本文为您介绍如何进行PyODPS的采样。
前提条件
请提前完成如下操作：
在DataWorks上完成业务流程创建，本例使用DataWorks简单模式。详情请参见创建业务流程。
操作步骤
创建表并导入数据。
下载鸢尾花数据集iris.data，重命名为iris.csv。
创建表pyodps_iris并上传数据集iris.csv。操作方法请参见建表并上传数据。
建表语句如下。
CREATE TABLE if not exists pyodps_iris ( sepallength DOUBLE comment '片长度(cm)', sepalwidth DOUBLE comment '片宽度(cm)', petallength DOUBLE comment '瓣长度(cm)', petalwidth DOUBLE comment '瓣宽度(cm)', name STRING comment '种类' );
- 登录DataWorks控制台。
在左侧导航栏上单击工作空间列表。
选择操作列中的。
在数据开发页面，右键单击已经创建的业务流程，选择。
在新建节点对话框，输入节点名称，并单击确认。
在PyODPS节点输入采样代码。
以下代码仅支持在未开启项目级Schema模式的项目下执行，若您已开启项目级Schema模式执行以下代码时需指定Schema。指定Schema的方法详情，请参见Schema。
#采样 from odps.df import DataFrame iris = DataFrame(o.get_table('pyodps_iris')) #按份数采样 print iris.sample(parts=10).head(5) #分为10，分默认取第0份 print iris.sample(parts=10,i=0).head(5) #手动指定取第0份 print iris.sample(parts=10,i=[2,5]).head(5) #分成10份，取第2和第5份 print iris.sample(parts=10,columns=['name','sepalwidth']).head(5) #根据name和sepalwidth的值做采样 #按比例、条数采样 print iris.sample(n=100).head() #选取100条数据 print iris.sample(frac=0.3).head() #采样30%的数据 #按权重列采样 print iris.sample(n=100,weights='sepallength').head() print iris.sample(n=100,weights='sepalwidth',replace=True).head() #分层采样 print iris.sample(strata='name',n={'Iris-setosa' : 10,'Iris-versicolor' : 10}).head() print iris.sample(strata='name',frac={'Iris-setosa': 0.5,'Iris-versicolor': 0.4}).head()单击运行。
在运行日志中查看运行结果。
完整的运行结果如下。
Collection: ref_0 odps.Table name: WB_BestPractice_dev.`pyodps_iris` schema: sepallength : double # 片长度(cm) sepalwidth : double # 片宽度(cm) petallength : double # 瓣长度(cm) petalwidth : double # 瓣宽度(cm) name : string # 种类 Sample[collection] _input: _parts: Scalar[int8] 10 _i: _replace: Scalar[boolean] False Collection: ref_0 odps.Table name: WB_BestPractice_dev.`pyodps_iris` schema: sepallength : double # 片长度(cm) sepalwidth : double # 片宽度(cm) petallength : double # 瓣长度(cm) petalwidth : double # 瓣宽度(cm) name : string # 种类 Sample[collection] _input: _parts: Scalar[int8] 10 _i: _replace: Scalar[boolean] False Collection: ref_0 odps.Table name: WB_BestPractice_dev.`pyodps_iris` schema: sepallength : double # 片长度(cm) sepalwidth : double # 片宽度(cm) petallength : double # 瓣长度(cm) petalwidth : double # 瓣宽度(cm) name : string # 种类 Sample[collection] _input: _parts: Scalar[int8] 10 _i: _replace: Scalar[boolean] False Collection: ref_0 odps.Table name: WB_BestPractice_dev.