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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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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"])
+-----+------+------+| 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.
Published by Isshin Inada
Edited by 0 others
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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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