This topic describes how to sample data using PyODPS.
Prerequisites
Before you start, complete the following steps:
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Activate MaxCompute.
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Activate DataWorks.
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Create a workflow in DataWorks. This example uses a workspace in basic mode.
Procedure
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Download the test dataset and import it into MaxCompute.
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Download and decompress the Iris dataset. Rename the iris.data file to iris.csv.
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Create a table named pyodps_iris and upload the iris.csv dataset. For more information, see Create a table and upload data.
Use the following statement to create the table.
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 'Species' );
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Log in to the DataWorks console and select a region in the upper-left corner.
In the left-side navigation pane, click Workspaces.
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In the Actions column, click Go to > Data Development.
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On the Data Development page, right-click the created workflow and choose Create Node > MaxCompute > PyODPS 2.
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In the Create Node dialog box, enter a node name and click OK.
After you create the PyODPS node, you are automatically redirected to the node editor. Enter the following sampling code.
The following code runs only in projects where project-level schema mode is disabled. If this mode is enabled for your project, you must specify a schema. For more information, see Schema.
# Sampling from odps.df import DataFrame iris = DataFrame(o.get_table('pyodps_iris')) # Partition-based sampling print iris.sample(parts=10).head(5) # Divide the dataset into 10 partitions and return the first partition (partition 0) by default. print iris.sample(parts=10,i=0).head(5) # Explicitly return partition 0. print iris.sample(parts=10,i=[2,5]).head(5) # Divide the dataset into 10 partitions and return partitions 2 and 5. print iris.sample(parts=10,columns=['name','sepalwidth']).head(5) # Perform sampling based on the values in the name and sepalwidth columns. # Count- and fraction-based sampling print iris.sample(n=100).head() # Select 100 rows of data. print iris.sample(frac=0.3).head() # Sample 30% of the data. # Weighted sampling print iris.sample(n=100,weights='sepallength').head() print iris.sample(n=100,weights='sepalwidth',replace=True).head() # 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()In the top toolbar, click Run (
).View the results on the Run Log tab.
Complete output
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
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