`pyodps_iris` schema: sepallength : double # 片长度(cm) sepalwidth : double # 片宽度(cm) petallength : double # 瓣长度(cm) petalwidth : double # 瓣宽度(cm) name : string # 种类 Sample[collection] _input: _parts: Scalar[int8] 10 _i: _sampled_fields: name = Column[sequence(string)] 'name' from collection ref_0 sepalwidth = Column[sequence(float64)] 'sepalwidth' from collection ref_0 _replace: Scalar[boolean] False Executing RandomSample... Command: PAI -name RandomSample -project algo_public -Dreplace="false" -Dlifecycle="1" -DoutputTableName="tmp_pyodps_1570690014_69f3d75d_9537_4c9c_87ea_a5f6ad8d2e07" -DsampleSize="100" -DinputTableName="WB_BestPractice_dev.pyodps_iris"; Instance ID: 20191010064654985g6co9592 Sub Instance: create_output (20191010064700688g5cbn62m_71ee0561_bcc4_4147_b849_f74688353fb6) Sub Instance: without_replacement (20191010064703694g9cbn62m_93a8a15b_ffd1_4afe_8928_19f28455a15c) Try to fetch data from tunnel sepallength sepalwidth petallength petalwidth name 0 5.1 3.5 1.4 0.2 Iris-setosa 1 4.9 3.0 1.4 0.2 Iris-setosa 2 4.7 3.2 1.3 0.2 Iris-setosa 3 4.6 3.1 1.5 0.2 Iris-setosa 4 5.0 3.6 1.4 0.2 Iris-setosa 5 4.6 3.4 1.4 0.3 Iris-setosa 6 4.4 2.9 1.4 0.2 Iris-setosa 7 4.9 3.1 1.5 0.1 Iris-setosa 8 4.8 3.4 1.6 0.2 Iris-setosa 9 4.8 3.0 1.4 0.1 Iris-setosa 10 4.3 3.0 1.1 0.1 Iris-setosa 11 5.1 3.5 1.4 0.3 Iris-setosa 12 5.7 3.8 1.7 0.3 Iris-setosa 13 5.1 3.8 1.5 0.3 Iris-setosa 14 5.1 3.7 1.5 0.4 Iris-setosa 15 4.6 3.6 1.0 0.2 Iris-setosa 16 5.1 3.3 1.7 0.5 Iris-setosa 17 4.8 3.4 1.9 0.2 Iris-setosa 18 5.0 3.4 1.6 0.4 Iris-setosa 19 5.2 3.5 1.5 0.2 Iris-setosa 20 5.2 3.4 1.4 0.2 Iris-setosa 21 4.8 3.1 1.6 0.2 Iris-setosa 22 5.2 4.1 1.5 0.1 Iris-setosa 23 5.5 4.2 1.4 0.2 Iris-setosa 24 4.9 3.1 1.5 0.1 Iris-setosa 25 5.0 3.2 1.2 0.2 Iris-setosa 26 4.4 3.0 1.3 0.2 Iris-setosa 27 5.1 3.4 1.5 0.2 Iris-setosa 28 5.0 3.5 1.3 0.3 Iris-setosa 29 4.5 2.3 1.3 0.3 Iris-setosa .. ... ... ... ... ... 