dense_rank()
Описание
Функция dense_rank() вычисляет плотный ранг значений в окне. Возвращает ранг текущей строки без пропусков в нумерации.
Параметры
Нет параметров
Возвращаемое значение
Long - ранг текущей строки
Пример использования
from pyspark.sql import SparkSession
from pyspark.sql.window import Window
from pyspark.sql.functions import dense_rank
# Создаем SparkSession
spark = SparkSession.builder.appName("dense_rank_example").getOrCreate()
# Создаем DataFrame
data = [
("A", 100),
("B", 200),
("C", 200),
("D", 300),
("E", 300)
]
df = spark.createDataFrame(data, ["name", "value"])
# Определяем окно
window = Window.orderBy("value")
# Вычисляем плотный ранг
result = df.select(
"name",
"value",
dense_rank().over(window).alias("dense_rank")
).show()
# Результат:
# +----+-----+-----------+
# |name|value|dense_rank |
# +----+-----+-----------+
# | A| 100| 1|
# | B| 200| 2|
# | C| 200| 2|
# | D| 300| 3|
# | E| 300| 3|
# +----+-----+-----------+
Примечания
- Функция должна использоваться с оконной спецификацией
- При одинаковых значениях присваивается одинаковый ранг
- Следующий ранг не пропускается (в отличие от
rank()) - Для вычисления ранга с пропусками используйте
rank() - Для получения номера строки используйте
row_number() - Для вычисления кумулятивного распределения используйте
cume_dist()
XML функции
aggregate functions
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array functions pyspark
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basic functions
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bitwise functions
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call functions
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sketch functions
sort functions
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string functions pyspark
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- uuid()
struct functions
system functions
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- input_file_block_length()
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- spark_partition_id()
- user()
type functions
window functions
- cume_dist()
- lag(col, offset, default)
- lead(col, offset, default)
- monotonically_increasing_id()
- nth_value(col, n)
- ntile(n)
- percent_rank()
- rank()
- row_number()
- window(timeColumn, windowDuration, slideDuration, startTime)
xml functions
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агрегатные функции
битовые операции
оконные функции
- diff(col)
- session_window(timeColumn, gapDuration)
- sliding_window(timeColumn, windowDuration, slideDuration)
- tumbling_window(timeColumn, windowDuration)