以下代码示例展示了一个基本的preprocess函数实现: importpandasaspdfromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportStandardScaler,LabelEncoderdefpreprocess(dataframe):# 处理缺失值dataframe=dataframe.dropna()# 类别编码le=LabelEncoder()forcolumnindataframe.select_dtypes(include=['object...
OneHotEncoderfromsklearn.composeimportColumnTransformerfromsklearn.pipelineimportPipeline# 创建示例数据data={'Area':[1500,2500,None,3000,3500],'Location':['Suburb','City','Suburb','City','Suburb'],'Price':[300000,500000,400000,None,600000]}df=pd.DataFrame(data)# 分离特征和目标...
get_dataset_1M()函数实现了从文件中读取MovieLens 1M数据集,将其转换为用户-电影评分矩阵,并返回训练集和测试集。具体来说,该函数使用pandas库中的read_csv函数读取训练集和测试集文件,并将其存储在名为training_set和test_set的DataFrame中。然后,这两个数据集被转换为numpy数组,以便进行进一步处理。 在获取训练集...
如果您加载它,那么您将拥有类似DataFrame Y M 1 2 30 2019 1 A E H1 2020 2 B F I2 2021 3 C G J Set multi-index usinig year and month df = df.set_index(['Y','M']) 1 2 3Y M 2019 1 A E H2020 2 B F I2021 3 C G J 使用stack()对其进行重塑 df = df.stack() Y M...
Bioconductor 是与特定版本的 R 绑定的，正常来说当 Bioconductor 的包都来自同一版本时，它们的效果最佳...
dataframe2 = gpd.read_file('./Meta_data/NYC/Administrative_data/Area/Area.shp') # dataframe2 = dataframe2.to_crs('EPSG:4326') seleceted_colums2 = ['OBJECTID', 'zone', 'geometry'] area_dataframe = dataframe2[seleceted_colums2] ## area_dataframe = area_dataframe[area_dataframe['...
# df = pd.DataFrame(pleading_data, columns=['Date', 'Text']) question = f""" I have a legal case description and require two distinct pieces of information: 1. Summary: Please provide a concise summary of the case, focusing on the facts and events. Exclude any information about the ...
问ValueError:管道不包含名为[‘preprocess_companies_node’的节点]EN问题是，在使用0.17.3+时，您...
()# Take the set of files and read them all into a single pandas dataframeinput_files=[os.path.join(args.train,file)forfileinos.listdir(args.train)]iflen(input_files)==0:raiseValueError(('There are no files in {}.\n'+'This usually ...
any integer value between 1980 and 2020 'Price': np.random.normal(loc=110000, scale=20000, size=size), # normally distributed prices 'Type': np.random.choice(['Single Family', 'Townhouse', 'Condo', 'Duplex'], size) # type of the house } # Create a DataFrame df = pd.DataF...