# 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]) # ...
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]+=...
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....
f.writelines("{0:<6}\t{1:<20}\t{2:<6}\t{3:.4%}\t{4:.4%}".format(i+1, word, count, count/total_word, cum_fre/total_word)) f.write('\n') 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.axi...
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...
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...
>>>x.count([1,2]) 2 >>> x.count(1) 1 >>> x.count('a') 1 1. 计算字母和数字出现的次数 str='abc123abc456aa' d={} for x in str: print x if not x in d: d[x]=1 else: d[x]=d[x]+1 print d {'a': 4, 'c': 2, 'b': 2, '1': 1, '3': 1, '2': 1...
[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....
first_patient = load_scan(INPUT_FOLDER + patients[0])first_patient_pixels = get_pixels_hu(first_patient)plt.hist(first_patient_pixels.flatten(), bins=80, color='c')plt.xlabel("Hounsfield Units (HU)")plt.ylabel("Frequency")plt.show()# Show some slice in the middleplt.imshow(first_pati...
(returns_count, 0) AS frequency FROM ( SELECT ss_customer_sk, -- return order ratio COUNT(distinct(ss_ticket_number)) AS orders_count, -- return ss_item_sk ratio COUNT(ss_item_sk) AS orders_items, -- return monetary amount ratio SUM( ss_net_paid ) AS orders_money FROM store_...