如何将新列添加到Spark数据帧（使用PySpark）？

Question

我有一个Spark数据框（使用PySpark 1.5.1），希望添加一个新列。

我试过以下方法，但没有成功：

type(randomed_hours) # => list

# Create in Python and transform to RDD

new_col = pd.DataFrame(randomed_hours, columns=['new_col'])

spark_new_col = sqlContext.createDataFrame(new_col)

my_df_spark.withColumn("hours", spark_new_col["new_col"])

使用以下命令时也出错：

my_df_spark.withColumn("hours",  sc.parallelize(randomed_hours))

那么，如何使用PySpark将新列（基于Python向量）添加到现有的数据帧中？

Answer 1

不能向Spark中的DataFrame添加任意列。只有使用文本才能创建新列（其他文本类型在How to add a constant column in a Spark DataFrame?中描述）

from pyspark.sql.functions import lit

df = sqlContext.createDataFrame(
    [(1, "a", 23.0), (3, "B", -23.0)], ("x1", "x2", "x3"))

df_with_x4 = df.withColumn("x4", lit(0))
df_with_x4.show()

## +---+---+-----+---+
## | x1| x2|   x3| x4|
## +---+---+-----+---+
## |  1|  a| 23.0|  0|
## |  3|  B|-23.0|  0|
## +---+---+-----+---+

转换现有列：

from pyspark.sql.functions import exp

df_with_x5 = df_with_x4.withColumn("x5", exp("x3"))
df_with_x5.show()

## +---+---+-----+---+--------------------+
## | x1| x2|   x3| x4|                  x5|
## +---+---+-----+---+--------------------+
## |  1|  a| 23.0|  0| 9.744803446248903E9|
## |  3|  B|-23.0|  0|1.026187963170189...|
## +---+---+-----+---+--------------------+

包括使用join：

from pyspark.sql.functions import exp

lookup = sqlContext.createDataFrame([(1, "foo"), (2, "bar")], ("k", "v"))
df_with_x6 = (df_with_x5
    .join(lookup, col("x1") == col("k"), "leftouter")
    .drop("k")
    .withColumnRenamed("v", "x6"))

## +---+---+-----+---+--------------------+----+
## | x1| x2|   x3| x4|                  x5|  x6|
## +---+---+-----+---+--------------------+----+
## |  1|  a| 23.0|  0| 9.744803446248903E9| foo|
## |  3|  B|-23.0|  0|1.026187963170189...|null|
## +---+---+-----+---+--------------------+----+

或使用函数/udf生成：

from pyspark.sql.functions import rand

df_with_x7 = df_with_x6.withColumn("x7", rand())
df_with_x7.show()

## +---+---+-----+---+--------------------+----+-------------------+
## | x1| x2|   x3| x4|                  x5|  x6|                 x7|
## +---+---+-----+---+--------------------+----+-------------------+
## |  1|  a| 23.0|  0| 9.744803446248903E9| foo|0.41930610446846617|
## |  3|  B|-23.0|  0|1.026187963170189...|null|0.37801881545497873|
## +---+---+-----+---+--------------------+----+-------------------+

性能方面的内置函数（pyspark.sql.functions）映射到Catalyst表达式，通常比Python用户定义的函数更受欢迎。

如果要将任意RDD的内容添加为列，可以

• 添加row numbers to existing data frame
• 调用RDD上的zipWithIndex，并将其转换为数据帧
• 使用索引作为联接键联接两者