Rapidata results

RapidataResults

Bases: dict

A specialized dictionary class for handling Rapidata API results. Extends the built-in dict class with specialized methods.

to_pandas

to_pandas(split_details: bool = False) -> DataFrame

Warning

This method is currently under development. The structure of the results may change in the future.

Converts the results to a pandas DataFrame.

For Compare results (exactly 2 assets), creates standardized A_/B_ columns for each metric (plus Both_/Neither_ when those options are present). For Ranking results with N assets, creates one <asset>_<metric> column per asset. For non-compare results, flattens nested dictionaries into columns with underscore-separated names.

Parameters:

Name	Type	Description	Default
split_details	bool	If True, splits each datapoint by its detailed results, creating a row for each response with global metrics copied.	False

Returns:

Type	Description
DataFrame	pd.DataFrame: A DataFrame containing the processed results

Raises:

Type	Description
ValueError	If split_details is True but no detailed results are found

Source code in src/rapidata/rapidata_client/results/rapidata_results.py

def to_pandas(self, split_details: bool = False) -> "pd.DataFrame":
    """
    Warning:
        This method is currently under development. The structure of the results may change in the future.

    Converts the results to a pandas DataFrame.

    For Compare results (exactly 2 assets), creates standardized ``A_``/``B_``
    columns for each metric (plus ``Both_``/``Neither_`` when those options
    are present). For Ranking results with N assets, creates one
    ``<asset>_<metric>`` column per asset. For non-compare results, flattens
    nested dictionaries into columns with underscore-separated names.

    Args:
        split_details: If True, splits each datapoint by its detailed results,
                      creating a row for each response with global metrics copied.

    Returns:
        pd.DataFrame: A DataFrame containing the processed results

    Raises:
        ValueError: If split_details is True but no detailed results are found
    """
    import pandas as pd
    from pandas.core.indexes.base import Index

    if "results" not in self or not self["results"]:
        return pd.DataFrame()

    if self["info"].get("orderType") is None:
        managed_print(
            "Warning: Results are old and Order type is not specified. Dataframe might be wrong."
        )

    if split_details and not self._has_detailed_results():
        raise ValueError("No detailed results found in the data")

    # Compare/Ranking have an asset-aware shape; route them to the
    # compare-specific path even when split_details=True so we keep the
    # A_/B_ (or per-asset) column splitting.
    if self["info"].get("orderType") in ("Compare", "Ranking"):
        return self._compare_to_pandas(split_details=split_details)

    if split_details:
        return self._to_pandas_with_detailed_results()

    # Get the structure from first item
    first_item = self["results"][0]
    columns = []
    path_map = {}  # Maps flattened column names to paths to reach the values

    # Build the column structure once
    self._build_column_structure(first_item, columns, path_map)

    # Extract data using the known structure
    data = []
    for item in self["results"]:
        row = []
        for path in path_map.values():
            value = self._get_value_from_path(item, path)
            row.append(value)
        data.append(row)

    return pd.DataFrame(data, columns=Index(columns))

to_json

to_json(path: str = './results.json') -> None

Saves the results to a JSON file.

Parameters:

Name	Type	Description	Default
path	str	The file path where the JSON should be saved. Defaults to "./results.json".	'./results.json'

Source code in src/rapidata/rapidata_client/results/rapidata_results.py

def to_json(self, path: str = "./results.json") -> None:
    """
    Saves the results to a JSON file.

    Args:
        path: The file path where the JSON should be saved. Defaults to "./results.json".
    """
    with open(path, "w") as f:
        json.dump(self, f)