class_docstr: Base Data type class
class_docstr_tok: ["Base","Data","type","class"]
docstr: Forms :obj:`InputData` of ``ts`` type from columns of different variant of the same variable.
  
  Args:
      file_path: path to the source csv file.
      delimiter: delimiter for pandas DataFrame.
      task: the :obj:`Task` that should be solved with data.
      is_predict: indicator of stage to prepare the data to. ``False`` means fit, ``True`` means predict.
      columns_to_drop: ``list`` with names of columns to ignore.
      target_column: ``string`` with name of target column, used for predict stage.
      index_col: name or index of the column to use as the :obj:`Data.idx`.
  
          If ``None``, then check the first column's name and use it as index if succeeded
          (see the param ``possible_idx_keywords``).
  
          Set ``False`` to skip the check and rearrange a new integer index.
      possible_idx_keywords: lowercase keys to find. If the first data column contains one of the keys,
          it is used as index. See the :const:`POSSIBLE_TS_IDX_KEYWORDS` for the list of default keywords.
  
  Returns:
      An instance of :class:`InputData`.
code: def from_csv_time_series(
      cls,
      file_path: PathType,
      delimiter: str = ",",
      task: Union[Task, str] = "ts_forecasting",
      is_predict: bool = False,
      columns_to_drop: Optional[List] = None,
      target_column: Optional[str] = "",
      index_col: Optional[Union[str, int]] = None,
      possible_idx_keywords: Optional[List[str]] = None,
  ) -> InputData:
      """
      Forms :obj:`InputData` of ``ts`` type from columns of different variant of the same variable.
  
      Args:
          file_path: path to the source csv file.
          delimiter: delimiter for pandas DataFrame.
          task: the :obj:`Task` that should be solved with data.
          is_predict: indicator of stage to prepare the data to. ``False`` means fit, ``True`` means predict.
          columns_to_drop: ``list`` with names of columns to ignore.
          target_column: ``string`` with name of target column, used for predict stage.
          index_col: name or index of the column to use as the :obj:`Data.idx`.\n
              If ``None``, then check the first column's name and use it as index if succeeded
              (see the param ``possible_idx_keywords``).\n
              Set ``False`` to skip the check and rearrange a new integer index.
          possible_idx_keywords: lowercase keys to find. If the first data column contains one of the keys,
              it is used as index. See the :const:`POSSIBLE_TS_IDX_KEYWORDS` for the list of default keywords.
  
      Returns:
          An instance of :class:`InputData`.
      """
  
      possible_idx_keywords = (
          possible_idx_keywords or POSSIBLE_TS_IDX_KEYWORDS
      )
      if isinstance(task, str):
          task = Task(TaskTypesEnum(task))
  
      df = get_df_from_csv(
          file_path,
          delimiter,
          index_col,
          possible_idx_keywords,
          columns_to_drop=columns_to_drop,
      )
      idx = df.index.to_numpy()
  
      if target_column is not None:
          time_series = np.array(df[target_column])
      else:
          time_series = np.array(df[df.columns[-1]])
  
      if is_predict:
          # Prepare data for prediction
          len_forecast = task.task_params.forecast_length
  
          start_forecast = len(time_series)
          end_forecast = start_forecast + len_forecast
          input_data = InputData(
              idx=np.arange(start_forecast, end_forecast),
              features=time_series,
              target=None,
              task=task,
              data_type=DataTypesEnum.ts,
          )
      else:
          # Prepare InputData for train the pipeline
          input_data = InputData(
              idx=idx,
              features=time_series,
              target=time_series,
              task=task,
              data_type=DataTypesEnum.ts,
          )
  
      return input_data
  
imports: from __future__ import annotations
  import glob
  import os
  from copy import copy, deepcopy
  from dataclasses import dataclass, field
  from typing import Any, Iterable, List, Optional, Tuple, Union
  import numpy
  import pandas
  from golem.core.log import default_log
  from golem.utilities.requirements_notificator import warn_requirement
  from fedot.core.data.array_utilities import atleast_2d
  from fedot.core.data.load_data import JSONBatchLoader, TextBatchLoader
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
label_desc: Use image node_id 5 for calling the Data obj's underlying member method code with example usage: obj.from_csv_time_series(cls, file_path, delimiter, task, is_predict, columns_to_drop, target_column, index_col, possible_idx_keywords) and returns: input_data