具有特定形状的多个字云图

Question

我有一个相当具体的问题。在出版方面，我想为用LDA生成的每一个主题做一个字云情节。

我目前的代码如下：

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud
sns.set()

topics_df = pd.DataFrame({'Unnamed: 0': {0: 'Topic1', 1: 'Topic2', 2: 'Topic3', 3: 'Topic4', 4: 'Topic5'}, 'Terms per Topic': {0: 'charge, model, infrastructure, station, time, base, propose, drive, public, service, range, problem, study, paper, network, user, travel, battery, city, method', 1: 'cost, emission, fuel, car, high, price, low, reduce, hybrid, carbon, battery, gas, alternative, benefit, compare, find, conventional, efficiency, gasoline, total', 2: 'market, technology, policy, paper, change, development, support, mobility, business, industry, government, focus, study, transition, innovation, decision, develop, technological, case, lead', 3: 'consumer, policy, adoption, effect, model, purchase, bev, factor, evs, study, incentive, choice, preference, subsidy, influence, environmental, state, level, suggest, analysis', 4: 'energy, system, electricity, demand, transport, scenario, impact, increase, power, phev, transportation, sector, potential, base, show, consumption, reserve, term, economic, grid'}})

wc = WordCloud(background_color="white", colormap="Dark2",
               max_font_size=150, random_state=42)

plt.rcParams['figure.figsize'] = [20, 15]

# Create subplots for each topic
for i in range(5):
    wc.generate(text=topics_df["Terms per Topic"][i])   
    plt.subplot(5, 4, i+1)
    plt.imshow(wc, interpolation="bilinear")
    plt.axis("off")
    plt.title(topics_df.index[i])
plt.show()

我想要的输出结果是，每个字云图都是圆形的，周围有一个黑色的边缘。我想使用PCA图表上的每一个字云。

Answer 1

你可以试试这个：

码

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud
sns.set()
plt.rcParams['figure.figsize'] = [20, 15]

topics_df = pd.DataFrame({'Unnamed: 0': {0: 'Topic1', 1: 'Topic2', 2: 'Topic3', 3: 'Topic4', 4: 'Topic5'}, 'Terms per Topic': {0: 'charge, model, infrastructure, station, time, base, propose, drive, public, service, range, problem, study, paper, network, user, travel, battery, city, method', 1: 'cost, emission, fuel, car, high, price, low, reduce, hybrid, carbon, battery, gas, alternative, benefit, compare, find, conventional, efficiency, gasoline, total', 2: 'market, technology, policy, paper, change, development, support, mobility, business, industry, government, focus, study, transition, innovation, decision, develop, technological, case, lead', 3: 'consumer, policy, adoption, effect, model, purchase, bev, factor, evs, study, incentive, choice, preference, subsidy, influence, environmental, state, level, suggest, analysis', 4: 'energy, system, electricity, demand, transport, scenario, impact, increase, power, phev, transportation, sector, potential, base, show, consumption, reserve, term, economic, grid'}})

x, y = np.ogrid[:300, :300]
mask = (x - 150) ** 2 + (y - 150) ** 2 > 130 ** 2
mask = 255 * mask.astype(int)

wordcloud = WordCloud(background_color="white", mask=mask, contour_width=0.1, 
                      contour_color="black",  max_font_size=150, random_state=42,
                      colormap="Dark2")

for i in range(5):
    wordcloud.generate(text=topics_df["Terms per Topic"][i])   
    plt.subplot(5, 4, i+1)
    plt.imshow(wordcloud, interpolation="bilinear")
    plt.axis("off")
    plt.title(topics_df.index[i])
plt.show()

结果

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