Tech Pay
Billionaire Age
Company Data
will push to another branch with something like https://github.com/peter-evans/create-pull-request/
Online Matplotlib Compilers:
https://www.tutorialspoint.com/execute_matplotlib_online.php
https://python-fiddle.com/examples/matplotlib
be mindful of networking:
Line 113 in 36d7dec
https://marketplace.visualstudio.com/search?target=VSCode, 33528
https://marketplace.visualstudio.com/search?target=VS, 11684
https://atom.io/packages, 9193
https://plugins.jetbrains.com, 6159
https://packagecontrol.io/stats, 5241
https://marketplace.eclipse.org
https://www.bls.gov/emp/tables/fastest-growing-occupations.htm
https://www.bls.gov/emp/tables/fastest-growing-occupations-alt.htm
https://www.bls.gov/emp/tables/occupations-most-job-growth.htm
https://www.bls.gov/ooh/most-new-jobs.htm
https://www.visualcapitalist.com/the-20-fastest-growing-jobs-in-the-next-decade/
shelter
plumbing ~45%
electricity ~90%
heat
internet ~66%
laundry
kitchen
refrigerator ~75%
dishwasher
microwave
oven
stove
https://globaldatalab.org/areadata/
https://ourworldindata.org/energy-access
https://www.followthemoney.org/
CA $30
TX $10
FL $8
NY $4.5
PA $15
IL $23
OH $7
GA $10
NC $23
MI $6
NJ $1
VA $1
WA $18
AZ $12
MA $13
TN $3.5
IN $9.5
MO $20
MD $1
import matplotlib.pyplot as plt
data = [33, 37, 96, 97, 114, 394, 673, 957, 902, 609, 524, 1012, 1202, 1465, 560, 600]
x_values = [2009 + i for i in range(len(data))]
x_values_with_asterisk = [str(x) if x != 2024 else '2024*' for x in x_values]
plt.figure(figsize=(10, 6))
plt.plot(x_values, data, marker='o')
plt.title('GWWC Lifetime 10% Pledges by Year')
plt.xlabel('Year')
plt.ylabel('Pledges')
plt.xticks(ticks=x_values, labels=x_values_with_asterisk)
plt.ylim(ymin=0)
plt.grid(True, which='both', linestyle='--', linewidth=0.5, color='grey')
plt.show()
https://gs.statcounter.com/os-market-share#monthly-200901-202407 / https://gs.statcounter.com/os-market-share#quarterly-200901-202403
https://gs.statcounter.com/browser-market-share#monthly-200901-202407 / https://gs.statcounter.com/browser-market-share#quarterly-200901-202403
https://gs.statcounter.com/search-engine-market-share#monthly-200901-202407 / https://gs.statcounter.com/search-engine-market-share#quarterly-200901-202403
https://gs.statcounter.com/social-media-stats#monthly-200903-202407 / https://gs.statcounter.com/social-media-stats#quarterly-200903-202403
ideally bottom corner
plotly?
Online Matplotlib Compilers:
https://www.tutorialspoint.com/execute_matplotlib_online.php
https://python-fiddle.com/examples/matplotlib
8 plt.plot
6 plt.xlabel
6 plt.show
4 plt.ylabel
4 plt.annotate
3 plt.title
3 plt.figure (figsize)
plt.gca (set_scientific)
plt.ylim
plt.xticks
plt.grid
plt.legend
https://developer.spotify.com/documentation/web-api/reference/search
https://medium.com/@nargessmi87/how-to-customize-the-openstreetmap-marker-icon-and-binding-popup-ab2254bddec2
https://stackoverflow.com/questions/63719299/get-top-100-artists-by-genre-spotify-api
https://stackoverflow.com/questions/17854633/get-birthdate-birthplace-from-mediawiki-xml-format-api
https://en.wikipedia.org/wiki/List_of_countries_by_life_expectancy
https://en.wikipedia.org/wiki/List_of_countries_by_wealth_per_adult
https://en.wikipedia.org/wiki/Forbes_list_of_the_world%27s_highest-paid_athletes
https://en.wikipedia.org/wiki/Forbes_list_of_highest-earning_musicians
https://chartmasters.org/most-streamed-artists-ever-on-spotify/
https://www.apartments.com/
grouped bar chart?
