Large Dataset Output

# output from large data set (that does not work)

def _convert_pandas_statistics(stat):
        content_dict = {}
        percentiles = []
        if "25%" in stat:
            percentiles = [0] * 100
            percentiles[24] = stat["25%"]
            percentiles[49] = stat["50%"]
            percentiles[74] = stat["75%"]
        if "mean" in stat:
            content_dict["mean"] = stat["mean"]
        if "mean" in stat and "count" in stat:
            content_dict["sum"] = stat["mean"] * stat["count"]
        if "max" in stat:
            content_dict["maximum"] = stat["max"]
        if "std" in stat:
            content_dict["stdDev"] = stat["std"]
        if "min" in stat:
            content_dict["minimum"] = stat["min"]
        if percentiles:
            content_dict["approxPercentiles"] = percentiles
        return content_dict

df = fv.get_batch_data()
stats = df.describe()
final_stats = []
for col in stats.columns:
    stat = _convert_pandas_statistics(stats[col].to_dict())
    stat["dataType"] = (
                "Fractional"
                if isinstance(stats[col].dtype, type(np.dtype(np.float64)))
                else "Integral"
            )
    stat["isDataTypeInferred"] = "false"
    stat["column"] = col.split(".")[-1]
    stat["completeness"] = 1
    final_stats.append(stat)
final_stats

