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:ifwordinword_count:word_count[word]+=...
# 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]) # ...
count_dict = defaultdict(int) for item in lists: count_dict[item] += 1 3)使用集合(set)和列表(list) 先使用set去重,然后循环的把每一个元素和每一个元素对应的次数lists.count(item)组成一个元组放在列表里面 lists = ['a', 'a', 'b', 5, 6, 7, 5] count_set = set(lists) count_list ...
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(".,?!-") # 如果单词已存在于字典中,则增加其频率;否则...
path = pkg_resources.resource_filename( "symspellpy", "frequency_dictionary_en_82_765.txt") bigram_path = pkg_resources.resource_filename( "symspellpy", "frequency_bigramdictionary_en_243_342.txt") spellchecker.load_dictionary(dictionary_path, term_index=0, count_index=1) spel...
data.fillna(0,inplace=True) 去除重复值使用drop_duplicates()方法,它会自动检测并删除数据集中的重复行: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 data=pd.read_csv('data_with_duplicates.csv')# 删除重复行 unique_data=data.drop_duplicates() ...
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....
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 ...
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...
# Histogramfig = plt.figure(figsize = (6,4))title = fig.suptitle("Sulphates Content in Wine", fontsize=14)fig.subplots_adjust(top=0.85, wspace=0.3) ax = fig.add_subplot(1,1,1)ax.set_xlabel("Sulphates")ax.set_ylabel("Frequency")ax...