Question

我有一个Pandas数据框，其中有一列：犯罪类型。该列包含16个不同的＆＃34;类别＆＃34;犯罪，我希望将其视为文字云，根据数据框中的频率调整字数。

我尝试使用以下代码执行此操作：

将数据输入：

fields = ['Crime type']

text2 = pd.read_csv('allCrime.csv', usecols=fields)

生成单词云：

wordcloud2 = WordCloud().generate(text2)
# Generate plot
plt.imshow(wordcloud2)
plt.axis("off")
plt.show()

然而，我收到此错误：

TypeError: expected string or bytes-like object

我能够使用以下代码从完整数据集创建一个早期的词云，但我希望词云只能生成特定列中的单词，犯罪类型＆＃39; （＆＃39; allCrime.csv＆＃39;包含大约13列）：

text = open('allCrime.csv').read()
wordcloud = WordCloud().generate(text)
# Generate plot
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

我是Python和Pandas的新手（并且通常编码！）所以感谢所有的帮助。

Answer 1

问题在于，您使用的WordCloud.generate方法需要一个字符串，它会计算单词实例，但您提供pd.Series。

根据您想要生成的单词云，您可以执行以下操作：

wordcloud2 = WordCloud().generate(' '.join(text2['Crime Type']))，它会连接数据框列中的所有单词，然后计算所有实例。
使用WordCloud.generate_from_frequencies手动传递单词的计算频率。

Answer 2

您可以在删除单个列的所有后缀词时生成词云。假设您的数据框为df，col名称为comment，那么以下代码可以提供帮助：

#Final word cloud after all the cleaning and pre-processing
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
comment_words = ' '
stopwords = set(STOPWORDS) 

# iterate through the csv file 
for val in df.comment: 

   # typecaste each val to string 
   val = str(val) 

   # split the value 
   tokens = val.split() 

# Converts each token into lowercase 
for i in range(len(tokens)): 
    tokens[i] = tokens[i].lower() 

for words in tokens: 
    comment_words = comment_words + words + ' '


wordcloud = WordCloud(width = 800, height = 800, 
            background_color ='white', 
            stopwords = stopwords, 
            min_font_size = 10).generate(comment_words) 

# plot the WordCloud image                        
plt.figure(figsize = (8, 8), facecolor = None) 
plt.imshow(wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 

plt.show()

Answer 3

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Answer 4

您需要创建一个串联的输入文本。这可以通过join函数来完成。

fields = ['Crime type']
text2 = pd.read_csv('allCrime.csv', usecols=fields)

text3 = ' '.join(text2['Crime Type'])
wordcloud2 = WordCloud().generate(text3)
# Generate plot
plt.imshow(wordcloud2)
plt.axis("off")
plt.show()

Answer 5

使用以下方法可以轻松完成：

df = pd.read_csv('allCrime.csv')
data = df['Crime type'].value_counts().to_dict()
wc = WordCloud().generate_from_frequencies(data)

plt.imshow(wc)
plt.axis('off')
plt.show()

从单列Pandas数据帧生成单词云

5 个答案: