Module polars_lazy::dsl

Domain specific language for the Lazy API.

This DSL revolves around the Expr type, which represents an abstract operation on a DataFrame, such as mapping over a column, filtering, group_by, or aggregation. In general, functions on LazyFrames consume the LazyFrame and produce a new LazyFrame representing the result of applying the function and passed expressions to the consumed LazyFrame. At runtime, when LazyFrame::collect is called, the expressions that comprise the LazyFrame’s logical plan are materialized on the actual underlying Series. For instance, let expr = col("x").pow(lit(2)).alias("x2"); would produce an expression representing the abstract operation of squaring the column "x" and naming the resulting column "x2", and to apply this operation to a LazyFrame, you’d use let lazy_df = lazy_df.with_column(expr);. (Of course, a column named "x" must either exist in the original DataFrame or be produced by one of the preceding operations on the LazyFrame.)

There are many, many free functions that this module exports that produce an Expr from scratch; col and lit are two examples. Expressions also have several methods, such as pow and alias, that consume them and produce a new expression.

Several expressions are only available when the necessary feature is enabled. Examples of features that unlock specialized expression include string, temporal, and dtype-categorical. These specialized expressions provide implementations of functions that you’d otherwise have to implement by hand.

Because of how abstract and flexible the Expr type is, care must be take to ensure you only attempt to perform sensible operations with them. For instance, as mentioned above, you have to make sure any columns you reference already exist in the LazyFrame. Furthermore, there is nothing stopping you from calling, for example, any with an expression that will yield an f64 column (instead of bool), or col("string") - col("f64"), which would attempt to subtract an f64 Series from a string Series. These kinds of invalid operations will only yield an error at runtime, when collect is called on the LazyFrame.

Re-exports

• pub use functions::*;

Modules

• binary
• catdtype-categorical
• consts
• dttemporal
• functions
  Functions
• stringstrings
• udf

Structs

• ArrayNameSpace
  Specialized expressions for Series of DataType::List.
• CategoricalNameSpace
  Specialized expressions for Categorical dtypes.
• ChainedThen
  Utility struct for the when-then-otherwise expression.
• ChainedWhen
  Utility struct for the when-then-otherwise expression.
• DatetimeArgs
  Arguments used by datetime in order to produce an Expr of Datetime
• DurationArgs
  Arguments used by duration in order to produce an Expr of Duration
• FieldsMapper
• JoinOptions
• ListNameSpace
  Specialized expressions for Series of DataType::List.
• MetaNameSpace
  Specialized expressions for Categorical dtypes.
• RollingCovOptions
• SpecialEq
  Wrapper type that has special equality properties depending on the inner type specialization
• StrptimeOptions
• StructNameSpace
  Specialized expressions for Struct dtypes.
• Then
  Utility struct for the when-then-otherwise expression.
• UserDefinedFunction
  Represents a user-defined function
• When
  Utility struct for the when-then-otherwise expression.

Enums

• AggExpr
• BooleanFunction
• CategoricalOrdering
• Excluded
• Expr
  Expressions that can be used in various contexts. Queries consist of multiple expressions. When using the polars lazy API, don’t construct an Expr directly; instead, create one using the functions in the polars_lazy::dsl module. See that module’s docs for more info.
• FunctionExpr
• Operator
• WindowMapping
• WindowType

Traits

• BinaryUdfOutputField
• ExprEvalExtensioncumulative_eval or list_eval
• FunctionOutputField
• IntoListNameSpacelist_eval
• ListNameSpaceExtensionlist_eval
• RenameAliasFn
• SeriesBinaryUdf
  A wrapper trait for any binary closure Fn(Series, Series) -> PolarsResult<Series>
• SeriesUdf
  A wrapper trait for any closure Fn(Vec<Series>) -> PolarsResult<Series>
• UdfSchema

