I have a pandas dataframe with a column such as :
df1 = pd.DataFrame({ 'val': [997.95, 997.97, 989.17, 999.72, 984.66, 1902.15]})
I have 2 types of events that can be detected from this column, I wanna label them 1 and 2 .
I need to get the indexes of each label , and to do so I need to find where the 'val' column has changed a lot (± 7 ) from previous row.
Expected output:
one = [0, 1, 3, 5]
two = [2, 4 ]
Use Series.diff with mask for test less values like 0, last use boolean indexing with indices:
m = df1.val.diff().lt(0)
#if need test less like -7
#m = df1.val.diff().lt(-7)
one = df1.index[~m]
two = df1.index[m]
print (one)
Int64Index([0, 1, 3, 5], dtype='int64')
print (two)
nt64Index([2, 4], dtype='int64')
If need lists:
one = df1.index[~m].tolist()
two = df1.index[m].tolist()
Details:
print (df1.val.diff())
0 NaN
1 0.02
2 -8.80
3 10.55
4 -15.06
5 917.49
Name: val, dtype: float64