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Outline
ParametersReturn ValueExamplesComputing the correlation of two numeric PySpark columns Related
chevron_left PySpark DataFrame
Method aliasMethod coalesceMethod collectMethod colRegexMethod corrMethod countMethod covMethod describeMethod distinctMethod dropMethod dropDuplicatesMethod dropnaMethod exceptAllMethod fillnaMethod filterMethod foreachMethod groupByMethod headMethod intersectMethod intersectAllMethod joinMethod limitMethod orderByMethod printSchemaMethod randomSplitMethod repartitionMethod replaceMethod sampleMethod sampleByMethod selectMethod selectExprMethod showMethod sortMethod summaryMethod tailMethod takeMethod toDFMethod toJSONMethod toPandasMethod transformMethod unionMethod unionByNameMethod whereMethod withColumnMethod withColumnRenamedProperty columnsProperty dtypesProperty rdd
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chevron_left PySpark DataFrame
Method aliasMethod coalesceMethod collectMethod colRegexMethod corrMethod countMethod covMethod describeMethod distinctMethod dropMethod dropDuplicatesMethod dropnaMethod exceptAllMethod fillnaMethod filterMethod foreachMethod groupByMethod headMethod intersectMethod intersectAllMethod joinMethod limitMethod orderByMethod printSchemaMethod randomSplitMethod repartitionMethod replaceMethod sampleMethod sampleByMethod selectMethod selectExprMethod showMethod sortMethod summaryMethod tailMethod takeMethod toDFMethod toJSONMethod toPandasMethod transformMethod unionMethod unionByNameMethod whereMethod withColumnMethod withColumnRenamedProperty columnsProperty dtypesProperty rdd
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PySpark DataFrame | corr method

Machine Learning
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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.

Related

PySpark DataFrame | cov method
PySpark DataFrame's cov(~) method returns the covariance of two specified numeric columns as a float.
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Published by Isshin Inada
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Official PySpark Documentation
https://spark.apache.org/docs/3.2.0/api/python/reference/api/pyspark.sql.DataFrame.corr.html
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