MrN00b0t

class Pokemon:
  def __init__(self, name='Default', level=1, element='Grass', maxhealth=100, curr_health=10, concious=True, attack=1, defence=1, speed=1):
    self.name = name
    self.level = level
    self.element = element
    self.maxhealth = maxhealth
    self.curr_health = curr_health
    self.concious = concious
    self.attack = attack
    self.defence = defence

MrN00b0t

# names of hurricanes
names = ['Cuba I', 'San Felipe II Okeechobee', 'Bahamas', 'Cuba II', 'CubaBrownsville', 'Tampico', 'Labor Day', 'New England', 'Carol', 'Janet', 'Carla', 'Hattie', 'Beulah', 'Camille', 'Edith', 'Anita', 'David', 'Allen', 'Gilbert', 'Hugo', 'Andrew', 'Mitch', 'Isabel', 'Ivan', 'Emily', 'Katrina', 'Rita', 'Wilma', 'Dean', 'Felix', 'Matthew', 'Irma', 'Maria', 'Michael']

# months of hurricanes
months = ['October', 'September', 'September', 'November', 'August', 'September', 'September', 'September', 'September', 'September', 'September', 'October', 'September', 'August', 'September', 'September', 'August', 'August', 'September', 'September', 'August', 'October', 'September', 'September', 'July', 'August', 'September', 'October', 'August', 'September', 'October', 'September', 'September', 'October']

# years of hurricanes
years = [1924, 1928, 1932, 1932, 1933, 1933, 1935, 1938, 1953, 1955, 1961, 1961, 1967, 1969, 1971, 1977, 1979, 1980, 1988, 1989, 1992, 1998, 2003, 2004, 2005, 2005, 2005, 2

MrN00b0t

letters = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"]
points = [1, 3, 3, 2, 1, 4, 2, 4, 1, 8, 5, 1, 3, 4, 1, 3, 10, 1, 1, 1, 1, 4, 4, 8, 4, 10]
letter_to_points = {letter:point for letter, point in zip(letters,points)}
#add lower case letter point values
letter_to_points.update({letter.lower():point for letter, point in zip(letters, points)})
letter_to_points[' '] = 0

#Establish the score of a given word
def score_word (word):
  point_total = 0

MrN00b0t

# These are the emails you will be censoring. The open() function is opening the text file that the emails are contained in and the .read() method is allowing us to save their contexts to the following variables:
email_one = open("email_one.txt", "r").read()
email_two = open("email_two.txt", "r").read()
email_three = open("email_three.txt", "r").read()
email_four = open("email_four.txt", "r").read()

#list of terms provided in challenge 2
proprietary_terms = ["she", "personality matrix", "sense of self", "self-preservation", "learning algorithm", "her", "herself"]

#list of terms provided in challenge three. I added 'distressing'

MrN00b0t

import requests
from bs4 import BeautifulSoup
import pandas as pd

prefix = "https://s3.amazonaws.com/codecademy-content/courses/beautifulsoup/"
webpage_response = requests.get('https://s3.amazonaws.com/codecademy-content/courses/beautifulsoup/shellter.html')

webpage = webpage_response.content
soup = BeautifulSoup(webpage, "html.parser")

MrN00b0t

/* Top 25 Schools using an app */
SELECT email_domain, COUNT(user_id) as no_of_users
FROM users
GROUP BY 1
ORDER BY 2 DESC
LIMIT 25;

/* Count number of users in New York */
SELECT city, COUNT(user_id) as no_of_users
FROM users

MrN00b0t

import random

money = 100

#Write your game of chance functions here
def coin_toss(guess, bet):
  if bet > money:
    print('You don\'t have enough green to place that bet!')
    print('The maximum bet you can make is £' + str(money))
    return 0

MrN00b0t

import codecademylib3_seaborn
import pandas as pd
import matplotlib.pyplot as plt

# load rankings data here:
wood_coaster = pd.read_csv('Golden_Ticket_Award_Winners_Wood.csv')
steel_coaster = pd.read_csv('Golden_Ticket_Award_Winners_Steel.csv')

#Examine datasets
print(wood_coaster.head(5))

MrN00b0t

import pandas as pd
pd.set_option('display.max_colwidth', -1)

#Load csv into Data Frame
jeopardy = pd.read_csv('jeopardy.csv')

#Investigate DataFrame Structure
print(jeopardy.head())
#Does not show full DataFrame; try different method

MrN00b0t

-- This is the first query:

SELECT DISTINCT year from population_years;

-- Add your additional queries below:
SELECT ROUND(MAX(population), 2) AS 'Largest Population of Gabon in Dataset (mil)'
FROM population_years
WHERE country = 'Gabon';
SELECT country AS 'Countries with Smallest Populations in 205 (Ascending)'
FROM population_years