00- Tutorials/Python for Beginners Tutorial/Python for Beginners (run).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a37eb6cb",
   "metadata": {},
   "source": [
    "## Machine Learning for Neuroscience, <br>Department of Brain Sciences, Faculty of Medicine, <br> Imperial College London\n",
    "### Contributors: Francesca Palermo, Nan Fletcher-Lloyd, Alex Capstick, Yu Chen\n",
    "**Winter 2022**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9246f7fe",
   "metadata": {},
   "source": [
    "# Python for Beginners"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b161ecd",
   "metadata": {},
   "source": [
    "This tutorial is adapted from the tutorials provided by DataCamp (https://www.datacamp.com) where further examples and practice questions can be found."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecd203f",
   "metadata": {},
   "source": [
    "## Basic Python Syntax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3630749f",
   "metadata": {},
   "source": [
    "### Print Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a29ec6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, World!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello, World!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d21585f1",
   "metadata": {},
   "source": [
    "### Operators & Booleans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8229166",
   "metadata": {},
   "source": [
    "Operators allow simple outcomes to be obtained.\n",
    "+ The sum of values is found using +\n",
    "+ The difference between values is found using -\n",
    "+ The product of values is found using *\n",
    "+ The quotient of values is found using /"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28cc2b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e61f575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 - 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13aeabcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 * 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fdf0527e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 / 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2023817",
   "metadata": {},
   "source": [
    "Booleans allow us to compare two values. \n",
    "+ less than is <\n",
    "+ greater than is >\n",
    "+ equal to or less than is <= \n",
    "+ equal to or greater than is >= "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9304d93a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b2e1d30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 > 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5b7bc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 <= 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc3bcb2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 >= 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a50b99b",
   "metadata": {},
   "source": [
    "### Creating and Assigning Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d45f2d7",
   "metadata": {},
   "source": [
    "A number can be assigned to a name (the variable) and the value returned by calling that same variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa0221a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "variable_1 = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34bb5e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4366f64c",
   "metadata": {},
   "source": [
    "### Data Types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd89a7e",
   "metadata": {},
   "source": [
    "Values in python can take several different forms. Some of the most common forms are listed.\n",
    "+ int represents integers\n",
    "+ float represents decimal numbers\n",
    "+ str or \"strings\" represents text."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe104671",
   "metadata": {},
   "source": [
    "## Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2ea703",
   "metadata": {},
   "source": [
    "Lists are a type of data structure in which values can be stored and accessed. An advantage of lists is that the data types do not need to be the same (see below, where we have an integer, float, and string, respectively). \n",
    "\n",
    "A list can be built by assigning a name to a set of values in square brackets (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d45b3c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "list = [5, 5.0,'five']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fedb57d",
   "metadata": {},
   "source": [
    "Each item within the list can be accessed by calling the name assigned to the list alongside the position of that item within the list in squared brackets. Note, the first value in a list is always given the positional value 0, the second the value 1, and so on, so that in a list of N data points, the final data point will have the positional value N-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0da4b6dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e8e4e5",
   "metadata": {},
   "source": [
    "Lists can also be built inside of lists. \n",
    "\n",
    "In the example below, each value is separated into its own list by data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cb7cd923",
   "metadata": {},
   "outputs": [],
   "source": [
    "list = [[\"int\", 5], [\"float\", 5.5], [\"str\", 'five']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c23de84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['int', 5], ['float', 5.5], ['str', 'five']]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1416474b",
   "metadata": {},
   "source": [
    "\n",
    "Other types of data structures such as dataframes, series, and arrays will be discussed in further detail later in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c7fe35",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e0405b",
   "metadata": {},
   "source": [
    "### Built-in Functions & Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7ccd81",
   "metadata": {},
   "source": [
    "Python has several set functions and methods which can be called with regards to an object. Methods are all functions, acting on objects, but not all functions are methods, as these only need take objects as inputs. Below we explore an incomprehensive list of the functions and methods available using Python."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc90030",
   "metadata": {},
   "source": [
    "First, let's create a simple list of numerical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1c4a1d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "heights = [160, 165, 185, 155, 175, 180]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88a71167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[160, 165, 185, 155, 175, 180]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2e9d22",
   "metadata": {},
   "source": [
    "Great! But these heights are out of order. To order these, we apply the sort () method. This will order values in ascending order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8c68943",
   "metadata": {},
   "outputs": [],
   "source": [
    "heights.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cd91900d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[155, 160, 165, 175, 180, 185]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a62be27",
   "metadata": {},
   "source": [
    "The sort () method is one way of trying to find the minimum and maximum values within a list. An alternative to this is to call the max () and min () functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a8784696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "185"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(heights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d01babbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(heights)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7586cb38",
   "metadata": {},
   "source": [
    "Let's now create a second list of names that correspond to these heights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3cfa72aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['Charlie','Brooke','Taylor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5cf47bd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Charlie', 'Brooke', 'Taylor']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10865d4b",
   "metadata": {},
   "source": [
    "We can check the length of this list to make sure it matches the previous list. We do this using the len () function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9b78da32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a121e1d",
   "metadata": {},
   "source": [
    "Oops! Looks like we're missing a few names. How do we add these on? There are two methods:\n",
    "* The append () method adds a single element.\n",
    "* The extend () method adds multiple elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ce9cac69",
   "metadata": {},
   "outputs": [],
   "source": [
    "names.append('Morgan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6481209a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Charlie', 'Brooke', 'Taylor', 'Morgan']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e269b17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "names.extend(['Riley','Jamie'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "11910a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Charlie', 'Brooke', 'Taylor', 'Morgan', 'Riley', 'Jamie']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec51abda",
   "metadata": {},
   "source": [
