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# io
> 
# module pixeltable.io
Functions for importing and exporting Pixeltable data.
## func create\_label\_studio\_project()
```python Signature theme={null}
create_label_studio_project(
t: Table,
label_config: str,
name: str | None = None,
title: str | None = None,
media_import_method: Literal['post', 'file', 'url'] = 'post',
col_mapping: dict[str, str] | None = None,
sync_immediately: bool = True,
s3_configuration: dict[str, Any] | None = None,
**kwargs: Any
) -> UpdateStatus
```
Create a new Label Studio project and link it to the specified [`Table`](./table).
* A tutorial notebook with fully worked examples can be found here:
[Using Label Studio for Annotations with Pixeltable](https://docs.pixeltable.com/notebooks/integrations/using-label-studio-with-pixeltable)
The required parameter `label_config` specifies the Label Studio project configuration,
in XML format, as described in the Label Studio documentation. The linked project will
have one column for each data field in the configuration; for example, if the
configuration has an entry
```
```
then the linked project will have a column named `image`. In addition, the linked project
will always have a JSON-typed column `annotations` representing the output.
By default, Pixeltable will link each of these columns to a column of the specified [`Table`](./table)
with the same name. If any of the data fields are missing, an exception will be raised. If
the `annotations` column is missing, it will be created. The default names can be overridden
by specifying an optional `col_mapping`, with Pixeltable column names as keys and Label
Studio field names as values. In all cases, the Pixeltable columns must have types that are
consistent with their corresponding Label Studio fields; otherwise, an exception will be raised.
The API key and URL for a valid Label Studio server must be specified in Pixeltable config. Either:
* Set the `LABEL_STUDIO_API_KEY` and `LABEL_STUDIO_URL` environment variables; or
* Specify `api_key` and `url` fields in the `label-studio` section of `$PIXELTABLE_HOME/config.toml`.
**Requirements:**
* `pip install label-studio-sdk`
* `pip install boto3` (if using S3 import storage)
**Parameters:**
* **`t`** (`Table`): The table to link to.
* **`label_config`** (`str`): The Label Studio project configuration, in XML format.
* **`name`** (`str | None`): An optional name for the new project in Pixeltable. If specified, must be a valid
Pixeltable identifier and must not be the name of any other external data store
linked to `t`. If not specified, a default name will be used of the form
`ls_project_0`, `ls_project_1`, etc.
* **`title`** (`str | None`): An optional title for the Label Studio project. This is the title that annotators
will see inside Label Studio. Unlike `name`, it does not need to be an identifier and
does not need to be unique. If not specified, the table name `t.name` will be used.
* **`media_import_method`** (`Literal['post', 'file', 'url']`, default: `'post'`): The method to use when transferring media files to Label Studio:
* `post`: Media will be sent to Label Studio via HTTP post. This should generally only be used for
prototyping; due to restrictions in Label Studio, it can only be used with projects that have
just one data field, and does not scale well.
* `file`: Media will be sent to Label Studio as a file on the local filesystem. This method can be
used if Pixeltable and Label Studio are running on the same host.
* `url`: Media will be sent to Label Studio as externally accessible URLs. This method cannot be
used with local media files or with media generated by computed columns.
The default is `post`.
* **`col_mapping`** (`dict[str, str] | None`): An optional mapping of local column names to Label Studio fields.
* **`sync_immediately`** (`bool`, default: `True`): If `True`, immediately perform an initial synchronization by
exporting all rows of the table as Label Studio tasks.
* **`s3_configuration`** (`dict[str, Any] | None`): If specified, S3 import storage will be configured for the new project. This can only
be used with `media_import_method='url'`, and if `media_import_method='url'` and any of the media data is
referenced by `s3://` URLs, then it must be specified in order for such media to display correctly
in the Label Studio interface.
The items in the `s3_configuration` dictionary correspond to kwarg
parameters of the Label Studio `connect_s3_import_storage` method, as described in the
[Label Studio connect\_s3\_import\_storage docs](https://labelstud.io/sdk/project.html#label_studio_sdk.project.Project.connect_s3_import_storage).
`bucket` must be specified; all other parameters are optional. If credentials are not specified explicitly,
Pixeltable will attempt to retrieve them from the environment (such as from `~/.aws/credentials`).
