帮我修改错误import pandas import pandas as pd import np import numpy np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) import matplotlib.pyplot matplotlib.pyplot.rcParams['font.sans-serif'] = 'SimHei' matplotlib.pyplot.rcParams['axes.unicode_minus'] = False data = numpy.load('D:\\temp\\test\\国民经济核算季度数据.npz') name = data['columns'] values = data['values'] matplotlib.pyplot.figure(figsize = (8,7)) matplotlib.pyplot.scatter(values[:,0],values[:,3], marker = 'o',c = '#0099FF') matplotlib.pyplot.scatter(values[:,0],values[:,4], marker = '*',c = '#00FF00') matplotlib.pyplot.scatter(values[:,0],values[:,5], marker = '+',c = '#FF3300') matplotlib.pyplot.xlabel('年份') matplotlib.pyplot.ylabel('生产总值（亿元）') matplotlib.pyplot.xticks(range(0,70,4),values[range(0,70,4),1],rotation=45) matplotlib.pyplot.title('2000-2017年各产业季度生产总值散点图') matplotlib.pyplot.legend(['第一产业','第二产业','第三产业']) matplotlib.pyplot.savefig('D:\\temp\\test\\2022231168许开国c.png') matplotlib.pyplot.show name1 = pandas.DataFrame(list(data['columns'])) values1 = pandas.DataFrame(list(data['values'])) with pd.ExcelWriter('D:\\temp\\test\\2022231201许开国c.xlsx') as writer: name1.to_excel(writer, sheet_name= 'Sheet1', index=False, header=True) values1.to_excel(writer, sheet_name='Sheet2', index=False, header=True)

numpy库与pandas库axis=0，axis= 1轴的用法详解

对数据进行操作时，经常...In [1]: import numpy as np #生成一个3行4列的数组 In [2]: a = np.arange(12).reshape(3,4) In [3]: a Out[3]: array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) #axis= 0 对a

pandas to_excel 添加颜色操作

import pandas as pd import numpy as np 接下来，创建一个 DataFrame 示例，这里我们设置了二级标题： python columns = [['A', 'A', 'B', 'B', 'C'], ['a', 'b', 'c', 'd', 'e']] demo_df = pd.DataFrame...

Exploratory_Data_Analysis_Visualization_Python：使用PyData生态系统进行数据分析和可视化：Pandas，Matplotlib Numpy和Seaborn

Python中的探索性数据分析...一维和二维数据的NumPy和Pandas 异同皮尔逊的r函数脾气暴躁的创建数组索引和选择基本操作大熊猫子集数据框使用索引缺失数据通过...分组合并数据框（合并，联接，串联）数据透

优化以下代码使其提取csv文件中有小数点的行import numpy as np import pandas as pd data = pd.read_csv(r'C:\Users\Admin\Desktop\bisaifiles\population.csv',header=None) data decimal_data = df[df.dtypes == float] print(decimal_data.index.tolist())

import pandas as pd # 读取csv文件 data = pd.read_csv(r'C:\Users\Admin\Desktop\bisaifiles\population.csv', header=None) # 将数据类型转换为float data = data.apply(pd.to_numeric, errors='coerce') # ...

import numpy as np import matplotlib.pyplot as plt import pandas as pd dataset = pd.read_csv('E:\Program Files (x86)\Apriori-Association-Rules-main\Market_Basket_Optimisation.csv', header = None) print(dataset)运行后，可视化显示各类商品的累计购买量应该如何写代码

import numpy as np import matplotlib.pyplot as plt import pandas as pd # 读入数据集 dataset = pd.read_csv('E:\Program Files (x86)\Apriori-Association-Rules-main\Market_Basket_Optimisation.csv', ...

