CODEWITHSUNDEEP
pandasdataframe
Ioan Kats
Ioan Kats

Reputation: 521

Pandas check that a list is is_monotonic_increasing but with specific step

Lets say that we have these columns in a df:

   A  B  C
0  1  0  1
1  2  2  2
2  3  4  4
3  4  6  6
4  5  8  8

I know that I can check that every specific columns with monotonic_increasing like that

df['A'].is_monotonic_increasing.

I was wondering if there is a way to check/validate that the monotonic_increasing has a specific step. Let me explain. I would like for example to check that df['A'] is monotonic_increasing with step 1, the df['B'] is monotonic_increasing with step 2.

Is there a way to check that out ?

Upvotes: 0

Views: 521

Answers (2)

noah
noah

Reputation: 2786

One liner to get all columns at once:

 df.diff().iloc[1] * (df.diff().nunique() == 1)

Output:

A    1.0
B    2.0
C    0.0
Name: 1, dtype: float64

Output is the step size, or 0 if not monotonic increasing.

Upvotes: 0

Quang Hoang
Quang Hoang

Reputation: 150785

I don't think there's a function for that. We can build a two-line function:

def step_incr(series, step=1):
    tmp = np.arange(len(series)) * step
    return series.eq(series.iloc[0]+tmp).all()

step_incr(df['A'], step=1)
# True

step_incr(df['B'], step=1)
# False

Another way to check is looking at the values of differences:

def is_step(series):
    uniques = series.diff().iloc[1:].unique()

    if len(uniques) == 1:
        return True, uniques[0]
    return False, None

is_step(df['A'])
# (True, 1.0)

is_step(df['B'])
# (True, 2.0)

is_step(df['C'])
# (False, None)

Upvotes: 2

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