",
    "    elif operation == 'multiply':\n",
    "        # Step 3: Create an expression for multiplication\n",
    "        result = input1 * input2\n",
    "    elif operation == 'divide':\n",
    "        # Step 3: Create an expression for division, with a check for division by zero\n",
    "        if input2 != 0:\n",
    "            result = input1 / input2\n",
    "        else:\n",
    "            result = 'Error: Division by zero'\n",
    "    else:\n",
    "        result = 'Error: Invalid operation'\n",
    "    \n",
    "    return result\n",
    "    #return print(f'{operation} {input1} from {input2}, and the result equals {result}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "0aaf645f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# Example usage:\n",
    "input1 = 10\n",
    "input2 = 10\n",
    "operation = 'divide'\n",
    "print(calculate(input1, input2, operation))  # Output: should be? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "726bcd57",
   "metadata": {},
   "source": [
    "## 2. Plotting using `Matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "d208bb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = np.linspace(-5.,5.,101) #1D array of x-positions\n",
    "yy = np.linspace(-5.,5.,101) #1D array of y-positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "2800112b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101,)"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "22a54b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5.  -4.9 -4.8 -4.7 -4.6 -4.5 -4.4 -4.3 -4.2 -4.1 -4.  -3.9 -3.8 -3.7\n",
      " -3.6 -3.5 -3.4 -3.3 -3.2 -3.1 -3.  -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3\n",
      " -2.2 -2.1 -2.  -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.  -0.9\n",
      " -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1  0.   0.1  0.2  0.3  0.4  0.5\n",
      "  0.6  0.7  0.8  0.9  1.   1.1  1.2  1.3  1.4  1.5  1.6  1.7  1.8  1.9\n",
      "  2.   2.1  2.2  2.3  2.4  2.5  2.6  2.7  2.8  2.9  3.   3.1  3.2  3.3\n",
      "  3.4  3.5  3.6  3.7  3.8  3.9  4.   4.1  4.2  4.3  4.4  4.5  4.6  4.7\n",
      "  4.8  4.9  5. ]\n"
     ]
    }
   ],
   "source": [
    "print(xx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "41a8307e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101,)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(xx).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "6b4d3c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.plot(np.cos(xx), color='cornflowerblue', linewidth=7.0, label='My Cosine Function')\n",
    "#plt.plot(np.sin(xx), color='orange', linewidth=7.0, label='My Sine Function')\n",
    "plt.axhline(0, linestyle='--', color='black', alpha=0.3)\n",
    "\n",
    "plt.xlim(0,100)\n",
    "plt.ylim(-1.1,1.1)\n",
    "plt.title('Cosine Wave', fontsize=20)\n",
    "plt.xlabel('X', fontsize=20)\n",
    "plt.ylabel('Y', fontsize=20)\n",
    "plt.legend()\n",
    "# plt.grid()\n",
    "\n",
    "# why did i have to do this? try commenting it out and see what changes \n",
    "new_x_labels = np.linspace(-5, 5, 11)\n",
    "plt.xticks(ticks=np.linspace(0, 100, 11), labels=new_x_labels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "aec51e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Questions!\n",
    "#1.  How many dimensions is the graph above?\n",
    "# hint: How many axis are there on the graph?\n",
    "\n",
    "#2. Is this data discrete or continuous? \n",
    "# hint: are the points is there a smooth transition between points or is it step-wise?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdbd30f6",
   "metadata": {},
   "source": [
    "Now let's review arrays again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "b274f51c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n",
      "Array shape: (3, 3)\n"
     ]
    }
   ],
   "source": [
    "# simple arrays \n",
    "# think about what you are creating ... what *kind* of array is this? \n",
    "simple_array = np.array([[1.,0.,0.],[0.,1.,0.],[0.,0.,1.]])\n",
    "print(simple_array)\n",
    "print(f'Array shape: {simple_array.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "8a9f7bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tic Tac Toe')"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "plt.imshow(simple_array)\n",
    "\n",
    "plt.axhline(0.5, color='white')\n",
    "plt.axhline(1.5, color='white')\n",
    "plt.axvline(0.5, color='white')\n",
    "plt.axvline(1.5, color='white')\n",
    "\n",
    "plt.title('Tic Tac Toe', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "4f6f1a90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Questions!\n",
    "# How many dimensions are in this plot?\n",
    "# How about now? Is this data discrete or continuous? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "16512e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now you have a tic tac toe game!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842c7de3",