import numpy as np import pandas as pd def localmin(points, pixel_size): x_min = np.min(points[:, 0]) y_min = np.min(points[:, 1]) x_max = np.max(points[:, 0]) y_max = np.max(points[:, 1]) w = x_max - x_min h = y_max - y_min wn = w // pixel_size + 1 hn = h // pixel_size + 1 x_bins = np.array([i * pixel_size for i in range(int(wn + 1))]) y_bins = np.array([i * pixel_size for i in range(int(hn + 1))]) df = pd.DataFrame(points, columns=['x', 'y', 'z', 'classification', 'indices']) df['x_bin'] = pd.cut(df['x'], bins=x_bins) df['y_bin'] = pd.cut(df['y'], bins=y_bins) result = df.groupby(['x_bin', 'y_bin']).apply(lambda x: x.loc[x['z'].idxmin()])[['x', 'y', 'z', 'classification', 'indices']] return result a = np.random.random([100,3]) b = np.random.random([100, 1])//0.5 c = np.arange(1000).reshape([100,1]) a = np.concatenate([a,b,c],axis=-1) d = localmin(a,0.2) 如何从a中删除d中的点

import numpy as np import pandas as pd def localmin(points, pixel_size): x_min = np.min(points[:, 0]) y_min = np.min(points[:, 1]) x_max = np.max(points[:, 0]) y_max = np.max(points[:, 1]) ...

import pandas as pd import numpy as np #读取训练样本 data = pd . read_csv ("D:\\数据挖掘\\实验\\实验六\\train.csv") #处理 data _ received 属性并转为时间类型 data ['date_received']= data['date_received']. astype('str').apply(lambda x :x.split ('.')[0]) data ['date_received']= pd . to_datetime ( data['date_received']) 报错 data ['date']= pd.to_datetime(data['date']) ^ SyntaxError: invalid syntax解决

这个错误的原因是你的代码中有语法错误，可能是因为你在引入 Pandas 和 NumPy 库时没有使用正确的语法。你可以尝试按照下面的方式修改代码： python import pandas as pd import numpy as np # 读取训练样本 ...

import pandas as pd import numpy as np from csv import reader from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split filename = 'D:/111/20news-18828.tar/20newsgroups.srv' with open(filename, 'rt', encoding='UTF-8') as file: A = file.readlines() data = np.array(A) df = pd.DataFrame(data, columns=['text']) # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(df['text'], df['category'], random_state=42) # 对文本进行特征提取 tfidf = TfidfVectorizer(stop_words='english') X_train_tfidf = tfidf.fit_transform(X_train.astype(str)) X_test_tfidf = tfidf.transform(X_test.astype(str)) # 使用多项式朴素贝叶斯模型进行训练和预测 clf = MultinomialNB() clf.fit(X_train_tfidf, y_train) y_pred = clf.predict(X_test_tfidf.toarray()) # 输出模型的准确率 print('Accuracy:', accuracy_score(y_test, y_pred))是否有错误

import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn...

如何修改使得import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split filename = 'D:/111/20news-18828.tar/20newsgroups.srv' with open(filename, 'r', encoding='UTF-8') as file: A = file.readlines() data = np.array(A) df = pd.DataFrame(data, columns=['text'])df['category'] = df['text'].apply(lambda x: x.split('\t')[0]) df['text'] = df['text'].apply(lambda x: x.split('\t')[1]) # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(df['text'], df['category'], random_state=42) # 对文本进行特征提取 tfidf = TfidfVectorizer(stop_words='english') X_train_tfidf = tfidf.fit_transform(X_train.astype(str)) X_test_tfidf = tfidf.transform(X_test.astype(str)) # 使用多项式朴素贝叶斯模型进行训练和预测 clf = MultinomialNB() clf.fit(X_train_tfidf, y_train) y_pred = clf.predict(X_test_tfidf) # 输出模型的准确率 print('Accuracy:', accuracy_score(y_test, y_pred))能够从我的文件中分类

import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn.model_selection ...