VersionWarning: No training dataset version was provided to initialise batch scoring . Defaulting to version 1.
2023-05-02 07:33:03,136 INFO: USE `workshop_featurestore`
2023-05-02 07:33:03,784 INFO: SELECT `fg0`.`close_0` `close_0`, `fg0`.`ema_0` `ema_0`, `fg0`.`high_0` `high_0`, `fg0`.`low_0` `low_0`, `fg0`.`open_0` `open_0`, `fg0`.`rsi_0` `rsi_0`, `fg0`.`sma_0` `sma_0`, `fg0`.`volume_0` `volume_0`, `fg0`.`close_1` `close_1`, `fg0`.`ema_1` `ema_1`, `fg0`.`high_1` `high_1`, `fg0`.`low_1` `low_1`, `fg0`.`open_1` `open_1`, `fg0`.`rsi_1` `rsi_1`, `fg0`.`sma_1` `sma_1`, `fg0`.`volume_1` `volume_1`, `fg0`.`close_10` `close_10`, `fg0`.`ema_10` `ema_10`, `fg0`.`high_10` `high_10`, `fg0`.`low_10` `low_10`, `fg0`.`open_10` `open_10`, `fg0`.`rsi_10` `rsi_10`, `fg0`.`sma_10` `sma_10`, `fg0`.`volume_10` `volume_10`, `fg0`.`close_11` `close_11`, `fg0`.`ema_11` `ema_11`, `fg0`.`high_11` `high_11`, `fg0`.`low_11` `low_11`, `fg0`.`open_11` `open_11`, `fg0`.`rsi_11` `rsi_11`, `fg0`.`sma_11` `sma_11`, `fg0`.`volume_11` `volume_11`, `fg0`.`close_12` `close_12`, `fg0`.`ema_12` `ema_12`, `fg0`.`high_12` `high_12`, `fg0`.`low_12` `low_12`, `fg0`.`open_12` `open_12`, `fg0`.`rsi_12` `rsi_12`, `fg0`.`sma_12` `sma_12`, `fg0`.`volume_12` `volume_12`, `fg0`.`close_13` `close_13`, `fg0`.`ema_13` `ema_13`, `fg0`.`high_13` `high_13`, `fg0`.`low_13` `low_13`, `fg0`.`open_13` `open_13`, `fg0`.`rsi_13` `rsi_13`, `fg0`.`sma_13` `sma_13`, `fg0`.`volume_13` `volume_13`, `fg0`.`close_14` `close_14`, `fg0`.`ema_14` `ema_14`, `fg0`.`high_14` `high_14`, `fg0`.`low_14` `low_14`, `fg0`.`open_14` `open_14`, `fg0`.`rsi_14` `rsi_14`, `fg0`.`sma_14` `sma_14`, `fg0`.`volume_14` `volume_14`, `fg0`.`close_15` `close_15`, `fg0`.`ema_15` `ema_15`, `fg0`.`high_15` `high_15`, `fg0`.`low_15` `low_15`, `fg0`.`open_15` `open_15`, `fg0`.`rsi_15` `rsi_15`, `fg0`.`sma_15` `sma_15`, `fg0`.`volume_15` `volume_15`, `fg0`.`close_16` `close_16`, `fg0`.`ema_16` `ema_16`, `fg0`.`high_16` `high_16`, `fg0`.`low_16` `low_16`, `fg0`.`open_16` `open_16`, `fg0`.`rsi_16` `rsi_16`, `fg0`.`sma_16` `sma_16`, `fg0`.`volume_16` `volume_16`, `fg0`.`close_17` `close_17`, `fg0`.`ema_17` `ema_17`, `fg0`.`high_17` `high_17`, `fg0`.`low_17` `low_17`, `fg0`.`open_17` `open_17`, `fg0`.`rsi_17` `rsi_17`, `fg0`.`sma_17` `sma_17`, `fg0`.`volume_17` `volume_17`, `fg0`.`close_18` `close_18`, `fg0`.`ema_18` `ema_18`, `fg0`.`high_18` `high_18`, `fg0`.`low_18` `low_18`, `fg0`.`open_18` `open_18`, `fg0`.`rsi_18` `rsi_18`, `fg0`.`sma_18` `sma_18`, `fg0`.`volume_18` `volume_18`, `fg0`.`close_19` `close_19`, `fg0`.`ema_19` `ema_19`, `fg0`.`high_19` `high_19`, `fg0`.`low_19` `low_19`, `fg0`.`open_19` `open_19`, `fg0`.`rsi_19` `rsi_19`, `fg0`.`sma_19` `sma_19`, `fg0`.`volume_19` `volume_19`, `fg0`.`close_2` `close_2`, `fg0`.`ema_2` `ema_2`, `fg0`.`high_2` `high_2`, `fg0`.`low_2` `low_2`, `fg0`.`open_2` `open_2`, `fg0`.`rsi_2` `rsi_2`, `fg0`.`sma_2` `sma_2`, `fg0`.`volume_2` `volume_2`, `fg0`.`close_3` `close_3`, `fg0`.`ema_3` `ema_3`, `fg0`.`high_3` `high_3`, `fg0`.`low_3` `low_3`, `fg0`.`open_3` `open_3`, `fg0`.`rsi_3` `rsi_3`, `fg0`.`sma_3` `sma_3`, `fg0`.`volume_3` `volume_3`, `fg0`.`close_4` `close_4`, `fg0`.`ema_4` `ema_4`, `fg0`.`high_4` `high_4`, `fg0`.`low_4` `low_4`, `fg0`.`open_4` `open_4`, `fg0`.`rsi_4` `rsi_4`, `fg0`.`sma_4` `sma_4`, `fg0`.`volume_4` `volume_4`, `fg0`.`close_5` `close_5`, `fg0`.`ema_5` `ema_5`, `fg0`.`high_5` `high_5`, `fg0`.`low_5` `low_5`, `fg0`.`open_5` `open_5`, `fg0`.`rsi_5` `rsi_5`, `fg0`.`sma_5` `sma_5`, `fg0`.`volume_5` `volume_5`, `fg0`.`close_6` `close_6`, `fg0`.`ema_6` `ema_6`, `fg0`.`high_6` `high_6`, `fg0`.`low_6` `low_6`, `fg0`.`open_6` `open_6`, `fg0`.`rsi_6` `rsi_6`, `fg0`.`sma_6` `sma_6`, `fg0`.`volume_6` `volume_6`, `fg0`.`close_7` `close_7`, `fg0`.`ema_7` `ema_7`, `fg0`.`high_7` `high_7`, `fg0`.`low_7` `low_7`, `fg0`.`open_7` `open_7`, `fg0`.`rsi_7` `rsi_7`, `fg0`.`sma_7` `sma_7`, `fg0`.`volume_7` `volume_7`, `fg0`.`close_8` `close_8`, `fg0`.`ema_8` `ema_8`, `fg0`.`high_8` `high_8`, `fg0`.`low_8` `low_8`, `fg0`.`open_8` `open_8`, `fg0`.`rsi_8` `rsi_8`, `fg0`.`sma_8` `sma_8`, `fg0`.`volume_8` `volume_8`, `fg0`.`close_9` `close_9`, `fg0`.`ema_9` `ema_9`, `fg0`.`high_9` `high_9`, `fg0`.`low_9` `low_9`, `fg0`.`open_9` `open_9`, `fg0`.`rsi_9` `rsi_9`, `fg0`.`sma_9` `sma_9`, `fg0`.`volume_9` `volume_9`
FROM `workshop_featurestore`.`historical_data_21_1` `fg0`
UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.

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