   "metadata": {},
   "source": [
    "Let's return back to our cosine graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "329aed32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# you already ran these but to remind you \n",
    "# xx = np.linspace(-5.,5.,101) #1D array of x-positions\n",
    "# yy = np.linspace(-5.,5.,101) #1D array of y-positions\n",
    "\n",
    "\n",
    "XX,YY = np.meshgrid(xx,yy) #2D arrays of xy coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "b7da8fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101, 101)\n"
     ]
    }
   ],
   "source": [
    "Z = np.cos(XX) + np.sin(YY) \n",
    "# how is this different from our previous 2D plot? \n",
    "print(Z.shape)\n",
    "# what just happened to your dimensions?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b712382f",
   "metadata": {},
   "source": [
    "Although Z is a 2D array, it represents a surface in 3D space when plotted with XX and YY. Therefore, the plot below is a 3D plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "35f1284c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y')"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.imshow(Z, vmin=-2,vmax=2)\n",
    "ax=plt.gca()\n",
    "ax.axis('equal')\n",
    "plt.axis('off')\n",
    "\n",
    "plt.colorbar()\n",
    "plt.title(\"Contour plot \\n Z = cos(XX) + sin(YY)\")\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "fc99b13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Questions!\n",
    "# How many dimensions are in this plot?\n",
    "# How about now? Is this data discrete or continuous? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "b243ccdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 100.5, 100.5, -0.5)"
      ]
     },
     "execution_count": 264,
     "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": [
    "# but how boring is that... when you can switch up your color palettes\n",
    "plt.imshow(Z, cmap = 'jet')\n",
    "ax=plt.gca()\n",
    "ax.axis('equal')\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c97780",
   "metadata": {},
   "source": [
    "Now we will put everything together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "66a2ec1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "757d427dbd994c9ebd9a283177f11019",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(interactive(children=(FloatSlider(value=-6.283185307179586, description='x min', max=0.0, min=-…"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interactive, FloatSlider, Text\n",
    "import ipywidgets as widgets\n",
    "\n",
    "\n",
    "# function to compute Z based on the given trigonometric functions\n",
    "def calculate_Z(x, y, func_x, func_y):\n",
    "    XX, YY = np.meshgrid(x, y)\n",
    "    Z = eval(f'np.{func_x}(XX)') + eval(f'np.{func_y}(YY)')\n",
    "    return XX, YY, Z\n",
    "\n",
    "# interactive function to update the plot\n",
    "def update_plot(x_min, x_max, y_min, y_max, func_x, func_y):\n",
    "    x = np.linspace(x_min, x_max, 100)\n",
    "    y = np.linspace(y_min, y_max, 100)\n",
    "    XX, YY, Z = calculate_Z(x, y, func_x, func_y)\n",
    "    \n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.contourf(XX, YY, Z, levels=50, cmap='viridis')\n",
    "    plt.colorbar()\n",
    "    plt.title(f\"Contour plot of Z = {func_x}(XX) + {func_y}(YY)\")\n",
    "    plt.xlabel('X')\n",
    "    plt.ylabel('Y')\n",
    "    plt.show()\n",
    "\n",
    "# interactive stuff \n",
    "# sliders for adjusting the x and y axis limits\n",
    "x_min_slider = FloatSlider(value=-2*np.pi, min=-10, max=0, step=0.1, description='x min')\n",
    "x_max_slider = FloatSlider(value=2*np.pi, min=0, max=10, step=0.1, description='x max')\n",
    "y_min_slider = FloatSlider(value=-2*np.pi, min=-10, max=0, step=0.1, description='y min')\n",
    "y_max_slider = FloatSlider(value=2*np.pi, min=0, max=10, step=0.1, description='y max')\n",
    "\n",
    "# text boxes for changing the trigonometric functions\n",
    "func_x_text = Text(value='cos', description='function for x')\n",
    "func_y_text = Text(value='sin', description='function for y')\n",
    "\n",
    "# create interactive plot\n",
    "interactive_plot = interactive(update_plot, x_min=x_min_slider, x_max=x_max_slider, \n",
    "                               y_min=y_min_slider, y_max=y_max_slider, \n",
    "                               func_x=func_x_text, func_y=func_y_text)\n",
    "\n",
    "# display the interactive plot\n",
    "widgets.VBox([interactive_plot])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "f68e990c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Questions!\n",
    "# How can visualizing data affect how you interpret data? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a11c1c7",
   "metadata": {},
   "source": [
    "## 3. Complex Functions and Dynamics in NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "bccb0d02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of 1D array: 15\n",
