### Question :

I trained a LogisticRegression model in PySpark (ML package) and the result of the prediction is a PySpark DataFrame (`cv_predictions`

) (see [1]). The `probability`

column (see [2]) is a `vector`

type (see [3]).

```
[1]
type(cv_predictions_prod)
pyspark.sql.dataframe.DataFrame
[2]
cv_predictions_prod.select('probability').show(10, False)
+----------------------------------------+
|probability |
+----------------------------------------+
|[0.31559134817066054,0.6844086518293395]|
|[0.8937864350711228,0.10621356492887715]|
|[0.8615878905395029,0.1384121094604972] |
|[0.9594427633777901,0.04055723662220989]|
|[0.5391547673698157,0.46084523263018434]|
|[0.2820729747752462,0.7179270252247538] |
|[0.7730465873083118,0.22695341269168817]|
|[0.6346585276598942,0.3653414723401058] |
|[0.6346585276598942,0.3653414723401058] |
|[0.637279255218404,0.362720744781596] |
+----------------------------------------+
only showing top 10 rows
[3]
cv_predictions_prod.printSchema()
root
...
|-- rawPrediction: vector (nullable = true)
|-- probability: vector (nullable = true)
|-- prediction: double (nullable = true)
```

How do I create parse the `vector`

of the PySpark DataFrame, such that I create a new column that just pulls the first element of each `probability`

vector?

This question is similar to, but the solutions in the links below didn’t work/weren’t clear to me:

How to access the values of denseVector in PySpark

How to access element of a VectorUDT column in a Spark DataFrame?

##
Answer #1:

Update:

It seems like there is a bug in spark that prevents you from accessing individual elements in a dense vector during a select statement. Normally you should would be able to access them just like you would a numpy array, but when trying to run the code previously posted, you may get the error `pyspark.sql.utils.AnalysisException: "Can't extract value from probability#12;"`

So, one way to handle this to avoid this silly bug is to use a udf. Similar to the other question, you can define a udf in the following way:

```
from pyspark.sql.functions import udf
from pyspark.sql.types import FloatType
firstelement=udf(lambda v:float(v[0]),FloatType())
cv_predictions_prod.select(firstelement('probability')).show()
```

Behind the scenes this still accesses the elements of the DenseVector like a numpy array, but it doesn’t throw the same bug as before.

Since this is getting a lot of upvotes, I figured I should strike through the incorrect portion of this answer.

~~ Original answer:~~

A dense vector is just a wrapper for a numpy array. So you can access the elements in the same way that you would access the elements of a numpy array.

There are several ways to access individual elements of an array in a dataframe. One is to explicitly call the column `cv_predictions_prod['probability']`

in your select statement. By explicitly calling the column, you can perform operations on that column, like selecting the first element in the array. For example:

```
cv_predictions_prod.select(cv_predictions_prod['probability'][0]).show()
```

~~should solve the problem. ~~