Pyspark将元组的RDD转换为数据框

Question

I have a rdd of tuples where the first two lines look like this:

[[('n', 12.012457082117459), ('s', 0.79112758892014912)],
[('t', 3.6243409329763652),('vn', 3.6243409329763652),('n', 52.743253562212828),('v', 11.644347760553064)]]

In each tuple, the first value, eg: 'n','s','t', is the desired column name, and the second value, eg: 12.012, 0.7911.... is the desired values for each column. However, in each list(row) of rdd, we can see that not all column names are there. For example, in the first row, only

'n', 's' 

appeared, while there is no

's' 

in the second row. So I want to convert this rdd to a dataframe, where the values should be 0 for columns that do not show up in the original tuple. In other words, the first two rows might look like this:

n     s      t       vn     omitted.....
12    0.79   0       0      ..... 
52    0      3.62    3.62    .......

I tried following:

row = Row('l','eng','q','g','j','b','nt','z','n','d','f','i','k','s','vn','nz','v','nrt','tg','nrfg','t','ng','zg','a')
df = tup_sum_data.map(row).toDF()

Where strings in Row() are my desired column names. But i got following error:

TypeError                                 Traceback (most recent call last)
/Users/1/Documents/spark/python/pyspark/sql/types.py in _infer_type(obj)
968         try:
--> 969             return _infer_schema(obj)
970         except TypeError:

/Users/1/Documents/spark/python/pyspark/sql/types.py in _infer_schema(row)
991     else:
--> 992         raise TypeError("Can not infer schema for type: %s" % type(row))
993 

TypeError: Can not infer schema for type: <class 'numpy.float64'>
During handling of the above exception, another exception occurred:
TypeError                                 Traceback (most recent call last)
/Users/1/Documents/spark/python/pyspark/sql/types.py in _infer_type(obj)
968         try:
--> 969             return _infer_schema(obj)
970         except TypeError:

/Users/1/Documents/spark/python/pyspark/sql/types.py in _infer_type(obj)
969             return _infer_schema(obj)
970         except TypeError:
--> 971             raise TypeError("not supported type: %s" % type(obj))
972 
973 

TypeError: not supported type: <class 'tuple'>

Some lines in the error codes are omitted. Could anyone help me figure out how to deal with this? Thank you !

UPDATE I converted data types from np.float64 to float, and there is no error. However, the dataframe does not look like what I wanted; it looked like this:

+--------------------+
|                   l|
+--------------------+
|[[n,12.0124570821...|
|[[t,3.62434093297...|
|[[a,0.44628710262...|
|[[n,16.7534769832...|
|[[n,17.6017774340...|
+--------------------+
only showing top 5 rows

So can anyone help me how to get a correctly formatted dataframe? Thank you !

Answer 1

from pyspark.sql.types import *
from pyspark.sql import *

data_frame_schema = StructType([
    StructField("n", FloatType()),
    StructField("s", FloatType()),
    StructField("t", FloatType()),
    StructField("v", FloatType()),
    StructField("vn", FloatType())
])

raw_list = [[('n', 12.012457082117459), ('s', 0.79112758892014912)], \
[('t', 3.6243409329763652),('vn', 3.6243409329763652),('n', 52.743253562212828),('v', 11.644347760553064)]]

raw_rdd = sc.parallelize(raw_list)

# dict_to_row = lambda d: Row(n=d.get("n"), s=d.get("s"), t=d.get("t"), v=d.get("v"), vn=d.get("vn"))
dict_to_row = lambda d: Row(n=d.get("n", 0.0), s=d.get("s", 0.0), t=d.get("t", 0.0), v=d.get("v", 0.0), vn=d.get("vn", 0.0))

row_rdd = raw_rdd.map(lambda l: dict_to_row(dict(l)))
df = spark.createDataFrame(row_rdd, data_frame_schema)
df.show()

Pasting the above into the pyspark shell yields output:

+---------+----------+--------+---------+--------+
|        n|         s|       t|        v|      vn|
+---------+----------+--------+---------+--------+
|12.012457|0.79112756|     0.0|      0.0|     0.0|
| 52.74325|       0.0|3.624341|11.644348|3.624341|
+---------+----------+--------+---------+--------+