How to detect constant absolute delta in integer series?

I have integer series as follows:

data1 = [1, 2, 3, 4, 3, 2, 1, 2, 1, 1]

data2 = [4, 0, 0, 0, 8, 0, 0, 0]


We can see data1 seems to be "continuous" while data2 is not, as data1 has a maximum constant absolute delta of 1.

data1

data2

data1

How can I decide using Pandas that data1 is "continuous", and data2 is not?

data1

2 Answers
2

Define continuous to mean "consecutive differences are at most 1 in absolute value". To detect this, you can use .diff():

.diff()

In [1]: series1, series2 = pd.Series(data1), pd.Series(data2)

In [2]: series1.diff().fillna(0).abs().max()
Out[2]: 1.0

In [3]: series2.diff().fillna(0).abs().max()
Out[3]: 8.0


So series1.diff().fillna(0).abs().max() <= 1 will evaluate to True, and series2.diff().fillna(0).abs().max() <= 1 will evaluate to False.

series1.diff().fillna(0).abs().max() <= 1

True

series2.diff().fillna(0).abs().max() <= 1

False

Similar to Andrey's solution but this takes advantage of pandas' rolling windows series method.

data1.rolling(2).apply(lambda x: abs(np.diff(x)) <= 1).all()
>>> True

data2.rolling(2).apply(lambda x: abs(np.diff(x)) <= 1).all()
>>> False


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