Data Visualization  & EDA with  Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a92285b",
   "metadata": {},
   "source": [
    "# Looping through string\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9c02166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 b\n",
      "1 a\n",
      "2 n\n",
      "3 a\n",
      "4 n\n",
      "5 a\n"
     ]
    }
   ],
   "source": [
    "fruit='banana'\n",
    "index=0\n",
    "while index < len(fruit):\n",
    "    letter=fruit[index]\n",
    "    \n",
    "    print(index, letter)\n",
    "    index=index + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f777b9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Implement iteration with continue\n",
      "Enter your data>>>Ghana is most peaceful country in Africa\n",
      "Enter your data>>>#\n",
      "Enter your data>>>done\n",
      "End!\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n Implement iteration with continue\")\n",
    "while True:\n",
    "    line=input(\"Enter your data>>>\")\n",
    "    if line[0]=='#':\n",
    "        continue\n",
    "        \n",
    "    if line=='done':\n",
    "        break\n",
    "        print(line)\n",
    "        \n",
    "print('End!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2d89b4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Printing in reverse order\n",
      "5 a\n",
      "4 n\n",
      "3 a\n",
      "2 n\n",
      "1 a\n",
      "0 b\n"
     ]
    }
   ],
   "source": [
    "#Printing in the reverse order\n",
    "\n",
    "print(\"\\n Printing in reverse order\")\n",
    "index=len(fruit)-1\n",
    "\n",
    "while index >=0:\n",
    "    letter=fruit[index]\n",
    "    print(index, letter)\n",
    "    index -=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c5aaa7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Letter wise iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ed57b45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E\n",
      "g\n",
      "y\n",
      "p\n",
      "t\n"
     ]
    }
   ],
   "source": [
    "Country='Egypt'\n",
    "for letter in Country:\n",
    "    print(letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3f5132",
   "metadata": {},
   "source": [
    "# Iterating and slicing a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5eb2a140",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of a in  government of the republic of Ghana is : 2\n"
     ]
    }
   ],
   "source": [
    "#Looking for and counting \n",
    "\n",
    "\n",
    "word='government of the republic of Ghana'\n",
    "count=0\n",
    "\n",
    "for letter in word:\n",
    "    if letter=='a':\n",
    "        count +=1\n",
    "print(\"Number of a in \", word, \"is :\", count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "221f49da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#String Slicing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "180f901b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welc\n",
      "e\n",
      "e to High Coll\n",
      "Welcome to H\n",
      "lcome to High Colleges of Technology\n",
      "Welcome to High Colleges of Technology\n",
      "Welcome to High Colleges of Technology\n"
     ]
    }
   ],
   "source": [
    "s=\"Welcome to High Colleges of Technology\"\n",
    "\n",
    "print(s[0:4])\n",
    "print(s[6:7])\n",
    "print(s[6:20])\n",
    "print(s[:12])\n",
    "print(s[2:])\n",
    "print(s[:])\n",
    "print(s)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38765920",
   "metadata": {},
   "source": [
    "# Parsing and Extracting Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b20bf0b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
      "20\n",
      "From sumailaayamba12\n",
      "['From', 'sumailaayamba12']\n",
      "SUMAILAAYAMBA12\n",
      "\n",
      ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n"
     ]
    }
   ],
   "source": [
    "my_data=\"From sumailaayamba12@gmail.com Thursday August 11 04:13:30 2022 \"\n",
    "find_loc=my_data.find('@')\n",
    "\n",
    "print(\"\\n<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>\")\n",
    "print(find_loc)\n",
    "print(my_data[:find_loc])\n",
    "my_data2=my_data[:find_loc]\n",
    "my_name=my_data2.split(' ')\n",
    "\n",
    "print(my_name)\n",
    "print(my_name[1].replace('.', '').upper())\n",
    "print(\"\\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212c7101",
   "metadata": {},
   "source": [
    "# Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7e38d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a series using the Pandas library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d072bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a series from array using pandas and numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28512269",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a demo of Pandas series\n",
      "A    81\n",
      "B   -34\n",
      "C    22\n",
      "D    62\n",
      "E    99\n",
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "my_array=np.array([81, -34, 22, 62,99])\n",
    "my_series=pd.Series(my_array, index=['A', 'B', 'C', 'D', 'E'])\n",
    "print(\"This is a demo of Pandas series\")\n",
    "print(my_series)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aed2d507",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-34\n"
     ]
    }
   ],
   "source": [
    "print(my_series[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0d6c4c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ibraihim    19\n",
      "Ali         70\n",
      "Samadu      97\n",
      "Baba        72\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Create series from dictionary using pandas\n",
    "\n",
    "my_dict={'Samadu':97, 'Ali':70, 'Ibraihim':19, 'Baba':72}\n",
    "my_series2=pd.Series(my_dict, index=['Ibraihim', 'Ali', 'Samadu', 'Baba'])\n",
    "print(my_series2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "35fdee91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Name  Age\n",
      "0   Samadu   34\n",
      "1  Alimatu   22\n",
      "2   Babama   17\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "A Pandas Data Frame\n",
    "A data frame is a two-dimensional data structure. In other words, data is\n",
    "aligned in a tabular fashion in rows and columns. In the following table,\n",
    "you have two columns and three rows of data. Listing 1-30 shows how to\n",
    "create a data frame using the Pandas library\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "#Creating a DataFrame Using the pandas library\n",
    "my_data=[['Samadu', 34], ['Alimatu', 22], ['Babama', 17]]\n",
    "DataFrame1=pd.DataFrame(my_data, columns=['Name', 'Age'])\n",
    "print(DataFrame1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2818c525",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Name  Age\n",
      "100    Samadu   22\n",
      "1001  Alimatu   55\n",
      "102      Omar   62\n",
      "103     Salwa   11\n"
     ]
    }
   ],
   "source": [
    "#Create a data frame using a dictionary\n",
    "data2={'Name':['Samadu', 'Alimatu', 'Omar', 'Salwa'], 'Age':[22, 55, 62, 11]}\n",
    "dataFrame2=pd.DataFrame(data2, index=[100, 1001, 102, 103])\n",
    "print(dataFrame2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "85bfd2c0",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Samadu</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>Alimatu</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  Age\n",
       "100    Samadu   22\n",
       "1001  Alimatu   55"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d150af01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100      Samadu\n",
       "1001    Alimatu\n",
       "102        Omar\n",
       "103       Salwa\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame2['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d028361c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100     22\n",
       "1001    55\n",
       "102     62\n",
       "103     11\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame2['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dcc4725",
   "metadata": {},
   "source": [
    "# Pandas Panels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f3fc4e74",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pandas' has no attribute 'panel'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-835b5228d000>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m         (np.random.randn(4, 2))}\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpanel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_temp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Temperature Day 1'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\pandas\\__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m    242\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_SparseArray\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"module 'pandas' has no attribute '{name}'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'panel'"
     ]
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "A panel is a 3D container of data that can be created from\n",
    "different data structures such as from a dictionary of data frames\n",
    "\"\"\"\n",
    "#Creating a panel\n",
    "my_temp={'Temperature Day 1' :pd.DataFrame(np.random.randn(4, 3)), 'Temperature Day 2':pd.DataFrame\n",
    "        (np.random.randn(4, 2))}\n",
    "        \n",
    "p=pd.Panel(my_temp)\n",
    "print(p['Temperature Day 1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e136c26",
   "metadata": {},
   "source": [
    "# Python Lambdas and the Numpy Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14aa820f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Python Lambdas and the Numpy Library\n",
    "The lambda operator is a way to create small anonymous functions, in\n",
    "other words, functions without names. These functions are throwaway\n",
    "functions; they are just needed where they have been created. The lambda\n",
    "feature is useful mainly for Lisp programmers. Lambda functions are used\n",
    "in combination with the functions filter(), map(), and reduce().\n",
    "Anonymous functions refer to functions that aren’t named and are\n",
    "created by using the keyword lambda. A lambda is created without using\n",
    "the def keyword; it takes any number of arguments and returns an\n",
    "evaluated expression\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "73ce83a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The summmation of 7 + 10 =  17\n"
     ]
    }
   ],
   "source": [
    "#Anonymous Function Definition\n",
    "\n",
    "\n",
    "summation=lambda var, var2: var + var2 \n",
    "#Call summation as a function\n",
    "\n",
    "print(\"The summmation of 7 + 10 = \", summation(7, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b444eb8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference of 20 - 11 =  9\n"
     ]
    }
   ],
   "source": [
    "sub=lambda a, b: a - b\n",
    "print(\"The difference of 20 - 11 = \", sub(20, 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b8a28d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=lambda x, y : x * y\n",
    "result(2, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d3fa311a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "297"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result(9, 33)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbc1cae",
   "metadata": {},
   "source": [
    "# The map() Function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b80553ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "The map() function is used to apply a specific function on a sequence of\n",
    "data. The map() function has two arguments.\n",
    "r = map(func, seq)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "312c91f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[60.44, 77.0, 86.9, 77.0]\n",
      "[15.799999999999999, 25.0, 30.500000000000004, 25.0]\n"
     ]
    }
   ],
   "source": [
    "#Using the map() Function\n",
    "\n",
    "def fahrenheit(T):\n",
    "    return ((float(9)/5) * T + 32)\n",
    "\n",
    "def celsius(T):\n",
    "    return (float(5)/9) * (T - 32)\n",
    "\n",
    "Temp=(15.8, 25, 30.5, 25)\n",
    "\n",
    "F=list(map(fahrenheit, Temp))\n",
    "\n",
    "C=list(map(celsius, F))\n",
    "\n",
    "print(F)\n",
    "print(C)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e390d05c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102.56\n",
      "97.7\n",
      "99.14\n",
      "100.03999999999999\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Celsius=[39.2, 36.5, 37.3, 37.8]\n",
    "\n",
    "Fahrenheit=map(lambda x: (float(9)/5) * x + 32, Celsius)\n",
    "for x in Fahrenheit:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6057f81b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#The filter() Function\n",
    "\"\"\"\n",
    "The filter() function is an elegant way to filter out all elements of a list\n",
    "for which the applied function returns true.\n",
    "For instance, the function filter(func, list1) needs a function\n",
    "called func as its first argument. func returns a Boolean value, in other\n",
    "words, either true or false. This function will be applied to every element\n",
    "of the list list1. Only if func returns true will the element of the list be\n",
    "included in the result list.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "81d48ecb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "8\n",
      "34\n"
     ]
    }
   ],
   "source": [
    "#Using filter() function\n",
    "\n",
    "fib=[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n",
    "\n",
    "result=filter(lambda x: x % 2==0, fib)\n",
    "\n",
    "for x in result:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c42cae8f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'reduce' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-c7d4b1692193>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m47\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m42\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m102\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m13\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'reduce' is not defined"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "The reduce () Function\n",
    "The reduce() function continually applies the function func to a sequence\n",
    "seq and returns a single value.\n",
    "The reduce() function is used to find the max value in a sequence of\n",
    "integers\n",
    "\"\"\"\n",
    "\n",
    "#Using the reduce() Function\n",
    "\n",
    "f=lambda a, b: a if (a > b) else b\n",
    "reduce(f, [47, 11, 42, 102, 13])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f915da52",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Python Numpy Package\n",
    "Numpy is a Python package that stands for “numerical Python.” It is a\n",
    "library consisting of multidimensional array objects and a collection of\n",
    "routines for processing arrays.\n",
    "The Numpy library is used to apply the following operations:\n",
    "• Operations related to linear algebra and random\n",
    "number generation\n",
    "• Mathematical and logical operations on arrays\n",
    "• Fourier transforms and routines for shape\n",
    "manipulation\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "21b8f7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8, 10, 12],\n",
       "       [14, 16, 18]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Demo of Numpy Function\n",
    "\n",
    "a=np.array([[1, 2, 3 ], [4, 5, 6]])\n",
    "b=np.array([[7, 8, 9], [10, 11, 12]])\n",
    "np.add(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "58848653",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'array' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-56-f99292da9762>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m18\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubtract\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'array' is not defined"
     ]
    }
   ],
   "source": [
    "array([[8, 10, 12], [14, 16, 18]])\n",
    "np.subtract(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "824959e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Data Cleaning and Manipulation Techniques\n",
    "Keeping accurate data is highly important for any data scientist.\n",
    "Developing an accurate model and getting accurate predictions from\n",
    "the applied model depend on the missing values treatment. Therefore,\n",
    "handling missing data is important to make models more accurate and\n",
    "valid.\n",
    "Numerous techniques and approaches are used to handle missing data\n",
    "such as the following:\n",
    "• Fill NA forward\n",
    "• Fill NA backward\n",
    "• Drop missing values\n",
    "• Replace missing (or) generic values\n",
    "• Replace NaN with a scalar value\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c243fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "dataset.fillna(0) # Fill missing values with zero value\n",
    "dataset.fillna(method='pad') # Fill methods Forward\n",
    "dataset.fillna(method=' bfill') # Fill methods Backward\n",
    "dataset.dropna() # remove all missing data\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "de21f947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Lion\n",
       "1    Tiger\n",
       "2     Bear\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals=['Lion', 'Tiger', 'Bear']\n",
    "pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "03809ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    92\n",
       "1    18\n",
       "2    44\n",
       "3    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks=[92, 18, 44, 22]\n",
