mteb/BrightProStackoverflowRetrieval huggingface dataset:
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  * __
  * [ Meta ](../meta.html)
  * polars.Expr.... 

#  polars.Expr.meta.root_names  #

Expr.meta.  root_names  (  )  →  [ list
](https://docs.python.org/3/library/stdtypes.html#list "\(in Python v3.12\)")
[  [ str  ](https://docs.python.org/3/library/stdtypes.html#str "\(in Python
v3.12\)") ]  [ [source]  ](https://github.com/pola-rs/polars/blob/main/py-
polars/polars/expr/meta.py#L169-L188) #

    

Get a list with the root column name.

Examples

    
    
    >>> e = pl.col("foo") * pl.col("bar")
    >>> e.meta.root_names()
    ['foo', 'bar']
    >>> e_filter = pl.col("foo").filter(pl.col("bar") == 13)
    >>> e_filter.meta.root_names()
    ['foo', 'bar']
    >>> e_sum_over = pl.sum("foo").over("groups")
    >>> e_sum_over.meta.root_names()
    ['foo', 'groups']
    >>> e_sum_slice = pl.sum("foo").slice(pl.len() - 10, pl.col("bar"))
    >>> e_sum_slice.meta.root_names()
    ['foo', 'bar']
    

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  * __
  * [ List ](../list.html)
  * polars.Serie... 

#  polars.Series.list.last  #

Series.list.  last  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L546-L561) #

    

Get the last value of the sublists.

Examples

    
    
    >>> s = pl.Series("a", [[3, 2, 1], [], [1, 2]])
    >>> s.list.last()
    shape: (3,)
    Series: 'a' [i64]
    [
        1
        null
        2
    ]
    

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  * ` Series.list.last()  `

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  * [ Functions ](../functions.html)
  * polars.cum_count 

#  polars.cum_count  #

polars.  cum_count  (  _ *  columns  :  [ str
](https://docs.python.org/3/library/stdtypes.html#str "\(in Python v3.12\)") _
, _ reverse  :  [ bool
](https://docs.python.org/3/library/functions.html#bool "\(in Python v3.12\)")
=  False  _ )  →  Expr  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/functions/lazy.py#L170-L212) #

    

Return the cumulative count of the non-null values in the column.

This function is syntactic sugar for ` col(columns).cum_count()  ` .

If no arguments are passed, returns the cumulative count of a context. Rows
containing null values count towards the result.

Parameters  :

    

***columns**

    

Name(s) of the columns to use.

**reverse**

    

Reverse the operation.

Examples

    
    
    >>> df = pl.DataFrame({"a": [1, 2, None], "b": [3, None, None]})
    >>> df.select(pl.cum_count("a"))
    shape: (3, 1)
    ┌─────┐
    │ a   │
    │ --- │
    │ u32 │
    ╞═════╡
    │ 1   │
    │ 2   │
    │ 2   │
    └─────┘
    

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  * __
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  * polars.Expr.... 

#  polars.Expr.str.to_uppercase  #

Expr.str.  to_uppercase  (  )  →  Expr  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/expr/string.py#L512-L530) #

    

Transform to uppercase variant.

Examples

    
    
    >>> df = pl.DataFrame({"foo": ["cat", "dog"]})
    >>> df.with_columns(foo_upper=pl.col("foo").str.to_uppercase())
    shape: (2, 2)
    ┌─────┬───────────┐
    │ foo ┆ foo_upper │
    │ --- ┆ ---       │
    │ str ┆ str       │
    ╞═════╪═══════════╡
    │ cat ┆ CAT       │
    │ dog ┆ DOG       │
    └─────┴───────────┘
    

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  * polars.sum_h... 

#  polars.sum_horizontal  #

polars.  sum_horizontal  (  _ *  exprs  :  IntoExpr  |  Iterable  [  IntoExpr  ]  _ )  →  Expr  [ [source]  ](https://github.com/pola-rs/polars/blob/main/py-polars/polars/functions/aggregation/horizontal.py#L180-L212) # 
    

Sum all values horizontally across columns.

Parameters  :

    

***exprs**

    

Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.

Examples

    
    
    >>> df = pl.DataFrame(
    ...     {
    ...         "a": [1, 8, 3],
    ...         "b": [4, 5, None],
    ...         "c": ["x", "y", "z"],
    ...     }
    ... )
    >>> df.with_columns(sum=pl.sum_horizontal("a", "b"))
    shape: (3, 4)
    ┌─────┬──────┬─────┬─────┐
    │ a   ┆ b    ┆ c   ┆ sum │
    │ --- ┆ ---  ┆ --- ┆ --- │
    │ i64 ┆ i64  ┆ str ┆ i64 │
    ╞═════╪══════╪═════╪═════╡
    │ 1   ┆ 4    ┆ x   ┆ 5   │
    │ 8   ┆ 5    ┆ y   ┆ 13  │
    │ 3   ┆ null ┆ z   ┆ 3   │
    └─────┴──────┴─────┴─────┘
    

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  * __
  * [ Computation ](../computation.html)
  * polars.Expr.exp 

#  polars.Expr.exp  #

Expr.  exp  (  )  →  Self  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/expr/expr.py#L628-L647) #

    

Compute the exponential, element-wise.

Examples

    
    
    >>> df = pl.DataFrame({"values": [1.0, 2.0, 4.0]})
    >>> df.select(pl.col("values").exp())
    shape: (3, 1)
    ┌──────────┐
    │ values   │
    │ ---      │
    │ f64      │
    ╞══════════╡
    │ 2.718282 │
    │ 7.389056 │
    │ 54.59815 │
    └──────────┘
    

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  * __
  * [ List ](../list.html)
  * polars.Expr.... 

#  polars.Expr.list.explode  #

Expr.list.  explode  (  )  →  Expr  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/expr/list.py#L1002-L1033) #

    

Returns a column with a separate row for every list element.

Returns  :

    

Expr

    

Expression with the data type of the list elements.

See also

` ExprNameSpace.reshape  `

    

Reshape this Expr to a flat Series or a Series of Lists.

Examples

    
    
    >>> df = pl.DataFrame({"a": [[1, 2, 3], [4, 5, 6]]})
    >>> df.select(pl.col("a").list.explode())
    shape: (6, 1)
    ┌─────┐
    │ a   │
    │ --- │
    │ i64 │
    ╞═════╡
    │ 1   │
    │ 2   │
    │ 3   │
    │ 4   │
    │ 5   │
    │ 6   │
    └─────┘
    

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page")

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  * ` Expr.list.explode()  `

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  * [ List ](../list.html)
  * polars.Serie... 

#  polars.Series.list.min  #

Series.list.  min  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L204-L218) #

    

Compute the min value of the arrays in the list.

Examples

    
    
    >>> s = pl.Series("values", [[4, 1], [2, 3]])
    >>> s.list.min()
    shape: (2,)
    Series: 'values' [i64]
    [
        1
        2
    ]
    

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