数据预处理(excel加载对于数值类数据进行异常值和缺失值的识别与处理)

1、数据预处理（excel加载对于数值类数据进行异常值和缺失值的识别与处理）importpandasaspdimportnumpyasnpfromscipyimportstatsfromsklearnimportdatasetsdataframe=pd.read_excel(rC:UsersShinelonDesktoppopulation.xls,sheet_name=O,header=O)#使用sklearn.datasets中的make_blobs创建仿真数据集feature,_=datasets.make_blobs(n_samples=10,neatures=2,centers=1,ra

2、ndom_state=1)print(查看仿真数据集feature：n)print(feature)print(n*2)#设置索引#dataframe=dataframe.set_index(dataframe姓名)print(dataframe)#print(n*2)#查看有多少行数据#print(有多少行数据(0表示行)：,dataframe.shapeO,end=n)print(有几列(1表示列)：,dataframe.shape1,end=n)print(n*2)#按字符索引查询#print(dataframe.loc小王)#多条件查询#print(多条件查找：n,(dataframe

3、年龄23)&(dataframe爱好=足球)print(n*2)#自动计算数值型的列#print(自动计算数值型的列:n,dataframe.describe()print(n*2)#查看缺失值#print(查看缺失值isnulln)print(对年龄列查看缺失值：,dataframe年龄.isnull().sum()#print(处理缺失值(过滤)notnull:n)print(dataframedataframe年龄.notnull()print(n*2)print(处理缺失值取众数平均数中位数:n)print(fillna.(data.mode()data.mean()data.medi

4、an()n)zongshu=dataframe年龄.mode()#多个众数时采用第一个zongshu=zongshu0dataframe=dataframe.fillna(zongshu,axis=1)print(dataframe)print(n*2)print(处理缺失值插值法:n)print(fillna.(method,limit),method方法limit前向或后向填充时最大的填充范Hn)print(dataframe.fillna(method=ffill)print(n*2)print(*3)print(识别异常值：n)featureO,O=1e5feature0,1=1e5#

5、print(feature)print(四分位法：n)defindicies_of_outerliers(x):q1,q3=np.percentile(x,25,75)iqr=q3-q1lower_bound=q1-1.5*iqrupper_bound=q3+1.5*iqrreturnnp.where(xupper_bound)|(xlower_bound)outelier=indicies_of_outerliers(feature)num_outelier=0foriteminenumerate(outelier):print(item)num_outelier+=1print(共：,nu

6、m_outelier,个异常值)print(n*2)print(异常值处理)print(1.丢弃)foriteminenumerate(outelier):drop_row=item1print(drop_row)after_drop_feature=np.delete(feature,drop_row0,axis=0)print(afterdrop:)print(after_drop_feature)print(2.置空)NaN_outlier_feature=featureforiteminenumerate(outelier):index_outlier=item1print(index_outlier)NaN_outlier_featurein