CODEWITHSUNDEEP
pythonnumpyapache-sparkpyspark
Vaibhav Rathi
Vaibhav Rathi

Reputation: 350

How to convert a pyspark dataframe column to numpy array

I am trying to convert a pyspark dataframe column having approximately 90 million rows into a numpy array.

I need the array as an input for scipy.optimize.minimize function.

I have tried both converting to Pandas and using collect(), but these methods are very time consuming.

I am new to PySpark, If there is a faster and better approach to do this, Please help.

Thanks

This is how my dataframe looks like.

+----------+
|Adolescent|
+----------+
|       0.0|
|       0.0|
|       0.0|
|       0.0|
|       0.0|
|       0.0|
|       0.0|
|       0.0|
|       0.0|
|       0.0|
+----------+

Upvotes: 11

Views: 39302

Answers (2)

qwr
qwr

Reputation: 10921

Another way is to convert the selected column to RDD, then flatten by extracting the value of each Row (can abuse .keys()), then convert to numpy array:

x = df.select("colname").rdd.map(lambda r: r[0]).collect()  # python list
np.array(x)  # numpy array

Upvotes: 1

pissall
pissall

Reputation: 7399

#1

You will have to call a .collect() in any way. To create a numpy array from the pyspark dataframe, you can use:

adoles = np.array(df.select("Adolescent").collect()) #.reshape(-1) for 1-D array

#2

You can convert it to a pandas dataframe using toPandas(), and you can then convert it to numpy array using .values. 

pdf = df.toPandas()
adoles = df["Adolescent"].values

Or simply:

adoles = df.select("Adolescent").toPandas().values #.reshape(-1) for 1-D array

#3

For distributed arrays, you can try Dask Arrays

I haven't tested this, but assuming it would work the same as numpy (might have inconsistencies):

import dask.array as da
adoles = da.array(df.select("Adolescent").collect()) #.reshape(-1) for 1-D array

Upvotes: 27

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