你可以一次概括过滤器的语句：

from pyspark.sql.functions import col, count, when
# Converts all unmatched filters to NULL and drops them.
df = df.select([when(col(c).contains('substring'), col(c)).alias(c) for c in df.columns]).na.drop()

或
您可以简单地遍历列并应用相同的过滤器：

for col in df.columns:
    df = df.filter(df[col].contains("substring"))

您可以搜索所有列并填充下一个 Dataframe 和联合结果，如下所示：

columns = ["language", "else"]
data = [
    ("Java", "Python"),
    ("Python", "100000"),
    ("Scala", "3000"),
]
df = spark.createDataFrame(data).toDF(*columns)
df.cache()
df.show()

schema = df.schema
df2 = spark.createDataFrame(data=[], schema=schema)

for col in df.columns:
    df2 = df2.unionByName(df.filter(df[col].like("%Python%")))

df2.show()
+--------+------+
|language|  else|
+--------+------+
|  Python|100000|
|    Java|Python|
+--------+------+

结果将包含前2行，因为它们在某些列中具有值“Python”。