    "Perfect! Now, we can use the index () method to find out the names of the smallest and tallest people."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889b33b4",
   "metadata": {},
   "source": [
    "First, let's double check the type of each dataframe using the type () function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "742ea449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(heights)\n",
    "type(names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c40afed",
   "metadata": {},
   "source": [
    "Now, let's find the index number of the minimum and maximum heights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82a816cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_height = min(heights)\n",
    "max_height = max(heights)\n",
    "min_index = heights.index(min_height)\n",
    "max_index = heights.index(max_height)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9e22c8",
   "metadata": {},
   "source": [
    "With the min and max hieght index positions, we can then discern the names of the smallest and tallest people in our group of six."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "24e3c491",
   "metadata": {},
   "outputs": [],
   "source": [
    "smallest_name = names[min_index]\n",
    "tallest_name = names[max_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "86022cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Charlie'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallest_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "83cf22e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jamie'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tallest_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f24bb6",
   "metadata": {},
   "source": [
    "It worked! Charlie has the smallest height and Jamie has the tallest."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a060ed",
   "metadata": {},
   "source": [
    "### User-Defined Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad95d245",
   "metadata": {},
   "source": [
    "There are four main steps to defining your own function:\n",
    "1. Use keyword def and follow up with function name.\n",
    "2. Add parameters to function with parentheses, ending line with a colon.\n",
    "3. Add statements that function should execute.\n",
    "4. End function with return if function should output or without otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "50fd28df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hello():\n",
    "    print(\"Hello World!\")\n",
    "    return"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e84ef929",
   "metadata": {},
   "source": [
    "Now, let's run the function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8dd13e41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "hello()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b1e4511",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63e3fc8",
   "metadata": {},
   "source": [
    "### Installing packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576fc90a",
   "metadata": {},
   "source": [
    "Packages are collections of modules which themselves are a collection of classes, functions, and variables etc.\n",
    "\n",
    "In Jupyter Notebook this will look as follows: \n",
    "\n",
    "!pip install --user package_name_here -U"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b937adf",
   "metadata": {},
   "source": [
    "### Importing packages with an alias"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2c22c4",
   "metadata": {},
   "source": [
    "Before we can begin to use a package, we must import it into our environment, and we can do so in a way that assigns an alias to the package (usually a recognisable short-hand). \n",
    "\n",
    "For example: \n",
    "\n",
    "import package_name_here as alias"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a1caa6",
   "metadata": {},
   "source": [
    "## Pandas for Beginners"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3faa1db2",
   "metadata": {},
   "source": [
    "Pandas is a python library that allows easy handling of tabular data. \n",
    "\n",
    "Pandas can take data from a wide range of sources, including Excel and CSV files.\n",
    "\n",
    "Pandas allow data to be stored and accessed as series or dataframes. Series objects are like arrays and dataframes are built from a set of series objects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c4f42d",
   "metadata": {},
   "source": [
    "First, we want to install pandas. Providing you're using Jupyter Notebook, pandas should already be installed; however, running the following code allows us to check: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "829dbb8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages (1.5.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/nanfletcher-lloyd/opt/anaconda3/lib/python3.8/site-packages (from pandas) (2021.1)\n",
      "Requirement already satisfied: numpy>=1.20.3 in /Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages (from pandas) (1.21.6)\n",
      "Requirement already satisfied: six>=1.5 in /Users/nanfletcher-lloyd/opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --user pandas -U"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c69f76",
   "metadata": {},
   "source": [
    "Next, we want to import pandas with the alias pd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cbb00b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4757739",
   "metadata": {},
   "source": [
    "Now, let's look at importing a csv file. \n",
    "\n",
    "We do this with the read command and can do so using the path_to_file or from a URL.\n",
    "\n",
    "See below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "621e2c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     total_bill   tip     sex smoker   day    time  size\n",
      "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
      "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
      "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
      "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
      "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
      "..          ...   ...     ...    ...   ...     ...   ...\n",
      "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
      "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
      "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
      "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
      "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
      "\n",
      "[244 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "tips = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv\")\n",
    "print(tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3527d4",
   "metadata": {},
   "source": [
    "In the above example, we assigned the name df to the csv file.\n",
    "\n",
    "To read the csv file using path_to_file, the same code is used, replacing the URL with either file.csv if the file is in your working directory (the relative path) or the absolute path."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50223692",
   "metadata": {},
   "source": [
    "Since there are many rows in the DataFrame, we see that most the data is truncated.\n",
    "\n",
    "To see the first or last few entries, we use the commands head or tail, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "35104861",
   "metadata": {},
   "outputs": [
    {
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       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
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       "      <th>size</th>\n",
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       "      <td>1.01</td>\n",
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       "      <td>Dinner</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
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       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4007b0f7",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
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       "      <th>size</th>\n",
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       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>241</th>\n",
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       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589e86e5",
   "metadata": {},
   "source": [
    "In the above examples, we only see the first and last five entries. \n",
    "\n",
    "The number of entries that can be seen using this method can be changed between the values of 1 and 60. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a50a6716",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
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       "      <td>No</td>\n",
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       "      <td>Female</td>\n",
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       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.77</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.88</td>\n",
       "      <td>3.12</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.04</td>\n",
       "      <td>1.96</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.78</td>\n",
       "      <td>3.23</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4\n",
       "5       25.29  4.71    Male     No  Sun  Dinner     4\n",
       "6        8.77  2.00    Male     No  Sun  Dinner     2\n",
       "7       26.88  3.12    Male     No  Sun  Dinner     4\n",
       "8       15.04  1.96    Male     No  Sun  Dinner     2\n",
