Codecademy export

script.py

import pandas as pd
# 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, 2005, 2007, 2007, 2016, 2017, 2017, 2018]

# maximum sustained winds (mph) of hurricanes
max_sustained_winds = [165, 160, 160, 175, 160, 160, 185, 160, 160, 175, 175, 160, 160, 175, 160, 175, 175, 190, 185, 160, 175, 180, 165, 165, 160, 175, 180, 185, 175, 175, 165, 180, 175, 160]

# areas affected by each hurricane
areas_affected = [['Central America', 'Mexico', 'Cuba', 'Florida', 'The Bahamas'], ['Lesser Antilles', 'The Bahamas', 'United States East Coast', 'Atlantic Canada'], ['The Bahamas', 'Northeastern United States'], ['Lesser Antilles', 'Jamaica', 'Cayman Islands', 'Cuba', 'The Bahamas', 'Bermuda'], ['The Bahamas', 'Cuba', 'Florida', 'Texas', 'Tamaulipas'], ['Jamaica', 'Yucatn Peninsula'], ['The Bahamas', 'Florida', 'Georgia', 'The Carolinas', 'Virginia'], ['Southeastern United States', 'Northeastern United States', 'Southwestern Quebec'], ['Bermuda', 'New England', 'Atlantic Canada'], ['Lesser Antilles', 'Central America'], ['Texas', 'Louisiana', 'Midwestern United States'], ['Central America'], ['The Caribbean', 'Mexico', 'Texas'], ['Cuba', 'United States Gulf Coast'], ['The Caribbean', 'Central America', 'Mexico', 'United States Gulf Coast'], ['Mexico'], ['The Caribbean', 'United States East coast'], ['The Caribbean', 'Yucatn Peninsula', 'Mexico', 'South Texas'], ['Jamaica', 'Venezuela', 'Central America', 'Hispaniola', 'Mexico'], ['The Caribbean', 'United States East Coast'], ['The Bahamas', 'Florida', 'United States Gulf Coast'], ['Central America', 'Yucatn Peninsula', 'South Florida'], ['Greater Antilles', 'Bahamas', 'Eastern United States', 'Ontario'], ['The Caribbean', 'Venezuela', 'United States Gulf Coast'], ['Windward Islands', 'Jamaica', 'Mexico', 'Texas'], ['Bahamas', 'United States Gulf Coast'], ['Cuba', 'United States Gulf Coast'], ['Greater Antilles', 'Central America', 'Florida'], ['The Caribbean', 'Central America'], ['Nicaragua', 'Honduras'], ['Antilles', 'Venezuela', 'Colombia', 'United States East Coast', 'Atlantic Canada'], ['Cape Verde', 'The Caribbean', 'British Virgin Islands', 'U.S. Virgin Islands', 'Cuba', 'Florida'], ['Lesser Antilles', 'Virgin Islands', 'Puerto Rico', 'Dominican Republic', 'Turks and Caicos Islands'], ['Central America', 'United States Gulf Coast (especially Florida Panhandle)']]

# damages (USD($)) of hurricanes
damages = ['Damages not recorded', '100M', 'Damages not recorded', '40M', '27.9M', '5M', 'Damages not recorded', '306M', '2M', '65.8M', '326M', '60.3M', '208M', '1.42B', '25.4M', 'Damages not recorded', '1.54B', '1.24B', '7.1B', '10B', '26.5B', '6.2B', '5.37B', '23.3B', '1.01B', '125B', '12B', '29.4B', '1.76B', '720M', '15.1B', '64.8B', '91.6B', '25.1B']

# deaths for each hurricane
deaths = [90,4000,16,3103,179,184,408,682,5,1023,43,319,688,259,37,11,2068,269,318,107,65,19325,51,124,17,1836,125,87,45,133,603,138,3057,74]

