๋ณธ๋ฌธ ๋ฐ”๋กœ๊ฐ€๊ธฐ

Visualization

[Word Cloud] Mask๋ฅผ ์ด์šฉํ•œ Word Cloud + Python 1. ํ•„์š” ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ import ๋ฐ mask ์ด๋ฏธ์ง€ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ from urllib import request from wordcloud import WordCloud, ImageColorGenerator import matplotlib.pyplot as plt from PIL import Image # ๋งˆ์Šคํฌ ์ด๋ฏธ์ง€๋ฅผ url์—์„œ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ url = "https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQ9aACep8NBAyAioqO412qTTgDh5p2Ovz-VyA&usqp=CAU" res = request.urlopen(url).read() img = Image.open(BytesIO(res)) img = img.resize((600,1000)) mas.. ๋”๋ณด๊ธฐ
Word2Vec์„ ํ™œ์šฉํ•œ ๋‹จ์–ด ์œ ์‚ฌ์„ฑ Word2Vec์„ ํ™œ์šฉํ•œ ๋‹จ์–ด ์‹œ๊ฐํ™” ์ด๋ฏธ ํ•™์Šต๋œ ๋ชจ๋ธ Word2Vec์„ ํ™œ์šฉํ•œ ์œ ์‚ฌ๋‹จ์–ด โญโญโญ coffee์™€ ์œ ์‚ฌํ•œ ๋‹จ์–ด tea : 69.4% cocoa : 64.3% beans : 61.9% espresso : 61.3% drinks : 60.4% drink : 58.9% beer : 58.7% wine : 58.4% starbucks : 58.2% sugar : 55.7% ๋‚˜๋ผ ๋ถ„๋ฅ˜ ๋ชจ๋ธ ๋งŒ๋“ค๊ธฐ โญ ๋‚˜๋ผ๋กœ ๋ถ„๋ฅ˜ํ•œ ๋‹จ์–ด: ['senegal', 'latvia', 'colombia', 'serbia', 'nepal', 'maldives', 'croatia', 'brazil', 'uganda', 'slovakia'] ๋‚˜๋ผ๊ฐ€ ์•„๋‹Œ ๋‹จ์–ด :['qbs', 'allmänna', '1,617', 'zingiber'.. ๋”๋ณด๊ธฐ
[์ฐจํŠธ] ์›ํ˜• ์ฐจํŠธ ์‹œ๊ฐํ™” ๐Ÿค– ์„ค๋ช… Pie ์ฐจํŠธ์™€ ๋น„์Šทํ•˜์ง€๋งŒ Pie์ฐจํŠธ์˜ ๊ฒฝ์šฐ ํ•˜๋‚˜์˜ Pie์—๋Š” ํ•˜๋‚˜์˜ ๋ฐ์ดํ„ฐ ์ข…๋ฅ˜๋งŒ ๋“ค์–ด๊ฐˆ ์ˆ˜ ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. ์›ํ˜• ์ฐจํŠธ์˜ ๊ฒฝ์šฐ, ๊ฐ๋„์— ๋”ฐ๋ผ์„œ ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค์ค‘์œผ๋กœ ์Œ“์„ ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ํŠน์ง• Programming Language Python Libraries plotly [ .graph_objects ] numpy Data [start, end] (0~1์‚ฌ์ด ๊ฐ’์„ ๊ฐ€์ง„๋‹ค.) ์‚ฌ์šฉ์ž ์„ค์ • ๊ฐ€๋Šฅ ๋ถ€๋ถ„ base_r = 0.5 # ๊ฒ€์€์ƒ‰ ์›์˜ ๋ฐ˜์ง€๋ฆ„ num_points = 360 # ์ ์„ ์ฐ๋Š” ๊ฐœ์ˆ˜ r_gap = 0.1 # ์„  ์‚ฌ์ด ๊ฐ„๊ฒฉ mode='lines', # ์ข…๋ฅ˜ line_width=10, # ์„  ๊ตต๊ธฐ name='FXNNXC' + str(index+1) # Label ๊ตฌํ˜„ ๋งŒ๋“ ์ด: Christ.. ๋”๋ณด๊ธฐ