    "pd.Series(marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2e574b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Animal    78\n",
       "Omar      83\n",
       "Salwa     70\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quize={\"Animal\":78, \"Omar\": 83, \"Salwa\":70}\n",
    "a=pd.Series(quize)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "431dfe0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.loc['Animal']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e33d8950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['Omar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ce174cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.loc['Omar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a9b08e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['Animal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "da04ef0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9315fc53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    39\n",
       "1    44\n",
       "2     1\n",
       "3     9\n",
       "4     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q=pd.Series([39, 44, 1, 9, 0])\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c046a848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n",
      "83\n",
      "84\n",
      "93\n",
      "93\n"
     ]
    }
   ],
   "source": [
    "total=0\n",
    "for i in q:\n",
    "    total +=\n",
    "    print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c917f7ea",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'loc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-72-8d3a25d6f207>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m99\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m88\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m33\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m29\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Animal'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m00\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'str' object has no attribute 'loc'"
     ]
    }
   ],
   "source": [
    "test=[93, 22, 74, 21, 30]\n",
    "marks=pd.Series(test)\n",
    "\n",
    "d=pd.Series([99, 88, 33, 29])\n",
    "s.loc['Animal']=00\n",
    "\n",
    "\n",
    "newSeries=d.appned(marks)\n",
    "newSeries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63400654",
   "metadata": {},
   "source": [
    "# Running Basic Inferential Analaysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "416de9cd",
   "metadata": {},
   "outputs": [
    {
     "ename": "URLError",
     "evalue": "<urlopen error [Errno 11001] getaddrinfo failed>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mD:\\conda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1353\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1354\u001b[1;33m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[0;32m   1355\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n",
      "\u001b[1;32mD:\\conda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1254\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1255\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1300\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1301\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1249\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1250\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1251\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1009\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1010\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1011\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    949\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 950\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    951\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1417\u001b[1;33m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1418\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    920\u001b[0m         \u001b[1;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 921\u001b[1;33m         self.sock = self._create_connection(\n\u001b[0m\u001b[0;32m    922\u001b[0m             (self.host,self.port), self.timeout, self.source_address)\n",
      "\u001b[1;32mD:\\conda\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    786\u001b[0m     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 787\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    788\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    917\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 918\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    919\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-73-9ba7ee620c18>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tips'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0msb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"total_bill\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"tip\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Total Bill\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\seaborn\\utils.py\u001b[0m in \u001b[0;36mload_dataset\u001b[1;34m(name, cache, data_home, **kws)\u001b[0m\n\u001b[0;32m    483\u001b[0m                                   os.path.basename(full_path))\n\u001b[0;32m    484\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 485\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mget_dataset_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    486\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"'{name}' is not one of the example datasets.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0murlretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\site-packages\\seaborn\\utils.py\u001b[0m in \u001b[0;36mget_dataset_names\u001b[1;34m()\u001b[0m\n\u001b[0;32m    417\u001b[0m     \"\"\"\n\u001b[0;32m    418\u001b[0m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"https://github.com/mwaskom/seaborn-data\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 419\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    420\u001b[0m         \u001b[0mhtml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    523\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m         \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maudit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'urllib.Request'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 525\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[0;32m    543\u001b[0m                                   '_open', req)\n\u001b[0;32m    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 502\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    503\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1395\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1396\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1397\u001b[1;33m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[0;32m   1398\u001b[0m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0;32m   1399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1355\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0;32m   1356\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1357\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1358\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1359\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>"
     ]
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "df=sb.load_dataset('tips')\n",
    "sb.regplot(x=\"total_bill\", y=\"tip\", data=df)\n",
    "plt.xlabel(\"Total Bill\")\n",
    "plt.ylabel(\"Bill Tips\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd6c2cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff9e3b0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "862709df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "816ff39f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fa5b5b12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age       10.258562\n",
      "Height    73.695345\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "d={\n",
    "    'Name':pd.Series(['Sumaila', 'Hamid', 'Samadu', 'Majid', 'Osman', 'Gamel', 'Alim']),\n",
    "    'Age':pd.Series([22, 44, 11, 21, 25, 20, 30]),\n",
    "    'Height':pd.Series([111.33, 132.44, 221.412, 293.00, 121.20, 123.10, 100.22])\n",
    "}\n",
    "    \n",
    "df=pd.DataFrame(d)\n",
    "\n",
    "print(df.std()) #Calculate and print the standard deviation\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6149d6ec",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Text' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-76-aeccc34bc54d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0mText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Frequency'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Binomial'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Text' is not defined"
     ]
    }
   ],
   "source": [
    "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Binomial')]\n",
    "\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "cac717f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Age      Height\n",
      "count   7.000000    6.000000\n",
      "mean   24.714286  167.080333\n",
      "std    10.258562   73.695345\n",
      "min    11.000000  111.330000\n",
      "25%    20.500000  121.675000\n",
      "50%    22.000000  127.770000\n",
      "75%    27.500000  199.169000\n",
      "max    44.000000  293.000000\n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bf081be8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Values in the Distribution\n",
      "Age        24.714286\n",
      "Height    167.080333\n",
      "dtype: float64\n",
      "Mode Values in the Distribution\n",
      "0    111.330\n",
      "1    121.200\n",
      "2    123.100\n",
      "3    132.440\n",
      "4    221.412\n",
      "5    293.000\n",
      "dtype: float64\n",
      "Median Values in the Distribution\n",
      "127.77\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Values in the Distribution\")\n",
    "print(df.mean())\n",
    "\n",
    "print(\"Mode Values in the Distribution\")\n",
    "print(df['Height'].mode())\n",
    "\n",
    "\n",
    "print(\"Median Values in the Distribution\")\n",
    "print(df['Height'].median())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "62468a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Temperature in Fahrenneit')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v5s2bePXVV+Hi4oIDBw4gIyMDALBjxw54e3srX2dJSYly+rfeegshISHo3bs32rdvj7y8POzbt095DMTFxQU//PADiouLoVAoEBAQgFWrVsHQ0BBdu3bFN998A6BsS2XkyJGIiIh4rNdZndq8l7Xxyy+/QKFQ4P79+/j111/Rr18/dO3aFcnJyYiLiwNQdpDT3d292oP50dHR8PT0xPDhw2FtbY3IyMhK60t5O5qZmeHq1asoKiqCXC7HoUOHanxt1bVndcv29vbG4MGD0apVK8TExCiXLcXV1RUHDx7EgwcPoFAoav3L38fHB0uXLlWeaVZaWoqvvvoKly5dgo2NTa3m8U9nz56ttN5VpKenh5CQEGzduhWFhYXVTj9+/Hjs2rUL0dHRyvuioqIQGhoKe3t7ODs7Izo6GikpKQCgPD5TcWvrn6ytrSttEaWlpcHDwwPnz5+Hs7Mzjh8/jqSkJADAH3/8gXfeeadKfebm5ggPD8fBgweV9+Xk5CA9PR2dOnWq8TumXE2fnSNHjmDs2LHo1q0bPv30UwwePBjnz5+XfE11qUltSZQLCAjAO++8g2PHjmH48OFIT0/He++9B5lMhjZt2uCLL76o9HwPDw/89ttvGDBgABQKBfr27Yv79+8jLy8PHTp0QIsWLTBs2DDlgTYAmDx5MoKDgzF48GCUlJSgS5cuCAgIqLGuwYMH488//8TAgQPxzDPPoE2bNvDy8sKlS5dq/dpOnTqF8PBwKBQKrF69uspWQLmUlBQMHjwYMpkMq1atgqmpKVxcXPDxxx9j3LhxkMlkMDQ0xJdffgmZTIauXbviP//5D3x8fPDll1/Czc0NW7ZsUf6K7NWrFy5fvow2bdoAAD755BMEBwfD09MTpaWlcHBwgL+/PwBg5cqVCAwMxKBBg1BcXAwPDw+888471f7afhy1eS9ro7CwEMOGDUN+fj5GjRoFZ2dnAGVnVS1fvhxFRUUQQmD58uWwsLCotKsKAMaNG4cFCxZg9+7dAMp+3V65cgUA8Prrr2PmzJkIDAzEnDlz0KNHDwwYMADPPfccevbsqdx99U81tWdFU6ZMwfLly7F27Vro6uritddew82bN2t8vX369MHly5cxdOhQGBsbw97eHtnZ2SrbadCgQRBCYMaMGSgpKUFRURE6d+6M7777TrnVVlhYiHfffbfKtOXvy82bN5WPa2lpwdDQECtXrqyyq6acjY0N/Pz8MH/+/Goft7KywsaNG7FmzRoEBwdDoVDAzMwMGzZsUG6dLFy4ED4+PigtLUXLli2xceNGGBkZSb5OPT09fPXVV1i6dCm+/vprlJSUYNq0acrdgUuWLMGMGTMghICOjg42bNhQ5YCxiYkJvvvuO4SEhGD58uV45plnoKenh4kTJ8LZ2Rm2traS3zEVSX12SktLERUVBQ8PD+jr68PExASBgYGSr6kuyYTUfg3SOE/S/4Eqq03fGCJ6pMntbiIiorrDLQkiIpJUb1sSZ86cgZeXFwAgOTkZI0eOxKhRo7Bw4ULlKWzh4eEYMmQI3nvvPRw5cqS+SiEioidULyHx3//+F/Pnz1eewrds2TL4+vpi+/btEEIgIiICmZmZCA0NRVhYGLZs2YJVq1Y9cS9OIiKqH/USEpaWlli/fr3y9oULF+Dk5ASg7HS8mJgYnD17Ft26dYOenh6MjIxgaWn5WGf5EBFR/auXU2Dd3d0rne4ohFB2cDEwMEBubi7y8vIqnZZmYGBQ5XSwcvHx8fVRJhFRk1axV/+TUks/iYq9f/Pz82FsbAxDQ8NK3dLz8/NrPJe5Ll5sU5CYmAgHB4eGLqPBsR0eYVs8wrZ4pK5+XKvlFNhOnTohNjYWQFnvSEdHR3Tp0gXx8fEoKipCbm4ukpKSqu2qT0REDUctWxJ+fn7KYQZsbGzg7u4ObW1teHl5YdSoURBCYPr06ZXGnSEiooZXbyFhYWGB8PBwAGVjo2zbtq3Kc9577z2899579VUCERE9Jfa4JiIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkk66lqQXC6Hv78/UlNToaWlhcDAQOjo6MDf3x8ymQy2trZYuHAhtLSYW0REmkJtIfHHH3+gpKQEYWFhOH78ONasWQO5XA5fX1/07NkTCxYsQEREBNzc3NRVEhERqaC2n+3W1tYoLS2FQqFAXl4edHR0cOHCBTg5OQEAXF1dERMTo65yiIioFtS2JaGvr4/U1FQMGDAA2dnZ2LhxI+Li4iCTyQAABgYGyM3NlZw+MTFRXaVqtMLCQrYF2A4VsS0eYVvUPbWFxLfffgsXFxd89tlnSEtLg7e3N+RyufLx/Px8GBsbS07v4OCgjjI1XmJiItsCbIeK2BaPsC0eiY+Pr5P5qG13k7GxMYyMjAAAJiYmKCkpQadOnRAbGwsAiIqKgqOjo7rKISKiWlBbSIwdOxYXLlzAqFGj4O3tjenTp2PBggVYv349RowYAblcDnd3d3WVQ/UgJwd4662y/4moaVDb7iYDAwOsXbu2yv3btm1TVwlUj346nQqfz4qReuQlvDLkb6wP0cPgbi82dFlE9JTYKYGe2k+nUzF7eyJuR7cDIENqdDvM3p6In06nNnRpRPSUGBL01FYcuoz041aA+P87hAwZMVZYcehyg9ZFRE+PIdEEqfvYQModOXL/soYoKdt7KUq08SDOGil35CqmJCJNpzIkjhw5Uun2//73v3orhp7eT6dT8fKQG4iIEHhlyA217PJRnLV7tBVRTsggztrV+7KJqH5JHrg+cuQITp06hQMHDuD06dMAgNLSUkRGRmLgwIFqK5Bq79GxgTfx6NjAUQCot4PIOTnA3RNWECWVf2+IEm3cPWmFnBzA1LReFk1EaiAZEvb29sjJyUGLFi1gbW0NAJDJZPDw8FBbcfR4JI8NtLpcbyGxciUAUf0GqVBoYdUqYMmSelk0EamBZEg899xz8PT0xIABAzgyayMheWygx/V6W+aFC4CRUdm/6pw7V2+LJiI1kAwJPz8/hISEYODAgcrxlYQQkMlkiIiIUFuBVHsNcWzgxx/rbdZEpAEkQyIkJAQAEBkZCQDIycmBiYmJMjBIs/DYABHVB5X7keLi4uDh4YGRI0di3bp12LVrlzrqosdUm2MDdYFDbxA1LypDYs2aNdi2bRvMzc0xadIk7NixQx110WMqPzZgbl71n5FR3RwbaIjTa4moYakcu0lLSwumpqaQyWRo0aIFDAwM1FEXPab6PjbQEKfXElHDU7klYWlpiZCQEOTk5GDz5s1o27atOuoiDcOhN4iaJ5UhsXjxYrRt2xbdu3eHvr4+AgMD1VEXaRgOvUHUPKnc3aStrY3OnTujQ4cOAIAzZ86gR48e9V4YaRYOvUHUPKkMCR8fH2RnZ6NNmzbKfhIMieaFp9cSNV8qQyIrKwthYWHqqIU0FIfeIGq+VB6TsLa2Rnp6ujpqIQ2ljtNrNQn7ghA9onJL4tSpU+jbty/MzMyU90VHR9drUaRZmtPQG7wMK1FlKkPi0KFD6qiDqMGxLwhRVSpDIjExETt37kRRUZHyvmXLltVrUUQNoSGGWifSdCpDwt/fH6NHj8YLL7ygjnqIGkxDDLVOpOlUhoS5uTmGDx+ujlqIGhT7ghBVpTIkXnzxRWzevBkODg7KYcJdXFzqvTAidWJfEKLqqQwJuVyOGzdu4MaNG8r7GBLU1LAvCFH1VIbEsmXLcOPGDdy8eRMdO3ZE69at1VEXkVrxMqxE1VMZEtu2bcPhw4dx//59eHp6Ijk5GQsWLFBHbURq05z6ghA9DpU9rg8cOIBvv/0WRkZG8Pb2xpkzZ9RRFxERaQCVISFE2eke5Qet9fT06rciIiLSGCp3N3l4eOCDDz7A7du38fHHH+Ott95SR11ERKQBVIZEr1694OzsjCtXrsDa2hr29vbqqIuIiDSAypCYN28eduzYgfbt26ujHiIi0iAqQ0JfXx+ff/45rK2toaVVdghjxIgR9V4YERE1PJUHrrt16wZjY2NkZWUhMzMTmZmZ6qiLiOip8LogdUPllsTkyZNx8eJFFBYWqqMeIqKnxuuC1B2VITF16lTk5ubC3NwcAHiNayLSaLwuSN1SGRLZ2dnYvn17nSxs06ZNiIyMhFwux8iRI+Hk5AR/f3/IZDLY2tpi4cKFyuMeRERPgtcFqVsqv5Hbtm2LtLS0p15QbGwsTp8+jR07diA0NBR37tzBsmXL4Ovri+3bt0MIgYiIiKdeDhE1b5LXBbkjb+DKGifJLYnykV6Li4tx8OBBmFYYJ/lJrnEdHR0NOzs7TJkyBXl5eZg9ezbCw8Ph5OQEAHB1dcXx48fh5ub22PMmIirH64LULcmQeJIgqEl2djZu376NjRs34tatW5g8eTKEEMrhPgwMDJCbmys5fWJiYp3W01gVFhayLcB2qIht8UhmphyZMXYQJdqV7hcl2sg4YYnY2MswNlY0UHWNk8pjEgkJCdi7dy/k8rJNtYyMDGzZsuWxF2RqagobGxvo6enBxsYGLVq0wJ07d5SP5+fnw9jYWHJ6BweHx15mU5SYmMi2ANuhIrbFI2vXZkIG7WofkwltHDjQsdlcFyQ+Pr5O5qPymERQUBCcnJyQl5eHtm3bVtrt9Di6d++OY8eOQQiB9PR0FBQUwNnZGbGxsQCAqKgoODo6PtG8iaiq5thP4Nq1ljAyAszNq/4zMuJ1QZ6Eyi0JY2NjeHh44Pjx4/j0008xevToJ1pQ3759ERcXh2HDhkEIgQULFsDCwgIBAQFYtWoVbGxs4O7u/kTzJqLKmms/gfXrb3Grqo6pDAmZTIarV6+ioKAA169ff6oe17Nnz65y37Zt2554fkRUFfsJUF1SubvJ398fV69ehZeXF2bOnImRI0eqoy4iekKS/QQOXW7QuqhxktySOHfuHF555RXY2trC1tYWALB37161FUZET0ayn0CP6w1cGTVGklsSK1asUP4dFBSklmKI6OmxnwDVJcmQKL9sKQBcuXJFLcUQ0dPJyQHunrBSbkWUEyXauHvSqlmd6UR1QzIkyju5EVHjsXIlAFH9x1ootLBqlXrrocZP8phEeno6du7cqezXsHPnTuVjvOgQkWa6cKGsP4CRUfWPs58APS7JkBg0aJDydNeKfxOR5vrxx4augJoayZDw8fFRZx1ERKSBePGGOtQch0EgoqaNIVFHfjqdipeH3EBEhMArQ27gp9OpDV0SEdFTUzksBwBkZWWhqKhIebtt27b1VlBjxGEQiKipUhkSixYtQlRUFFq3bq28/kNYWJg6ams0eLlEImqqVIbE2bNn8fvvv/Pa0zXgMAhE1FSp/Oa3srKqtKuJquIwCETUVKnckkhLS0Pfvn1hZWUFANzd9A+PhkGonLcVh0F4wus0ERE1OJUhERISoo46Gq3aDIPQXC6XSERNj2RI7Nq1C8OHD0dYWFiVcZxmzJhR74U1FhwGgYiaMsmQeOGFFwAANjY2aiumMWpMwyDk5ADDhgG7d3MXGDUeXG8blmRIvPHGGwAAT09PtRVD9ae5XvOYGjeutw2P57U2A486+7XDo85+iewVThqN661mYEg0A7zmMTVGXG81g8qzm9LT07FixQpkZ2fD3d0dHTt2xKuvvqqO2qiOsLMfNUZcbzWDyi2JgIAADB06FMXFxXB0dMTSpUvVURfVIXb2o8aI661mUBkSRUVFcHZ2hkwmg42NDVq0aKGOuqiO8JrH1BhxvdUcKkNCT08Px44dg0KhQEJCAvT09NRRF9URXvOYGiOut5pD5TGJwMBABAcHIzs7G1u3bsWiRYvUUBbVFXb2o8aI663mUBkS33zzDVavXq2OWqgeNKbOfkTluN5qDpW7m5KSkvDgwQN11EJERBpG5ZZEUlISevbsCTMzM+UYTtHR0fVeGBERNTyVIXHkyBF11EFERBpIZUjMmTOnyn3Lli2rl2KIiEizqAyJgQMHAgCEELh48SIyMjLqvSgiItIMKkOifDRYAHB1dcW4cePqtSAiItIcKkOi4kHqzMxM3L17t14LIiIizaEyJA4cOKD8W09Pj8cjiIiaEZUh8dprr2H48OHK299//z06d+78xAvMysrCkCFDsHXrVujo6MDf3x8ymQy2trZYuHAhtLQ4ejkRkaaQDIn9+/cjMjISsbGxOHnyJABAoVDgypUrGDNmzBMtTC6XY8GCBWjZsiWAsrOkfH190bNnTyxYsAARERFwc3N7onkTUePCy5I2DjVevvS5555DTk4ORowYAQDQ0tJCu3btnnhhwcHBeP/997F582YAwIULF+Dk5ASg7KD48ePHGRJEzQAvS9p4SIaEiYkJevbsiZ49eyIjIwMlJSUQQuD27dt4/vnnH3tBe/fuhZmZGd544w1lSAghlL24DQwMkJubKzl9YmLiYy+zKSosLGRbgO1QUWNri8jruVgdmYPb0f1QflnSmdsikXo7Ff1sJEb0q6XG1haNgcpjEnPnzkVCQgIKCgpQUFAAS0tLhIeHP/aC9uzZA5lMhhMnTiAxMRF+fn64d++e8vH8/HwYGxtLTu/g4PDYy2yKEhMT2RZgO1TU2Nrio58jkXnCutJlSe+etMb251Iw5d9OTzXvxtYW9Sk+Pr5O5qPyKPH169dx4MABuLi44H//+98TX3Tohx9+wLZt2xAaGgoHBwcEBwfD1dUVsbGxAICoqCg4Ojo+0byJqPGQvCzpHXkDV0bVURkSBgYGkMlkePjwIczMzCCX190b6efnh/Xr12PEiBGQy+Vwd3evs3kTkWbiZUkbF5W7mzp37owtW7agdevWmD59OkpLS596oaGhocq/t23b9tTzI6LG4dFlSSv/Pq14WVKe6aRZVIbE4MGD0bp1a7Rs2RJRUVHo0qWLOuoiapKa+2mftbks6ZIl6q2JaqZyd9O8efNgaGgIHR0d9OvXD+bm5uqoi6jJ+el0Kl4ecgMREQKvDLmBn06nNnRJald+WVJz86r/jIx4WVJNpHJLQl9fH59//jmsra2VvaHL+00QUe38dDoVs7cn4nb0myg/7XP29qMA0Kz6B/CypI2PypDo1q0bgLLhNIjoyaw4dBnpx60qnfaZEWOFFa0uN6uQoMZHZUj4+PggJiYGt27dQpcuXWBtba2OuoiaFMnTPntcb+DKiGqmMiRWrVqFO3fuICkpCbq6uti8eTNWrVqljtqImgye9kmNlcoD1/Hx8Vi+fDn09fXh6emJW7duqaMuoibj0WmflX+TVTztk0hTqQyJ0tJSFBUVQSaTobS0lEN5Ez2m2pz2SaSpVO5u8vb2xpAhQ3Dv3j0MHz4cY8eOVUNZRE1H+WmfRhJj1/G0T9JkKkNiwIAB6NWrF27evAkLCws8++yz6qiLqMmo69M+m3uHPFIvlfuOTp06hTFjxmDy5MkYP348h+ElakDskEfqpjIkgoKCEBISgujoaHzxxRdYvHixOuoion941CGvHR51yEtE5HXp67AQPS2VIWFkZIQOHToAAOzs7JSXHiUi9ZLqkPfdqewGrYuaNpXHJFq1aoV58+bh9ddfx4ULF6BQKLBz504AHJ6DSJ2kOuTdYYc8qkcqQ8LGxgYAkJycDENDQzg5OSEzM7PeCyOiyqQ65JUkdGiQeqh5UBkSkydPxtWrV1FcXKy8j8OFE6lXTddhyIp9iddhoHqjMiQmTJiA4uJi5fWnZTIZvvzyy3ovjIgeqalDHhQyXoeB6o3KkCgqKuLV44gaWE0d8kpLFTh3jiMhUP1QGRKOjo44duwY2rdvr7yvbdu29VoUEVVWU4e8xMSrcHBwUF8x1KyoDImsrCx8/vnnlXY3hYWF1XthRETU8FSGxI0bN/Drr7+qoxYiItIwKndk2tnZISEhAcXFxcp/RETUPKjckoiLi8PRo0eVt2UyGSIiIuqzJiIi0hAqQ2Lfvn0AgJycHJiYmEAmk9V7UUREpBlqtSWxePFilJaWon///mjbti2GDx+ujtqIiKiBqTwmsWbNGmzbtg3m5uaYNGkSduzYoY66iIhIA6gMCS0tLZiamkImk6FFixYwMDBQR11ERKQBVIaEpaUlQkJCkJOTg82bN7MjHRFRMyIZEr6+vgCAxYsXo23btujevTueeeYZBAYGqqs2IiJqYJIHru/du1f2BB0djBw5Um0FERGR5pAMiZSUFKxatarax2bMmFFvBRERkeaQDImWLVvC2tpanbUQEZGGkQwJc3NzeHp6qrMWIiLSMJIHrl9++WV11kFERBpIMiT8/PzUWQcREWkgXs6KiIgkqRy7qa7I5XLMnTsXqampKC4uxuTJk9GhQwf4+/tDJpPB1tYWCxcuhJYWc4uISFOoLSR++eUXmJqaYsWKFcjOzoanpyfs7e3h6+uLnj17YsGCBYiIiICbm5u6SiIiIhXU9rO9f//+mDZtmvK2trY2Lly4ACcnJwCAq6srYmJi1FUOERHVgtq2JMoHBszLy8PUqVPh6+uL4OBg5fUpDAwMkJubKzl9YmKiWurUdIWFhWwLsB0qYls8wraoe2oLCQBIS0vDlClTMGrUKAwaNAgrVqxQPpafnw9jY2PJaR0cHNRRosZLTExkW4DtUBHb4hG2xSPx8fF1Mh+17W66e/cuxo0bh1mzZmHYsGEAgE6dOiE2NhYAEBUVBUdHR3WVQ0REtaC2kNi4cSMePHiAr776Cl5eXvDy8oKvry/Wr1+PESNGQC6Xw93dXV3lEBFRLahtd9P8+fMxf/78Kvdv27ZNXSUQEdFjYqcEIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIiksSQICIiSQwJIiKSxJAgIiJJDAkiIpLEkCAiIkkMCSIikqTT0AUoFAosWrQIly9fhp6eHoKCgmBlZdXQZRERETRgS+L3339HcXExdu7cic8++wxffPFFQ5dERET/r8FDIj4+Hm+88QYAoGvXrjh//nwDV0REROUafHdTXl4eDA0Nlbe1tbVRUlICHZ3KpcXHx6u7NI3FtijDdniEbfEI26JuNXhIGBoaIj8/X3lboVBUCYju3buruywiIoIG7G567bXXEBUVBQBISEiAnZ1dA1dERETlZEII0ZAFlJ/ddOXKFQgh8Pnnn6N9+/YNWRIREf2/Bg8JKc3x1Fi5XI65c+ciNTUVxcXFmDx5Mjp06AB/f3/IZDLY2tpi4cKF0NLSQnh4OMLCwqCjo4PJkyejb9++DV1+vcjKysKQIUOwdetW6OjoNNu22LRpEyIjIyGXyzFy5Eg4OTk1y7aQy+Xw9/dHamoqtLS0EBgY2CzXizNnzmDlypUIDQ1FcnJyrV9/YWEhZs2ahaysLBgYGCA4OBhmZmY1L0xoqEOHDgk/Pz8hhBCnT58WkyZNauCK6t/u3btFUFCQEEKIe/fuiT59+oiJEyeKkydPCiGECAgIEL/99pvIyMgQHh4eoqioSDx48ED5d1NTXFwsPvnkE/H222+La9euNdu2OHnypJg4caIoLS0VeXl5Yt26dc22LQ4fPiymTp0qhBAiOjpa+Pj4NLu22Lx5s/Dw8BDDhw8XQojHev1bt24V69atE0IIsX//fhEYGKhyeQ1+TEJKczw1tn///pg2bZrytra2Ni5cuAAnJycAgKurK2JiYnD27Fl069YNenp6MDIygqWlJS5dutRQZdeb4OBgvP/++2jdujUANNu2iI6Ohp2dHaZMmYJJkybhzTffbLZtYW1tjdLSUigUCuTl5UFHR6fZtYWlpSXWr1+vvP04r7/i96qrqytOnDihcnkaGxJSp8Y2ZQYGBjA0NEReXh6mTp0KX19fCCEgk8mUj+fm5iIvLw9GRkaVpsvLy2uosuvF3r17YWZmplyhATTbtsjOzsb58+exdu1aLF68GDNnzmy2baGvr4/U1FQMGDAAAQEB8PLyanZt4e7uXukM0Md5/RXvL3+uKg1+CqyU2pwa2xSlpaVhypQpGDVqFAYNGoQVK1YoH8vPz4exsXGVtsnPz6+0QjQFe/bsgUwmw4kTJ5CYmAg/Pz/cu3dP+XhzagtTU1PY2NhAT08PNjY2aNGiBe7cuaN8vDm1xbfffgsXFxd89tlnSEtLg7e3N+RyufLx5tQW5bS0Hv3WV/X6K95f/lyV86/7kutGczw19u7duxg3bhxmzZqFYcOGAQA6deqE2NhYAEBUVBQcHR3RpUsXxMfHo6ioCLm5uUhKSmpy7fPDDz9g27ZtCA0NhYODA4KDg+Hq6tos26J79+44duwYhBBIT09HQUEBnJ2dm2VbGBsbK7/sTUxMUFJS0mw/I+Ue5/W/9tpr+OOPP5TPrU0fNI0/u6k5nRobFBSEX3/9FTY2Nsr75s2bh6CgIMjlctjY2CAoKAja2toIDw/Hzp07IYTAxIkT4e7u3oCV1y8vLy8sWrQIWlpaCAgIaJZtsXz5csTGxkIIgenTp8PCwqJZtkV+fj7mzp2LzMxMyOVyjBkzBi+//HKza4tbt25hxowZCA8Px40bN2r9+gsKCuDn54fMzEzo6uoiJCQEzz33XI3L0tiQICKihqexu5uIiKjhMSSIiEgSQ4KIiCQxJIiISBJDgoiIJDX93mnUbG3evBkxMTHQ0tKCTCbD9OnT8fLLL1f73IqnFFa0cuVK2NjYYMiQIZXu/+uvv/Cf//wHJSUlePjwIYYMGYIPPvhAspazZ89izpw56NevH1544QWEh4dj4sSJGDhw4NO/UKJ6xJCgJunatWuIjIzEjh07IJPJlL22f/nll6eed0pKCoKCgvD111/D3NwchYWFGDNmDNq1awdXV9dqp4mOjsb7778PLy8vjB07FsuXL0fHjh2fuhai+saQoCbJzMwMt2/fxu7du+Hq6goHBwfs3r0bAHDx4kUEBgZCW1sbLVq0QGBgYKVpDx06hA0bNsDMzEzZQamin3/+GYMHD4a5uTkAoGXLltiyZQv09fWVw72npKSgtLQUH374ISwsLLB7927o6uqioKAA58+fx7x587B69WocPXoU+/fvh0wmw8CBAzFmzBikpaUhICAARUVFyvratGmjnoYj+qc6G7+WSMOcP39e+Pv7iz59+gh3d3dx8OBBIYQQnp6e4uLFi0KIsqGnP/30U5GSkqIcevlf//qXuHfvnlAoFOKjjz4Se/bsqTTfgIAAcfjw4WqXGRoaKpYuXSqEECI3N1e4ubmJrKwssW7dOrF9+3YhhBCjR48W165dE1evXhXvv/++KCkpEaWlpcLLy0skJSWJadOmiaNHjwohhIiJiREzZsyo+8YhqiVuSVCTlJycDENDQyxbtgwAcO7cOUyYMAE9e/ZERkYGHBwcAAA9evRASEiIcrq7d+/C0NAQzz77LACgW7duVebdtm3bSgPsAcClS5cghEBSUhJ69eoFoGyQyvbt2yMlJaXaGq9cuYLbt29j7NixAID79+/j5s2buHLlCjZt2oSvv/4aQgjo6uo+XWMQPQWe3URN0uXLl7Fo0SIUFRUBKLsOgZGREbS1tdG6dWvltQXi4uLw0ksvKaczNTVFbm6ucsTZc+fOVZm3h4cHdu3apXxOfn4+FixYgIyMDLRv3x5//fUXgLLh7q9cuQILC4tqa7SxsUGHDh3w/fffIzQ0FEOGDIGdnR1sbGwwc+ZMhIaGYvHixU1qzCFqfLglQU3S22+/jaSkJAwfPhz6+voQQmD27NkwMjJCUFAQAgMDIYSAtrY2Pv/8c+V0Ojo6WLZsGcaPHw8TE5Nqh6e3sLDArFmz4OPjA21tbeTn52PYsGHo06cPiouLERAQgJEjR6KoqAg+Pj5o1apVtTXa29vD2dkZI0eORHFxMbp06YLnn38efn5+yoArLCzEvHnz6q2diFThAH9ERCSJu5uIiEgSQ4KIiCQxJIiISBJDgoiIJDEkiIhIEkOCiIgkMSSIiEgSQ4KIiCT9H08+eXBw/hkfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "X=[590,540,740,130,810,300,320,230,470,620,770,250]\n",
    "Y = [32,36,39,52,61,72,77,75,68,57,48,48]\n",
    "\n",
    "plt.scatter(X, Y)\n",
    "plt.xlim(0, 1000)\n",
    "plt.ylim(0, 100)\n",
    "\n",
    "#Scatter plot color\n",
    "\n",
    "plt.scatter(X, Y, s=60, c='blue', marker='^')\n",
    "\n",
    "#Change axes ranges\n",
    "plt.xlim(0, 1000)\n",
    "plt.ylim(0, 100)\n",
    "\n",
    "#Add title\n",
    "plt.title('Relationship between Temperature and ICEDN Coffee Sales')\n",
    "\n",
    "#Add x and y  labels\n",
    "plt.xlabel('Sold Coffee')\n",
    "plt.ylabel('Temperature in Fahrenneit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "090f9397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nE10qAbfb7R+rr6+XJMXGxsrhcMjj8QTcp76+Xt27dw9dSAAIc5FmB+jRo4f69u2ryspKDRgwQJJUWVmpgQMHKjIyUsnJyaqsrPTP93q9OnnypJKSkppdp8vlUkSE6T3XrNraWlVVVZkdo1ldJZ9hGPJ43K3ON2POhQteSU3PD1UewzCafR2t/hpL1s9o9XySlJCQEPRc08tAkqZMmaJ169ZpyJAh8nq92rBhg7KysiRJkydP1vTp07Vnzx6NHTtW69evV58+fTRq1Khm1xcfHy+73R6i9G1XVVXVphcp1LpKPpvNpujomFbnmzPH3ez8UOWx2WzNvo5Wf40l62e0er62CvrP523btjU5vmXLlnaHyM/P1/Dhw5WVlaUpU6Zo7Nixmjt3riRp8ODBKioqUmFhoZxOpz744AMVFxdb+s0eALqaFrcMzp8/798MWrFiha6//noZhuFfXltbq9WrV2vGjBltelCn0xlwamh0dLQKCgpUUFDQ5PyMjAxlZGS06TEAAMFrdTdRTk6Ozp49K0maOnVqwLKoqCj9/Oc/75RgANrH7W5UbGzPJpcZhiGbzab+/eN1/PjR0AaDJbVYBtdcc43ef/99SdJdd92l7du3hyQUgPYzjAtyOr9rcpnHc/GYxr59PUMbCpYV9AFkigChlJw8XNXVriaXXfqr9vLTkQG0T9BlsHfvXv3Lv/yLTpw44T9ucOmX8tNPP+20gLg6VVe75HSebXLZpb9qy8qiQxsKCGNBl8HKlSs1fvx4Pfnkk4qMtMQZqQCADhL0u7rL5VJpaam6devWmXkAACYI+nMGI0eO1GeffdaZWQAAJgl6y2DQoEGaNWuWJkyYoGuvvTZg2bJlyzo8GAAgdIIug++++06ZmZn+/wcAhI+gy+DJJ5/szBwAABMFXQbr1q1rdtlDDz3UIWEAAOYIugyOHg38yPrZs2d19OhRTZ48ucNDAQBCK+gy2LBhwxVjb7/9tl577bUODQQACL12fQPMpEmT9N5773VUFgCASYLeMrh05dJLGhsb9dprr6lPnz4dnQkAEGJBl8G4ceNks9n8tw3D0DXXXKPHH3+8M3IBAEIo6DLYtWtXwG273a5rr72Wy1MAQBgIugwGDBigv/zlL3r33XdVXV2tvn37auLEierZs2cnxgMAhELQZXDs2DHNnj1bsbGxGjBggKqqqrRq1Sq98MILuuGGGzozIwCgkwVdBqtWrdKMGTM0b948/9j69eu1cuVKvfjii50SDgAQGkGfWnr06FE98MADAWNz5szR//3f/3V4KABAaAVdBt27d1d1dXXAmMvlUo8ePTo8FAAgtILeTTR16lTl5eVp/vz56t+/v1wul4qLizVt2rTOzAfAZC19H/Ul/fvH6/jxoy3OgbUFXQYPPvigLly4oDVr1ujMmTPq37+/pk6desWuIwDhpaXvo75k376eIcmCzhPUbqJ3331XL774ohYuXKh33nlH+/fvV2xsrG644QbZ7fbOzggA6GStlsF7772nhQsXBnz62Ofz6eabb1Z+fr7ef//9Tg0IAOh8re4m2rBhg1asWKEpU6b4x7p3764lS5YoISFBzz33nMaNG9epIQEAnavVLYNjx47pjjvuaHLZ3XffrfLy8g4JcuTIEd1zzz1KTU3VpEmT/J9daGxs1PLly+V0OpWWlqY1a9bI5/N1yGMCAC5qdcvAZrPJMIwml9nt9oDdRz+Uz+fTvHnz9Mgjj+juu+/WZ599pvvuu09DhgzR7t27VV5erh07dqihoUG5ubnq3bu3Zs+e3e7HBQBc1OqWwfDhw/Xuu+82uaysrEw/+tGP2h3i3LlzOnPmjHw+nwzDkM1mU0REhKKjo1VaWqq8vDz16tVLcXFxysvLU0lJSbsfEwDwV62WQU5Ojh577DHt2rVLXq9XkuT1erVr1y49/vjjHfIXeq9evTRz5kwtW7ZMw4cP11133aWcnBwNHDhQX3/9tZKTk/1zExMTdeLECTU0NLT7cQEAF7W6m+imm27SkiVLtHTpUjU0NKhHjx46e/asYmJitHjxYmVmZrY7hM/nk8Ph0KpVq/Szn/1Mn3zyiR588EENGDBAkuRwOPxzHQ6HDMOQ2+1WVFRUux8bABDkh86ysrL005/+VB9++KG++eYb9e3bVykpKR32Zvz73/9ee/fu1eLFiyVJKSkpys7O1qZNmyRJbrfbP7e+vl6SFBsb2yGPDQBowyeQo6OjddNNN3VKiJqaGjU2NgaMRUZG6tprr1Xfvn1VWVnp30qorKzUwIEDFRnZfHSXy6WIiHZ9vXOnqq2tVVVVldkxmmWFfIZhyONxN7nswgWvpIvLmptzOTPmXJ7RCnm+71I+wzBafa1bei0un9PRPzNW+DlsidXzSVJCQkLQc4Mug840fvx4rVmzRv/1X/+l++67T59//rk2b96s/Px8VVRUaN26dRoyZIi8Xq82bNigrKysFtcXHx9v6U9GV1VVtelFCjUr5LPZbIqOjmlmqdu/rPk5f2XOHHez862R+WI+m83W6mvd8mvx1zkd/TNjhZ/Dllg9X1tZogwGDRqk9evX61e/+pWefvpp9e7dWw888ICmTZsmj8ejoqIiZWVlqbGxUVOmTNHcuXPNjox2CObCZ5fvGgTQ+SxRBpKUnp6u9PT0K8ajo6NVUFCggoICE1KhMwRz4bOysujQhAEgqQ3fZwAACF+UAQCAMgAAUAYAAFEGAABRBgAAUQYAAFEGAABRBgAAUQYAAFEGAABRBgAAUQYAAFEGAABRBgAAUQYAAFnoy20AhJ7b3ajY2J6tzOFb564GlAFwFTOMC3I6v2txDt86d3VgNxEAgDIAAFAGAABRBgAAcQAZnSA5ebiqq13NLufsFMB6KAN0uOpql5zOs80u5+wUwHrYTQQAYMsAQPsF8+G1/v3jdfz40dAEQptRBgDaLZgPr+3b1zM0YfCDWGY30ZkzZ/Twww9rzJgxGjdunAoLC+X1etXY2Kjly5fL6XQqLS1Na9askc/nMzsuAIQVy2wZzJ8/X4MGDdJ7772nc+fO6e/+7u+0ZcsWnTlzRuXl5dqxY4caGhqUm5ur3r17a/bs2WZHBoCwYYktgyNHjqiyslKPPfaYYmJiFBcXp40bNyojI0OlpaXKy8tTr169FBcXp7y8PJWUlJgdGQDCiiXK4OOPP9b111+vDRs2aMKECZo0aZK2bdsmh8Ohr7/+WsnJyf65iYmJOnHihBoaGkxMDADhxRK7ic6dO6dDhw5pzJgxevPNN+VyuTRnzhzZbDZJksPh8M91OBwyDENut1tRUVFmRQaAsGKJLYOoqCg5HA4tXLhQ0dHRSkpK0owZM7R3715JgZ9Yra+vlyTFxsaakhUAwpEltgySkpLk8Xjk8XgUExMjSfJ6vZKkvn37qrKyUgMGDJAkVVZWauDAgYqMbD66y+VSRIQleq5JtbW1qqqqMjtGs1rKl56eoa++qmnx/hdfy5YvOdHa8pbmXLjgleRu93o6c87lGa2Q5/vMeA4Nw2jTz31X/j2xioSEhKDnWqIMxo8fr759+2rlypVatmyZXC6XXnrpJeXm5urkyZNat26dhgwZIq/Xqw0bNigrK6vF9cXHx8tut4cm/A9QVVXVphcp1FrKd/r0KY0bd67F+5eVRSs6OqbFOa0tb3mO27+sfevpzDnuZudbI3Pon0Obzdamn/uu/HvSFVmiDKKiorR582YVFhbqlltuUWRkpKZPn657771XDQ0NKioqUlZWlhobGzVlyhTNnTvX7MgAEFYsUQbSxb/mi4uLrxiPjo5WQUGBCgoKTEgFAFcH6+5YBwCEDGUAAKAMAACUAQBAlAEAQJQBAECUAQBAlAEAQJQBAECUAQBAlAEAQJQBAECUAQBAlAEAQJQBAECUAQBAlAEAQJQBAECUAQBAlAEAQJQBAECUAQBAlAEAQJQBAECUAQBAlAEAQJQBAEAWLIO6ujplZmZq06ZNkqTGxkYtX75cTqdTaWlpWrNmjXw+n8kpASC8RJod4Pt++ctf6uTJk/7bzz77rMrLy7Vjxw41NDQoNzdXvXv31uzZs01MCQDhxVJbBr/73e/0xRdfKDU11T9WWlqqvLw89erVS3FxccrLy1NJSYmJKQEg/FimDKqrq/XUU0+pqKhIEREXY50/f15ff/21kpOT/fMSExN14sQJNTQ0mBUVAMKOJcrA6/VqyZIlys/PV3x8vH+8rq5OkuRwOPxjDodDhmHI7XaHPCcAhCtLHDMoLi5WXFyc7rrrroDxSyVw+Rt/fX29JCk2NrbZ9blcLv/WhRXV1taqqqrK7BjNaimfYRjyeFov4tbmtGcdFy54JbnbvZ7OnHN5Rivk+T4znkPDMNr0c9+Vf0+sIiEhIei5liiDN954Q6dPn9aYMWMkXdwi+Oijj7R//3717dtXlZWVGjBggCSpsrJSAwcOVGRk89Hj4+Nlt9tDkv2HqKqqatOLFGot5bPZbIqOjml1Ha3Nad863P5lHZGlc+a4m51vjcyhfw5tNlubfu678u9JV2SJMtixY0fA7ezsbE2cOFE5OTkqKirSunXrNGTIEHm9Xm3YsEFZWVnmBAWAMGXdfSn/X35+voYPH66srCxNmTJFY8eO1dy5c82OBQBhxRJbBt+3efNm//9HR0eroKBABQUFJiYCgPBm+S0DAEDnowwAAJQBAIAyAACIMgAAyKJnE8EcycnDVV3tkmEYstlsTc7hMiBAeKIM4Fdd7ZLTeVYeT/Ofni0riw5xKgChwG4iAABbBgBCw+1uVGxszxbn9O8fr+PHj4YmEAJQBgBCwjAuyOn8rsU5+/b1DE0YXIHdRAAAygAAQBkAAEQZAABEGQAARBkAAEQZAABEGQAARBkAAEQZAABEGQAAxLWJAFjI5Reza+57NbiYXeegDABYxuUXs2vuezW4mF3nYDcRAIAyAABQBgAAWagMDhw4oHvvvVejR4/Wrbfequeee06GYaixsVHLly+X0+lUWlqa1qxZI5/PZ3ZcAAgrljiA/M033ygvL09Lly5VVlaWTpw4oTlz5qhXr146deqUysvLtWPHDjU0NCg3N1e9e/fW7NmzzY4NAGHDElsGLpdLEydO1LRp02S325WYmKiMjAwdPHhQpaWlysvLU69evRQXF6e8vDyVlJSYHRkAwoolymDkyJFavXq1/3ZDQ4P+8Ic/6IYbbtDXX3+t5ORk/7LExESdOHFCDQ0NZkQFgLBkiTK4nMfj0cKFCxUTE6M77rhDkuRwOPzLHQ6HDMOQ2+02KyIAhB1LlcGpU6c0c+ZM1dbW6oUXXlD37t0lKeCNv76+XpIUGxtrSkYACEeWOIAsSUePHlVubq5uu+02FRQUqFu3bpKkvn37qrKyUgMGDJAkVVZWauDAgYqMbD66y+VSRISlei5AbW2tqqqqOmx96ekZ+uqrmhbnXLhgKDLyyo/2X87j8cjjcevCBa+k5re8PJ7Wt8pam9OedVyeryOydMaclp5DK2Tuys+hYRgd+vvzQ3X073FnSEhICHquJcrgq6++Uk5Oju6//37Nnz8/YNmUKVO0bt06DRkyRF6vVxs2bFBWVlaL64uPj5fdbu/ExO1TVVXVphepNadPn9K4cedanFNWFq0f/9jT6pyLH/9v+jIAl7S0LNg57VvHX/N1RJbOmdP8c2iNzF33ObTZbB36+/NDdfTvsdksUQYlJSU6e/asNm7cqI0bN/rH09PT9dRTT6moqEhZWVlqbGzUlClTNHfuXBPTAkD4sUQZ5OfnKz8/v9nlBQUFKigoCGEiALi6WHfHOgAgZCgDAIA1dhMBQLAu/wKc5vAFOG1HGQDoUi7/Apzm8AU4bcduIgAAWwYAwg+7ktqOMgAQdtiV1HbsJgIAUAYAAMoAACDKAAAgygAAIMoAACDKAAAgygAAIMoAACDKAAAgygAAIK5NBOAqxcXsAlEGAK5KXMwuEGVgouTk4aqudrU452r6ywSAeSgDE1VXu+R0nm1xztX0lwkA83AAGQBAGQAA2E30g4RyX38wZzy43e52Pw6Aqxtl8AOEcl9/MGc8lJVFd8hjAbh6UQYA0IyWtswNw5DNZgubM/66TBmUl5dr+fLlKi8vV79+/bRixQqNGzeuwx8nmF1A7JYBrg4tbZl7PG5FR8e0uhegq5xC3iXKoKGhQfPmzdOMGTO0efNm7d69W/PmzdPOnTvVr1+/Dn2sYHYBsVsGQLC6yinkXaIM9u3bp/r6ev3iF7+QzWbTpEmTlJaWpt/+9rfKzc01O16Tgtm8ZAsDgFV0iTKoqKhQcnKybDabfywxMVGff/65ialaFszmJVsYAKyiS5RBXV2dYmJiAsZiYmJUX1/fpvVwPABAR2vt9O+u8p7SJcogNjb2iifU7XYrNjY2YMwwDEmS1+ttcj1nz36jW275c4uP9d57fRQd3fT9L+nePbZdc2w2r6KivO1eT2fNuZSvsx4r3J+/yzNaJc/38Rx23HMYGxulH//4TLPrCOY9JTY2ttn3rfaKiIgI2KvSHJtx6R3Uwnbv3q2lS5fqvffe84/l5ubqxhtvVF5enn+soaFBH3/8sRkRAcCSUlJSZLfbW53XJbYMnE6n7Ha7nn/+ef3iF7/Q7t27tW/fPi1btixgXmRkpEaMGBF0EwJAuIuICO6qQ11iy0CSjh07pscff1yffvqp+vXrp3/8x3/UrbfeanYsAAgLXaYMAACdh6uWAgDCswy2bdumjIwMpaamaurUqdq7d6/Zka7w0Ucf6Z577tGNN96o2267Ta+88orZkZr0+eefa+TIkZY8MP+b3/xGmZmZSk1N1Z133qm33nrL7EgqLy/X9OnTdeONNyozM1Pvv/++2ZECHDhwQPfee69Gjx6tW2+9Vc8995ysuHOgrq5OmZmZ2rRpk9lRrnDmzBk9/PDDGjNmjMaNG6fCwsJOOxPohzhy5IjuuecepaamatKkSXrxxReDu6MRZj777DNj9OjRxqeffmr4fD5j69atxqhRo4z6+nqzo/mdPn3aGDt2rPHyyy8bPp/POHjwoDFixAjj008/NTtaALfbbdx5553G9ddfbxw5csTsOAF2795tpKWlGR999JHh8/mMsrIyY9SoUcZnn31mWiaPx2PceuutxsaNG42GhgZj165dRkpKivHVV1+Zlulyf/7zn43Ro0cbr7zyinHhwgWjoqLCmDRpkrFlyxazo11h6dKlxg033GBs3LjR7ChXuOeee4x//ud/Nurr641Tp04Zt99+u/HrX//a7FiGYRiG1+s1br75ZuPVV181fD6f8cknnxgpKSnG/v37W71v2G0ZfPHFF/J6vf6/diIiIuRwOCx1dtG2bduUkpKie+65RzabTampqXr11VfVv39/s6MFWLVqlSZOnGh2jCadOnVKOTk5GjlypGw2myZMmKDExEQdPnzYtEyXXzalW7duAZdNsQKXy6WJEydq2rRpstvtSkxMVEZGhg4ePGh2tAC/+93v9MUXXyg1NdXsKFc4cuSIKisr9dhjjykmJkZxcXHauHGjMjIyzI4mSTp37pzOnDkjn8/nv+xNRESEoqNbv9pBlzi19PsaGhp07ty5JpeNHz9egwcPVlZWlux2u7p166b169cH9WSEKuPHH3+shIQELV68WLt371a/fv2Un5+vQYMGWSJfnz599Pbbb+vIkSMqKSnR888/H7Jcl2sp47Rp0wIK/sSJEzp27JiGDBkSqnhXsPplU0aOHKnVq1f7bzc0NOgPf/iD7r77bhNTBaqurtZTTz2lF198UUuXLjU7zhU+/vhjXX/99dqwYYNeffVV2e12TZs2TfPmzTM7miSpV69emjlzppYtW6bHHntMXq9X+fn5GjlyZKv37ZJlcOjQId1///1NLnvnnXf0ox/9SEuWLNGIESP0xhtvaOHChSotLVVCQoIlMt50000qKyvTv/7rv+rJJ5/Unj17lJ+fr5KSkpC9mbWUr6ysTL/85S+1adMmdevWLSR5mtJSxg8//FDdu3eXJH355ZeaO3eu7r777qB+6DtLR102JRQ8Ho8WLVqkmJgYzZw50+w4ki5eOWDJkiXKz89XfHy82XGadO7cOR06dEhjxozRm2++KZfLpTlz5qhHjx6WeB59Pp8cDodWrVqln/3sZ/rkk0/04IMPKjExUbfffnuL9+2SZeB0OlVeXt7ksieeeEI9e/bU6NGjJUlTp07V66+/rm3btunhhx+2RMa5c+fq5ptv1k9+8hNJ0sSJEzVu3Djt2rUrZGXQXD7DMDR79mzl5uYqMTExJFma09JzeMmBAweUn5+vO+64w/S/JIO9bIrZTp06pYcfflgxMTF64YUX5HA4zI4kSSouLlZcXJzuuusus6M0KyoqSg6HQwsXLpTNZlNSUpJmzJihnTt3WqIMfv/732vv3r1avHixpIufPs7OztaWLVtaLYOwO2ZQXV2txsbGgLHIyEhFRlqn95KSklRbWxswdvlxDjPV1NTo4MGDWr16tcaMGaMxY8ZIku6//37927/9m8npApWWlmrOnDnKz8/XP/3TP5l+XCgpKUl/+tOfAsYqKyuVnJxsUqIrHT16VNOmTdOQIUP0H//xH7rmmmvMjuT3xhtvqKyszP9zd/DgQa1du9ZSl6lPSkqSx+ORx+Pxj1npTKKampof/v7XyQe3Q27r1q1Gamqq8cEHHxher9fYsWOHMWrUKOP48eNmR/P77LPPjGHDhvmP+L/zzjvGiBEjjPLycrOjNcmKZxOVlZUZw4cPN/74xz+aHcXP4/EYt9xyi7Fhwwb/2USjRo0yTp48aXY0wzAM49SpU0ZaWpqxbt06s6MEZebMmZY7m8jj8RgTJ040CgoKDI/HY1RUVBgTJ040XnrpJbOjGYZhGJ9//rkxfPhwY/PmzYbX6zU+/fRTIz093XjllVdavW9YfgL5hRde0H//93/rz3/+s/72b/9Wixcv1s0332x2rAD79u3T6tWrVVFRobi4OP393/+9Zc5I+L7BgwfrlVde0YgRI8yO4jdjxgx9+OGHV+yjX7RoUbPHGULBypdNWbt2rdavX3/Fbqv09HQ988wzJqVqXnZ2tiZOnKicnByzowRwuVwqLCzUoUOHFBkZqenTp2v+/Pmmb5lesnv3bv3qV7/SF198od69eys7Ozuo34mwLAMAQNuE3TEDAEDbUQYAAMoAAEAZAABEGQAARBkAAEQZAABEGQAARBkAAEQZAB3mt7/9rW688UZ9+eWX/ttOp1NfffWVycmA1nE5CqADLViwQN99951WrFihu+66S0VFRbrtttvMjgW0ijIAOtC3336rO++8U5GRkbrttttUUFBgdiQgKOwmAjpQr1699NOf/lQ1NTWW+jpJoDVsGQAd6JNPPtGMGTP0k5/8RMeOHdNvfvMbU786FAgWWwZAB/F4PPqHf/gH5eTkaOXKlfJ6vVq3bp3ZsYCgUAZAB1mzZo1sNptyc3PVrVs3FRYWatOmTfrwww/Njga0it1EAAC2DAAAlAEAQJQBAECUAQBAlAEAQJQBAECUAQBAlAEAQJQBAECUAQBA0v8DaJgxsr21Bl0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt, numpy as np, pandas as pd, seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('classic')\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "#Create some data\n",
    "\n",
    "my_data=np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n",
    "\n",
    "my_data=pd.DataFrame(my_data, columns=['x', 'y'])\n",
    "\n",
    "#Plot the data with seaborn\n",
    "\n",
    "sns.displot(my_data['x'])\n",
    "sns.displot(my_data['y']);\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5402e62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in 'xy':\n",
    "    sns.kdeplot(my_data[col], shade=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa1abe07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c58240d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\conda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y, data. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('white'):\n",
    "    sns.jointplot(\"x\", \"y\", my_data, kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf727c22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82a40a51",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\conda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y, data. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " with sns.axes_style('white'):\n",
    "        sns.jointplot(\"x\", \"y\", my_data, kind='hex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3f8c2f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    " sns.pairplot(my_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51975a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\"\"\"\n",
    "Plotly\n",
    "The Plotly Python graphing library makes interactive, publication-quality\n",
    "graphs online. Different dynamic graphs formats can be generated online\n",
    "or offline.\"\"\"\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "08e7acfe",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'plotly'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-108-3a5610ac57ea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m  \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
     ]
    }
   ],
   "source": [
    "import  plotly.graph_objs as go, numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f38fdef4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2a67c2ea",
   "metadata": {},
   "source": [
    "# Running Basic Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "364fb79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x236e74d8a90>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd, numpy as np\n",
    "%matplotlib inline\n",
    "df=pd.DataFrame(np.random.randn(200, 6), index=pd.date_range('1/9/2009', periods=200), columns=list('ABCDEF'))\n",
    "\n",
    "df.plot(figsize=(20, 10)).legend(bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520f1e10",
   "metadata": {},
   "source": [
    "# Bar Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "89bb3bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x236e7421280>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd, numpy as np\n",
    "%matplotlib inline\n",
    "df=pd.DataFrame(np.random.rand(20, 5), columns=['Jan', 'Feb', 'March', 'April', 'May'])\n",
