API

内置模块和功能

我们提供了大量的内置函数供用户使用。下面是一个内置函数的列表。只要在脚本开头写上 import greptime 或 from greptime import * 就可以调用它们。

Function	Description
`pow(v0, v1)`	Raise a number `v0` to a power of `v1`.
`clip(v0, v1, v2)`	Clip all elements in a vector `v0` to a range between vectors `v1` and `v2`.
`diff(v0)`	Calculate the difference between adjacent elements in a vector `v0`.
`mean(v0)`	Calculate the mean of a vector `v0`.
`polyval(v0, v1)`	Evaluate a polynomial `v0` at points `v1`. similar to `numpy.polyval`.
`argmax(v0)`	Return the index of the maximum value in a vector `v0`. similar to `numpy.argmax`.
`argmin(v0)`	Return the index of the minimum value in a vector `v0`. similar to `numpy.argmin`.
`percentile`	Calculate the `q`-th percentile of a vector `v0`. similar to `numpy.percentile`.
`scipy_stats_norm_cdf`	Calculate the cumulative distribution function for the normal distribution. similar to `scipy.stats.norm.cdf`.
`scipy_stats_norm_pdf`	Calculate the probability density function for the normal distribution. similar to `scipy.stats.norm.pdf`.

Function	Description
`sqrt(v)`	Calculate the square root of a number `v`.
`sin(v)`	Calculate the sine of a number `v`.
`cos(v)`	Calculate the cosine of a number `v`.
`tan(v)`	Calculate the tangent of a number `v`.
`asin(v)`	Calculate the arcsine of a number `v`.
`acos(v)`	Calculate the arccosine of a number `v`.
`atan(v)`	Calculate the arctangent of a number `v`.
`floor(v)`	Calculate the floor of a number `v`.
`ceil(v)`	Calculate the ceiling of a number `v`.
`round(v)`	Calculate the nearest integer of a number `v`.
`trunc(v)`	Calculate the truncated integer of a number `v`.
`abs(v)`	Calculate the absolute value of a number `v`.
`signum(v)`	Calculate the sign(gives 1.0/-1.0) of a number `v`.
`exp(v)`	Calculate the exponential of a number `v`.
`ln(v)`	Calculate the natural logarithm of a number `v`.
`log2(v)`	Calculate the base-2 logarithm of a number `v`.
`log10(v)`	Calculate the base-10 logarithm of a number `v`.

这些函数是由 DataFusion 绑定的。

Function	Description
`random(len)`	Generate a random vector with length `len`.
`approx_distinct(v0)`	Calculate the approximate number of distinct values in a vector `v0`.
`median(v0)`	Calculate the median of a vector `v0`.
`approx_percentile_cont(values, percent)`	Calculate the approximate percentile of a vector `values` at a given percentage `percent`.
`array_agg(v0)`	Aggregate values into an array.
`avg(v0)`	Calculate the average of a vector `v0`.
`correlation(v0, v1)`	Calculate the Pearson correlation coefficient of a vector `v0` and a vector `v1`.
`count(v0)`	Calculate the count of a vector `v0`.
`covariance(v0, v1)`	Calculate the covariance of a vector `v0` and a vector `v1`.
`covariance_pop(v0, v1)`	Calculate the population covariance of a vector `v0` and a vector `v1`.
`max(v0)`	Calculate the maximum of a vector `v0`.
`min(v0)`	Calculate the minimum of a vector `v0`.
`stddev(v0)`	Calculate the sample standard deviation of a vector `v0`.
`stddev_pop(v0)`	Calculate the population standard deviation of a vector `v0`.
`sum(v0)`	Calculate the sum of a vector `v0`.
`variance(v0)`	Calculate the sample variance of a vector `v0`.
`variance_pop(v0)`	Calculate the population variance of a vector `v0`.

Method	Description
`select_columns(columns: List[str])`	select columns from DataFrame
`select(columns: List[Expr]])`	select columns from DataFrame using `PyExpr`
`filter(condition: Expr)`	filter DataFrame using `PyExpr`
`aggregate(group_expr: List[Expr], aggr_expr: List[Expr])`	Perform an aggregate query with optional grouping expressions.
`limit(skip: int, fetch: Optional[int])`	Limit the number of rows returned from this DataFrame. `skip` - Number of rows to skip before fetch any row; `fetch` - Maximum number of rows to fetch, after skipping skip rows.
`union(other: DataFrame)`	Union two DataFrame
`union_distinct(other: DataFrame)`	Union two DataFrame, but remove duplicate rows
`distinct()`	Remove duplicate rows
`sort(expr: List[Expr])`	Sort DataFrame by `PyExpr`, Sort the DataFrame by the specified sorting expressions. Any expression can be turned into a sort expression by calling its sort method.
`join(right: DataFrame, left_cols: List[str], right_cols: List[str], filter: Optional[Expr])`	Join two DataFrame using the specified columns as join keys. Eight Join Types are supported: `inner`, `left`, `right`, `full`, `leftSemi`, `leftAnti`, `rightSemi`, `rightAnti`.
`intersect(other: DataFrame)`	Intersect two DataFrame
`except(other: DataFrame)`	Except two DataFrame
`collect()`	Collect DataFrame to a list of `PyVector`

Method	Description
`col(name: str)`	Create a `PyExpr` that represents a column
`lit(value: Any)`	Create a `PyExpr` that represents a literal value
`sort(ascending: bool, null_first: bool)`	Create a `PyExpr` that represents a sort expression
comparison operators: `==`, `!=`, `>`, `>=`, `<`, `<=`	Create `PyExpr` from compare two `PyExpr`
logical operators: `&`, `\|`, `~`	Create `PyExpr` from logical operation between two `PyExpr`