CODEWITHSUNDEEP
pythonapache-sparkpyspark
mikeL
mikeL

Reputation: 1114

Convert DataFrame of numpy arrays to Spark DataFrame

I have a pandas DataFrame consisting of one column of integers and another column of numpy arrays

DataFrame({'col_1':[1434,3046,3249,3258], 'col_2':[np.array([1434, 1451, 1467]),np.array([3046, 3304]),
    np.array([3249, 3246, 3298, 3299, 3220]),np.array([3258, 3263, 3307])]})


   col_1    col_2
0   1434    [1434, 1451, 1467]
1   3046    [3046, 3304]
2   3249    [3249, 3246, 3298, 3299, 3220]
3   3258    [3258, 3263, 3307]

that I want to convert to a Spark DataFrame in the following format

df=sc.parallelize([  [1434,[1434, 1451, 1467]],
          [3046,[3046, 3304]],
          [3249,[3046, 3304]],
          [3258,[3258, 3263, 3307]]]).toDF(['col_1','col_2'])


df.select('col_1',explode(col('col_2')).alias('col_2')).show(14)


+-----+-----+
|col_1|col_2|
+-----+-----+
| 1434| 1434|
| 1434| 1451|
| 1434| 1467|
| 3046| 3046|
| 3046| 3304|
| 3249| 3046|
| 3249| 3304|
| 3258| 3258|
| 3258| 3263|
| 3258| 3307|
+-----+-----+

if I attempt to convert the pandas DataFrame directly into a Spark DataFrame I get the error

not supported type: <type 'numpy.ndarray'>

Upvotes: 3

Views: 5799

Answers (1)

titipata
titipata

Reputation: 5389

I guess one way is to convert each row in DataFrame to list of integer.

df.col_2 = df.col_2.map(lambda x: [int(e) for e in x])

Then, convert it to Spark DataFrame directly

df_spark = spark.createDataFrame(df)
df_spark.select('col_1', explode(col('col_2')).alias('col_2')).show(14)

Upvotes: 6

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