    "df.plot.bar(figsize=(20, 10)).legend(bbox_to_anchor=(1.1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a48290bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2d8a723",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "63961e4a",
   "metadata": {},
   "source": [
    "# Stacked bar plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a6696ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x236e5f67d00>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd, numpy as np\n",
    "%matplotlib inline\n",
    "df=pd.DataFrame(np.random.rand(20, 5), columns=['Jan', 'Feb', 'March', 'April', 'May'])\n",
    "df.plot.bar(stacked=True,figsize=(20, 10)).legend(bbox_to_anchor=(1.1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5a4cf5",
   "metadata": {},
   "source": [
    "# Horizontal bar plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "1b50dd53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x236e1f22bb0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd, numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "df=pd.DataFrame(np.random.rand(20, 5), columns=['Jan', 'Feb', 'March', 'April', 'May'])\n",
    "df.plot.barh(stacked=True,figsize=(20, 10)).legend(bbox_to_anchor=(1.1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e1b371",
   "metadata": {},
   "source": [
    "# Using the Bar's bin Attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "e08aae93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x236eca0bf70>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "df=pd.DataFrame(np.random.rand(20, 5), columns=['Jan', 'Feb', 'March', 'April', 'May'])\n",
    "df.plot.hist(bins=20, figsize=(10, 6)).legend(bbox_to_anchor=(1.2, 1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b22137a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9622c86e",
   "metadata": {},
   "source": [
    "# Multiple Histograms per Column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba03df82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'April'}>,\n",
       "        <AxesSubplot:title={'center':'May'}>],\n",
       "       [<AxesSubplot:title={'center':'June'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd, numpy as np\n",
    "\n",
    "df=pd.DataFrame({'April':np.random.randn(1000) + 1, 'May':np.random.randn(1000), 'June':np.random.randn(1000)-1}, columns=['April', 'May', 'June'])\n",
    "df.hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80eb820c",
   "metadata": {},
   "source": [
    "# Creating a Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a67a9806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd, numpy as np\n",
    "\n",
    "df=pd.DataFrame(np.random.rand(20, 5), \n",
    "               columns=['Jan', 'Feb', 'March', 'April', 'May'])\n",
    "df.plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ab2ea0",
   "metadata": {},
   "source": [
    "# Creating an Area Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29db18b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13e3cfef220>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd, numpy as  np\n",
    "\n",
    "\n",
    "df=pd.DataFrame(np.random.rand(20, 5),  columns=['Jan', 'Feb', 'March', 'April', 'May'])\n",
    "    \n",
    "df.plot.area(figsize=(6, 4)).legend(bbox_to_anchor=(1.3, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2f7c0b",
   "metadata": {},
   "source": [
    "# Creating scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6384f32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Temperature over two months'}, xlabel='Feb', ylabel='Jan'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd, numpy as np\n",
    "\n",
    "df=pd.DataFrame(np.random.rand(20, 5), columns=['Jan', 'Feb', 'March', 'April', 'May'])\n",
    "df.plot.scatter(x='Feb', y='Jan', title='Temperature over two months')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab62806d",
   "metadata": {},
   "source": [
    "# Exercises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9d811a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "\n",
    "5. Write a Python script to create a data frame for the\n",
    "following table:\n",
    "Name    Mobile_Sales     TV_Sales\n",
    "Ahmed    2540            2200\n",
    "Omar     1370            1900\n",
    "Ali      1320            2150\n",
    "Ziad     2000            1850\n",
    "Salwa    2100            1770\n",
    "Lila     2150            2000\n",
    "\n",
    "For the created data frame in the previous question, do the following:\n",
    "A. Create a bar plot of the sales volume\n",
    "B. Create a pie chart of item sales\n",
    "\n",
    "C. Create a box plot of item sales\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "947dd9c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2cda68db",
   "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>Mobile_Sales</th>\n",
       "      <th>TV_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ayamba</th>\n",
       "      <td>2540</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sumaila</th>\n",
       "      <td>1370</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ali</th>\n",
       "      <td>1320</td>\n",
       "      <td>2150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samadu</th>\n",
       "      <td>2000</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baba</th>\n",
       "      <td>2100</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ibrahim</th>\n",
       "      <td>2150</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mobile_Sales  TV_sales\n",
       "Name                           \n",
       "Ayamba           2540      2200\n",
       "Sumaila          1370      1900\n",
       "Ali              1320      2150\n",
       "Samadu           2000      1850\n",
       "Baba             2100      1770\n",
       "Ibrahim          2150      2000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt, pandas as pd, numpy as np\n",
    "\n",
    "salesMen=['Ayamba', 'Sumaila', 'Ali', 'Samadu', 'Baba', 'Ibrahim']\n",
    "Mobile_Sales=[2540, 1370, 1320, 2000, 2100, 2150]\n",
    "TV_sales=[2200, 1900, 2150, 1850, 1770, 2000]\n",
    "\n",
    "df=pd.DataFrame()\n",
    "df['Name']=salesMen\n",
    "df['Mobile_Sales']=Mobile_Sales\n",
    "df['TV_sales']=TV_sales\n",
    "\n",
    "df.set_index(\"Name\", drop=True, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "915a5a17",
   "metadata": {},
   "source": [
    "# Bar plot of the sales volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2d3dae42",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-32-642c1658cc84>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-32-642c1658cc84>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    plt.xlabel(\"Salesmen\") plt.ylabel(\"Sales\")\u001b[0m\n\u001b[1;37m                           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "df.plot.bar(figsize=(20, 10),\n",
    "rot=0).legend(bbox_to_anchor=(1.1, 1))\n",
    "plt.xlabel(\"Salesmen\") plt.ylabel(\"Sales\")\n",
    "plt.title('Sales Volume for two sales in \\nJanuary and April 2022')plt.show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "476d0c2f",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-37-62a7a86729ee>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-37-62a7a86729ee>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    df.plot.bar( figsize=(20, 10), rot=0).legend(bbox_to_anchor=(1.1, 1)), plt.xlabel('Salesmen') plt.ylabel('Sales')\u001b[0m\n\u001b[1;37m                                                                                                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "df.plot.bar( figsize=(20, 10), rot=0).legend(bbox_to_anchor=(1.1, 1)) plt.xlabel('Salesmen') plt.ylabel('Sales')\n",
    "plt.title('Sales Volume for two salesmen in \\nJanuary and April 2022')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c5afd2",
   "metadata": {},
   "source": [
    "# Pie chart of item sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "34fab77d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='Mobile_Sales'>,\n",
       "       <AxesSubplot:ylabel='TV_sales'>], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ScYEaMAPbcHNbjk53iA8+qEevXt/QvHlzPDw8Ui1l48aN3Lhxg7Vr1zJr1iw++uijVPclI2PvXLlyhWXLl7F81XJitbEYSxkxlTVBLtLuxPwba9uNlOLy6hYK/AXu99wxhBlo3aY13/X5jmrVqiV7xfdNyLbDNsgOjRMiiiKnTp1i9rzZbAzYiDK3koTyCVAKy/e9I3ERhL0eiNrYVHbwGFiNu/tyTKaHtGnThu++65V4LDEljBs3jm3btvHll18ybNiwVOqRkbFfQkNDWbNmDQuWLODe3Xu4lHRBV1oHhUjefMKZCAfleSXCFYECBQowqN8gvvzyy8TTQSlBth22QXZonIj4+HiWLl3Kb3N+4+HDh5jLmTH5m8BHamVp4A8gqBSYU1e0829E4BwuLssQhGVUrVqdH38cQYMGDdI085KRcQbu3bvHqLGjWL92PepCahJKJVi2lBw3E4P10ANXQXFIgUKrpE+fvgwc+F1iDIqM/SBX23YCdDods2fPxi+/H0OnDuVu8bsYBhgwNXJwZwZQPFGA2d8KPQlAZUymORiNDzh5shaNG7ehfPnqbN26FbPZbIUxZGQci5CQEDp37UyxEsXYeG0jpl4mEtonQAVkZ+Y1aqA0mHVmjMY+zJ//kMKFi9G6dQeHTibqjMgOjQNjMBhYuHAheQrkYdjUYUR9FEVC5wTwx/G2lt6ECOYwM1Dfyh1nQxTHodc/5MqVtrRr14ciRcqyYsUKDAYHPkMqI5NMQkND6d23N4WKFGL12dUYuxnRN9Vbgnxl/stlEAQN8Cs6XQAm0022b3enZMmyfP11Vx4+fCi1QhnkLSeHxGQysXr1aoaPHk6UPoqEWq/iY5zNPY0BpgGEkb5LTTpgJW5uU8mRw4UFC2YkqX0iI+MsPH36lEmTJzHv93kIRQR0tXUOv4qb7oggzBQQo7oA//vXk3+hVo/DbN5Mt27dGDPmhyS1lmRsi+zQOBCiKFpqg3w/kPDocLQ1tVAW5w3WuwPCBhdEvdFGAxqB31Grx1C3bm3mzZuemF1TRsaRiYyMZOrPU5k5ayZCQQFtLS3kllqVg3AXWCuAMRLI8pZGV9BoRiOK+/n++6GMHDkcV1dX22mUAZxvTu+03Lt3j48afUS7b9vxoMQDtD21ln1uZ3VmAMJAIIcNB1QC/dDrb/Pnn7kpWbIs338/gtjY1J6wkpGRFrPZzJw5c8iTPw+zts5C116H9gvZmUkJihMKMNbg7c4MQFl0uq3o9Yf49dedFC1a1mp1mGSSj+zQ2Dl6vZ7RY0ZTolQJjr04hq6nDirj3I7MKxShCsz6UhKMnAOjcT5G4wlmzw4kf/5irFy50uHqmshkbG7fvk31WtUZ+uNQElokoG2nhbxSq3IwnoM52AzMSuYF1dBqg3j0qD+ff96Gli3bERoamp4KZf6B7NDYMRcuXKBSqVJMnjARfT09hs8M4C61KtshPhGxFJKSCn+02qNERk6ne/cRfPTRZ4SFhUmoR0bm/RiNRqZOnUrZ8mW5IFxA200LhaVW5ZgIpwUEZR6gYgqucgH6YjDcZNcuKFy4BHPmzJVPUtoA2aGxQ/R6PWNGjqRm1aq0vHeP9oKA+xHBkgDX3jEAWivcYkF8IQK1sdRaSe4t9cXU3NxO4efXhcKFq1CkSFkKFGhEtmwzEITP0WqvEBiYmeLFy7J9+/ZUj5FaHj16RPHixdm7dy8Aw4cP5/PPP7e5Dhn75ubNmxQtXJCx08aib6/H2NDoGCcerWU3Unt70+FGHYjnRETD2PfKf7PtWIfB8D8SEgIYOnQadet+IslqTUayHXJxSjsjODiY1p9/jnjvHieMRioAUcDOBIjfjaUAnL1iAGYCVg05SeFpIxdvMD0GUhaQ5+5+FD+/HsTEfE5Y2GRE0RWN5jre3otxdw8iJGQ1ev1a9PrVtG79NV991ZbZs6enqZRCSsiZMyerV6+mcGF5qi3zX0RRZN7cuQwbPBg/gwGFG2DdEmbpR7rYjRTiCfQnaTmYiyAo3BDp/M5Lk2M7tNqLnD7dmxIlyrF69VKbOhQZyXbIDo0dsXPnTjq2bcvXej2/GI2JE6sswCKg/VnQVQObxsmmBBMWozQQaZJy6YDfIrGk9kyZQ+PtvYj4+A8IC/sl8bH4+Fro9UXx8+uJh0cgcXF1gQ4YDLVYu7YjBw74s2nT6lSVUUgparWaypUrp/s4Mo5HVFQUX7dty8UjR9ip11MeKBYPCVuBlhKLSw52YTde6Xjt0JhBOCEg6r/hfRsZybUdev0q9PpVtGzZnq5dv2X69J9tchIqI9kOecvJDjCZTIwaMYJ2LVvye3w8M//hzLymJfCxIOC62gHS9Guw+BO2vqXBGLq4POdNFffi4mrx7NlADAYfvLw2U6xYcRSKTGi1f/LoUWdq1apH8eLF2bRpEwCzZ8+mZcuWHDx4kIYNG1K+fHm6dOnCs2fPCAgIoEGDBlSsWJHvvvuOly9fJo4TERHBiBEjqFWrFqVLl6ZWrVr89NNP6PWWCsT/Xjb+N7GxsUycOJH69etTpkwZqlevzrBhw5KMIeN8XLt2jarlymE6coRLOh31gKzAKkB1GbgvpboUYk924y6IcQBT3ys7ZbajMQbDBZYuDaJcuSoUL16czZs3A7LtsAbyCo3EPH36lPYtWxJy9iynDAZKv6PtAlGkSBRwBKhrG30Zhbi4Onh7LyF37l68fNmC+PiqmM1ZABWRkT0BcHW9/o8rXBDF4RiN1YEeLFy4hCZNmgDw4MEDfv31VwYMGIBOp2P8+PF07NgRNzc3fvjhBx4+fMgvv/xCvnz5GDJkCGazma5duyKKIj/88ANeXl4EBgaydOlS8uXLR8eOHd+rf8iQIdy6dYv+/fuTM2dOLl26xJw5c8iaNSvDhw+3+vslIz0bN26k01dfMdBgYJzZnGR2+gnwjULB6rUiCd+LGeJUpDVRnFRgNtYCvN7bNuW2ozBabSD3739PwYK7uXbtGi1bWpbSZNuRNmSHRkKCgoJo06QJ1aKj2aTXv/dPJzeWldEBh0FbEciU7hIzDM+fD8TFJRovr614eh5CFAV0uuLExX3EixffYDa/7bdjOf1w7twjatf+mJYtGxMbG8vChQupVKkSAEePHmX37t0cPHiQPHksgQ3Hjx/n0qVLAISHh+Ph4cGYMWMoWbIkALVq1eL48eOcOXPmvUZJp9MRHx/P2LFjqVevHgA1atTg0qVLnD59Oo3vjIy9YTKZGDl8OL/PnMkqg4Fmb2n3m9nMHh082QBiO5tKdGyegfle8o9qp852qDCZxgO7+e23WWTN6g0g2440Ijs0ErFx40a+ad+eCUYjA0XxDQuWb6YbsASB86tFjD3TU2HGQhTVhIdP4vnz/nh4HMTd/QTu7qfJlm0OXl4BhISsfuf1BsMILl4MICJiERqNCxUqVEh8Lnv27Hh7eycaJIAsWbJw584dAHLlysXatWsRRZGQkBCCg4O5efMmkZGR+Pn5ATBnzhzAMoP7NxqNhhUrVgCWGj337t3j9u3bBAcHo9GkLSjh0aNHfPjhh5w8eRJvb+809SWTduLi4viiaVOCT5wgyGCgxDvaegJrgQa3wHgT3tlYJhHhtADKvIiG8slqn1bbYTINYfDg8dStWwlBEKxuO95FetoOKZAdGglY9L//MaBPHzYYDHyWwmsVwApRpGwYcJ6UpUeQeS9Gow/R0e2Jjm4PGPHy2o6PzxiyZZtDQkK1d1ypRq8PIDa2JUrlNQ4fPsyHH36Y+Kybm1uS1oKQ1IUNCAhgxowZPHv2jJw5c1KhQgU0Gg2iKKLVajlw4AAAhw8fpkePHv8Z/eDBg0yePJmQkBC8vb0pW7Ysrq6ucu4LJyIyMpLPP/oIzfXrnNLpyJyMa2oD3wkCv2+ChKGiYxzhlhKt5ag2ph9TfGnqbUdR9PrTXL78KVmyQExMDFmyZEl8Ni22Izk4k+2Qg4JtzC9TpzKkb1/2pMKZeU1xYIwg4L5LsBzokUkTrq4XKVy4Cq6uF/71jJKXL1sSF1cXjeYurwP/BOFvQ6FQxP+jvQJR/BDQ8OmnTVmyZGmyxj99+jSjR4+mffv2nDx5kmPHjjF79uzEFZFdu3aRO7clV/3ly5cJDw/HZDJx9+5djh49yv379+nfvz+lS5fGzc2NQ4cOMWTIEJ4/f05wcDDlypWjXbt2BAcHA7B582a6dOnCmDFjqFSpEnXr1uXgwYP88ssvVKtWjVq1arF169YkGtesWUPdunWpV68ec+f+nSQsLCyMvn37Uq9ePcqVK0fz5s05f/58sl63TPJ5/PgxdapWxffaNfYk05l5zU+iSG4jKN69UCADlqPaLu7AN8lqbj3bUQCjsQsmE9So0YDnz58na/z32Y738dp21KxZk6NHj3Ly5EkWLlxIwYIFk3W9vSE7NDZCFEVGDB3KL2PH8qdeT+009ve9KOJnBsVaq8jL0Oj1BRAEPVmyrHjDsyZUqofodEUxmz0BUCojEp91czv7hmtUGAy76dmzP4sWLX7v+BcvXkQQBLp3755oiMLDw7l9+zaiKLJu3brEvBXFixdn9erVuLi44Onpya5du7h+/ToGg4HMmTPz6aef4ubmRr9+/UhISKBAgQKcOHECDw8Pfv/998QxAwMDKVu2LGfPnqV58+b069cPDw8Pjh8/Tq9evfjxxx+TzPBu3brFrl27WLJkCRs2bGDLli0AjBo1Ch8fH/bt28eZM2coVaoUv/7663tfs0zyuXXrFjUqVqT6w4ds0OtTmJDAcpBngyji8gC4lA4CnQUROCEg6t+dd+afWNd2WI553b1biA8+qE94ePh7x3+f7Xgfr21H165d8fGxlF2Pi4vj3LlzDlnqJUM7NBMnTqR48eKJAVZgCdT19/cH4OzZs9SsWTPN45hMJnp368bq2bMJ1OnwT3OPlpXjlaKIyz3gLyt0mIExm7Pw7NlAvLx2kyfPN2TKtBU3t1NkyrSdPHm+QamMIDKyF/Hx1TCbNeTIMQF39yNkzryO7NmnYza/aR2/LgbDLnr3Hsi1a9feOX7ZsmUxm81MmDCBEydOsHHjRr766iv0ej3Pnz/n/v37NGjQAIDatWuzfv16TCYTXl5e7N+/n8KFC+Pi4sK2bdsoVKgQO3bsQKVSER8fT0JCAk+ePCFLlixJDGS2bNlo06YNgiBQvXp1RFGka9euKJVK6tSpQ1xcHNHR0Yntv//+ezw9PSlUqBAdOnRg165dgOVvaMiQIYBlFcHLyytZhlgmeVy8eJFaVavS/vlz/mcwpDpGoCLwgyDgtl2ABGsqdCKCgXiAycm+xPq2Q0CvX8eDB2WpXr0ecXFx7xz/XbYjIeH9v+iSJUvi4uLC1KlTCQwMZMeOHXTs2JFnz54l63p7I8PG0Gi1WrZv307r1q1Zvnw506dP/0+bypUrc/z48TSNYzQa6di2LRd27+a4TmfV2nDVgB6CwJKNEG9PRzN1jjduVNS3GAz5yZJlFTlyTMHFJQaTKStxcTUJC5uM0Wj5zYWGziB79l/Jnbsven1RQkN/wc+v91t6rY3BsJNDh9qRN+/byxt/8MEHjBgxguXLl7N161Z8fX1p3LgxKpWK+fPnYzab+fLLLwFYsWIF8fHxhISE4ObmhiiK3L9/nx49erBgwQJmzZpFjhw5KFCgAFFRUTx58oTx48ejUCSdu2TNmjXxvkKhwNXVFbXaYlxf79H/cw/9nwGGPj4+iU7LvXv3+OWXX3jy5AlFihTBw8PDIWd29siNGzf4uG5dBsfEMNwK7+lIUWSzWeDGSjB2t4JAayOx3RCCBERjPSzh1MnH+rZDiV6/gsePu7JuXQB58uR669jvsh3Lli1LzEXzNgoWLMjUqVOZM2cOvXr1IkeOHNStW5e2bdsybtw4wsPDE1duHAFBzKDWZ9OmTaxdu5aZM2fSqFEjDhw4gI+PD0FBQfTs2ZMLFy4kuZ8aRFGke6dOnFy/nkNaLTmt/BrAkmCzCBBeDumzgtpDCvNUlj5IX46gUn3OwoWz+fbbb5N9VWxsLLVr12b69OmJRzIBVq9ezZ9//snOnTuZMWMGDx48IFu2bLi6ujJkyBDCwsJo0KABy5cvT8xiPH36dC5cuMDKlSvZvHkzS5YsYefOnQD/+Zz/82RTfHw8H374IUePHk00bPPmzePChQvMnTuXatWqMW7cOJo1sxweXr9+PQsWLODQoUPWeOMyLPfu3aNWlSp0efGCH60YnHkDqADoGwNVrdZt2rAHu+HOq9WZK0AZCYX8EzNKZW+yZdvL2bOBSU46ybyZDLtCs27dOtq2bYufnx81a9Zk9erVDBo0yKpjjB8zhr3r1nFSp0sXZwYsc4mlQPPLoK+OJVmNVKiw1EMxpaGPSyAc8kDUPUnd9SY19uXMgGX7aQfduzdBEAS++SZ5AYfbt28nW7Zs1K9fP8nj7dq1Y9GiRZw4cYLmzZvTpk0bMmXKxIIFCwDLHrjZbE5Mq3727Fk2btxIvnz5Uv0Kpk2bxtixYwkJCWH16tWMGTMGg8GAXq9PHOfWrVssWbLkvbNCmXfz+PFjPqxZkzZRUYy38kmTklg2VEbthYSS2EcuK2vYjbSyD4TLBRCN9uLMACgwGn8nMrI79et/ypkzx5KcfpL5LxkyhubGjRvcv38/MdCyQ4cOrF+/Hq029ZWa/83CBQuY9fPP7NXp0r1G3KdAc4UCtzV2UBZBRdrSkD8HUV8YS4bO1NzszZl5TT0Mhu1069bnPyeI3sb69esTsw//k9y5c1OjRg2WL19OgQIFKFSoEJkzZ6Zo0aIAFC5cmAEDBtC9e3eqVKnClClTaN++Pffu3cNgeFNZ4fdTqFAh6tevT69evejRoweffPIJHh4ejB8/nkmTJlGxYkWGDBlC27ZtiYyMJDIyMlXjZHSePXvGx7VrU//ZM6abTMnOT5USBgDlRQH1cjuwF69Jq91Iy00ELoFonJD+rzPFCBgMvxMSkp9GjZpb9TvKGcmQW05jxoxh06ZNeHr+vVcaFRXFhAkTyJ8/f5q3nLZt20aHNm3YazBQy9ri38JToDAQUwNoaKNB0wHFIgXmR19jWXdyRrai0XQkMPBPqxWM69+/P/7+/inazpKxPxISEqhbrRoFb91ijV6friFx94BSgLY+chmVEyAc9kDUS7nn9T7icHVtwCef5Gfz5nX/iYlLDRMnTmTlypVs2LCB8uWTl0TQGqRnss4Mt0ITGxvLjh07mDNnDtu2bUu8de/ePTFjYlo4ceIEHb74gpU2dGbAUoB7LuB6Enhhw4GtiQjmcDPQQGol6Uhz9PofadiwyRuz/qaE0NBQjhw5wsmTJ2natKmV9MlIgSiKdP7qK9R//cWKdHZmAAoCswHXwziuvbAGZuCkgKjvJrWS9+CBVruTP/64yHffDUpz4P2/D8U4CxnOoflnXIKvr2/irV27dty9exeTKfUbuTdv3qTJJ5/wi15PCytqTi5fAdURUDtCRe438RJLgCCfSywkfRHFAcTEtOTjj5sQG5v6WeHKlSsZNGgQQ4cOlcsSODhTJk3i+K5dbNZq01I0PkV0AWoLAq7LHNReWIPbvDrG/pPEQpJDDrTavSxatI7Zs+ekqaddu3aRL18+evfuzf79+wkPD0en01G5cmWOHj2a2O7WrVtUrFiRhIQEbt++TZcuXahdu7bdJuvMcA7N++IS+vTpk6p+Y2JiaN6oEd3i4+mVVpGpRACWiCLCMxFOSSQiLYSBoFECWd/b1LERMBpn8PBhdtq1+zbVs63vv/+ec+fO0aZNGyvrk7El27dv56fx49mejocH3oQALDeLKKNF+MOGA9sRipMKMH6I5ZiTI1AInW4rQ4YM58SJE6nu5U2HYjQaDZ988klijimAHTt28Mknn+Dm5kb//v2pVq0aR44csdtknRnOodm2bRv9+/d/43OLFy/mwoULiTEz1apVS1b8jCiKdO3YkTxhYfwkcf2LgsAkwG0fjpdAKxwEs+PkPEgbKnS6DRw4cI4JExxhdiiTHly7do2v2rZlqcFABQnGzwUsAjQngTAJBEhJBJgfJr+qtv1QHaNxMs2afUFERMT7m/+Ldx2Kad68Ofv370en0yGKIrt3707czl64cCGdO3dGr9fbbbLODHts25rMnjWL43v3cl6ns4vcdv2BZaLA9TUipi5Sq0k+ilAFZoM9HZtMb7Kj1W5l4sRafPBBNT7++GOpBcnYkNeruv31eqRcY2sLbFAo2LNSJGGwmGGmuUKQAlwKIBpLvr+xnSGK/Xj58jgtW7bnyJE/cHFJ/jfP2rVriY+Pp169eomPRUVFsX37dtq0aUOWLFk4fPgw2bJlw2QyUa2apbDmlStX6NGjBy9fvqRo0aIYjcYk/dpDss4M8tFNP86ePcuIoUNZb+Pl4nfhgqUsghACvDvrvl0hPhGBGlLLsDHlMRim0a7dt/JR5wzGgN69yfP0KePsoKrxQrMZ1zgRdkitxEYkgHjRjGicJLWSVCKg1y/i3LlHjBkzPtlXve9QjCAING3alL1797J3716aNGmCQqEgLCyMIUOGMH78eAIDA1m6dGliiaBERULKYrGePn2aeP/x48fkzp0bvV5P7969+eabbzh16hSrVq1KLPuSHGSHJg3ExsbSrkULRhmNpL3ik3UpDwwWBNy3CmB8b3Pp0YMYLUKqa5A7Mt2Ija1I58695LIBGYRNmzaxecMGVtjJqm42YCWgugA8lFiMLTgPgosnlvUpRyUTWu0mfvnlN/bu3ZusK953KOZ1ss7AwEAOHTqUmAH8bck6U5vXCizJOuPi4rh58yarV6+mdevWaU7WKTs0aaBfjx7kffqU7+30S2isKJLdKMJGGw5qALSpuD3i1aexMJbjTqm9pT3xVL58zShWrDgazfUkj3t5baZYseIoFJaVlDx5OpI7d480jwcCOt0i9uw5xNq1cvl0Z+fx48d0//ZbFuj1Vq3tllY+A9oLgiVBp62z9qbWbqTmFg+cAFHfCWvaDZDCdpTGYJhJhw6dk7XC6+zJOjNkYj1rsGnTJrq3b89lvR6/9zeXjCPAx4ChE5A/nQczgHo66CUMRlbjjZ7U13JSq2+RP38z9PrCJCRUJSJibOJzXl6b8fUdwZ07JzGbvVGrbwEu6PVFrCOerbi7f8vNm1fIm9eevupkrIXZbKZhnTrkOX2aZWmY3aYXL4HiQFgJ4EsbDeoEdgOktB0iGk1TmjTJTEDAKiv057jJOuUVmlTw8uVLvuvenRl27syAJQloB0HAbZ1gSSKVnpgsRikEiJbgFgLoiQRSX0vIy2srOl0poqPbkCnTTgTh7TM3vb64FZ0ZgOYYDK348stvkwTPyTgPs2fNIvjcOWbZoTMDluIhawHVTeAvGw3qBHYDpLQdAjrdQrZv35Pssipvw9GTdcoOTSoYO3IkxeLj+UpqIclkuijimiDCHtuMl9oqTNa4pQ0TXl47iIurQ0zMpygUsXh6vn1v2nrLxn9jMMzg/Pl7zJzpaEdJZd7H48ePGT1iBIu1Wit8VtOPekBPQcBtg/Aq0aVtcFy7AdLbjlzo9TPo0qU3UVFRqe7F0ZN1yg5NCrl48SIL5s9nnlabLoXj0oOswP8AzRksRZ9k3oi7+3GUyqfExDTBZPIhPr46mTPbMgAJLIF+SxgxYjShoaE2HlsmPRncty9NzGbqv7+p5EwVRXyNIoIc0pUs7MN2fEVcXBkGDRqW6h4cPVmn7NCkALPZTK9vv2WA2YyjZS5oCXyoEHB11LIINsDLaxtabWn0+sIAvHzZDHf3M6hU92yspB7wCd9/P9LG48qkFwcPHmTPrl38mszTGlLjBqwXwSUYuCq1GvvHPmyHgE63gJUrV3HqlCOmik87skOTAhYtWkTYrVuMcsD4BgFYaBYRokQ4+t7mGQ5BiMXT8yCxsR+jULxEoXhJQkJ1zGaNBDMt0Ol+Yd26dZw7d87mY8tYF51OR5/OnfnRYCCX1GJSQBVgGOC+RbDWISCnxL5sR0FMpsH07j04Q6aAkB2aZBIREcHwQYOYrdU6TNWPf+MHTAfc/gRiJBZjZ2TKtBeFIoHs2WdQpEgVihSpQqFCdVEodHh5bcX2yXwKYjYPoGfPARnSMDkT03/9FdeICFJXJU5axgAFzKC0zuEZp8TebIcofs/168Fs2rTJpuPaA7JDk0xGDRtGHaPR4etAdwfKIKBcLbUS+8LLaxsJCeUICVmR5BYRMRKl8hmenn/aXJPZPIIrV+4QEBBg87EzGhMnTqR48eJcunQp8bGgoKDEbKhnz56lZs2Up898+vQpkyZMYI5W65B1ZtTAelFEeASclVqNfWJ/tsMTnW4C/fsPQ6fT2XhsaZEdmmRw//59VqxaxVQn+HAogBWiiBAGXJRYjJ2gVD7Bze0MMTHNSEioluQWFdUeozEbXl5SOBWZ0Okm8d1335OQ4GiVRh0HrVbL9u3bad26NcuXL39jm8qVK3P8+PEU9z31p5+oC9RKo0YpKQNMBNx2A7ESi7Ez7Nd2dCIy0oO5c+dJMLZ0yA5NMpg0fjytFAqKSy3ESpQARgkCbjuEtKZecAosy8ICMTEN3/CskpiYxnh4BKJUSlGO+Buio7MzffoMCcbOGOzatYt8+fLRu3dv9u/f/8bKvv9crUkuoaGhzJs3jx+dYCI0GCgjCqhWyIcK/on92g4XtNpfGTNmQoaqESc7NO/hwYMHrFi1ilEOcjohuQwXRfzMIKyzft9pKVyQ1ltq8PLaTkJCRUymN5cXjYlpiiCYACliWRRotT8xdep04uPjJRjf+Vm3bh1t27bFz8+PmjVrsnq1dfZjJ/34I40UCipapTdpcQHWiiKKCBEC02cMR7MbYO+2oyEmU+UMNRmSSx+8h15duxK1ciVrncyhAQgCagOGDkBRK3ToJCnM7Q8RN7eK/PprN3r37i21GKfixo0bfP311xw9ehQ3NzeOHTvGkCFDOHLkCJcuXaJnz55cuHCBoKCgxPvJ4eHDhxQvXJjTRiNl0/k12JLfgUECaPsDWazUqWw30pHDeHi0JCzsIZ6enlKLSXccMU7NZoSEhLBs+XLOGh2hXHXKqQZ0FwSWbISE70XSXPZXBfpBpKyw3R/AhfJY6yy5HjXOZ5QEEhK+Z8KEkXTv3h2lUv6ztRZr164lPj6eevXqJT4WFRXF9u3byZ8/9cXPJo0fT3OFwqmcGYCeQAACJ5aDrr+V5sKpsRvJ5QRw0gsMIe9s5px2A6AuZnNRFi78H4MGDZRaTLojbzm9g6kTJ9LExYXSUgtJRyaLIl46EbZZqUMVFruQzJsiTIHFtbJWEnNnNEoAbYiKEjPkUcz0IjY2lh07djBnzhy2bduWeOvevTsrVqxIdb/h4eEsW7HC6bapwZLPaqUo4vJChANW7DiFdiNZNxWWk1mGfmRcuyGQkDCMyZOno3fCz+O/salDI4oiR49aZuIRERGMGDGCqVOnEhcXZ0sZyeLJkycsXrKE0U4Q0PcuMgFLAfVlwNaZ9s1gfmoGPrLxwI6IEq12MGPHTpXz0liJ7du3ky1bNurXr4+vr2/irV27dty9exeTKXVLBvPnzaOeUum0EyE/YCGgCQQiJBbzLm4ABhcs2XQyMs2Ji/NgzZo1UgtJd2zq0EydOpUxYywfrrFjx/Lo0SNu3rzJ+PHjbSkjWSxZvJj6SqXTLRm/iU+BZgoFbmtsfIIhilfLzJ/adlyHpRP37j3kwAFrTo0zLuvXr6dJkyb/eTx37tzUqFGDPn1SngpPp9Mxb+ZMBmqdO7Vue+ATQcBthQB2mjhdOKEAw+dYsulkZBQkJAxl/PifMTtglvuUYNOg4I8//piVK1fi6elJ9erV2bNnD97e3jRo0ICgoCBbyXgvZrOZIn5+/BoWRkupxdiICKAwEFsT+NhGg94AYasa0clXwayJIIynRo1AAgP3Sy1F5g2sXr2aCd26cSMhwWGK16aWp0AxIKoS8F+/UFoeA4sB830g9bFQzoMOjSYvu3evo0GDBlKLSTdsukITHR2Nr68vJ0+eJHfu3OTNmxeVSmV3XuPhw4eJffHC4bMCp4ScwFxAcwLLyoktCAfMjlTdRnpEsRcnTx7hwYMHUkuReQPzfv2VXhnAmQHIASwH1OeARxKL+RdCkAJBKIXszLxGg9HYkd9/XyK1kHTFpg5N8eLFmTNnDkuWLOHDDz8kNjaWKVOmULasfW3sLJ43j68Nhgy3UNkRqI6AepVtzLHiiQLRYF+/e/snJ2r1x6xaJdeusDcuX77MhatX+UZqITakKdBGEHBbbUdbT7EgXjUjmqZKrcSuMJk6sW3bJqKjo6WWkm7Y1KGZMGECV69eJV++fPTt25ebN29y48YNu4qhefHiBZu3b6eLna0a2QIBWCqK8Ey0JKlJZ8yhIpDy+jgZHa32KxYuXCkHB9sZq5Yvp5VCYbX0LI7CXFEkU4II9nIA75yAoMwCGWqNPTmUQaUqw7p16ZBN1U6QLLFeZGQk3t7eUgz9TubMmcOa77/nRAaunTMNGK2AhKGAWzoNogWmAFzBUi1GJvnEo1L5cuLEISpXriy1GBkscXcFfX35/elTGkstRgIOYgntN3wNFJJQiBGLAUsYC4yTUIi98jtlyy7n8uVTUgtJF2y6QmMwGPj555+pWLEiDRo0ICQkhBYtWhAaauvzwm9n8ezZdMnAzgxAf6CwKKBIz1N+EYBSQHZmUoM70IqlS1dKLUTmFadOnSImOjrDJiD4EOgqCLivEyxOhVTcAIwuwCgJRdgzX3L9+kWuX78utZB0waYpR6dNm8atW7dYsmQJ3bt3J2fOnBQtWpTx48czf/58W0p5I1euXOF2cDBfSC1EYpTAKlGkcgiYrwOlUnCxgeRl/HwEgsILMU2VVN5E6jN+urmdwtv7f7i6XkYQtBiNfsTEfEJkZDdE0b7ShhsMHVm16kuiop7h6enJggULpJaUoVm3ahWtRDHDxd39k19FkV0GCFkH4lcpvDi5duN9BAKGxkBK656lLVOw49iOrCiVTVmzZi0PHtzH3d3dqWyHTR2aPXv2sHXrVrJmzQqARqNh3Lhx1K9f35Yy3sq2rVv5xMWFTE5a6iAllAcGCgJzt0B8MTF5nxQDMEsJMcl7/0SigcxpUPkGlD5gvE9KjZO7+1H8/HoQE/M5YWGTEUVXNJrreHsvxt09iJCQ1aS9NoQ1qYder+HFixcZokaLPWMymQhYu5ZVBoPUUiTFHVgvQp07YEjJRCiFduP97CDFdiWVdgMcz3bodM3YsOFXqlVzvtVxmzo0JpMJtdoyh3kduiOKIiqVypYy3sqO9evpJedESWS8KLLWCPEbgS+TcYEJi1HasAHc3dNZ3RuIj4cvvgD0pNQweXsvIj7+A8LCfvlHd7XQ64vi59cTD49A4uLqWldvmlCg139BRMRR8ubNK7WYDM3Ro0cRExKoJ7UQO6A6MEgQmLUZEgqLoEnGRQ5sN8ARbcen3LnzDeXLF8Fdivc7HbGpQ1O7dm1GjhzJyJEjEQSBhIQEJk2aRK1atWwp442EhoZy9to1PpNaiB3hBqwQ4ZObYHgI5Evmhe7u4OGRjsqsj4vLc4xG3/88HhdXi2fPBmIw+ADg6nqZbNlm4+p6AYVCi8Hgx4sXnYiOtnh8Xl6byZFjKo8fz8fHZwwq1QN0upKEhU1Grb5H9uy/olKFkZBQgfDwSRiNljw8CkUs2bLNwNPzIErlU0wmT+Li6vL06UjMZi8ABCGOnDkn4+m5DxCIjKxLZOSLxMnBo0eP+PDDD5k5cyaNGjVKfA3NmjWjZMmSTJkyJT3fwgzLzu3baWoy2dEcXFrGiyJbTAJ3VoG5SwoudEC7AY5pO2Jiyic5mOMstsOmQcEjRoxAq9VSu3ZtXr58SaVKlQgPD2f48OG2lPFGVq9ejVkQyOfuTl1gCZaV0IxOfaCDIOC2zo7yTKQDcXF18PAIJHfuXnh67kOhiHr1jIrIyJ7o9SVQKkPJk+drzGYNoaHTePJkFgZDPnx8xqJW30zsSxDi8PUdyosXnQgL+wWV6hF+fj3JkeMXIiP7EhHxA25uF8ie/efEa3x9h+DpeZBnz/rz+PFCoqK+wctrJ97e8xLb5Mo1CE/PfTx7Npjw8B/x8rqOUqkgPj6l8QKOQ2RkJNOmTQPg4sWLfPjhh7Rq1Yq7d+9KrOxvDuzYwcfyNnUiGiBAFHEJAS5IrSb9cUTb4eam4tEjO8uGaAVsukLj5eXF/Pnzef78OY8fP8bHxwcfHx9bSngrx4KCoGVLtEWKEHjiBEfPnKGryUReQeBzvZ7vgOJSi5SI6aLI1nhI2APOuoT1/PlAXFyi8fLaiqfnIURRQKcrTlzcR7x48Q1msxcazS202rKEhk7ndX2YhISKFClSDXf3s+j1JQBQKAw8f/4dMTFNAXB1vYK39/8ICVlJQkLVxMc8PAIBEAQdCkU8ERFjiYurB0B8fA1cXS/h7n4aALX6Jp6ehwkNnUZMzOevxq5AoUJ1ePr0qa3eJpszZswYtFotoijy448/0qBBA9zd3Rk3bhwrV0p/yis8PJyrwcHYRxSg/VAOGCcITNwhklAcS4CNk+KYtsOTQoU6YXQyR9wmDs2+ffve+HhYWFji/YYNG9pCyhsxmUwcOngQJk6EUqUwN2oEJhPi9es8DApi/rFjzAsJwc3NjWrx8XwLdMDG3qCEZAX+B3x1BnRVseQ8dzJEUU14+CSeP++Ph8dB3N1P4O5+mmzZ5uDlFUBIyGri4uq9Mhp61Oq/UKvv4ep6CQBB0CfpT6v1T7xvNGZ/9djfWZFNpqwoFC9fja3h0aMVACiVoajV91Crb6NWByOKliAEN7fzAIlGy9KHDwkJPkREPLPqe2FPXL58mX379hEeHs7NmzdZunQpnp6eVKlSRWppABw6dIgK7u5kj4uTWord8b0oshGBK8tFjL2kVpN+OKbtqIFOZ1kBdSZs8p38vv03QRAkdWguXbqEzmCA4v9Yg3FxgbJloWxZzF27wvPnJJw5w9Hjxzl85gydzGbyAU0MBr4Dikol3ka0ApYoBA6tAV1/581QazT6EB3dnujo9oARL6/t+PiMIVu2OYSHTyJHjilkzrweQTBgMOQjIeH1F2vS98Rk+m8sgCi+PUuhh8dBcuSYjFodgtHojVZbFlF05fU+n4vLS0RRhdmc9EST0ViI588PO91M6zV6vR6FQsGxY8coWbIkmTNn5sWLF2g0yYk2TX8O7N7NR0685ZcWlMB6UaRMOHAS+EBiQemMY9kOAaMxh+zQpIZDhw7ZYphUExQUhKpUKQwu7wjry5YNGjX6e/Xm2jUenDrF3MBA5jx6hPur1ZtOQDucb/VGAP5nFin6AjgK1JFYkBVxdb2In183Hj9emGR2BEpevmyJp+dBNJq7eHv/TubMAYSF/UpcXG1E0Q1BSCBz5oA0ja9S3SdXrv68fNmKR496YzRatmFz5eqPWm2JFTGZsiAIBhSKaMzmv4+kurgIiCKcO3eOPHnyAPyn2Ksjx9jUqlWLPn36cPv2bbp3787Dhw/5/vvv7aJisCiK7N+7l8VyCYq3UhT4Ffh+H2hLYfUsDVLjyLZDEDx49izs1X1L/T5Htx02DQoGCAkJ4ezZs5w5c4YzZ85w4sQJli1bZmsZSTh28iTxxYol/wIXFyhXDrp3R1yxAjZsIL5PH47UqMHXGg1qtZqCKhX9gXvpptr2+AHTAdc/gViJxVgRvb4AgqAnS5YVb3jWhEr1EJ2uKG5uF9FqSxMb2zBxxuThcexVu9R/qWk011EoDLx40TXRIAlCHG5u5xL7jY+37J9bTilYUCiicHO7CGTnwIEDifloIiIiEtuEh4c7dPDfxIkTqVGjBt999x0dOnRAr9dTrVo1xowZI7U0QkJCePL8uVyN7D30AaogoFnhfDXIHdt2hPPyZTRGo9FpbIdNFxLmzp3L7NmzUalUCIKA0WhEFEXKly/Pt99+a0spSTgeFATfpKFGbvbs0Lgx5saNwWhEvHaN+6dOMTswkFmPH+Ph6soHCQl8C7TFsVdvugNLELiwWsTYQ2o11sFszsKzZwPJmXMySmUk0dEtMBp9USojyJx5A0plBJGRvfDy2oK390KyZFmGTlccV9crZMv2O6IoIAjaVI+v05VEFF3Inn0q0dFf4uLygqxZl+Li8gxRtAQQGgyFePmyKTlzTkKhSMBgyI2390LAjNmcjePHzzNyZGbKly/P4sWLyZkzJy4uLsydO5fMmR13Wuzq6kqnTp24du0a+/bto169enz11Vd2seV08eJFSrq74y7Hz7wTBbBaFCnxHDgESL+4ZjUc23ZYuHr1KhUqVHAK22HT79Z169axdOlSzGYzu3fvZuzYsUycODEx2Z4UREdHE/LXX1CihHU6VCqhfHkoXx6xRw94+pS406c5dPw4B86doyNQUBRp+ir2pqB1RrUZCmC5KFI+FLgIVHhDI6mWKdMwblTUtxgM+cmSZRU5ckzBxSUGkykrcXE1CQubjNGYl8jI7iiVT/H2no9CoUOvL0B4+Fi8vHbg5pb686kGQ0HCwqaSLdsccufuhcmUg7i4ukRHtyVnznG4uIRjMvkQFvYTOXJ44+09D0EwEh3dBqMxN4LwgMuXrwAwefJkxo0bx7Bhw8iRIwc9e/YkMDAw1dqkJjw8nH79+nHnzh1EUWTjxo20atWKhQsXUrVqVUm1XbhwgQoZPDtwcskLzAN6HAVdOSD7Gxo5oN0Ax7YdSuWfnDx5kgoVKjiF7bBpte2KFSty/vx5IiIi6NatG9u2bSM2NpbPPvuMI0eO2EpGEg4ePEiTr78mYfXq9B/MYIBr1+DUKYRjxxBDQ/FwdaVmQgKdgTZIsAeYSsYLAlNdIOF7kcQCNlZPYZ4K0pDC3HF5jCDkJTY21ukyf/bt25cCBQowYMAAPvjgA86cOcPq1avZunUrAQFpiz9IK80bNqTO/v0MklSF4yACnykE/vQE7UDREpgHst2QlNG0bfuAdevetGXmeNh0hSZXrlxERESQM2dOwsLC0Ov1aDQaYmJibCkjCefOnUMsbqMMMyoVVKgAFSog9uwJERHEnT7NgcBA9p0/TztBoJAo0sxgoD/JT8wrBSNEkZUmgeD1IHZ89aAK+M747iJziwV42g345R2N0oAxbUXmHJPcqFRZuH79OpUrV5ZajFU5e/Ys06dPR6lUJgYutmvXjt9++01iZXDh4kX6Sy3CgRCAZWaRoi9Buwdo/OqJ5NiNf3MF+EMJxqdYZRqYIe0GQAUuXNgttQirYVOHpnHjxnz11VesWbOGmjVr0r9/f9RqNcVSEpBrZa7dvIn21ekQm5MzJ3z+OebPPweDAfHqVe6eOsVvx44xPSwMTzc3asbH0xlojX2t3qixVOSufReMd4HCr55Qvbq9CTMQKWLJzueV/iIzDAIqVVmuXLnidA5NpkyZCA8PT1KvKiwsjCxZskgnCnj+/DkPnz59446rzNvJCSwF2p0GfQUg96sn3mU3/o0IwmkB0dgayGJ1jRmLYjx4cBtRFBMnDI6MTb8j+/Tpw4ABA3B3d2fs2LFkz54dFxcXpk6daksZSbh6+zbkzv3+humNSgX+/tCrF+KaNbB2LbE9erC/alXaqlQoNRqKKpUMBR5KrfUV1YHugoDbhmSWRYjkVTvpcg45KwkJZbl06YrUMqxOmzZt6NmzJ7t27cJkMnHkyBH69+9Pq1atJNV19epV8rm5kVVSFY5JS6CFQoHbqlSWUwkBMVIEZlpZWUakCDpdbJIkt46MTWNo7BFvX19ejBoFZey4lLpeD1euIJw6BYGBiOHheLq6UjshgS5AC6RbvYkBigAR5V8JeRfXQNiuQdSlPqpf5m0s4IMPNnLixH6phVgVs9nM8uXLCQgI4MmTJ/j4+NCqVSu6dOmCy7vyRqUzy5cvZ1mfPvwpn3BKFS+AYsCzclg8nBSgWK/AfKsCmM9ZX1gGxM0tH3v3rqJOHcdPLmaz78HNmzcnBvE9ffqUr776Cn9/f4YNG4Zer3/P1elDXFwcL8LDwc9PkvGTjVoNlSoh9umDuHYtrFlDbI8e/FGlCq2VSlQaDcWVSoYBT2wsLROWQp7qS0DoexqHASY7f68dltLcunVDahFWR6FQ0KlTJ3bv3s3Fixf5448/6N69u6TODMCDBw/IJ5HdcgayAqsA1WXgfgoujAbzTTOYpY+hchYUimLcvn1bahlWwSYxNOvXr+e3335LrKo9ceJEtFot8+fPZ9myZcydO5eBAwfaQkoS7ty5g8rTE4PE+/EpxtcXmjXD3KwZ6PWYL1/mr1On+CUwkJ+fPiWTqyu14+PpCjQj/b3Wz4CmCgU714hoB799wU/xRIHZWC6d1WRUchMVFY7ZbEahsKdoq9QxceLE97YZNWqUDZS8mYd375JPPrKdJj4BvlUoWLVWtJyWTI6PegYUqhyY9Y6/mmAvaLXFuHXLORwam1i+tWvXMmfOHJo3b05CQgKHDh1i4MCBVKtWjR9++IFdu3bZQsZ/CA4ORpMnDzhyMJRaDZUrI/bti7huHaxaRUy3buytVImW/1i9+QHLAkl6MddsRhkjwoG3tzGHijhVzQS7wgez2ciLFy+kFmIV4uLi3nuTkge3b5NfUgXOwXSzGW+diLAhGY0NwGkw64elt6wMhclUmOvXg6WWYRVsskITEhKSePriypUriKJIpUqVAMibNy/PnklTLTgiIgIxq5OF9eXKBc2bY27e3LJ6c+kSf506xZTAQCY/fYqXqyt1EhLoBnyO9TzanMAcoOdx0Fbmv4cPEoB4EWhqpRFlkuKBUulOREQE2bJlk1pMmpk8ebLUEt7Jw5AQu06r4Ch4AuuA+rfAeBN4V37TKyCIKkRsv5rv3GQnIuK51CKsgk0cGoVCgcFgQKVSce7cOUqVKoWrq+XMf3h4eOJ9W/P8+XOMnp7vb+ioqNVQpQpilSrQrx88fszL06fZHRjIzkuXUCiVFDOZaGk00g/wTeNwX2Mpi3BqNej7/GvrKRxQKcBQ+E2XylgBtdqHiIgISpYsKbUUq6HT6di5cyfh4eGJhfMMBgN//fUX8+bNk0STKIo8jIgg7/ubyiSDWsB3gsDvmyBh6D8Sdf4TEYQTAqLhC+wrgYUzkI3nz2WHJtlUrFiRjRs30qxZM3bs2EHTpn/P0rdv3065ctLEVTx79gy9Mzs0/8bPD1q0wNyiBeh0mC9d4tbJk0wODGRSZCSZNRrqJiTQA2hEys2GACwVRUo+BU4D/8xMHwYKRZZUndKUSR4KRU7Cw8OllmFVRowYwdmzZ8maNStarZasWbNy+fJlWrZM4dEYK6LVatEaDOSQTIHz8ZMoss0ocG81mDu9ocEDEF+IwAwbK8sIZCMqSnZoks3AgQP55ptvmDhxIvny5eOrr74C4Ouvv+by5cuSVdsOe/YM0cGKb1kNjQaqVkWsWhX694dHj4gOCmJnYCDbr1xBoVRSwmiklclEXyxbSskhNzAa+GkPaIvxd/LNx2DWFQZepsOL+Sepy/hZsGADVKrHiT+LogsmUzZiY+vz7NkgzOYsyerHy2szvr4juHPnJGazd4p1pAWz2SdJtVxn4NixY2zdupWnT5+yZMkSZs2aRUBAAIcOHZJMU2yspdR8BpoK2YSlokiDB2A+C/wri4YQKCCa/LH8faeHDcm4dgOyERsbaeMx0webODQlSpTg4MGDBAcHU6JEicRilBUrVmTEiBGSLZGHP3sGRYtKMrbdkScP5MmDuVUr0GoxX7zIjVOnmBgYyIQXL8ii0VAvIYHuWE4nvGn1RgsUQEk4Rkvhlhn/bnEGSF8HUokPRu6TGuMUE9OQFy8sVdcFwYBK9Yjs2WehUoXw+PFS6wpNB/R651uhUSgU+Pn5kSlTJm7csBxLb9GiBTNmzJBMU2xsLC6CgCZjp/CyGknsBsDOV7d/ICIC50kv+5GR7QZ4YzBoiY+Pd/hacDYrfeDp6fmfraUBAwb8p129evU4fPiwTTQ9i4yETJlsMpZD4eoK1asjVq+euHoTFRTE9sBAtl69iotKRQmDgTYmE334u3CuHgjHyAY24I7t/zDiiecLvnilJOWGyWTKgVb7d+mAhAQAJb6+w1EqH2E0SlQiI5kYjdl4+tQ5ZlqvyZcvH+fOnaNSpUpotVoiIiJQKpVotdIlZ4yNjcVTpUKQ89BYBdluSE1WQOD58+eyQ2NtoqOjbTaWwWCwlByQeTuCAHnzQt68mFu3hoQETBcvcv3kScYfP864qCiyvlq9+frVJe6444GHpLKthcn02uG1HO13db1MtmyzcXW9gEKhxWDw48WLTkRHf5nkOg+PE2TLNgel8glabVmePh2BTvf3Orq7+zG8vRfg6noNMKLXFyIysg+xsWkpC6FCr5ewYnE60K1bN7p27crOnTtp3bo1X375JS4uLtStW1cyTbGxsXhKnNjPGZHthlR2wwWl0pX4+Pg09GEf2J1DY8sCWaIoOnYOGilwc4MPPkD84AMYOBBCQngRFMS2wEC2XLkCDr0ML8KrZW9BMKFSPcDbewFxcXUwGv1QKkPJk+dr4uJqERo6DUEwkSXLWnx8xpKQUAG9/u8zpzlzjuX5834YjX54e88nT55vuXdvP2ZzVlxdL+Pn153o6DZERvZEoYgja9bF+PoO5t69I5hMqd1DV2IwOJdD07BhQ8qVK0f27Nnp378/hQsXJjY2VtKg4NjYWDydIHmhjLVwdLsBguCCyZSScuf2id05NLZEdmjSiCBAvnyQLx/mNm3g+XNo3VpqVcDPpHzpOIosWdaQJcuaJI+aTBoePvwEmIhG8wit1ovQ0BzAMQASEnwpUgTc3Se+Mkx3AXj6tAwvXz4CHpGQUJKCBW+QJUsPIiPLoVb/RVycHxERauAwAAaDH/nzX8LV9Qfi4lK7RH2MuLjs72/mYLi5uaFUKjEajRiNRnx9fRPj8KRAp9O98WSxjKOTUe0GmM0G2aFxdMxms+zQWBOJ8gn9Gx+fn1AoUvZ7dXEpgMGgRa9/nWlXQKFQolZnJX/+LcTHhyCKBkwmyJUrEIVCjUKhQqFwBbzx8tqNm9salMpMgC8eHqvx8Pj7kLrZ7EeWLMfRaDYCloWsXLn+fNWHGhcXdyAz3t5r8PKKTdXrjokRiYyslapr7ZXNmzczYcIELly4wK+//sqOHTsQBIEOHTrQq1cvSTSp1WrkogfWwQickFrEKzKq3QAICxPlLSdHR16hcU4WLwYPj5Rtff3wA5Qta6Jdu6TBplFR8YwYUYjPPsvCl19GEBCQg2PHMmM0CuTMaaBo0QQCA6FDB2jYUOTECVi1SmTNmqSznUWLjISHqxk5UkSnE1i1yoezZy377Lly6cmTR0dQEPTvL1KpUuq27VasAKPRudK9va71ZjQa2bhxI4sWLSJ79uy0b99eUodG79Bbq7bHjCU11f5X/19XqXiiUqHV6cBstout6oxqNwCaNFHh4eH48UuyQyM7NDLvIEsWEx4eJp4+VbF7tzeBgZnp3DmMMmXiUKtF9HqBwMCkR0lNJgG9XkCt/tvAvHypxMPDYqzWrcvJjRvu9O//iMKFtahUIk+eqAkK8kqTVrMZyatQW5uwsDBq1KjBmTNnUKlUVKhQAfg7F4wUaDQa2aF5C2Ysh6v3A6eA60olj9VqEvR6MJkQcuRAKFwYc5EiUKAAFCwIWbNCixaS6rY2jmQ3AMxm0Slsh905NIUL2y41vpu7O+h0NhtPxvF49kxJTIySnDljCQ52I18+LRUr/v1levWqZVbz7++3K1c8qFTJ0i4qyoW7d1357DPLkergYFdKl46nRImExPbXrqX9uKTRCG5uznVqL2fOnJw+fZrNmzdTs2ZNAHbs2EHevNKtRKnVanTmjJ3z2gxcAf7A4rhcc3HhkUZDvF4PRiNC9uwIhQphLlrU4rgUKAD58iGq1fzHFZS40Gh64Eh2QxTBYDBLGpdmLWzu0Bw5coS1a9cSERHBggUL+N///sfQoUNRvTo+HRAQYDMtWbNkAQlnejL2RXS0C3fu/B0H9OKFZXalUpmpWzeKs2czsWePNwcOZMHPT8eDB67s3p0NQbDMuF4jCCIbNuRAq1Xg5mZm585seHqaqFs3CoACBXRcvOhBYKAX3t5Gbt1y58CBLADo9ak/PRMXp8DPz7kS8g8aNIg+ffqQKVMmli5dysmTJxkzZgyzZ8+WTJNarUafgRyaa1gcl5PAVRcXQjQa4gwGMBgQvL1RFCqEqWhRy2rLa8dFo/mv4+KkOLrd0GotDk1WJyjUbFOHJiAggHnz5tGuXTsWLlyIi4sLQUFBTJ06lVGjRtlSCgDZZYfGusTEAJZEVVKQ1nEvXMjEhQuW/WlBEHFzM1OkSALffBNG7tx6GjWKJDpayZ493hgMCnLm1NO+fThBQV4EB7sl9uPiItK+fQQbNuQgOlpJ0aIJ9OoVkRjs17r1U/R6gU2bLM6Hr6+eXr2esGFDToKDXfngg9Sldo+JUTtFpe1/0qBBA4KCglC8OiadM2dOjh07huc/arBNmzaNwYMH20yTRqNB64QOzW1gL5Yg3SsKBQ9dXYl97bhkzYpQsODfKy4FC1ocFzc3rHU2RrYb0tiN11+BmZ2gDJAgirbbDG7UqBEzZ86kePHiVKlShTNnzhAWFkbr1q0JDAy0lYxEevTqxcKXL6FbN5uP7XTEx6P+8muEmFh0SLeN551JydqNRpxg9TTFDBvmQffuM+nSpYvUUmxKxYoVOX/+vM3Gi4yMJFu2bMTgmPWc7gF7gONYHJf7rq7EGo2Iej1C5swIBQpgLlbs7xWX/PkhPTPI6vUoW7fDGCNdluuMbDeCg2HgQDeio+VTTikiMjKSoq9qJ71OoJcjRw5Lxl4JyJY1K4rQULkCdFrR61F/05XcsZmYxkI0aN7a9BtNez5s9pKvv35rkzShUmVMowTw8qXgdCs0ycGGczIAsmbNilqpJNRoxJ4rwT3kb8flsiBw382Nl68cFzJlQvF6xeUfjovo6Wn7rSK1GuPGtWAwwMSJFDgVxmxmI5D0wMZZzvKjYhyzZ1t8LGuSke1GTAxkyeIcJYBs6tCUKVOGZcuW0blz58THNm3aJFlxyixZsqCOi0O6qjBOgNmMqkt3sj2H38TfyEKWdzZvpmvNht1L6NpVrjphbaKjTRnSobFldvHX4/lmyULYs2d24dA8weK4BGJxXILd3HhpMmHW6cDTE0X+/ElXXAoUgEyZ7Gsi98qb0Jy+yNd8j+cb1r7qUpfCikIsWXyfX6fZlXqHxuLQOP52E9jYofnhhx/o3LkzGzZsID4+njZt2hAaGsqSJUtsKSMRX19flJHOVczPppjNKHv0xutRLDP5/b3ODEA72rHWsIxjx8w0aJD+EjMS0dGGDOnQSIFvzpyEPntm0zEjsDgux4BLwF13d6JfOy7u7ijy50csVgzxn45L5sz25bi8iwUL8DC7U4c6b20y2jiOLpe/5vRpqFrVhtqcmBcvIEeOnFLLsAo2dWiKFCnC3r17OXz4ME+ePMHHx4d69eqRSaKK1wULFsT05IkkYzsDisFDcL8TykzmkYPkna5RoqSCrhoBAUE0aOAwptbuiYuD+HgjPj4+UkvJEOTy8yPs+vV06fs5luDcY8BF4I67Oy/MZsxaLbi5ociXD7FoUcRChf52XLJmdRzH5U2YzbjuOkA7OuLC2/Oh5CUvVY01mD3rJMuWizhB6hTJCQ2FIkWk2SWxNjZxaKKiopL8XKNGjcT7JpOJqKgosmTJYgspSShUqBAJ4eGg15NhN1BTiTBqNJqLN5nBXPzwS9G1fejDN7dPEhwMhQqlk8AMxqNHkDWrh7xCYyN88+UjNI19RGE5Dn0Ei+Pyl5sbL0QRk1YLrq4o8uZNuuLyKgmd2RmTgW7dCjo9jWn83qY/MJLWz5qyZ4+Jzz9Pf2nOTni4K/7+xaSWYRVs4tBUr179rfvcoigiCAI3btywhZQk5MqVC6VajTE8HCRM1OVwTJ2K6vgZpjODghRM8eV++OHnkpstW0IZPDijZKtIXx49giJFUv67cAZsHRQMkL9wYS5rNMlKzPkSS+bcI8AF4LabG8/B4rioVBbH5d8rLtmzO6fj8hZcV6yjBS1w5/2nqdxxp5muDQvmr6NBg/Q9gJURCA11oZCTzCxt4tAcPHjQFsOkGIVCQa58+QgJDZUdmuQydy6qvQeZys+UoMT727+FDoZv+PWPyfToAZ6OePbVznj0CIoXLyO1DKvRo0cPOnToQJ06b4+neM2aNWve28balChRgg1KZRKHJh6L43IYS/r/266uPBMEjFotKJUo8uSxOC6FC//tuOTIkaEclzdy6hSG6Eha0jLZl3SjG3tN21m9OoFu3eRJUVp4/NggOzQpwdPTk8yZM/9n68keKFSokMWhkXk/K1ei3LiVH5lABSqkqauGNGSucjr79+ucrYyLJDx5oqFOHedxaPLkycOQIUPInDkz7dq1o1WrVm9N/CXFKckSJUpwPSGBBsAtV1eeCgIGrRZcXFD4+UHRophfr7gULAg5c2JWpD6bqzOjmreAukJ9sovZk32NAgW9tP35ZcNkmjQBX990FOjExMTAy5d62aFJCfXr1+f8+fNJtp5ebzVJueUEUKFkSQIfPLBatkunZcsWlEtWMIIRVKe6VbqsnfARAQG7ad5clGuEppHHj1UUK+Yc++AAo0ePZtiwYezevZuNGzcya9YsPv30Uzp06ECZMtI7boULF8akVHK4QgXEcuX+PhLt6ys7Linh3j3MISG0Y3SKL21IQ1YKS1mwIIKxYx06JFoy7t+HHDkyO0WWYLCRQ7Nr1y7APreeqlapguvBgzhfeTQrsm8fylnz6E9/GmC9s9Y96UnLZ7u4eBH8/a3WbYZDFOHBA51TOTRgqZnUvHlzmjdvTnBwMAEBAXTv3h0/Pz86dOhA8+bNJdVWsFgx7nz2GSRjW0zmzQi/zaSsohyFzKlbIfjBMIZ+x3pz/TqUKmVlcRmA27ehYsUKUsuwGjaZSuTKlQsAPz8/vLy8uHjxIn/88QdXr17F29sbP7+UnZKxJpUqVUJ7+7alVLHMfzl+HNXkX+kmdONz3n+kQI+euGT+ExAoaCzC+vUCcXFY5abXp+5l7tiRje++KwLAsmU+jB9v5VSkrzhxwosePYoRG/v2P71/akkOjx5ZkqxKlaDSFhQqVIhhw4axZ88e8ufPz4gRI6SWRLWKFeHOHallOC5RUSivXOcrc4cU2Y1//stHPkqYSjF9uqUmka3tBji27fjrLxXVqtW1hjy7wKZ5aM6fP0+PHj3Inj07vr6+PH78mJ9++olFixZJNrssWrQoKpUK0/37UCT5H4QMwaVLqEdPoK3wJV+IX7y3uR497ZStiTTGJH8MEQjCascvvb1h7VrHPoVfs2Y0pUolf83w2jXw9y+N2pFf9Hs4ffo0mzZtYt++fVSoUIEZM2ZILYkqFSuyJSBAopKKTsCsWfgKuSgjlkm53fg3d6FJk9Rf7gx2A1JuO/76S02fPpXTUZFtsalDM3nyZAYPHsyXX36Z+NjSpUsZN26cJCcVwHLSqYy/P2dv3ZIdmn9y+zbqQcP5jMZ0Ejsl6xIDBiKNMWzYIM1Ryvh4+OILy2qFIxsmb28j3t7JXzG8fl1JrVofpqMiaQgNDWXz5s1s2bKFmJgYmjdvzubNmylY0D6Op/v7+2OeNElqGY6JXo/maBBfif0xYpTthpVIie1ISIB79+KoVKlSOquyHTZ1aB4+fMgXXySd6Xfs2JFZs2bZUsZ/qF2lChf++ksODH5NSAiaPgOpK9ahr9j3P0Xi3oe7O3h4pJM2G7J/f1b27cuKXi9Qrlwcbdo8xcvL8imZNi0PPj56XrxQceuWG7VqRfPll0+5d8+VnTuzcfeuK3q9guzZDXz00Qvq1IlO0vetW+7s2eNNaKiaHDkMtGjxjPLlLTOrHTuysX9/VmbNsmxn9OhRjK5dn3DpkieXL3vi5mbm88+fU65cLKtW+XDzpjvPnp1g7969NGrUyLZvUjrRqVMngoKCKF26NL179+azzz5Do3l70VMpKF++PNqICIiMtEzxZZLPsmW4mlTUpz56LPs9zmI3wDFsx7x5PhQp4sE333zD4MGDncJ2JDuGJjIyklGjRlGrVi0qVKhAgwYNmDp1Klpt8ks7Vq9enb179yZ57PDhw1SsWDH5iv/F4sWL6dixY6qvB6jxwQe4XruWpj6chqdP0XTtTWVTRb4Xv0dhmzAruyMsTM3hw5n54ounfPHFU27ccGfBglxJ2pw4kZksWQz06BFKtWoxREYqmT49D0qlma5dQ+nR4wk5chhYvdqHR4+STv3Wrs1JnTrR9OwZiqurmf/9LxcxMW/P4752rQ9eXiZ69HhC/vxaVq/OyW+/5cHXV0doaCi5cuVi+PDhvHjxIl3eD1vz6NEjAgICCAgIoGXLlnbnzABkzpyZUhUrwunTUktxOFy37KYtbVHhfBVqHcV2mEw68uXLTbZs2ZzGdiR7hWbQoEH4+vqyc+dOsmTJwsOHDxkyZAhjxozh559/fue1PXv2BCAmJobBgwezdu1a/Pz8ePr0KUFBQdSqVSttryKN1KtXj/jgYEuVrqxZJdUiKVFRaL7tTmlDMcaKY95ZU8XZEUXo1+8xvr4GALy8TMyZ48dff7lRtGgCABqNmXbtIlC++iu6csWDAgW0dOsWmvhYkSIJDBpUhL/+cidPnr8jD9u2fUqVKjGv+jYyaVJ+goNdE2da/yZfPi1ffPEUgGzZDFy65EmBAlry5XtOtmw+jB49mrZt23L79m2qVauWHm+JTXn+/DmlS5eWWsZ7+aJpUyYfOYLOCWa3NmPHDszaBJqQhqAXO8ZRbMflywn07NmeqlWrOo3tSPb0+8KFCzRq1Cix5lK+fPkYOXIkWV85APv27aN169ZUq1aNSpUqMXDgQBISLL+80NBQXrx4QWhoKAqFguDgYFQqFY8fPwbg0qVLXH9V6E0URebOnUvjxo3x9/enVq1azJkzJ1FHaGgoXbp0wd/fn+bNm3P37t3E5zZv3szn/4ou9ff3Jygo6J2vLXv27BQpXRouXEju2+F8xMej+aYrhRJy8ZP4k1POnFKCn58u0SABlC4dh4uLyP37romP5cxpSDQ+AGXLxjF48CMAnjxRc/68J3v3WrYijMak23aFCyck3s+e3TJOfPzb/xwLFfp7JfT10nWBAlouXhSoXbt+4t9lTEwaAitlUkyTJk0wnz5tCcCQSRaaZWtoKjTFE+dMEe4ItiN3bi1Xr2r5+OOPncp2JHuFplGjRgwfPpymTZtStWpV/P39KV++POXLlyc0NJShQ4eyaNEiqlSpwqNHj2jXrh07d+6kTZs2lCxZkv3797NixQqKFi1Kly5d2Lp1K7///jtVq1ZlxIgRzJo1i/nz57Nnzx62bNnCqlWr8PX15cSJE3Tq1IkmTZqQP39+BgwYQKFChTh16hTBwcF07tyZIlYI5m36ySfMOncOQwPr5VlxGPR61F93IVeMFz+LP+OK6/uvcXJe/+G/RqEADw8TUVF/r1plypQ0+M5shoCAHBw7lhmjUSBnTkPijOzf5YbU6r8Tgb1OKiiKb49VcnX9b+IwtVrk1Ck3pk5t9dZaaY6KXq9/79HsyZMn20jN2/H39ydTpkxEXr4MThRcmW6cP48hMoLWtJZaSbrhCLYjLEwkTx4fChYsyIMHD5L1uhyBZDs0kyZNYuvWrezevZuAgADi4+OpUKECP/zwAyVLlmTnzp3kzZuX6Ohonj17RtasWQkPD0+8vnbt2hQtWpRJkyZx/vx5jEZjYrBf5cqVE0851alTh8qVK5MjRw7Cw8MRRRGVSkVERAQuLi5cvHiRuXPnotFoKFmyJF988QXnz59P8xvxWePGzF22DIMokqHS1hqNqDp1I3ukghnib047a0op8fFJt9vMZoiNdcHD4+0ZSXfv9iYwMDOdO4dRpkwcarWIXi8QGJg+WThfvICQEB0NGzZ0iv3vf+PuAFUHBUGgWZMmrDh5EpPs0LwX5ezfqamojY/ZR2op6YYj2I4HDxR88onzlSpPtkPj4uJCq1ataNWqFUajkRs3brBs2TI6d+7MoUOH2Lx5MwEBAWg0GkqVKoVWq01SBTdr1qxMnz6dmzdv0rRpU0JDQ+nduzfTpk0jLCwssa3RaGTKlCkEBgbi4+OTmObcbDYTERGBUqkke/a/a37kyZPHKg5NzZo1QauFv/4CJ8u4+lbMZlx69cHrSTwzmEdmnCP9tTV4+FBDVJQLWbJYZlsXL3piNgsUK/b2rCPBwW7ky6elYsXYxMeuXrUc20iPgtB370KdOh/g5eXldA6NWq1m9OiUp8OXghZNmrCud28S+vTJWJOhlBISgnj/Hu0YIrWSdMURbMf9+0r69v3U+h1LTLJiaI4ePUrlypWJi7MEHSmVSsqWLcvUqVNJSEhg5cqVbN26lY0bN3Lw4EFmz56N97+OMQqCwB9//MGsWbPInTs3Go2GqlWrMmvWLC5dupTYbtq0acTHx3P06FF27NjBuHHjMJksHwwfHx+MRmOSlZ9/3lcoFBj+sZet0+kS43jeh1qt5uNPPkEIDExWe2dAMXAQHnfCmMVMcpBDajl2hYuLyNy5fly65MHRo5lZudKHMmXiKFLk7af6ChTQEhzsxoEDWbhxw429e7OybJkvgmCZbVmbu3fVNG/e1ur92gNieljxdOLDDz/E+PQpPHwotRS7RpgxkxKKkhSnuNRS0hVHsB3R0WYaNmxo9X6lJlkrNFWqVMHLy4vRo0fTt29f8ufPz8uXL1m5ciW5c+cmU6ZMKBQK1Go1RqORLVu2cOnSJapWrZqkn/j4+MQApNdkzpwZs/nvpbjY2FjUajUKhYLY2Fh+/vlnzGYzBoMBPz8/qlatytSpU5k4cSJPnjxh/fr1FChQAICCBQsSEhLC+fPnKVu2LPPmzUvRm9GpQwcOfPcdCZ06Of1MS/hhFK6X/2IGc8hNbqv2HS9R6lRrjlu0aAJFiiSwfLkvZjNUqRJD69ZP33lNo0aRREcr2bPHG4NBQc6cetq3DycoyIvgYDfriXvFw4cGmqQlPaodU7my42QvdXd3p1a9evx58iTkT5+09w5PbCyqC1f4Shz/1ibOYDfAMWxHhQrl8HCWpD//QBCTORUKDQ1l1qxZHD9+nKioKMsfca1aDBo0KPEc+5EjR9BoNJQvXx5vb28iIyOZP38+w4cPx93dnfDwcHx9fcmUKRM3btxgzpw5TJ48OTGmZufOndy/f5/hw4dz69YtPD09adiwIVevXqVRo0Z06tSJZ8+eMXr0aE6dOoWPjw8ffPABd+7cYeXKlQD88ssvbNu2DZPJRNu2bTlw4ACjR49O1nE0rVaLt48PCVOmgBPXxWHyFDT7DjOTmVadLaWq9IGVcZYU5u9j3z7YsaMIV678JbUUGWD+/PkMXriQ+OnTpZZin0ydSq4/LrFKXPWf3Fay3bAdogjffOPGzJmradGihdRyrE6yHRprEBISQufOnXn27Bk5c+YkIiKCPHnyMH/+fEkLVP6Tjt9+y9q4OEx9+kgtJX2YMwfVpu38wi+Up7zVu9ejx0DajrBe5CKjhVEsX57yBKwqlfMbJYAhQ9xo124CgwcPllqKDBbblr9gQcTNm8HLS2o59oXRiOunzelr7MVnfPbGJtawG2/iAAeY7TKD//0PcuZ8e7uMYjdu3YIhQ1x5+vQFrq7Od5rVpg4NgMFg4OzZs0RGRpIrVy7KlSuHUmnTCgzv5MCBA3zerh26devAxckSyy1bhnL5aiYwgepUl1rNO2nj2pQm7WL4+mupldgfT55Ap04uhIQ8Iee7rLSMTSldsSLX69SB5s2llmJfLFuG5/JNbGITamzvNXRUf0nRmk8ZM+btp4wyCr//rkCt/oKVK9dKLSVdsEle+6ioqMRbXFwcJUuWpGbNmhQqVIjY2FiioqJsISNZ1K9fHzdBgIsXpZZiXTZuRLl8NT/wg907MwCNtM3YvBlMcoGt/7B3r8CnnzaUnRk7Y9iAAbht22Y5pyuTiOvG7XzBF5I4MwAj9GM4etTMjRuSDG83GI3w559qOnT4Rmop6YZNlkaqV6+eJPGXKIqJP7++f8NOPm0uLi50/uYb5u7cic5Z8krs24dy7gIGMID61JdaTbLoSEc2atdw/LiZOnWkVmM/mEywb5+GRYt6Sy1F5l+0bduW/kOGkHD2LPzrQESG5cABjHExNKWpZBJKUYpSYhlmzrzG77+Lzn7e460cOwYeHln5+OOPpZaSbtjEoSldujT379/nk08+oVmzZuTJk8cWw6aafn36MLN4cQgPBx8HTwAVGIhq8q90pdtb96/tETVqyuoqsTHgHHXqyDPe15w7B+DmFJVxnQ2NRsN3vXvz8+bNaGWHBgD1/5bRSGhMZlHaHFfjzONpf681R49C3bqSSpGMHTvc6N17IC7OFkrxD2yy5bRp0yYCAgLIkSMHP/zwA8OGDePEiRNkzpwZPz8/uwkIfk2BAgX4uFEjFNu3Sy0lbVy8iHrMRNrRji/4Qmo1KaYPfbh23YwTZeZOM7t3a/j22652FXcm8ze9e/XCePEi8ocWuHYNY0QoX4jS2x5vvKmnb8js2QJ6/fvbOxvBwXD9upEuXbpILSVdsYlDA1CoUCEGDhzIwYMH6devHxcvXqRRo0YMHjyYY8eO2UpGsvl+wACUu3aBTie1lNRx6xbqwSP4nM/4lm+lVpMq8pOf3Eoftm7NoGvE/yIkBE6eNNG7d1+ppci8BR8fH75o2xblli1SS5Ecl5mzqa6ojh/2MWEdxCB0sUoy4q9m2zYl7dq1/U/CW2fDZg7NP6lWrRo//fQTc+fO5fbt23Tv3l0KGe+kXr165PHzgwMHpJaSch48QNN3EPXFuvQV+yLguA7BF/oO7NkjksyEz07N+vUqvvzyC/Llyye1FJl3MHTgQEuioJcvpZYiHeHhCH/dpYO5g9RKElGjpr2uE0uXQnS01GpsR0yM5WusX7+BUktJd2zu0Dx+/JgFCxbQpEkTevfuTbVq1Vi/fr2tZbwXQRAYPmAAbps3O9aphfBwNN37UsVckaHiUId2ZgA+4zPUgpr9+6VWIi1Pn8L+/WaGDx8ltRSZ91ChQgUqVq4Mu3ZJLUU6ZsygsKIwpSgltZIktKMdXmRm6VLHtospYdMmgcqVK1GxYkWppaQ7NnFoXrx4wZo1a/jyyy/5/PPPuX37NoMHD+bo0aOMGjWKcuXK2UJGiunYsSMeWi0cOSK1lOQRFYWmUw/KGIszxjwGFxw/+EuBgg+09dgYIKRLkTZHYeNGFz79tCElnTmDtRMxYtAgXLdts5yVzWjEx6MOumBXqzP/ZJBuODt3ihmi9FZsLGzerOTHH6dKLcUm2CSxXpkyZciUKRONGzemYcOGeHp6/qdN6dKl01tGqliwYAEDpkxBu2iRfSfai49H0+5rCsf4ME2chivOkwUyiijaKFswfTqULSu1GtsTHQ3t2ys5fPgEVapUkVqOTDIwmUzkKVyYsG++gfqOkSrBavz2Gzl2nGStuNZuJ1VdlZ3w9n/Izz870Op7Kli2TODevaocOXJKaik2wSYOTYkSJf4eUBD+U0nXnvLQ/Bu9Xk/+okUJ69AB7LU6qV6Puv035Il0Z5Y4Cw+cr+hYD5du5KoVzLhxzm2A3sTSpQIPH1bnzz9PSC1FJgXMnj2b72fORLtggX1PhqyJ2Yxro2b0NHSlGc2kVvNWHvCArspvmTIFnCXd2L+JjYX27VVs376PevXqSS3HJti89IEjsnTpUnqPGYN26VKwt+OyRiOqb7qQI9TIPHEemZE230N6cZrTjFAMIyAg5fWdHJmnT+Gbb5QcOnSM6tXtP8OzzN/o9XqKlinDw0aNoGVLqeXYhrVrcVu4is1ssvtV4hGM4HHeIJYuFZ3S31y+XCA4OOOszoBEp5wcjY4dO5Jdo4E//pBaSlLMZlx69ibzk3hmijOd1pkBqEpVsqo9M1yc5ZIlKpo0aSI7Mw6IWq3mf3PmoF6+HF68kFqOTXBdu5nWtLJ7ZwZgJCN5FiGwd6/USqxPZCQEBCiZMOFnqaXYFNmhSQZKpZJpkyahWbYM4uKklpOIYsBgPO5GMItZZCe71HLSnYbaJhmqvtOtW3D4MPz8829SS5FJJQ0bNqRBvXqoFi+WWkr6c+wYhpgXtKCF1EqShSeefK5rxYIFEB8vtRrrsnSpio8/bkidDFY3RnZokkmbNm2oWLo0LsuXSy0FAOGHkbheuc1MZpCLXFLLsQnf8i3aBIGTJ6VWkv6IIsyf78rAgYPInz+/1HJk0sC8GTMQDh2CmzellpKuqH9fREOhIVnJKrWUZNOTnigMbqx1ouLTf/0F+/eL/PrrzHe269ixI4sXL2b27Nn06NHDKmM/evSI4sWLExkZ+cbnu3btyooVK6wy1puQHZpkIggCi+bNQ9i+He7elVbMpMmoT55jOtMpQAFptdgQNWpK6/3ZuNH5P7bHjsHjx66MGDFSaikyaaRgwYIMHTIE1zlzHCunVUq4fRtj6CO+FL+UWkmKUKCgh7Yf69ZBRITUatKO2QyzZ1smQoULF5Zazn9YtGgRX3/9dbr17/zfDFakVKlSfNevH66zZyNZUpTZs1Ht/5Of+ZniFJdGg4T0Fvtw5YqZkBCplaQfCQmwcKErkyb9QqZMmaSWI2MFfhg+HK/oaJwyYANQzJhFJUVl8uF4Waw/5VN8FDlZsMDxvw737YPIyEyMGjUmRdfFxMTw3XffUbVqVVq1asU5SxVcHj16hL+/P6NHj6Zy5cosW7aMly9fMmzYMD788EPKly/PJ598woF/ZdRft24dH3/8Mf7+/gwfPhyDwQD8vSr0+v7cuXNp2bIlFSpUoGPHjly9epWOHTvi7+9PmzZtePz4cYpeh+P/Bm3M+LFj8YiIQJLUtUuXoty8nYlMpBz2mYwwvSlEIXIpczh1faelS13In78MnTt3llqKjJVwd3dn3owZaBYvtpyndSYiI1HcuE17czuplaSaEfoxHDli5tYtqZWknshIWLBAzaxZ8/HwSFnqjnPnzvHxxx9z4sQJWrduTY8ePYh+VR8iPj4eT0/PxOemTZtGXFwcO3bs4Ny5c3z22WdMmDAhSX9//fUXO3bsYPv27Rw6dIg/3nKgZuPGjcycOZPAwECePn1Kly5dGDlyJCdOnECj0bBkyZIUvQ7ZoUkhnp6ezJ81C/X8+ZZPkK3YuBHlijWMYhRVqWq7ce2QNvoO7NrlnPWdrl+HnTsVLFmyCoVC/vN0Jlq2bEmVChVwWbZMainWZcYM8gp5KU95qZWkmtKUpqRYmpkzHTMjuSjCjBkaPv64MS1TkSKgSpUqNGnSBKVSSbt27ciePTtHjx5NfL5Zs2ao1Wo8PT3p168fkyZNQq1WExoaioeHB+Hh4Un669+/P66uruTNm5cyZcrw8C1pmRs3bkzevHnx9PSkbNmy1K5dmxIlSuDm5kaVKlXkFRpb0KpVKxp/9BGaadNss/W0dy/KuQsYxCDqUjf9x7NzmtAElaDi0CGplVgXnQ5++cWV0aPHUbz4u7cTnTGgz9kRBIGFc+Yg7Nxpidx0BvR6NMfP8pXYweHrxo01j+PuXUv8mqOxfz/cvOnG778vStX1uXPnTvKzr69vEiclZ86cifcjIiLo3bs3NWrUYMiQIVy+fPk/yXK9vLwS76tUKkxvOZqaNevfAeQuLi5JrlMoFP/p933IDk0qEASBJQsWkOnePdixI30HO3YM1dTp9KAHn/Jp+o7lIChQUF1blw0bHHM29TYWL3YhZ87iDB36vdRS3kh6B/RlBEqWLMmI4cNx/eknnGKJceFC3M1uTjHRyk526uo/Ys5sAb1eajXJ5+lTmDNHxaJFy8mWLVsq+3ia5OfHjx//x8l5zcCBA6lVqxanTp1i/fr1tGnTJlVjguW71JrIDk0qyZo1K+tWrkS1cCHpFqF6/jyqsRNpL7SnNa3TZwwHpRe9ePJE5Pp1qZVYh/PnYdcuF1atCkCZwmzUzhLQl1EYO3o05fPlQzVrltRS0obZjOvO/bSnnd3WbEopQxhCfIySrVulVpI8RBGmTdPQokUbmjZtmup+Tp8+zb59+zAYDCxdupSEhATq1n2zkxobG4tGo0GhUPDo0SPmzJkDWDJjS43s0KSBDz/8kB5duuA6dar1q+revIl66Eia0ZRvxG+s27cT4I03BcRCbN7s+B/h589h8mQNv/46g6JFi6b4emcJ6MsouLi4sGntWtzOnLEcS3FUtm1D1OloTGOplVgNNWra675lyRJLUVh7JyBA4NEjL2bOnJumfmrVqsXatWupWrUqf/zxB4sWLXprYPFPP/3E2rVr8ff3p3PnznzyySe4urrylx1so9pZYSLH45epU9lTsSL3Fy/GZKVYBh48QNN3MPXFevQWezv83nR60cXUjVFHRtC3L2R1nFxeSTAa4ccfXWnUqDk9e/ZMVR+vA/oA2rVrx/Llyzl69Cj+/v7A3wF9arWafv36Jd63VkAfQNmyZRFFMbEQbZUqVey24Kw94Ofnx9oVK2jepg2GEiUgn+Mdd3ZdsY7mtMAdd6mlWJX2tGcL61m2LIb+/e13T/vSJVi2TMnhwzvJkiVLiq9fuXLle9vkyZOHW/86+lWvXr3/FLvs1KlT4v1/t1+wYMEbx/z3+FOmTEnyc79+/d6r7984/vRWYlxdXdm9dSua3bvhX0v3qSI8HE33vlQTKzNEHCI7M++gOtXJrPJg926plaSeBQtcEMX8LFiwONX7yc4S0Pc2IiMjGTVqFLVq1aJChQo0aNCAqVOnotVqrdJ/alm8eDEdO3ZM9fWNGzemb69euI4f73jxNEFBGKKe0xLnLLo5QPc9O3aIvMWXl5xnz2DCBA3Tp8+iatWMfer1n8gOjRUoVqwYAWvXovrtN7h9O/UdRUWh6dSDssYSjDKPcpp96fTkY+3nbN7kmPWdDh2C/fs1bNmyC3f31M9ynSWg720MGjQIo9HIzp07uXjxIsuWLePcuXOMGZOy5GH2yM9TplApf37UU6dKl6wzFSjnLqC+oj45yCG1lHShJjUpoMjP3Ln29xVpNMKECa40btzSaiccnQX7+205KI0bN2bsyJG4jh0LUVEp7yA2Fs3XXSmizcNE80RUqKyu0RnpTGfi4gSCgqRWkjLu3YNp01SsWrU+zSnKnSWg721cuHCBRo0aJS6r58uXj5EjRyauEO3bt4/WrVtTrVo1KlWqxMCBA0l4teIxfPhwpkyZQseOHalQoQLNmzfn8uXL9O7dG39/fz777DOuv4osF0WRuXPn0rhxY/z9/alVq1bi+wMQGhpKly5d8Pf3p3nz5tz9RwmUzZs38/nnnyfR7e/vT9B7PphKpZKtGzbgfe8eitWr0/xe2YR79zCHPKSdAyfSSw6jDeM5f97Mqxh7u2H+fCWQtlVdZ0V2aKzIDyNG8EnNmrj++GPKgoT1etTfdCVPbBamilPRoEk/kU6GGjWlDBUcqr7T8+cwapSGwYO//8+XYGpwloC+t9GoUSOGDx/OpEmTOHDgAM+fP6d8+fKMGDGC0NBQhg4dyrBhwwgKCmLbtm2cPXuWnTt3Jl4fEBDA8OHDOX36NJkyZaJDhw507NiRoKAgSpQowaxXp4327NnDli1bWLJkCRcuXODnn39m9uzZPHjwAIABAwaQM2dOTp06xeTJk/nzzz+t8vqyZ8/O3u3bUa1bhyNUXhVmzKScoiyFKCS1lHQlP/mpZKrGrFmC3awAb9wocOSIO1u27MbNzU1qOXaHIFpro1sGgLi4OKrUrMndnDnRDx8O78v2ajSi+rozOcPMzBXnkpnMthHqRNzhDj0U3VixAvz8pFbzbmJjYeBAV2rUaMmyZavkGVYyMJlMbN26ld27d3P+/Hni4+OpUKECP/zwAyVLliQ8PJy8efMSHR3NvXv3GDNmDA0bNqRv374MHz4crVbLjBkzAJgxYwZBQUGsfVVeeePGjSxfvpwdO3YQGxtLfHw8OXLkICIigjt37tCjRw+WLl1Krly5+PDDDzl+/DjZs2cH4LfffuP8+fOsXLmSzZs3s2TJkiSOlL+/P/Pnz6datWrJep3r16/n627d0P/yC7wnsaJkREWhatGWSfxEZSpLrSbdiSWWLzTN6PudmcYSH+b680/LEe1Dh47KcTNvwXGmtQ6Ch4cHf/7xBznv3kU5d+6798XNZpTde5M5NIGZ4kzZmUklRSiCryo727bZt3Og18OYMRqKFq3FokXLZGcmmbi4uNCqVSsWL17MmTNn2LhxI3ny5KFz584kJCSwefNmatWqRcuWLVm8eDFarTZJQHJyg5eNRiNTpkyhWrVqdO3aNdE5MZvNREREoFQqE50ZsJwAsSZt27blp7FjUX//vf1mEp49G1/Bh0pUklqJTfDEk890LZk/X9q47YsX4ZdfVGzYsFl2Zt6B7NCkAz4+Phw7eJDMJ06geEeqeEX/gXjci2AWs8hG6jI8ylhopWvHjh0iEh98eSsmE0yapEalKsnGjdtQqeQYqeRw9OhRKleuTFxcHGCJOSlbtixTp04lISGBlStXsnXrVjZu3MjBgweZPXs23t7eSfpIruM4bdo04uPjOXr0KDt27GDcuHGJJ7x8fHwwGo1JTo/9875CoUhMQAig0+kS43hSwpDBg/lx5EjUQ4fCnTspvj5dMRrRHDnFV+JXGer0ZS96oTC48mpRz+YEB8OYMSrmzJlPY6mXiewc2aFJJwoUKMCRAwdw37YNYfPm/zwvDP8B16t3mclMcpFLAoXORXOao0SJlcIarIoowuzZSkJDc7N794E0nWjKaFSpUgUvLy9Gjx5NcHAwJpOJFy9eMG/ePHLnzk2mTJlQKBSo1WqMRiMBAQFcunQpiXORXGJjY1Gr1SgUCmJjY/npp58wm80YDAb8/PyoWrUqU6dOJT4+njt37rB+/frEawsWLEhISAjnz5/HYDAwb968VL/mYd9/z9hhwywrNcHBqe7H6ixdiqtJRX3qS63EpihQ0E3bl7VrLWUGbMnDhzB8uIbBg0fQuXNn2w7ugMgOTTpSunRpDuzZg2bpUtiz5+8nJv6EOug8vzGd/OSXTqAToUBBFW1tAuysvpPZDDNnKjl71pv9+4+kutZKRsXNzY3Vq1ej0Wj49ttv8ff359NPP+Xhw4esWLGCL7/8knLlyvHRRx9Ru3ZtDh48SIsWLbidivQJ/fv3JyIigmrVqvHpp5+iUqmoUKFCYsD0b7/9RkJCAjVr1qRv37589NFHideWL1+eTp068d1331GnTh0EQaBIkSKpft0/jBjBqEGDLCs19+6luh9r4rplN21pmyFPYH7GZ+RU5GThAtt9Zd67B4MGaejWbSBjxoyz2biOjBwUbAMOHz7Mp02aoO3aFe7fR7V1F9OYRlnKSi3NqXjGM9op2zBrFpQsKbUayzbTL7+o+OsvHw4dCiR/ftl5lUkZY8aN4+fZs9FNnw5Sfn527UL16yw2swlPPKXTISFXuMJAl++YOzf9Y7bv3oXvv1fTt+/3jBv3oxxvl0zkFRobUK9ePQ7+8Qea//0Pl607mMhE2ZlJB7KTnfwUsIv6TgYDTJyo5uHDfAQGnpadGZlU8eO4cQzu3RvNkCFImbZWs3Q1TYUmGdaZAShLWUqKJZk1M31Xge/cgSFD1AwYMJLx4yfIzkwKkN7yZxBq1KjBloAAMnlk4rLLZUTkhbH04FtjF/780yxpYTmdDsaO1RAVVYSjR0+RK5ccIyWTeib++CP9u3WzODUhIbYXcPEihufhtBZb235sO2OMeTx37kBgYPr0f/kyDB6s5vvvxzJ6tONnwrY1skNjQz799FNOnj3J4WyHma6ajhErV+iWoRa18FK7S1bfKToaRozQIIqlOXz4RJJjvjIyqUEQBKb89BP9OnVCM2CA5QyvDXGZNZfaitr44mvTce2RHOSgjuFDZs8WSEXc+TvZvx+GD1cxdepMRoz4wbqdZxBkh8bGlChRglPnThGcP5jhmuFE4wA16h2MDxMas2mT7bN73r8Pffu6kjdvA/bvP0LmzHJeIRnrIAgCP0+ZwoyffkL1ww8IGzfapvbT48dw757TlzlICUMZSnyMC1u2WKc/UYSVKxXMnu3Gli076Nmzp3U6zoDIDo0E5MmTh6ALQeT5MA99XPsQjB0dzXQCutCF2Bg4c8Z2Y548Cf36qfj220Fs2bITT8+MG2sgkz4IgkDPnj05eugQ3ps2oZ48mfROvCT89hslFCUojp1mLpYANWraab9l6VLSvLVtMMDPP6vYuzcbgYGn+OSTT6wjMoMiOzQS4enpyeYdm+k6tCv9VP04xjGpJTkNrrhSwlDWJvWdRBHWrhWYOFHN4sWrmDDhJxTvK3chI5MGqlevztULFygfH4/rd9/BkyfpM1BsLKrzV+lg7pA+/TswHeiAJ14sW5b6gN2nT2HIEFfCwopw5sxFypUrZ0WFGRPZ8kqIQqFg3I/jWLV+FT+7/swyxTJM2EkVNAent9iHCxfM6WbrAeLjYfJkNTt3ZuPYsZN88cUX6TeYjMw/8PX15fiff9KpUSPUvXrB6dPWH2TePLLhTTWSV4sqo9FfO5QdO8RUHT4LCoLu3dVUqtSG48fPkjt3busLzIDIDo0d0KJFC06cPsGx3McY6jqUcMLff5HMOylGMXKqsrF9e/ocebx+HXr0cEWvr8S5c1eoWLFiuowjI/M2VCoV82bNYvHcuajHj0exerX14mrMZlz3H6WD2AGF/DXxRmpRi3xCPubNTf77YzLB//7nwoQJGmbNWsSSJSvkzOFWRP6k2glly5bl0o1LVPyyIt1U3TjAAaklOTwtdW3ZsV1Er7denyYTLF+uYMgQFd999yOHDgXi6yuf/pCRjq+++opTgYHk3LcPzbhxlpLuaWXVKlyMAh/zcdr7cmJGG8dx7ryZCxfe3zY8HAYPduXChfycOXOBjh07pr/ADIbs0NgRnp6eLFq6iFUbV7HAawET1ROJIUZqWQ5LK1ohiNar7/T4MQwY4MrJk/k5ceI0Q4cOleNlZOwCf39/rp4/zweurmg6d4ZDh9K0WuO6YRtf0AY1aiuqdD4KUpCKpirMnPH2U5VmM2zbBl26qKhcuS1nz16hpD2kMndCZGtshzRt2pSrt6+irqOmm2s3TnJSakkOiQIFlbU1CQhI27aTyQRbtkCPHirq1v2GCxeuUaFCBeuIlJGxEtmyZePQ3r0snTmTrAsW4DpsWOqyCx88iDHuJc1oZn2RTshocQzhYQL79//3uUePLKsymzb5sGXLLhYtWiZvMaUjskNjp/j4+LBr3y4mzp7Izx4/M1ozmlBCpZblcPShD/fvi9y6lbrrr12DPn1c2bkzD5s372TOnPm4ublZV6SMjJUQBIF27dpx7/ZtOlWrhrJHDxSLF6foeLd64VIaC43JjJxHKTl44kljfXN+/x0SEiyPmUywYYNA9+5KatXqxPXrd/j4Y3n7Lr2RHRo7RhAEunbtyl/3/6J4u+J0UXZhuWI5OnRSS3MYcpCDfEI+Nm9O2SpNVJSlsOTQoWq++uoHrl+/Q8OGDdNHpIyMlcmcOTPzZs8m6PhxSt+8iWuXLpZkSe/j+nWMEaF8Icon9lJCH/qAzpV16+D8eejZ05X9+/Oyb9+fzJ49T85LZSPkatsOxOnTp+nVuRfhd8Ppqe1JTWoiIBcuex9HOcqPLmPZvBm8vN7d1miEnTth6VI19ep9yMyZ8yhQoIBNdMrIpAdms5lFixYxaNgwjGXLouvdG94SyO7SvSfV7mb7f3v3HhPVnfdx/D1XBoECwrQqXpBCXQctDIuiMooK1jLamvqkWesiBusaN7Elz67bbbrtaveChl0bb709DbZ4xWDBttqWxkeFtqkXEK+LLlqBVWAYYIvAcJvL8wctW/vU1u4Wh4Pf18lJTmbml3zPP5PP+Z7f+R3+7P7zHa5S+XLJJVf1Fv7+Bl544Q9kZmai18scpDtJAo3CuFwucnJyeG71cwxzDiO9I51JTJJg8z0eM1h5IqODWy0V43LBRx/B7t0GDAYjmza9htVqvbNFCtGP7HY7//2b37A3Px/XokV4Fi4EP79//cBmQ7sojY1sJJpo7xWqMHbs7NDtoMhTROrDqbz2xmvy5KOXyC0nhdFoNKxYsYKq2irSfp/Gev/1ZPpmUkaZvMH7O8zuTCU/X4XbffPnLhcUFUFGhi/79oWRlfU/VFR8LmFGDDpGo5Gdb73FkaIiHjx/Hv3ixai+vn7/xk1EqCMkzNymVlp5Q/0GS7VL0afqOXfhHPvf2y9hxoukQ6Nwra2tbN68mb+u+ytj3GP4ecfPiSdeOjbf4MDBY/r5/PGPHiZP7r21dOQI7Nrli0oVxJo1WaSlpaHVar1dqhD9zuPxUFxczAt/+hPHPvsMt9WK9u0DPO95julM93Z5A9plLvOu7l0OeQ6RMCmB7E3ZTJo0ydtlCSTQDBotLS1s2riJzRs2E+AMYH7HfOYyF39kMtpXMlVPoXrwb/z0px4OHPDBz28ov/vdH0hPT0en03m7PCG84uTJk/z2+ec58tFHJOuTWdi9EBMmb5c1oHTTTQklvOf7Hn93/p3FixezKnMVZrPZ26WJr5FAM8h0dXWRn5/P5r9s5sLfLpBCCgucC4ggwtuleY0HD2c4wy7tLk5TxvTp0/jVr54lNTUVjUbj7fKEGBAqKyvZsnELb257kzGqMSzoWEASSXf14no2bBxQHeB9/fsEG4NZ9etVLF26lODgYG+XJr6FBJpBrKysjK0bt5K3N49IXSRJjiSSSMKI0dul3RFVVFGsKuao4SgtmhaWLlvKipUrZJVOIb5Da2srubm5bMreRIOtgWmeaST2JBJPPAYM3i6v37lxc4pTvGt4l2Pdx3h4zsM89eunSE5OlpXBBzgJNHeB5uZm9u3bx+43d/PpiU+JNkQzwzGDGcwglFBvl/ej+irElPiWcL3nOqlzUlmUvogFCxZgMAz+P2Mhfixut5vjx49TsK+AgrwC6hrqmKydTGJnIlOZOqhuZ7fQwglOUKovpVRVispHxS9++QtW/nIlY8aM8XZ54jZJoLnL2Gw2CgoK2PPWHj4r/QyTr4nY9ljMmDFhUlx7uZ12znKW0+rTlBpK+0LMz5b8jPnz58uCVkL8CDweD+fPn6egoIC3d73NxSsXifOJY1rHNBJJJIQQb5f4g7hwcYlLvSHGr5QKRwUToiYwb+E8rPOsTJkyRR4QUCAJNHex+vp6Dh48yKEPDnH4fw/T0trCgz4PEuOIwYyZSCIHXMBpp50KKihXlXN2yFkqHBWMHj6alLkppDycgtVqlRAjRD+7evUqhYWF7Nu5jxNnTvCA7wNEdkUS6YwkiigiiMAHH2+X2ceNGxs2znOek/qTlKpKcWvczEmZw7zH5jF37lyGDx/u7TLFf0gCjQB6r8AuXrzI4cOHOfT+IYpLirnRfoOIIRFEdkcytmcs4YQzlrGEENLvj4W7cFFLLZ/zOVe4QpVvFVe4Qm1HLSONI5k9ZzbJc5OZNWsWo0aN6tdahBC3ZrPZOHLkCKdOnaLs0zJOnz1NS3sLY4eM5f6e+7m/+36iiCKSyH6/TeXESS21VH+51ehr+IfuH1R1VuH0OImOimbef83DarWSkJAgXZhBRgKN+FYej4fq6mrKy8spKyvjzIkzXDh/gar6KnzUPtzrcy9GlZGQnhBCu0MJJZQQQjBgwOdbNjduur62ddNNF120007jl1uToYkmbRN2lx17lx21Ws1PIn5CTHwMsfGxxMTEMHHiRIzGu2NSsxBK5PF4uHbtGqdOnaK8vJzST0spLy+ntqmWEUNGEKoOJcAdQEBP737Pl1sggX3H93APPvjgxEk33Thx0kMP3XTT9o3tBjeoM9RRo6mhpqMGtUbNA2MewDTRxIS4CYwfPx6TyURkZKQszzDISaARP4jD4aCmpoZr1679a6+5Rs3lGupq63B0OOjs7MTR6aCzu5OO7g5cbhcAeq0eg86AwceAr48vBoOBAP8AwkaHMSpiFKNGjyIsLIyRI0cSFhZGeHi4XEEJMUjY7XbOnTtHQ0MDTU1NNDU10djYSGNdI422RpqbmmlqbuKfN/5Ji6Olb5xKpUKv1ffuOj2B/oEEBQYRFBREcEgwQ+8dSuS4SEwmE+PHjyc8PFyWY7hLSaAR/a6npwe1Wi1/MkKI2+J0Ounq6sLHxweNRoNKJSufi+8ngUYhxo0bh8FgQK1W4/F48Pf3x2q18swzz9xWF2PJkiXMnDmTJ5988g5UK4QQQtxZ0s9XkJ07dzJx4kQArl+/zrJlywgNDWXFihVerkwIIYTwLln2UKHCwsKYPn06Fy5c6PssLy+PRx99lPj4eBISEli7di1fb8BdvnyZxx9/nMmTJ7Ny5Urq6+uB3kl8L7/8MlarFbPZjMViYevWrXf8nIQQ4naMGzeOmJgYzGYzsbGxWCwWsrKycDqdtzV+yZIl5OTk9HOV4k6TQKNQVVVVfPzxx6SkpABw5swZXnrpJTZs2EBpaSm5ubkUFhZy7NixvjFHjx7lxRdfpKSkhMDAQFavXg3ABx98QGFhIdu2baO8vJzs7Gy2bNlCdXW1V85NCCG+z86dOykvL+f06dPs3buX4uJitm3b5u2yhBfJLScFSU9PR6PR4HK5cDgcmEwmEhMTgd4rlnfeeYfhw4fT3NxMa2srAQEB2Gy2vvFPPPEEJlPvW3RXr16NxWLBZrMxY8YM4uPjMRqN2Gw2PB4POp2OhoYGWfZbCDHg3apjvXv3bmpra9FoNKSmprJmzZq+CcZfdayrq6uJi4tj7dq1DBs2DI/HwyuvvMLBgwepq6vDz8+PRYsWsWrVKm+dnrhN0qFRkO3bt1Na2rumw/Hjx4mKiiIjIwOPx4Nareb1119nypQpLF68mLy8PNxuN263u2/8iBEj+o6NRiNarRabzYbT6WT9+vUkJCSwfPlyDhw4AHDTWCGEGKikYy1AOjSKFRQUxPLly3nkkUdobm4mPz+fc+fOUVRURGBgIAAWi+WmMXa7ve+4vr4ep9NJWFgYGzZswOFwUFJSgsFgoKuri/3799/J0xFCiB9EOtbimyTQKJTD4SAvL4/w8HCCg4Npa2tDp9Oh1Wrp7OwkJycHu91OT09P35g9e/aQkpLCiBEjWLduHbNmzSIkJIS2tjb0ej1qtZq2tjays7Nxu903jRVCiIFk+/btfU99fvHFF2RlZZGRkcH+/fv7OtYffvghQUFBREdH33bHevTo0axfv55PPvmE++67jwkTJgDSsVYCCTQKkpaWhlrde5dQq9USFxfHq6++ilqtZtmyZVRUVGCxWBgyZAhTp05l9uzZVFZW9o1PTk7m6aefprGxEYvFwrp16wDIzMzk2WefJSEhAX9/fx566CFiY2OprKz8f10eIYQYaKRjLUACjWJcunTpO78fOnTodz6GuGPHjlt+Fx4eTl5e3r9dmxBCeJN0rAXIpGAhhBAKlJaWhtlsxmw2k5SUxPXr12/qWPv5+WGxWEhOTubq1au37FjPnDkTtVp9U8e6oaGBhIQEUlNT0el0fR1rMbDJqw+EEEIIoXjSoRFCCCGE4kmgEUIIIYTiSaARQgghhOJJoBFCCCGE4kmgEUIIIYTiSaARQgghhOJJoBFCCCGE4kmgEUIIIYTiSaARQgghhOJJoBFCCCGE4kmgEUIIIYTiSaARQgghhOJJoBFCCCGE4kmgEUIIIYTiSaARQgghhOJJoBFCCCGE4kmgEUIIIYTi/R/E3hax5iljSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.pie(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac9be10",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9194523d",
   "metadata": {},
   "source": [