      "Mean of 2D array: 3.5\n"
     ]
    }
   ],
   "source": [
    "print('Sum of 1D array:', array_sum)\n",
    "print('Mean of 2D array:', array_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "5452488f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using random numbers to simulate dice throws\n",
    "number_throws = 10000\n",
    "number_dice = 3\n",
    "type_dice = 6\n",
    "\n",
    "dice_throws = np.random.randint(1, type_dice + 1, (number_throws, number_dice))\n",
    "sum_throws = np.sum(dice_throws, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "e283e51c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Yfny5di/fz8aNmwINzc3KBQKeHp64siRIzhy5Ahefvllje7zvyoqKoJSqXzmUQcvLy8AwI0bN1Tai4uL8ffff0sh74033lA5AvZkyOzUqRM6deqEJUuWYPv27QgODsbXX39d4R8VjRo1gqmpaYWf3SdfHxcXFwgh4OzsjJdeeqnScZS9dpcuXULPnj2l9pKSEmRmZj41FJatr1QqceXKFZWjQhW9X08qO6p0/vz5So/6lvUpO5JHtQ9PmVGd9+Ql6/Xr10fz5s1VDvmXfQfQk5cw9+vXD6Wlpfj4449V2levXg2FQoG+ffsCgDQvYv369Sr91q1bV+U6y/46fvLoh65v6VDV1+B5mJmZPfOy8efVr18/nDhxAklJSVJbYWEhNm3ahGbNmqnMc+rWrRuKioqwZs0adO3aVQpq3bp1w5dffombN28+c/6Quvusqry8vArn7nz22WcAAG9v76eu7+/vD1tbW2zbtg0PHz6U2rdu3YrS0lL06tULAPDiiy+qnI4u+yLHu3fvlvtMlh0Bqey0mb6+PoKCgrBr1y5cv35dak9PT8fevXtV+g4ZMgT6+vpYuHBhuf0IIaSfX29vbzRq1AgbN25EcXGxyjiq8hkq+5yuXbtWpb0qP1+9e/eGubk5li5dqvIaltUIPA6eLi4uWLFiRYVzu578ugiqeXiEiOo8Dw8P+Pv7w8vLC9bW1jh16hS+//57hIaGSn3K/ooOCwtDUFAQ9PX1MWLECAwYMAA9evTAu+++i6tXr6JNmzbYt28f/ve//yE8PFz6q9DLywtDhw7FmjVrcPv2bemy+4sXLwKo2pEQCwsLdO/eHVFRUSgpKUGTJk2wb98+ZGZmauFVqbqqvgbPw8vLCxs2bMDixYvRvHlz2Nraqvz1/1/MmTMHO3bsQN++fREWFgZra2vExMQgMzMTP/zwg8p8Gl9fXxgYGCAjIwOTJk2S2rt3744NGzYAQJUCkTr7rKpDhw4hLCwMr776KlxdXVFcXIwjR45g586d8Pb2xqhRo566vrGxMT788EOMGTMG3bt3x+jRo3H9+nV89NFH6NatG4YMGfLU9WNiYrB+/Xq88sorcHFxwf379/Hpp5/CwsIC/fr1q3S9hQsXIi4uDt26dcPbb7+NR48eYd26dfD09FSZw+fi4oLFixdj7ty5uHr1KgYPHgxzc3NkZmbixx9/xKRJkzBjxgwYGhpi8eLFmDx5Mnr27Inhw4cjMzMTW7ZsqdIcorZt2+L111/H+vXrce/ePXTu3BkJCQlVOoprYWGB1atX480330SHDh2k7w/77bff8ODBA8TExEBPTw+fffYZ+vbtC09PT4wbNw5NmjTBjRs3cPDgQVhYWOCnn3565r5Ih3RzcRtR1ZRd/lrZJb8VXSb+5GX3ixcvFh07dhRWVlbC1NRUuLm5iSVLloji4mKpz6NHj8TUqVNFo0aNhEKhULks+P79+yIiIkI4ODgIQ0ND4erqKj788EOVS2uFEKKwsFCEhIQIa2trUb9+fTF48GCRkZEhAKhcBl92yfytW7fKjefPP/8Ur7zyirCyshKWlpbitddeEzdv3qz00v0ntzFmzBhhZmZWpdepIpX1q+prADUvJc/Ozhb9+/cX5ubmKpdOV/a+Hzx4UABQudzcycmp0q9VuHLlinj11VeFlZWVMDExER07dhSxsbEV9u3QoYMAIJKTk6W2P//8UwAQjo6OVR5TVfdZ1dfq8uXL4o033hAvvviiMDU1FSYmJsLT01PMnz9fFBQUVLmuHTt2iDZt2ghjY2NhZ2cnQkNDpa8aeJqUlBTx+uuvi6ZNmwpjY2Nha2srXn75ZXHq1Kly4/n3Z1QIIQ4fPiy8vLyEkZGRePHFF8XGjRulz+6TfvjhB9G1a1dhZmYmzMzMhJubmwgJCREZGRkq/davXy+cnZ2FsbGx8Pb2FomJicLPz++Zl90LIcQ///wjwsLChI2NjTAzMxMDBgwQWVlZz7zsvszu3btF586dhampqbCwsBAdO3YUO3bsUOlz5swZMWTIEGFjYyOMjY2Fk5OTGDZsmEhISHhmfaRbCiGqaaYlkQylpqaiXbt2+OqrrxAcHKzrcoiIqBKcQ0SkIf/880+5tjVr1kBPT6/Sb4gmIqKagXOIiDQkKioKp0+fRo8ePWBgYIA9e/Zgz549mDRpUpVuK0BERLrDU2ZEGhIfH4+FCxfiwoULKCgoQNOmTTF69Gi8++67MDDg3x5ERDUZAxERERHJHucQERERkewxEBEREZHscWJDFSiVSty8eRPm5uZavdUAERERaY4QAvfv34eDg8MzvxiVgagKbt68yauEiIiIaqmsrCy88MILT+3DQFQFZTe0zMrKgoWFhY6rISIioqrIz8+Ho6Ojyo2pK8NAVAVlp8ksLCwYiIiIiGqZqkx34aRqIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQNdF0BEVJM0m/Oz1rZ9dVl/rW2biP4bHiEiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItnjFzMSUY3GL0okourAI0REREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7Og1EiYmJGDBgABwcHKBQKLBr1y5pWUlJCWbPno1WrVrBzMwMDg4OeOONN3Dz5k2Vbdy5cwfBwcGwsLCAlZUVJkyYgIKCApU+Z8+eRbdu3WBiYgJHR0dERUVVx/CIiIioltBpICosLESbNm0QHR1dbtmDBw+QkpKCyMhIpKSkYOfOncjIyMDAgQNV+gUHByMtLQ3x8fGIjY1FYmIiJk2aJC3Pz89H79694eTkhNOnT+PDDz/EggULsGnTJq2Pj4iIiGoHnX4xY9++fdG3b98Kl1laWiI+Pl6l7eOPP0bHjh1x/fp1NG3aFOnp6YiLi8PJkyfh7e0NAFi3bh369euHFStWwMHBAdu2bUNxcTE2b94MIyMjeHp6IjU1FatWrVIJTkRERCRftWoO0b1796BQKGBlZQUASEpKgpWVlRSGACAwMBB6enpITk6W+nTv3h1GRkZSn6CgIGRkZODu3bsV7qeoqAj5+fkqDyIiIqq7ak0gevjwIWbPno3XX38dFhYWAIDs7GzY2tqq9DMwMIC1tTWys7OlPnZ2dip9yp6X9XnS0qVLYWlpKT0cHR01PRwiIiKqQWpFICopKcGwYcMghMCGDRu0vr+5c+fi3r170iMrK0vr+yQiIiLdqfE3dy0LQ9euXcOBAweko0MAYG9vj9zcXJX+jx49wp07d2Bvby/1ycnJUelT9rysz5OMjY1hbGysyWEQERFRDVajjxCVhaFLly5h//79sLGxUVnu6+uLvLw8nD59Wmo7cOAAlEolfHx8pD6JiYkoKSmR+sTHx6NFixZo0KBB9QyEiIiIajSdBqKCggKkpqYiNTUVAJCZmYnU1FRcv34dJSUlePXVV3Hq1Cls27YNpaWlyM7ORnZ2NoqLiwEA7u7u6NOnDyZOnIgTJ07g119/RWhoKEaMGAEHBwcAwMiRI2FkZIQJEyYgLS0N33zzDT766CNMnz5dV8MmIiKiGkanp8xOnTqFHj16SM/LQsqYMWOwYMEC7N69GwDQtm1blfUOHjwIf39/AMC2bdsQGhqKgIAA6OnpYejQoVi7dq3U19LSEvv27UNISAi8vLzQsGFDzJs3j5fcExERkUSngcjf3x9CiEqXP21ZGWtra2zfvv2pfVq3bo0jR46oXR8RERHJQ42fVE1ENUezOT9rbdtXl/XX2raJiJ6lRk+qJiIiIqoODEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQke7yXGRGRjvEecUS6xyNEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHs6DUSJiYkYMGAAHBwcoFAosGvXLpXlQgjMmzcPjRs3hqmpKQIDA3Hp0iWVPnfu3EFwcDAsLCxgZWWFCRMmoKCgQKXP2bNn0a1bN5iYmMDR0RFRUVHaHhoRERHVIga63HlhYSHatGmD8ePHY8iQIeWWR0VFYe3atYiJiYGzszMiIyMRFBSECxcuwMTEBAAQHByMv/76C/Hx8SgpKcG4ceMwadIkbN++HQCQn5+P3r17IzAwEBs3bsS5c+cwfvx4WFlZYdKkSdU6XiJNazbnZ61s9+qy/lrZLhFRTaXTQNS3b1/07du3wmVCCKxZswbvvfceBg0aBAD44osvYGdnh127dmHEiBFIT09HXFwcTp48CW9vbwDAunXr0K9fP