If a title is not specified, Pixeltable will use the default `'Pixeltable-S3-Import-Storage'`.
All other parameters use their Label Studio defaults.
* **`kwargs`** (`Any`): Additional keyword arguments are passed to the `start_project` method in the Label
Studio SDK, as described in the
[Label Studio start\_project docs](https://labelstud.io/sdk/project.html#label_studio_sdk.project.Project.start_project).
**Returns:**
* `UpdateStatus`: An `UpdateStatus` representing the status of any synchronization operations that occurred.
**Examples:**
Create a Label Studio project whose tasks correspond to videos stored in the `video_col` column of the table `tbl`:
```python theme={null}
config = """
"""
create_label_studio_project(tbl, config)
```
Create a Label Studio project with the same configuration, using `media_import_method='url'`, whose media are stored in an S3 bucket:
```python theme={null}
create_label_studio_project(
tbl,
config,
media_import_method='url',
s3_configuration={'bucket': 'my-bucket', 'region_name': 'us-east-2'},
)
```
## func export\_images\_as\_fo\_dataset()
```python Signature theme={null}
export_images_as_fo_dataset(
tbl: pxt.Table,
images: exprs.Expr,
image_format: str = 'webp',
classifications: exprs.Expr | list[exprs.Expr] | dict[str, exprs.Expr] | None = None,
detections: exprs.Expr | list[exprs.Expr] | dict[str, exprs.Expr] | None = None
) -> fo.Dataset
```
Export images from a Pixeltable table as a Voxel51 dataset. The data must consist of a single column
(or expression) containing image data, along with optional additional columns containing labels. Currently, only
classification and detection labels are supported.
The [Working with Voxel51 in Pixeltable](https://docs.pixeltable.com/examples/vision/voxel51) tutorial contains a
fully worked example showing how to export data from a Pixeltable table and load it into Voxel51.
Images in the dataset that already exist on disk will be exported directly, in whatever format they
are stored in. Images that are not already on disk (such as frames extracted using a
[`frame_iterator`](./video#iterator-frame_iterator)) will first be written to disk in the specified
`image_format`.
The label parameters accept one or more sets of labels of each type. If a single `Expr` is provided, then it will
be exported as a single set of labels with a default name such as `classifications`.
(The single set of labels may still containing multiple individual labels; see below.)
If a list of `Expr`s is provided, then each one will be exported as a separate set of labels with a default name
such as `classifications`, `classifications_1`, etc. If a dictionary of `Expr`s is provided, then each entry will
be exported as a set of labels with the specified name.
**Requirements:**
* `pip install fiftyone`
**Parameters:**
* **`tbl`** (`pxt.Table`): The table from which to export data.
* **`images`** (`exprs.Expr`): A column or expression that contains the images to export.
* **`image_format`** (`str`, default: `'webp'`): The format to use when writing out images for export.
* **`classifications`** (`exprs.Expr | list[exprs.Expr] | dict[str, exprs.Expr] | None`): Optional image classification labels. If a single `Expr` is provided, it must be a table
column or an expression that evaluates to a list of dictionaries. Each dictionary in the list corresponds
to an image class and must have the following structure:
```python theme={null}
{'label': 'zebra', 'confidence': 0.325}
```
If multiple `Expr`s are provided, each one must evaluate to a list of such dictionaries.
* **`detections`** (`exprs.Expr | list[exprs.Expr] | dict[str, exprs.Expr] | None`): Optional image detection labels. If a single `Expr` is provided, it must be a table column or an
expression that evaluates to a list of dictionaries. Each dictionary in the list corresponds to an image
detection, and must have the following structure:
```python theme={null}
{
'label': 'giraffe',
'confidence': 0.99,
# [x, y, w, h], fractional coordinates
'bounding_box': [0.081, 0.836, 0.202, 0.136],
}
```
If multiple `Expr`s are provided, each one must evaluate to a list of such dictionaries.
**Returns:**
* `'fo.Dataset'`: A Voxel51 dataset.
