How to "describe" a column in pandas for python-CodePudding

I'm using pandas in python and I have a dataframe where one column is a timestamp and others contain data.

The blue line stays constant for a while, then suddenly increases to zero, then at some point descends again to about -98 and stays there until it suddenly goes up to zero. What I need is a new column with the status of the blue color: constant,sudden increase, constant, decrease, constant, sudden increase, constant or somehow an object that describes the data:

blue line{ 
'08.02.2022 08:30:00.000' : 'sudden increase',
'08.02.2022 10:39:30.000' : 'start decrease',
'08.02.2022 10:59:40.000' : 'end decrease',
'08.02.2022 13:50:30.000' : 'sudden increase' 
}

Is there a package for something like this? I hope it isn't too far feched

Kind Regards, Alexander

CodePudding user response：

constant,sudden increase, constant, decrease, constant, sudden increase, constant

Then I suggest taking looking at

Now you can use numpy.select to map thresholds in the gradient to your labels.

Then, in pandas it is easy to filter the rows by changes in labels:

# input data
df = pd.read_excel('test.xlsx').set_index('time')
df.index = pd.to_datetime(df.index)

# compute gradient
grad = df['Col1'].diff()
# set labels
thresh_sudden = 10
thresh_gradual = 0.25
df['state'] = np.select([grad>thresh_sudden, grad<-thresh_gradual],
                        ['sudden increase', 'start decrease'],
                        'constant')  # default

# add end of decrease
df.loc[df['state'].eq('start decrease') & df['state'].shift(-1).eq('constant'),
       'state'] = 'end decrease'

# find changing points
change = df['state'].ne(df['state'].shift())
df['change'] = df['state'].where(change)

# slice
df[change]

output:

                      Col1      Col2            state           change
time                                                                  
2022-08-02 08:00:00 -98.05       NaN         constant         constant
2022-08-02 08:29:50 -96.99       NaN  sudden increase  sudden increase
2022-08-02 08:30:10  -0.01       NaN         constant         constant
2022-08-02 10:39:30  -0.89  1.351546   start decrease   start decrease
2022-08-02 10:59:20 -97.86  1.415426     end decrease     end decrease
2022-08-02 10:59:30 -98.04  1.415426         constant         constant
2022-08-02 13:50:20 -98.09  0.441630  sudden increase  sudden increase
2022-08-02 13:50:50  -0.12  0.441630         constant         constant