import pandas as pd import numpy as np # 读取数据 data = pd.read_excel('数据.xlsx') # 提取指标列 indicators = data.iloc[1:50:2, 2:].values indicators=pd.DataFrame(indicators) print(indicators) # 数据归一化 normalized_data = (indicators - indicators.min(axis=0)) / (indicators.max(axis=0) - indicators.min(axis=0)) print(normalized_data) # 灰色关联度计算函数 def grey_correlation(x, y): """ 计算两列数据的灰色关联度 :param x: 第一列数据 :param y: 第二列数据 :return: 灰色关联度 """ n = len(x) lambda_value = 0.5 # 灰色关联度衡量因子，默认取0.5 # 累加生成级比累加矩阵 c = np.abs(x - y) c_max, c_min = np.max(c), np.min(c) rho = (c_min + lambda_value * c_max) / (c + lambda_value * c_max) r = np.sum(rho) / n return r # 计算各指标之间的灰色关联度 corr_matrix = np.zeros((len(normalized_data[0]), len(normalized_data[0]))) for i in range(len(normalized_data[0])): for j in range(len(normalized_data[0])): corr_matrix[i, 1] = grey_correlation(normalized_data[:, i], normalized_data[:, j]) # 输出灰色关联度矩阵 print(corr_matrix)

import pandas as pd import numpy as np # 读取数据 data = pd.read_excel('数据.xlsx') # 提取指标列 indicators = data.iloc[1:50:2, 2:].values indicators = pd.DataFrame(indicators) print(indicators) #...

import pandas as pd from numpy import * food=pd.read_csv('hot-spicy pot.csv') food.head(10) food['taste'].head(5) import pandas as pd from numpy import * from sklearn.feature_extraction.text import TfidfVectorizer #1.读取数据 print('Step1:read data...') food=pd.read_csv('hot-spicy pot.csv') food.head(10) #2.将菜品的描述构造成TF-IDF向量 print('Step2:make TF-IDF...') tfidf=TfidfVectorizer(stop_words='english') tfidf_matrix=tfidf.fit_transform(food['taste']) tfidf_matrix.shape #3.计算两个菜品的余弦相似度 print('Step3:compute similarity...') from sklearn.metrics.pairwise import pairwise_distances cosine_sim=pairwise_distances(tfidf_matrix,metric="cosine") #推荐函数，输出与其最相似的10个菜品 def content_based_recommendation(name,consine_sim=cosine_sim): idx=indices[name] sim_scores=list(enumerate(cosine_sim[idx])) sim_scores=sorted(sim_scores,key=lambda x:x[1]) sim_scores=sim_scores[1:11] food_indices=[i[0]for i in sim_scores] return food['name'].iloc[food_indices] #4.根据菜名及特点进行推荐 print('Step4:recommend by name...') #建立索引，方便使用菜名进行数据访问 indices=pd.Series(food.index,index=food['name']).drop_duplicates() result=content_based_recommendation("celery") result from sklearn.metrics.pairwise import pairwise_distances cosine_sim=pairwise_distances(tfidf_matrix,metric="cosine") tfidf_matrix.shape 请用中文逐行详细注释，这段代码

import pandas as pd from numpy import * from sklearn.feature_extraction.text import TfidfVectorizer 2. 读取数据，并将菜品的描述构造成 TF-IDF 向量 python food=pd.read_csv('hot-spicy pot.csv...

import numpy as np from datetime import datetime, timedelta import pandas as pd test = pd.read_excel("C:\\Users\\1data\\Desktop\\DBM成本收入核算\\test.xlsx") start_date = pd.to_datetime(test['合同开始日期']) end_date = pd.to_datetime(test['合同截止日期']) test['合同周期月数'] = round((end_date - start_date) / np.timedelta64(1, 'M')) start_date_col = '合同开始日期' end_date_col = '合同截止日期' new_col = '日期' for index, row in test.iterrows(): start_date = pd.to_datetime(row[start_date_col]) end_date = pd.to_datetime(row[end_date_col]) date_list = [] if start_date.day <= 15: while start_date <= end_date: date_list.append(start_date) start_date = start_date + timedelta(days=30) else: start_date = start_date + timedelta(days=30) while start_date <= end_date + timedelta(days=30): date_list.append(start_date) start_date = start_date + timedelta(days=30) test.loc[index, new_col] = ','.join([str(date.date()) for date in date_list]) df_tmp=test['日期'].str.split(',',expand=True) df_tmp=df_tmp.stack() df_tmp = df_tmp.reset_index(level=1,drop=True) df_tmp.name='日期' df_new = test.drop(['日期'], axis=1).join(df_tmp).reset_index().drop(columns='index') print(df_new) df_new.to_excel('income_test.xlsx',index=False) 将这段代码导出的excel中合同开始日期字段不显示时分秒且新增日期字段的取年月的字段合同周期年月