6xYsQIODg7Ytm0biouLsXnzZhgZGcHT0xOpqalYtWoVAxEREREBqMFziDIzM5GdnY3AwECpzdLSEj4+PkhKSgIAJCUlwcrKSgpDABAYGAg9PT0kJydLfbp37w4jIyOpT1BQEDIyMnD37t0K911UVIT8/HyVBxEREdVdNTYQZWdnAwDs7OxU2u3s7KRl2dnZsLW1VVluYGAAa2trlT4VbePf+3jS0qVLYWlpKT0cHR3/+4CIiIioxqqxgUiX5s6di3v37kmPrKwsXZdEREREWlRjA5G9vT0AICcnR6U9JydHWmZvb4/c3FyV5Y8ePcKdO3dU+lS0jX/v40nGxsawsLBQeRAREVHdVWMDkbOzM+zt7ZGQkCC15efnIzk5Gb6+vgAAX19f5OXl4fTp01KfAwcOQKlUwsfHR+qTmJiIkpISqU98fDxatGiBBg0aVNNoiIiIqCbTaSAqKChAamoqUlNTATyeSJ2amorr169DoVAgPDwcixcvxu7du3Hu3Dm88cYbcHBwwODBgwEA7u7u6NOnDyZOnIgTJ07g119/RWhoKEaMGAEHBwcAwMiRI2FkZIQJEyYgLS0N33zzDT766CNMnz5dR6MmIiKimkanl92fOnUKPXr0kJ6XhZQxY8Zg69atmDVrFgoLCzFp0iTk5eWha9euiIuLk76DCAC2bduG0NBQBAQEQE9PD0OHDsXatWul5ZaWlti3bx9CQkLg5eWFhg0bYt68ebzknoiIiCQ6DUT+/v4QQlS6XKFQYNGiRVi0aFGlfaytraUvYaxM69atceTIkeeuk4iIiOq2GjuHiIiIiKi66PQIERERVT9t3fIF4G1fqPbiESIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPY0Eory8PE1shoiIiEgn1A5Ey5cvxzfffCM9HzZsGGxsbNCkSRP89ttvGi2OiIiIqDqoHYg2btwIR0dHAEB8fDzi4+OxZ88e9O3bFzNnztR4gURERETaZqDuCtnZ2VIgio2NxbBhw9C7d280a9YMPj4+Gi+QiIiISNvUPkLUoEEDZGVlAQDi4uIQGBgIABBCoLS0VLPVEREREVUDtY8QDRkyBCNHjoSrqytu376Nvn37AgDOnDmD5s2ba7xAIiIiIm1TOxCtXr0azZo1Q1ZWFqKiolC/fn0AwF9//YW3335b4wUSERERaZvagcjQ0BAzZswo1x4REaGRgoiIiIiqm9qBqGnTpvD394efnx/8/f3h4uKijbqIiIiIqo3ak6o/+OADmJiYYPny5XB1dYWjoyNGjRqFTz/9FJcuXdJGjURERERapfYRolGjRmHUqFEAHs8bOnz4MGJjY/H2229DqVTySjMiIiKqddQORADw4MEDHD16FIcOHcLBgwdx5swZtGzZEv7+/houj4iIiEj71A5EnTt3xpkzZ+Du7g5/f3/MmTMH3bt3R4MGDbRRHxEREZHWqT2H6Pfff4eZmRnc3Nzg5uYGd3d3hiEiIiKq1dQORLdv38aBAwfQqVMn7N27F126dEGTJk0wcuRIfPrpp9qokYiIiEir1A5ECoUCrVu3RlhYGL7//nvs2bMHvXr1wnfffYe33npLGzUSERERaZXac4hSUlJw6NAhHDp0CEePHsX9+/fRqlUrTJ06FX5+ftqokYiIiEir1A5EHTt2RLt27eDn54eJEyeie/fusLS01EZtRERERNVC7UB0584dWFhYaKMWIiIiIp1QOxCVhaHTp08jPT0dAODh4YH27dtrtjIiIiKiaqL2pOrc3Fz06NEDHTp0QFhYGMLCwuDt7Y2AgADcunVLo8WVlpYiMjISzs7OMDU1hYuLC95//30IIaQ+QgjMmzcPjRs3hqmpKQIDA8vdQuTOnTsIDg6GhYUFrKysMGHCBBQUFGi0ViIiIqq91A5EU6dORUFBAdLS0nDnzh3cuXMH58+fR35+PsLCwjRa3PLly7FhwwZ8/PHHSE9Px/LlyxEVFYV169ZJfaKiorB27Vps3LgRycnJMDMzQ1BQEB4+fCj1CQ4ORlpaGuLj4xEbG4vExERMmjRJo7USERFR7aX2KbO4uDjs378f7u7uUpuHhweio6PRu3dvjRZ37NgxDBo0CP379wcANGvWDDt27MCJEycAPD46tGbNGrz33nsYNGgQAOCLL76AnZ0ddu3ahREjRiA9PR1xcXE4efIkvL29AQDr1q1Dv379sGLFCjg4OGi0ZiIiIqp91D5CpFQqYWhoWK7d0NAQSqVSI0WV6dy5MxISEnDx4kUAwG+//YajR4+ib9++AIDMzExkZ2cjMDBQWsfS0hI+Pj5ISkoCACQlJcHKykoKQwAQGBgIPT09JCcna7ReIiIiqp3UPkLUs2dPTJs2DTt27JCOrty4cQMREREICAjQaHFz5sxBfn4+3NzcoK+vj9LSUixZsgTBwcEAgOzsbACAnZ2dynp2dnbSsuzsbNja2qosNzAwgLW1tdTnSUVFRSgqKpKe5+fna2xMREREVPOofYTo448/Rn5+Ppo1awYXFxe4uLjA2dkZ+fn5KnN7NOHbb7/Ftm3bsH37dqSkpCAmJgYrVqxATEyMRvfzpKVLl8LS0lJ6ODo6anV/REREpFtqHyFydHRESkoK9u/fj99//x0A4O7urnLaSlNmzpyJOXPmYMSIEQCAVq1a4dq1a1i6dCnGjBkDe3t7AEBOTg4aN24srZeTk4O2bdsCAOzt7ZGbm6uy3UePHuHOnTvS+k+aO3cupk+fLj3Pz89nKCIiIqrD1ApEJSUlMDU1RWpqKnr16oVevXppqy4AwIMHD6Cnp3oQS19fX5qr5OzsDHt7eyQkJEgBKD8/H8nJyZgyZQoAwNfXF3l5eTh9+jS8vLwAAAcOHIBSqYSPj0+F+zU2NoaxsbGWRkVEREQ1jVqByNDQEE2bNkVpaam26lExYMAALFmyBE2bNoWnpyfOnDmDVatWYfz48QAe32g2PDwcixcvhqurK5ydnREZGQkHBwcMHjwYwOOjV3369MHEiROxceNGlJSUIDQ0FCNGjOAVZkRERATgOU6Zvfvuu/i///s/fPnll7C2ttZGTZJ169YhMjISb7/9NnJzc+Hg4IDJkydj3rx5Up9Zs2ahsLAQkyZNQl5eHrp27Yq4uDiYmJhIfbZt24bQ0FAEBARAT08PQ4cOxdq1a7VaOxEREdUeageijz/+GJcvX4aDgwOcnJxgZmamsjwlJUVjxZmbm2PNmjVYs2ZNpX0UCgUWLVqERYsWVdrH2toa27dv11hdREREVLeoHYjKTkURERER1RVqB6L58+drow4iIiIinVE7EJUpLi5Gbm5uuW+nbtq06X8uioiIiKg6qR2ILl68iAkTJuDYsWMq7UIIKBSKarsCjYiIiEhT1A5E48aNg4GBAWJjY9G4cWMoFApt1EVERERUbdQORKmpqTh9+jTc3Ny0UQ8RERFRtVP7XmYeHh74+++/tVELERERkU5UKRDl5+dLj+XLl2PWrFk4dOgQbt++rbKMd4UnIiKi2qhKp8ysrKxU5goJIRAQEKDSh5OqiYiIqLaqUiA6ePCgtusgIiIi0pkqBSI/Pz/p/69fvw5HR8dyV5cJIZCVlaXZ6oiIiIiqgdqTqp2dnXHr1q1y7Xfu3IGzs7NGiiIiIiKqTmoHorK5Qk8qKChQucM8ERERUW1R5e8hmj59OoDHd5ePjIxEvXr1pGWlpaVITk5G27ZtNV4gERERkbZVORCdOXMGwOMjROfOnYORkZG0zMjICG3atMGMGTM0XyERERGRllU5EJVdaTZu3Dh89NFHsLCw0FpRRERERNVJ7Vt3bNmyRRt1ENV6zeb8rLVtX13WX2vbJiKi55hUTURERFTXMBARERGR7DEQERERkexVKRC1b98ed+/eBQAsWrQIDx480GpRRERERNWpSoEoPT0dhYWFAICFCxeioKBAq0URERERVacqXWXWtm1bjBs3Dl27doUQAitWrED9+vUr7Dtv3jyNFkhERESkbVUKRFu3bsX8+fMRGxsLhUKBPXv2wMCg/KoKhYKBiIiIyuHXUlBNV6VA1KJFC3z99dcAAD09PSQkJMDW1larhRERERFVF7W/mFGpVGqjDiIiIiKdUTsQAcCVK1ewZs0apKenAwA8PDwwbdo0uLi4aLQ4IiIiouqg9vcQ7d27Fx4eHjhx4gRat26N1q1bIzk5GZ6enoiPj9dGjURERERapfYRojlz5iAiIgLLli0r1z579mz06tVLY8URERERVQe1jxClp6djwoQJ5drHjx+PCxcuaKQoIiIiouqkdiBq1KgRUlNTy7WnpqbyyjMiIiKqldQ+ZTZx4kRMmjQJf/zxBzp37gwA+PXXX7F8+XJMnz5d4wUSERERaZvagSgyMhLm5uZYuXIl5s6dCwBwcHDAggULEBYWpvECiYiIiLRN7UCkUCgQERGBiIgI3L9/HwBgbm6u8cKIiIiIqstzfQ9RGQYhIiIiqgvUnlRNREREVNcwEBEREZHsMRARERGR7KkViEpKShAQEIBLly5pqx4iIiKiaqdWIDI0NMTZs2e1VQsRERGRTqh9ymzUqFH4/PPPtVELERERkU6ofdn9o0ePsHnzZuzfvx9eXl4wMzNTWb5q1SqNFUdERERUHdQOROfPn0f79u0BABcvXlRZplAoNFMVERERUTVSOxAdPHhQG3UQERER6cxzX3Z/+fJl7N27F//88w8AQAihsaKIiIiIqpPagej27dsICAjASy+9hH79+uGvv/4CAEyYMAHvvPOOxgu8ceMGRo0aBRsbG5iamqJVq1Y4deqUtFwIgXnz5qFx48YwNTVFYGBgua8FuHPnDoKDg2FhYQErKytMmDABBQUFGq+ViIiIaie1A1FERAQMDQ1x/fp11KtXT2ofPnw44uLiNFrc3bt30aVLFxgaGmLPnj24cOECVq5ciQYNGkh9oqKisHbtWmzcuBHJyckwMzNDUFAQHj58KPUJDg5GWloa4uPjERsbi8TEREyaNEmjtRIREVHtpfYcon379mHv3r144YUXVNpdXV1x7do1jRUGAMuXL4ejoyO2bNkitTk7O0v/L4TAmjVr8N5772HQoEEAgC+++AJ2dnbYtWsXRowYgfT0dMTFxeHkyZPw9vYGAKxbtw79+vXDihUr4ODgoNGaiYiIqPZR+whRYWGhypGhMnfu3IGxsbFGiiqze/dueHt747XXXoOtrS3atWuHTz/9VFqemZmJ7OxsBAYGSm2Wlpbw8fFBUlISACApKQlWVlZSGAKAwMBA6OnpITk5ucL9FhUVIT8/X+VBREREdZfagahbt2744osvpOcKhQJKpRJRUVHo0aOHRov7448/sGHDBri6umLv3r2YMmUKwsLCEBMTAwDIzs4GANjZ2amsZ2dnJy3Lzs6Gra2tynIDAwNYW1tLfZ60dOlSWFpaSg9HR0eNjouIiIhqFrVPmUVFRSEgIACnTp1CcXExZs2ahbS0NNy5cwe//vqrRotTKpXw9vbGBx98AABo164dzp8/j40bN2LMmDEa3de/zZ07F9OnT5ee5+fnMxQRERHVYWofIWrZsiUuXryIrl27YtCgQSgsLMSQIUNw5swZuLi4aLS4xo0bw8PDQ6XN3d0d169fBwDY29sDAHJyclT65OTkSMvs7e2Rm5ursvzRo0e4c+eO1OdJxsbGsLCwUHkQERFR3aX2ESLg8Tydd999V9O1lNOlSxdkZGSotF28eBFOTk4AHk+wtre3R0JCAtq2bQvg8dGc5ORkTJkyBQDg6+uLvLw8nD59Gl5eXgCAAwcOQKlUwsfHR+tjICIioprvuQLR3bt38fnnnyM9PR0A4OHhgXHjxsHa2lqjxUVERKBz58744IMPMGzYMJw4cQKbNm3Cpk2bADyevxQeHo7FixfD1dUVzs7OiIyMhIODAwYPHgzg8RGlPn36YOLEidi4cSNKSkoQGhqKESNG8AozIiIiAvAcp8wSExPRrFkzrF27Fnfv3sXdu3exdu1aODs7IzExUaPFdejQAT/++CN27NiBli1b4v3338eaNWsQHBws9Zk1axamTp2KSZMmoUOHDigoKEBcXBxMTEykPtu2bYObmxsCAgLQr18/dO3aVQpVRERERGofIQoJCcHw4cOxYcMG6OvrAwBKS0vx9ttvIyQkBOfOndNogS+//DJefvnlSpcrFAosWrQIixYtqrSPtbU1tm/frtG6iIiIqO5Q+wjR5cuX8c4770hhCAD09fUxffp0XL58WaPFEREREVUHtQNR+/btpblD/5aeno42bdpopCgiIiKi6lSlU2Znz56V/j8sLAzTpk3D5cuX0alTJwDA8ePHER0djWXLlmmnSiIiIiItqlIgatu2LRQKBYQQUtusWbPK9Rs5ciSGDx+uueqIiIiIqkGVAlFmZqa26yAiIiLSmSoForIvQiQiIiKqi57rixlv3ryJo0ePIjc3F0qlUmVZWFiYRgojIiIiqi5qB6KtW7di8uTJMDIygo2NDRQKhbRMoVAwEBEREVGto3YgioyMxLx58zB37lzo6al91T4RERFRjaN2onnw4AFGjBjBMERERER1htqpZsKECfjuu++0UQsRERGRTqh9ymzp0qV4+eWXERcXh1atWsHQ0FBl+apVqzRWHBEREVF1eK5AtHfvXrRo0QIAyk2qJiIiIqpt1A5EK1euxObNmzF27FgtlENERERU/dSeQ2RsbIwuXbpooxYiIiIinVA7EE2bNg3r1q3TRi1EREREOqH2KbMTJ07gwIEDiI2NhaenZ7lJ1Tt37tRYcURERETVQe1AZGVlhSFDhmijFiIiIiKdUDsQbdmyRRt1EBEREekMv26aiIiIZE/tI0TOzs5P/b6hP/744z8VRERERFTd1A5E4eHhKs9LSkpw5swZxMXFYebMmZqqi4iIiKjaqB2Ipk2bVmF7dHQ0Tp069Z8LIiIiIqpuGptD1LdvX/zwww+a2hwRERFRtdFYIPr+++9hbW2tqc0RERERVRu1T5m1a9dOZVK1EALZ2dm4desW1q9fr9HiiIiIiKqD2oFo8ODBKs/19PTQqFEj+Pv7w83NTVN1EREREVUbtQPR/PnztVEHERERkc7wixmJiIhI9qp8hEhPT++pX8gIAAqFAo8ePfrPRRERERFVpyoHoh9//LHSZUlJSVi7di2USqVGiiIiIiKqTlUORIMGDSrXlpGRgTlz5uCnn35CcHAwFi1apNHiiIiIiKrDc80hunnzJiZOnIhWrVrh0aNHSE1NRUxMDJycnDRdHxEREZHWqRWI7t27h9mzZ6N58+ZIS0tDQkICfvrpJ7Rs2VJb9RERERFpXZVPmUVFRWH58uWwt7fHjh07KjyFRkRERFQbVTkQzZkzB6ampmjevDliYmIQExNTYb+dO3dqrDgiIqLn0WzOz1rb9tVl/bW2bdKdKgeiN95445mX3RMRERHVRlUORFu3btViGUSap62/EPnXIRFR3cNvqiYiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItmrVYFo2bJlUCgUCA8Pl9oePnyIkJAQ2NjYoH79+hg6dChycnJU1rt+/Tr69++PevXqwdbWFjNnzsSjR4+quXoiIiKqqWpNIDp58iQ++eQTtG7dWqU9IiICP/30E7777jscPnwYN2/exJAhQ6TlpaWl6N+/P4qLi3Hs2DHExMRg69atmDdvXnUPgYiIiGqoWhGICgoKEBwcjE8//RQNGjSQ2u/du4fPP/8cq1atQs+ePeHl5YUtW7bg2LFjOH78OABg3759uHDhAr766iu0bdsWffv2xfvvv4/o6GgUFxfrakhERERUg9SKQBQSEoL+/fsjMDBQpf306dMoKSlRaXdzc0PTpk2RlJQEAEhKSkKrVq1gZ2cn9QkKCkJ+fj7S0tIq3F9RURHy8/NVHkRERFR3VfnWHbry9ddfIyUlBSdPniy3LDs7G0ZGRrCyslJpt7OzQ3Z2ttTn32GobHnZsoosXboUCxcu1ED1REREVBvU6CNEWVlZmDZtGrZt2wYTE5Nq2+/cuXNx79496ZGVlVVt+yYiIqLqV6MD0enTp5Gbm4v27dvDwMAABgYGOHz4MNauXQsDAwPY2dmhuLgYeXl5Kuvl5OTA3t4eAGBvb1/uqrOy52V9nmRsbAwLCwuVBxEREdVdNToQBQQE4Ny5c0hNTZUe3t7eCA4Olv7f0NAQCQkJ0joZGRm4fv06fH19AQC+vr44d+4ccnNzpT7x8fGwsLCAh4dHtY+JiIiIap4aPYfI3NwcLVu2VGkzMzODjY2N1D5hwgRMnz4d1tbWsLCwwNSpU+Hr64tOnToBAHr37g0PDw+MHj0aUVFRyM7OxnvvvYeQkBAYGxtX+5iIiIio5qnRgagqVq9eDT09PQwdOhRFRUUICgrC+vXrpeX6+vqIjY3FlClT4OvrCzMzM4wZMwaLFi3SYdVERERUk9S6QHTo0CGV5yYmJoiOjkZ0dHSl6zg5OeGXX37RcmVERERUW9XoOURERERE1YGBiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkz0DXBZA8NJvzs9a2fXVZf61tm4iI5IFHiIiIiEj2GIiIiIhI9hiIiIiISPY4h4iIiEgDOFeyduMRIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikr0aHYiWLl2KDh06wNzcHLa2thg8eDAyMjJU+jx8+BAhISGwsbFB/fr1MXToUOTk5Kj0uX79Ovr374969erB1tYWM2fOxKNHj6pzKERERFSD1ehAdPjwYYSEhOD48eOIj49HSUkJevfujcLCQqlPREQEfvrpJ3z33Xc4fPgwbt68iSFDhkjLS0tL0b9/fxQXF+PYsWOIiYnB1q1bMW/ePF0MiYiIiGogA10X8DRxcXEqz7du3QpbW1ucPn0a3bt3x7179/D5559j+/bt6NmzJwBgy5YtcHd3x/Hjx9GpUyfs27cPFy5cwP79+2FnZ4e2bdvi/fffx+zZs7FgwQIYGRnpYmhERERUg9ToI0RPunfvHgDA2toaAHD69GmUlJQgMDBQ6uPm5oamTZsiKSkJAJCUlIRWrVrBzs5O6hMUFIT8/HykpaVVuJ+ioiLk5+erPIiIiKjuqjWBSKlUIjw8HF26dEHLli0BANnZ2TAyMoKVlZVKXzs7O2RnZ0t9/h2GypaXLavI0qVLYWlpKT0cHR01PBoiIiKqSWpNIAoJCcH58+fx9ddfa31fc+fOxb1796RHVlaW1vdJREREulOj5xCVCQ0NRWxsLBITE/HCCy9I7fb29iguLkZeXp7KUaKcnBzY29tLfU6cOKGyvbKr0Mr6PMnY2BjGxsYaHgURERHVVDX6CJEQAqGhofjxxx9x4MABODs7qyz38vKCoaEhEhISpLaMjAxcv34dvr6+AABfX1+cO3cOubm5Up/4+HhYWFjAw8OjegZCRERENVqNPkIUEhKC7du343//+x/Mzc2lOT+WlpYwNTWFpaUlJkyYgOnTp8Pa2hoWFhaYOnUqfH190alTJwBA79694eHhgdGjRyMqKgrZ2dl47733EBISwqNAREREBKCGB6INGzYAAPz9/VXat2zZgrFjxwIAVq9eDT09PQwdOhRFRUUICgrC+vXrpb76+vqIjY3FlClT4OvrCzMzM4wZMwaLFi2qrmEQERFRDVejA5EQ4pl9TExMEB0djejo6Er7ODk54ZdfftFkaURERFSH1Og5RERERETVgYGIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkr0bfuoO0o9mcn7W27avL+mtt20RERNrCQERERFQL8Y9bzeIpMyIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0DXRdAQLM5P2tt21