# write your update damages function here:
def clean_damages(damages):
  new_damages = []
  for damage in damages:
    if "M" in damage:
      damage = list(damage)
      damage = ''.join(damage[:-1])
      damage = float(damage)
      damage *= 1000000
      new_damages.append(damage)
    elif "B" in damage:
      damage = list(damage)
      damage = ''.join(damage[:-1])
      damage = float(damage)
      damage *= 1000000000
      new_damages.append(damage)
    else:
      new_damages.append(damage)
  return new_damages

updated_damages = clean_damages(damages)


df = pd.DataFrame({
  'names': names,
  'months': months,
  'years': years,
  'max_sustained_winds': max_sustained_winds,
  'areas_affected': areas_affected,
  'damages': updated_damages,
  'deaths': deaths
})

# write your construct hurricane dictionary function here:
def build_dictionary(keys, names, months, years, max_sustained_winds, areas_affected, damages, deaths):
  new_dictionary = dict()
  for index in range(0, len(keys)):
    new_dictionary[keys[index]] = {"Name": names[index], "Month": months[index], "Year": years[index], "Max Sustained Wind": max_sustained_winds[index], "Areas Affected": areas_affected[index], "Damage": updated_damages[index], "Deaths": deaths[index]}     
  return new_dictionary  

hurricane_names = build_dictionary(names, names, months, years, max_sustained_winds, areas_affected, updated_damages, deaths)

# write your construct hurricane by year dictionary function here:
hurricane_years = build_dictionary(years, names, months, years, max_sustained_winds, areas_affected, updated_damages, deaths)

# write your count affected areas function here:

def affected_count(subject, dictionary):
  affected_count = {}  
  for key in dictionary:
    for item in dictionary[key][subject]:
      if item in affected_count:
        affected_count[item] += 1
      else:
        affected_count[item] = 1
  return affected_count

affected_areas_count = affected_count("Areas Affected", hurricane_names)
#print(affected_areas_count)

# write your find most affected area function here:
def highest_count(dictionary):
  maximum_hit = 0
  max_hit_subject = ''
  for key in dictionary:
    if dictionary[key] > maximum_hit:
      maximum_hit = dictionary[key]
      max_hit_subject = key
  return max_hit_subject, maximum_hit

#print(highest_count(affected_areas_count))

# write your greatest number of deaths function here:
def subject_count(subject, dictionary):
  count_dict = {}
  for key in dictionary:
    if dictionary[key][subject] == "Damages not recorded":
      count_dict[key] = float(0.0)
    else:
      count_dict[key] = dictionary[key][subject]
  return count_dict

death_count_dict = subject_count("Deaths", hurricane_names)
#print(death_count_dict)

max_death_count = highest_count(death_count_dict) 
#print(max_death_count)

# write your catgeorize by mortality function here:
def rating(subject, dictionary, lower_bounds_list):
  rating_dictionary = {}
  for key in dictionary:
    if dictionary[key] > lower_bounds_list[3]:
      rating_dictionary[key] = {subject: 4}
    elif dictionary[key] > lower_bounds_list[2]:
      rating_dictionary[key] = {subject: 3}
    elif dictionary[key] > lower_bounds_list[1]:
      rating_dictionary[key] = {subject: 2}
    elif dictionary[key] > lower_bounds_list[0]:
      rating_dictionary[key] = {subject: 1}
    else:
      rating_dictionary[key] = {subject: 0}
  return rating_dictionary

mortality_bounds_list = [0, 100, 500, 1000, 10000]
mortality_scaling_list = rating("Mortality Scaling", death_count_dict, mortality_bounds_list)
print(mortality_scaling_list)

# write your greatest damage function here:

damage_count_list = subject_count("Damage", hurricane_names)
#print(damage_count_list)

highest_damage = highest_count(damage_count_list)
print(highest_damage)

# write your catgeorize by damage function here:
damage_bounds_list = [0, 100000000, 1000000000, 10000000000, 50000000000]

damage_scaling_dict = rating("Damage Scaling", damage_count_list, damage_bounds_list)
print(damage_scaling_dict)