       "9       14.78  3.23    Male     No  Sun  Dinner     2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab05b3fd",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
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       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "234       15.53  3.00    Male    Yes   Sat  Dinner     2\n",
       "235       10.07  1.25    Male     No   Sat  Dinner     2\n",
       "236       12.60  1.00    Male    Yes   Sat  Dinner     2\n",
       "237       32.83  1.17    Male    Yes   Sat  Dinner     2\n",
       "238       35.83  4.67  Female     No   Sat  Dinner     3\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb3f0be",
   "metadata": {},
   "source": [
    "You can change the shape of the dataframe by selecting for specific columns or rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "981b8038",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      tip   day\n",
      "0    1.01   Sun\n",
      "1    1.66   Sun\n",
      "2    3.50   Sun\n",
      "3    3.31   Sun\n",
      "4    3.61   Sun\n",
      "..    ...   ...\n",
      "239  5.92   Sat\n",
      "240  2.00   Sat\n",
      "241  2.00   Sat\n",
      "242  1.75   Sat\n",
      "243  3.00  Thur\n",
      "\n",
      "[244 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "tips_1 = tips[['tip','day']]\n",
    "print(tips_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "32fdcdec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     tip  day\n",
      "0   1.01  Sun\n",
      "1   1.66  Sun\n",
      "2   3.50  Sun\n",
      "3   3.31  Sun\n",
      "4   3.61  Sun\n",
      "5   4.71  Sun\n",
      "6   2.00  Sun\n",
      "7   3.12  Sun\n",
      "8   1.96  Sun\n",
      "9   3.23  Sun\n",
      "10  1.71  Sun\n",
      "11  5.00  Sun\n",
      "12  1.57  Sun\n",
      "13  3.00  Sun\n",
      "14  3.02  Sun\n",
      "15  3.92  Sun\n",
      "16  1.67  Sun\n",
      "17  3.71  Sun\n",
      "18  3.50  Sun\n",
      "19  3.35  Sat\n",
      "20  4.08  Sat\n",
      "21  2.75  Sat\n",
      "22  2.23  Sat\n",
      "23  7.58  Sat\n",
      "24  3.18  Sat\n",
      "25  2.34  Sat\n",
      "26  2.00  Sat\n",
      "27  2.00  Sat\n",
      "28  4.30  Sat\n",
      "29  3.00  Sat\n",
      "30  1.45  Sat\n",
      "31  2.50  Sat\n",
      "32  3.00  Sat\n",
      "33  2.45  Sat\n",
      "34  3.27  Sat\n",
      "35  3.60  Sat\n",
      "36  2.00  Sat\n",
      "37  3.07  Sat\n",
      "38  2.31  Sat\n",
      "39  5.00  Sat\n",
      "40  2.24  Sat\n",
      "41  2.54  Sun\n",
      "42  3.06  Sun\n",
      "43  1.32  Sun\n",
      "44  5.60  Sun\n",
      "45  3.00  Sun\n",
      "46  5.00  Sun\n",
      "47  6.00  Sun\n",
      "48  2.05  Sun\n",
      "49  3.00  Sun\n"
     ]
    }
   ],
   "source": [
    "tips_1 = tips_1.iloc[0:50, :]\n",
    "print(tips_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3ea5bb",
   "metadata": {},
   "source": [
    "You can also rename columns to make them easier to remember and/or call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8c726eb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Tips Day of Week\n",
      "0   1.01         Sun\n",
      "1   1.66         Sun\n",
      "2   3.50         Sun\n",
      "3   3.31         Sun\n",
      "4   3.61         Sun\n",
      "5   4.71         Sun\n",
      "6   2.00         Sun\n",
      "7   3.12         Sun\n",
      "8   1.96         Sun\n",
      "9   3.23         Sun\n",
      "10  1.71         Sun\n",
      "11  5.00         Sun\n",
      "12  1.57         Sun\n",
      "13  3.00         Sun\n",
      "14  3.02         Sun\n",
      "15  3.92         Sun\n",
      "16  1.67         Sun\n",
      "17  3.71         Sun\n",
      "18  3.50         Sun\n",
      "19  3.35         Sat\n",
      "20  4.08         Sat\n",
      "21  2.75         Sat\n",
      "22  2.23         Sat\n",
      "23  7.58         Sat\n",
      "24  3.18         Sat\n",
      "25  2.34         Sat\n",
      "26  2.00         Sat\n",
      "27  2.00         Sat\n",
      "28  4.30         Sat\n",
      "29  3.00         Sat\n",
      "30  1.45         Sat\n",
      "31  2.50         Sat\n",
      "32  3.00         Sat\n",
      "33  2.45         Sat\n",
      "34  3.27         Sat\n",
      "35  3.60         Sat\n",
      "36  2.00         Sat\n",
      "37  3.07         Sat\n",
      "38  2.31         Sat\n",
      "39  5.00         Sat\n",
      "40  2.24         Sat\n",
      "41  2.54         Sun\n",
      "42  3.06         Sun\n",
      "43  1.32         Sun\n",
      "44  5.60         Sun\n",
      "45  3.00         Sun\n",
      "46  5.00         Sun\n",
      "47  6.00         Sun\n",
      "48  2.05         Sun\n",
      "49  3.00         Sun\n"
     ]
    }
   ],
   "source": [
    "tips_1.columns = ['Tips','Day of Week']\n",
    "print(tips_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fa750a",
   "metadata": {},
   "source": [
    "Pandas can be used to calculate descriptive statisitcs of a row, column, or group, such as in the examples below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8bd45d7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-46-e94c6762f83e>:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  tips_mean = tips_1.mean(axis=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Tips    3.0896\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips_mean = tips_1.mean(axis=0)\n",
    "tips_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e059c8c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-2251c7c30969>:1: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  tips_median = tips_1.median(axis=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Tips    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips_median = tips_1.median(axis=0)\n",
    "tips_median"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad64df14",
   "metadata": {},
   "source": [
    "Note, to return the mean value across several columns, use axis=1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d8abbd",
   "metadata": {},
   "source": [
    "But what if we wanted to find the mean tip for each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4a1d4b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day of Week\n",
       "Sat    3.077273\n",
       "Sun    3.099286\n",
       "Name: Tips, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips_day_mean = tips_1.groupby(['Day of Week'])['Tips'].mean()\n",
    "tips_day_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da63e5fd",
   "metadata": {},
   "source": [
    "Pandas dataframes can be further modified by combining and filtering data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f69987",
   "metadata": {},
   "source": [
    "Let's do this in two stages. First, say we want to find the sum of the total bill and tip for each customer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "493bd276",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips #print unmodified df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b94c94e5",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>24.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>26.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>28.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>34.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>29.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>24.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>19.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>21.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size  amount\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2   18.00\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3   12.00\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3   24.51\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2   26.99\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4   28.20\n",
       "..          ...   ...     ...    ...   ...     ...   ...     ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3   34.95\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2   29.18\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2   24.67\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2   19.57\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2   21.78\n",
       "\n",
       "[244 rows x 8 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips['amount'] = tips['total_bill'] + tips['tip'] \n",
    "#creates new column 'amount' in df and enters the sum of total_bill and tip by row \n",
    "tips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a682e0c",
   "metadata": {},
   "source": [
    "Now, let's say we only want these values for those customers who visited on a Saturday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3d2543eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>amount</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat</td>\n",
       "      <td>20.65</td>\n",
       "      <td>3.35</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sat</td>\n",
       "      <td>17.92</td>\n",
       "      <td>4.08</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat</td>\n",
       "      <td>20.29</td>\n",
       "      <td>2.75</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>23.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat</td>\n",
       "      <td>15.77</td>\n",
       "      <td>2.23</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat</td>\n",
       "      <td>39.42</td>\n",
       "      <td>7.58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>47.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Sat</td>\n",
       "      <td>35.83</td>\n",