70 7.1 3.0 5.9 2.1 Iris-virginica 71 7.6 3.0 6.6 2.1 Iris-virginica 72 7.3 2.9 6.3 1.8 Iris-virginica 73 7.2 3.6 6.1 2.5 Iris-virginica 74 6.5 3.2 5.1 2.0 Iris-virginica 75 6.8 3.0 5.5 2.1 Iris-virginica 76 5.8 2.8 5.1 2.4 Iris-virginica 77 7.7 3.8 6.7 2.2 Iris-virginica 78 7.7 2.6 6.9 2.3 Iris-virginica 79 7.7 2.8 6.7 2.0 Iris-virginica 80 6.3 2.7 4.9 1.8 Iris-virginica 81 6.7 3.3 5.7 2.1 Iris-virginica 82 6.2 2.8 4.8 1.8 Iris-virginica 83 6.1 3.0 4.9 1.8 Iris-virginica 84 6.4 2.8 5.6 2.1 Iris-virginica 85 7.2 3.0 5.8 1.6 Iris-virginica 86 7.4 2.8 6.1 1.9 Iris-virginica 87 7.9 3.8 6.4 2.0 Iris-virginica 88 6.3 2.8 5.1 1.5 Iris-virginica 89 6.3 3.4 5.6 2.4 Iris-virginica 90 6.4 3.1 5.5 1.8 Iris-virginica 91 6.0 3.0 4.8 1.8 Iris-virginica 92 6.9 3.1 5.4 2.1 Iris-virginica 93 6.9 3.1 5.1 2.3 Iris-virginica 94 5.8 2.7 5.1 1.9 Iris-virginica 95 6.8 3.2 5.9 2.3 Iris-virginica 96 6.7 3.3 5.7 2.5 Iris-virginica 97 6.3 2.5 5.0 1.9 Iris-virginica 98 6.2 3.4 5.4 2.3 Iris-virginica 99 5.9 3.0 5.1 1.8 Iris-virginica [100 rows x 5 columns] Executing RandomSample... Command: PAI -name RandomSample -project algo_public -Dreplace="false" -DsampleRatio="0.3" -DoutputTableName="tmp_pyodps_1570690039_e1867332_72ea_4656_928d_3bd6e31d87c7" -Dlifecycle="1" -DinputTableName="WB_BestPractice_dev.pyodps_iris"; Instance ID: 20191010064720117gmpms38 Sub Instance: create_output (20191010064725740grcbn62m_b338a671_6047_4360_8792_41d2e748e41f) Sub Instance: without_replacement (20191010064728747gtcbn62m_6c9914da_d5c3_4336_b076_163edb1bf48a) Try to fetch data from tunnel sepallength sepalwidth petallength petalwidth name 0 5.1 3.5 1.4 0.2 Iris-setosa 1 4.7 3.2 1.3 0.2 Iris-setosa 2 4.6 3.1 1.5 0.2 Iris-setosa 3 4.8 3.4 1.6 0.2 Iris-setosa 4 5.7 4.4 1.5 0.4 Iris-setosa 5 5.1 3.5 1.4 0.3 Iris-setosa 6 5.0 3.4 1.6 0.4 Iris-setosa 7 5.2 3.4 1.4 0.2 Iris-setosa 8 4.9 3.1 1.5 0.1 Iris-setosa 9 5.5 3.5 1.3 0.2 Iris-setosa 10 4.4 3.2 1.3 0.2 Iris-setosa 11 5.0 3.3 1.4 0.2 Iris-setosa 12 5.7 2.8 4.5 1.3 Iris-versicolor 13 5.2 2.7 3.9 1.4 Iris-versicolor 14 5.0 2.0 3.5 1.0 Iris-versicolor 15 5.6 2.9 3.6 1.3 Iris-versicolor 16 5.8 2.7 4.1 1.0 Iris-versicolor 17 6.1 2.8 4.0 1.3 Iris-versicolor 18 6.6 3.0 4.4 1.4 Iris-versicolor 19 6.8 2.8 4.8 1.4 Iris-versicolor 20 6.0 2.9 4.5 1.5 Iris-versicolor 21 5.4 3.0 4.5 1.5 Iris-versicolor 22 5.7 2.9 4.2 1.3 Iris-versicolor 23 5.7 2.8 4.1 1.3 Iris-versicolor 24 6.3 2.9 5.6 1.8 Iris-virginica 25 4.9 2.5 4.5 1.7 Iris-virginica 26 6.7 2.5 5.8 1.8 Iris-virginica 27 6.4 2.7 5.3 1.9 Iris-virginica 28 6.8 3.0 5.5 2.1 Iris-virginica 