For a time series dataset:
A, How do I find the local minima (nadir values) for each ID? (local mins)
B, How do I find any subsequent values that are 2 greater than each local minima. (local mins + 2)
import pandas as pd
df = pd.DataFrame({'id': [1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2], 'value': [8,5,3,2,1,2,3,5, 1.5, 3, 1, 1.5, 2, 3, 4, 0.4]})
For A, I was able to use the following code to find all the nadir/local minimum values from the dataset, but they are not grouped by each id. How do I modify this to group them by id?
nadir_min = df.value[(df.value.shift(1) > df.value) & (df.value.shift(-1) > df.value)]
nadir_min
Out[1]:
4 1.0
8 1.5
10 1.0
Name: value, dtype: float64
For B, I would like to get back the subsequent values after the nadir/local minimums that are two greater than the nadir/local minimums. For the example data above I would get back:
index id value
6 1 3.0
13 2 3.0
14 2 4.0
Perhaps a conditional loop would do the trick as it can store each local min and compare the subsequent values if they are 2 greater than it. However, the working dataset is MASSIVE and would take too long to run so I am trying something like this:
df['min_plus2'] = (df['value'] >= nadir_min + 2) & (df.index > nadir_min_index)
You do it with next code:
Answer for A:
Answer for B: