###
`polars.read_csv(source: 'str | Path | IO[str] | IO[bytes] | bytes', *, has_header: 'bool' = True, columns: 'Sequence[int] | Sequence[str] | None' = None, new_columns: 'Sequence[str] | None' = None, separator: 'str' = ',', comment_prefix: 'str | None' = None, quote_char: 'str | None' = '"', skip_rows: 'int' = 0, skip_lines: 'int' = 0, schema: 'SchemaDict | None' = None, schema_overrides: 'Mapping[str, PolarsDataType] | Sequence[PolarsDataType] | None' = None, null_values: 'str | Sequence[str] | dict[str, str] | None' = None, missing_utf8_is_empty_string: 'bool' = False, ignore_errors: 'bool' = False, try_parse_dates: 'bool' = False, n_threads: 'int | None' = None, infer_schema: 'bool' = True, infer_schema_length: 'int | None' = 100, batch_size: 'int' = 8192, n_rows: 'int | None' = None, encoding: 'CsvEncoding | str' = 'utf8', low_memory: 'bool' = False, rechunk: 'bool' = False, use_pyarrow: 'bool' = False, storage_options: 'StorageOptionsDict | None' = None, skip_rows_after_header: 'int' = 0, row_index_name: 'str | None' = None, row_index_offset: 'int' = 0, sample_size: 'int' = 1024, eol_char: 'str' = '\n', raise_if_empty: 'bool' = True, truncate_ragged_lines: 'bool' = False, decimal_comma: 'bool' = False, glob: 'bool' = True) -> 'DataFrame'`

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###
`polars.DataFrame(data: 'FrameInitTypes | None' = None, schema: 'SchemaDefinition | None' = None, *, schema_overrides: 'SchemaDict | None' = None, strict: 'bool' = True, orient: 'Orientation | None' = None, infer_schema_length: 'int | None' = 100, nan_to_null: 'bool' = False, height: 'int | None' = None) -> 'None'`

Error fetching doc for polars.DataFrame: 'charmap' codec can't encode characters in position 3256-3268: character maps
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###
`polars.Series(name: 'str_ | ArrayLike | None' = None, values: 'ArrayLike | None' = None, dtype: 'PolarsDataType | None' = None, *, strict: 'bool' = True, nan_to_null: 'bool' = False) -> 'None'`

A Series represents a single column in a Polars DataFrame.

Parameters
----------
name : str, default None
Name of the Series. Will be used as a column name when used in a DataFrame.
When not specified, name is set to an empty string.
values : ArrayLike, default None
One-dimensional data in various forms. Supported are: Sequence, Series,
pyarrow Array, and numpy ndarray.
dtype : DataType, default None
Data type of the resulting Series. If set to `None` (default), the data type is
inferred from the `values` input. The strategy for data type inference depends
on the `strict` parameter:

    - If `strict` is set to True (default), the inferred data type is equal to the
      first non-null value, or `Null` if all values are null.
    - If `strict` is set to False, the inferred data type is the supertype of the
      values, or :class:`Object` if no supertype can be found. **WARNING**: A full
      pass over the values is required to determine the supertype.
    - If no values were passed, the resulting data type is :class:`Null`.

strict : bool, default True
Throw an error if any value does not exactly match the given or inferred data
type. If set to `False`, values that do not match the data type are cast to
that data type or, if casting is not possible, set to null instead.
nan_to_null : bool, default False
In case a numpy array is used to create this Series, indicate how to deal
with np.nan values. (This parameter is a no-op on non-numpy data).

Examples
--------
Constructing a Series by specifying name and values positionally:

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

Notice that the dtype is automatically inferred as a polars Int64:

> > > s.dtype
> > > Int64

Constructing a Series with a specific dtype:

> > > s2 = pl.Series("a", [1, 2, 3], dtype=pl.Float32)
> > > s2
> > > shape: (3,)
> > > Series: 'a' [f32]
[
1.0
2.0
3.0
]

It is possible to construct a Series with values as the first positional argument.
This syntax considered an anti-pattern, but it can be useful in certain
scenarios. You must specify any other arguments through keywords.

> > > s3 = pl.Series([1, 2, 3])
> > > s3
> > > shape: (3,)
> > > Series: '' [i64]
[
1
2
3
]

---

### `polars.col(...)`

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### `polars.when(*predicates: 'IntoExprColumn | Iterable[IntoExprColumn] | bool', **constraints: 'Any') -> 'pl.When'`

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###
`polars.DataFrame.filter(self, *predicates: 'IntoExprColumn | Iterable[IntoExprColumn] | bool | list[bool] | np.ndarray[Any, Any]', **constraints: 'Any') -> 'DataFrame'`

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### `polars.DataFrame.select(self, *exprs: 'IntoExpr | Iterable[IntoExpr]', **named_exprs: 'IntoExpr') -> 'DataFrame'`

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###
`polars.DataFrame.with_columns(self, *exprs: 'IntoExpr | Iterable[IntoExpr]', **named_exprs: 'IntoExpr') -> 'DataFrame'`

Error fetching doc for polars.DataFrame.with_columns: 'charmap' codec can't encode characters in position 936-963:
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Error fetching doc for polars.DataFrame.groupby: type object 'DataFrame' has no attribute 'groupby'

###
`polars.DataFrame.join(self, other: 'DataFrame', on: 'str | Expr | Sequence[str | Expr] | None' = None, how: 'JoinStrategy' = 'inner', *, left_on: 'str | Expr | Sequence[str | Expr] | None' = None, right_on: 'str | Expr | Sequence[str | Expr] | None' = None, suffix: 'str' = '_right', validate: 'JoinValidation' = 'm:m', nulls_equal: 'bool' = False, coalesce: 'bool | None' = None, maintain_order: 'MaintainOrderJoin | None' = None) -> 'DataFrame'`

Error fetching doc for polars.DataFrame.join: 'charmap' codec can't encode characters in position 3846-3872: character
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