# DatasetMeta


Metadata of one dataset, in the style of pyreadstat's metadata object.


Usage

``` python
DatasetMeta(
    dataset,
    collection,
    file_label,
    structure,
    number_rows,
    column_names,
    column_labels,
    column_types,
    column_mandatory,
    column_roles,
)
```


A read-only view: the field names and parallel-tuple layout follow pyreadstat, extended with the CDISC dimensions (`structure`, `mandatory`, `role`). The data itself is reached through the module attribute of the same name (e.g. [pharmaverseadam.adsl](pharmaverseadam.adsl.md#pharmadata.pharmaverseadam.adsl)).


## Attributes


`dataset: str`  
The dataset name, e.g. `"adsl"`.

`collection: str`  
The collection it belongs to, e.g. `"pharmaverseadam"`.

`file_label: str or None`  
The dataset label, equivalent to a SAS dataset label.

`structure: str or None`  
The CDISC structure, e.g. `"One record per subject"`; None when the source specifications do not provide one.

`number_rows: int`  
Row count, read from the parquet footer without loading any data.

`number_columns: int`  
Column count, i.e. `len(column_names)`.

`column_names: tuple of str`  
The column names, in column order.

`column_labels: tuple of str or None`  
The column labels, parallel to `column_names`.

`column_types: tuple of str or None`  
The column types, parallel to `column_names`.

`column_mandatory: tuple of str or None`  
The CDISC mandatory flags, parallel to `column_names`.

`column_roles: tuple of str or None`  
The CDISC roles, parallel to `column_names`.

`file_format: str`  
Always `"parquet"`, the storage format of every dataset.

`file_encoding: str`  
Always `"utf-8"`: the encoding of the shipped parquet data, not of the original source file.


## Examples

``` python
>>> meta = pharmaverseadam.meta("adsl")
>>> meta.dataset
'adsl'
>>> meta.file_label
'Subject Level Analysis'
>>> meta.column_names_to_labels["STUDYID"]
'Study Identifier'
>>> (meta.number_rows, meta.number_columns)
(306, 55)
```


## Attributes

| Name | Description |
|----|----|
| [column_names_to_labels](#column_names_to_labels) | The column names mapped to their labels, in column order. |
| [number_columns](#number_columns) | Column count, i.e. `len(column_names)`. |
| [specs](#specs) | Column-level specifications of the dataset, in the active format. |

------------------------------------------------------------------------


### column_names_to_labels


The column names mapped to their labels, in column order.


`column_names_to_labels: dict[str, str | None]`


#### Examples

``` python
>>> pharmaverseadam.adsl_meta.column_names_to_labels["AGE"]
'Age'
```

------------------------------------------------------------------------


### number_columns


Column count, i.e. `len(column_names)`.


`number_columns: int`


------------------------------------------------------------------------


### specs


Column-level specifications of the dataset, in the active format.


`specs: pa.Table | pl.DataFrame | pd.DataFrame`


#### Examples

``` python
>>> pharmaverseadam.adsl_meta.specs.columns
['variable', 'label', 'type', 'mandatory', 'role']
```


## Methods

| Name | Description |
|----|----|
| [__repr__()](#__repr__) | One line: which dataset, and how big. |

------------------------------------------------------------------------


### \_\_repr\_\_()


One line: which dataset, and how big.


Usage

``` python
__repr__()
```