approx_percentile(col, percentage[, accuracy])

Описание

Функция approx_percentile() возвращает приблизительный процентиль числового столбца. Процентиль - это значение, ниже которого находится указанный процент данных.

Параметры

• col: Column - числовой столбец для расчета процентиля
• percentage: double - процент (от 0.0 до 1.0)
• accuracy: int (опционально) - точность расчета (по умолчанию 10000)

Возвращаемое значение

Double - приблизительный процентиль

Пример использования

from pyspark.sql.functions import approx_percentile
from pyspark.sql import SparkSession

# Создаем SparkSession
spark = SparkSession.builder.appName("approx_percentile_example").getOrCreate()

# Создаем DataFrame
data = [(i,) for i in range(1, 101)]
df = spark.createDataFrame(data, ["value"])

# Используем approx_percentile для разных процентилей
result = df.agg(
    approx_percentile("value", 0.25).alias("25th_percentile"),
    approx_percentile("value", 0.5).alias("median"),
    approx_percentile("value", 0.75).alias("75th_percentile"),
    approx_percentile("value", 0.9, 1000).alias("90th_percentile_low_accuracy"),
    approx_percentile("value", 0.9, 100000).alias("90th_percentile_high_accuracy")
).show()

# Результат:
# +---------------+-------+---------------+------------------------+-------------------------+
# |25th_percentile|median |75th_percentile|90th_percentile_low_accuracy|90th_percentile_high_accuracy|
# +---------------+-------+---------------+------------------------+-------------------------+
# |25.0           |50.0   |75.0          |90.0                    |90.0                      |
# +---------------+-------+---------------+------------------------+-------------------------+

Примечания

• Чем выше значение accuracy, тем точнее результат, но больше потребление памяти
• Для точного расчета процентиля используйте percentile()
• Для расчета медианы можно использовать approx_percentile(col, 0.5)
• Для расчета квартилей используйте значения 0.25, 0.5 и 0.75

XML функции

• from_xml(col, schema)
• schema_of_xml(xml)
• to_xml(col)

aggregate functions

• any_value(col[, ignoreNulls])
• approxCountDistinct(col[, rsd])
• array_agg(col)
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basic functions

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bitmap functions

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bitwise functions

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call functions

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comparison functions

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conditional functions

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conversion functions

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csv functions

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datetime functions pyspark

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encryption functions

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expression functions

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java functions

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map functions

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mathematical functions pyspark

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normal functions

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null functions

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sketch functions

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sort functions

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string functions pyspark

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• sentences(str, lang, country)
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• url_decode(str)
• url_encode(str)
• uuid()

struct functions

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system functions

• current_catalog()
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• spark_partition_id()
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type functions

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window functions

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• ntile(n)
• percent_rank()
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• row_number()
• window(timeColumn, windowDuration, slideDuration, startTime)

xml functions

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• xpath_number(xml, path)
• xpath_short(xml, path)
• xpath_string(xml, path)

агрегатные функции

• approx_count_distinct(col, rsd)
• std(col)
• sumDistinct(col)

битовые операции

• bitwise_and(col1, col2)

оконные функции

• diff(col)
• session_window(timeColumn, gapDuration)
• sliding_window(timeColumn, windowDuration, slideDuration)
• tumbling_window(timeColumn, windowDuration)

операторы сравнения

• <>(left, right)
• <=>(left, right)
• =(left, right)

предикатные функции

• isnotnan(col)

системные функции

• database()
• session_user()

условные функции

• case_when(condition1, value1, condition2, value2, ..., else_value)
• if(condition, value1, value2)

функции для работы с битовыми картами

• bitmap_bit_position(bitmap, bit)
• bitmap_bucket_number(bitmap)

функции преобразования типов

• to_decimal(col, scale=0)

функции шифрования

• aes_decrypt(col, key)