mteb/BrightProStackoverflowRetrieval huggingface dataset:
/../api/polars.exceptions.SchemaError.html)
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  * [ __ ](../../../index.html)
  * [ Python API reference ](../../index.html)
  * __
  * [ List ](../list.html)
  * polars.Serie... 

#  polars.Series.list.reverse  #

Series.list.  reverse  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L322-L336) #

    

Reverse the arrays in the list.

Examples

    
    
    >>> s = pl.Series("a", [[3, 2, 1], [9, 1, 2]])
    >>> s.list.reverse()
    shape: (2,)
    Series: 'a' [list[i64]]
    [
        [1, 2, 3]
        [2, 1, 9]
    ]
    

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"next page")

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  * ` Series.list.reverse()  `

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

#  polars.Series.list.all  #

Series.list.  all  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L35-L61) #

    

Evaluate whether all boolean values in a list are 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.all()
    shape: (6,)
    Series: '' [bool]
    [
        true
        false
        false
        true
        true
        null
    ]
    

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__ On this page

  * ` Series.list.all()  `

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e_equal.html)
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    * [ polars.threadpool_size ](../../api/polars.threadpool_size.html)

  * [ __ ](../../../index.html)
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  * __
  * [ Meta ](../meta.html)
  * polars.Expr.... 

#  polars.Expr.meta.tree_format  #

Expr.meta.  tree_format  (  _ *  _ , _ return_as_string  :  [ bool  ](https://docs.python.org/3/library/functions.html#bool "\(in Python v3.12\)") =  False  _ )  →  [ str  ](https://docs.python.org/3/library/stdtypes.html#str "\(in Python v3.12\)") |  [ None  ](https://docs.python.org/3/library/constants.html#None "\(in Python v3.12\)") [ [source]  ](https://github.com/pola-rs/polars/blob/main/py-polars/polars/expr/meta.py#L295-L315) # 
    

Format the expression as a tree.

Parameters  :

    

**return_as_string:**

    

If True, return as string rather than printing to stdout.

Examples

    
    
    >>> e = (pl.col("foo") * pl.col("bar")).sum().over(pl.col("ham")) / 2
    >>> e.meta.tree_format(return_as_string=True)  
    

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  * ` Expr.meta.tree_format()  `

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  * __
  * [ Array ](../array.html)
  * polars.Expr.arr.max 

#  polars.Expr.arr.max  #

Expr.arr.  max  (  )  →  Expr  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/expr/array.py#L46-L67) #

    

Compute the max values of the sub-arrays.

Examples

    
    
    >>> df = pl.DataFrame(
    ...     data={"a": [[1, 2], [4, 3]]},
    ...     schema={"a": pl.Array(pl.Int64, 2)},
    ... )
    >>> df.select(pl.col("a").arr.max())
    shape: (2, 1)
    ┌─────┐
    │ a   │
    │ --- │
    │ i64 │
    ╞═════╡
    │ 2   │
    │ 4   │
    └─────┘
    

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  * ` Expr.arr.max()  `

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

#  polars.Expr.name.map_fields  #

Expr.name.  map_fields  (  _ function  :  Callable  [  [  [ str
](https://docs.python.org/3/library/stdtypes.html#str "\(in Python v3.12\)") ]
,  [ 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#L287-L306) #

    

Rename fields of a struct by mapping a function over the field name.

Parameters  :

    

**function**

    

Function that maps a field name to a new name.

Notes

This only take effects for struct.

Examples

    
    
    >>> df = pl.DataFrame({"x": {"a": 1, "b": 2}})
    >>> df.select(pl.col("x").name.map_fields(lambda x: x.upper())).schema
    OrderedDict({'x': Struct({'A': Int64, 'B': Int64})})
    

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polars.Expr.name.prefix_fields  __ ](polars.Expr.name.prefix_fields.html "next
page")

__ On this page

  * ` Expr.name.map_fields()  `

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>>> table_path = "gs://bucket/path/to/delta-table/"
    >>> storage_options = {"SERVICE_ACCOUNT": "SERVICE_ACCOUNT_JSON_ABSOLUTE_PATH"}
    >>> pl.scan_delta(
    ...     table_path, storage_options=storage_options
    ... ).collect()  
    

Creates a scan for a Delta table from Azure. Supported options for Azure are
available [ here
](https://docs.rs/object_store/latest/object_store/azure/enum.AzureConfigKey.html#variants)
.

Following type of table paths are supported,

  * az://<container>/<path>

  * adl://<container>/<path>

  * abfs[s]://<container>/<path>

    
    
    >>> table_path = "az://container/path/to/delta-table/"
    >>> storage_options = {
    ...     "AZURE_STORAGE_ACCOUNT_NAME": "AZURE_STORAGE_ACCOUNT_NAME",
    ...     "AZURE_STORAGE_ACCOUNT_KEY": "AZURE_STORAGE_ACCOUNT_KEY",
    ... }
    >>> pl.scan_delta(
    ...     table_path, storage_options=storage_options
    ... ).collect()  
    

Creates a scan for a Delta table with additional delta specific options. In
the below example, ` without_files  ` option is used which loads the table
without file tracking information.

    
    
    >>> table_path = "/path/to/delta-table/"
    >>> delta_table_options = {"without_files": True}
    >>> pl.scan_delta(
    ...     table_path, delta_table_options=delta_table_options
    ... ).collect()  
    

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g.assert_frame_equal.html)
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  * __
  * [ Functions ](../functions.html)
  * polars.quantile 

#  polars.quantile  #

polars.  quantile  (

     _ column  :  [ str  ](https://docs.python.org/3/library/stdtypes.html#str "\(in Python v3.12\)") _ , 
     _ quantile  :  [ float  ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.12\)") |  Expr  _ , 
     _ interpolation  :  RollingInterpolationMethod  =  'nearest'  _ , 
)  →  Expr  [ [source]  ](https://github.com/pola-rs/polars/blob/main/py-
polars/polars/functions/lazy.py#L1516-L1533) #

    

Syntactic sugar for ` pl.col("foo").quantile(..)  ` .

Parameters  :

    

**column**

    

Column name.

**quantile**

    

Quantile between 0.0 and 1.0.

**interpolation** {‘nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’}

    

Interpolation method.

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  * ` quantile()  `

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  * polars.Serie... 

#  polars.Series.list.median  #

Series.list.  median  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L236-L250) #

    

Compute the median value of the arrays in the list.

Examples

    
    
    >>> s = pl.Series("values", [[-1, 0, 1], [1, 10]])
    >>> s.list.median()
    shape: (2,)
    Series: 'values' [f64]
    [
            0.0
            5.5
    ]
    

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  * ` Series.list.median()  `

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