29 5.7 2.5 5.0 2.0 Iris-virginica 30 5.8 2.8 5.1 2.4 Iris-virginica 31 6.5 3.0 5.5 1.8 Iris-virginica 32 6.0 2.2 5.0 1.5 Iris-virginica 33 6.3 2.7 4.9 1.8 Iris-virginica 34 7.2 3.2 6.0 1.8 Iris-virginica 35 6.2 2.8 4.8 1.8 Iris-virginica 36 6.1 3.0 4.9 1.8 Iris-virginica 37 6.4 2.8 5.6 2.1 Iris-virginica 38 7.2 3.0 5.8 1.6 Iris-virginica 39 6.3 2.8 5.1 1.5 Iris-virginica 40 6.4 3.1 5.5 1.8 Iris-virginica 41 6.0 3.0 4.8 1.8 Iris-virginica 42 6.8 3.2 5.9 2.3 Iris-virginica 43 6.3 2.5 5.0 1.9 Iris-virginica 44 6.2 3.4 5.4 2.3 Iris-virginica Executing WeightedSample... Command: PAI -name WeightedSample -project algo_public -DinputTableName="WB_BestPractice_dev.pyodps_iris" -DsampleSize="100" -DprobCol="sepallength" -Dreplace="false" -DoutputTableName="tmp_pyodps_1570690063_6a62857e_8f85_4ea7_99ef_08aa259546d4" -Dlifecycle="1"; Instance ID: 20191010064743533gnpms38 Sub Instance: create_output (20191010064748787gkdbn62m_8d47bfb7_e470_4cce_8b69_28811f190083) Sub Instance: without_replacement (20191010064751793gmdbn62m_230a1d26_5c2e_440e_a31d_fe9e63c6f906) Try to fetch data from tunnel sepallength sepalwidth petallength petalwidth name 0 4.9 3.0 1.4 0.2 Iris-setosa 1 4.7 3.2 1.3 0.2 Iris-setosa 2 5.0 3.6 1.4 0.2 Iris-setosa 3 5.4 3.9 1.7 0.4 Iris-setosa 4 5.0 3.4 1.5 0.2 Iris-setosa 5 4.4 2.9 1.4 0.2 Iris-setosa 6 4.8 3.4 1.6 0.2 Iris-setosa 7 4.8 3.0 1.4 0.1 Iris-setosa 8 5.4 3.9 1.3 0.4 Iris-setosa 9 5.1 3.5 1.4 0.3 Iris-setosa 10 5.7 3.8 1.7 0.3 Iris-setosa 11 4.6 3.6 1.0 0.2 Iris-setosa 12 5.0 3.4 1.6 0.4 Iris-setosa 13 5.2 3.5 1.5 0.2 Iris-setosa 14 5.2 3.4 1.4 0.2 Iris-setosa 15 4.7 3.2 1.6 0.2 Iris-setosa 16 4.8 3.1 1.6 0.2 Iris-setosa 17 5.5 4.2 1.4 0.2 Iris-setosa 18 4.9 3.1 1.5 0.1 Iris-setosa 19 5.0 3.2 1.2 0.2 Iris-setosa 20 5.5 3.5 1.3 0.2 Iris-setosa 21 4.9 3.1 1.5 0.1 Iris-setosa 22 5.1 3.4 1.5 0.2 Iris-setosa 23 4.5 2.3 1.3 0.3 Iris-setosa 24 4.8 3.0 1.4 0.3 Iris-setosa 25 5.1 3.8 1.6 0.2 Iris-setosa 26 4.6 3.2 1.4 0.2 Iris-setosa 27 5.3 3.7 1.5 0.2 Iris-setosa 28 5.0 3.3 1.4 0.2 Iris-setosa 29 7.0 3.2 4.7 1.4 Iris-versicolor .. ... ... ... ... ... 