How do you perform conditional operations on different elements in a Pandas DataFrame?

In order to give a better explanation, will separate the approach by creating two different functions:

1. The first one will be the event detection, let’s call it detect_event.

2. The second one will calculate the the price between the current event and the previous one, in the list generated by the first function. We will call it calculate_price_change.

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Starting with the first function, here it is key to understand very well the goals we want to reach or the constraints/conditions we want to satisfy.

Will leave two, of more, potential options, given the various interpretations of the question:

A. The initial is what I could get from my initial understanding

B. The second part will be one of the interpretations one could get from @Iyar Lyn comment (I can see more interpretations, but won’t consider in this answer as the approach will be similar).

Within option A, we will create a function to detect where a stock is under 5 or 25

def detect_event(df):
    # Create a list of the indexes of the events where stock was under 5 or over 25
    events = []
    # Loop through the dataframe
    for i in range(len(df)):
        # If stock is under 5, add the index to the list
        if df['stock'][i] < 5:
            events.append(i)
        # If stock is over 25, add the index to the list
        elif df['stock'][i] > 25:
            events.append(i)
    # Return the list of indexes of the events where stock was under 5 or over 25
    return events

The comments make it self-explanatory, but, basically, this will return a list of indexes of the rows where stock is under 5 or over 25.

With OP’s df this will return

events = detect_event(df)

[Out]:
[0, 4, 5, 9]

Within the option B, assuming one wants to know the events where the stock went from under 5 to over 25, and vice-versa, consecutively (there are more ways to interpret this), then one can use the following function

def detect_event(df):
    # Create a list of the indexes of the events where we will store the elements in the conditions
    events = []

    for i, stock in enumerate(df['stock']):
        # If the index is 0, add the index of the first event to the list of events
        if i == 0:
            events.append(i)
        # If the index is not 0, check if the stock went from over 25 to under 5 or from under 5 to over 25
        else:
            # If the stock went from over 25 to under 5, add the index of the event to the list of events
            if stock < 5 and df['stock'][i-1] > 25:
                events.append(i)
            # If the stock went from under 5 to over 25, add the index of the event to the list of events
            elif stock > 25 and df['stock'][i-1] < 5:
                events.append(i)

    # Return the list of events
    return events

With OP’s df this will return

events = detect_event(df)

[Out]:
[0, 5]

Note that 0 is the element in the first position, that we are appending by default.

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As for the second function, once the conditions are well defined, meaning we know clearly what we want, and adapted the first function, detect_event, accordingly, we can now detect the changes in the prices.

In order to detect the price change between the events that satisfy the conditions we defined previously, one will use a different function: calculate_price_change.

This function will take both the dataframe df and the list events generated by the previous function, and return a list with the prices diferences.

def calculate_price_change(df, events):
    # Create a list to store the price change between the most recent event where stock was under 5 vs the most recent event where stock was over 25
    price_change = []
    # Loop through the list of indexes of the events
    for i, event in enumerate(events):
        # If the index is 0, the price change is 0
        if i == 0:
            price_change.append(0)
        # If the index is not 0, calculate the price change between the current and past events
        else:
            price_change.append(df['price'][event] - df['price'][events[i-1]])
    return price_change

Now we if one calls this last function using the df and the list created with the first function detect_event, one gets the following

price_change = calculate_price_change(df, events)

[Out]:
[0, 10, -10, 10]

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Notes:

• As it is, the question gives room for multiple interpretations. That’s why my initial flag for "Needs details or clarity". For the future one might want to review: How do I ask a good question?
  and its hyperlinks.

• I understand that sometimes we won’t be able to specify everything that we want (as we might not even know – due to various reasons), so communication is key. Therefore, appreciate Iyar Lin‘s time and contributions as they helped improve this answer.