import numpy as np from datetime import datetime, timedelta import pandas as pd test = pd.read_excel("C:\\Users\\1data\\Desktop\\DBM成本收入核算\\test.xlsx") start_date = pd.to_datetime(test['...

优化这个代码import xarray as xr import netCDF4 as nc import pandas as pd import numpy as np import datetime import matplotlib.pyplot as plt import cartopy.mpl.ticker as cticker import cartopy.crs as ccrs import cartopy.feature as cfeature ds = xr.open_dataset('C:/Users/cindy/Desktop/SP.nc', engine='netcdf4') # 读取原始数据 ds_temp = xr.open_dataset('C:/Users/cindy/Desktop/SP.nc') # 区域提取* south_asia = ds_temp.sel(latitude=slice(38, 28), longitude=slice(75, 103)) indian_ocean = ds_temp.sel(latitude=slice(5, -15), longitude=slice(60, 100)) # 高度插值 south_asia_200hpa = south_asia.t.interp(level=200) indian_ocean_200hpa = indian_ocean.t.interp(level=200) south_asia_400hpa = south_asia.t.interp(level=400) indian_ocean_400hpa = indian_ocean.t.interp(level=400) # 区域平均 TTP = south_asia_400hpa.mean(dim=('latitude', 'longitude'))#.values TTIO = indian_ocean_400hpa.mean(dim=('latitude', 'longitude'))# TTP_200hpa = south_asia_200hpa.mean(dim=('latitude', 'longitude')) TTIO_200hpa = indian_ocean_200hpa.mean(dim=('latitude', 'longitude')) tlup=(TTP-TTIO)-(TTP_200hpa-TTIO_200hpa)-(-5.367655815) # 定义画图区域和投影方式 fig = plt.figure(figsize=[10, 8]) ax = plt.axes(projection=ccrs.PlateCarree()) # 添加地图特征 ax.set_extent([60, 140, -15, 60], crs=ccrs.PlateCarree()) ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidths=0.5) ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='lightgray') ax.add_feature(cfeature.OCEAN.with_scale('50m'), facecolor='white') # 画距平场 im = ax.contourf(TTP_200hpa, TTP, tlup, cmap='coolwarm', levels=np.arange(-4, 4.5, 0.5), extend='both') # 添加色标 cbar = plt.colorbar(im, ax=ax, shrink=0.8) cbar.set_label('Temperature anomaly (°C)') # 添加经纬度坐标轴标签 ax.set_xticks(np.arange(60, 105, 10), crs=ccrs.PlateCarree()) ax.set_yticks(np.arange(-10, 40, 10), crs=ccrs.PlateCarree()) lon_formatter = cticker.LongitudeFormatter() lat_formatter = cticker.LatitudeFormatter() ax.xaxis.set_major_formatter(lon_formatter) ax.yaxis.set_major_formatter(lat_formatter) # 添加标题和保存图片 plt.title('Temperature anomaly at 400hPa over South Asia and the Indian Ocean') plt.savefig('temperature_anomaly.png', dpi=300) plt.show()

import xarray as xr, netCDF4 as nc, pandas as pd, numpy as np, datetime import matplotlib.pyplot as plt import cartopy.mpl.ticker as cticker, cartopy.crs as ccrs, cartopy.feature as cfeature 2. ...

