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PySpark DataFrame | corr method

Machine Learning
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PySpark
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PySpark DataFrame
schedule Jul 1, 2022
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PySpark DataFrame's corr(~) method returns the correlation of the specified numeric columns as a float.

Parameters

1. col1 | string

The first column.

2. col2 | string

The second column.

3. method | string | optional

The type of correlation to compute. The only correlation type supported currently is the Pearson Correlation Coefficient.

Return Value

A float.

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([("Alex", 180, 80), ("Bob", 170, 70), ("Cathy", 160, 70)], ["name", "height", "weight"])
df.show()
+-----+------+------+
| name|height|weight|
+-----+------+------+
| Alex| 180| 80|
| Bob| 170| 70|
|Cathy| 160| 70|
+-----+------+------+

Computing the correlation of two numeric PySpark columns

To compute the correlation between the height and weight columns:

df.corr("height","weight")
0.8660254037844387

Here, we see that the height and weight are positively correlated with a Pearson correlation coefficient of around 0.87.

robocat
Published by Isshin Inada
Edited by 0 others
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