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Sort data in PyODPS

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This topic describes how to sort data in a PyODPS DataFrame by using the sort() method. Each call to the sort() method is compiled into a MaxCompute SQL statement that contains an ORDER BY clause.

Prerequisites

Before you start, complete the following steps:

Procedure

  1. Download the test dataset and import it into MaxCompute.

    1. Download and decompress the Iris dataset. Rename the iris.data file to iris.csv.

    2. 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'
);

  2. Log in to the DataWorks console and select a region in the upper-left corner.

  3. In the left-side navigation pane, click Workspaces.

  4. In the Actions column, click Go to > Data Development.

  5. On the Data Development page, right-click the created workflow and choose Create Node > MaxCompute > PyODPS 2.

  6. In the Create Node dialog box, enter a node name and click OK.

  7. In the PyODPS node, enter the following code.

    from  odps.df import DataFrame

iris = DataFrame(o.get_table('pyodps_iris'))

# Sort the data by a single column. By default, data is sorted in ascending order.
print(iris.sort('sepalwidth').head(5))

# The following examples show two ways to sort data in descending order.

# Method 1: Set the ascending parameter to False.
print(iris.sort('sepalwidth', ascending=False).head(5))

# Method 2: Add a hyphen (-) prefix to the column name.
print(iris.sort(-iris.sepalwidth).head(5))

# Sort the data by multiple columns.
print(iris.sort(['sepalwidth', 'petallength']).head(5))

# To use a different sort order for each column, pass a list of Booleans to the `ascending` parameter.
# The length of the list must match the number of columns to sort.
print(iris.sort(['sepalwidth', 'petallength'], ascending=[True, False]).head(5))
print(iris.sort(['sepalwidth', -iris.petallength]).head(5))

  8. Click Run.

  9. View the results on the Log tab.运行日志

    The following is the complete run log:

    Sql compiled:
CREATE TABLE tmp_pyodps_d1b06785_dc18_4288_ad34_de860de1be08 LIFECYCLE 1 AS
SELECT *
FROM WB_BestPractice_dev.`pyodps_iris` t1
ORDER BY sepalwidth
LIMIT 10000
Instance ID: 20191010061554817gwml0lim

   sepallength  sepalwidth  petallength  petalwidth             name
0          5.0         2.0          3.5         1.0  Iris-versicolor
1          6.0         2.2          5.0         1.5   Iris-virginica
2          6.2         2.2          4.5         1.5  Iris-versicolor
3          6.0         2.2          4.0         1.0  Iris-versicolor
4          5.5         2.3          4.0         1.3  Iris-versicolor
Sql compiled:
CREATE TABLE tmp_pyodps_3cb90bb2_fb95_43fb_ae84_f2b5a27d72dc LIFECYCLE 1 AS
SELECT *
FROM WB_BestPractice_dev.`pyodps_iris` t1
ORDER BY sepalwidth DESC
LIMIT 10000
Instance ID: 20191010061601287gs086792


   sepallength  sepalwidth  petallength  petalwidth         name
0          5.7         4.4          1.5         0.4  Iris-setosa
1          5.5         4.2          1.4         0.2  Iris-setosa
2          5.2         4.1          1.5         0.1  Iris-setosa
3          5.8         4.0          1.2         0.2  Iris-setosa
4          5.4         3.9          1.3         0.4  Iris-setosa
Sql compiled:
CREATE TABLE tmp_pyodps_97b080bb_e014_48e8_a310_4b45fcd6a2ed LIFECYCLE 1 AS
SELECT *
FROM WB_BestPractice_dev.`pyodps_iris` t1
ORDER BY sepalwidth DESC
LIMIT 10000
Instance ID: 20191010061606927g6emz192

   sepallength  sepalwidth  petallength  petalwidth         name
0          5.7         4.4          1.5         0.4  Iris-setosa
1          5.5         4.2          1.4         0.2  Iris-setosa
2          5.2         4.1          1.5         0.1  Iris-setosa
3          5.8         4.0          1.2         0.2  Iris-setosa
4          5.4         3.9          1.3         0.4  Iris-setosa
Sql compiled:
CREATE TABLE tmp_pyodps_6fe37b6e_6705_4052_b733_211eb9bd16ac LIFECYCLE 1 AS
SELECT *
FROM WB_BestPractice_dev.`pyodps_iris` t1
ORDER BY sepalwidth, petallength
LIMIT 10000
Instance ID: 20191010061611714gn586792

   sepallength  sepalwidth  petallength  petalwidth             name
0          5.0         2.0          3.5         1.0  Iris-versicolor
1          6.0         2.2          4.0         1.0  Iris-versicolor
2          6.2         2.2          4.5         1.5  Iris-versicolor
3          6.0         2.2          5.0         1.5   Iris-virginica
4          4.5         2.3          1.3         0.3      Iris-setosa
Sql compiled:
CREATE TABLE tmp_pyodps_a52c805c_94a1_4a75_a6af_4fc9ed06ae68 LIFECYCLE 1 AS
SELECT *
FROM WB_BestPractice_dev.`pyodps_iris` t1
ORDER BY sepalwidth, petallength DESC
LIMIT 10000
Instance ID: 20191010061616553gw3m9592

   sepallength  sepalwidth  petallength  petalwidth             name
0          5.0         2.0          3.5         1.0  Iris-versicolor
1          6.0         2.2          5.0         1.5   Iris-virginica
2          6.2         2.2          4.5         1.5  Iris-versicolor
3          6.0         2.2          4.0         1.0  Iris-versicolor
4          6.3         2.3          4.4         1.3  Iris-versicolor
Sql compiled:
CREATE TABLE tmp_pyodps_aac5538e_9b40_4078_b3c6_852b99c663c1 LIFECYCLE 1 AS
SELECT *
FROM WB_BestPractice_dev.`pyodps_iris` t1
ORDER BY sepalwidth, petallength DESC
LIMIT 10000
Instance ID: 20191010061621329gvmkc292


   sepallength  sepalwidth  petallength  petalwidth             name
0          5.0         2.0          3.5         1.0  Iris-versicolor
1          6.0         2.2          5.0         1.5   Iris-virginica
2          6.2         2.2          4.5         1.5  Iris-versicolor
3          6.0         2.2          4.0         1.0  Iris-versicolor
4          6.3         2.3          4.4         1.3  Iris-versicolor
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