DataFrameStatFunctions
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package sql
Builds a Bloom filter over a specified column.
Builds a Bloom filter over a specified column.
the column over which the filter is built
expected number of items which will be put into the filter.
expected number of bits of the filter.
2.0.0
Builds a Bloom filter over a specified column.
Builds a Bloom filter over a specified column.
name of the column over which the filter is built
expected number of items which will be put into the filter.
expected number of bits of the filter.
2.0.0
Builds a Bloom filter over a specified column.
Builds a Bloom filter over a specified column.
the column over which the filter is built
expected number of items which will be put into the filter.
expected false positive probability of the filter.
2.0.0
Builds a Bloom filter over a specified column.
Builds a Bloom filter over a specified column.
name of the column over which the filter is built
expected number of items which will be put into the filter.
expected false positive probability of the filter.
2.0.0
Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
the name of the column
the name of the column to calculate the correlation against
The Pearson Correlation Coefficient as a Double.
val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) .withColumn("rand2", rand(seed=27)) df.stat.corr("rand1", "rand2", "pearson") res1: Double = 0.613...
1.4.0
Calculates the correlation of two columns of a DataFrame.
Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in MLlib's Statistics.
the name of the column
the name of the column to calculate the correlation against
The Pearson Correlation Coefficient as a Double.
val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) .withColumn("rand2", rand(seed=27)) df.stat.corr("rand1", "rand2") res1: Double = 0.613...
1.4.0
Builds a Count-min Sketch over a specified column.
Builds a Count-min Sketch over a specified column.
the column over which the sketch is built
relative error of the sketch
confidence of the sketch
random seed
a CountMinSketch over column colName
2.0.0
Builds a Count-min Sketch over a specified column.
Builds a Count-min Sketch over a specified column.
the column over which the sketch is built
depth of the sketch
width of the sketch
random seed
a CountMinSketch over column colName
2.0.0
Builds a Count-min Sketch over a specified column.
Builds a Count-min Sketch over a specified column.
name of the column over which the sketch is built
relative error of the sketch
confidence of the sketch
random seed
a CountMinSketch over column colName
2.0.0
Builds a Count-min Sketch over a specified column.
Builds a Count-min Sketch over a specified column.
name of the column over which the sketch is built
depth of the sketch
width of the sketch
random seed
a CountMinSketch over column colName
2.0.0
Calculate the sample covariance of two numerical columns of a DataFrame.
Calculate the sample covariance of two numerical columns of a DataFrame.
the name of the first column
the name of the second column
the covariance of the two columns.
val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) .withColumn("rand2", rand(seed=27)) df.stat.cov("rand1", "rand2") res1: Double = 0.065...
1.4.0
Computes a pair-wise frequency table of the given columns.
Computes a pair-wise frequency table of the given columns. Also
known as a contingency table. The number of distinct values for
each column should be less than 1e4. At most 1e6 non-zero pair
frequencies will be returned. The first column of each row will be
the distinct values of col1 and the column names will be the
distinct values of col2. The name of the first column will be
col1_col2. Counts will be returned as Longs. Pairs that have no
occurrences will have zero as their counts. Null elements will be
replaced by "null", and back ticks will be dropped from elements if
they exist.
The name of the first column. Distinct items will make the first item of each row.
The name of the second column. Distinct items will make the column names of the DataFrame.
A DataFrame containing for the contingency table.
val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3))) .toDF("key", "value") val ct = df.stat.crosstab("key", "value") ct.show() +---------+---+---+---+ |key_value| 1| 2| 3| +---------+---+---+---+ | 2| 2| 0| 1| | 1| 1| 1| 0| | 3| 0| 1| 1| +---------+---+---+---+
1.4.0
Finding frequent items for columns, possibly with false positives.
Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in <a href="http://dx.doi.org/10.1145/762471.762473">here, proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%. This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting `DataFrame`.
the names of the columns to search frequent items in.
A Local DataFrame with the Array of frequent items for each column.
1.4.0
Finding frequent items for columns, possibly with false positives.
Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in <a href="http://dx.doi.org/10.1145/762471.762473">here, proposed by Karp, Schenker, and Papadimitriou. This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting `DataFrame`.
the names of the columns to search frequent items in.
A Local DataFrame with the Array of frequent items for each column.
val rows = Seq.tabulate(100) { i => if (i % 2 == 0) (1, -1.0) else (i, i * -1.0) } val df = spark.createDataFrame(rows).toDF("a", "b") // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns // "a" and "b" val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4) freqSingles.show() +-----------+-------------+ |a_freqItems| b_freqItems| +-----------+-------------+ | [1, 99]|[-1.0, -99.0]| +-----------+-------------+ // find the pair of items with a frequency greater than 0.1 in columns "a" and "b" val pairDf = df.select(struct("a", "b").as("a-b")) val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1) freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show() +----------+ | freq_ab| +----------+ | [1,-1.0]| | ... | +----------+
1.4.0
Applies an action to the underlying DataFrameStatFunctions.
Applies an action to the underlying DataFrameStatFunctions, it is used for transformations that can fail due to an AnalysisException.
Returns a stratified sample without replacement based on the fraction given on each stratum.
Returns a stratified sample without replacement based on the fraction given on each stratum.
stratum type
column that defines strata
sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.
random seed
a new DataFrame that represents the stratified sample
val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3))).toDF("key", "value") val fractions = Map(1 -> 1.0, 3 -> 0.5) df.stat.sampleBy("key", fractions, 36L).show() +---+-----+ |key|value| +---+-----+ | 1| 1| | 1| 2| | 3| 2| +---+-----+
1.5.0
Applies a transformation to the underlying DataFrameStatFunctions.
Applies a transformation to the underlying DataFrameStatFunctions, it is used for transformations that can fail due to an AnalysisException.
Unpack the underlying DataFrameStatFunctions into a DataFrame.
Unpack the underlying DataFrameStatFunctions into a DataFrame, it is used for transformations that can fail due to an AnalysisException.