from collections import defaultdict
import pprint
skip = (
' Washer & Dryer Hookups',
' Doorman',
' Furnished',
' Lofts',
' Gated',
' Fireplace',
' Patio',
' Office',
' Den',
' Yard',
' Clubhouse',
' Business Center',
' Controlled Access',
' Playground',
' Basement',
' Concierge',
)
national = defaultdict(lambda: 0)
for city in cities:
print(city)
city = cities[city]
map = {}
total = city[0]
s = city[1]
for line in s.splitlines():
if line.startswith(skip):
continue
line = line.replace(',', '').strip(')')
map[round(int(line.split('(')[1])/total, 4)] = line.split('(')[0]
national[line.split('(')[0]] += round(int(line.split('(')[1])/total/11, 2)
pprint.PrettyPrinter().pprint(map)
pprint.PrettyPrinter().pprint(national)
cities = {
'nyc': (4885, '''Air Conditioning(1,662)
In Unit Washer & Dryer(1,337)
Washer & Dryer Hookups(518)
Dishwasher(1,488)
Wheelchair Access(253)
Parking(833)
Laundry Facilities(1,722)
Fitness Center(1,490)
Pool(382)
Elevator(1,455)
Doorman(1,334)
Dog Friendly(2,496)
Cat Friendly(2,285)
Furnished(534)
Lofts(10)
Utilities Included(1,340)
Gated(195)
Fireplace(124)
Patio(129)
Garage(648)
Hardwood Floors(1,581)
Balcony(497)
Office(207)
Den(53)
Yard(28)
Clubhouse(420)
Business Center(331)
Controlled Access(755)
Playground(570)
Basement(77)
Walk-In Closets(719)
Concierge(1,163)'''),
'la': (22795, '''Air Conditioning(13,898)
In Unit Washer & Dryer(11,316)
Washer & Dryer Hookups(1,925)
Dishwasher(14,426)
Wheelchair Access(3,584)
Parking(16,976)
Laundry Facilities(8,937)
Fitness Center(11,542)
Pool(12,181)
Elevator(10,497)
Doorman(1,201)
Dog Friendly(14,990)
Cat Friendly(15,515)
Furnished(4,450)
Lofts(2,632)
Utilities Included(7,011)
Gated(10,821)
Fireplace(4,449)
Patio(7,126)
Garage(9,723)
Hardwood Floors(9,296)
Balcony(10,620)
Office(2,520)
Den(2,176)
Yard(1,062)
Clubhouse(7,492)
Business Center(5,765)
Controlled Access(10,261)
Playground(1,067)
Basement(76)
Walk-In Closets(9,400)
Concierge(3,209)'''),
'chi': (28270, '''Air Conditioning(14,565)
In Unit Washer & Dryer(11,974)
Washer & Dryer Hookups(1,333)
Dishwasher(15,150)
Wheelchair Access(3,235)
Parking(14,931)
Laundry Facilities(12,524)
Fitness Center(10,769)
Pool(6,397)
Elevator(9,946)
Doorman(3,958)
Dog Friendly(16,780)
Cat Friendly(18,922)
Furnished(2,662)
Lofts(931)
Utilities Included(15,093)
Gated(5,936)
Fireplace(2,511)
Patio(3,024)
Garage(8,774)
Hardwood Floors(14,674)
Balcony(5,356)
Office(2,361)
Den(1,222)
Yard(1,275)
Clubhouse(3,152)
Business Center(5,586)
Controlled Access(5,565)
Playground(472)
Basement(669)
Walk-In Closets(7,314)
Concierge(4,646)'''),
'sf': (5263, '''Air Conditioning(1,168)
In Unit Washer & Dryer(2,598)
Washer & Dryer Hookups(168)
Dishwasher(2,625)
Wheelchair Access(927)
Parking(3,361)