70 7.2 3.6 6.1 2.5 Iris-virginica 71 6.4 2.7 5.3 1.9 Iris-virginica 72 5.7 2.5 5.0 2.0 Iris-virginica 73 5.8 2.8 5.1 2.4 Iris-virginica 74 6.4 3.2 5.3 2.3 Iris-virginica 75 7.7 3.8 6.7 2.2 Iris-virginica 76 6.9 3.2 5.7 2.3 Iris-virginica 77 5.6 2.8 4.9 2.0 Iris-virginica 78 7.7 2.8 6.7 2.0 Iris-virginica 79 6.3 2.7 4.9 1.8 Iris-virginica 80 6.7 3.3 5.7 2.1 Iris-virginica 81 7.2 3.2 6.0 1.8 Iris-virginica 82 6.2 2.8 4.8 1.8 Iris-virginica 83 6.1 3.0 4.9 1.8 Iris-virginica 84 7.2 3.0 5.8 1.6 Iris-virginica 85 7.9 3.8 6.4 2.0 Iris-virginica 86 6.4 2.8 5.6 2.2 Iris-virginica 87 6.3 2.8 5.1 1.5 Iris-virginica 88 6.1 2.6 5.6 1.4 Iris-virginica 89 6.3 3.4 5.6 2.4 Iris-virginica 90 6.4 3.1 5.5 1.8 Iris-virginica 91 6.0 3.0 4.8 1.8 Iris-virginica 92 6.9 3.1 5.1 2.3 Iris-virginica 93 5.8 2.7 5.1 1.9 Iris-virginica 94 6.8 3.2 5.9 2.3 Iris-virginica 95 6.7 3.3 5.7 2.5 Iris-virginica 96 6.7 3.0 5.2 2.3 Iris-virginica 97 6.5 3.0 5.2 2.0 Iris-virginica 98 6.2 3.4 5.4 2.3 Iris-virginica 99 5.9 3.0 5.1 1.8 Iris-virginica [100 rows x 5 columns] Executing WeightedSample... Command: PAI -name WeightedSample -project algo_public -DinputTableName="WB_BestPractice_dev.pyodps_iris" -DsampleSize="100" -DprobCol="sepalwidth" -Dreplace="true" -DoutputTableName="tmp_pyodps_1570690082_f55e899c_3cb4_4eeb_ade4_b8cb79e018dc" -Dlifecycle="1"; Instance ID: 2019101006480392g9ers38 Sub Instance: create_output (20191010064808827g9ebn62m_fb70c859_913a_4830_9248_8c8eaf134f1d) Sub Instance: with_replacement (20191010064811833gdebn62m_07544cc2_1d7d_4fa5_972d_196eb6b9f537) sepallength sepalwidth petallength petalwidth name 0 5.1 3.5 1.4 0.2 Iris-setosa 1 4.6 3.4 1.4 0.3 Iris-setosa 2 5.0 3.4 1.5 0.2 Iris-setosa 3 5.0 3.4 1.5 0.2 Iris-setosa 4 4.4 2.9 1.4 0.2 Iris-setosa 5 4.8 3.4 1.6 0.2 Iris-setosa 6 5.8 4.0 1.2 0.2 Iris-setosa 7 5.8 4.0 1.2 0.2 Iris-setosa 8 5.1 3.5 1.4 0.3 Iris-setosa 9 5.1 3.5 1.4 0.3 Iris-setosa 10 5.1 3.5 1.4 0.3 Iris-setosa 11 5.1 3.7 1.5 0.4 Iris-setosa 12 4.6 3.6 1.0 0.2 Iris-setosa 13 4.8 3.4 1.9 0.2 Iris-setosa 14 5.0 3.0 1.6 0.2 Iris-setosa 15 5.0 3.4 1.6 0.4 Iris-setosa 16 5.2 3.4 1.4 0.2 Iris-setosa 17 4.8 3.1 1.6 0.2 Iris-setosa 18 4.8 3.1 1.6 0.2 Iris-setosa 19 5.4 3.4 1.5 0.4 Iris-setosa 20 5.4 3.4 1.5 0.4 Iris-setosa 21 5.4 3.4 1.5 0.4 Iris-setosa 22 5.2 4.1 1.5 0.1 Iris-setosa 23 5.2 4.1 1.5 0.1 Iris-setosa 24 5.5 4.2 1.4 0.2 Iris-setosa 25 5.0 3.2 1.2 0.2 Iris-setosa 26 4.9 3.1 1.5 0.1 Iris-setosa 27 4.4 3.0 1.3 0.2 Iris-setosa 28 5.1 3.4 1.5 0.2 Iris-setosa 29 5.0 3.5 1.3 0.3 Iris-setosa .. ... ... ... ... ... 