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# mypy: allow-untyped-defs
from typing import Any, Optional
_pandas: Any = None
_WITH_PANDAS: Optional[bool] = None
def _try_import_pandas() -> bool:
try:
import pandas # type: ignore[import]
global _pandas
_pandas = pandas
return True
except ImportError:
return False
# pandas used only for prototyping, will be shortly replaced with TorchArrow
def _with_pandas() -> bool:
global _WITH_PANDAS
if _WITH_PANDAS is None:
_WITH_PANDAS = _try_import_pandas()
return _WITH_PANDAS
class PandasWrapper:
@classmethod
def create_dataframe(cls, data, columns):
if not _with_pandas():
raise RuntimeError("DataFrames prototype requires pandas to function")
return _pandas.DataFrame(data, columns=columns) # type: ignore[union-attr]
@classmethod
def is_dataframe(cls, data):
if not _with_pandas():
return False
return isinstance(data, _pandas.core.frame.DataFrame) # type: ignore[union-attr]
@classmethod
def is_column(cls, data):
if not _with_pandas():
return False
return isinstance(data, _pandas.core.series.Series) # type: ignore[union-attr]
@classmethod
def iterate(cls, data):
if not _with_pandas():
raise RuntimeError("DataFrames prototype requires pandas to function")
yield from data.itertuples(index=False)
@classmethod
def concat(cls, buffer):
if not _with_pandas():
raise RuntimeError("DataFrames prototype requires pandas to function")
return _pandas.concat(buffer) # type: ignore[union-attr]
@classmethod
def get_item(cls, data, idx):
if not _with_pandas():
raise RuntimeError("DataFrames prototype requires pandas to function")
return data[idx : idx + 1]
@classmethod
def get_len(cls, df):
if not _with_pandas():
raise RuntimeError("DataFrames prototype requires pandas to function")
return len(df.index)
@classmethod
def get_columns(cls, df):
if not _with_pandas():
raise RuntimeError("DataFrames prototype requires pandas to function")
return list(df.columns.values.tolist())
# When you build own implementation just override it with dataframe_wrapper.set_df_wrapper(new_wrapper_class)
default_wrapper = PandasWrapper
def get_df_wrapper():
return default_wrapper
def set_df_wrapper(wrapper):
global default_wrapper
default_wrapper = wrapper
def create_dataframe(data, columns=None):
wrapper = get_df_wrapper()
return wrapper.create_dataframe(data, columns)
def is_dataframe(data):
wrapper = get_df_wrapper()
return wrapper.is_dataframe(data)
def get_columns(data):
wrapper = get_df_wrapper()
return wrapper.get_columns(data)
def is_column(data):
wrapper = get_df_wrapper()
return wrapper.is_column(data)
def concat(buffer):
wrapper = get_df_wrapper()
return wrapper.concat(buffer)
def iterate(data):
wrapper = get_df_wrapper()
return wrapper.iterate(data)
def get_item(data, idx):
wrapper = get_df_wrapper()
return wrapper.get_item(data, idx)
def get_len(df):
wrapper = get_df_wrapper()
return wrapper.get_len(df)
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