words=text.split()# 初始化一个空字典用于存储单词计数 word_count={}# 遍历单词列表并统计单词出现次数forwordinwords:# 去除标点符号 word=word.strip('.,!?()[]{}"\'')# 如有需要可以转换为小写 # word=word.strip('.,!?()[]{}"\'').lower()ifword:ifwordin
df.groupby('区域')['订单号'].count().reset_index()如果要对同一个字段做不同的运算，可以使用....
# 计算 RFM 分数 def calculate_rfm(df): # Recency 分数(越小越好) df['R_Score'] = pd.qcut(df['Last_Login_Days_Ago'], q=5, labels=[5, 4, 3, 2, 1]) # Frequency 分数(越高越好) df['F_Score'] = pd.qcut(df['Purchase_Frequency'], q=5, labels=[1, 2, 3, 4, 5]) # ...
def spell_correction(sentence_list):max_edit_distance_dictionary= 3 prefix_length = 4 spellchecker = SymSpell(max_edit_distance_dictionary, prefix_length) dictionary_path = pkg_resources.resource_filename( "symspellpy", "frequency_dictionary_en_82_765.txt") bigram_path = pkg_reso...
def count_word_frequency(text): # 初始化一个空字典来存储单词频率 word_frequency = {} # 将文本转换为小写并分割成单词列表 words = text.lower().split() # 遍历单词列表并计算频率 for word in words: # 去除标点符号 word = word.strip(".,?!-") ...
cnt.append(count) # 关闭文件 f.close() # 绘制频率图 plt.bar(list(range(1, len(cnt)+1)), cnt, align='center') plt.axis([1, len(cnt)+1, 1, cnt[len(cnt)//30]]) plt.title('Word frequency') plt.xlabel('X axis') plt.ylabel('Y axis') ...
word, store the split word in a list as the first element inside a tuple. Store the frequency count of the word as an integer as the second element of the tuple. Create a tuple for every word in this fashion and store the tuples in a list called 'corpus', then return ...
[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22) plt.xlabel(x_var) plt.ylabel("Frequency") plt.ylim(0,25) plt.xticks(ticks=bins[::3], labels=[round(b,1)for b in bins[::3]]) plt....
apple apple orange banana orange" my_lst = list(my_text.split()) # 将文本分割成单词并存入列表my_set = set(my_text.split()) # 将文本分割成单词并存入集合中，去除重复 word_frequency = {word: my_lst.count(word) for word in my_set} # 使用字典统计每个单词的频率print(word_frequency...
names.append(name.strip())returnnames# 提取文本信息defextract_content(filename):returnopen_file(filename)# 统计人物出现频数defcount_frequency(names, content): data = []fornameinnames: num =sum(1forlineincontentifnameinline) data.append((name, num))returndata# 生成词云defgenerate_wordcloud(data...