70 5.6 2.7 4.2 1.3 Iris-versicolor 71 5.7 2.9 4.2 1.3 Iris-versicolor 72 6.3 3.3 6.0 2.5 Iris-virginica 73 7.1 3.0 5.9 2.1 Iris-virginica 74 6.3 2.9 5.6 1.8 Iris-virginica 75 7.3 2.9 6.3 1.8 Iris-virginica 76 7.2 3.6 6.1 2.5 Iris-virginica 77 6.4 2.7 5.3 1.9 Iris-virginica 78 6.8 3.0 5.5 2.1 Iris-virginica 79 6.8 3.0 5.5 2.1 Iris-virginica 80 5.8 2.8 5.1 2.4 Iris-virginica 81 6.2 2.8 4.8 1.8 Iris-virginica 82 6.2 2.8 4.8 1.8 Iris-virginica 83 6.1 3.0 4.9 1.8 Iris-virginica 84 6.4 2.8 5.6 2.1 Iris-virginica 85 7.2 3.0 5.8 1.6 Iris-virginica 86 7.4 2.8 6.1 1.9 Iris-virginica 87 7.4 2.8 6.1 1.9 Iris-virginica 88 7.4 2.8 6.1 1.9 Iris-virginica 89 6.4 3.1 5.5 1.8 Iris-virginica 90 6.0 3.0 4.8 1.8 Iris-virginica 91 6.9 3.1 5.4 2.1 Iris-virginica 92 6.7 3.1 5.6 2.4 Iris-virginica 93 6.7 3.1 5.6 2.4 Iris-virginica 94 6.7 3.1 5.6 2.4 Iris-virginica 95 6.9 3.1 5.1 2.3 Iris-virginica 96 6.9 3.1 5.1 2.3 Iris-virginica 97 6.7 3.0 5.2 2.3 Iris-virginica 98 6.5 3.0 5.2 2.0 Iris-virginica 99 5.9 3.0 5.1 1.8 Iris-virginica [100 rows x 5 columns] Executing StratifiedSample... Command: PAI -name StratifiedSample -project algo_public -Dlifecycle="1" -DoutputTableName="tmp_pyodps_1570690104_6cc52795_2a86_4634_a905_740b3a426d3f" -DsampleSize="Iris-setosa:10,Iris-versicolor:10" -DstrataColName="name" -DinputTableName="WB_BestPractice_dev.pyodps_iris"; Instance ID: 20191010064824633gaco9592 Sub Instance: create_output (20191010064829870g4fbn62m_dfa297c5_23b5_43f5_bb17_83ba8d263630) Sub Instance: stratified_sampling (20191010064831874g7fbn62m_fc9eddb7_42f1_49fe_8206_891ef451fb76) Try to fetch data from tunnel sepallength sepalwidth petallength petalwidth name 0 5.4 3.9 1.7 0.4 Iris-setosa 1 4.3 3.0 1.1 0.1 Iris-setosa 2 5.4 3.9 1.3 0.4 Iris-setosa 3 5.1 3.3 1.7 0.5 Iris-setosa 4 4.7 3.2 1.6 0.2 Iris-setosa 5 4.5 2.3 1.3 0.3 Iris-setosa 6 5.0 3.5 1.6 0.6 Iris-setosa 7 5.1 3.8 1.9 0.4 Iris-setosa 8 4.8 3.0 1.4 0.3 Iris-setosa 9 5.0 3.3 1.4 0.2 Iris-setosa 10 7.0 3.2 4.7 1.4 Iris-versicolor 11 5.5 2.3 4.0 1.3 Iris-versicolor 12 6.5 2.8 4.6 1.5 Iris-versicolor 13 5.6 3.0 4.5 1.5 Iris-versicolor 14 5.7 2.6 3.5 1.0 Iris-versicolor 15 5.5 2.4 3.7 1.0 Iris-versicolor 16 