問題描述

PROBNORM:解釋

PROBNORM : explanation

SAS 中的 PROBNORM 函數(shù)返回標(biāo)準(zhǔn)正態(tài)分布的觀測值小于或等于 x 的概率.

The PROBNORM function in SAS returns the probability that an observation from the standard normal distribution is less than or equal to x.

pyspark中有沒有等價的功能?

Is there any equivalent function in pyspark?

推薦答案

恐怕PySpark中沒有這樣的實現(xiàn)方法.
但是，您可以利用 Pandas UDF 使用基本的 Python 包定義您自己的自定義函數(shù)！這里我們將使用 scipy.stats.norm 模塊從標(biāo)準(zhǔn)正態(tài)分布中獲取累積概率.

I'm afraid that in PySpark there is no such implemented method.
However, you can exploit Pandas UDFs to define your own custom function using basic Python packages! Here we are going to use scipy.stats.norm module to get cumulative probabilities from a standard normal distribution.

我正在使用的版本:

• Spark 3.1.1
• 熊貓 1.1.5
• scipy 1.5.2

示例代碼

import pandas as pd
from scipy.stats import norm
import pyspark.sql.functions as F
from pyspark.sql.functions import pandas_udf


# create sample data
df = spark.createDataFrame([
    (1, 0.00),
    (2, -1.23),
    (3, 4.56),
], ['id', 'value'])


# define your custom Pandas UDF
@pandas_udf('double')
def probnorm(s: pd.Series) -> pd.Series:
    return pd.Series(norm.cdf(s))


# create a new column using the Pandas UDF
df = df.withColumn('pnorm', probnorm(F.col('value')))


df.show()

+---+-----+-------------------+
| id|value|              pnorm|
+---+-----+-------------------+
|  1|  0.0|                0.5|
|  2|-1.23|0.10934855242569191|
|  3| 4.56| 0.9999974423189606|
+---+-----+-------------------+

編輯

如果您的工作人員也沒有正確安裝 scipy，您可以使用 Python 基礎(chǔ)包 math 和一點 統(tǒng)計知識.

Edit

If you do not have scipy properly installed on your workers too, you can use the Python base package math and a little bit of statistics knowledge.

import math
from pyspark.sql.functions import udf

def normal_cdf(x, mu=0, sigma=1):
    """
    Cumulative distribution function for the normal distribution
    with mean `mu` and standard deviation `sigma`
    """
    return (1 + math.erf((x - mu) / (sigma * math.sqrt(2)))) / 2

my_udf = udf(normal_cdf)

df = df.withColumn('pnorm', my_udf(F.col('value')))

df.show()

+---+-----+-------------------+
| id|value|              pnorm|
+---+-----+-------------------+
|  1|  0.0|                0.5|
|  2|-1.23|0.10934855242569197|
|  3| 4.56| 0.9999974423189606|
+---+-----+-------------------+

結(jié)果其實是一樣的.

這篇關(guān)于pyspark中的probnorm函數(shù)等效的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網(wǎng)！