This topic describes how to use a PyODPS node to sample data.
Prerequisites
The following operations are performed:
MaxCompute and DataWorks have been activated. For more information, see Activate MaxCompute and DataWorks and Purchase guide.
A workflow is created in the DataWorks console. In this example, a workflow is created for a DataWorks workspace in basic mode. For more information, see Create a workflow.
Procedure
Download the test dataset and import it to MaxCompute.
Download the dataset iris.data from iris and rename iris.data as iris.csv.
Create a table named pyodps_iris and upload the dataset iris.csv to the table. For more information, see Create tables and upload data.
Sample statement:
CREATE TABLE if not exists pyodps_iris ( sepallength DOUBLE comment 'sepal length (cm)', sepalwidth DOUBLE comment 'sepal width (cm)', petallength DOUBLE comment ''petal length (cm)', petalwidth DOUBLE comment 'petal width (cm)', name STRING comment 'type' );
Log on to the DataWorks console.
In the left-side navigation pane, click Workspace.
Find your workspace, and choose in the Actions column.
On the DataStudio page, right-click the created workflow and choose .
In the Create Node dialog box, specify Name and click Confirm.
After creating a PyODPS node, you will automatically enter the node editing page and input the following sample code.
The following example code is only supported in projects where the project-level schema mode is not enabled. If you have already enabled the project-level schema mode, you must specify a schema when executing the code. For more information on how to specify the schema, see Schema.
# Sample data. from odps.df import DataFrame iris = DataFrame(o.get_table('pyodps_iris')) # Sample data by part. print iris.sample(parts=10).head(5) # Divide the target data into 10 parts. Part 0 is obtained by default. print iris.sample(parts=10,i=0).head(5) # Divide the target data into 10 parts and obtain part 0. print iris.sample(parts=10,i=[2,5]).head(5) # Divide the target data into 10 parts and obtain part 2 and part 5. print iris.sample(parts=10,columns=['name','sepalwidth']).head(5) # Divide the target data into 10 parts and sample data based on the values of name and sepalwidth. # Sample data based on the specified number of data records or proportion. print iris.sample(n=100).head() # Obtain 100 data records. print iris.sample(frac=0.3).head() # Obtain 30% of the target data. # Sample data based on the weight column. print iris.sample(n=100,weights='sepallength').head() print iris.sample(n=100,weights='sepalwidth',replace=True).head() # Perform stratified sampling. 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()Click the Run icon in the toolbar.
View the running result of the PyODPS 2 node on the Run Log tab.
In this example, the following information appears on the Run Log tab:
Collection: ref_0 odps.Table name: WB_BestPractice_dev.`pyodps_iris` schema: sepallength : double # Sepal length (cm) sepalwidth : double # Sepal width (cm) petallength : double # Petal length (cm) petalwidth : double # Petal width (cm) name : string # Type 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 # Sepal length (cm) sepalwidth : double # Sepal width (cm) petallength : double # Petal length (cm) petalwidth : double # Petal width (cm) name : string # Type 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 # Sepal length (cm) sepalwidth : double # Sepal width (cm) petallength : double # Petal length (cm) petalwidth : double # Petal width (cm) name : string # Type 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 # Sepal length (cm) sepalwidth : double # Sepal width (cm) petallength : double # Petal length (cm) petalwidth : double # Petal width (cm) name : string # Type 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