**Examples:**
Export the images in the `image` column of the table `tbl` as a Voxel51 dataset, using classification labels from `tbl.classifications`:
```python theme={null}
export_images_as_fo_dataset(
tbl, tbl.image, classifications=tbl.classifications
)
```
## func export\_lancedb()
```python Signature theme={null}
export_lancedb(
table_or_query: pxt.Table | pxt.Query,
db_uri: Path,
table_name: str,
batch_size_bytes: int = 134217728,
if_exists: Literal['error', 'overwrite', 'append'] = 'error'
) -> None
```
Exports a Query's data to a LanceDB table.
This utilizes LanceDB's streaming interface for efficient table creation, via a sequence of in-memory pyarrow
`RecordBatches`, the size of which can be controlled with the `batch_size_bytes` parameter.
**Requirements:**
* `pip install lancedb`
**Parameters:**
* **`table_or_query `** (`Any`): Table or Query to export.
* **`db_uri`** (`Path`): Local Path to the LanceDB database.
* **`table_name `** (`Any`): Name of the table in the LanceDB database.
* **`batch_size_bytes `** (`Any`): Maximum size in bytes for each batch.
* **`if_exists`** (`Literal['error', 'overwrite', 'append']`, default: `'error'`): Determines the behavior if the table already exists. Must be one of the following:
* `'error'`: raise an error
* `'overwrite'`: overwrite the existing table
* `'append'`: append to the existing table
## func export\_parquet()
```python Signature theme={null}
export_parquet(
table_or_query: pxt.Table | pxt.Query,
parquet_path: Path,
partition_size_bytes: int = 100000000,
inline_images: bool = False,
_write_md: bool = False
) -> None
```
Exports a query result or table to one or more Parquet files. Requires pyarrow to be installed.
Pixeltable column types are mapped to Parquet types as follows:
* String: string
* Int: int64
* Float: float32
* Bool: bool
* Timestamp: timestamp\[us, tz=UTC]
* Date: date32
* UUID: uuid
* Binary: binary
* Image: binary (when `inline_images=True`)
* Audio, Video, Document: string (file paths)
* Array (requires shape to be known):
* fixed\_shape\_tensor for fixed-shape arrays
* list for ragged arrays (one or more dimensions are None)
* Json: struct
* Schema is inferred from data via `pyarrow.infer_type()`
* Fields that contain empty dicts cannot be mapped to a Parquet type and will result in an exception
**Parameters:**
* **`table_or_query `** (`Any`): Table or Query to export.
* **`parquet_path `** (`Any`): Path to directory to write the parquet files to.
* **`partition_size_bytes `** (`Any`): The maximum target size for each chunk. Default 100\_000\_000 bytes.
* **`inline_images `** (`Any`): If True, images are stored inline in the parquet file. This is useful
for small images, to be imported as pytorch dataset. But can be inefficient
for large images, and cannot be imported into pixeltable.
If False, will raise an error if the Query has any image column.
Default False.
## func import\_csv()
```python Signature theme={null}
import_csv(
tbl_name: str,
filepath_or_buffer: str | os.PathLike,
schema_overrides: dict[str, typing.Any] | None = None,
primary_key: str | list[str] | None = None,
num_retained_versions: int = 10,
comment: str = '',
**kwargs: Any
) -> pixeltable.catalog.table.Table
```
Creates a new base table from a csv file. This is a convenience method and is equivalent
to calling `import_pandas(table_path, pd.read_csv(filepath_or_buffer, **kwargs), schema=schema)`.
See the Pandas documentation for [`read_csv`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)
for more details.
**Returns:**
* `pixeltable.catalog.table.Table`: A handle to the newly created [`Table`](./table).
## func import\_excel()
```python Signature theme={null}
import_excel(
tbl_name: str,
io: str | os.PathLike,
*,
schema_overrides: dict[str, typing.Any] | None = None,
primary_key: str | list[str] | None = None,
num_retained_versions: int = 10,
comment: str = '',
**kwargs: Any
) -> pixeltable.catalog.table.Table
```
Creates a new base table from an Excel (.xlsx) file. This is a convenience method and is
equivalent to calling `import_pandas(table_path, pd.read_excel(io, *args, **kwargs), schema=schema)`.
See the Pandas documentation for [`read_excel`](https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html)
for more details.
