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script.py

# 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]

# 1
# Update Recorded Damages
conversion = {"M": 1000000,
              "B": 1000000000}

# test function by updating damages
def update_damages(damages):
  damages_updated=[]
  for i in damages:
    if i=="Damages not recorded":
      damages_updated.append(i)
    else:

      if i[-1]== "M":
        damages_updated.append(int(float(i[:-1])*1000000))
      else:

        damages_updated.append(int(float(i[:-1])*1000000000))

  return damages_updated

print(update_damages(damages))
print("\n")


# 2 
# Create a Table
def create_table(Name, Month, Year, Max_sustained_wind, Areas_affected, Damage, Death):

  table={}

  for i in range(len(Name)):
    table[Name[i]]={"Name":Name[i],"Month":Month[i],"Year": Year[i], "Max Sustained Wind":Max_sustained_wind[i], "Areas Affected":Areas_affected[i], "Damage":Damage[i], "Deaths":Death[i]}
  
  return table

#print(create_table(names,months, years,max_sustained_winds,areas_affected,damages, deaths))

  


# Create and view the hurricanes dictionary

table=create_table(names,months, years,max_sustained_winds,areas_affected,update_damages(damages), deaths)

# 3
# Organizing by Year
def update_by_year(table, years):

  aux=0
  year= []         # lista de anos únicos
  table_updated={} # dicionario de retorno da função
  year.append(years[0])

  for i in years:
      
      if year[aux]==i:
          continue
      else:
          year.append(i)
          aux+=1

        
  for i in year:

    list_dict = []   # lista de dicionários com os anos

    for x,y in table.items():
        
      if y["Year"]==i:

        list_dict.append(y)
                       
      else:
        continue     
        
    table_updated[i]=list_dict
    
  return table_updated
                
                


#print(update_by_year(table, years))

# create a new dictionary of hurricanes with year and key




# 4
# Counting Damaged Areas

def count_affected_area(hurricane_dict, areas_affected):

  areas = []
  areaf_count = {}
  aux = 0

  areas.append(areas_affected[0][0])

  for i in areas_affected:
    for j in i:

      if j in areas:
        continue
      else: 
        areas.append(j)

  
  for k in areas:
    aux=0
    for x,y in hurricane_dict.items():
      for j in y["Areas Affected"]:
        if k == j:
          aux+=1
        else:
          continue
  
    areaf_count[k]=aux

  return(areaf_count)


# create dictionary of areas to store the number of hurricanes involved in

areaf=count_affected_area(table,areas_affected)
  

#print(areaf)


# 5 
# Calculating Maximum Hurricane Count

def find_most_affected_hurricane(hurri_dict):

  most = 0

  for i in list(hurri_dict.values()):
    if i <= most:
      continue
    else:
      most = i

  return most

# find most frequently affected area and the number of hurricanes involved in

print(find_most_affected_hurricane(areaf))

# 6
# Calculating the Deadliest Hurricane

def deadliest_hurricane(table):
  
  deaths=0

  for x,y in table.items():
    if y["Deaths"] <= deaths:
      continue
    else:
      deaths=y["Deaths"]

  for x,y in table.items():
    if deaths == y["Deaths"]:
      return x
    else:
      continue


# find highest mortality hurricane and the number of deaths

print(deadliest_hurricane(table))

# 7
# Rating Hurricanes by Mortality
def rate_hurricane(table):

  rate = {}
  rate_1 = []
  rate_2 = []
  rate_3 = []
  rate_4 = []
  rate_5 = []

  for x,y in table.items():

    if y["Deaths"] > 10000:
      rate_5.append(y)

    elif y["Deaths"] > 1000:
      rate_4.append(y)

    elif y["Deaths"] > 500:
      rate_3.append(y)

    elif y["Deaths"] > 100:
      rate_2.append(y)

    else:
      rate_1.append(y)
  
    rate["1"] = rate_1
    rate["2"] = rate_2
    rate["3"] = rate_3
    rate["4"] = rate_4
    rate["5"] = rate_5

  return rate


# categorize hurricanes in new dictionary with mortality severity as key
#print(rate_hurricane(table))

# 8 Calculating Hurricane Maximum Damage

def greatest_damage(table):

  vulcan_damage = []
  damage= 0

  for x,y in table.items():
    if y["Damage"] == "Damages not recorded":
      continue
    else:
      if y["Damage"] <= damage:
        continue
      else:
        damage=y["Damage"]
  
  for x,y in table.items():
    if y["Damage"]==damage:
      vulcan_damage.append(x)

  vulcan_damage.append(damage)
  
  return vulcan_damage



# find highest damage inducing hurricane and its total cost
greatest_damage(table)
#print(greatest_damage(table))

# 9
# Rating Hurricanes by Damage

damage_scale = {0: 0,
                1: 100000000,
                2: 1000000000,
                3: 10000000000,
                4: 50000000000}
  
# categorize hurricanes in new dictionary with damage severity as key

def rate_hurricane_usd(table):

  rate = {}
  rate_1 = []
  rate_2 = []
  rate_3 = []
  rate_4 = []
  rate_5 = []

  for x,y in table.items():

    if y["Damage"] == "Damages not recorded":
      continue
    
    else:

      if y["Damage"] > 50000000000:
        rate_5.append(y)

      elif y["Damage"] > 10000000000:
        rate_4.append(y)

      elif y["Damage"] > 1000000000:
        rate_3.append(y)

      elif y["Damage"] > 100000000:
        rate_2.append(y)

      else:
        rate_1.append(y)
  
    rate["1"] = rate_1
    rate["2"] = rate_2
    rate["3"] = rate_3
    rate["4"] = rate_4
    rate["5"] = rate_5

  return rate

print(rate_hurricane_usd(table))