Convert a standard python key value dictionary list to pyspark data frame

Question

Consider i have a list of python dictionary key value pairs , where key correspond to column name of a table, so for below list how to convert it into a pyspark dataframe with two cols arg1 arg2?

 [{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""}]

How can i use the following construct to do it?

df = sc.parallelize([
    ...
]).toDF

Where to place arg1 arg2 in the above code (...)

Solution 1

The other answers work, but here's one more one-liner that works well with nested data. It's may not the most efficient, but if you're making a DataFrame from an in-memory dictionary, you're either working with small data sets like test data or using spark wrong, so efficiency should really not be a concern:

d = {any json compatible dict}
spark.read.json(sc.parallelize([json.dumps(d)]))

Solution 2

Old way:

sc.parallelize([{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""}]).toDF()

New way:

from pyspark.sql import Row
from collections import OrderedDict

def convert_to_row(d: dict) -> Row:
    return Row(**OrderedDict(sorted(d.items())))

sc.parallelize([{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""},{"arg1": "", "arg2": ""}]) \
    .map(convert_to_row) \ 
    .toDF()

Solution 3

For anyone looking for the solution to something different I found this worked for me: I have a single dictionary with key value pairs - I was looking to convert that to two PySpark dataframe columns:

So

{k1:v1, k2:v2 ...}

Becomes

 ---------------- 
| col1   |  col2 |
|----------------|
| k1     |  v1   |
| k2     |  v2   |
 ----------------

lol= list(map(list, mydict.items()))
df = spark.createDataFrame(lol, ["col1", "col2"])

Solution 4

I had to modify the accepted answer in order for it to work for me in Python 2.7 running Spark 2.0.

from collections import OrderedDict
from pyspark.sql import SparkSession, Row

spark = (SparkSession
        .builder
        .getOrCreate()
    )

schema = StructType([
    StructField('arg1', StringType(), True),
    StructField('arg2', StringType(), True)
])

dta = [{"arg1": "", "arg2": ""}, {"arg1": "", "arg2": ""}]

dtaRDD = spark.sparkContext.parallelize(dta) \
    .map(lambda x: Row(**OrderedDict(sorted(x.items()))))

dtaDF = spark.createDataFrame(dtaRdd, schema) 

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