CODEWITHSUNDEEP
pythonpandas
Saturnix
Saturnix

Reputation: 10564

Pandas: determine where 2 time series are intersecting and in which direction

This is a follow up to this question: determine the coordinates where two pandas time series cross, and how many times the time series cross

I have 2 series in my Pandas dataframe, and would like to know where they intersect.

   A    B
0  1  0.5
1  2  3.0
2  3  1.0
3  4  1.0
4  5  6.0

With this code, we can create a third column that will contain True everytime the two series intersect:

df['difference'] = df.A - df.B

df['cross'] = np.sign(df.difference.shift(1))!=np.sign(df.difference)
np.sum(df.cross)-1

Now, instead of a simple True or False, I would to know in which direction the intersection took place. For example: from 1 to 2, it intersected upwards, from 2 to 3 downwards, from 3 to 4 no intersections, from 4 to 5 upwards.

enter image description here

   A    B  Cross_direction
0  1  0.5  None
1  2  3.0  Upwards
2  3  1.0  Downwards
3  4  1.0  None
4  5  6.0  Upwards

In pseudo-code, it should be like this:

cross_directions = [none, none, ... * series size]
for item in df['difference']:
    if item > 0 and next_item < 0:
        cross_directions.append("up")
    elif item < 0 and next_item > 0:
        cross_directions.append("down")

The problem is that next_item is unavailable with this syntax (we obtain that in the original syntax using .shift(1)) and that it takes a lot of code.

Should I look into implementing the code above using something that can group the loop by 2 items at a time? Or is there a simpler and more elegant solution like the one from the previous question?

Upvotes: 0

Views: 1812

Answers (2)

nandneo
nandneo

Reputation: 505

You can use numpy.select.

Below code should work for you, the code is as follows:

df = pd.DataFrame({'A': [1, 2, 3, 4,5], 'B': [0.5, 3, 1, 1, 6]})
df['Diff'] = df.A - df.B
df['Cross'] = np.select([((df.Diff < 0) & (df.Diff.shift() > 0)), ((df.Diff > 0) & (df.Diff.shift() < 0))], ['Up', 'Down'], 'None')

#Output dataframe
   A    B  Diff Cross
0  1  0.5   0.5  None
1  2  3.0  -1.0    Up
2  3  1.0   2.0  Down
3  4  1.0   3.0  None
4  5  6.0  -1.0    Up

Upvotes: 7

Saturnix
Saturnix

Reputation: 10564

My very lousy and redundant solution.

dataframe['difference'] = dataframe['A'] - dataframe['B']
dataframe['temporary_a'] = np.array(dataframe.difference) > 0
dataframe['temporary_b'] = np.array(dataframe.difference.shift(1)) < 0
cross_directions = []
for index,row in dataframe.iterrows():
    if not row['temporary_a'] and not row['temporary_b']:
        cross_directions.append("up")
    elif row['temporary_a'] and row['temporary_b']:
        cross_directions.append("down")
    else:
        cross_directions.append("not")
dataframe['cross_direction'] = cross_directions

Upvotes: 0

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