报错ValueError: np.nan is an invalid document, expected byte or unicode string. 怎么修改import pandas as pd from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 读取电影评论数据集 data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk') x = v.fit_transform(df['eview'].apply(lambda x: np.str_(x))) # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42) # 创建CountVectorizer对象进行词频统计和向量化 count_vectorizer = CountVectorizer() X_train_count = count_vectorizer.fit_transform(X_train) X_test_count = count_vectorizer.transform(X_test) # 创建TfidfVectorizer对象进行TF-IDF计算和向量化 tfidf_vectorizer = TfidfVectorizer() X_train_tfidf = tfidf_vectorizer.fit_transform(X_train) X_test_tfidf = tfidf_vectorizer.transform(X_test) # 创建逻辑回归分类器并在CountVectorizer上进行训练和预测 classifier_count = LogisticRegression() classifier_count.fit(X_train_count, y_train) y_pred_count = classifier_count.predict(X_test_count) accuracy_count = accuracy_score(y_test, y_pred_count) print("Accuracy using CountVectorizer:", accuracy_count) # 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测 classifier_tfidf = LogisticRegression() classifier_tfidf.fit(X_train_tfidf, y_train) y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf) accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf) print("Accuracy using TfidfVectorizer:", accuracy_tfidf)

import numpy as np from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import Logistic...

import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity data = pd.read_excel('工作簿1.xlsx') feature_columns = ['城市', '职业', '幸运色'] # 选择适当的列作为项目的特征 data['combined_features'] = data[feature_columns].apply(lambda x: ' '.join(x.dropna().astype(str)), axis=1) vectorizer = TfidfVectorizer() item_features = vectorizer.fit_transform(data['combined_features']) similarity_matrix = cosine_similarity(item_features) user_id = 1 # 假设用户ID为1 user_ratings = data[data['user_id'] == user_id]['rating'] user_ratings = user_ratings.reset_index(drop=True) user_ratings = user_ratings.values.reshape(-1, 1) recommended_items = pd.Series(user_ratings[:len(data)], index=data['幸运色']).sort_values(ascending=False) print(recommended_items.head(10)) 报错ValueError: Length of values (1) does not match length of index (1000) 如何进行修改

这个错误是由于用户评分数据的长度与数据集的长度不匹配导致的。根据代码的逻辑，代码尝试使用用户的评分数据来预测用户对所有项目的评分，并输出推荐的项目列表。根据错误信息提示，用户评分数据的长度为1，而...

from mlxtend.frequent_patterns import apriori import numpy as np from future import print_function import pandas as pd inputfile = '/Users/mac/Desktop/apr.txt' #输入事务集文件 data1 = pd.read_csv(inputfile, header=None, dtype = object) print(u'\n转换原始数据至0-1矩阵...') ct = lambda x : pd.Series(1, index = x[pd.notnull(x)]) #转换0-1矩阵的过渡函数 b = map(ct, data1.values()) #用map方式执行 data1 = pd.DataFrame(b).fillna(0) #实现矩阵转换，空值用0填充 print('\n转换完毕') del b #删除中间变量b，节省内存 support = 0.06 #最小支持度 confidence = 0.75 #最小置信度 ms = '---' #连接符，默认'--'，用来区分不同元素，如A--B。需要保证原始表格中不含有该字符 print(u'\n开始搜索关联规则...') find_rule(data1, support, confidence, ms) print(u'\n搜索完成') 报错'numpy.ndarray' object is not callable 求修改代码

import numpy as np from __future__ import print_function import pandas as pd inputfile = '/Users/mac/Desktop/apr.txt' #输入事务集文件 data1 = pd.read_csv(inputfile, header=None, dtype = object) ...

import pandas as pd import numpy as np import lyapunov import matplotlib.pyplot as plt # 读取csv文件 data = pd.read_csv('77.csv', header=None) # 将第一列设为时间戳 data[0] = pd.to_datetime(data[0], format='%Y%m%d%H%M%S') # 将第二列数据转换为numpy数组 values = np.array(data[1]) # 计算lyapunov置数 lyap = lyapunov.mLE(values, tau=1, embDim=5) # 绘图 plt.plot(lyap) plt.show()以上代码运行时报错如下ValueError: time data '1' does not match format '%Y%m%d%H%M%S' (match)，改写代码解决问题

