mteb/BrightProStackoverflowRetrieval huggingface dataset:
(../../api/polars.exceptions.SchemaError.html)
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  * __
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  * polars.Serie... 

#  polars.Series.list.mean  #

Series.list.  mean  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L220-L234) #

    

Compute the mean value of the arrays in the list.

Examples

    
    
    >>> s = pl.Series("values", [[3, 1], [3, 3]])
    >>> s.list.mean()
    shape: (2,)
    Series: 'values' [f64]
    [
        2.0
        3.0
    ]
    

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  * ` Series.list.mean()  `

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

#  polars.Series.list.any  #

Series.list.  any  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L63-L89) #

    

Evaluate whether any boolean value in a list is true.

Returns  :

    

Series

    

Series of data type ` Boolean  ` .

Examples

    
    
    >>> s = pl.Series(
    ...     [[True, True], [False, True], [False, False], [None], [], None],
    ...     dtype=pl.List(pl.Boolean),
    ... )
    >>> s.list.any()
    shape: (6,)
    Series: '' [bool]
    [
        true
        true
        false
        false
        false
        null
    ]
    

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  * ` Series.list.any()  `

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  * __
  * [ Manipulation/selection ](../modify_select.html)
  * polars.Expr.arg_true 

#  polars.Expr.arg_true  #

Expr.  arg_true  (  )  →  Self  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/expr/expr.py#L535-L563) #

    

Return indices where expression evaluates ` True  ` .

Warning

Modifies number of rows returned, so will fail in combination with other
expressions. Use as only expression in ` select  ` / ` with_columns  ` .

See also

[ ` Series.arg_true  `
](../../series/api/polars.Series.arg_true.html#polars.Series.arg_true
"polars.Series.arg_true")

    

Return indices where Series is True

[ ` polars.arg_where  ` ](polars.arg_where.html#polars.arg_where
"polars.arg_where")

    

Examples

    
    
    >>> df = pl.DataFrame({"a": [1, 1, 2, 1]})
    >>> df.select((pl.col("a") == 1).arg_true())
    shape: (3, 1)
    ┌─────┐
    │ a   │
    │ --- │
    │ u32 │
    ╞═════╡
    │ 0   │
    │ 1   │
    │ 3   │
    └─────┘
    

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  * __
  * [ Name ](../name.html)
  * polars.Expr.... 

#  polars.Expr.name.suffix_fields  #

Expr.name.  suffix_fields  (  _ suffix  :  [ str
](https://docs.python.org/3/library/stdtypes.html#str "\(in Python v3.12\)") _
)  →  Expr  [ [source]  ](https://github.com/pola-rs/polars/blob/main/py-
polars/polars/expr/name.py#L329-L348) #

    

Add a suffix to all fields name of a struct.

Parameters  :

    

**suffix**

    

Suffix to add to the filed name

Notes

This only take effects for struct.

Examples

    
    
    >>> df = pl.DataFrame({"x": {"a": 1, "b": 2}})
    >>> df.select(pl.col("x").name.suffix_fields("_suffix")).schema
    OrderedDict({'x': Struct({'a_suffix': Int64, 'b_suffix': Int64})})
    

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#  polars.Expr.list.mean  #

Expr.list.  mean  (  )  →  Expr  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/expr/list.py#L258-L276) #

    

Compute the mean value of the lists in the array.

Examples

    
    
    >>> df = pl.DataFrame({"values": [[1], [2, 3]]})
    >>> df.with_columns(mean=pl.col("values").list.mean())
    shape: (2, 2)
    ┌───────────┬──────┐
    │ values    ┆ mean │
    │ ---       ┆ ---  │
    │ list[i64] ┆ f64  │
    ╞═══════════╪══════╡
    │ [1]       ┆ 1.0  │
    │ [2, 3]    ┆ 2.5  │
    └───────────┴──────┘
    

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  * polars.Expr.floordiv 

#  polars.Expr.floordiv  #

Expr.  floordiv  (  _ other  :  Any  _ )  →  Self  [ [source]
](https://github.com/pola-rs/polars/blob/main/py-
polars/polars/expr/expr.py#L5386-L5419) #

    

Method equivalent of integer division operator ` expr  //  other  ` .

Parameters  :

    

**other**

    

Numeric literal or expression value.

See also

[ ` truediv  ` ](polars.Expr.truediv.html#polars.Expr.truediv
"polars.Expr.truediv")

    

Examples

    
    
    >>> df = pl.DataFrame({"x": [1, 2, 3, 4, 5]})
    >>> df.with_columns(
    ...     pl.col("x").truediv(2).alias("x/2"),
    ...     pl.col("x").floordiv(2).alias("x//2"),
    ... )
    shape: (5, 3)
    ┌─────┬─────┬──────┐
    │ x   ┆ x/2 ┆ x//2 │
    │ --- ┆ --- ┆ ---  │
    │ i64 ┆ f64 ┆ i64  │
    ╞═════╪═════╪══════╡
    │ 1   ┆ 0.5 ┆ 0    │
    │ 2   ┆ 1.0 ┆ 1    │
    │ 3   ┆ 1.5 ┆ 1    │
    │ 4   ┆ 2.0 ┆ 2    │
    │ 5   ┆ 2.5 ┆ 2    │
    └─────┴─────┴──────┘
    

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  * polars.Serie... 

#  polars.Series.list.tail  #

Series.list.  tail  (  _ n  :  [ int  ](https://docs.python.org/3/library/functions.html#int "\(in Python v3.12\)") |  Expr  =  5  _ )  →  Series  [ [source]  ](https://github.com/pola-rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L760-L779) # 
    

Slice the last ` n  ` values of every sublist.

Parameters  :

    

**n**

    

Number of values to return for each sublist.

Examples

    
    
    >>> s = pl.Series("a", [[1, 2, 3, 4], [10, 2, 1]])
    >>> s.list.tail(2)
    shape: (2,)
    Series: 'a' [list[i64]]
    [
        [3, 4]
        [2, 1]
    ]
    

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  * ` Series.list.tail()  `

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  * polars.Expr.or_ 

#  polars.Expr.or_  #

Expr.  or_  (  _ *  others  :  Any  _ )  →  Self  [ [source]
](https://github.com/pola-rs/polars/blob/main/py-
polars/polars/expr/expr.py#L5014-L5054) #

    

Method equivalent of bitwise “or” operator ` expr  |  other  |  ...  ` . 

Parameters  :

    

***others**

    

One or more integer or boolean expressions to evaluate/combine.

Examples

    
    
    >>> df = pl.DataFrame(
    ...     data={
    ...         "x": [5, 6, 7, 4, 8],
    ...         "y": [1.5, 2.5, 1.0, 4.0, -5.75],
    ...         "z": [-9, 2, -1, 4, 8],
    ...     }
    ... )
    >>> df.select(
    ...     (pl.col("x") == pl.col("y"))
    ...     .or_(
    ...         pl.col("x") == pl.col("y"),
    ...         pl.col("y") == pl.col("z"),
    ...         pl.col("y").cast(int) == pl.col("z"),
    ...     )
    ...     .alias("any")
    ... )
    shape: (5, 1)
    ┌───────┐
    │ any   │
    │ ---   │
    │ bool  │
    ╞═══════╡
    │ false │
    │ true  │
    │ false │
    │ true  │
    │ false │
    └───────┘
    

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