PySpark
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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 | cov method
schedule Aug 12, 2023
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PySpark DataFrame's cov(~) method returns the covariance of two specified numeric columns as a float.
Parameters
1. col1 | string
The first column.
2. col2 | string
The second column.
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 covaraince of two numeric PySpark columns
To compute the covariance between the height and weight columns:
df.cov("height","weight")
50.0
Here, we see that the covariance between height and weight is 50 (positive correlation).
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.cov.html
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