删除空列的快速方法[PySpark]

Question

有没有一种简单的方法可以在 pyspark 中删除巨大数据集（300+ col >100k 行）的空列？例如 df.dropna(axis=1,how='all') 在 Python

Answer 1

是的，您可以简单地使用中的答案。我给它添加了一个 threshold 参数：

import pyspark.sql.functions as F

# Sample data
df = pd.DataFrame({'x1': ['a', '1', '2'],
                   'x2': ['b', None, '2'],
                   'x3': ['c', '0', '3'] })
df = sqlContext.createDataFrame(df)
df.show()

def drop_null_columns(df, threshold=0):
    """
    This function drops all columns which contain null values.
    :param df: A PySpark DataFrame
    """
    null_counts = df.select([F.count(F.when(F.col(c).isNull(), c)).alias(c) for c in df.columns]).collect()[0].asDict()
    to_drop = [k for k, v in null_counts.items() if v > threshold]
    df = df.drop(*to_drop)
    return df

# Drops column b2, because it contains null values
drop_null_columns(df).show()

输出

+---+---+
| x1| x3|
+---+---+
|  a|  c|
|  1|  0|
|  2|  3|
+---+---+

第 x2 列已删除。

可以边用边用threshold=df.count()

Answer 2

这是@pissall 的 fn 的扩展功能。:

def drop_null_columns(df, threshold=-1):
    """
    This function drops all columns which contain null values.
    If threshold is negative (default), drop columns that have only null values.
    If threshold is >=0, drop columns that have count of null values bigger than threshold. This may be very computationally expensive!
    Returns PySpark DataFrame.
    """
    if threshold<0:
        max_per_column = df.select([F.max(c).alias(c) for c in df.columns]).collect()[0].asDict()
        to_drop = [k for k, v in max_per_column.items() if v == None]
    else:
        null_counts = df.select([F.count(F.when(F.col(c).isNull(), c)).alias(c) for c in df.columns]).collect()[0].asDict()
        to_drop = [k for k, v in null_counts.items() if v > threshold]
    df = df.drop(*to_drop)
    return df

删除空列的快速方法[PySpark]

Quick way to delete empty column [PySpark]

col

is-empty

pyspark

输出