I need assistance with identifying the date when a threshold value is first exceeded in multiple time series columns. The columns are related to different site locations and the index is a date time value. I am looking for a function similar to "idxmax" but specifically for returning the index when the threshold is surpassed for the first time. Being new to Python, I would appreciate some guidance on how to achieve this task. Thank you.
One potential solution is to utilize the idxmax function:
In [23]: s = pd.Series([10, 20, 30, 40, 50], index=pd.date_range('2022-01-01', periods=5, freq='D'))
In [24]: s
Out[24]:
2022-01-01 10
2022-01-02 20
2022-01-03 30
2022-01-04 40
2022-01-05 50
Freq: D, dtype: int64
In [25]: s >= 30
Out[25]:
2022-01-01 False
2022-01-02 False
2022-01-03 True
2022-01-04 True
2022-01-05 True
Freq: D, dtype: bool
In [26]: (s >= 30).idxmax()
Out[26]: Timestamp('2022-01-03 00:00:00')
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