PySpark数据帧修改列

Question

我有如下输入数据帧，其中输入列是动态的，即它可以是n个数字 - 就像input1到input2

+----+----+-------+------+------+
|dim1|dim2|  byvar|input1|input2|
+----+----+-------+------+------+
| 101| 102|MTD0001|     1|    10|
| 101| 102|MTD0002|     2|    12|
| 101| 102|MTD0003|     3|    13|

想修改如下列，怎么可能？

+----+----+-------+----------+------+
|dim1|dim2|  byvar|TRAMS_NAME|values|
+----+----+-------+----------+------+
| 101| 102|MTD0001|    input1|     1|
| 101| 102|MTD0001|    input2|    10|
| 101| 102|MTD0002|    input1|     2|
| 101| 102|MTD0002|    input2|    12|
| 101| 102|MTD0003|    input1|     3|
| 101| 102|MTD0003|    input2|    13|

我使用了create_map Spark方法，但它是硬编码的方式。还有其他方法可以实现相同的方法吗？

Answer 1

这是使用 stack() 函数解决问题的另一种解决方案。当然，它可能更简单一些，但限制是您必须显式放置列名。

希望这有帮助！

# set your dataframe
df = spark.createDataFrame(
[(101, 102, 'MTD0001', 1, 10),
(101, 102, 'MTD0002', 2, 12),
(101, 102, 'MTD0003', 3, 13)],
['dim1', 'dim2', 'byvar', 'v1', 'v2']
)
df.show()
+----+----+-------+---+---+
|dim1|dim2|  byvar| v1| v2|
+----+----+-------+---+---+
| 101| 102|MTD0001|  1| 10|
| 101| 102|MTD0002|  2| 12|
| 101| 102|MTD0003|  3| 13|
+----+----+-------+---+---+
result = df.selectExpr('dim1', 
'dim2', 
'byvar', 
"stack(2, 'v1', v1, 'v2', v2) as (names, values)")
result.show()
+----+----+-------+-----+------+
|dim1|dim2|  byvar|names|values|
+----+----+-------+-----+------+
| 101| 102|MTD0001|   v1|     1|
| 101| 102|MTD0001|   v2|    10|
| 101| 102|MTD0002|   v1|     2|
| 101| 102|MTD0002|   v2|    12|
| 101| 102|MTD0003|   v1|     3|
| 101| 102|MTD0003|   v2|    13|
+----+----+-------+-----+------+

如果我们想动态地设置要堆叠的列，我们只需要设置未更改的列，在您的示例中是dim1、dim2和byvar，并使用 for 循环创建堆栈句子。

# set static columns
unaltered_cols = ['dim1', 'dim2', 'byvar']
# extract columns to stack
change_cols = [n for n in df.schema.names if not n in unaltered_cols]
cols_exp = ",".join(["'" + n + "'," + n for n in change_cols])
# create stack sentence
stack_exp = "stack(" + str(len(change_cols)) +',' + cols_exp + ") as (names, values)"
# print final expression
print(stack_exp)
# --> stack(2,'v1',v1,'v2',v2) as (names, values)
# apply transformation
result = df.selectExpr('dim1', 
'dim2', 
'byvar', 
stack_exp)
result.show()
+----+----+-------+-----+------+
|dim1|dim2|  byvar|names|values|
+----+----+-------+-----+------+
| 101| 102|MTD0001|   v1|     1|
| 101| 102|MTD0001|   v2|    10|
| 101| 102|MTD0002|   v1|     2|
| 101| 102|MTD0002|   v2|    12|
| 101| 102|MTD0003|   v1|     3|
| 101| 102|MTD0003|   v2|    13|
+----+----+-------+-----+------+

如果我们运行相同的代码但使用不同的数据帧，您将获得所需的结果。

df = spark.createDataFrame(
[(101, 102, 'MTD0001', 1, 10, 4),
(101, 102, 'MTD0002', 2, 12, 5),
(101, 102, 'MTD0003', 3, 13, 5)],
['dim1', 'dim2', 'byvar', 'v1', 'v2', 'v3']
)
# Re-run the code to create the stack_exp before!
result = df.selectExpr('dim1', 
'dim2', 
'byvar', 
stack_exp)
result.show()
+----+----+-------+-----+------+
|dim1|dim2|  byvar|names|values|
+----+----+-------+-----+------+
| 101| 102|MTD0001|   v1|     1|
| 101| 102|MTD0001|   v2|    10|
| 101| 102|MTD0001|   v3|     4|
| 101| 102|MTD0002|   v1|     2|
| 101| 102|MTD0002|   v2|    12|
| 101| 102|MTD0002|   v3|     5|
| 101| 102|MTD0003|   v1|     3|
| 101| 102|MTD0003|   v2|    13|
| 101| 102|MTD0003|   v3|     5|
+----+----+-------+-----+------+

Answer 2

Sample DataFrame:

df.show() #added more columns to show code is dynamic
+----+----+-------+------+------+------+------+------+------+
|dim1|dim2|  byvar|input1|input2|input3|input4|input5|input6|
+----+----+-------+------+------+------+------+------+------+
| 101| 102|MTD0001|     1|    10|     3|     6|    10|    13|
| 101| 102|MTD0002|     2|    12|     4|     8|    11|    14|
| 101| 102|MTD0003|     3|    13|     5|     9|    12|    15|
+----+----+-------+------+------+------+------+------+------+

对于Spark2.4+，您可以使用explode、arrays_zip、array和element_at来动态执行此操作，以获得您的 2 列。只要输入列的名称以'input'开头，此操作就有效

from pyspark.sql import functions as F
df.withColumn("vals",
F.explode(F.arrays_zip(F.array([F.array(F.lit(x),F.col(x)) for x in df.columns if x!=['dim1','dim2','byvar']]))))
.select("dim1", "dim2","byvar","vals.*").withColumn("TRAMS_NAME", F.element_at("0",1))
.withColumn("VALUES", F.element_at("0",2)).drop("0").show()
+----+----+-------+----------+------+
|dim1|dim2|  byvar|TRAMS_NAME|VALUES|
+----+----+-------+----------+------+
| 101| 102|MTD0001|    input1|     1|
| 101| 102|MTD0001|    input2|    10|
| 101| 102|MTD0001|    input3|     3|
| 101| 102|MTD0001|    input4|     6|
| 101| 102|MTD0001|    input5|    10|
| 101| 102|MTD0001|    input6|    13|
| 101| 102|MTD0002|    input1|     2|
| 101| 102|MTD0002|    input2|    12|
| 101| 102|MTD0002|    input3|     4|
| 101| 102|MTD0002|    input4|     8|
| 101| 102|MTD0002|    input5|    11|
| 101| 102|MTD0002|    input6|    14|
| 101| 102|MTD0003|    input1|     3|
| 101| 102|MTD0003|    input2|    13|
| 101| 102|MTD0003|    input3|     5|
| 101| 102|MTD0003|    input4|     9|
| 101| 102|MTD0003|    input5|    12|
| 101| 102|MTD0003|    input6|    15|
+----+----+-------+----------+------+