Pyspark 数据框 OrderBy 列列表 [英] Pyspark dataframe OrderBy list of columns

问题描述

在写入 csv 之前，我尝试在 pyspark 数据框中使用 OrderBy 函数，但如果我有列列表，我不确定是否使用 OrderBy 函数.

代码:

Cols = ['col1','col2','col3']df = df.OrderBy(cols,ascending=False)

解决方案

根据文档字符串/签名:

<块引用>

签名:df.orderBy(*cols, **kwargs)文档字符串:返回一个按指定列排序的新 DataFrame.:param cols: 要排序的列名或列名列表.:param 升序:布尔值或布尔值列表(默认为 True).

两者

df = spark.createDataFrame([(1, 2, 3)] )cols = ["_1", "_2", "_3"]df.orderBy(cols, 升序=假)

和

df.orderBy(*cols, 升序=假)

有效，以及与 list[pyspark.sql.Column] 等效.

I am trying to use OrderBy function in pyspark dataframe before I write into csv but I am not sure to use OrderBy functions if I have a list of columns.

Code:

Cols = ['col1','col2','col3']
df = df.OrderBy(cols,ascending=False)

解决方案

As per docstring / signature:

Signature: df.orderBy(*cols, **kwargs)
Docstring:
Returns a new :class:`DataFrame` sorted by the specified column(s).
:param cols: list of :class:`Column` or column names to sort by.
:param ascending: boolean or list of boolean (default True).

Both

df = spark.createDataFrame([(1, 2, 3)] )
cols = ["_1", "_2", "_3"]

df.orderBy(cols, ascending=False)

and

df.orderBy(*cols, ascending=False)

are valid, as well as equivalents with list[pyspark.sql.Column].

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