【问题标题】：TypeError: Image data cannot be converted to float,where my code is going wrong?TypeError：图像数据无法转换为浮点数，我的代码哪里出错了？
【发布时间】：2020-11-01 08:33:38
【问题描述】：

def calculate_frequencies(file_contents):
    # Here is a list of punctuations and uninteresting words you can use to process your text
    punctuations = '''!()-[]{};:'"\,<>./?@#$%^&*_~'''
    uninteresting_words = ["the", "a", "to", "if", "is", "it", "of", "and", "or", "an", "as", "i", "me", "my", \
    "we", "our", "ours", "you", "your", "yours", "he", "she", "him", "his", "her", "hers", "its", "they", "them", \
    "their", "what", "which", "who", "whom", "this", "that", "am", "are", "was", "were", "be", "been", "being", \
    "have", "has", "had", "do", "does", "did", "but", "at", "by", "with", "from", "here", "when", "where", "how", \
    "all", "any", "both", "each", "few", "more", "some", "such", "no", "nor", "too", "very", "can", "will", "just"]

    # LEARNER CODE START HERE
    dict1=[]
    d ={}
    for words in file_contents.split():
        if words.isalpha() and words.lower() not in uninteresting_words:
            dict1.append(words.lower())
    for words in dict1:
        if words not in d:
            d[words] =0
        d[words]+=file_contents.split().count(words)


        return d
    #wordcloud
    cloud = WordCloud(width=900,height=500, max_words=1628,relative_scaling=1,normalize_plurals=False)
    cloud.generate_from_frequencies(calculate_frequencies)
    return cloud.to_array()`enter code here`)

【问题讨论】：

标签： word-cloud

【解决方案1】：

# LEARNER CODE START HERE
words = file_contents.split(" ")
words_list = []

frequency={}
file_contents=file_contents.split()
for word in words:
    for uninteresting_word in uninteresting_words:
        if word is not uninteresting_word:
            words_list.append(word)
for word in words_list:
    if not word.isalpha():
        word =''.join([letter for letter in word if word.isalpha()])
words_dict = {}
for word in words_list:
    if word not in words_dict.keys():
        words_dict[word] = words_list.count(word)

#wordcloud
cloud = wordcloud.WordCloud()
cloud.generate_from_frequencies(words_dict)
return cloud.to_array()

【讨论】：

请尝试提供一些描述以获取更多详细信息。

【解决方案2】：

这是一个简短的答案

学习者代码从这里开始

for s in file_contents:
    if s in punctuations:
        file_contents = file_contents.replace(s, "")
files = file_contents.lower().split()
result = {}
for word in files:
    if word.isalpha() == True and word not in uninteresting_words:
        if word in result:
            result[word] += 1
        else:
            result[word] = 1
         
#wordcloud
cloud = wordcloud.WordCloud()
cloud.generate_from_frequencies(result)
return cloud.to_array()

【讨论】：