    "# A box plot of items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c2a50717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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paVFHR4ciIyMlSU6nUwkJCeetw263y2KxDKRkXMLO/AfJbrcrJsZsLQA+n4MHD/Y7E9LQ0CC73X7edZgJGR48Ho+OHj36me39hpCWlhZlZmbqu9/9rpYvX96nbfXq1Tpy5Ig2b96smE99W8yfP1+LFy/Wzp07NWPGDG3cuFGjR4/W9OnTZbFYNGXKFBUUFCg3N1f79u3Tli1bVFxcfIHDBAAEApvN1m94iI6OPus7A5enfkNIaWmpTpw4oZKSEpWUlPiWp6Wladu2bbJarbrhhhv6bLN9+3YlJiYqPz9f69atU1NTk5KSklRcXOybySgqKlJeXp5mzZql6OhorVq1SikpKX4eHgAACFRBvb29AX/Q3ePxqLq6WsnJyRyOuQx4PNLbbzcpPf1K8dcNDC9ut5Sb26qf/WyEwsJMV4Oh1t/3N2d5IuBYLFJi4ikCCDAMcQk+PokQAgAAjCCEAAAAIwghAADACEIIAAAwghCCgOPxSB9+GCKPx3QlAIChRAhBwOnokObOvVIdHaYrAeBvkZHSm2826f/fLBuXOUIIAOCi4RJ8fBIhBAAAGEEIAQAARhBCAACAEf0+wA4YqK4uqZ8neA9IW1vfn4NltUo8ERwAAg8hBH7R1SWNHHn6p7+MG+effmw26fhxgggQCM5cgj92rDg5FYQQ+Ed39+kAUl8vxcQMvr+GhgbZ7fZB99PWdjrMdHcTQoBAcOYS/NZW/3xW4NJGCIFfxcT454MlOrqXDygAGOY4MRUAABhBCAEAAEYQQgAAgBGcEwIA6BeX4GMoEEIAAOfFJfgYKoQQAMB5cQk+hgohBAAwIFyCD3/jxFQAAGAEIQQAABhBCAEAAEYQQgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCAACAEYQQAABgBCEEAAAYQQgBAABGEEIAAIARhBAAAGAET9GF34SpS2rr9ktfQe3tp5/TPVhtUpisknjONwAEGkII/MapeMWMa/JLX3a/9CLFSKrTWEmH/dQjAMBfCCHwm3g5daS+WzExg++roaFBdvvgo0hbmzRxnFVHBl8SAMDPCCHwG7dsUozt9PTDIPVGR8svaUaS2y+9AAD8jRNTAQCAEYQQAABgBCEEAAAYQQgBAABGEEIAAIARhBAAAGDEgELI7t27deedd+raa69Venq6NmzYoN7eXvX09CgvL08zZ85UamqqCgsL5fV6fdtt27ZN8+bNU3JysjIyMtTY2Ohra25uVmZmphwOh9LT07V161b/jw4AAASsfu8TcuzYMS1dulSrV6/WrbfeqoMHD+r73/++Ro4cqebmZn344Yf64x//qO7ubmVlZWnUqFG69957deDAAeXm5urZZ59VcnKyNmzYoKysLG3ZskWSlJOTo6lTp6q4uFg1NTXKysrSl7/8ZU2dOnXIBw0AAMzrdyakoaFBs2fP1u233y6LxaL4+HjNnTtXe/bsUXl5uZYuXaqRI0cqLi5OS5cuVWlpqSSpoqJCaWlpSk1NldVqVXZ2tpqamlRVVSWn06m9e/cqJydHVqtVDodDCxYsUFlZ2ZAPGAAABIZ+Q8i0adNUUFDge93d3a0dO3Zo8uTJ+sc//qGEhARfW3x8vA4ePKju7m7V1dVp0qRJvjaLxaLx48ertrZWTqdTY8aMUVRUVJ9t9+/f769xAQCAAPe5Tkx1u93KycmRzWbTzTffLEkKDw/3tYeHh6u3t1ddXV1yuVyy2fo+uTQ8PFwul0sdHR1ntdlsNnV2dl7oOAAAwCVmwM+OaW5u1gMPPCCbzaYXX3xRvb29kqSuri7fOmdCREREhMLDw+V2931qR2dnpyIjIxUREdFnuzP9REREnLeGhoYGBQdzQU8gam8PkmRXQ0ODoqN7/dBfu+rr6wOuLuByxP6NC/XJi1XOZUAh5MyJo3PmzNGjjz6q0NBQSdIVV1whp9OpL33pS5Ikp9Opr3zlKwoJCVFCQoKcTqevD4/Ho0OHDmnixImKjY1VS0uLOjo6FBkZ6dv2k4d2zsVut8tisQykZFxkbW2nf9rtdr88d66+vl7jxo0bdD/+rgu4HLF/40J5PB4dPXr0M9v7nVZoaWlRZmam7r77bj3++OO+ACJJCxcuVFFRkY4ePaqWlhb98pe/1K233ipJmj9/vt5++23t3LlT3d3deuaZZzR69GhNnz5d8fHxmjJligoKCuR2u1VVVaUtW7bolltuGfyIAQDAJaHfmZDS0lKdOHFCJSUlKikp8S1PS0vTz3/+c+Xn5+vWW29VT0+PFi5cqB/84AeSpMTEROXn52vdunVqampSUlKSiouLfTMZRUVFysvL06xZsxQdHa1Vq1YpJSVliIYJAAACTVDvmZM7ApjH41F1dbWSk5M5HBOg2tqkESOk1lYF3HStP+sCLkfs37hQ/X1/c5YnAAAwghACAACMIIQAAAAjCCEAAMAIQggAADCCEAIAAIwghAAAACMIIQAAwIgBP8AOAHD5ClOX1Nbtl76C2tv/78Evg9EmhckqydbvqghMhBAAQL+cilfMuCa/9GX3Sy9SjKQ6jZV02E894mIjhAAA+hUvp47Ud/vl9ugNDQ2y2wcfRdrapInjrDoy+JJgCCEEANAvt2xSjO309MMg9UZH++1hL26/9AJTODEVAAAYQQgBAABGEEIAAIARhBAAAGAEIQQAABhBCAEAAEYQQgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCAACAEYQQAABgBCEEAAAYQQgBAABGEEIAAIARhBAAAGAEIQQAABhBCAEAAEYQQgAAgBGEEAAAYESI6QIAAJeGtjb/9NPeHuSXvvxVD8whhAAAzstqPf1z3Dh/9Wj3V0ey2f6vPlx6CCEAgPOy2aTOTqm7e/B9tbWdDjP19VJMzOD7s1pP14dLEyEEANAvm82/X/YxMf4JIbi0cWIqAAAwghACAACMIIQAAAAjCCEAAMAIQggA4KKJjJTefLNJkZGmK0EgIIQAAC4ai0VKTDwli8V0JQgEhBAAAGAEIQQAABjxuUJIdXW1UlNTfa9dLpdWr16t6667TjNnztSKFSt0/PhxX/u2bds0b948JScnKyMjQ42Njb625uZmZWZmyuFwKD09XVu3bvXDcAAAwKViQCHE6/WqtLRUmZmZ6unp8S1/5plndOzYMf3pT3/SW2+9pfb2duXn50uSDhw4oNzcXK1du1aVlZVyOBzKysrybZuTk6MJEyZo165dKiws1Jo1a1RTU+Pn4QEAgEA1oBBSUFCgsrIyLV++vM/yjz76SF6v1/faYrEoIiJCklRRUaG0tDSlpqbKarUqOztbTU1NqqqqktPp1N69e5WTkyOr1SqHw6EFCxaorKzMj0MDAAQaj0f68MMQeTymK0EgGFAIWbJkicrLy3XVVVedtby6ulopKSlKSUnRkSNHtGLFCklSXV2dJk2a5FvXYrFo/Pjxqq2tldPp1JgxYxQVFeVrj4+P1/79+/0xJgBAgOrokObOvVIdHaYrQSAYUAiJi4s753Kv16uFCxfqr3/9q/77v/9bV155pR5++GFJp88XsX3qaUfh4eFyuVzq6Og4q81ms6mzs/NCxgAAAC5BF/wU3Z6eHq1YsUIvvfSSRo0aJUnKy8vT9ddfr4aGBoWHh8vtdvfZprOzU5GRkYqIiFBXV1eftq6uLt+hHAAAMPxdcAhxuVxqbW3tc6JqSMjp7kJDQ5WQkCCn0+lr83g8OnTokCZOnKjY2Fi1tLSoo6NDkf//tnlOp1MJCQnnfc+GhgYFB3NVcSBqbw+SZFdDQ4Oio3v90F+76uvrA64uAIPDPnl5+eR5o+dywSFkxIgRcjgcys/P11NPPSWLxaInnnhCM2bMUFxcnObPn6/Fixdr586dmjFjhjZu3KjRo0dr+vTpslgsmjJligoKCpSbm6t9+/Zpy5YtKi4uPu972u12WbjNXkBqazv90263KyZm8P3V19dr3Lhxg+7H33UBGBz2ycuLx+PR0aNHP7N9UNMKTz31lL7whS/om9/8pubOnetbJkmJiYnKz8/XunXrNHPmTL333nsqLi72hYiioiI1NjZq1qxZWrlypVatWqWUlJTBlAMAAC4hQb29vQE/H+bxeFRdXa3k5GRmQgJUW5s0YoTU2qqAmwnxZ10ABsftlnJzW/Wzn41QWJjpajDU+vv+5gQLAMBFExYmPfRQGwEEkgghAADAEEIIAAAw4oKvjgEA4NO6urrU3d193nXa29vVduYymc9gtVrPuqklhh9CCADAb+Lj49XU1DTofsaOHavDhw/7oSIEMkIIAMBvnE5nvzMhDQ0Nstvt513HarX6sywEKEIIAMBvbDZbv4dRoqOjFcM18xAnpgIAAEMIIQAAwAhCCAAAMIIQAgAAjCCEAAAAIwghAADACEIIAAAwghACAACMIIQAAAAjCCEAAMAIQggAADCCEAIAAIwghAAAACMIIQAAwAhCCAAAMIIQAgAAjCCEAAAAIwghAADACEIIAAAwghACAACMIIQAAAAjCCEAAMAIQggAADCCEAIAAIwghAAAACMIIQAAwAhCCAAAMIIQAgAAjCCEAAAAIwghAADACEIIAAAwghACAACMIIQAAAAjCCEAAMAIQggAADCCEAIAAIwghAAAACMIIQAAwIjPFUKqq6uVmpraZ9lzzz2nG264QQ6HQxkZGXI6nb62bdu2ad68eUpOTlZGRoYaGxt9bc3NzcrMzJTD4VB6erq2bt06yKEAAIBLyYBCiNfrVWlpqTIzM9XT0+NbvmnTJpWWlurXv/613n33XSUlJWnlypWSpAMHDig3N1dr165VZWWlHA6HsrKyfNvm5ORowoQJ2rVrlwoLC7VmzRrV1NT4eXgAACBQDSiEFBQUqKysTMuXL++z/De/+Y0efvhhxcfHKzQ0VDk5OVq3bp0kqaKiQmlpaUpNTZXValV2draamppUVVUlp9OpvXv3KicnR1arVQ6HQwsWLFBZWZn/RwgAAALSgELIkiVLVF5erquuusq3zOVyyel0yuVy6bbbbtN1112nlStXauTIkZKkuro6TZo0ybe+xWLR+PHjVVtbK6fTqTFjxigqKsrXHh8fr/379/trXAAAIMANKITExcWdtaytrU29vb0qLS3V008/re3btys2Nlb333+/pNMhxWaz9dkmPDxcLpdLHR0dZ7XZbDZ1dnZe6DgAAMAl5oKvjrFarZKk733ve7Lb7YqIiNDDDz+smpoaHT58WOHh4XK73X226ezsVGRkpCIiItTV1dWnraurSxERERdaDgAAuMSEXOiGo0aNUmxsrNrb233LvF6vJKm3t1cJCQl9rpTxeDw6dOiQJk6cqNjYWLW0tKijo0ORkZGSJKfTqYSEhPO+Z0NDg4KDuao4ELW3B0myq6GhQdHRvX7or1319fUBVxeAwfPX/o3AdyYXfJYLDiGStGjRIhUXF2vmzJm64oorVFhYqOTkZNntds2fP1+LFy/Wzp07NWPGDG3cuFGjR4/W9OnTZbFYNGXKFBUUFCg3N1f79u3Tli1bVFxcfN73s9vtslgsgykZQ6St7fRPu92umJjB91dfX69x48YNuh9/1wVg8Py1fyPweTweHT169DPbBxVCVqxYofDwcC1ZskQnTpzQjBkz9PTTT0uSEhMTlZ+fr3Xr1qmpqUlJSUkqLi72hYiioiLl5eVp1qxZio6O1qpVq5SSkjKYcgAAwCUkqLe3N+DnqD0ej6qrq5WcnMxMSIBqa5NGjJBaWxVwMyH+rAvA4DETcvno7/ubEywAAIARhBAAAGAEIQQAABhBCAEAAEYQQgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCAACAEYQQAABgBCEEAAAYQQgBAABGEEIAAIARhBAAAGAEIQQAABgRYroADC9tbf7pp709yC99+aseAID/EULgF1br6Z/jxvmrR7u/OpLN9n/1AQACByEEfmGzSZ2dUnf34PtqazsdZurrpZiYwfdntZ6uDwAQWAgh8Bubzb9f9jEx/gkhAIDAxImpAADACEIIAAAwghACAACMIIQAAAAjCCEIOJGR0ptvNiky0nQlAIChRAhBwLFYpMTEU7JYTFcCABhKhBAAAGAEIQQAABhBCAEAAEYQQgAAgBGEEAQcj0f68MMQeTymKwEADCVCCAJOR4c0d+6V6ugwXQkAYCgRQgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCAACAEYQQBJywMCknp1VhYaYrAQAMJUIIAk5YmPTQQ22EEAAY5gghAADACEIIAAAwghACAACMIIQAAAAjCCEAAMAIQggCzsmT0pw5Y3TypOlKAABDiRCCgOP1SrW1ofJ6TVcCABhKnyuEVFdXKzU19Zxt69ev1/z58/ss27Ztm+bNm6fk5GRlZGSosbHR19bc3KzMzEw5HA6lp6dr69atF1A+AAC4VA0ohHi9XpWWliozM1M9PT1ntVdWVur555/vs+zAgQPKzc3V2rVrVVlZKYfDoaysLF97Tk6OJkyYoF27dqmwsFBr1qxRTU3NIIcDAAAuFQMKIQUFBSorK9Py5cvPamttbdUjjzyie+65p8/yiooKpaWlKTU1VVarVdnZ2WpqalJVVZWcTqf27t2rnJwcWa1WORwOLViwQGVlZf4ZFQAACHgDCiFLlixReXm5rrrqqrPaHnnkEd1999366le/2md5XV2dJk2a5HttsVg0fvx41dbWyul0asyYMYqKivK1x8fHa//+/Rc6DgAAcIkZUAiJi4s75/LNmzfr5MmTWrJkyVltLpdLNputz7Lw8HC5XC51dHSc1Waz2dTZ2TnQujGMBQdLkyb1KJjTpgFgWAu50A3r6upUXFyszZs3Kygo6Kz28PBwud3uPss6OzsVGRmpiIgIdXV19Wnr6upSRETEhZaDYSQqSnrrrWZFRY0zXQoAYAhdcAh54403dOLECd18882SpJ6eHnV3dyslJUWvvvqqEhIS5HQ6fet7PB4dOnRIEydOVGxsrFpaWtTR0aHIyEhJktPpVEJCwnnfs6GhQcH89/iy0N7ervr6etNlABgC7N+XD28/91q44BBy33336b777vO9Li8v1wsvvKDXXntNkjR//nwtXrxYO3fu1IwZM7Rx40aNHj1a06dPl8Vi0ZQpU1RQUKDc3Fzt27dPW7ZsUXFx8Xnf0263y2KxXGjJuITU19dr3DhmQoDhiP378uHxeHT06NHPbL/gENKfxMRE5efna926dWpqalJSUpKKi4t9IaKoqEh5eXmaNWuWoqOjtWrVKqWkpAxVOQAAIMAE9fb29pouoj8ej0fV1dVKTk5mJuQywf+UgOGL/fvy0d/3NydYAAAAIwghCDhut/TkkzH61MVVAIBhhhCCgON2S7/4xQhCCAAMc4QQAABgBCEEAAAYQQgBAABGEEIAAIARhBAAAGAEIQQBJzJSevPNJv3/xwoBAIYpQggCjsUiJSaeEjfHBYDhjRACAACMIIQAAAAjCCEAAMAIQggAADCCEIKA4/FIH34YIo/HdCUAgKFECEHA6eiQ5s69Uh0dpisBAAwlQggAADAixHQBuPx0dXWpu7v7M9vb2vr+/CxWq1U2m82PlQEALiZCCC66+Ph4NTU19bveuHHnbx87dqwOHz7sp6oAABcbIQQXndPpPO9MiCQ1NDTIbrefdx2r1erPsgAAFxkhBBedzWbr9zBKdHS0YmJiLlJFAAATODEVAAAYQQgBAABGEEIAAIARhBAAAGAEIQQAABhBCAEAAEYQQgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCAACAEYQQAABgBCEEAAAYQQgBAABGEEIAAIARhBAAAGAEIQQAABhBCAEAAEYQQgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCAACAEYQQAABgxOcKIdXV1UpNTfW9bmtr0+rVq/X1r39dM2fO1LJly9TY2Ohr37Ztm+bNm6fk5GRlZGT0aWtublZmZqYcDofS09O1detWPwwHAABcKgYUQrxer0pLS5WZmamenh7f8scee0zHjx/XH/7wB73zzjsaNWqUHnzwQUnSgQMHlJubq7Vr16qyslIOh0NZWVm+bXNycjRhwgTt2rVLhYWFWrNmjWpqavw8PAAAEKgGFEIKCgpUVlam5cuX91nu9XqVnZ2tESNGKDw8XBkZGdq7d6+6u7tVUVGhtLQ0paamymq1Kjs7W01NTaqqqpLT6dTevXuVk5Mjq9Uqh8OhBQsWqKysbEgGCQAAAs+AQsiSJUtUXl6uq666qs/y9evXKykpyff6T3/6kyZOnCir1aq6ujpNmjTJ12axWDR+/HjV1tbK6XRqzJgxioqK8rXHx8dr//79gx0PAAC4RIQMZKW4uLh+16moqFBJSYmeffZZSZLL5ZLNZuuzTnh4uFwul8LCws5qs9ls6uzsHGjdAADgEjegEHI+Xq9XTz31lDZt2qSNGzf6TlwNDw+X2+3us25nZ6ciIyMVERGhrq6uPm1dXV2KiIg453v09vZKkjwez2DLxSXC6/Xy9w0MU+zfl48zf89nvsc/bVAhxO1268EHH9TBgwdVWlqq+Ph4X1tCQoKcTmefQg4dOqSJEycqNjZWLS0t6ujoUGRkpCTJ6XQqISHhnO/j9XolSR988MFgysUl5ujRo6ZLADBE2L8vL2e+xz9tUCFk9erVOnLkiDZv3qyYmJg+bfPnz9fixYu1c+dOzZgxQxs3btTo0aM1ffp0WSwWTZkyRQUFBcrNzdW+ffu0ZcsWFRcXn7vIkBBdffXVCg4OVlBQ0GBKBgAAF0lvb6+8Xq9CQs4dNy44hNTX12vr1q2yWq264YYb+rRt375diYmJys/P17p169TU1KSkpCQVFxfLYrFIkoqKipSXl6dZs2YpOjpaq1atUkpKyjnfKzg4WFar9UJLBQAAASio97MO1AAAAAwhbtsOAACMIIRcxhITE3XVVVfp2LFjZ7XdfvvtSkxMPGfbpz3zzDN97ob7SY2Njbrmmmt0/PhxNTQ0DLjP/vz973/XD37wA6WkpOiaa67RwoULVVpaOuDtv/GNb+iPf/zjoOsAhoPvfe97uuaaa3TNNddo6tSpuuqqq3yvb7zxRv3rv/7rObdbu3atvv/97w9ZXbm5uXr88ceHrH+YN+hLdHFpi4qK0uuvv67vfOc7vmV1dXX66KOP/NL/2LFjVVVVJUnq6OjwS58nT57UkiVLlJ2draKiIoWEhOj999/X8uXLFRISokWLFvnlfYDLRUlJie/Pjz/+uFwul372s59JkmpqarRo0SIdPnxYX/rSl3zrud1uvfrqq/r5z39+0evF8MFMyGXupptu0pYtW/ose/XVVzVv3rw+y+rr67Vs2TLNnDlTN9xwg37605/2uddLe3u7srOzlZqaqttvv1179uyRpPPOfhw4cED33nuvZsyYoXnz5unll18eUM1Op1NtbW265ZZbZLVaFRwcrJSUFOXm5vouA+vt7dWGDRt000036ZprrtGsWbNUVFR0zv5aW1u1evVqzZo1S2lpafrpT3+q7u5uSdJHH32ke+65RykpKZozZ45++tOfcn8DXFamTp2qqVOn6ve//32f5a+//rpiYmJ0/fXX99vH9u3bdfPNNyslJUULFizQ7373O1/bG2+8oUWLFmnmzJm69tprtWLFinPeuNLj8ehXv/qVbrzxRt8DU1taWiSdvgfVypUrNXPmTM2aNUvLly/Xxx9/PMiR42IghFzm/vmf/1l///vfVV9fL+n0l/eWLVu0cOFC3zrd3d2699579cUvflHvvPOOysrKVF1drSeeeMK3zp49ezR37lz99a9/1aJFi5SVlaXW1tbPfN+Ojg7de++9+trXvqa//vWveuqpp1RUVKS3336735onT56s8ePH69vf/rY2btyod999Vy6XS9/61rd0xx13SDr9Afn73/9eL7zwgqqqqpSfn69nnnlGBw8ePKu/VatWqaOjQ6+//roqKir097//XevXr5ck/cd//Idmzpyp9957T7/5zW/0+uuv6y9/+cvAfrnAMHHXXXepvLy8zw2nXn75ZS1evFjBwef/GvF4PPrRj36kRx99VLt37/Y92PT48eNqamrSD3/4Q61atUrvvvuuKioqtHv3br322mtn9fOf//mf+v3vf6/nn39eO3bs0Fe+8hUtX75cvb292rRpk06cOKE///nPeuONN9TV1aUXXnjB778H+B8h5DIXFRWl9PR0305fWVmpMWPGaNy4cb519uzZo6NHj+rHP/6xwsPDFRcXpx/+8Id65ZVXfDMPM2bM0IIFCxQSEqK77rpLo0eP1o4dOz7zff/85z8rIiJC3//+9xUaGqrJkyfrO9/5zoDO67BarXr55Ze1YMECbd++Xffee69SU1OVnZ2tI0eOSJKuv/56/fa3v1VcXJxaWlrU29ur0NBQX/sZR48e1dtvv61/+7d/U3R0tEaNGqUVK1b46oiJidHOnTv15ptvKjo6Wu+8885Zl6QDw93NN9+skydP6t1335V0ejbygw8+GNChT4vFoujoaJWXl6uyslKpqal6//33NXLkSH3hC1/Qa6+9phkzZqi1tVVHjx7VyJEjfTMcn1RWVqZly5bpy1/+ssLCwvTQQw+prq5ONTU1iomJ0f79+/Xqq6+qtbVVzz33nH74wx/6/fcA/+OcEGjhwoUqKCjQfffdp4qKCt1666192j/++GNdccUVfe7VYrfb5Xa7fVOeY8eO7bPNmDFjzvlBcsbhw4fV0NDQ594wXq+3T/g5n5iYGC1btkzLli2Ty+XSnj17tH79euXk5Oi3v/2tTp06pZ/97Gf6y1/+ori4OE2dOtX3Hp/U2Ngo6fSH7CedOnVKH3/8sX7yk5/omWeeUX5+vpqampSWlqbHHntsQM9TAoYLm82mW2+9Vb/73e903XXX6eWXX9ZNN92kkSNHDmj7F154QUVFRXrggQfU09Ojb3/721q5cqVCQ0NVXl6ul19+WWFhYUpKSlJXV9c5b/Hd2Nion/zkJ3rsscd8y7xerw4fPqxFixaps7NTpaWlysvL0+TJk/WTn/xE11xzjd9+BxgahBAoLS1NP/7xj1VdXa0///nPWr16dZ9DKVdeeaX+8Y9/qLu72xdEDh06pNDQUI0YMUKS9I9//KNPn4cPHz4rmHxSXFycJk+e3OfY8Mcff6xTp071W+/69ev1/vvv6ze/+Y0kKSIiQmlpaQoNDdXSpUslSYWFhXK5XNqxY4dsNpvcbrdeeeWVc9YRFBSkd955x/dU587OTh05ckSjRo3S7t27tXz5cq1evVoHDx7UI488ovXr1/tO2gMuF4sXL9Ztt92mEydO6JVXXvE9rLQ/LpdLTU1NKiwsVG9vr6qqqnT//fcrMTFRoaGheuWVV/Rf//VfGjNmjO99ziUuLk4//vGPNXv2bN+yuro62e121dbW6vrrr1dGRoaOHz+uDRs2aOXKldq+ffugx42hxeEYKCQkRDfddJMeeeQRpaamKjo6uk/7tGnT9KUvfUlPPPGEOjs71dLSooKCAt18882+UFJZWak33nhDPT09+vWvf63Ozs7zHraYPXu2mpubtXnzZp06dUrNzc2699579dxzz/Vb77x58/S3v/1Nv/rVr3yHWurr6/Xiiy/qxhtvlHT6CpozJ62ePHlS69atk9frVU9PT5++4uLi9PWvf11PPPGETp48KZfLpby8PGVnZysoKEiFhYVav369uru7NXr0aFksFsXGxn7O3zBw6YuPj9f06dO1du1ajR07VtOmTRvQdh6PR8uWLdOrr74qSfriF7+ooKAgxcbG6uTJk747Yp86dUovv/yy/va3v521n0qnbxtQVFSkw4cPy+v16qWXXtK3vvUtnThxQq+99pp+9KMf6dixY4qJiVFERAT76SWCEAJJ0i233KLa2tqzDsVIUmhoqH75y1+qpaVFs2fP1i233KKrr75aeXl5vnVmzZqlTZs2KTU1Vdu2bVNJSYnv4YTnEhMTo+eff15/+MMf9LWvfU233Xabrr32Wv3oRz/qt9akpCS9+OKLeu+997Rw4UJNnz5dS5Ys0aRJk/Tv//7vkqQHH3xQR44c0cyZM/Uv//IvCg0NVXJysmpra8/q7+c//7k8Ho+++c1v6oYbblBra6s2btwoScrPz9f//u//6mtf+5pmz56tL3zhC7r//vv7rREYju666y699tpruvvuuwe8TXR0tJ5++mmVlJTI4XBo8eLFuuuuu/SNb3xDt912m6ZNm6Ybb7xRaWlpeuutt/Stb31L+/fvP6ufzMxMpaen67vf/a5SUlJUXl6uZ599VnFxcbrvvvsUHx+vm266SSkpKXr//feVn5/vz6FjiHDbdgAAYAQzIQAAwAhOTEXAWbRokerq6s7ZFh0dfd5LfwFcXPv27etzx+VPu/vuu7lcFp+JwzEAAMAIDscAAAAjCCEAAMAIQggAADCCEAIAAIwghAAAACMIIQAAwIj/B1X+mt5NBOwtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f850d309",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5912dd0a",
   "metadata": {},
   "source": [
    "# C. Create a area plot of item sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4dc097",
   "metadata": {},
   "source": [
    "# Create area plot of item sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "af3b2b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13e3ef51310>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.area(figsize=(6, 4)).legend(bbox_to_anchor=(1.3, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95ffc7cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e8574fd3",
   "metadata": {},
   "source": [
    "# E. Create a stacked bar plot of item sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "88ee1a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13e3ee8eac0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BiL4AAAAAAAUi+gIAAAAAFIjoCwAAAABQIKIvAAAAAECBtCj1AAAAAAAbvGWlHoAV+D1hPSb6AgAAAJRIfX19ylqUpeGKhlKPwkqUtShLfX19qceA1Sb6AgAAAJRIeXl5GpY3JHk2SftSj0MTi9KwfMeUl3s6Kusf0RcAAACg5Lol6VDqIWhiYakHgDXmjyoAAAAAAApE9AUAAAAAKBCPdwAAAGDt8W33zZPfF4ANiugLAADAv62+vj5lLcrScEVDqUfhA5S1KEt9fX2pxwBgHRB9AQAA+LeVl5enYXlDkmeTtC/1OKxgURqW75jyck95BNgQiL4AAACsRd2SdCj1EKxgYakHAGAd8kd8AAAAAAAFIvoCAAAAABSI6AsAAAAAUCCiLwAAAABAgaxS9H3sscfyH//xH9lll10yePDgTJgwIQ0NDamtrc2YMWOy2267ZeDAgbn88stTX1/f+Llp06Zlv/32S9++fXPkkUfm9ddfb9w3d+7cDB8+PP3798/gwYNzzz33rP2zAwAAAADYwLT4qDcsWLAgI0eOzFlnnZVhw4bllVdeyXHHHZdOnTpl7ty5mTFjRu67777U1NRkxIgR6dy5c4455pi88MILGT16dK677rr07ds3EyZMyIgRIzJlypQkyahRo9K7d+9MnDgxTz/9dEaMGJGtttoqvXv3/thPGgAAAACgqD7ySt9Zs2Zl7733zpe//OVUVFSkR48eGTJkSP7yl7/kzjvvzMiRI9OpU6dsuummGTlyZCZNmpQkmTx5cvbcc88MHDgwlZWVOfnkkzNnzpw88cQTmTlzZqZPn55Ro0alsrIy/fv3z8EHH5zbb7/9Yz9hAAAAAIAi+8jou9NOO2XcuHGNr2tqavL73/8+O+ywQ954441st912jft69OiRV155JTU1NXnxxRez/fbbN+6rqKjIlltumeeffz4zZ85M165d065duyaffe6559bWeQEAAAAAbJBW64vcli1bllGjRqWqqioHHnhgkqR169aN+1u3bp2GhoZUV1dnyZIlqaqqavL51q1bZ8mSJVm8ePEK+6qqqrJ06dI1PQ8AAAAAALIKz/R9z9y5c/Otb30rVVVV+fGPf5yGhoYkSXV1deN73ou2bdq0SevWrbNs2bImx1i6dGnatm2bNm3aNPnce8dp06bNGp8IAACsdcs++i2sY35PAAA+0ipF3/e+aG3ffffNOeeck5YtWyZJNt5448ycOTPdunVLksycOTNbb711WrRoke222y4zZ85sPEZdXV1effXVbLvttunYsWPmzZuXxYsXp23bto2fff+jIlZm1qxZKS9frYuTm6VFixaVegQ+wqxZs9K+fftSj8H7WDfrB2un+bF2mj/rpnl65513UtaiLA1XNJR6FFairEVZXn311Wy00UalHoX38e+c9YN/7zQ/1k7zV5R1U19fX+oRWIc+MvrOmzcvw4cPz1FHHZUTTzyxyb6hQ4dm/Pjx6dWrV+rq6nLttddm2LBhSZKDDjoohx12WB566KHsuuuuueaaa9KlS5fsvPPOqaioSK9evTJu3LiMHj06zz77bKZMmZKJEyd+6Czdu3dPRUXFmp9tM7Fw4cJSj8BH6N69ezp06FDqMXgf62b9YO00P9ZO82fdNE8bbbRRGpY3JHk2yfr/H3nFsigNy3fMlltuae00M/6ds37w753mx9pp/oqyburq6vLmm2+WegzWkY+MvpMmTcrbb7+dH/3oR/nRj37UuH3PPffMZZddlrFjx2bYsGGpra3N0KFDc/zxxydJevbsmbFjx+bCCy/MnDlzsuOOO2bixImN0Xb8+PEZM2ZM9thjj7Rv3z5nnnlmBgwY8DGdJgAArIluSdb//8grFnEEAOCjfGT0PeWUU3LKKad84P5zzjkn55xzzkr3DRkyJEOGDFnpvq5du+a6665bxTEBAAAAAFgV6/8DcgEAAAAAaCT6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgLUo9AMBqWVbqAVgpvy8AAADQbIi+wHqhvr4+ZS3K0nBFQ6lH4QOUtShLfX19qccAAACADZ7oC6wXysvL07C8IcmzSdqXehxWsCgNy3dMebmnBgEAAECpib7AeqZbkg6lHoIVLCz1AAAAAMD/45IsAAAAAIACEX0BAAAAAApE9AUAAAAAKBDP9AWADcGyUg/ACvyeAAAAHxPRFwAKrL6+PmUtytJwRUOpR2ElylqUpb6+vtRjAAAABSP6AkCBlZeXp2F5Q5Jnk7Qv9Tg0sSgNy3dMebmnbQEAAGuX6AsAG4RuSTqUegiaWFjqAQAAgIJyaQkAAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgqxV9n3zyyQwcOLDxdW1tbcaMGZPddtstAwcOzOWXX576+vrG/dOmTct+++2Xvn375sgjj8zrr7/euG/u3LkZPnx4+vfvn8GDB+eee+5ZC6cDAAAAALBhW6XoW19fn0mTJmX48OGpra1t3H711VdnxowZue+++zJlypQ8+OCDufnmm5MkL7zwQkaPHp3zzz8/jz76aPr3758RI0Y0fnbUqFHZZptt8vDDD+fyyy/Pd7/73Tz99NNr+fQAAAAAADYsqxR9x40bl9tvvz0nnnhik+133nlnRo4cmU6dOmXTTTfNyJEjM2nSpCTJ5MmTs+eee2bgwIGprKzMySefnDlz5uSJJ57IzJkzM3369IwaNSqVlZXp379/Dj744Nx+++1r/wwBAAAAADYgqxR9//M//zN33nlnPvWpTzVuW7hwYd54441st912jdt69OiRV155JTU1NXnxxRez/fbbN+6rqKjIlltumeeffz4zZ85M165d065duyaffe6559bGOQEAAAAAbLBarMqbNt100xW2LVmyJEnSunXrxm2tW7dOQ0NDqqurs2TJklRVVTX5TOvWrbNkyZK0atVqhX1VVVVZunTpap8AAAAAAAD/v9X6Irf3ey/2VldXN257L9q2adMmrVu3zrJly5p8ZunSpWnbtm3atGnT5HPvHadNmzZrOg4AAAAAAFnFK31XZqONNsrGG2+cmTNnplu3bkmSmTNnZuutt06LFi2y3XbbZebMmY3vr6ury6uvvpptt902HTt2zLx587J48eK0bdu28bPvf1TEysyaNSvl5WvcqZuNRYsWlXoEPsKsWbPSvn37Uo/B+1g36wdrp/mxdpo/66Z5snaaP2un+bFu1g/WTvNj7TR/RVk39fX1pR6BdWiNo2+SDB06NOPHj0+vXr1SV1eXa6+9NsOGDUuSHHTQQTnssMPy0EMPZdddd80111yTLl26ZOedd05FRUV69eqVcePGZfTo0Xn22WczZcqUTJw48UN/Xvfu3VNRUfHvjNwsLFy4sNQj8BG6d++eDh06lHoM3se6WT9YO82PtdP8WTfNk7XT/Fk7zY91s36wdpofa6f5K8q6qaury5tvvlnqMVhH/q3oe8opp2Ts2LEZNmxYamtrM3To0Bx//PFJkp49e2bs2LG58MILM2fOnOy4446ZOHFiY7QdP358xowZkz322CPt27fPmWeemQEDBvz7ZwQAAAAAsAFbrei722675Yknnmh83apVq5xzzjk555xzVvr+IUOGZMiQISvd17Vr11x33XWr8+MBAAAAAPgI6/8DcgEAAAAAaCT6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUiOgLAAAAAFAgoi8AAAAAQIGIvgAAAAAABSL6AgAAAAAUSMmj74wZM3LYYYelX79+2W+//fLwww+XeiQAAAAAgPVWSaNvTU1NvvnNb2bIkCF59NFHc+aZZ+ab3/xm5s+fX8qxAAAAAADWWyWNvo888kiWLl2ab3zjG2nZsmX22WefDBw4MHfddVcpxwIAAAAAWG+VNPq++OKL2W677VJWVta4rUePHnnuuedKOBUAAAAAwPqrpNF3yZIlqaqqarKtqqoqS5cuLdFEAAAAAADrtxal/OFt2rRJdXV1k23V1dVp06ZNk20NDQ1Jkrq6unU228eprq4ubdu2TfJ2kmKcU3EsStI2dXV1hfnnrSism+bO2mmurJ3mzLppzqyd5szaaa6sm+bO2mmurJ3mrFjr5r1zeK+zUWxlDSX8nX7wwQdz1lln5Q9/+EPjthEjRqRfv34ZOXJk47aampo89dRTpRgRAAAAAAqjT58+qaysLPUYfMxKeqXvbrvtloqKilx33XX5xje+kQcffDCPPPJIzj777Cbva9GiRfr06ZPy8vImz/8FAAAAAD5aQ0ND6uvr06JFSXMg60hJr/RNkueffz7f/e5387e//S2bbLJJzjzzzAwePLiUIwEAAAAArLdKHn0BAAAAAFh7yks9AAAAAAAAa4/oy1rxj3/8o9QjAAAAAAAp8Re5sf558sknM27cuMybNy/19fVJktra2ixYsCBPP/10iaeD9VNdXV0qKipKPQY0K3vssUf+8Ic/pF+/fh/4Ja6PP/74Op4KAABg/SD6slrOO++89OrVK3369MlLL72UffbZJzfeeGO+8Y1vlHo0aPZefvnlTJw4cYU/NHn55Zfzpz/9qcTTQfNy5ZVXJkmuvfbaD4y+wAf785///IH7dt1113U4Cax/XnvttcybNy/vff1NbW1tnnvuuRx99NGlHQwAVoMvcmO17Lzzzvnzn/+c2bNn59xzz81PfvKTzJgxI2eccUbuvvvuUo8HzdrXv/71tGnTJh07dszcuXPTv3///OIXv8jhhx+eE088sdTjAVAg/fr1a/K6uro6ZWVl2XbbbTNlypQSTQXN34QJE3L11VenZcuWKSsry/Lly9PQ0JCdd945kyZNKvV40GzNmTMnN9xwQ15//fXGC1zec+2115ZoKtiwudKX1dK5c+dUVFSke/fuefHFF5MkPXv2zOuvv17iyaD5e+aZZ/LQQw9l9uzZufTSS3Pqqadmzz33zNixY0Vf+Bcf9liHhoaGlJeX5y9/+cs6ngrWH0888UST18uWLcs111zjcULwESZNmpSbbrop9fX1uffeezNmzJhccMEFqaysLPVo0KyNGjUqlZWV2XXXXVNe7uujoDkQfVktvXv3ziWXXJLTTz89m222WaZOnZpWrVqlXbt2pR4Nmr0OHTqkbdu22WqrrfLcc88lSQYMGJBXXnmlxJNB83Pdddd94L66urqccMIJ63AaWP+1atUq3/rWt7LXXnvl5JNPLvU40GwtXrw4gwYNyvz58/P000+nsrIy3/nOd3LggQfm7LPPLvV40Gw999xzefjhh9OqVatSjwL8P6Ivq+Xss8/O2WefnXfeeSff/va3c+KJJ6ampibnnXdeqUeDZm/77bfPDTfckKOPPjodOnTIX/7yl1RVVbnqClZi4MCBTV7X1NRk8uTJueWWWzJz5swMGjSoRJPB+uvpp5929RV8hM022yzz58/PJptskrlz56ampiatWrXKokWLSj0aNGu9e/fOK6+8kk9+8pOlHgX4fzzTl39LbW1tamtr06ZNm1KPAs3ec889l1GjRuX666/P9OnTc8YZZ6ShoSGjRo3K8ccfX+rxoFmaP39+fvazn+W2227LO++8k+OOOy5f+9rXstlmm5V6NGjWDj744Cava2trM2vWrBxzzDE5/fTTSzQVNH8TJkzI5MmTc+utt+aiiy7K0qVLU1lZmXnz5nmmL3yIZ555Jscee2z23HPPdOjQock+V8lDaYi+rLa77rorU6dOzfz587P55pvn0EMPzec+97lSjwXrnXnz5mXx4sXp0aNHqUeBZmf69Om5+eab86tf/Sqf+cxncvjhh+eMM87I5MmTs+mmm5Z6PGj27rrrriavy8vLs80222SnnXYq0USw/rj33nuz9957p7a2NuPGjcvixYtzyimnZKuttir1aNBsHXXUUXnjjTfSp0+fFe5kvPjii0s0FWzYRF9WyzXXXJPbb789hx9+eLp27ZpZs2bl1ltvzTe/+c18/etfL/V40Czdf//9H/mez3/+8+tgElh/7LDDDjn00ENzwgknNF7V++lPf1r0BWCdeOONNzJv3rxsvvnm6dy5c6nHgWavX79+eeihh9wFDM2IZ/qyWn7xi1/kpptuyjbbbNO4bZ999skJJ5wg+sIHuOSSSz50f1lZmegL/+KII47IlClTMmPGjBx22GE58MADU1ZWVuqxoNk78sgjP3Kt3HLLLetoGlj/zJs3L9/+9rfz5z//OS1atEhdXV0GDx6ciy++OBtttFGpx4Nma5tttsk//vEP0ReaEVf6slr23nvv/PKXv0zr1q0bty1ZsiSDBw/OI488UsLJACiaZcuWZerUqbntttvy8ssvZ8mSJZkwYUL22muvUo8GzdaNN96YJHnxxRfz29/+Nl/96lfTvXv3zJs3L7fddluGDBmSc845p8RTQvN1wgknpEOHDvn2t7+dLl26ZO7cubnkkktSUVGRyy+/vNTjQbN1zTXX5Be/+EUOPPDAdOrUqckfQB5zzDElnAw2XKIvq+Ttt99Okvz0pz/Nc889l9GjR2fzzTfP/Pnz8/3vfz89evTwRVTwAR544IHsu+++H/iYh7KysgwZMmQdTwXrl7/97W/5+c9/nqlTp6ZLly4ZOnRoTjrppFKPBc3Wl770pVx00UXZYYcdGre98MILOfnkk3PvvfeWcDJo3nbbbbc8+OCDqaysbNy2ePHi7LPPPi5ygQ9x5JFHrnR7WVmZO0ygRERfVskOO+yQsrKyvPePy/v/1K6hoSFlZWX529/+VqrxoFk76KCDMnXq1Oyzzz4r3V9WVpYHHnhgHU8F66fFixdn8uTJuf322/O///u/pR4Hmq1+/frl0UcfTcuWLRu3LV26NJ/5zGfy+OOPl3AyaN6++tWv5rzzzvMHJgCs90RfVsns2bM/8j3dunVbB5MAAPBRhg8fns022yynnXZaOnfunDfeeCOXXHJJli5dmmuuuabU40Gzc9NNNyVJnn/++fzud7/L1772tWy++eZ544038vOf/zz77ruvR6PASvz0pz/NEUcc0biGVsbjHaA0RF/WyMKFC1NfX99kW8eOHUszDKxHXnvttcybN6/xqvna2to899xzOfroo0s7GACFMnfu3Jxyyin561//2vhlVAMHDsz3v//9fOITnyj1eNDsfNCt6e9xizqs3HHHHZcf/vCHHu8AzZDoy2qZPHlyzjvvvCxZsqRxm8c7wKqZMGFCrr766rRs2TJlZWVZvnx5GhoasvPOO2fSpEmlHg+AApo9e3bmz5+fTTfdNJtvvnmpxwEAYB1pUeoBWL+MGzcup512Wj772c+mvLy81OPAemXSpEm56aabUl9fn3vvvTdjxozJBRdc0OSLQgBgbXn/3SWzZ8/Oyy+/7O4SWAUPP/zwSu/M+u///u8STwbNV11dXaZNm5ZXX301y5cvb7LPl+9CaYi+rJaampp87WtfE3xhDSxevDiDBg3K/Pnz8/TTT6eysjLf+c53cuCBB+bss88u9XgAFMiH3V0i+sIH+973vpe77ror7du3b7yj8c033/zAL+QF/unss8/Ob37zm/Tu3bvJl4gCpSP6sloOPfTQ/PCHP8zw4cPTooV/fGB1bLbZZpk/f3422WSTzJ07NzU1NWnVqlUWLVpU6tEAKBh3l8Caue+++zJp0qQsWrQot912W8aNG5cJEybklVdeKfVo0KxNmzYtd999d7p3717qUYD/R7VjtfTv3z/f+ta3ctVVV6VVq1ZN9j3++OMlmgrWD1/4whdyxBFH5NZbb81nPvOZnHLKKamsrMwnP/nJUo8GQMG4uwTWzPLly7PDDjtkwYIFefbZZ5Mkw4cPz+c+97kSTwbNW5cuXbLJJpuUegzgfURfVsv555+fY489Np/+9KdTUVFR6nFgvXLiiSdmm222SZs2bTJmzJiMGzcuixcvzqWXXlrq0QAoGHeXwJrZfPPN8+KLL2bbbbfNP/7xj7z77rspLy9v8kXWwP/v7bffTpIccsghOeusszJq1Ki0b9++yXs6duy47gcDUtbw3tPpYRUMGDAgjz32WKnHAADgQ0yYMCGTJ0/OrbfemosuuihLly5NZWVl5s2bl0mTJpV6PGi2Jk2alCuuuCJ33313Jk6cmGeeeSYtWrRImzZtcsMNN5R6PGh2dthhh5SVlTV+8WFZWVnjvveei/23v/2tVOPBBk30ZbWMGTMmO++8c770pS+VehRY7/z973/PD37wg7z++uupr69vsm/KlCklmgqAorr33nuz9957p7a2tvHuklNOOSVbbbVVqUeDZm369OnZYYcdkiQ33nhjFi9enGOOOSadO3cu8WTQ/MyePfsj39OtW7d1MAnwr0RfVsvw4cPz0EMPpUuXLunUqVOTfaIVfLiDDz44n/zkJ7P77runvLy8yb5DDjmkRFMBAPBBFixYIPbCGlq+fHmef/759OrVq9SjwAbJM31ZLQcddFAOOuigUo8B66VZs2blzjvvTMuWLUs9CgAF5+4SWD319fW58cYb88wzz2TIkCEZNGhQjjnmmMyYMSPbbLNNrrnmmmy99dalHhOarQceeCAXXHBB5s2bl/dfW1hVVZUnnniihJPBhsuVvqwVdXV1vtgNPsKJJ56Yo446KrvttlupRwGg4NxdAqvn4osvzv/93//ls5/9bB588MG0adMm22+/fY499tj8/Oc/z+uvv55rr7221GNCs3XAAQfkC1/4Qtq2bZvp06fn0EMPzdVXX539998/Rx99dKnHgw2S6MtqefnllzNx4sTMmzev8aqR2travPzyy/nTn/5U4umgeXvqqadyxBFHpHfv3it8o63/iABgberXr18effRRd5fAKtpzzz1z++23Z7PNNsvLL7+cAw44II888kg6dOiQJUuWZPDgwXnkkUdKPSY0W3379s3jjz+e2bNn54wzzsikSZMya9asDB8+PNOmTSv1eLBB8ngHVst///d/p02bNtl4440zd+7c9O/fP7/4xS9yxBFHlHo0aPbe+yLEXXbZxZXxAHysdt999zz++OPuLoFV9O6772azzTZLkmy99dapqqpKhw4dkiRt2rTJ8uXLSzkeNHtdunRJbW1tNt9887z00ktJku7du+fNN98s8WSw4RJ9WS3PPPNMHnroocyePTuXXnppTj311Oy5554ZO3ZsTjzxxFKPB83aSy+9lD//+c9p0cL/9ALw8Ro5cqS7S2A1lJWVNXn9r1fJu0EWPtwuu+ySM844IxdeeGE++clP5vrrr09lZWW6dOlS6tFgg6U8sFo6dOiQtm3bZquttspzzz2XJBkwYEBeeeWVEk8GzV+fPn3ywgsvZIcddij1KAAUnLtLYPU0NDTk2WefbYy7dXV1TV6LvvDhzj777Fx++eWpra3Nf/3Xf2XUqFFZvHhxLrzwwlKPBhss0ZfVsv322+eGG27I0UcfnQ4dOuQvf/lLqqqq/McErIJPfepTOfLII7PnnnumU6dOTa4oOfvss0s4GQBF4+4SWD1Lly7Nl770pSbb3v/6X68EBpq68847c8YZZ6Rt27bp3Lmz5/hCM+D/C2S1nHnmmRk1alT222+/nHjiiTnqqKPS0NCQUaNGlXo0aPbefvvtfO5zn0uSLFmypMTTAFBk7i6B1fP3v/+91CPAem38+PG+6weambIG96mwGpYtW5ZWrVo1vp43b14WL16cHj16lHAqAADe79JLL80vfvELd5cAsE6ce+65ad++fYYOHZpNNtmkyb93OnbsWLrBYAMm+rJadtlll+y///754he/mIEDB5Z6HFivjB8//gP3nXTSSetwEgCK7qyzzvrAfRdffPE6nASADUG/fv2ydOnSxtdlZWVpaGhIWVlZ/va3v5VwMthwib6slhkzZmTq1Km555570tDQkIMPPjhf/OIXs+2225Z6NGj2Ro4c2eT122+/naeffjoHHXRQLrnkkhJNBQAA8O+ZPXv2B+7r1q3bOpwEeI/oyxp77LHHMm3atPz2t79Nhw4dMmzYsAwdOtStG7AafvOb3+Tuu+/OlVdeWepRACiQZcuWZerUqZk3b17q6+uTJLW1tXn++edzzTXXlHg6AIpo0aJF+f3vf5958+alW7du+exnP5vWrVuXeizYYPkiN9ZITU1N/vGPf+Stt97KwoUL06VLlzz++OOZMGFCzjzzzBW++RZYuX322Sff+c53Sj0GAAVz1lln5bHHHkunTp1SXV2dTp06Zfr06f5/NAA+Fo8//nhGjBiRLl26pGvXrpk9e3YuvPDC/OhHP8onP/nJUo8HGyTRl9Xyhz/8Iffcc0/uv//+dOzYMUOHDs0pp5ySrbbaKknyu9/9Lt/+9rf9BwWsxNtvv93kdW1tbe6+++506dKlNAMBUFgPPvhg/vd//zdvvPFGbrzxxlx11VW544478pvf/KbUowFQQBdffHFOP/30HHbYYY3bbrrppnz3u9/NrbfeWsLJYMMl+rJaTj311Oy333657rrrMmDAgCTJn/70p1x88cW59tpr07Nnz3z1q18t8ZTQPH36059u/Bbb956s0759+3zve98r5VgAFFB5eXm6deuW9u3bN36BziGHHOJxQgB8LF599dUVWsCRRx6Zq666qkQTAaIvq+Whhx5KZWVlampqcscdd+SWW27Jiy++mN133z1J0rVr15xxxhklnhKapwceeKDxrxcuXJglS5Zkp512SsuWLUs4FQBFtOWWW+Yvf/lLdtlll1RXV2f+/Plp0aJFqqurSz0aAAX06U9/Ovfdd1++8IUvNG77v//7v/Tv37+EU8GGTfRltbz99tv52c9+lttuuy3vvPNOjjvuuFx//fXZbLPNSj0aNFvLli3LmDFj0qlTp5x55pmZPn16hg8fnqVLl6Zbt265+eab07Vr11KPCUCBHHfccTn22GMzderUfOUrX8lhhx2WioqK7LXXXqUeDYACGTlyZJJ/fonb6aefnp///Ofp1q1b3njjjTzyyCPZY489SjwhbLjKGt67xxg+xPTp03PzzTfnV7/6VT7zmc/k8MMPzxlnnJHJkydn0003LfV40KyNHTs2f/nLX/Lf//3f2WmnnXLYYYele/fuufTSSzNhwoTMmjUrY8eOLfWYABTM3Llz06VLlyxdujQ333xzNtpoo/zHf/xHKisrSz0aAAUxfvz4j3zPSSedtA4mAf6VK31ZJV/96ldz6KGHZtq0aa7qhdV0//3356abbsoWW2yRBQsW5Mknn8w555yTioqKHHnkkTnwwANLPSIABfFBd5csWbIk3bp1y5AhQ9xdAsBa07Zt21KPAHwA0ZdVcsQRR2TKlCmZMWNGDjvssBx44IGNX0gFfLi33norW2yxRZLkiSeeSJs2bdKrV68kyUYbbZQlS5aUcjwACuQHP/hBXnrppRx++OFJkosuuih77bVX490l3//+991dAsBa85vf/OZD95eVleWYY45ZR9MA7yf6skrOPvvsnHHGGZk6dWomTZqUSy65JEuWLMnf//53j3eAj9C2bdssWrQo7du3z6OPPpr+/funvLw8SfLKK69ko402KvGEABSFu0sAWJd+8pOflHoE4AOUl3oA1h+tWrXKl7/85dx+++25+eab86UvfSmnnnpqPv/5z6/Sc3xgQzV48OBcdNFF+d3vfpe777678T+4ly5dmvHjx+czn/lMiScEoCjcXQIAQCL6soZ69eqV8847Lw8++GCOPvro/PrXvy71SNBsnXbaaZkzZ05OOeWU7LXXXhk2bFiS5LOf/WyefPLJnHLKKaUdEIDCeO/ukiTuLgEA2ICVNTQ0NJR6CIAN0YMPPphdd901VVVVpR4FgII455xzsnz58uy///4ZPXp0vvOd7+SQQw7J0qVLc/bZZ6dVq1a56KKLSj0mAAAfM9EXAAAK4h//+EdOPfXUPPnkk9l///1z8cUXp6ysLLvuums6dOiQW2+91fcxAABsAERfAAAoOHeXAABsWERfAAAAAIAC8UVuAAAAAAAFIvoCAAAAABSI6AsAAAAAUCCiLwAA6dmzZ04//fQVto8YMSJXX311CSYCAADWlOgLAECSZOrUqbn77rtLPQYAAPBvEn0BAEiSHHbYYTnvvPMye/bsle6vqanJBRdckP322y99+/bN4MGDc/vttzfu79mzZ2677bbss88+6du3b84999z88Y9/zP77759+/frllFNOyfLly5Mky5Yty6WXXpq99947u+++e0aPHp2FCxeuk/MEAICiE30BAEiSfOUrX8mnP/3pfOc730l9ff0K+3/84x/nqaeeym233ZbHH388J5xwQi644IIsXry48T2//vWvc/fdd+d//ud/8otf/CJXXXVVbr311kydOjUPP/xwHnjggSTJZZddlr/+9a+54447cv/996e2tjZnn332OjtXAAAoMtEXAIBG559/fl599dVcd911K+z76le/mokTJ6ZDhw6ZO3duqqqqsmzZsrzzzjuN7zniiCPSrl27bLvtttl4443z5S9/OZ07d063bt2y3Xbb5fXXX09DQ0PuuOOOfPvb387GG2+cdu3aZfTo0Zk2bVoWLFiwLk8XAAAKqUWpBwAAoPno1KlTLr300hx//PH5zGc+02Tfu+++m/PPPz9PPPFEunfvnk9+8pNJ0uSq4E6dOjX+dUVFRdq3b9/4ury8PPX19VmwYEGqq6tz3HHHpaysrHF/q1atMmvWrHTu3PnjOj0AANggiL4AADSx++675+tf/3rOOOOMbLLJJo3bzz333GyxxRZ56KGH0rJly7z00ku56667mnz2/RH3g3Tq1CmVlZWZNGlStt9++yTJ8uXL88orr2SrrbZauycDAAAbII93AABgBaeffnpatWqVRx99tHHbu+++m8rKylRUVOStt97KZZddliSpra1drWOXl5fnkEMOyWWXXZYFCxakpqYmV155ZY466qjGL3oDAADWnOgLAMAKKisrc/nll6dVq1aN2/77v/87Dz/8cHbZZZd85StfyQ477JBu3brl+eefX+3jn3XWWdlqq61yyCGHZNCgQfnrX/+aH/3oR6mqqlqbpwEAABuksoaGhoZSDwEAAAAAwNrhSl8AAAAAgAIRfQEAAAAACkT0BQAAAAAoENEXAAAAAKBARF8AAAAAgAIRfQEAAAAACkT0BQAAAAAoENEXAAAAAKBARF8AAAAAgAIRfQEAAAAACuT/A8I+9Z4cOWYFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(stacked=True, figsize=(20, 10)).legend(bbox_to_anchor=(1.1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebe5fb86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "21e50e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list=[2, 4, 5, 1]\n",
    "list.count(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b1b15d",
   "metadata": {},
   "source": [
    "# Lists and Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b00b9fd0",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'list' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-51-af93b730e369>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mWord\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Egypt'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mList1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mWord\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mList1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'list' object is not callable"
     ]
    }
   ],
   "source": [
    "Word = 'Egypt'\n",
    "List1 = list(Word)\n",
    "print (List1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "115146f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4698aea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "staff_salary={'Sumaila':300, 'Hamid':12, 'Samadu':99, 'Ali':20}\n",
    "print(staff_salary['Ali'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e8bf26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define a function to iterate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "88e8cce4",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'salary' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-60-2137d94a9144>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'\\t'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNetsalary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mnetSalary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msalary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'salary' is not defined"
     ]
    }
   ],
   "source": [
    "def netSalary(salary):\n",
    "    return salary - (salary * 0.05)\n",
    "    print(\"Name \", 't', \"Net Salary\")\n",
    "    \n",
    "    for key, value in staff_salary.items():\n",
    "        print(key, '\\t', Netsalary(value))\n",
    "        \n",
    "netSalary(salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2fecbbe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "staff_salary={'Omar Ahmed':300, 'Ali Zidane':290, 'Ama':2384}\n",
    "\n",
    "STDMarks={\"Samadu Sule\":48, \"Sumaila Ayamba\":18, \"Dane Pop\":64}\n",
    "\n",
    "def cmp(a, b):\n",
    "    for key in a.items():\n",
    "        for key2 in b.items():\n",
    "            return (key > key2) - (key < key2)\n",
    "        \n",
    "print(cmp(STDMarks, staff_salary))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27f2cb4",
   "metadata": {},
   "source": [
    "# Sorting a Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a2078e15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 22), (23, 44), (99, 1)]\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "scores=[(23, 44), (99, 1), (00, 22)]\n",
    "print(sorted(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ac75ced9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 22), (23, 44), (99, 1)]\n"
     ]
    }
   ],
   "source": [
    "scores2=sorted(scores, key=lambda x: (x[0], x[1]))\n",
    "print(scores2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f70d4a3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "be7fbb1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33, 53, 22, 8, 4, 1)\n"
     ]
    }
   ],
   "source": [
    "test=(33, 53, 22)\n",
    "test2=(8, 4, 1)\n",
    "\n",
    "Combind=test + test2\n",
    "print(Combind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2ccfeba3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-ab1a7cca592d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'test' is not defined"
     ]
    }
   ],
   "source": [
    "del test\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbaec3cd",
   "metadata": {},
   "source": [
    "# Creating a Series with index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cb94d845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a   -0.358346\n",
      "b   -1.306928\n",
      "c   -0.235441\n",
      "dtype: float64\n",
      "Index(['a', 'b', 'c'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np, pandas as pd\n",
    "\n",
    "Series=pd.Series(np.random.randn(3), index=['a', 'b', 'c'])\n",
    "\n",
    "print(Series)\n",
    "print(Series.index)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a0d6ac51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.143258\n",
      "1   -0.375628\n",
      "2    0.026784\n",
      "3    0.137753\n",
      "dtype: float64\n",
      "RangeIndex(start=0, stop=4, step=1)\n"
     ]
    }
   ],
   "source": [
    "Series4=pd.Series(np.random.randn(4))\n",
    "print(Series4)\n",
    "print(Series4.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c558f6",
   "metadata": {},
   "source": [
    "# Creating a Series from a Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6ffc0536",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Series of non declared index\n",
      "m         8\n",
      "y      2022\n",
      "d    Friday\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "my_dict={'m':8, 'y':2022, 'd':'Friday'}\n",
    "print(\"\\n Series of non declared index\")\n",
    "\n",
    "my_series=pd.Series(my_dict)\n",
    "print(my_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4ad984bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Series of declared index\n",
      "y      2022\n",
      "m         8\n",
      "d    Friday\n",
      "s       NaN\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nSeries of declared index\")\n",
    "my_series2=pd.Series(my_series, index=['y', 'm', 'd', 's'])\n",
    "print(my_series2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "79b43bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m           8\n",
      "y        2022\n",
      "d    Saturday\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Altering a series\n",
    "my_series['d']='Saturday'\n",
    "print(my_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4bf8c71c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CREATE SERIES FROM SCALAR VALUE\n",
      "a    9.1\n",
      "b    9.1\n",
      "c    9.1\n",
      "d    9.1\n",
      "e    9.1\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Creating a series from a scalar value\n",
    "\n",
    "print(\"\\nCREATE SERIES FROM SCALAR VALUE\")\n",
    "my_scalar=pd.Series(9.1, index=['a', 'b', 'c', 'd', 'e'])\n",
    "print(my_scalar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b7bb70",
   "metadata": {},
   "source": [
    "# Vectorized Operations and Label Alignment With Series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3c5e4b7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Addition\n",
      "a    2\n",
      "b    4\n",
      "c    6\n",
      "d    8\n",
      "dtype: int64\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'serX' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-84-4283751f8f5a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_serX\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmy_serX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_serX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mserX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'serX' is not defined"
     ]
    }
   ],
   "source": [
    "my_serX=pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "print(\"Addition\");\n",
    "print(my_serX + my_serX)\n",
    "\n",
    "print(my_serX[1:] + serX[:-1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ecc8d19",
   "metadata": {},
   "source": [
    "# Using a Series Name Attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d213a1ad",
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.0  1.0\n",
       "b  2.0  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a Data Frame from a Dict of Series\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "my_dict={'one':pd.Series([1., 2., 3.],\n",
    "index=['a', 'b', 'c']),\n",
    "        \n",
    "        'two':pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}\n",
    "my_dict=pd.DataFrame(my_dict)\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5580b979",
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "d  NaN  4.0\n",
       "z  NaN  NaN\n",
       "w  NaN  NaN"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set index for the DataFrame\n",
    "\n",
    "pd.DataFrame(my_dict, index=['d', 'z', 'w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "4bf6e27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   two  three  one\n",
       "d  4.0    NaN  NaN\n",
       "b  2.0    NaN  2.0\n",
       "a  1.0    NaN  1.0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Control label appearance os the DataFrame\n",
    "\n",
    "pd.DataFrame(my_dict, index=['d', 'b', 'a'], columns=['two', 'three', 'one'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f66a42f",
   "metadata": {},
   "source": [
    "# Creating Data Frames from a Dict of Ndarrays/Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a33710d3",
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "0    1    5\n",
       "1    2    3\n",
       "2    3    2\n",
       "3    4    5"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # without index\n",
    "my_ndarr={'one':[1, 2, 3, 4], 'two':[5, 3, 2, 5]}\n",
    "pd.DataFrame(my_ndarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96a227d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Data Frame from a Record Array\n",
    "\n",
    "import pandas as pd, numpy as np\n",
    "my_record=np.zeros((2,), dtype=[('A', )])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e43077b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "93bbead9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "coin=random.randint(3, 5)\n",
    "print(coin)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "585a17d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, my name is -f\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(\"Hello, my name is\", sys.argv[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ba47b85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79ff352d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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