importpandasaspdimportnumpyasnpfromsklearnimportdatasetsimportseabornassnsiris=datasets.load _ iris (' target '，) ' filename'] ) x _ train=PD.data frame (iris.dame columns=iris.feature _ names (x _ trainsepallength (cm ) sepath ) 05.13.51.40.214.93.01.40.224.73.21.30.234.63.11.50.245.03.61.40.2 . 1456.73.05
如x _ train.describe (sepa llength (cm ) sepa lwidth (cm ) petal length (cm ) petal length (cm ) petalwidth ) cm ) count 150.000000000000150. 580001.199333 STD0. 8280660.4358661.76661 25 % 5.100000002.800000000.30000000 % 5.8000000000000000.3000000000000000000000000000000000000000000000000000000000000000000000 ength(cm ) 5.843333sepalwidth ) cm ) 3.057333petallength ) cm ) 3.758000petalwidth ) cm ) 1.19933333 dtype 3360 floo gth sepa lllength width ) cm ) sepallength ) cm ) 1.000000-0.1175700.87175701.000000-0.428440-0.366126 petal llength (cm 0.817941-0.3661260.9628651.000000 SNS.heat map (x _ train.corr )、annot=True、cmap=' rdylbu ' (matplotlib.cord ) 源站上可能有防盗链机制。 建议保存图像并直接上传(img-d0nzjovd
y _ train0=PD.data frame (iris.target )=0y_train1=PD.dataframe ) iris.target==1y _ train2=PD.data FATA axis=1) y _ train.columns=iris.target _ namesy _ trainsetosaversicolorvirginica0truefalsefalse1truefalse2truefalse . 145故障安全146故障安全147故障安全148故障安全149故障安全150 rows3 columns
train=PD.concat([x_train，Y_train]，axis=1) trainsepallength(cm ) sepalwidth ) cm ) petal length (cm ) pepet setosaversicolorvirginica 05.13.51.40.2 truefalsefalse 14.93.01.40.2 truefalsefalse 24.73.21.30.2 truefalsefalse efalsetrue 1466.32.55.01.9 falsefalsetrue 1476.53.05.22.0 falsefalsetrue
sns.heatmap(train.corr )，annot=True，cmap='RdYlBu ' ) matplotlib.axes._ subplots.axessubplotat0x1f 90 f 020