Laundry Facilities(1,447)
Fitness Center(2,657)
Pool(764)
Elevator(2,601)
Doorman(593)
Dog Friendly(3,459)
Cat Friendly(3,627)
Furnished(1,193)
Lofts(510)
Utilities Included(1,289)
Gated(1,288)
Fireplace(661)
Patio(1,160)
Garage(2,408)
Hardwood Floors(2,057)
Balcony(1,745)
Office(450)
Den(401)
Yard(199)
Clubhouse(1,557)
Business Center(984)
Controlled Access(1,957)
Playground(375)
Basement(93)
Walk-In Closets(1,985)
Concierge(1,037)'''),
'dc': (20189, '''Air Conditioning(16,439)
In Unit Washer & Dryer(14,181)
Washer & Dryer Hookups(861)
Dishwasher(15,311)
Wheelchair Access(5,936)
Parking(17,998)
Laundry Facilities(6,016)
Fitness Center(16,139)
Pool(12,165)
Elevator(13,773)
Doorman(1,844)
Dog Friendly(16,347)
Cat Friendly(17,345)
Furnished(2,703)
Lofts(1,511)
Utilities Included(5,898)
Gated(5,937)
Fireplace(2,841)
Patio(7,200)
Garage(13,235)
Hardwood Floors(9,164)
Balcony(11,104)
Office(1,328)
Den(3,381)
Yard(802)
Clubhouse(9,586)
Business Center(10,070)
Controlled Access(11,675)
Playground(2,075)
Basement(258)
Walk-In Closets(11,426)
Concierge(12,624)'''),
'dal': (24361, '''Air Conditioning(15,758)
In Unit Washer & Dryer(12,546)
Washer & Dryer Hookups(8,117)
Dishwasher(14,728)
Wheelchair Access(6,268)
Parking(20,197)
Laundry Facilities(8,534)
Fitness Center(18,109)
Pool(18,818)
Elevator(6,664)
Doorman(517)
Dog Friendly(22,813)
Cat Friendly(22,700)
Furnished(1,791)
Lofts(2,867)
Utilities Included(1,964)
Gated(10,588)
Fireplace(7,269)
Patio(12,415)
Garage(10,212)
Hardwood Floors(9,663)
Balcony(14,032)
Office(3,664)
Den(3,237)
Yard(3,254)
Clubhouse(12,773)
Business Center(14,094)
Controlled Access(10,567)
Playground(4,213)
Basement(194)
Walk-In Closets(13,980)
Concierge(3,599)'''),
'hou': (40483, '''Air Conditioning(26,201)
In Unit Washer & Dryer(22,696)
Washer & Dryer Hookups(9,241)
Dishwasher(27,491)
Wheelchair Access(8,087)
Parking(33,726)
Laundry Facilities(13,356)
Fitness Center(28,387)
Pool(33,942)
Elevator(9,940)
Doorman(562)
Dog Friendly(34,650)
Cat Friendly(34,710)
Furnished(2,461)
Lofts(4,093)
Utilities Included(4,417)
Gated(22,560)
Fireplace(8,074)
Patio(17,064)
Garage(16,785)
Hardwood Floors(15,767)
Balcony(20,253)
Office(5,293)
Den(3,751)
Yard(3,391)
Clubhouse(23,062)
Business Center(22,126)
Controlled Access(19,687)
Playground(7,652)
Basement(142)
Walk-In Closets(23,106)
Concierge(6,714)'''),
'bos': (40307, '''Air Conditioning(8,214)
In Unit Washer & Dryer(13,362)
Washer & Dryer Hookups(870)
Dishwasher(14,956)
Wheelchair Access(1,607)
Parking(12,669)
Laundry Facilities(18,547)
Fitness Center(6,095)
Pool(3,502)
Elevator(5,920)
Doorman(980)
Dog Friendly(9,529)
Cat Friendly(13,977)
Furnished(2,449)
Lofts(184)
Utilities Included(17,293)
Gated(2,836)
Fireplace(1,953)
Patio(3,142)
