mteb/BrightProStackoverflowRetrieval huggingface dataset:
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  * __
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  * polars.Expr.... 

#  polars.Expr.is_duplicated  #

Expr.  is_duplicated  (  )  →  Self  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/expr/expr.py#L3810-L3834) #

    

Return a boolean mask indicating duplicated values.

Returns  :

    

Expr

    

Expression of data type ` Boolean  ` .

Examples

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

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page")

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  * ` Expr.is_duplicated()  `

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ath, storage_options=storage_options)  
    

Reads a Delta table from Google Cloud storage (GCS). See a list of supported
storage options for GCS [ here
](https://docs.rs/object_store/latest/object_store/gcp/enum.GoogleConfigKey.html#variants)
.

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

Reads a Delta table from Azure.

Following type of table paths are supported,

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

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

  * abfs://<container>/<path>

See a list of supported storage options for Azure [ here
](https://docs.rs/object_store/latest/object_store/azure/enum.AzureConfigKey.html#variants)
.

    
    
    >>> 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.read_delta(table_path, storage_options=storage_options)  
    

Reads 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.read_delta(
    ...     table_path, delta_table_options=delta_table_options
    ... )  
    

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e Spark  ](/user-guide/migration/spark/)
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    * Misc  Misc 
      * [ Multiprocessing  ](/user-guide/misc/multiprocessing/)
      * [ Visualization  ](/user-guide/misc/visualization/)
      * [ Comparison with other tools  ](/user-guide/misc/comparison/)
  * [ API reference  ](/api/)
  * Development  Development 
    * [ Contributing  ](/development/contributing/)

Contributing

      * [ IDE configuration  ](/development/contributing/ide/)
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      * [ Continuous integration  ](/development/contributing/ci/)
      * [ Code style  ](/development/contributing/code-style/)
    * [ Versioning  ](/development/versioning/)
  * Releases  Releases 
    * [ Changelog  ](/releases/changelog/)
    * [ Upgrade guides  ](/releases/upgrade/)

Upgrade guides

      * [ Version 0.20  ](/releases/upgrade/0.20/)
      * [ Version 0.19  ](/releases/upgrade/0.19/)

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The page you are looking for does no longer exist. How you got here is a
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  * [ Temporal ](../temporal.html)
  * polars.Expr.... 

#  polars.Expr.dt.is_leap_year  #

Expr.dt.  is_leap_year  (  )  →  Expr  [ [source]  ](https://github.com/pola-
rs/polars/blob/main/py-polars/polars/expr/datetime.py#L806-L837) #

    

Determine whether the year of the underlying date is a leap year.

Applies to Date and Datetime columns.

Returns  :

    

Expr

    

Expression of data type ` Boolean  ` .

Examples

    
    
    >>> from datetime import date
    >>> df = pl.DataFrame(
    ...     {"date": [date(2000, 1, 1), date(2001, 1, 1), date(2002, 1, 1)]}
    ... )
    >>> df.with_columns(
    ...     leap_year=pl.col("date").dt.is_leap_year(),
    ... )
    shape: (3, 2)
    ┌────────────┬───────────┐
    │ date       ┆ leap_year │
    │ ---        ┆ ---       │
    │ date       ┆ bool      │
    ╞════════════╪═══════════╡
    │ 2000-01-01 ┆ true      │
    │ 2001-01-01 ┆ false     │
    │ 2002-01-01 ┆ false     │
    └────────────┴───────────┘
    

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"next page")

__ On this page

  * ` Expr.dt.is_leap_year()  `

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  * polars.Serie... 

#  polars.Series.list.max  #

Series.list.  max  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L188-L202) #

    

Compute the max value of the arrays in the list.

Examples

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

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__ On this page

  * ` Series.list.max()  `

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

#  polars.Series.list.lengths  #

Series.list.  lengths  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L1077-L1084) #

    

Return the number of elements in each list.

Deprecated since version 0.19.8:  This method has been renamed to [ ` len()  `
](polars.Series.list.len.html#polars.Series.list.len "polars.Series.list.len")
.

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page")

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  * ` Series.list.lengths()  `

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  * polars.Serie... 

#  polars.Series.list.arg_min  #

Series.list.  arg_min  (  )  →  Series  [ [source]  ](https://github.com/pola-
rs/polars/blob/py-0.20.23/py-polars/polars/series/list.py#L592-L612) #

    

Retrieve the index of the minimal value in every sublist.

Returns  :

    

Series

    

Series of data type ` UInt32  ` or ` UInt64  ` (depending on compilation).

Examples

    
    
    >>> s = pl.Series("a", [[1, 2], [2, 1]])
    >>> s.list.arg_min()
    shape: (2,)
    Series: 'a' [u32]
    [
        0
        1
    ]
    

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

#  polars.Expr.shuffle  #

Expr.  shuffle  (  _ seed  :  [ int  ](https://docs.python.org/3/library/functions.html#int "\(in Python v3.12\)") |  [ None  ](https://docs.python.org/3/library/constants.html#None "\(in Python v3.12\)") =  None  _ )  →  Self  [ [source]  ](https://github.com/pola-rs/polars/blob/main/py-polars/polars/expr/expr.py#L8813-L8838) # 
    

Shuffle the contents of this expression.

Parameters  :

    

**seed**

    

Seed for the random number generator. If set to None (default), a random seed
is generated each time the shuffle is called.

Examples

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

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#  polars.Series.list.n_unique  #

Series.list.  n_unique  (  )  →  Series  [ [source]
](https://github.com/pola-rs/polars/blob/py-0.20.23/py-
polars/polars/series/list.py#L359-L373) #

    

Count the number of unique values in every sub-lists.

Examples

    
    
    >>> s = pl.Series("a", [[1, 1, 2], [2, 3, 4]])
    >>> s.list.n_unique()
    shape: (2,)
    Series: 'a' [u32]
    [
        2
        3
    ]
    

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