       "      <td>4.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>40.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Sat</td>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>34.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Sat</td>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>29.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Sat</td>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>24.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Sat</td>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>19.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    day  total_bill   tip     sex smoker    time  size  amount\n",
       "0   Sat       20.65  3.35    Male     No  Dinner     3   24.00\n",
       "1   Sat       17.92  4.08    Male     No  Dinner     2   22.00\n",
       "2   Sat       20.29  2.75  Female     No  Dinner     2   23.04\n",
       "3   Sat       15.77  2.23  Female     No  Dinner     2   18.00\n",
       "4   Sat       39.42  7.58    Male     No  Dinner     4   47.00\n",
       "..  ...         ...   ...     ...    ...     ...   ...     ...\n",
       "82  Sat       35.83  4.67  Female     No  Dinner     3   40.50\n",
       "83  Sat       29.03  5.92    Male     No  Dinner     3   34.95\n",
       "84  Sat       27.18  2.00  Female    Yes  Dinner     2   29.18\n",
       "85  Sat       22.67  2.00    Male    Yes  Dinner     2   24.67\n",
       "86  Sat       17.82  1.75    Male     No  Dinner     2   19.57\n",
       "\n",
       "[87 rows x 8 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips_day = tips.set_index('day').loc['Sat'].reset_index()\n",
    "tips_day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05bd03e",
   "metadata": {},
   "source": [
    "And what if we only want these values for those customers who had size 3, or 3 or more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "42c3ac57",
   "metadata": {},
   "outputs": [],
   "source": [
    "tips_size1 = tips[tips['size'] == 3] # == means only\n",
    "tips_size2 = tips[tips['size'] >= 3] # > greater than, >= greater than or qual to, < smaller than, <= smaller than or equal to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0a9e3d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>24.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.33</td>\n",
       "      <td>1.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.29</td>\n",
       "      <td>3.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.97</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>20.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.65</td>\n",
       "      <td>3.35</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>24.06</td>\n",
       "      <td>3.60</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>27.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16.31</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>18.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>16.93</td>\n",
       "      <td>3.07</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>18.69</td>\n",
       "      <td>2.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>31.27</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>36.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>16.04</td>\n",
       "      <td>2.24</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>18.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>28.55</td>\n",
       "      <td>2.05</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>30.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>17.59</td>\n",
       "      <td>2.64</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>20.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>20.08</td>\n",
       "      <td>3.15</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>23.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>17.07</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>20.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>44.30</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>46.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>38.07</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>42.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>25.71</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>29.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>22.82</td>\n",
       "      <td>2.18</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>18.64</td>\n",
       "      <td>1.36</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>17.26</td>\n",
       "      <td>2.74</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>16.21</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>18.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>24.52</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>50.81</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>60.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>45.35</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>48.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>20.90</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>24.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>18.15</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>21.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>23.10</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>27.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>18.71</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "      <td>22.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>16.47</td>\n",
       "      <td>3.23</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "      <td>19.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>26.59</td>\n",
       "      <td>3.41</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>30.06</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>32.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>28.17</td>\n",
       "      <td>6.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>34.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>15.98</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "      <td>18.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>15.69</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>18.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>35.83</td>\n",
       "      <td>4.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>40.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>34.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill    tip     sex smoker   day    time  size  amount\n",
       "1         10.34   1.66    Male     No   Sun  Dinner     3   12.00\n",
       "2         21.01   3.50    Male     No   Sun  Dinner     3   24.51\n",
       "16        10.33   1.67  Female     No   Sun  Dinner     3   12.00\n",
       "17        16.29   3.71    Male     No   Sun  Dinner     3   20.00\n",
       "18        16.97   3.50  Female     No   Sun  Dinner     3   20.47\n",
       "19        20.65   3.35    Male     No   Sat  Dinner     3   24.00\n",
       "35        24.06   3.60    Male     No   Sat  Dinner     3   27.66\n",
       "36        16.31   2.00    Male     No   Sat  Dinner     3   18.31\n",
       "37        16.93   3.07  Female     No   Sat  Dinner     3   20.00\n",
       "38        18.69   2.31    Male     No   Sat  Dinner     3   21.00\n",
       "39        31.27   5.00    Male     No   Sat  Dinner     3   36.27\n",
       "40        16.04   2.24    Male     No   Sat  Dinner     3   18.28\n",
       "48        28.55   2.05    Male     No   Sun  Dinner     3   30.60\n",
       "64        17.59   2.64    Male     No   Sat  Dinner     3   20.23\n",
       "65        20.08   3.15    Male     No   Sat  Dinner     3   23.23\n",
       "71        17.07   3.00  Female     No   Sat  Dinner     3   20.07\n",
       "102       44.30   2.50  Female    Yes   Sat  Dinner     3   46.80\n",
       "112       38.07   4.00    Male     No   Sun  Dinner     3   42.07\n",
       "114       25.71   4.00  Female     No   Sun  Dinner     3   29.71\n",
       "129       22.82   2.18    Male     No  Thur   Lunch     3   25.00\n",
       "146       18.64   1.36  Female     No  Thur   Lunch     3   20.00\n",
       "152       17.26   2.74    Male     No   Sun  Dinner     3   20.00\n",
       "162       16.21   2.00  Female     No   Sun  Dinner     3   18.21\n",
       "165       24.52   3.48    Male     No   Sun  Dinner     3   28.00\n",
       "170       50.81  10.00    Male    Yes   Sat  Dinner     3   60.81\n",
       "182       45.35   3.50    Male    Yes   Sun  Dinner     3   48.85\n",
       "186       20.90   3.50  Female    Yes   Sun  Dinner     3   24.40\n",
       "188       18.15   3.50  Female    Yes   Sun  Dinner     3   21.65\n",
       "189       23.10   4.00    Male    Yes   Sun  Dinner     3   27.10\n",
       "200       18.71   4.00    Male    Yes  Thur   Lunch     3   22.71\n",
       "205       16.47   3.23  Female    Yes  Thur   Lunch     3   19.70\n",
       "206       26.59   3.41    Male    Yes   Sat  Dinner     3   30.00\n",
       "210       30.06   2.00    Male    Yes   Sat  Dinner     3   32.06\n",
       "214       28.17   6.50  Female    Yes   Sat  Dinner     3   34.67\n",
       "223       15.98   3.00  Female     No   Fri   Lunch     3   18.98\n",
       "231       15.69   3.00    Male    Yes   Sat  Dinner     3   18.69\n",
       "238       35.83   4.67  Female     No   Sat  Dinner     3   40.50\n",
       "239       29.03   5.92    Male     No   Sat  Dinner     3   34.95"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips_size1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3d3a9887",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>24.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>28.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.88</td>\n",
