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"""Helper functions that depend on the DataFrame class."""
from .helpers import list_data_frames
from .data_frame import DataFrame
# --------------------------------------------------------------------
[docs]def load_data_frame(name):
"""Retrieves a :class:`~getml.data.DataFrame` handler of data in the
getML engine.
A data frame object can be loaded regardless if it is held in
memory (accessible through the 'Data Frames' tab in the getML
monitor) or not. It only has to be present in the current project
and thus listed in the output of
:func:`~getml.data.list_data_frames`.
Args:
name (str):
Name of the data frame.
Examples:
.. code-block:: python
d, _ = getml.datasets.make_numerical(population_name = 'test')
d2 = getml.data.load_data_frame('test')
Raises:
TypeError: If any of the input arguments is of wrong type.
ValueError:
If `name` does not corresponding to a data frame on the
engine.
Returns:
:class:`~getml.data.DataFrame`:
Handle the underlying data frame in the getML engine.
Note:
The getML engine knows to different states of a data frame
object. First, the current instance in memory (RAM) that
holds the most recent changes applied via the Python API
(listed under the 'in_memory' key of
:func:`~getml.data.list_data_frames`). Second, the
version stored to disk by calling the
:meth:`~getml.data.DataFrame.save` method (listed under the
'in_project_folder' key). If a data frame object corresponding
to `name` is present in both of them, the most recent version
held in memory is loaded. To load the one from memory instead,
you use the :meth:`~getml.data.DataFrame.load` method.
In order to load a data frame object from a different project,
you have to switch projects first. Caution: any changes
applied after the last call to
:meth:`~getml.data.DataFrame.save` will be lost. See
:func:`~getml.engine.set_project` and
:class:`~getml.data.DataFrame` for more details about the
lifecycles of the models.
"""
if not isinstance(name, str):
raise TypeError("'name' must be of type str")
data_frames_available = list_data_frames()
# First, attempt to load a data frame held in memory.
if name in data_frames_available['in_memory']:
return DataFrame(name).refresh()
if name in data_frames_available['in_project_folder']:
return DataFrame(name).load()
raise ValueError("No data frame holding the name '" +
name+"' present on the getML engine")
# --------------------------------------------------------------------
[docs]def exists(name):
"""
Returns true if a data frame named 'name' exists.
Args:
name (str):
Name of the data frame.
"""
if not isinstance(name, str):
raise TypeError("'name' must be of type str")
all_df = list_data_frames()
return (
name in (all_df["in_memory"] + all_df["in_project_folder"])
)
# --------------------------------------------------------------------
[docs]def delete(name):
"""
If a data frame named 'name' exists, it is deleted.
Args:
name (str):
Name of the data frame.
"""
if not isinstance(name, str):
raise TypeError("'name' must be of type str")
if exists(name):
DataFrame(name).delete()
# --------------------------------------------------------------------