Transformational

`polars_ta.wq.transformational`

Functions:

Name	Description
`bool_`	int转成bool
`clamp`	Limits input value between lower and upper bound in inverse = false mode (which is default). Alternatively, when inverse = true, values between bounds are replaced with mask, while values outside bounds are left as is.
`cut`	分箱
`fill_nan`	填充`nan`为`null`
`fill_null`	填充`null`为`value`
`float_`	int转成float
`int_`	bool转int
`left_tail`	NaN everything greater than maximum, maximum should be constant.
`lit_`	将常量封装成lit
`logit`	对数几率，拉伸中间部分
`nop`	空操作
`purify`	Clear infinities (+inf, -inf) by replacing with null.
`right_tail`	NaN everything less than minimum, minimum should be constant.
`sigmoid`	sigmoid激活函数
`tail`	If (x > lower AND x < upper) return newval, else return x. Lower, upper, newval should be constants.

`bool_(a: Expr) -> Expr`

int转成bool

`clamp(x: Expr, lower: float = 0, upper: float = 0, inverse: bool = False, mask: float = None) -> Expr`

Limits input value between lower and upper bound in inverse = false mode (which is default). Alternatively, when inverse = true, values between bounds are replaced with mask, while values outside bounds are left as is.

Examples:

df = pl.DataFrame({
    'a': [None, 1, 2, 3, 4, 5, 6],
}).with_columns(
    out1=clamp(pl.col('a'), 2, 5, False),
    out2=clamp(pl.col('a'), 2, 5, True),
)
shape: (7, 3)
┌──────┬──────┬──────┐
│ a    ┆ out1 ┆ out2 │
│ ---  ┆ ---  ┆ ---  │
│ i64  ┆ i64  ┆ i64  │
╞══════╪══════╪══════╡
│ null ┆ null ┆ null │
│ 1    ┆ 2    ┆ 1    │
│ 2    ┆ 2    ┆ null │
│ 3    ┆ 3    ┆ null │
│ 4    ┆ 4    ┆ null │
│ 5    ┆ 5    ┆ null │
│ 6    ┆ 5    ┆ 6    │
└──────┴──────┴──────┘

References

https://platform.worldquantbrain.com/learn/operators/detailed-operator-descriptions#clampx-lower-0-upper-0-inverse-false-mask

`cut(x: Expr, b: float, *more_bins) -> Expr`

分箱

Parameters:

Name	Type	Default
`x`	`Expr`	required
`b`	`float`	required
`*more_bins`		`()`

Examples:

df = pl.DataFrame({
    'a': [None, 1, 1, 1, 2, 2, 3, 10],
}).with_columns(
    out1=cut(pl.col('a'), 2, 5, 20),
)
shape: (8, 2)
┌──────┬──────┐
│ a    ┆ out1 │
│ ---  ┆ ---  │
│ i64  ┆ u32  │
╞══════╪══════╡
│ null ┆ null │
│ 1    ┆ 0    │
│ 1    ┆ 0    │
│ 1    ┆ 0    │
│ 2    ┆ 0    │
│ 2    ┆ 0    │
│ 3    ┆ 1    │
│ 10   ┆ 2    │
└──────┴──────┘

`fill_nan(x: Expr) -> Expr`

填充nan为null

`fill_null(x: Expr, value=0) -> Expr`

填充null为value

`float_(a: Expr) -> Expr`

int转成float

`int_(a: Expr) -> Expr`

bool转int

`left_tail(x: Expr, maximum: float = 0) -> Expr`

NaN everything greater than maximum, maximum should be constant.

Examples:

df = pl.DataFrame({
    'a': [None, 1, 2, 3, 4, 5],
}).with_columns(
    out=left_tail(pl.col('a'), 3),
)
shape: (6, 2)
┌──────┬──────┐
│ a    ┆ out  │
│ ---  ┆ ---  │
│ i64  ┆ i64  │
╞══════╪══════╡
│ null ┆ null │
│ 1    ┆ 1    │
│ 2    ┆ 2    │
│ 3    ┆ 3    │
│ 4    ┆ null │
│ 5    ┆ null │
└──────┴──────┘

`lit_(a: Expr) -> Expr`

将常量封装成lit

`logit(x: Expr) -> Expr`

对数几率，拉伸中间部分

`nop(x: Expr) -> Expr`

空操作

`purify(x: Expr) -> Expr`

Clear infinities (+inf, -inf) by replacing with null.

Examples:

df = pl.DataFrame({
    'a': [None, 1., 2., float('nan'), float('inf'), float('-inf')],
}).with_columns(
    out=purify(pl.col('a')),
)
shape: (6, 2)
┌──────┬──────┐
│ a    ┆ out  │
│ ---  ┆ ---  │
│ f64  ┆ f64  │
╞══════╪══════╡
│ null ┆ null │
│ 1.0  ┆ 1.0  │
│ 2.0  ┆ 2.0  │
│ NaN  ┆ null │
│ inf  ┆ null │
│ -inf ┆ null │
└──────┴──────┘

`right_tail(x: Expr, minimum: float = 0) -> Expr`

NaN everything less than minimum, minimum should be constant.

Examples:

df = pl.DataFrame({
    'a': [None, 1, 2, 3, 4, 5],
}).with_columns(
    out=right_tail(pl.col('a'), 3),
)
shape: (6, 2)
┌──────┬──────┐
│ a    ┆ out  │
│ ---  ┆ ---  │
│ i64  ┆ i64  │
╞══════╪══════╡
│ null ┆ null │
│ 1    ┆ null │
│ 2    ┆ null │
│ 3    ┆ 3    │
│ 4    ┆ 4    │
│ 5    ┆ 5    │
└──────┴──────┘

References

https://platform.worldquantbrain.com/learn/operators/detailed-operator-descriptions#right_tail

`sigmoid(x: Expr) -> Expr`

sigmoid激活函数

`tail(x: Expr, lower: float = 0, upper: float = 0, newval: float = 0) -> Expr`

If (x > lower AND x < upper) return newval, else return x. Lower, upper, newval should be constants.

Examples:

df = pl.DataFrame({
    'a': [None, 1, 2, 3, 4, 5, 6],
}).with_columns(
    out=tail(pl.col('a'), 2, 5, 1),
)
shape: (7, 2)
┌──────┬──────┐
│ a    ┆ out  │
│ ---  ┆ ---  │
│ i64  ┆ i64  │
╞══════╪══════╡
│ null ┆ null │
│ 1    ┆ 1    │
│ 2    ┆ 2    │
│ 3    ┆ 1    │
│ 4    ┆ 1    │
│ 5    ┆ 5    │
│ 6    ┆ 6    │
└──────┴──────┘