Functions

• all
  Selects all columns. Shorthand for col("*").
• all_horizontal
  Create a new column with the bitwise-and of the elements in each row.
• any_horizontal
  Create a new column with the bitwise-or of the elements in each row.
• apply_binary
  Like map_binary, but used in a group_by-aggregation context.
• apply_multiple
  Apply a function/closure over the groups of multiple columns. This should only be used in a group_by aggregation.
• arange
  Generate a range of integers.
• arg_sort_byrange
  Find the indexes that would sort these series in order of appearance. That means that the first Series will be used to determine the ordering until duplicates are found. Once duplicates are found, the next Series will be used and so on.
• arg_wherearg_where
  Get the indices where condition evaluates true.
• as_structdtype-struct
  Take several expressions and collect them into a StructChunked.
• avg
  Find the mean of all the values in the column named name. Alias for mean.
• binary_expr
  Compute op(l, r) (or equivalently l op r). l and r must have types compatible with the Operator.
• cast
  Casts the column given by Expr to a different type.
• coalesce
  Folds the expressions from left to right keeping the first non-null values.
• col
  Create a Column Expression based on a column name.
• cols
  Select multiple columns by name.
• concat_expr
• concat_list
  Concat lists entries.
• concat_strconcat_str and strings
  Horizontally concat string columns in linear time
• count
  Count expression.
• cov
  Compute the covariance between two columns.
• cum_fold_exprsdtype-struct
  Accumulate over multiple columns horizontally / row wise.
• cum_reduce_exprsdtype-struct
  Accumulate over multiple columns horizontally / row wise.
• date_rangetemporal
  Create a date range from a start and stop expression.
• date_rangestemporal
  Create a column of date ranges from a start and stop expression.
• datetimetemporal
  Construct a column of Datetime from the provided DatetimeArgs.
• datetime_rangedtype-datetime
  Create a datetime range from a start and stop expression.
• datetime_rangesdtype-datetime
  Create a column of datetime ranges from a start and stop expression.
• dtype_col
  Select multiple columns by dtype.
• dtype_cols
  Select multiple columns by dtype.
• durationtemporal
  Construct a column of Duration from the provided DurationArgs
• first
  First column in DataFrame.
• fold_exprs
  Accumulate over multiple columns horizontally / row wise.
• format_strconcat_str and strings
  Format the results of an array of expressions using a format string
• int_range
  Generate a range of integers.
• int_ranges
  Generate a range of integers for each row of the input columns.
• is_not_null
  A column which is false wherever expr is null, true elsewhere.
• is_null
  A column which is true wherever expr is null, false elsewhere.
• last
  Last column in DataFrame.
• lit
  Create a Literal Expression from L. A literal expression behaves like a column that contains a single distinct value.
• map_binary
  Apply a closure on the two columns that are evaluated from Expr a and Expr b.
• map_list_multiple
  Apply a function/closure over multiple columns once the logical plan get executed.
• map_multiple
  Apply a function/closure over multiple columns once the logical plan get executed.
• max
  Find the maximum of all the values in the column named name. Shorthand for col(name).max().
• max_horizontal
  Create a new column with the maximum value per row.
• mean
  Find the mean of all the values in the column named name. Shorthand for col(name).mean().
• median
  Find the median of all the values in the column named name. Shorthand for col(name).median().
• min
  Find the minimum of all the values in the column named name. Shorthand for col(name).min().
• min_horizontal
  Create a new column with the minimum value per row.
• not
  Negates a boolean column.
• pearson_corr
  Compute the pearson correlation between two columns.
• quantile
  Find a specific quantile of all the values in the column named name.
• reduce_exprs
  Analogous to Iterator::reduce.
• repeat
  Create a column of length n containing n copies of the literal value. Generally you won’t need this function, as lit(value) already represents a column containing only value whose length is automatically set to the correct number of rows.
• rolling_corrrolling_window
• rolling_covrolling_window
• spearman_rank_corrrank and propagate_nans
  Compute the spearman rank correlation between two columns. Missing data will be excluded from the computation.
• sum
  Sum all the values in the column named name. Shorthand for col(name).sum().
• sum_horizontal
  Create a new column with the sum of the values in each row.
• ternary_expr
• time_rangedtype-time
  Generate a time range.
• time_rangesdtype-time
  Create a column of time ranges from a start and stop expression.
• when
  Start a when-then-otherwise expression.

Type Aliases

• GetOutput