5.0 2.3 3.3 1.0 Iris-versicolor 17 5.6 2.7 4.2 1.3 Iris-versicolor 18 5.7 3.0 4.2 1.2 Iris-versicolor 19 5.1 2.5 3.0 1.1 Iris-versicolor Executing StratifiedSample... Command: PAI -name StratifiedSample -project algo_public -DsampleRatio="Iris-setosa:0.5,Iris-versicolor:0.4" -DoutputTableName="tmp_pyodps_1570690128_a68477cd_19e5_4fe0_bb39_4712f76dd967" -Dlifecycle="1" -DstrataColName="name" -DinputTableName="WB_BestPractice_dev.pyodps_iris"; Instance ID: 20191010064848733gbers38 Sub Instance: create_output (20191010064853918gwfbn62m_4eb22c22_7051_4372_8d13_05c5a417aa87) Sub Instance: stratified_sampling (20191010064855924g0gbn62m_b4242ac7_bd5a_47a8_a1f2_3367a6a101a7) Try to fetch data from tunnel sepallength sepalwidth petallength petalwidth name 0 4.9 3.0 1.4 0.2 Iris-setosa 1 4.7 3.2 1.3 0.2 Iris-setosa 2 5.0 3.6 1.4 0.2 Iris-setosa 3 5.4 3.9 1.7 0.4 Iris-setosa 4 5.0 3.4 1.5 0.2 Iris-setosa 5 5.4 3.7 1.5 0.2 Iris-setosa 6 4.8 3.4 1.6 0.2 Iris-setosa 7 4.8 3.0 1.4 0.1 Iris-setosa 8 5.8 4.0 1.2 0.2 Iris-setosa 9 5.4 3.4 1.7 0.2 Iris-setosa 10 5.1 3.7 1.5 0.4 Iris-setosa 11 4.8 3.4 1.9 0.2 Iris-setosa 12 5.0 3.0 1.6 0.2 Iris-setosa 13 5.0 3.4 1.6 0.4 Iris-setosa 14 5.2 3.5 1.5 0.2 Iris-setosa 15 5.2 3.4 1.4 0.2 Iris-setosa 16 4.7 3.2 1.6 0.2 Iris-setosa 17 5.2 4.1 1.5 0.1 Iris-setosa 18 5.0 3.2 1.2 0.2 Iris-setosa 19 5.1 3.4 1.5 0.2 Iris-setosa 20 4.5 2.3 1.3 0.3 Iris-setosa 21 5.0 3.5 1.6 0.6 Iris-setosa 22 5.1 3.8 1.9 0.4 Iris-setosa 23 5.1 3.8 1.6 0.2 Iris-setosa 24 5.3 3.7 1.5 0.2 Iris-setosa 25 7.0 3.2 4.7 1.4 Iris-versicolor 26 6.4 3.2 4.5 1.5 Iris-versicolor 27 6.9 3.1 4.9 1.5 Iris-versicolor 28 6.5 2.8 4.6 1.5 Iris-versicolor 29 5.7 2.8 4.5 1.3 Iris-versicolor 30 6.6 2.9 4.6 1.3 Iris-versicolor 31 5.6 2.9 3.6 1.3 Iris-versicolor 32 5.6 3.0 4.5 1.5 Iris-versicolor 33 5.6 2.5 3.9 1.1 Iris-versicolor 34 6.1 2.8 4.7 1.2 Iris-versicolor 35 6.8 2.8 4.8 1.4 Iris-versicolor 36 5.5 2.4 3.8 1.1 Iris-versicolor 37 5.5 2.4 3.7 1.0 Iris-versicolor 38 6.0 2.7 5.1 1.6 Iris-versicolor 39 5.6 3.0 4.1 1.3 Iris-versicolor 40 5.5 2.6 4.4 1.2 Iris-versicolor 41 6.1 3.0 4.6 1.4 Iris-versicolor 42 5.7 3.0 4.2 1.2 Iris-versicolor 43 5.7 2.9 4.2 1.3 Iris-versicolor 44 6.2 2.9 4.3 1.3 Iris-versicolor
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