可以尝试将时间戳的格式修改为正确的格式，例如： # 读取csv文件 data = pd.read_csv('77.csv', header=None) # 将第一列设为时间戳，修改日期格式为"%Y-%m-%d %H:%M:%S" data[0] = pd.to_datetime(data[0], ...

import pandas as pd from numpy import * movies=pd.io.parsers.read_csv('F:\python练习/1movie_metadata.csv') movies.head() movies['genres'].head() from sklearn.feature_extraction.text import TfidfVectorizer tfidf=TfidfVectorizer(stop_words='english') movies['genres']=movies['genres'].fillna('') tfidf_matrix=tfidf.fit_transform(movies['genres']) tfidf_matrix.shape from sklearn.metrics.pairwise import linear_kernel cosine_sim=linear_kernel(tfidf_matrix,tfidf_matrix) indices=pd.Series(movies.index,index=movies['movie_title']).drop_duplicates() def get_recommendation(title,consine_sim=cosine_sim): idx=indices[title] sim_scores=list(enumerate(cosine_sim[idx])) sim_scores=sorted(sim_scores,key=lambda x:x[1],reverse=True) sim_scores=sim_scores[1:11] movie_indices=[i[0]for i in sim_scores] return print(movies['movie_title'].iloc[movie_indices]) get_recommendation('Avatar?') 几个参数

这段代码中涉及到几个参数： 1. stop_words='english'：在使用TfidfVectorizer时，将英语中的常用词作为停用词，不进行考虑。 2. fillna('')：将数据框中的缺失值填充为一个空字符串。 3. cosine_sim=linear_...

import pandas as pd import matplotlib import numpy as np import matplotlib.pyplot as plt import jieba as jb import re from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.feature_selection import chi2 import numpy as np from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.naive_bayes import MultinomialNB def sigmoid(x): return 1 / (1 + np.exp(-x)) import numpy as np #定义删除除字母,数字，汉字以外的所有符号的函数 def remove_punctuation(line): line = str(line) if line.strip()=='': return '' rule = re.compile(u"[^a-zA-Z0-9\u4E00-\u9FA5]") line = rule.sub('',line) return line def stopwordslist(filepath): stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()] return stopwords df = pd.read_csv('./online_shopping_10_cats/online_shopping_10_cats.csv') df=df[['cat','review']] df = df[pd.notnull(df['review'])] d = {'cat':df['cat'].value_counts().index, 'count': df['cat'].value_counts()} df_cat = pd.DataFrame(data=d).reset_index(drop=True) df['cat_id'] = df['cat'].factorize()[0] cat_id_df = df[['cat', 'cat_id']].drop_duplicates().sort_values('cat_id').reset_index(drop=True) cat_to_id = dict(cat_id_df.values) id_to_cat = dict(cat_id_df[['cat_id', 'cat']].values) #加载停用词 stopwords = stopwordslist("./online_shopping_10_cats/chineseStopWords.txt") #删除除字母,数字，汉字以外的所有符号 df['clean_review'] = df['review'].apply(remove_punctuation) #分词，并过滤停用词 df['cut_review'] = df['clean_review'].apply(lambda x: " ".join([w for w in list(jb.cut(x)) if w not in stopwords])) tfidf = TfidfVectorizer(norm='l2', ngram_range=(1, 2)) features = tfidf.fit_transform(df.cut_review) labels = df.cat_id X_train, X_test, y_train, y_test = train_test_split(df['cut_review'], df['cat_id'], random_state = 0) count_vect = CountVectorizer() X_train_counts = count_vect.fit_transform(X_train) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) 已经写好以上代码，请补全train和test函数

以下是train和test函数的代码： python def train(X_train_tfidf, y_train): clf = MultinomialNB().fit(X_train_tfidf, y_train) return clf def test(clf, X_test): X_test_counts = count_vect.transform...

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