preprocessing.curate_data.remove_duplicate_timestamps

preprocessing.curate_data.remove_duplicate_timestamps(
    df,
    time_col='Time (UTC)',
    agg='mean',
)

Resolve duplicate timestamps across all data columns. Groups rows that share the same time_col value and collapses them using the chosen aggregation. All columns except time_col are aggregated. The resulting frame is sorted chronologically, re-indexed, and returned.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Input dataframe containing time_col and one or more data columns.	required
time_col	str	Name of the column that holds timestamps. Defaults to "Time (UTC)".	'Time (UTC)'
agg	str | Callable	Aggregation applied when collapsing duplicate rows. Accepts any string recognised by pandas.core.groupby.GroupBy.agg() ("mean", "median", "min", "max", "sum", "std", "var", "first", "last") as well as "mode" (most frequent value per group) or any custom callable. Defaults to "mean".	'mean'

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: Deduplicated dataframe with unique time_col rows,
	pd.DataFrame	sorted ascending by timestamp.

Raises

Name	Type	Description
	KeyError	If time_col is not present in df.

Examples

Mean-aggregate two data columns with the default time column:

import pandas as pd
from spotforecast2_safe.preprocessing.curate_data import remove_duplicate_timestamps
df = pd.DataFrame(
     {
         "Time (UTC)": [
             "2026-01-01 00:00:00",
             "2026-01-01 00:00:00",
             "2026-01-01 01:00:00",
         ],
         "Load A": [100.0, 120.0, 130.0],
         "Load B": [200.0, 220.0, 210.0],
     }
    )
out = remove_duplicate_timestamps(df)
print(f"len(out): {len(out)}")
print(f"Load A: {float(out.loc[0, 'Load A'])}")
print(f"Load B: {float(out.loc[0, 'Load B'])}")

len(out): 2
Load A: 110.0
Load B: 210.0

Median aggregation on a custom time column:

import pandas as pd
from spotforecast2_safe.preprocessing.curate_data import remove_duplicate_timestamps
df2 = pd.DataFrame(
    {
        "ts": ["2026-01-01", "2026-01-01", "2026-01-02"],
        "value": [10.0, 30.0, 20.0],
    }
)
out2 = remove_duplicate_timestamps(
    df2, time_col="ts", agg="median"
)
print(f"Value: {float(out2.loc[0, 'value'])}")

Value: 20.0