       "      <td>3.12</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>20.45</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>24.01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>26.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>15.69</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>18.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>35.83</td>\n",
       "      <td>4.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>40.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>34.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker  day    time  size  amount\n",
       "1         10.34  1.66    Male     No  Sun  Dinner     3   12.00\n",
       "2         21.01  3.50    Male     No  Sun  Dinner     3   24.51\n",
       "4         24.59  3.61  Female     No  Sun  Dinner     4   28.20\n",
       "5         25.29  4.71    Male     No  Sun  Dinner     4   30.00\n",
       "7         26.88  3.12    Male     No  Sun  Dinner     4   30.00\n",
       "..          ...   ...     ...    ...  ...     ...   ...     ...\n",
       "227       20.45  3.00    Male     No  Sat  Dinner     4   23.45\n",
       "230       24.01  2.00    Male    Yes  Sat  Dinner     4   26.01\n",
       "231       15.69  3.00    Male    Yes  Sat  Dinner     3   18.69\n",
       "238       35.83  4.67  Female     No  Sat  Dinner     3   40.50\n",
       "239       29.03  5.92    Male     No  Sat  Dinner     3   34.95\n",
       "\n",
       "[84 rows x 8 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips_size2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fddf0d75",
   "metadata": {},
   "source": [
    "Another asset of pandas is that it can convert strings to datetime objects, using pd.to_datetime(df['column']). From here on, datetime objects can be modified to display year, month, day etc. only. This is also a particularly useful function for dealing with transitions.\n",
    "\n",
    "Further, more in-depth tutorials for pandas can be found here: https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html#user-guide."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2769fd92",
   "metadata": {},
   "source": [
    "## NumPy for Beginners"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b3f4d7",
   "metadata": {},
   "source": [
    "NumPy stands for numeric or numerical python and is another Python library with the tools for solving mathematical models of problems on a computer.\n",
    "\n",
    "One of these tools is the NumPy array.\n",
    "\n",
    "A NumPy array is a data structure in which data can be stored and accessed as a multi-dimensional array object. \n",
    "\n",
    "Arrays have several advantages over Python lists, as they are a more compact data structure allowing for efficient computation of matrices and arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00af71cb",
   "metadata": {},
   "source": [
    "At a structural level, an array is a combination of four pointers:\n",
    "+ data \n",
    "+ dtype \n",
    "+ shape\n",
    "+ strides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "341334b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec2eaf0",
   "metadata": {},
   "source": [
    "We will now show you how to make arrays of zeros, ones, and using random values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f77ee2d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c55b8cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a74b41fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.17298548, 0.53932039, 0.14323977],\n",
       "       [0.0983449 , 0.32428726, 0.48228903],\n",
       "       [0.65616659, 0.87096367, 0.1289875 ]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2591f2",
   "metadata": {},
   "source": [
    "Here, the two numbers in parentheses indicate the number of rows and columns in the matrix."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dd461d0c",
   "metadata": {},
   "source": [
    "To create an array with a number, use the following approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dcbeb2f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 7, 7],\n",
       "       [7, 7, 7],\n",
       "       [7, 7, 7]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((3,3),7) # where the last number is the fill-in value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06118ae3",
   "metadata": {},
   "source": [
    "To create an array of evenly-spaced values, we use the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "87f4dda8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 2, 9) \n",
    "# where 0 and 2 indicate the start and endpoint values of the array (the range) and the final number 9 indicates the total number of values."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2a8b67f7",
   "metadata": {},
   "source": [
    "Further, more in-depth tutorials for NumPy can be found here: https://numpy.org/doc/stable/user/index.html#user."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f38226",
   "metadata": {},
   "source": [
    "## Visualisations in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0370e125",
   "metadata": {},
   "source": [
    "There are two main data visualisation packages that can be used with Python. These are Matplotlib and Seaborn."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6ccb7a",
   "metadata": {},
   "source": [
    "While the two are complementary, Seaborn specifically targets statistical data visualisation and extends Matplotlib by working with different parameters past the default Matplotlib parameters. Here we will focus on the use of Seaborn extending Matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85bfdf18",
   "metadata": {},
   "source": [
    "### Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3f72bb12",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e5e4a6",
   "metadata": {},
   "source": [
    "### Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8062f010",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b47df66",
   "metadata": {},
   "source": [
    "Now, let's load one of the built-in data sets in the Seaborn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "13a1bed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8a644575",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaee3656",
   "metadata": {},
   "source": [
    "Seaborn allows us to visualise data using different types of plots. Here, we will demonstrate the differences between  swarmplots, factor/barplots, violinplots, and boxplots. This can best be visualised by plotting multiple subplots in one plot using plt.subplots*. We use figsize to control the size of the overall plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a2197f26",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/nanfletcher-lloyd/.local/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=2, ncols=2, figsize=(10,8))\n",
    "\n",
    "sns.swarmplot(x=\"day\", y=\"tip\", data=tips, ax=axes[0,0])\n",
    "sns.barplot(x=\"day\", y=\"tip\", data=tips, ax=axes[0,1])\n",
    "sns.violinplot(x=\"day\", y=\"tip\", data=tips, ax=axes[1,0])\n",
    "sns.boxplot(x=\"day\", y=\"tip\", data=tips, ax=axes[1,1])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6a104a82",
   "metadata": {},
   "source": [
    "As can be seen in the plot above, each plot type provides a slightly different perspective.\n",
    "\n",
    "The first four plots (swarmplot, barplot, violinplot, and boxplot) are good for visualising the difference between two groups (i.e. where one variable is categorical). To differing degrees, each of these plots show the distribution of values in each group (here, the distribution of tips over different days of the week).\n",
    "\n",
    "The swarmplot shows distribution of tip size across the differnt days.\n",
    "\n",
    "The barplot represents an estimate of central tendency (mean, median etc. - here it is median) by the height with some indication of uncertainty around that estimate by the errorbars.\n",
    "\n",
    "The violinplot shows distribution of tip size and give an estimate of kernel density ()\n",
    "\n",
    "The boxplot shows the distribution of the tip size, where all plot components correspond to an actual data point (minimum, lower quartile, median quartile, upper quartile, and maximum, as well as showing each individual outlier).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc4d970",
   "metadata": {},
   "source": [
    "Another plot that is good for visualising data is the scatterplot. This plot allows you to visualise whether there is any relationship between two sets of continuous data (here, the relationship between the size of the tip and the total bill)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6ebaf52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_bill', ylabel='tip'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(y=\"tip\", x=\"total_bill\", data=tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86b863f2",
   "metadata": {},
   "source": [
    "One of the major motivations for using Seaborn with Matplotlib is that it allows us more control over the paramaters of the plot, such as adding another variable, changing the color palette, setting the limits of the x and y axes, and providing labels and titles etc. Such changes can help us to make our plots more visually appealing and easier to explain."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990ce54d",
