这里所需要的只是导入StringType并使用lit和cast：

from pyspark.sql.types import StringType
from pyspark.sql.functions import lit

new_df = old_df.withColumn('new_column', lit(None).cast(StringType()))

完整示例：

df = sc.parallelize([row(1, "2"), row(2, "3")]).toDF()
df.printSchema()
# root
#  |-- foo: long (nullable = true)
#  |-- bar: string (nullable = true)

new_df = df.withColumn('new_column', lit(None).cast(StringType()))

new_df.printSchema()
# root
#  |-- foo: long (nullable = true)
#  |-- bar: string (nullable = true)
#  |-- new_column: string (nullable = true)

new_df.show()
# +---+---+----------+
# |foo|bar|new_column|
# +---+---+----------+
# |  1|  2|      null|
# |  2|  3|      null|
# +---+---+----------+

Scala的等价物可以在这里找到：Create new Dataframe with empty/null field values

我会将lit（None）强制转换为NullType而不是StringType，这样，如果我们必须过滤掉该列上的非空行......就可以很容易地完成如下操作

df = sc.parallelize([Row(1, "2"), Row(2, "3")]).toDF()

new_df = df.withColumn('new_column', lit(None).cast(NullType()))

new_df.printSchema() 

df_null = new_df.filter(col("new_column").isNull()).show()
df_non_null = new_df.filter(col("new_column").isNotNull()).show()

如果要强制转换为StringType，请注意不要使用lit（“None”）（带引号），因为它将无法在col（“new_column”）上搜索具有过滤条件.isNull（）的记录。

不带import StringType的选件

df = df.withColumn('foo', F.lit(None).cast('string'))

完整示例：

from pyspark.sql import functions as F
df = spark.range(1, 3).toDF('c')

df = df.withColumn('foo', F.lit(None).cast('string'))

df.printSchema()
#     root
#      |-- c: long (nullable = false)
#      |-- foo: string (nullable = true)

df.show()
#     +---+----+
#     |  c| foo|
#     +---+----+
#     |  1|null|
#     |  2|null|
#     +---+----+

df1.selectExpr("school","null as col1").show()

输出：

+--------------------+----+
|              school|col1|
+--------------------+----+
|Shanghai Jiao Ton...|null|
|   Peking University|null|
|Shanghai Jiao Ton...|null|
|    Fudan University|null|
|    Fudan University|null|
| Tsinghua University|null|
|Shanghai Jiao Ton...|null|
| Tsinghua University|null|
| Tsinghua University|null|
|   Peking University|null|

或在pyspark 2.2+中

df1.pandas_api().assign(new_column=None)