eX9dfatomIiOoKHiEiIiIi2eMRIiIiIqqSunxGg0eIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2ZBWIoqOj0axZM5iYmMDHxwcnTpzQdUlERERUA8gmEH3zzTeYPn065s+fj5SUFLRp0wZBQUHIzc3VdWlERESkY7IJRKtWrcLEiRMxbtw4eHh4YOPGjahXrx42b96s69KIiIhIx2QRiIqLi3H69GkEBgZKbXp6eggMDERSUpIOKyMiIqKaQBbfVP3333+jtLQUdnZ2Ku12dnb4/fffy/UvKipCUVGR9PzevXsAgPz8fK3Upyx6oJXtAhXXXN37q2v75Bi1o6bsk2PU/P50sU+OUfP709U+NbFNIcSzOwsZuHHjhgAgjh07ptI+c+ZM0bFjx3L958+fLwDwwQcffPDBBx914JGVlfXMrCCLI0QNGzaEvr4+cnJyVNpzcnJgb29frv/cuXMxffp06blSqcSdO3dgY2MDhUIhtefn58PR0RFZWVmwsLDQ3gB0iGOsGzjGuoFjrBs4xuojhMD9+/fh4ODwzL6yCERGRkbw8vJCQkICBg8eDOBxyElISEBoaGi5/sbGxjA2NlZps7KyqnT7FhYWdfZDXYZjrBs4xrqBY6wbOMbqYWlpWaV+sghEADB9+nSMGTMG3t7e6NixI9asWYPCwkKMGzdO16URERGRjskmEA0fPhy3bt3CvHnzkJ2djbZt2yIuLq7cRGsiIiKSH9kEIgAIDQ2t8BTZ8zI2Nsb8+fPLnV6rSzjGuoFjrBs4xrqBY6yZFEJU5Vo0IiIiorpLFl/MSERERPQ0DEREREQkewxEREREJHsMRERERCR7DERqWrp0KTp06ABzc3PY2tpi8ODByMjI0HVZWrVs2TIoFAqEh4fruhSNu3HjBkaNGgUbGxuYmpqiVatWOHXqlK7L0pjS0lJERkbC2dkZpqamcHFxwfvvv1+1+/rUUImJiRgwYAAcHBygUCiwa9culeVCCMybNw+NGzeGqakpAgMDcenSJd0U+5yeNsaSkhLMnj0brVq1gpmZGRwcHPDGG2/g5s2buiv4OTzrffy3t956CwqFAmvWrKm2+jShKmNMT0/HwIEDYWlpCTMzM3To0AHXr1+v/mKf07PGWFBQgNDQULzwwgswNTWFh4cHNm7cqJtin4GBSE2HDx9GSEgIjh8/jvj4eJSUlKB3794oLCzUdWlacfLkSXzyySdo3bq1rkvRuLt376JLly4wNDTEnj17cOHCBaxcuRINGjTQdWkas3z5cmzYsAEff/wx0tPTsXz5ckRFRWHdunW6Lu25FRYWok2bNoiOjq5weVRUFNauXYuNGzciOTkZZmZmCAoKwsOHD6u50uf3tDE+ePAAKSkpiIyMREpKCnbu3ImMjAwMHDhQB5U+v2e9j2V+/PFHHD9+vEq3XqhpnjXGK1euoGvXrnBzc8OhQ4dw9uxZREZGwsTEpJorfX7PGuP06dMRFxeHr776Cunp6QgPD0doaCh2795dzZVWgSZunipnubm5AoA4fPiwrkvRuPv37wtXV1cRHx8v/Pz8xLRp03RdkkbNnj1bdO3aVddlaFX//v3F+PHjVdqGDBkigoODdVSRZgEQP/74o/RcqVQKe3t78eGHH0pteXl5wtjYWOzYsUMHFf53T46xIidOnBAAxLVr16qnKA2rbIx//vmnaNKkiTh//rxwcnISq1evrvbaNKWiMQ4fPlyMGjVKNwVpQUVj9PT0FIsWLVJpa9++vXj33XersbKq4RGi/+jevXsAAGtrax1XonkhISHo378/AgMDdV2KVuzevRve3t547bXXYGtri3bt2uHTTz/VdVka1blzZyQkJODixYsAgN9++w1Hjx5F3759dVyZdmRmZiI7O1vlM2tpaQkfHx8kJSXpsDLtunfvHhQKxVPvuVjbKJVKjB49GjNnzoSnp6euy9E4pVKJn3/+GS+99BKCgoJga2sLHx+fp546rI06d+6M3bt348aNGxBC4ODBg7h48SJ69+6t69LKYSD6D5RKJcLDw9GlSxe0bNlS1+Vo1Ndff42UlBQsXbpU16VozR9//IENGzbA1dUVe/fuxZQpUxAWFoaYmBhdl6Yxc+bMwYgRI+Dm5gZDQ0O0a9cO4eHhCA4O1nVpWpGdnQ0A5W7JY2dnJy2rax4+fIjZs2fj9ddf1/lNNDVp+fLlMDAwQFhYmK5L0Yrc3FwUFBRg2bJl6NOnD/bt24dXXnkFQ4YMweHDh3VdnsasW7cOHh4eeOGFF2BkZIQ+ffogOjoa3bt313Vp5cjq1h2aFhISgvPnz+Po0aO6LkWjsrKyMG3aNMTHx9eqc9nqUiqV8Pb2xgcffAAAaNeuHc6fP4+NGzdizJgxOq5OM7799lts27YN27dvh6enJ1JTUxEeHg4HB4c6M0Y5KykpwbBhwyCEwIYNG3RdjsacPn0aH330EVJSUqBQKHRdjlYolUoAwKBBgxAREQEAaNu2LY4dO4aNGzfCz89Pl+VpzLp163D8+HHs3r0bTk5OSExMREhICBwcHGrc2QceIXpOoaGhiI2NxcGDB/HCCy/ouhyNOn36NHJzc9G+fXsYGBjAwMAAhw8fxtq1a2FgYIDS0lJdl6gRjRs3hoeHh0qbu7t7rbrC41lmzpwpHSVq1aoVRo8ejYiIiDp75M/e3h4AkJOTo9Kek5MjLasrysLQtWvXEB8fX6eODh05cgS5ublo2rSp9Dvo2rVreOedd9CsWTNdl6cRDRs2hIGBQZ3+HfTPP//g//7v/7Bq1SoMGDAArVu3RmhoKIYPH44VK1bourxyeIRITUIITJ06FT/++CMOHToEZ2dnXZekcQEBATh37pxK27hx4+Dm5obZs2dDX19fR5VpVpcuXcp9ZcLFixfh5OSko4o078GDB9DTU/27R19fX/rrtK5xdnaGvb09EhIS0LZtWwBAfn4+kpOTMWXKFN0Wp0FlYejSpUs4ePAgbGxsdF2SRo0ePbrc0YOgoCCMHj0a48aN01FVmmVkZIQOHTrU6d9BJSUlKCkpqTW/gxiI1BQSEoLt27fjf//7H8zNzaV5CZaWljA1NdVxdZphbm5ebk6UmZkZbGxs6tRcqYiICHTu3BkffPABhg0bhhMnTmDTpk3YtGmTrkvTmAEDBmDJkiVo2rQpPD09cebMGaxatQrjx4/XdWnPraCgAJcvX5aeZ2ZmIjU1FdbW1mjatCnCw8OxePFiuLq6wtnZGZGRkXBwcMDgwYN1V7SanjbGxo0b49VXX0VKSgpiY2NRWloq/R6ytraGkZGRrspWy7PexydDnqGhIezt7dGiRYvqLvW5PWuMM2fOxPDhw9G9e3f06NEDcXFx+Omnn3Do0CHdFa2mZ43Rz88PM2fOhKmpKZycnHD48GF88cUXWLVqlQ6rroSOr3KrdQBU+NiyZYuuS9OqunjZvRBC/PTTT6Jly5bC2NhYuLm5iU2bNum6JI3Kz88X06ZNE02bNhUmJibixRdfFO+++64oKirSdWnP7eDBgxX+DI4ZM0YI8fjS+8jISGFnZyeMjY1FQECAyMjI0G3RanraGDMzMyv9PXTw4EFdl15lz3ofn1QbL7uvyhg///xz0bx5c2FiYiLatGkjdu3apbuCn8OzxvjXX3+JsWPHCgcHB2FiYiJatGghVq5cKZRKpW4Lr4BCiFr8lbVEREREGsBJ1URERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREVKs8ePAAQ4cOhYWFBRQKBfLy8qq03oIFC6TbeQDA2LFjdfLt1f7+/ggPD6/2/RLR0zEQEcncrVu3MGXKFDRt2hTGxsawt7dHUFAQfv31V12XVqGYmBgcOXIEx44dw19//QVLS8vn2s5HH32ErVu3ara4fzl06JBagY2IdIv3MiOSuaFDh6K4uBgxMTF48cUXkZOTg4SEBNy+fVvXpVXoypUrcHd3/8/31XveIKULJSUlMDQ01HUZRHUajxARyVheXh6OHDmC5cuXo0ePHnByckLHjh0xd+5cDBw4EABw9epVKBQKpKamqqynUCikm1CWHQ3Zu3cv2rVrB1NTU/Ts2RO5ubnYs2cP3N3dYWFhgZEjR+LBgwdPremHH36Ap6cnjI2N0axZM6xcuVJa5u/vj5UrVyIxMREKhQL+/v6VbmfZsmWws7ODubk5JkyYgIcPH6osf/KUmVKpRFRUFJo3bw5jY2M0bdoUS5YskZZnZWVh2LBhsLKygrW1NQYNGoSrV69WuO+rV6+iR48eAIAGDRpAoVBg7NixKvuaNWsWrK2tYW9vjwULFqisr1AosGHDBgwcOBBmZmZSHRs2bICLiwuMjIzQokULfPnll9I6M2bMwMsvvyw9X7NmDRQKBeLi4qS25s2b47PPPgPw+D3r2LEjzMzMYGVlhS5duuDatWuVvp5EdZ6ub6ZGRLpTUlIi6tevL8LDw8XDhw8r7FN2M9EzZ85IbXfv3lW5mWjZDR47deokjh49KlJSUkTz5s2Fn5+f6N27t0hJSRGJiYnCxsZGLFu2rNJ6Tp06JfT09MSiRYtERkaG2LJlizA1NZVunnz79m0xceJE4evrK/766y9x+/btCrfzzTffCGNjY/HZZ5+J33//Xbz77rvC3NxctGnTRuozZswYMWjQIOn5rFmzRIMGDcTWrVvF5cuXxZEjR8Snn34qhBCiuLhYuLu7i/Hjx4uzZ8+KCxcuiJEjR4oWLVpUeKPcR48eiR9++EEAEBkZGeKvv/4SeXl5QojHN0q2sLAQCxYsEBcvXhQxMTFCoVCIffv2SesDELa2tmLz5s3iypUr4tq1a2Lnzp3C0NBQREdHi4yMDLFy5Uqhr68vDhw4IIQQYvfu3cLS0lI8evRICCHE4MGDRcOGDcXs2bOFEEL8+eefAoC4dOmSKCkpEZaWlmLGjBni8uXL4sKFC2Lr1q3i2rVrlb43RHUdAxGRzH3//feiQYMGwsTERHTu3FnMnTtX/Pbbb9JydQLR/v37pT5Lly4VAMSVK1ektsmTJ4ugoKBKaxk5cqTo1auXStvMmTOFh4eH9HzatGnCz8/vqWPy9fUVb7/9tkqbj49PpYEoPz9fGBsbSwHoSV9++aVo0aKFyh26i4qKhKmpqdi7d2+F65S9Jnfv3lVp9/PzE127dlVp69ChgxRchHgciMLDw1X6dO7cWUycOFGl7bXXXhP9+vUTQjx+T/T09MTJkyeFUqkU1tbWYunSpcLHx0cIIcRXX30lmjRpIoR4HCwBiEOHDlVYO5Ec8ZQZkcwNHToUN2/exO7du9GnTx8cOnQI7du3f64Jx61bt5b+387ODvXq1cOLL76o0pabm1vp+unp6ejSpYtKW5cuXXDp0iWUlpZWuY709HT4+PiotPn6+j61f1FREQICAipc/ttvv+Hy5cswNzdH/fr1Ub9+fVhbW+Phw4e4cuVKlesq8+/XCQAaN25c7nXx9vYuV2NFr016ejoAwMrKCm3atMGhQ4dw7tw5GBkZYdKkSThz5gwKCgpw+PBh+Pn5AQCsra0xduxYBAUFYcCAAfjoo4/w119/qT0OorqEgYiIYGJigl69eiEyMhLHjh3D2LFjMX/+fACAnt7jXxNCCKl/SUlJhdv598RfhUJRbiKwQqGAUqnUdPn/mamp6VOXFxQUwMvLC6mpqSqPixcvYuTIkWrvryqvi5mZmdrb9ff3x6FDh6TwY21tDXd3dxw9elQlEAHAli1bkJSUhM6dO+Obb77BSy+9hOPHj6u9T6K6goGIiMrx8PBAYWEhAKBRo0YAoHIE4d8TrDXJ3d293OX+v/76K1566SXo6+urtZ3k5GSVtqf9Y+/q6gpTU1MkJCRUuLx9+/a4dOkSbG1t0bx5c5VHZVerGRkZAYBaR7aeprLXxsPDQ3ru5+eHo0ePIiEhQZpw7u/vjx07duDixYvlJqG3a9cOc+fOxbFjx9CyZUts375dI7US1UYMREQydvv2bfTs2RNfffUVzp49i8zMTHz33XeIiorCoEGDADw+etKpUycsW7YM6enpOHz4MN577z2t1PPOO+8gISEB77//Pi5evIiYmBh8/PHHmDFjhlrbmTZtGjZv3owtW7bg4sWLmD9/PtLS0irtb2JigtmzZ2PWrFn44osvcOXKFRw/fhyff/45ACA4OBgNGzbEoEGDcOTIEWRmZuLQoUMICwvDn3/+WeE2nZycoFAoEBsbi1u3bqGgoECtMTxp5syZ2Lp1KzZs2IBLly5h1apV2Llzp8pr0717d9y/fx+xsbEqgWjbtm1o3LgxXnrpJQBAZmYm5s6di6SkJFy7dg379u3DpUuX4O7u/p9qJKrN+D1ERDJWv359+Pj4YPXq1bhy5QpKSkrg6OiIiRMn4v/+7/+kfps3b8aECRPg5eWFFi1aICoqCr1799Z4Pe3bt8e3336LefPm4f3330fjxo2xaNEilUvWq2L48OG4cuUKZs2ahYcPH2Lo0KGYMmUK9u7dW+k6kZGRMDAwwLx583Dz5k00btwYb731FgCgXr16SExMxOzZszFkyBDcv38fTZo0QUBAACwsLCrcXpMmTbBw4ULMmTMH48aNwxtvvPGfvghy8ODB+Oijj7BixQpMmzYNzs7O2LJli8pRnwYNGqBVq1bIycmBm5sbgMchSalUqpwuq1evHn7//XfExMTg9u3baNy4MUJCQjB58uTnro+otlOIf08MICIiIpIhnjIjIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZ+39X5Z5lqc5fLQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(sum_throws, bins=range(number_dice, number_dice*type_dice + 2), rwidth=0.8, align='left')\n",
    "plt.xlabel('Sum of dice throws')\n",
    "plt.ylabel('Number of throws')\n",
    "plt.title(f'Histogram for throw of {number_dice} {type_dice}-sided dice')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "b1b3a47f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70243f489ddf4619a8bf0e2b190318f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3, description='Dice', max=20, min=1), IntSlider(value=10000, descriptio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.simulate_dice_throws(number_dice, number_throws)>"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interact, IntSlider\n",
    "\n",
    "def simulate_dice_throws(number_dice, number_throws):\n",
    "    type_dice = 6\n",
    "    dice_throws = np.random.randint(1, type_dice + 1, (number_throws, number_dice))\n",
    "    sum_throws = np.sum(dice_throws, axis=1)\n",
    "\n",
    "    plt.hist(sum_throws, bins=range(number_dice, number_dice * type_dice + 2), rwidth=0.8, align='left')\n",
    "    plt.xlabel('Sum of dice throws')\n",
    "    plt.ylabel('Number of throws')\n",
    "    plt.title(f'Histogram for throw of {number_dice} {type_dice}-sided dice')\n",
    "    plt.show()\n",
    "\n",
    "interact(simulate_dice_throws,\n",
    "         number_dice=IntSlider(min=1, max=20, step=1, value=3, description='Dice'),\n",
    "         number_throws=IntSlider(min=1, max=10000, step=1, value=10000, description='Throws'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "46e81482",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Questions!\n",
    "# What can a graph like this tell you about *sampling*?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c63ccb5",
   "metadata": {},
   "source": [
    "## 4. Introduction to Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "5a898683",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame:\n",
      "      Name  Age  Score\n",
      "0    Alice   24     88\n",
      "1      Bob   27     92\n",
      "2  Charlie   22     95\n",
      "3    David   32     89\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Creating a DataFrame\n",
    "data = {\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David'],\n",
    "    'Age': [24, 27, 22, 32],\n",
    "    'Score': [88, 92, 95, 89]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print('DataFrame:')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "ce7cea3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Age: 26.25\n",
      "Max Score: 95\n"
     ]
    }
   ],
   "source": [
    "# Basic operations on DataFrame\n",
    "mean_age = df['Age'].mean()\n",
    "max_score = df['Score'].max()\n",
    "\n",
    "print('Mean Age:', mean_age)\n",
    "print('Max Score:', max_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "21d73bfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated DataFrame:\n",
      "      Name  Age  Score   Pass\n",
      "0    Alice   24     88  False\n",
      "1      Bob   27     92   True\n",
      "2  Charlie   22     95   True\n",