   "metadata": {},
   "source": [
    "Using the plot above, we will now add another categorical variable to this plot using the hue and style function to denote between the different groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6ebaf52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_bill', ylabel='tip'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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88HG3wX9A/zith/fdk9rrQaMgKlEFfUfSMtDbKeifjhrOqQw4ZTXGNvvyyw0YjGbbNMBrEIy8svn1iEvBK9Q211aUTlCBXxlwRkf4tulc3zg1mmBvV9s0IGs7fHEfhIyEuLmw5UU4sh76QVpVcQwq1aMMOGMifHhzSSIvfH2Eoqp6bp4WzXVTom037Nc/DqbeA5NvA70rbPuvSvUofYp6uKsMWKU19dSbzIR4u9l+roextnkUR8ttRbEh9XBXcTj+Hi72u3jLQK+CvtLHqBy/oiiKg1GBX1EUxcGowK8oiuJg7JLjF0L4AW8CowEJ3Cql3GaPtihKf5eaV8G+7HLMFsm4SF9Ghvvau0lKH2evh7v/Ar6RUi4SQrgAHnZqh6L0a4dyy7nu9e1U1pkAcHPW8f6d0xkf5Wffhil9ms1TPUIIX+Ac4C0AKWW9lLLM1u1QFJvL2gXHN2sTuVpud8PXB041BX0Ag9HCmu0Z3WunMuDZI8cfBxQCK4QQe4QQbwoh2iyCK4S4UwiRJIRIGuillxUHUHkKPr8X3lsMySvg3avgk9uhtHtBOru07apOWaU1mC19f36OYj/2CPx6YCLwmpRyAlANPN76ICnlcillopQyMTjYRlUVFYfzwo4XWLphKUs3LOWFHS/03oW8w2DRCnD2gPUPg7kernsPAmK7ddrLxoW32XfDlBicdGqWsNIxe+T4s4FsKeWOhtfraCfwK4ot2HQ1KmMtmBsKyEkz1FdrqZ5uzCqeGhfAS4vH8dLGo5gtkvvnD2X28KAearAyUNk88Esp84QQWUKIeCllKrAAOGzrdiiKTVU0pHqEDq5/D758WEv13PZ9t3r9Xm7OXDUxkrnxwUgJgV42KkSn9Gv2GtXzALCmYUTPCeAWO7VDGeAMRjP1Jott6vCfjk9DqsdkgMhE8I3WtruZ6mkU4KkCvtJ5dgn8Usq9QJvCQYrSUywWya70Ev7z4zFyy2q5aVoMF40JY5CP9cL1Nl2GMHR0+9vtsFgkOpWnV3qJKtKmDEiHcsu56a0dGM3a6JY/fXmYmnoz980banXcE1OfsEfzOnQ4t4J1yVkcPlXBokmRzBkeTLC325nfqChnoVOBXwgxEZiFNsv2FymljZ6GKUrXHD5V0RT0G73180kWTYps0+vvK04UVnHDm9ubVhDbfqKE35w7jAfnD1O9f6VHnXE4pxDiaWAVEAgEoY2//2NvN0xRusNN79Rmn6erE85OfTeAHjlV0WbZyP9tOU5uedux+orSHZ3p8d8IjJNSGgCEEC8Ce4G/9GK7FKVbxkT6EuLjSkFFXdO+xy8Y0acfgra3WIxOCPruR5XSX3Um8OcCboCh4bUrkNNrLVKUHjA42Iu1t09l24liCirqmDEksM/XrxkZ5kOQlwtFVfVN+x6YP5RwP7WQi9KzOhP4y4FDQoiNaDn+hcBOIcS/AaSUD/Zi+xSly4aGeDM0xNvezei02CBP1tw+la8P5JGaX8klY8KYMTTQ9stGKgNeZwL/pw1/Gm3unaYoihIf6kN8qI+9m6EMcGcM/FLKVbZoiKIoimIbHQZ+IcSHUsrFQogDaCkeK1LKsb3aMkVRbKKi1kilwUiglytuzm1HQykDz+l6/A81/H0EeLTFfgH8rdda1A9JKSmtMeLp4oSr+o8zcNRXg8UMbj7W2wNIal4FK39NJ72omgnR/lw/JZqoALUu0kDXYeCXUp5q2BwqpbQqGi6EGNGrrepHskpq+Cg5i0925zA8xIsHFgxjQrS/vZuldFd9NexZA3VVkLgMDqyDukqYckePBv8qg4mkjBI2HSkgMsCdOcNDiA+1zQPp3LIaPs74D7nuqbhEwSEJq9PieXDco3i52bm2kdKrTpfquQe4FxgshNjf4kvewC+93bD+oN5k5tUfj/H+rixAWxRj+8kSPr9vJsMG9Z/RJEo7DBVw8idI+RKOboDsnRA3F8Yu7tHA/+3hPH734b6m169vOcFHd09ncLBXj12jI6fKDO2WpS6srFOBf4A7XapnLbAB+H9Y18uvlFKW9Gqr+oncMgMfJWdb7aupN3M0v0oF/v7OJwwu/CvkJmtB390fLvs3+EX12CWKKuv4+zepVvuKq+tJziy1SeB3d2k/LenqbI/1mRRbOl2qpxxtDP/1tmtO/+LspMPD2clqzVPQFrxWuie3rJbdGaWkF1czOsKXCVH++Hq07YWeKKwiKaOEkmojk6L9GRvp2/XnLKXpYCiHsHFQcgJSN0BFrlZDv7YUDnwMU27vsR6/SVowmMxt9hdW1FFtMOHp1vDfs6oQyrO0dtWUNG/ruvc8qaNc/oHscjKKaxgX5YeHi6rjeLZKquvYl1XGwZwKhoR4MTHan1DfvlUfSv2rdkOEvzuPXRDPU58fatqXEOrNyLCB9QDQ1oor6/jdh3vZdqL5F8vHzo/nrjlDrJYUPFlUzY1v7uBUuaFp3xtLElk4ctDZX7S6CL56FLJ3wHVrIeMXyNgGsXPg0pdh4zNwcguMvaYp8L+w4wVSS7Qee3xA/FlX+gz1cWfZjFj++X1a0z5XvQ6LhMKqOi3wm+q1NXq3vAhXv62ln3avhNt+gIgJVuc72/Z4uzm3KUUdHxDPmz+fZFd6KS8tHsdVEyPP6p56w9neV3f/Xbqj3mTmja0neW3z8aZ95yaE8PdF4/D3dLFZO85EBf5uunJCBLGBniRnlhLp787k2ADC1BT7bknJr7QK+gD/+iGNC8eEERfk2bRvX1apVdAHePGbI0yJ9cfX4yz/k3kGwZxHtUXQV16s7bv+AwgaBoFDtLSPxWSV6umJZRtnDA6kfq6FLUcLCfF2Y9awQNbvz+X6KQ3X0bvAyMth73vw0VJt39R72k05daU9jUFxx4li/vFdKnsyYH92OQDPf3WEmUOD7F7N9Gzvy6bLabaSXlzD8p9OWO37/kgBxwqqmBwXYJc2tUcF/m7ycnNm9vBgZg9XC8L3FIOxbfqjzmShrlVapKqu7XFl1UbqzJauXdg7FHwiofCIlt7xDNaCPoBvRNfOeQZDB3mx/kAug3zcKKmu52/fpLLilsnWSyh6BGirdpU2BJToadozhx50qtzArvRSq30VBiPGrn4vHVSd0YzZ0mbaE7XtpPTsSQV+pc8ZGuKFr7sz5bXNJYrnxQcT5W+dkx4d4YOTTlj9R7tlZiwhXVm4pLoI1v9OC/ozHoLkt7Xe/+3fa73+dvTE6l0Bnq78buEIjuZXUFZrIjbQg6EhLR7smuohaQUc+BDG3wiZ2+HjW9tN9XSnPfGh3rjqddSZmgP9jVNjCO0Daxec7X3ZdFW1VqIDPZgU409yRvOHaLCXK4Nb/KbaFwgp23469TWJiYkyKSnJ3s1QbOhAdhn//uEY+3PKuHhMGDdPj7VK8wCYzBa2nyzh5Y2p5FfUcdO0GK4cH84g3y6m2rJ2QFEajL4a8g5A0VFt29nOqbvCVEj5CiYugZri5m3PoB67hJSSpPRSXtqYSnpxDYsTo1icGEmEv5rMdbaOF1Sx8teTfHc4n0kx/tw3byijwn3t0hYhRLKUss0ytyrwK32WwWiiqs6Mv4eL1UPd1qoNJgwms3V6pIsKyirJKDPi7+5MjL8Lzi59pH6/yQh657bbPaym3kRtvZkATxe7VAXNe+45AEKfesrm1+5JJrOFsloj3m56XNtZFMhWOgr8KtWj9FluznrcnM/8I+rppsezB36U92WVcefqJPIr6tDrBI+cH89N02Lwcu0D/01aBvpeCvoAHi56bQin2QRlGdozjpbbvcyQknrmg/oBvZOOoB7oiPQWNeBcUYDy2nr++NkB8htW7DJZJC9uSCHlVIWdW2YHFos2W/nNBZC1C1K/1rZzknvtknnPPUf6jTdhOHAAw4EDpN94U1PvX+l5faAroyj2V1Jt5EBO2yCfXVpLYqzt22NXOh14hWpF6VZeqP0dOBRc1fyUgUIFfkUB/D2ciQ/1JjWv0mp/WB+bcWkz4RPgnEdg49Pa6wte7HB0U09ozOmn33gTALFr3u21aykq1aMoAPh5uPDClWPwaygLIYS23m1CH5uFXVZTz6aUfP7vu1Q+3Z1NdklNz1/EYoGj38D3z2rzGlx94KNlvZrqaeQ2Ih63EbYdgumIVI9fURpMivHny/tnkVVSg6+7M0NCPDv1cNlWzBbJ2h2Z/O3b5gegk2L8+N9NkwjuytyFjuh04DUIwifCla9rdYq+edwmqZ7+Ppqnv+g7P9WK0gdEBXj02YVIMkuq+dcPaVb7kjPKSM2r7NnADxA1Ga5/D7xCtNctt5V+TwV+RWlHQYWB0hojwd6uBPSR4loms7SaWduo1thLZRVaBnoV9AcUleNX+heLBSpy2m73hLoqpJRsPVrIy1/t4YpXf2Hx67+yL6v0zO+1gQh/d85vVXnU192ZYSG9X7tfGVhU4FfOSt5zz9lvfLXFAsc2wpprofh483bRse6fuzIfNv0FQ+Zu9qYc5XHXj1h5gQs5pQbuXbOHggrDmc/Ryzxc9DxxcQL3zBlCpL87F44O5d3bphLbx+rAKH2fSvUoZ8WuMyulGYw1UHAQVlygLUriEwHSdOb3nklVHuxehfuBD7g1aDyemZsYMiWQ0aEz2ZVVTW65gZA+ULAsJtCTxy6I545zBuPp6mTXcgBK/6V6/Eqn9ImZlU7OEH+xVo++qkCrj7/obQge0f1zh43THmDWlOCZuYny4Yt4qewcdmVV4+7shJ9731mDVghBgKeLCvpKl6kev2J3nV4xyWKBEz/CzuXg5gt1lfD5/XDtuxA0tHuNqMyHve83vfTN3sTlM2/k0yNOPHfFKGICe2ekT8t7B9uvGKU4JhX4lU7p9MzK2nKoLtQCccvt02izYpKpDvSuYDRoa94GDta2yzLBXA++UXDDB1BwGL7/U8+leo58DrN+i3nwPJzeu5bxdUmsv+c2IoP9eq1SpT1Xi1Iclwr8ylk57azKukqtN75nNdzwIRz6FPa9Bzd+BMFnMRtz97sQN0sL9ClfaQuQFKTAxj/Cks9hyRfgHw0BgyF8krbdXWHj4LaN4BOBk4c/3LYRV58Ihnj07EpXitIX2C3wCyGcgCQgR0p5ib3aYU/ltfUkZ5Sy62QJsUFeTBscQEygDUZolGZCVb42SafldieEPvUUNXUm9maWcii3HL2TjqlxgdrIEmdPLYBueRGWz4Xw8TD+BvAIPO05W66SNDJwJBSlaIuieIVAYQqc+AEOfAwhCeDu1xzonZzbDfpVdSb2ZJay/Xgx4X7uTB8SyODgTgx5DB3dtJnlOpidR4pJK8hjYrQ/ibH+BHi2X2a3OzXk21vsvL87ml/JtuNFFFXVM2NIIBOi/XFzVs8j+hK7LcQihPgtkAj4nCnwD8SFWKSUvPXzSf7y1ZGmfSNCvVixbErvLtZuqID1D0PKelj8Dvz6KmTvgDs3a4H1DFrn4/fsmUtJdT3v3DqVCH93MJTDJ3eBoYwXRp1Dakkqw/2HI4ToXB7fbOKFpL9ZH1tWA9tegWVfQewsqutMSCnxcmv/getHSVk8um5/0+tIf3fW3jGN6E7OyC2srOOed5NIyihr2vfggmE8OH8oeifr8RDfpn9L/JOrAUh9/mbOjz2/U9cYqI4XVHHt8m0UVdU37Vt+8yTOGxVqx1Y5rj61EIsQIhK4GHge+K092mBv2aW1vLTxqNW+lLwqUvIqezfwu/nArN/AyS2wdrG276J/gF/n0iVtc9JzOV5YzeFT5UR4mGDHcq2W+4Sb2xzbqVy2oaztNWKuhj2+8P6NlC3+mGVfGzCYLNwzdwjz4oPxcW+eWZtfYeDFDSlWp8wureVQTnmnA//R/EqroA/w2uZjXDE+3Oo3h7znniM+JRXDgQMAxD+5mrwROx263syerFKroA/wt29TmRoXgK9H35gBrdhvOOc/gceADueaCyHuFEIkCSGSCgsLbdYwWzGZLRiM5jb760xt9/U4Nz/tAWmjoOHg0r0UU73J0pzqGXc9LPxT105UmdfOTgFLvsAcnsg3abXszS4nJa+Sh97fy8/Hiq2ONJktVNW1fdjbXqmDjtS3829gNEvqzb1UGmEAqa1v+72rMpgwmvv+Eq+OxOY9fiHEJUCBlDJZCDG3o+OklMuB5aClemzTOtsJ93dn0aRIPkzKbtrn7apn+CDv3r2woUIrt5u7G2Y/ArtXaT3/TqZ6Wuag4wPi2ZMBHi5OWrt1Ohh6LkRMBI/ApmMbUz3tnaON4BFtrsGQ+eDqyfcJf+bxj9OtDl/5azoLRw7CuSEFE+rrzq2zYnlt84mmY1z1OkaEdv77OiTYi0BPF4qrm3uu540c1KZ4W+hTT6lUTytjI/3Q6wQmS/N/2dtnxxHk3XeXIXRENs/xCyH+H3AzYALcAB/gEynlTR29ZyDm+AGySmv4JDmbT/fkkBDmw91zhjAuyq/3L5x3AHL3wuiroeR4w/ZVne71F1bWsfVoIZuPFmI2W7j9nMFMiG41+qWuCsrSIWgEmAxQXQC+0eDU9b7G379J4dXNx632XTQ6lFdumGi1GPup8lq+3JfL+zuziAvy5N55Q5gUE3Dac1cZjGSV1uKi1xET4EFqfiVv/HSCPVllXDImjMWTozp88D5QFgjvCWaLZNfJEl75MY288jqWzYjhgtGhPV89VOmUjnL8dnu4C9DQ43/EER/utlReU4+7ixMutpyJaTGDzqntdgfqjGZOFFVTWlNPlL9Wuri8th4353bKBtTXwK43YMfrcNWbkLUddr0JV/wP4mZrq5x0wf7sMq59fTu1DSkyvU6w9o6pTIlrf9RQh+1r5WRRNc9+cYgtRwtxdhLcN28oS2fE4uWip6behI+7c6+N4x+oDEYz9SYLPn1oxrMj6lMPdxVrdnno1TLQnyHoV9WZWL0tnb9/m4pFgp+HM28sSWRybAe9aL2btkZrdQGsvlybdBUzE1w9uxz0QUsjrLtnOr8cK8JolswYEsjYSL8Oj/d1P/P31WyRvLs9gy1HtedIRrPkn9+nMTbSj/kjQvDVD9wHkjmltSRnlHCyqJqxkX5MiPbDr4d+Ft2cndQQzj7MroFfSrkZ2GzPNihnlpJXwV+/aS4rUFZj5Pfr9vHR3TMI9Gond6vTQdwcGHEpHPpE23fOIxA6rtttGRXuy6hw366fwGLR2tewnVlcw4YDp9octi+rjPkjBm4N+qLKOn730V62nyhp2vfIecO5Z+5Qq7SZMjCpIm3KGZ0qa1uS+ERRDSXV9e0cjZbqSXpLC/qBQ7WJVp/fDxm/gh1Ti5SchKS3oaYEU9EJDNvf4Pu9x9p9oD44eGCXOk7Nr7QK+gCvbDpGRnG1nVqk2JJK9ShnFN7OvIIhwZ4EenWQFmhM9Yy4BOb9EUpPwM432k31dDYP321mExz+TBvRVJiCLmc3brnJnHvF5wj3aPbnlDd9kM0YEthxGmuAaH8osUUNWXUQKvArZzQi1JsnL07grxtSMFkkgZ4u/N8149idUUZafiUxQR4UVdbj7+nCuEhfogM9SfWbzc9hwyhINjNn+FQmXDQO96DIpnPmltXyxb5cPtiVxeAgT+6bN5SJMdYjgypqjew4WcxPRwsJ8XEjMcafgoo69maXMSbCl2mDA7UPpcp8cPEg72//xGNUHD4XXw6urXrxTnqt5k/+Idj1BjqdnrTLvmBzRQS1RhP3zRtMRa2JCVH+jIn0bT+FNYCEeLvi6+5Mea2xad+cYcFE+ffN9YZtoaiqjl3pJew8WUJCmA/ThwQO2O+HCvwOoLvDDT1d9SybHss5w4IoqzUS7e/Bp3tyeGXTMR5aMIwVv6QzYcJmfshNZV0uXB17L8+uq2jqQb/+0wleu3EiFwZp5zOZLaz4JZ03tmpj7U8WVfPL8SI+v28m8aE+Tdf9ZE82z35xuOl1pL87F4wOZcUv6QDMjw/h5cvj8N3yLIQk4JWYgHvx55BUAom3tA3+9dVQrF0zdd4b3LvJwvGiVHQCFidGIYDxPfiAsy/LKq3lwQVD2ZJaSFpBFTOGBBId4EGlwYSnq+OFhXqTmde3nGj6mQSYHOvPazdNImgAdgJUjr+/qS6E6uK226dhSEnt9spZznod8aE+TI0LRAj4+VgRt86MZd1ubQJaY5mF/YX7ySzUt8n//+O7VMprtH15FQZW/Zpu3UajhZS8yqbXp8pqeek765IW2aW1eLUISptSCzhWUo+lPA82PoVXyjM4HfsC06kTlH38kfUNNKZ6cpOpu+Y9Xj4eyvGiGgAsEt7flcXs4cGnD/q1ZXBii1aPqOV2P2QyW3hu/RFqjWZmDAlkT2YZb/18EsmAmyvZKRnFNbz9y0mrfbvSSzmaX9nBO/o3x/to78+qC2HL30DnDDPuh59f1rZn/w48245lz3vuOS3oN9SSSb/xJtxGxHd7otHbKS/jHJmKR9ACausj8fdoHqutEzraqzpRVWdqms2p1+nwcHWivsY6n9wyz2+qLcdgbJtvbv1suF64YBz3EK7Zv0J5NjJwODVOUzAct57ohZMext0AkVMo9RnHzyd/aXPuosq609/4ic3w0VI451Ft7sPPL8G1ayCh/xWXHRnug6+7M7vSS9mVri0m/+RFCYT59mKdqD7MaLFgtrT90DOeRamP/kT1+PsTadHGxG9/Fd6Yp9W+N9dr+22osXf/4fG3WDRFG1oZHxDPxJCJjA4aTVyIDmcn64e4d50zpClvHurrxuMXWC+XGBvowajwhjRP7h7CvlrGkinWFR09XZysng0PDvJkiC+4pn+gzQ5280UUH8UnpIjQx37TdFxZTT2/HCvikzQju0jAxdWFSTFt6+xHnqmIW9RUGHUV/PR3LeiPvgYi28yN6ReGhniz9o6p3DE7jrnDg3nl+glcPSnC3s2ymxh/T+aPCLbaN8jHlaEhnSjn3Q+pHn9/4jVI620e+VIrZuYVArN/C17B7R7e6VWzuqjEUAKeu7lw1LkcyriI4Z5XcOWYQIaFBhB2azivbUnnVEUdS2fEsnDkIKv3XjI2nAg/d7adKCbS353pQ4Kaa+G4+qA3VnKb+IJBsxfwYaqZocEeXDslht0ZpQwJ9mLmkEBumh5DiL4Maktg1m+p85uN88FX0NWWaqkdtN80/vV9GitapJb+eHECj5w3nMO5FRRWab38xYlRjI08w/wAZ3frKqZ+Udq+fqrbcyIGEE83Pc9cOorR4dl8dSCPxFh/ls2IJWKAPty1