**Returns:**
* `pixeltable.catalog.table.Table`: A handle to the newly created [`Table`](./table).
## func import\_huggingface\_dataset()
```python Signature theme={null}
import_huggingface_dataset(
table_path: str,
dataset: datasets.Dataset | datasets.DatasetDict | datasets.IterableDataset | datasets.IterableDatasetDict,
*,
schema_overrides: dict[str, Any] | None = None,
primary_key: str | list[str] | None = None,
**kwargs: Any
) -> pxt.Table
```
Create a new base table from a Huggingface dataset, or dataset dict with multiple splits.
Requires `datasets` library to be installed.
HuggingFace feature types are mapped to Pixeltable column types as follows:
* `Value(bool)`: `Bool`
`Value(int*/uint*)`: `Int`
`Value(float*)`: `Float`
`Value(string/large_string)`: `String`
`Value(timestamp*)`: `Timestamp`
`Value(date*)`: `Date`
* `ClassLabel`: `String` (converted to label names)
* `Sequence`/`LargeList` of numeric types: `Array`
* `Sequence`/`LargeList` of string: `Json`
* `Sequence`/`LargeList` of dicts: `Json`
* `Array2D`-`Array5D`: `Array` (preserves shape)
* `Image`: `Image`
* `Audio`: `Audio`
* `Video`: `Video`
* `Translation`/`TranslationVariableLanguages`: `Json`
**Parameters:**
* **`table_path`** (`str`): Path to the table.
* **`dataset`** (`datasets.Dataset | datasets.DatasetDict | datasets.IterableDataset | datasets.IterableDatasetDict`): An instance of any of the Huggingface dataset classes:
[`datasets.Dataset`](https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.Dataset),
[`datasets.DatasetDict`](https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.DatasetDict),
[`datasets.IterableDataset`](https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDataset),
[`datasets.IterableDatasetDict`](https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDatasetDict)
* **`schema_overrides`** (`dict[str, Any] | None`): If specified, then for each (name, type) pair in `schema_overrides`, the column with
name `name` will be given type `type`, instead of being inferred from the `Dataset` or `DatasetDict`.
The keys in `schema_overrides` should be the column names of the `Dataset` or `DatasetDict` (whether or not
they are valid Pixeltable identifiers).
* **`primary_key`** (`str | list[str] | None`): The primary key of the table (see [`create_table()`](./pixeltable#func-create_table)).
* **`kwargs`** (`Any`): Additional arguments to pass to `create_table`.
An argument of `column_name_for_split` must be provided if the source is a DatasetDict.
This column name will contain the split information. If None, no split information will be stored.
**Returns:**
* `pxt.Table`: A handle to the newly created [`Table`](./table).
## func import\_json()
```python Signature theme={null}
import_json(
tbl_path: str,
filepath_or_url: str,
*,
schema_overrides: dict[str, Any] | None = None,
primary_key: str | list[str] | None = None,
num_retained_versions: int = 10,
comment: str = '',
**kwargs: Any
) -> pxt.Table
```
Creates a new base table from a JSON file. This is a convenience method and is
equivalent to calling `import_data(table_path, json.loads(file_contents, **kwargs), ...)`, where `file_contents`
is the contents of the specified `filepath_or_url`.
**Parameters:**
* **`tbl_path`** (`str`): The name of the table to create.
* **`filepath_or_url`** (`str`): The path or URL of the JSON file.
* **`schema_overrides`** (`dict[str, Any] | None`): If specified, then columns in `schema_overrides` will be given the specified types
(see [`import_rows()`](./io#func-import_rows)).
* **`primary_key`** (`str | list[str] | None`): The primary key of the table (see [`create_table()`](./pixeltable#func-create_table)).
* **`num_retained_versions`** (`int`, default: `10`): The number of retained versions of the table
(see [`create_table()`](./pixeltable#func-create_table)).
* **`comment`** (`str`, default: `''`): A comment to attach to the table (see [`create_table()`](./pixeltable#func-create_table)).
* **`kwargs`** (`Any`): Additional keyword arguments to pass to `json.loads`.
**Returns:**
* `pxt.Table`: A handle to the newly created [`Table`](./table).
