Source code for getml.project.containers.data_frames

# Copyright 2022 The SQLNet Company GmbH
#
# This file is licensed under the Elastic License 2.0 (ELv2).
# Refer to the LICENSE.txt file in the root of the repository
# for details.
#


"""
Container for data frames in memory.
"""

from getml.data import DataFrame
from getml.data.helpers import list_data_frames
from getml.data.helpers2 import load_data_frame
from getml.utilities.formatting import _Formatter

# --------------------------------------------------------------------


[docs]class DataFrames: """ Container which holds all data frames associated with the running project that are currently stored in memory. The container supports slicing and is sort- and filterable. """ # ---------------------------------------------------------------- def __init__(self, data=None): self._in_memory = list_data_frames()["in_memory"] self._on_disk = list_data_frames()["on_disk"] if data is None: self.data = [load_data_frame(name) for name in self._in_memory] else: self.data = data # ---------------------------------------------------------------- def __getitem__(self, key): if isinstance(key, int): return self.data[key] if isinstance(key, slice): dfs_subset = self.data[key] return DataFrames(data=dfs_subset) if isinstance(key, str): if key in self.in_memory: return [df for df in self.data if df.name == key][0] if key in self.on_disk: raise AttributeError(f"DataFrame {key} not loaded from disk.") raise AttributeError(f"No DataFrame with name: {key}") raise TypeError( f"DataFrames can only be indexed by: int, slices, or str, not {type(key).__name__}" ) # ---------------------------------------------------------------- def __len__(self): return len(self.data) # ---------------------------------------------------------------- def __repr__(self): if len(self.in_memory) == 0: output = "No data frames in memory." else: output = self._format()._render_string() if len(self.on_disk) > 0: output += "\n\nOn disk:\n" output += "\n".join(self.on_disk) return output # ---------------------------------------------------------------- def _repr_html_(self): if len(self.in_memory) == 0: output = "<p>No data frames in memory.</p>" else: output = self._format()._render_html() if len(self.on_disk) > 0: output += "<p>On disk:</p>" output += "<br>".join(self.on_disk) return output # ---------------------------------------------------------------- def _format(self): headers = [["name", "rows", "columns", "memory usage"]] rows = [[df.name, df.nrows(), df.ncols(), df.memory_usage] for df in self.data] formatted = _Formatter(headers, rows) formatted[4].cell_template = "{:{width}.2f} MB" return formatted # ----------------------------------------------------------------
[docs] def delete(self): """ Deletes all data frames in the current project. Args: mem_only (bool): If called with the `mem_only` option set to True, the data frames will be kept on disk (in the project folder) and can be reloaded to memory through :meth:`getml.project.data_frames.load_all`. """ for name in self.on_disk: DataFrame(name).delete()
# ---------------------------------------------------------------- @property def in_memory(self): """ Returns the names of all data frames currently in memory. """ return self._in_memory # ----------------------------------------------------------------
[docs] def filter(self, conditional): """ Filters the data frames container. Args: conditional (callable): A callable that evaluates to a boolean for a given item. Returns: :class:`getml.pipeline.DataFrames`: A container of filtered data frames. Example: .. code-block:: python big_frames = getml.project.data_frames.filter(lambda frame: frame.memory_usage > 1000) """ dfs_filtered = [df for df in self.data if conditional(df)] return DataFrames(data=dfs_filtered)
# ----------------------------------------------------------------
[docs] def load(self): """ Loads all data frames stored in the project folder to memory. """ for df in self.on_disk: if df not in self.in_memory: self.data.append(load_data_frame(df))
# ---------------------------------------------------------------- @property def on_disk(self): """ Returns the names of all data frames stored in the project folder. """ return self._on_disk # ----------------------------------------------------------------
[docs] def retrieve(self): """ Retrieve a dict of all data frames in memory. """ return {df.name: df for df in self.data}
# ----------------------------------------------------------------
[docs] def save(self): """ Saves all data frames currently in memory to disk. """ for df in self.data: df.save()
# ----------------------------------------------------------------
[docs] def sort(self, key, descending=False): """ Sorts the data frames container. Args: key (callable, optional): A callable that evaluates to a sort key for a given item. descending (bool, optional): Whether to sort in descending order. Return: :class:`getml.pipeline.DataFrames`: A container of sorted data frames. Example: .. code-block:: python by_num_rows = getml.project.data_frames.sort(lambda frame: frame.nrows()) """ dfs_sorted = sorted(self.data, key=key, reverse=descending) return DataFrames(data=dfs_sorted)
# ----------------------------------------------------------------
[docs] def unload(self): """ Unloads all data frames in the current project from memory. """ for name in self.on_disk: DataFrame(name).unload()