a8mGzhroJRs6rTHVs3M5eARATQlMu6/DVE+j0z3cLa6uY9uxYjYczGNEqDfnjw49Y6G41YdXk1mRCYBep+d3kx6lrr4e58Of4PL1g3DVW1CWwSrfAIprKkgrP0CUT9SZ15I1G6HilLa4SsavkLkNdr5B5YIXkcPOx9vDDSmhus6Eh6u+eaJRebZWGdTDH8qzKdvwPYa0LEKfeop9WaVc/uqvVpdxcdKx4aHZuDk7kVFcjYeLE0NDvDqs79/k0GdaqmfUVdpvWYc/09b7Tbj09O9T+g0pJVUGE+4uTm3WXuiPVMmGgaAx1TP5Dq2m/k9/71Sqp6OcvsUieW9HJv9oeIj61YFTvLsjg4/umk50oKf2W4VHkJYfrynmhQOvt1kY/PdTfg+A3t0NoieBTxR8tASip7MpbFBTXf2JpontN64yH8oyIDgB0n+Cb/4A16zShl0mr4TZv8X782Vw9QoYeRlC54R36/ovvpFW22Vfft/8LUOrztmyLHO92UKFwciQEC9t8ZjOipoKF78MIy7SHjbEzYHIzq1c1pMKKw0kZ5SyP7uckeE+JMb4E+qgufmeJoRo+/M1AKnA3594DYJ5TwA6Lb3TcrsLcstq+c+Px6z25VfUcSSvkmhdsbZS16SlWpnl7f8j1ZJ1+sVU3P21Y0tPdHxMS6Z6SF4BBz+BGQ8AAgaNhhObtO3A4RA5BSKngm/4GWsKtfcwO3REPNMHn8fmo0VNx8UGeXRtfLZPmDZMtPg4pqLjHA29jNDSAnxz9+E0dL42Q7mX1dSZeGnjUd7bmdW07+KxYfy/K8c0FUTLLq3BYLQQ7ueGh4v6L660pX4q+huvQe1vd1J5bT1VBhNB3q5YpGy3goLFIsFsgPJMLbXhG60FvbAOrleZD06uWpkGUy0sWgGp3wBnGCWjd9FKQ7v6wC//hJITMPY62PQcBI+Ay1/T1u299t0uf7gBPH5hAgWV+zh8qoKpcQE8c+nIrteHr6vC8sNz6FO+wHf2i3gdWoFTaRrGO37COWxUl9tI8XFtiGjkJOvtVk4WV1sFfYCv9p/i9llxjAj15st9p/jLV4epMJiYPyKYP148snPrDSsORQV+ByGlZOfJEl7ckMKh3AouHBPKg/OHctusOLYcLWRBglaQLDldm7VIUJiWcnlrIfjFwJhrGGnOtTpnfEC8No/ghz9B6BgYdTWgg2+fgCWfE3/iY+tj2+PmC9lJMONB+P4Z2LdWS91MXAofLoUbP4KQzi1A3tHD7FBg7R1TKa81Eujlgpdr13vmdXoPDg+/n1GnDhDx02MAnJjzCjnlfswO6+JJqwvhiwchb5/2Pd/4NJRlwp0/aqUvWl6/nSGuoJVbOJBTwWMfN681vCmlkADP47xw5WjblvxW+jwV+B1EWkEVJ44d5q+Ta7jlB2em+FXhmruT6xInYzCameGRixlBfcQgoqsPYCmvR1TlIS76B2b3MExl1fw+ejxM+h3UFEPyKvAdBXvehdw9HEx4mE17TVTWXsC5Uycy/uO7ecLFlbrB55IZdw01Ol9OFFYSF+TVXNu+IlcrnOYVov32YqyFCTdruXR3P5h8G1TmUvTp95gKSjo9/8BtRNsPCj8Plx6ZkVtlMFFUVY8UzQ/+pNCRVdKikF11MeQf1NJepvrmbVfv9h+0ewbDeX+G1VfBu1dp+65+W/tNq5XYIA9Gh/twMLeiaV9MgDuDgzz5IaWgzfFf7T/F7xYO7911nFsqz4HSdO3ZR3UhlGeB0GlpwKBhkL1LW/ozaFi7bzdbJPuyyvghJR+BYEFCCOMi/dCpiqE9SgV+B2GoKuPykpV4bP+MtfNeIfjIO3jsSqJyyQ9M8DUwbcd9WHBi4qJV6Nbdok1Qmvs4bPoLTq4+OE29CyzhYDGC9yCIPx/ePBdCEjg09w0WrzlOTcN6q28KWHXxs5yz6SqOj3+K9UdqMfmvaXowHB8QT5R3FN9nND+EjS/bzRPTH9BKJn9xP8z5vZb6+f5pfC9ZS84/t7P60GoQcPPIm9u9xxd2vMCJshPELYwDQL/zr00Pn3uKr1Mdc3Nex7nsGLmz/x9Bh1Yw5KeHCF76o3aAlJD6FXzxAJz7J6jKg+2vwc2fwpD5Hc+gdvPXgqOhTHuW4RelpcJaCfB05Z/XjWf1tgx+SClg5tAgbp0ZS4iPW7ulBYaGeOJhqxIMRgPs+B9sewUu/y+krIchC7TfCL0GwfnPw0e3QMhIuG5tu+m7PZmlXLd8e9Nkv/9tOc4Hd01vd96F0nUq8DuISunG9vAlzMnZTswP9wCQPfMFDpZ48+etBQyd+yIJaW9QuW4zTolP4rLlYe3hrt4NznkMQ7UnBX95A5fwMEJ/d582l2Dmg7DrLX46XtoU9EGLfa8ecGLK4PMZsek26ha+zz9zU60eDEsp2z4oHnWv1tNP+w62/BUA0+jbKHj3Kwz79zP7Ban15kc2HG+sBQQ4u4Gxlvq6Snbk7WBH3g4AJoZ0MJKoG/TuPlTN/gOFg6/itp99eHDiS0z1LUUEDtEOEAIGz4OhC7XUFcDEZVTsO0XJ0ze1P4u6ulD7oChLh/lPw6//hnevbjfVA9rkq6cvHcVvFg7Hy1XftN7wmEhfZg8NYusx7UG2q17HHy8eia+tRqk4u2kpumMb4TPtZ4zB8+CyV7RnRWuu0Xr7F/61w2c27+/MtFqv12SRfJSUpQJ/D1OB30GMCPXhQLEn9e7BuFdq9XW8Q6KpqNNRaTBRIXzAyRlLXR0W6QpOLtpQUb0bCB1mg0TW1monMxuhNBOGzAMnF2qMba9XZZRkjXuIYJd3OFjS9uvtspi1wmq+0ZC7BwDpGYYxd3vbY421cGCdFmhHXgGHPuVepxDS/Iazv+xo2+MbvLDjBavfPM44t6AdXhEJmPyH8GJYDa7OOjyDPK3LSjt7gH9M82v/WEzHazo+YWOqpywT4i+BIXOhLAt8Yzp8i5NO4N8qdRXq48ZL144n5VQFlXUmhgR7MXyQjR/sOruDTwQUHNFeewWDZwgIJ5AmcPFqWzyvhUqDqZ197fyAKd2iAr+DCHSu55zs5egK9lA7+wnc9r+L7xe3cNmtmwm8OJipvy6B+moCrnsEl09vA2mGWb/VykNs/y+eU+/C819/Bt8o7YQz7oc3F4BHEHOG+vHqrgpaljo5b1Qot23IZGrYYkSxID6qOe/emOpp2SOPD4iHQ+u0IHHkc21iWuZ2nHf8hYjH1pLzz/fZ+ofztFQPaHn03e9A9k5I3QAp6/GPnMzSyTewJter+ZytNJab6K4Onxk0pnp2vQnjb4KqfPjhWQJu/pSAZXd0PIs6YhKETdBSXS23z1KwtyvB3l0fAdUtjameY99rPztHv9H+nb54UAv2sx+BjU/BZ/d2mOq5fmo03x7Ot9p37eS2zzqU7lEzdx1JQQqc2qfNNC3LgFP7IeFSTtWA96ltSOGEIWwKwXk/YTGZMJv0OHu5YamrxlxjxjnxsuZzGWsh5SsISaDKK47kjGLe3pZLucHIkumxVNaaWLszk8RYf+IHeRPg6UJskAfDBnk3947LMqEoTVuPt7oAitLYb6kmuugE7zvVMcM/gbGVJZQdBcOxrLYPd0vT4Y352sNmz2C4/QfrnnY7lm5Y2jypLGQiqy5c1YPf4AZlWVq6KuFSMNU1b3uFdLtEdp9XdEybcT3ycqgq0H7e9C7abwGhYyHtW/AO0x52t6Om3sS248W8vuUEOh3cec4Qpg8OwF3NR+iSjmbuqsA/QL2y+99c7jWEz4v3gasXD4TP04Zluno3r4IlZfvbLdelbbm/NSk5klfJS98dZfuJIm6YEs2ixCiGDfJGSklNvRm9DurNEle9rv0hhY3nlxLyD7F0z98pri0iozJTC8wz/wY+7cwfaEz1fHF/877LX4XRi7Rccwd6ItXTKR19bx1B63uHs/5e1BnNCIEahtpNqmSDg7nSI5bID5ayOHEZxpBQrZrnec9D4m1aCQaw/g/YcrtliuE0/0kLKuu4c3USWSVa7v/1rSf5Yv8pPr13BqG+7k0Lepx22Hzj+c1G7SEnkFGZ2fx1Uwe58cZUT9Q0uPRf2pq+u1fD4LnWJRxa6bVA31pH31tHcLp77+T3wtVZBfzepAJ/H1RdZ8JskU1T8LsiuSYH38FzGLTjDQCkbxTEzkI49dw/eUZxTVPQb3Sq3EBGXjGhRWlaLZvO5qn1LhAzi/iibU274gPiISCu/eP9IuGq5dp2QBxcrd3n6YK+oigaFfj7EIPRzM/HinjlhzSq683cM2cwCxIGdWni0ZayVGaPXARp2lj50ujz2XfKlUk+xm59oLTk4erUlKWx2l+eBj/cDHdugcZhjmciJWRt44mUbdqEH3M91HlD8UkI7CD4t/xQ6OgDoh1mi2yu7KkoDqj/1x0dQHZnlnL7qiT2ZZdzrKCK3320n5+OFnbpXH+MvpiA9Q9g9o6kOnIOAQfeZFTR1xzNK+ux9g4J9uTWmdYB9+rEQIYUfAeL34WAwZ0/mdmo1fwpSoPzXoBJt8Dx7ztO9ZwlKSV7Mkv57Yd7uemtHXy1P5eymvozv1FRBiDV4+9DvjuU32bfil/TOX9UaFPOs6NRIfnltaTmV/Hr8SJKa4w8PCkKy4QlnIi8itQKPRcNGkp50ES2nywlKbOcrJJazh81iEkxAZ1eXNvq2vU1uOnduH/eUIZFmDhVaiTA24zeLRePpH3UD5uHS95+bfx26GjI2a3NDQgd3f7J9S7aSJDIRK33HjRMm/Z/Fj350zmUW851y7c3lWfedryYf1wzlkWTonrk/B2x2cNkpXMq86HgsFb1tb6qedvV094tsykV+PuQAM+2KZ1ATxd0LR6ItTflv6ymnh25B/njhyVU1Jpwd3bivhiJrjSdgNp1jHcLRleWjp/PEbxdw/nzVymYLZI1OzJ56vJwSvTf8+jkR0/fOKOh6dpNpRO8RyICRhFgcWVR1Qr0WccoGHcflvJcXD65FS74q1Zp89J/wSd3akMul34BPuHtX0Pv0hzoi1KbJ/rkHdC2/WPP+D3sSHJGmVVNfoD/bDrGuV1MpXVWT80bUHqApWHxnA2PwcLnoDhNGyCw7GuInWnv1tmUCvx9yPwRIby+5TjVDeUPnHSC22cPxlmvO+3C6TmltWQW6qio1WY91hrNvJHqycPu0QQeXAFAVfR8fmEsx4tqrBaVXvtrGZefc/rp8K2vPfuFhkJolzkjPCJ5/OsMXp53BYOCq7jtG2demPUvxvmb8PNwhZoiWLsYXDy14ZYdBf2Wio/BO5dB4DA491l4/wZtQtNVy7tcnrm9lL7eSedwA24cmk4H8RdpE/42NvzGPP1+rQS4g1GBvw8ZHeHLR3fPYNuJYuqMZqYPCWRspN8Z32e2SMxm63051ZI6v5Cm1y5+4cSHB/GbDSlWx9WZJE66Lj7sjb8Ibxdvrp8Sze0bTuCm11NZZ+Cub3V8dNd0/CypoNNr+XsnF3DtZPkAnwg498/aGP0VF2ofGnMe61ZN/kmxAXi4OFnVFPrNgmH4uvdebx+sZw93WJpasR0nF/AObX7tHaaVJXEwKvD3MSPDfRgZ7tNmf2NOP2PZLbgMHkzY0805/nB/d+IG6ZqWGHR3duLZiQbC1v8Vhi0Enyhckt9maGQiw0LiSCuobnrv4qm+1LPvtG0KfeopMBpIX3Y7AFufOF9L9RQfRxc2jhunxuCk07F2RwbRAV78fuFgEpzzYPVNWo7/uveh7CSsuw0Wr9RG8IQkdHxBZ3etvr9ODxYTeIV17jeF0xgZ5sMHd07j6wN55FUYuGxcOJPjArp1zs5QOf0+xGKBw5/Dvve05UuLj8N3T0L4BIdL9aiZu/1M8cqVvJ6Q2+aBYU5JDQdyy/n+cD7HC6t5cl4IE2t+RTd0vhZIU75CDplPcrknb289yakKA0umxTA3PgT/dp4tWKmvgbpK8v6pjZv/7qoYhBDcXFIME5eARwBSSgqzjuG241/4ZHynlRieuATCJ8L2/2p12Be9Db/8S5vGv2x9u5UntZs8Bm+ei8UnivwZT+N6YiMBtVlw2b+71etXFMoy4cRmiL8YjNXN256B9m5Zr1AzdweIwGXLSG1Rb8a5IU0TEeBBuCjkPF0W5ri5OBtKoSgSPAK0VMnEJQghSPSD8ZF+WKTUpsPXlIIJ7cFqTal2rN4Fqosgc7v2/hM/QvwlvLVQx9HSoxzc/Rmjg0Zz85yXtK+jLVIdEj0MKubDodVacTLPUG1RFZNBK9i14kLtJhY+d/plI30iyL1iHauPubL6k1z8PS/gj+fFMdc1gC4umKgoGr9obbEfIYDAFtuORQX+fi7ON655mOWFkYj1v0G38M/axK30n+DOnyB8nNUPt76hfjvVxdpat+ETYPAc+Plf2hq3Iy7Rgn7yCi3fvnsVGA0cNWdaj1DxaNVLyjsA3/xBS9EIAd88CsFDIThee1BbcEj72tAFpy3NK/VufJjpzWtbtYXgq+pM3PX+Ydbd7UtibO+nZ5QBzpHLaTRQgb8fig+Ix1nnTJxvHImhiRhSVmtf+N19WgXOH5/XqkJe+PcOl7gDwFAOxzdpKyZFTNZKHE9aBnHnQPQ0OLkFpAWip2upmo4WW286XyWY6+CmT7QA/8GNYKyDDb/Xgv6YxdpwunevPm2qp6iqjjWtFhQH2J9dfsbAr8bNK8qZqcDfDzUGM22Y5ermIZ53PKgN8YyYqJXGRTb86UDgYG1x79emaUF/0GiY8zjkH9Jq3ji5gG8EHPgIQkaeeYRK7Ay440dtvL0Q2rZfDCTeqn2QTFwK42+A/MOnTfW4OzsR7utGYWWd1f4zPoug746brzeZVaVJpc9QgX8gmnyHFrQ3PKbl2MPHt39cdTGkfq2lbKrytV552veQsVXr5fvHgpsvRM2AxKU8IS2Qn6eth+sb0f4526ufM3guRE3Rnh+03G7NbASdHi83Zx47P54lK3Y1zTmIDfRgYrRfp78FXs6nGTpafFwbxufiYb3dC04UVfHZnhw2pxayYEQIl42PIC7IsWaJKn2PzUf1CCGigHeAQWjd0eVSyn+d7j0DYVTPyaJqNqUUsC+rlPkjBjFjSCAhPm3HDzfl6++5Flx9tD+Z27ViZ96hUFumLXYOUFcNrp7Nqzq9+qK2wEr4BGpLT6ErPEyK+0Rw9iDYtZ4jBXUcyK9lkKeeadFenCgz8/X+bKJ8nTk/IZiETbdA3Bxe8NRZpUvOCV9AQYkfW1OqCfZ2Y/ggL2pr65g9IpzYVkEst6yWrWlF/HyskGlxgZwzPJiogI6Dak5pDVvTivjleBHTI904J0JHZPRgzGmbOOQ0nNQKZzxcnBgd4UtM4GkCZk0J5CSzvu4Up6rzuNIjmu3CyCWjWyzMnncAakthw++RC/+EWeeK7quHqRh/NzlxVxAfHtT8/KMzSjO0BeFjZ0FFLpSe1BaVcdIeuBdX1XHryl3syy5vektijD9vLk20ni1cVw1Z22HQKMotbjjn7OTH0iByzL4sGDGIISE2Xj5RGTD60qgeE/A7KeVuIYQ3kCyE2CilPGyHtvSY0+WW88sN/GdTGpklNezPLueLfae4dWYsj184os2v/01lEfJ3kFmZiV6n5/eHNkPAUD4dNo3PMr5rvkYNMHGZNosWeCdnE+56d9L2bCG1JBVvF2/CvQ4gpeRo6dGm93135FxKDBb+/m3z2rQrdhWy7tqXGJa/gdSSQ1bpkjEei/jte2lNr71d9dx+zmDuXZPMylumNH2AVRmMPLf+MBsO5gHw5b5TzBwayKs3TGy3LEJFrZFnvzzExsMFDcfDnDhP/j1xP75f38nYsAmMvW4t+ISd+R8g/Wf48GYumfGA9nrDXVyyeHWLi52Cj27RfrNZ+CfE5/ehlxJmP0KuOZxbV+/nPzdMZHJnHx7XV8NP/4C972ozknev1oL3XT9D6ChA+7BvGfQBkjJKOVFYzcSYFt+P/APw7lXIMYsRfvF4bH2OaTOf4eZDE1mzI5O1t08jwt+9c+1SlE6weeCXUp4CTjVsVwohjgARQL8O/KfLLedVGHg09UsAHohcwK70UlZty+D6KdEMG6SNbumoLMJbC03a5KXoKXyesdH6GgUlUJDKWwuHc7T0KM7ZGcT5xnG09KjV8oKA1fumxS5m1a8ZVm0srzVyoNBEEH5t2v/dgSqr15V1JupNFo7mV3G0oKop8J8sqmkK+o1+OVbM8cIqJsW0Dagni6qbgn6jLSerOT5MMNFrEFz9VueCPmgprfE3wK+vaK/H3aDta+QTBle8BquvgPUPa73yc/9EZUEG/y0dQ35FGR8nZ3c+8Lt4wswHIWsHfHaPtu+Sf2rPTRroOij93KYkdPAImP0IYus/8AGqI2fzRX0ih/MqAUjJq1CBX+lRdi3LLISIBSYAO9r52p1CiCQhRFJhYddKE/cVHi5OWlBPSSUhzIfJsf7oncTpHrtac3KHumpk63fMfQKq85sCvdFi7NTp9DqByWJps79a58Oqqslt9res7dNISolOCCwtvmaWbc+pvb/9drR3XgCL1Gmpm9KTbYv9d8TJGdxa1Bxy92tKuTRxdmveJ5yQzh54H3qXxyP2MmaQGwZjq7oXZ7ymi5aKa+Tmq41majAk2JN58dYTzs4bOYjBwa1SVjpnbcJbA6OLLyWGFt/XDr5PitJVdnu4K4TwAj4GfiOlrGj9dSnlcmA5aDl+GzfvrHU04iXvuedwbtGTv5UXcRsRz0dzryU6oLkX11iSoTFfv/WJ85tSPZQcA4uRK0acZ32NQ59aXW+4/3CifaIRLcYmxwfE0/I5TnxAPDtTS1mcGMV/Nx9v2u/u7ITUOfFLVi0TJljfy/BQL74+2Hyvrnodnq56QnxcGdYi/xwX5Mm0wQFsP1HStG9MuC9DWge6BoODPUmM8Scpo7Rp37gwD+Li42FvEGz6C4SM7HyqZ/urMO567fX2/2ojiUZerr1uTPUYa7TCb1tfQvz4PKaL/o+KKg/yqi38IfEsSjQ3pnpydmkT0vauhY9vs0r1+Lq78NwVo9maVsSOE8VMHxLIrKHBeLu1+kDKPwDfPYkcPI8q/wT8kv/LspmT+CF0IvmVRuJDO57zoChdYZeSDUIIZ2A98K2U8qUzHd+fH+62TuG4jRmD24h4dL95lBBv64e77x15j3nrtElLfsvOw807HMpztIeSERO1B5NHN8DUe7TZsCsuhPP+AtHTWF24k5zKHKJ9okkISCDcZSipBTVU1Znx99AT4Qm7MqtIzqkm1EfP+cP9OVxkYs3OTKIDPJg9LIi/bkiltKaesZG+XD0xkkWTItE76SitrmNrSh7v7sohxMeNmUODKCyv4oKxUQwfZB2UKgsy+SDFyDeH8nlmrj/ewVHEBnX8cDK9qJov9+XyQ0oBc4f5c9lIfwZHhmlDPvWunV/BqzIfjv8AQxZorxu3vVsMG83apQX+kNFYSo5TU13JT2XBfJdh5upJkUyJDTi7tV6L0rR/m/gLoTy7edv5LNMytWXazOaYGZSa3XA98R2baoeRVObFtZOjSAhrW7tJUTqjo4e79hjVI4BVQImU8jedeU9/DvyNGnvykavfQd/BOrQv7ngRszRzovwEAW4BPDn5efycJRQfhbfO1+raB8TBqkvh0ldg0