PandasData

QuantConnect.Python.PandasData

PandasData(data: Any, time_as_column: bool = False)

Bases: Object

Organizes a list of data to create pandas.DataFrames

Initializes an instance of PandasData

is_custom_data

is_custom_data: bool

Gets true if this is a custom data request, false for normal QC data

levels

levels: int

Implied levels of a multi index pandas.Series (depends on the security type)

add

add(data: Any) -> None

add(trade_bar: TradeBar, quote_bar: QuoteBar) -> None

Signature descriptions:

• Adds security data object to the end of the lists

• Adds Lean data objects to the end of the lists

Parameters:

Name	Type	Description	Default
data	Optional[Any]	IBaseData object that contains security data	None
trade_bar	Optional[TradeBar]	TradeBar object that contains trade bar information of the security	None
quote_bar	Optional[QuoteBar]	QuoteBar object that contains quote bar information of the security	None

to_pandas_data_frame

to_pandas_data_frame(
    levels: int = 2,
    filter_missing_value_columns: bool = True,
) -> DataFrame

to_pandas_data_frame(
    pandas_datas: List[PandasData],
    skip_times_column: bool = False,
) -> Any

Signature descriptions:

• Get the pandas.DataFrame of the current PandasData state

• Helper method to create a single pandas data frame indexed by symbol

Parameters:

Name	Type	Description	Default
levels	Optional[int]	Number of levels of the multi index	2
filter_missing_value_columns	Optional[bool]	If false, make sure columns with "missing" values only are still added to the dataframe	True

Returns:

Type	Description
DataFrame | Any	pandas.DataFrame object.