Hurricane Analysis - BYAlejoGD

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’]

1

Update Recorded Damages

conversion = {“M”: 1000000,
“B”: 1000000000}

test function by updating damages

def updated_damages(damages):
update_damages=
for damage in damages:
if damage[0].isdigit():
if damage.find(‘M’)>0:
#print(str(float(damage.replace(‘M’,‘’))*conversion[“M”]))
update_damages.append(float(damage.replace(‘M’,‘’))*conversion[“M”])

        elif damage.find('B')>0:
            #print(str(float(damage.replace('B',''))*conversion["B"]))
            update_damages.append(float(damage.replace('B',''))*conversion["B"])
    else:
        update_damages.append(damage)
return update_damages

updated_damages=updated_damages(damages)

print(updated_damages)

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]

‘’’ #primera solución
def create_dictionary():
headers_list=[‘Name’,‘Month’,‘Year’,‘Max Sustained Wind’,‘Areas Affected’,‘Damage’,‘Deaths’]
hurrican={}
d=0
while d < len(names):
hurrican[headers_list[0]]=names[d]
hurrican[headers_list[1]]=months[d]
hurrican[headers_list[2]]=years[d]
hurrican[headers_list[3]]=max_sustained_winds[d]
hurrican[headers_list[4]]=areas_affected[d]
hurrican[headers_list[5]]=damages[d]
hurrican[headers_list[6]]=deaths[d]
print(hurrican)
d+=1
return hurrican

hurrican_dictionay=create_dictionary()
print(hurrican_dictionay)
‘’’

2

def create_dictionary(names, months, years, max_sustained_winds, areas_affected, updated_damages, deaths):
#Create dictionary of hurricanes with hurricane name as the key and a dictionary of hurricane data as the value.
hurricanes = dict()
num_hurricanes = len(names)
for i in range(num_hurricanes):
hurricanes[names[i]] = {“Name”: names[i],
“Month”: months[i],
“Year”: years[i],
“Max Sustained Wind”: max_sustained_winds[i],
“Areas Affected”: areas_affected[i],
“Damage”: updated_damages[i],
“Deaths”: deaths[i]}
return hurricanes

create hurricanes dictionary

hurricanes = create_dictionary(names, months, years, max_sustained_winds, areas_affected, updated_damages, deaths)

Create a Table

#print(hurricanes)

Create and view the hurricanes dictionary

3

Organizing by Year

def hurricanes_by_year(hurricanes):
dicthurricanes_by_year=dict()
for hurricanne in hurricanes.values():
if hurricanne[‘Year’] not in dicthurricanes_by_year:
dicthurricanes_by_year[hurricanne[‘Year’]]= [hurricanne]
elif hurricanne[‘Year’] in dicthurricanes_by_year:
dicthurricanes_by_year[hurricanne[‘Year’]].append(hurricanne)
print(dicthurricanes_by_year)
return dicthurricanes_by_year

print(hurricanes_by_year(hurricanes))

4

def count_affected_areas(hurricanes):
affected_areas_count = dict()
for cane in hurricanes:
for area in hurricanes[cane][‘Areas Affected’]:
if area not in affected_areas_count:
affected_areas_count[area]=1
else:
affected_areas_count[area]+=1
return(affected_areas_count)

Counting Damaged Areas

count_affected_areas(hurricanes)

5

Calculating Maximum Hurricane Count

def maximun_affected(count_of_affectations):
area=‘’
often=0
maximun_affected=dict()
for affectations in count_of_affectations:
if often < count_of_affectations[affectations]:
area=affectations
often=count_of_affectations[affectations]
maximun_affected[area]=often
return maximun_affected

maximun_affected(count_affected_areas(hurricanes))

6

Calculating the Deadliest Hurricane

def greatest_number_deaths(hurricanes):
hurricane=‘’
max_deaths=0
for hurricane in hurricanes:
if max_deaths < hurricanes[hurricane][‘Deaths’]:
max_deaths=hurricanes[hurricane][‘Deaths’]
hurricane=hurricanes[hurricane][‘Name’]
return max_deaths,hurricane

greatest_number_deaths(hurricanes)

find highest mortality hurricane and the number of deaths

7

Rating Hurricanes by Mortality

mortality_scale = {0: 0,
1: 100,
2: 500,
3: 1000,
4: 10000}

def hurricane_mortality(mortality_scale,hurricanes):
hurricane_mortality=dict()
first_value=0
last_value=0
for ms in mortality_scale.keys():
hurricane_mortality[ms]=
first_value=mortality_scale[ms]
if ms < len(mortality_scale.keys())-1:
last_value=mortality_scale[ms+1]-1
else:
first_value=mortality_scale[ms-1]+1
last_value=mortality_scale[ms]
for canes in hurricanes:
if hurricanes[canes][‘Deaths’] > first_value and hurricanes[canes][‘Deaths’] <last_value:
hurricane_mortality[ms].append(hurricanes[canes])

return hurricane_mortality

hurricane_mortality(mortality_scale,hurricanes)

8

Calculating Hurricane Maximum Damage

def hurican_maximun_damage(hurricanes):
cane_name=‘’
cane_costly=0
for hurricane in hurricanes:
#print(hurricanes[hurricane][‘Damage’])
if ‘Damages not recorded’ != hurricanes[hurricane][‘Damage’]:
if cane_costly < float(hurricanes[hurricane][‘Damage’]):
cane_costly=hurricanes[hurricane][‘Damage’]
cane_name=hurricanes[hurricane][‘Name’]
return cane_costly,cane_name

max_damage_cane, max_damage = hurican_maximun_damage(hurricanes)
print(max_damage_cane, max_damage)

find highest damage inducing hurricane and its total cost

9

Rating Hurricanes by Damage

damage_scale = {0: 0,
1: 100000000,
2: 1000000000,
3: 10000000000,
4: 50000000000}

def hurricane_damage(damage_scale,hurricanes):
hurricane_dmage=dict()
first_value=0
last_value=0
for ms in damage_scale.keys():
hurricane_dmage[ms]=
first_value=damage_scale[ms]
if ms < len(damage_scale.keys())-1:
last_value=damage_scale[ms+1]-1
else:
first_value=damage_scale[ms-1]+1
last_value=damage_scale[ms]
for canes in hurricanes:
if hurricanes[canes][‘Deaths’] > first_value and hurricanes[canes][‘Deaths’] <last_value:
hurricane_dmage[ms].append(hurricanes[canes])

return hurricane_dmage

hurricane_damage(damage_scale,hurricanes)

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Thanks for your recomendation, the link is DataScientistNLPCodeAcademi/HurricaneAnalysis/Hurricane Analysis.ipynb at 8c5681ee7d63a271a6a8ad6b695312aef7b83175 · alejandrogutierrezdiaz/DataScientistNLPCodeAcademi · GitHub