      "3    David   32     89  False\n"
     ]
    }
   ],
   "source": [
    "# Adding a new column\n",
    "passing_criteria = 90\n",
    "df['Pass'] = df['Score'] > passing_criteria\n",
    "print('Updated DataFrame:')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "8cec9276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 100.0)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.bar(df['Name'], df['Score'], color='slategray')\n",
    "plt.axhline(passing_criteria, color='red')\n",
    "plt.title('Graphing Test Scores \\n Using Pandas')\n",
    "plt.ylim(0,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8143355",
   "metadata": {},
   "source": [
    "### Penguins!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "52c18466",
   "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>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.921930</td>\n",
       "      <td>17.151170</td>\n",
       "      <td>200.915205</td>\n",
       "      <td>4201.754386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.459584</td>\n",
       "      <td>1.974793</td>\n",
       "      <td>14.061714</td>\n",
       "      <td>801.954536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.100000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.225000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>3550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.450000</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>4050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.500000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>4750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.600000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>6300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
       "count      342.000000     342.000000         342.000000   342.000000\n",
       "mean        43.921930      17.151170         200.915205  4201.754386\n",
       "std          5.459584       1.974793          14.061714   801.954536\n",
       "min         32.100000      13.100000         172.000000  2700.000000\n",
       "25%         39.225000      15.600000         190.000000  3550.000000\n",
       "50%         44.450000      17.300000         197.000000  4050.000000\n",
       "75%         48.500000      18.700000         213.000000  4750.000000\n",
       "max         59.600000      21.500000         231.000000  6300.000000"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins = pd.read_csv('penguins.csv')\n",
    "penguins.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "694b56e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['species', 'island', 'bill_length_mm', 'bill_depth_mm',\n",
       "       'flipper_length_mm', 'body_mass_g', 'sex'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "b89c87c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Adelie\n",
       "1    Adelie\n",
       "2    Adelie\n",
       "3    Adelie\n",
       "4    Adelie\n",
       "5    Adelie\n",
       "6    Adelie\n",
       "7    Adelie\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins['species'].head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "64472a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "336    Gentoo\n",
       "337    Gentoo\n",
       "338    Gentoo\n",
       "339    Gentoo\n",
       "340    Gentoo\n",
       "341    Gentoo\n",
       "342    Gentoo\n",
       "343    Gentoo\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins['species'].tail(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "bde3a3d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santossh/anaconda3/envs/conda_md/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/santossh/anaconda3/envs/conda_md/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/santossh/anaconda3/envs/conda_md/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/santossh/anaconda3/envs/conda_md/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/santossh/anaconda3/envs/conda_md/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Plot flipper length vs. body mass, colored by species\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "custom_colors = ['blue', 'magenta', 'forestgreen']\n",
    "sns.scatterplot(data=penguins, x='flipper_length_mm', y='body_mass_g', hue='species', palette=custom_colors) \n",
    "\n",
    "# Adding title and labels\n",
    "plt.title('Flipper Length vs. Body Mass by Species')\n",
    "plt.xlabel('Flipper Length (mm)')\n",
    "plt.ylabel('Body Mass (g)')\n",
    "plt.legend(title='Species')\n",
    "plt.grid()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29fa2470",
   "metadata": {},
   "source": [
    "## 5. Introduction to Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a479f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if time, pass presentation over to Eric "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c35a3d8e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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