Garage(5,998)
Hardwood Floors(13,933)
Balcony(4,781)
Office(1,144)
Den(1,141)
Yard(1,370)
Clubhouse(2,995)
Business Center(3,129)
Controlled Access(2,428)
Playground(362)
Basement(1,619)
Walk-In Closets(4,134)
Concierge(3,892)'''),
'phi': (8728, '''Air Conditioning(5,277)
In Unit Washer & Dryer(4,696)
Washer & Dryer Hookups(331)
Dishwasher(5,283)
Wheelchair Access(1,771)
Parking(5,645)
Laundry Facilities(3,786)
Fitness Center(4,591)
Pool(2,374)
Elevator(4,323)
Doorman(1,493)
Dog Friendly(5,670)
Cat Friendly(6,464)
Furnished(1,209)
Lofts(703)
Utilities Included(4,304)
Gated(1,923)
Fireplace(742)
Patio(1,265)
Garage(2,866)
Hardwood Floors(3,368)
Balcony(2,092)
Office(648)
Den(805)
Yard(435)
Clubhouse(2,535)
Business Center(2,569)
Controlled Access(3,718)
Playground(822)
Basement(637)
Walk-In Closets(2,781)
Concierge(2,520)'''),
'atl': (13125, '''Air Conditioning(8,081)
In Unit Washer & Dryer(7,351)
Washer & Dryer Hookups(3,700)
Dishwasher(7,872)
Wheelchair Access(3,632)
Parking(10,955)
Laundry Facilities(4,017)
Fitness Center(10,513)
Pool(10,911)
Elevator(4,189)
Doorman(616)
Dog Friendly(12,008)
Cat Friendly(12,013)
Furnished(1,639)
Lofts(1,135)
Utilities Included(1,455)
Gated(5,806)
Fireplace(2,412)
Patio(4,819)
Garage(5,058)
Hardwood Floors(3,911)
Balcony(6,784)
Office(1,979)
Den(1,272)
Yard(731)
Clubhouse(7,938)
Business Center(6,970)
Controlled Access(6,220)
Playground(2,249)
Basement(133)
Walk-In Closets(6,904)
Concierge(2,282)'''),
'sea': (13107, '''Air Conditioning(5,290)
In Unit Washer & Dryer(8,839)
Washer & Dryer Hookups(523)
Dishwasher(8,041)
Wheelchair Access(3,345)
Parking(11,090)
Laundry Facilities(2,496)
Fitness Center(9,762)
Pool(1,493)
Elevator(7,699)
Doorman(483)
Dog Friendly(12,088)
Cat Friendly(12,141)
Furnished(1,660)
Lofts(1,693)
Utilities Included(1,243)
Gated(5,566)
Fireplace(1,590)
Patio(5,003)
Garage(8,497)
Hardwood Floors(3,667)
Balcony(7,372)
Office(1,837)
Den(2,463)
Yard(434)
Clubhouse(5,832)
Business Center(4,649)
Controlled Access(7,401)
Playground(538)
Basement(174)
Walk-In Closets(6,195)
Concierge(3,198)'''),
'mia': (11902, '''Air Conditioning(5,880)
In Unit Washer & Dryer(5,636)
Washer & Dryer Hookups(421)
Dishwasher(5,609)
Wheelchair Access(1,865)
Parking(7,086)
Laundry Facilities(2,071)
Fitness Center(6,429)
Pool(8,598)
Elevator(3,919)
Doorman(410)
Dog Friendly(7,741)
Cat Friendly(7,625)
Furnished(2,399)
Lofts(119)
Utilities Included(3,070)
Gated(3,832)
Fireplace(115)
Patio(2,052)
Garage(3,674)
Hardwood Floors(976)
Balcony(4,293)
Office(508)
Den(742)
Yard(220)
Clubhouse(3,984)
Business Center(3,554)
Controlled Access(3,120)
Playground(2,180)
Basement(58)
Walk-In Closets(4,423)
Concierge(2,016)''')
}
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.