I loved doing this! You can find some tunes that I listened to while coding in the README file.
My project:
Tried my first time creating repo on Github!
Happy coding!
This is my code for this project. I also included comments on my thought process and how I arrived at my solution.
Hi everyone, I share my solution link on GitHub:
Happy coding.
Took me a while, but here is my code:
Hi everyone,
project was pretty interesting to do although it took me a bit of time. Here is my code!
I will be glad to hear your feedback.
https://github.com/MayaViko/hurricane_analysis_challenge_project.git
This is my project solustions. It was fun! All feedback is welcome, thanks!
Thank you for designing this practice!
Here’s my code
here is my code, this was a fun and challenging project
Here is my code:
Hi Annajiali,
I am struggling a lot with the logic behind the code in your solution for the first block.
if damage.find(‘M’) != -1:
I don’t understand how this works because, in theory, we are looking for strings that have the last character of ‘M’. Yet, it still works, but I can’t understand why.
.find('M')
looks for any M
in the string, and returns the index of the first M
it finds.
If there is no M
, then .find('M')
returns -1
.
So the if statement is meant to deal with a situation where there was no 'M'
.
@amdesanti , I think you’re interpreting the -1 as an index position. .find() only returns positive indices. I was confused by this myself. Here’s a simple example of the situation:
bob = “knob”
bob.find(“M”)
-1
bob.find(“b”)
3
Thanks for responding to this!
Yes, I looked up the method specs in the python documentation. I was getting it confused as the index position.
str.
find
(sub [, start [, end ]])
Return the lowest index in the string where substring sub is found within the slice s[start:end]
. Optional arguments start and end are interpreted as in slice notation. Return -1
if sub is not found.
This is my code, it was fun to make this exercise. It was also very reguarding to look that the answer was not as far as my solution… although i’m curious about how task 5 could be done wit defaultdict object…
My Hurricane Analysis project. Feel free to review my code. Any feedback that could help make my code better, eligible and more efficient is appreciated and it will be incorporated. Thank you in advance for all your help and time!
import operator
# 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 converse_damages(damages_list):
conversion = {"M": 1000000, "B": 1000000000}
updated_damages = [
float(record[:-1]) *
conversion[record[-1]] if record[-1] in conversion.keys() else
record if record == "Damages not recorded" else record
for record in damages_list
]
# since there is no elif statement for list comprehension, it was needed to create
# else statement that does exactly the same thing as the previous else statement
# https://stackoverflow.com/a/9987546/16672014
return updated_damages
updated_damages = converse_damages(damages)
for damage in updated_damages:
print(type(damage))
print(updated_damages)
# write your construct hurricane dictionary function here:
def create__hurricanes_dict_by_name(names, months, years, max_sustained_winds,
areas_affected, damages, deaths):
hurricanes = dict()
length_index = len(names)
for index in range(length_index):
hurricanes[names[index]] = {
"Name": names[index],
"Month": months[index],
"Year": years[index],
"Max Sustained Winds": max_sustained_winds[index],
"Areas Affected": areas_affected[index],
"Damages": updated_damages[index],
"Deaths": deaths[index],
}
return hurricanes
hurricanes_by_name = create__hurricanes_dict_by_name(names, months, years,
max_sustained_winds,
areas_affected,
updated_damages, deaths)
print(hurricanes_by_name)
# write your construct hurricane by year dictionary function here:
def hurricanes_dict_by_year(names, months, years, max_sustained_winds,
areas_affected, damages, deaths):
hurricanes = dict()
length_index = len(names)
for index in range(length_index):
hurricanes[years[index]] = {
"Name": names[index],
"Month": months[index],
"Year": years[index],
"Max Sustained Winds": max_sustained_winds[index],
"Areas Affected": areas_affected[index],
"Damages": damages[index],
"Deaths": deaths[index],
}
return hurricanes
hurricanes_by_year = hurricanes_dict_by_year(names, months, years,
max_sustained_winds,
areas_affected, updated_damages,
deaths)
hurricanes_by_year[1924]
hurricanes_by_year[2017]
# write your count affected areas function here:
def count_affected_arreas(dictionary):
areas_affected = dict()
counter = 0
for name in dictionary:
for area in dictionary[name]["Areas Affected"]:
if area in areas_affected:
areas_affected[area] += 1
else:
areas_affected[area] = 1
return areas_affected
count_affected_arreas = count_affected_arreas(hurricanes_by_name)
print(count_affected_arreas)
# write your find most affected area function here:
def find_most_affected_area(dictionary_with_counts):
result = []
sort_dictionary = sorted(dictionary_with_counts.items(),
key=lambda x: x[1],
reverse=True)
for i in sort_dictionary:
result += i[0], i[1]
return sort_dictionary
find_most_affected_area(count_affected_arreas)[:5]
# write your greatest number of deaths function here:
def find_most_lethal_hurricane(dictionary):
fatality_hurricanes = dict()
for key, value in dictionary.items():
fatality_hurricanes[key] = value['Deaths'], value['Year']
# fatality_hurricanes_sorted = dict(sorted(fatality_hurricanes.items(),
# key=lambda x: x[1],
# reverse=True))
# finding the key with the biggest value and returning both of them (key and value)
maximum_fatality = max(fatality_hurricanes.items(),
key=operator.itemgetter(1))[:2]
return maximum_fatality
find_most_lethal_hurricane(hurricanes_by_name)
# write your catgeorize by mortality function here:
def categ_hurricanes_mortality_scale(dictionary):
mortality_scale = {0: list(), 1: list(), 2: list(), 3: list(), 4: list()}
for name, value in dictionary.items():
if dictionary[name]["Deaths"] == 0:
mortality_scale[0].append(value)
elif dictionary[name]["Deaths"] <= 100:
mortality_scale[1].append(value)
elif dictionary[name]["Deaths"] <= 500:
mortality_scale[2].append(value)
elif dictionary[name]["Deaths"] <= 1000:
mortality_scale[3].append(value)
else:
mortality_scale[4].append(value)
return mortality_scale
categ_hurricanes_mortality_scale(hurricanes_by_name)
# write your greatest damage function here:
def greatest_dmg_hurricane(dictionary):
dmg_hurricanes_dict = dict()
for name in dictionary:
if dictionary[name]["Damages"] == "Damages not recorded":
continue
else:
dmg_hurricanes_dict[name] = dictionary[name]["Damages"]
greatest_dmg_hurricane = sorted(dmg_hurricanes_dict.items(),
key=lambda x: x[1],
reverse=True)[0]
return greatest_dmg_hurricane
greatest_dmg_hurricane(hurricanes_by_name)
# write your catgeorize by damage function here
def damage_rating_hurricanes(dictionary):
damage_rating = {0: list(), 1: list(), 2: list(), 3: list(), 4: list()}
for name, value in dictionary.items():
if dictionary[name]["Damages"] == "Damages not recorded":
damage_rating[0].append(value)
elif dictionary[name]["Damages"] <= 100000000:
damage_rating[1].append(value)
elif dictionary[name]["Damages"] <= 1000000000:
damage_rating[2].append(value)
elif dictionary[name]["Damages"] <= 10000000000:
damage_rating[3].append(value)
else:
damage_rating[4].append(value)
return damage_rating
damage_rating_hurricanes = damage_rating_hurricanes(hurricanes_by_name)
damage_rating_hurricanes[0]```
Hello, this is my code for this Project, any feedback would be great