Aggregate functions

Functions that fold the rows of a group into one value. Names are case-insensitive. Parameters of a function go in a first pair of parentheses, as in quantile(0.9)(x). The combinators extend every function here.

Name	Arguments	Description
`any`	`x`	Returns the first value met in a group.
`anyLast`	`x`	Returns the last value met in a group.
`argMax`	`arg, val`	Returns arg from the row where val is largest.
`argMin`	`arg, val`	Returns arg from the row where val is smallest.
`avg`	`x`	Returns the arithmetic mean of a numeric column.
`avgWeighted`	`x, weight`	Returns the mean of a column weighted by a second column.
`corr`	`x, y`	Returns the Pearson correlation coefficient of two columns.
`count`	`[x]`	Counts rows, or the rows where the argument is not NULL.
`covarPop`	`x, y`	Returns the population covariance of two columns.
`covarSamp`	`x, y`	Returns the sample covariance of two columns.
`deltaSum`	`x`	Sums the increases between consecutive rows and ignores decreases.
`entropy`	`x`	Returns the Shannon entropy of the values of a group.
`groupArray`	`x`	Collects the values of a group into an array.
`groupBitAnd`	`x`	Returns the bitwise AND of all values of a group.
`groupBitmap`	`x`	Counts the distinct integers of a group with a bitmap.
`groupBitOr`	`x`	Returns the bitwise OR of all values of a group.
`groupBitXor`	`x`	Returns the bitwise XOR of all values of a group.
`kolmogorovSmirnovTest`	`value, sample_index`	Returns the statistic and the p-value of the Kolmogorov-Smirnov test over two samples.
`kurtPop`	`x`	Returns the population kurtosis.
`kurtSamp`	`x`	Returns the sample kurtosis.
`mannWhitneyUTest`	`value, sample_index`	Returns the U statistic and the p-value of the Mann-Whitney test over two samples.
`max`	`x`	Returns the largest value of a column.
`maxMap`	`keys, values`	Takes the largest value for each key over the rows of a group.
`median`	`x`	Estimates the median of a group with a t-digest sketch.
`medianExact`	`x`	Returns the exact median, taking the nearest rank.
`medianExactInterpolated`	`x`	Returns the exact median, interpolating linearly between the two nearest values.
`min`	`x`	Returns the smallest value of a column.
`minMap`	`keys, values`	Takes the smallest value for each key over the rows of a group.
`quantile`	`(level)(x)`	Estimates the quantile at the given level, between 0 and 1, of a group with a t-digest sketch. Without the parameter the level is 0.5.
`quantileExact`	`(level)(x)`	Returns an exact quantile, taking the nearest rank.
`quantileExactInterpolated`	`(level)(x)`	Returns an exact quantile, interpolating linearly between the two nearest values.
`quantiles`	`(level, ...)(x)`	Estimates several quantiles of a group at once with a t-digest sketch and returns them as an array. Without parameters the levels are 0.25, 0.5, 0.75, 0.9 and 0.99.
`quantilesExact`	`(level, ...)(x)`	Returns several exact quantiles as an array, taking the nearest rank.
`quantilesExactInterpolated`	`(level, ...)(x)`	Returns several exact quantiles as an array, interpolating linearly between the two nearest values.
`retention`	`cond1, cond2, ...`	Returns an array that tells, for each later condition, whether it held in a row together with the first condition.
`sequenceCount`	`timestamp, cond1, cond2, ...`	Counts the event chains of a group that match a pattern of conditions.
`sequenceMatch`	`timestamp, cond1, cond2, ...`	Returns 1 when the events of a group match a pattern of conditions.
`simpleLinearRegression`	`x, y`	Fits a least-squares line and returns the slope and the intercept as an array.
`skewPop`	`x`	Returns the population skewness.
`skewSamp`	`x`	Returns the sample skewness.
`stddevPop`	`x`	Returns the population standard deviation.
`stddevSamp`	`x`	Returns the sample standard deviation.
`stochasticLinearRegression`	`target, feature1, ...`	Trains a linear regression model with stochastic gradient descent and returns the weights as an array.
`stochasticLogisticRegression`	`target, feature1, ...`	Trains a logistic regression model with stochastic gradient descent and returns the weights as an array.
`studentTTest`	`value, sample_index`	Returns the t statistic and the p-value of Student's t-test over two samples.
`sum`	`x`	Returns the sum of a numeric column.
`sumMap`	`keys, values`	Sums the values for each key over the rows of a group.
`topK`	`(k)(x)`	Returns the k most frequent values of a group, counted approximately. Without the parameter k is 10.
`uniq`	`x`	Estimates the number of distinct values with a HyperLogLog sketch.
`uniqExact`	`x`	Counts the exact number of distinct values.
`uniqUpTo`	`x`	Counts distinct values up to a limit and returns the limit plus one above it.
`varPop`	`x`	Returns the population variance.
`varSamp`	`x`	Returns the sample variance.
`welchTTest`	`value, sample_index`	Returns the t statistic and the p-value of Welch's t-test over two samples.
`windowFunnel`	`timestamp, cond1, cond2, ...`	Returns the longest chain of conditions that hold in order within a time window.