Terjemahan disediakan oleh mesin penerjemah. Jika konten terjemahan yang diberikan bertentangan dengan versi bahasa Inggris aslinya, utamakan versi bahasa Inggris.

Transform

3. 

5. 

Join View

Lihat untuk mempelajari selengkapnya.

Concatenate View

Lihat untuk mempelajari selengkapnya.

PySpark

from pyspark.sql.functions import from_unixtime, to_date, date_format
df = df.withColumn('DATE_TIME', from_unixtime('TIMESTAMP'))
df = df.withColumn( 'EVENT_DATE', to_date('DATE_TIME')).withColumn(
'EVENT_TIME', date_format('DATE_TIME', 'HH:mm:ss'))

df.info()

SELECT name, fare, pclass, survived FROM df

# Specify the subset of columns
# all rows having identical values in these columns will be dropped

subset = ["col1", "col2", "col3"]
df = df.dropDuplicates(subset)  

# to drop the full-duplicate rows run
# df = df.dropDuplicates()            
                        
        

col_a * col_b

Anda dapat memilih dari opsi berikut:

Anda dapat memilih dari opsi berikut:

Anda dapat memilih dari opsi berikut:

Anda dapat memilih dari opsi berikut:

Pengambilan sampel

Randomized split

9. Pilih Tambahkan.

Ordered split

12. Pilih Tambahkan.

Stratified split

11. Pilih Tambahkan.

Split by column keys

10. Pilih Tambahkan.

 "{"seq": 1,"name": {"first": "Nathaniel","last": "Ferguson"},"age": 59,"city": "Posbotno","state": "WV"}"
            

seq, name,                                    age, city, state
1, {"first": "Nathaniel","last": "Ferguson"}, 59, Posbotno, WV
            

seq, name,                                    age, city, state, name_first, name_last
1, {"first": "Nathaniel","last": "Ferguson"}, 59, Posbotno, WV, Nathaniel, Ferguson
            

                [1, 2, 3]
                [4, 5, 6]
                [7, 8, 9]
            

# A table with your image data is stored in the `df` variable
import cv2
import numpy as np
from pyspark.sql.functions import column

from sagemaker_dataprep.compute.operators.transforms.image.constants import DEFAULT_IMAGE_COLUMN, IMAGE_COLUMN_TYPE
from sagemaker_dataprep.compute.operators.transforms.image.decorators import BasicImageOperationDecorator, PandasUDFOperationDecorator


@BasicImageOperationDecorator
def my_transform(image: np.ndarray) -> np.ndarray:
  # To use the code snippet on your image data, modify the following lines within the function
    HYST_THRLD_1, HYST_THRLD_2 = 100, 200
    edges = cv2.Canny(image,HYST_THRLD_1,HYST_THRLD_2)
    return edges
    

@PandasUDFOperationDecorator(IMAGE_COLUMN_TYPE)
def custom_image_udf(image_row):
    return my_transform(image_row)
    

df = df.withColumn(DEFAULT_IMAGE_COLUMN, custom_image_udf(column(DEFAULT_IMAGE_COLUMN)))