## func import\_pandas()
```python Signature theme={null}
import_pandas(
tbl_name: str,
df: pandas.core.frame.DataFrame,
*,
schema_overrides: dict[str, typing.Any] | None = None,
primary_key: str | list[str] | None = None,
num_retained_versions: int = 10,
comment: str = ''
) -> pixeltable.catalog.table.Table
```
Creates a new base table from a Pandas
[`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), with the
specified name. The schema of the table will be inferred from the DataFrame.
The column names of the new table will be identical to those in the DataFrame, as long as they are valid
Pixeltable identifiers. If a column name is not a valid Pixeltable identifier, it will be normalized according to
the following procedure:
* first replace any non-alphanumeric characters with underscores;
* then, preface the result with the letter 'c' if it begins with a number or an underscore;
* then, if there are any duplicate column names, suffix the duplicates with '\_2', '\_3', etc., in column order.
**Parameters:**
* **`tbl_name`** (`str`): The name of the table to create.
* **`df`** (`pandas.core.frame.DataFrame`): The Pandas `DataFrame`.
* **`schema_overrides`** (`dict[str, typing.Any] | None`): If specified, then for each (name, type) pair in `schema_overrides`, the column with
name `name` will be given type `type`, instead of being inferred from the `DataFrame`. The keys in
`schema_overrides` should be the column names of the `DataFrame` (whether or not they are valid
Pixeltable identifiers).
**Returns:**
* `pixeltable.catalog.table.Table`: A handle to the newly created [`Table`](./table).
## func import\_parquet()
```python Signature theme={null}
import_parquet(
table: str,
*,
parquet_path: str,
schema_overrides: dict[str, Any] | None = None,
primary_key: str | list[str] | None = None,
**kwargs: Any
) -> pxt.Table
```
Creates a new base table from a Parquet file or set of files. Requires pyarrow to be installed.
**Parameters:**
* **`table`** (`str`): Fully qualified name of the table to import the data into.
* **`parquet_path`** (`str`): Path to an individual Parquet file or directory of Parquet files.
* **`schema_overrides`** (`dict[str, Any] | None`): If specified, then for each (name, type) pair in `schema_overrides`, the column with
name `name` will be given type `type`, instead of being inferred from the Parquet dataset. The keys in
`schema_overrides` should be the column names of the Parquet dataset (whether or not they are valid
Pixeltable identifiers).
* **`primary_key`** (`str | list[str] | None`): The primary key of the table (see [`create_table()`](./pixeltable#func-create_table)).
* **`kwargs`** (`Any`): Additional arguments to pass to `create_table`.
**Returns:**
* `pxt.Table`: A handle to the newly created table.
## func import\_rows()
```python Signature theme={null}
import_rows(
tbl_path: str,
rows: list[dict[str, Any]],
*,
schema_overrides: dict[str, Any] | None = None,
primary_key: str | list[str] | None = None,
num_retained_versions: int = 10,
comment: str = ''
) -> pxt.Table
```
Creates a new base table from a list of dictionaries. The dictionaries must be of the
form `{column_name: value, ...}`. Pixeltable will attempt to infer the schema of the table from the
supplied data, using the most specific type that can represent all the values in a column.
If `schema_overrides` is specified, then for each entry `(column_name, type)` in `schema_overrides`,
Pixeltable will force the specified column to the specified type (and will not attempt any type inference
for that column).
All column types of the new table will be nullable unless explicitly specified as non-nullable in
`schema_overrides`.
**Parameters:**
* **`tbl_path`** (`str`): The qualified name of the table to create.
* **`rows`** (`list[dict[str, Any]]`): The list of dictionaries to import.
* **`schema_overrides`** (`dict[str, Any] | None`): If specified, then columns in `schema_overrides` will be given the specified types
as described above.
* **`primary_key`** (`str | list[str] | None`): The primary key of the table (see [`create_table()`](./pixeltable#func-create_table)).
* **`num_retained_versions`** (`int`, default: `10`): The number of retained versions of the table
(see [`create_table()`](./pixeltable#func-create_table)).
* **`comment`** (`str`, default: `''`): A comment to attach to the table (see [`create_table()`](./pixeltable#func-create_table)).
**Returns:**
* `pxt.Table`: A handle to the newly created [`Table`](./table).
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