EhtyKWzO0u/WWaV11914SosFolFSquRKvVmMy5OzcHNaLag1wkyimvYmlbI4VOVnDMsiClxAQR6WRdIMJotOAmBSVqsztEk/zB8uAQu+zcWN190Hy7V6uvEzDjj96d1u2zFZLYghOhzSzHWm80463RWv70pytnqS6N6ZgI3AweEEHsb9j0hpfzaDm2xmcaRN+0F/dYjgkwWEwU1BWw7XsTIMF9iXH20Hv8nWnVMPAIgaKgW+E9DpxPosA4crYOrc8OHQmyQZ5uhma01HutCBwHaXK/V33/3anR6Vy0dYqzV8vRnCGD2CPrA2Q3ftCF7fT8Ux2CPUT0/Aw7XjWm9VGJLrUcEJQQkcE7AKF7ZnMEV4yOIGekJsx6G9K3aAZNvh2DrssbOHdTU/+vOv2KyaAu06HV6fj/l9x030mLRSjh4BkHo2ObtjiaAtRY+Hq5+UyvJYKzRVuAaPM9h66EoSl+lZu72Mc46Z67zmkfUj3/APOpZXM3pyLXPIkYv0soceIXCya284CGsfkuYEzWnaVRPy4fLJouJ2zZqw2reWmg6/cVL02HdLeDkTN2cJ3H99jFk6FjEDR92rhxy/mH4/AFt5IzOGTb9GcLGdirVoyiK7ajA3we0DNQjA0fiL8twyt/PzPLbQVoQ9RWQeBvcuE4b9rf7HVJLUkktTcVgMgBa/Z6bR97c5tzR3tEYUr7Vtq86//QNCRxM7aI1uL93Ja4bfos5YBg581/FW/hw+sUZG5jrAQtc/4GWjlp7badTPYqi2I4K/HYmpSSWG9mTkQPAtgz4od7IR+e/jEf2T3BkPebLXsUpbrbW40//GUZdQcChN1mdcCfZ0sg7BduBFuUeGtJKec89x+xWk8LyRqR3mHaqraunpLSECGkha+rTbBZT+OmXKqbEZXPB6LDTll4AtFTPrd9pNc+FgNs2Nm/3d+XZWvG62Fna6KrG7fZqDilKH6cCf0+qq4aKHAge3rT9j/QvOFCkBd74gHiivKOae+Y5uylxCePfPxwjr0LrubvqdXx0pQ/ulTla2eQFT6Mry8Ty1SMYptyGx7rbsLj68Mysh/Bb/xhxERNxmqXNHG0s99BVurIMIjY9SOm4u3gyezo/HdfG1288UsjWtCJeuWHCmdeo9Y9pf7tBvyybbDbBnjWw+QU473nI+EUrbnf7DxDZZsCEovR5KvD3FGMd5UnL8f35X5y87CX8C9Pw2/Yal132f6w6vKrpsKbhs0Vp8M5luI9dyuCgi5sCv5uzE36WcoR3GJzcCtm7EF6DEAVHqImaRNF5fyL68wfw+/I3SK8QzCMfYNT2EjJeuKW5PPONN+E2amSbSWGxa9497S1Y/AeTfv4Kjuni+Gn7Eauv/ZRWxInCaiZEd29x8r5aNvm0nPTaxLCTP2lrtAIseBqChtu3XYrSRX1zLFt/5OxKsc8gMBuJ+/BW/Da9AGETSK8ra/943yi48G94JP2X346XuOq1f4ryWiPvnIoi1TeUrItf1EoXFByhcuqdvGTIwGCqaT6HcMZcVY0xMxNpbFWuwdxcfsBtRHzTcNLTcXdxojY0kbTS9mssWHpgzsdw/+G46Lr34WEXOp31xCwXL9CpIZdK/6R6/D3o65ocbk64GN99H2g75jzG/oJfmgqlWZVPcHaD4BHsXbCGb7P1PH1JAkIIAj1dmRTjT0buMWKS1zad2/vAOv549Vt4fHw70msQYu4fEF/9Fvf0tzFPvIvQP/6xw5796YaStpYQ5ouni57vj+STnFHWtD8xxp/BwV0rD9w6vXPVsKs4Wnq0/cVc+qLGVM+xjTD3D5D+q7bWQcQklepR+iUV+HuKsY6bTXp8932AKSQBfVkWvH89jyz5gm+Dx5BfnQ+AU2MvsSiNwyfSue47dwzGIqZFVTM7Uo8uNJogSzFBnlGQtx8u+odWvXHtYjzKc+CaVQiPAG2fTwT1RXVU/7gf74voVK++M6IDPfm/a8az4WAe3x/JZ/6IEC4eE4Z/O6WVO6N1emfVhatOc3Qf1JjqCRoGQ8/VKn/mJKlUj9JvqcDfU5xd8Y2aDiMuRX/hi9ookF//A+5+JGeut+rxAuAbxV6MGIwnmRzhxt+HpxCQ+R3Zw55F7vsecWILLPsKTmyG9F+0ETIBcdajSIYtxHUYhE6/BDi7nv2ZxAZ5cs/cIdx1zuAOyws7FP9o7Q+Am0/ztqL0Qyrw96ToaVqv0CNQW7O2YbvdB5rObghXLYi76EBnrsUz60fiy45AZZ6WRkjdAD/9DabcqRUbs8PQwZ4I+mdcq1fpGbWlUJEHgxIatk+dsayH4phU4O9pHoHtb7djQpQ/ni5O/JJlYG3kbB6KmoVL1s/aF2c8AB8t09aDnfVb8OzEzNlGRoP2DKH1tp30iyGb/V19Dex8Q1uI5qaPIeVrSHoLbvkaQsfYu3VKH6NG9djRiDAf3r9zGn++MI5bfHdrQd+jYQWoHa/DzN9A5SnY/po2uqczcnbDrjfAUNliu81yB8pA4+Kh1UUCeGsh/PIyjLj07DoMisNQPX4bOF2qY0ykH2OC9bDHArGz4eKX4MBHWm5/zDVatUtTLchOrA5lqILkFbD7He3X/H1rwWzU6tK79fyv/BaLpKDSoM096OKDX6UHBQ2DoQvh0Mfa60k3g3eofduk9El2WYjlbJ11Pf6aUsj4GSKnaMvjNW63XJSjk1qXQWijKA3KMiBurrZUYFkmxM3RRoKcjboqLS/rFwVl2Vp1y+Dh2gIjOt2Ze26lGVBwWKuqeeRLrf7pvvfhghe1Zw89LKe0htXbM1i7M5MQb1eevGgks4YFNZVuVmysvga2/Qd+fF7r+ecka/tVqseh9aV6/L0vJwk+uAkm3AxufrDtFbjifzD++rM+1WnLINSWw3dPw7FvtYVRfvk3lB6Hu3+B4LN8iOnqpf0B8Its3t+ZDyujQUsNbX9VWwbw8Kdamufqt7TZvyEjtZEoPcRikazensH/tpwAoKLWxG2rdvHxPTOYEN2pcm5KT2tM9VQVwpxHtY7A/g9Vqkdp18AM/OETYdq92rqrAGOvhSHzzuoUrZdMTL/xJtxGxFv3/N19YeGzUHgEPr9X23fNKggY3AM3cRac3WDqnZC9AzY2tO+85+HUPkh6G0Zc3KOpnoJKA2t3Zlrts0hIyatUgd+eoiZr8zvcvMErpHlbUVoZmIFf6EDX4tZ0enpt7RehA9Fi6n5vXuv0DdFq4Ddy8dSGgY5Z1OND+tycnQjxdqWi1rq+v4/bwPxx6ldaBnoV9JUODMyEbPYu+PXfMPY6mHo37F2jrSZ1FkKfeorYNe9qi6OPGUPsmnfb5vlry2HjM1B6QlsPN3AorFsGJcd77l46w2iAncshc5u2+HrUNFj/G6gtgbBxPX45Pw8Xnrgowara8pBgT8ZE+vX4tRRF6XkDs4sWMQkWrYSY6VovOGqatt0Fpy2D4O4L5z4Dk5bCkPkQPV1bxcoeqZ7Jd2gPcYcs0Ibx5R+AwGG9dslZQ4P55J4ZpJyqxMddz5hIP6LPVK9fUZQ+YWCO6lEURVE6HNUzMFM9iqIoSodU4FcURXEwKvAriqI4GBX4FUVRHIwK/IqiKA5GBX5FURQH0y+GcwohCoEMe7fDRoKAIns3wo7U/Tvu/TvyvUPv3H+MlLJNwaZ+EfgdiRAiqb1xt45C3b/j3r8j3zvY9v5VqkdRFMXBqMCvKIriYFTg73uW27sBdqbu33E58r2DDe9f5fgVRVEcjOrxK4qiOBgV+BVFURyMCvx2JIR4WwhRIIQ42GJfgBBioxAireHvAbmWoRAiSgjxoxDisBDikBDioYb9jnL/bkKInUKIfQ33/6eG/XFCiB1CiGNCiA+EEC72bmtvEkI4CSH2CCHWN7x2mPsXQqQLIQ4IIfYKIZIa9tnk518FfvtaCVzQat/jwA9SymHADw2vByIT8Dsp5UhgGnCfEGIkjnP/dcB8KeU4YDxwgRBiGvBX4GUp5VCgFLjNfk20iYeAIy1eO9r9z5NSjm8xft8mP/8q8NuRlPInoKTV7suBVQ3bq4ArbNkmW5FSnpJS7m7YrkT7zx+B49y/lFJWNbx0bvgjgfnAuob9A/b+AYQQkcDFwJsNrwUOdP8dsMnPvwr8fc8gKeWphu08YJA9G2MLQohYYAKwAwe6/4Y0x16gANgIHAfKpJSNq9hno30YDlT/BB4DLA2vA3Gs+5fAd0KIZCHEnQ37bPLzPzDX3B0gpJRSCDGgx9sKIbyAj4HfSCkrRIsV3Af6/UspzcB4IYQf8Ckwwr4tsh0hxCVAgZQyWQgx187NsZdZUsocIUQIsFEIkdLyi7358696/H1PvhAiDKDh7wI7t6fXCCGc0YL+GinlJw27Heb+G0kpy4AfgemAnxCisUMWCeTYq129bCZwmRAiHXgfLcXzLxzn/pFS5jT8XYD2wT8FG/38q8Df93wBLG3YXgp8bse29JqGfO5bwBEp5UstvuQo9x/c0NNHCOEOLER7zvEjsKjhsAF7/1LKP0gpI6WUscB1wCYp5Y04yP0LITyFEN6N28B5wEFs9POvZu7akRDiPWAuWjnWfOAZ4DPgQyAarRT1Yill6wfA/Z4QYhawFThAc473CbQ8vyPc/1i0h3dOaB2wD6WUfxZCDEbrAQcAe4CbpJR19mtp72tI9TwipbzEUe6/4T4/bXipB9ZKKZ8XQgRig59/FfgVRVEcjEr1KIqiOBgV+BVFURyMCvyKoigORgV+RVEUB6MCv6IoioNRgV9RFMXBqMCvOAwhhJ8Q4t4zHBMrhLihE+eKbVlOu52vLxNC/KeDr/3a+hxCiLmNpYkVpbepwK84Ej/gtIEfiAXOGPi7Q0o5ozfPryhnogK/4kheBIY0LHzx94Y/BxsWw7i2xTGzG455uKFXvlUIsbvhz9kE7SghxOaGRTWeadwphKg63ZsUpbep6pyKI3kcGC2lHC+EuBq4GxiHVjJjlxDip4ZjHpFSXgIghPAAFkopDUKIYcB7QGL7p29jCjAaqGk4/1dSyqSevSVFOXsq8CuOahbwXkNp5HwhxBZgMlDR6jhn4D9CiPGAGRh+FtfYKKUsBhBCfNJwTRX4FbtTgV9RTu9htAJ649BSo4azeG/rQliqMJbSJ6gcv+JIKgHvhu2twLUNq2AFA+cAO1sdA+ALnJJSWoCb0appdtbChsWz3dGW0Pulm+1XlB6hevyKw5BSFgshfmkYQrkB2A/sQ+uJPyalzBNCFANmIcQ+YCXwX+BjIcQS4Bug+iwuuRNtoZlI4F2V31f6ClWWWVEUxcGoVI+iKIqDUakeRekGIcT5wF9b7T4ppbzSHu1RlM5QqR5FURQHo1I9iqIoDkYFfkVRFAejAr+iKIqDUYFfURTFwfx/mOQo3CWW4MEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(y=\"tip\", x=\"total_bill\", data=tips, hue=\"day\", style=\"day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990ce54d",
   "metadata": {},
   "source": [
    "We can also change the color palette using the palette function and the limits of the x and y axes using the plt.x/ylim function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85459e2f",
   "metadata": {},
   "source": [
    "Here, we've chosen a color palette known as viridis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6ebaf52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 12.0)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(y=\"tip\", x=\"total_bill\", data=tips, hue=\"day\", style=\"day\", palette='viridis')\n",
    "plt.xlim(0, 60)\n",
    "plt.ylim(0, 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4cae06",
   "metadata": {},
   "source": [
    "Learn more about choosing and creating color palettes in seaborn using the links below: \n",
    "\n",
    "https://seaborn.pydata.org/tutorial/color_palettes.html - how to choose/create a color palette\n",
    "https://www.dropbox.com/s/8autfrvx6dpll96/pal.pdf - in-built seaborn color palettes by name\n",
    "https://medium.com/swlh/how-to-create-a-seaborn-palette-that-highlights-maximum-value-f614aecd706b - matplotlib colors by name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106fce98",
   "metadata": {},
   "source": [
    "Now create your own color palette!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "43a0ee11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 12.0)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = ['#04134e', '#c418aa', '#ad1822', '#d67a24']\n",
    "\n",
    "sns.scatterplot(y=\"tip\", x=\"total_bill\", data=tips, hue=\"day\", style=\"day\", palette=palette)\n",
    "plt.xlim(0, 60)\n",
    "plt.ylim(0, 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990ce54d",
   "metadata": {},
   "source": [
    "Finally, we can tidy the labels of the x and y axes and add a title. We can also edit the legend and move it outside of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6ebaf52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff34835c370>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(y=\"tip\", x=\"total_bill\", data=tips, hue=\"day\", style=\"day\", palette='viridis')\n",
    "plt.xlim(0, 60)\n",
    "plt.ylim(0, 12)\n",
    "plt.xlabel('Total Bill, £')\n",
    "plt.ylabel('Total Tip, £')\n",
    "plt.title('Categorical Scatterplot of Tips')\n",
    "plt.legend(title='Day of Week', ncol=4, bbox_to_anchor=[0.89, -0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8577a1da",
   "metadata": {},
   "source": [
    "It might be helpful to play around with the legend coordinates so you can see how this works.\n",
    "\n",
    "Try answering the following:\n",
    "\n",
    "Which coordinate determines the x and y position of the legend, respectively?\n",
    "\n",
    "In which direction does a negative value move the x and y position of the legend, respectively?\n",
    "\n",
    "In which direction does a positive value move the x and y position of the legend, respectively?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20e19fb",
   "metadata": {},
   "source": [
    "The first coordinate denotes the x position of the legend and the second coordinate denotes the y position of the legend (x, y).\n",
    "\n",
    "A negative value moves the x position of the legend to the left and the y position of the legend downwards.\n",
    "\n",
    "A positive value moves the x position of the legend to the right and the y position of the legend upwards."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9ac9420d",
   "metadata": {},
   "source": [
    "Further, more in-depth tutorials for Matplotlib and Seaborn can be found at the following links:\n",
    "\n",
    "Matplotlib - https://matplotlib.org/stable/index.html\n",
    "\n",
    "Seaborn - https://seaborn.pydata.org/tutorial/introduction.html"
   ]
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   "source": [
    "And that marks the end of this tutorial! Next, Machine Learning for Beginners."
   ]
  }
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