Codecademy export

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}
# Item 2:
def damage_converter(string_values):
  for i, string_value in enumerate(string_values):
    if string_value == "Damages not recorded":
      string_values[i] = 0
    else:
      for convert_char in conversion:
        if convert_char in str(string_value):
          string_values[i] = float(string_value.replace(convert_char,'')) * conversion[convert_char]
  
# test function by updating damages
damage_converter(damages)


# 2 
# Create a Table
def table_creator(storm_key, storm_names, storm_months, storm_years, storm_winds, storm_areas, storm_damages, storm_deaths, aggregate=False):
  """
  takes storm attribute lists.
    - lists must be of equal length. we're assuming for this exercise
  if aggregate is True, values are list of dicts
  returns list of dicts.
    key is name, value is dict of hurrican information 
  """
  table = {}
  if aggregate:
    for i in range(len(storm_key)):
      if storm_key[i] in table:
        table[storm_key[i]].append({
          "Name": storm_names[i],
          "Month": storm_months[i],
          "Year": storm_years[i],
          "Max Sustained Wind": storm_winds[i],
          "Areas Affected": storm_areas[i],
          "Damage": storm_damages[i],
          "Death": storm_deaths[i]
        })
      else:
        table[storm_key[i]] = [{
          "Name": storm_names[i],
          "Month": storm_months[i],
          "Year": storm_years[i],
          "Max Sustained Wind": storm_winds[i],
          "Areas Affected": storm_areas[i],
          "Damage": storm_damages[i],
          "Death": storm_deaths[i]
        }]
  else:
    for i in range(len(storm_key)):
      table[storm_key[i]] = {
        "Name": storm_names[i],
        "Month": storm_months[i],
        "Year": storm_years[i],
        "Max Sustained Wind": storm_winds[i],
        "Areas Affected": storm_areas[i],
        "Damage": storm_damages[i],
        "Deaths": storm_deaths[i]
      }
  return table

# Create and view the hurricanes dictionary
hurricane_table = table_creator(
  names,
  names,
  months,
  years,
  max_sustained_winds,
  areas_affected,
  damages,
  deaths
)
print(hurricane_table['Cuba I'])

# 3
# Organizing by Year
# modified the original function to allow a key to be set.


# create a new dictionary of hurricanes with year and key
hurricane_table_years = table_creator(
  years,
  names,
  months,
  years,
  max_sustained_winds,
  areas_affected,
  damages,
  deaths,
  aggregate=True
)
print(hurricane_table_years[1932])


# 4
# Counting Damaged Areas
def value_count(values):
  table = {}
  for value in values:
    if isinstance(value, list):
      for area in value:
        if area in table:
          table[area] += 1
        else:
          table[area] = 1
    else:
      if value in table:
        table[value] += 1
      else:
        table[value] = 1
  return table

# create dictionary of areas to store the number of hurricanes involved in
damaged_areas_table = value_count(areas_affected)


# 5 
# Calculating Maximum Hurricane Count
def greatest_impact(table, named_value=None):
  if named_value:
    count = 0
    for k,v in table.items():
      if v[named_value] > count:
        count = v[named_value]
    for k,v in table.items():
      if v[named_value] == count:
        return {k: v[named_value]}
  else:
    count = max(table.values())
    for k,v in table.items():
      if v == count:
        return {k: v}

# find most frequently affected area and the number of hurricanes involved in
most_damaged_area = greatest_impact(damaged_areas_table)
print(most_damaged_area)

# 6
# Calculating the Deadliest Hurricane
# greatest_impact() function works here as well

# find highest mortality hurricane and the number of deaths
most_dangerous_hurricane = greatest_impact(hurricane_table, "Deaths")
print(most_dangerous_hurricane)

# 7
# Rating Hurricanes by Mortality
mortality_scale = {
  0: 0,
  1: 100,
  2: 500,
  3: 1000,
  4: 10000
}
def group(table, grouping_map, named_column=None):
  """
  Table is assumed to be aggregate (k = name, v = int count)
  Grouping map is assumed to be 0-indexed (k = int, v = threshold)
  """
  output = {}
  for i in range(len(grouping_map) - 1):
    for storm in table:
      if named_column:
        if grouping_map[i] < table[storm][named_column] < grouping_map[i + 1]:
          if i + 1 in output:
            output[i + 1].append(table[storm])
          else:
            output[i + 1] = [table[storm]]
      else:
        if grouping_map[i] < table[storm] < grouping_map[i + 1]:
          if i + 1 in output:
            output[i + 1] = output[i + 1].append(table[storm])
          else:
            output[i + 1] = [table[storm]]
  return output

# categorize hurricanes in new dictionary with mortality severity as key
hurricanes_mortality_groupings = group(hurricane_table, mortality_scale, "Deaths")
# print(hurricanes_mortality_groupings)

# 8 Calculating Hurricane Maximum Damage
# reusing greatest_impact() because just refactored that function

# find highest damage inducing hurricane and its total cost
most_costly_hurricane = greatest_impact(hurricane_table, "Damage")

# 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
hurricanes_damage_groupings = group(hurricane_table, damage_scale, "Damage")