How do I get out of the second loop to increment my first loop in order to create two 2D arrays?-CodePudding

Context:

I started with a car dataframe with over 20 car models. Then I created an array with the number of occurences for each model. Now I am trying to seperate the models with less then 500 occurences from those with => 500 occurences into two different 2D arrays.

My code:

unique_models, count_of_models = np.unique(my_data_frame["model"], 
return_counts=True)
print(unique_models, count_of_models)

[' A1' ' A2' ' A3' ' A4' ' A5' ' A6' ' A7' ' A8' ' Q2' ' Q3' ' Q5' ' Q7' ' Q8' ' R8' 
 ' RS3' ' RS4' ' RS5' ' RS6' ' RS7' ' S3' ' S4' ' S5' ' S8' ' SQ5' ' SQ7' ' TT'] 
[1347    1 1929 1381  882  748  122  118  822 1417  877  397   69   28   33   31     
 29   39    1   18   12    3    4   16    8  336]

representative_models = np.empty((0, 2), int)
other_models = np.empty((0, 2), int)

for models in unique_models:

    for counts in count_of_models:

        if counts < 500:

            other_models = np.append(other_models, np.array([[models, counts]]), axis=0)

        else:

            representative_models = np.append(other_models, np.array([[models, counts]]), axis=0)

        unique_models  = 1

Everything works, except for one small thing. Somehow, the unique_models = 1 incrementation won't work and the second for loop will continue with the same unique_model but increment its count.

What I want is to get back in the first loop, increment the model, then go to the second loop and increment the count.

Hope this is clear enough, thanks :)

CodePudding user response：

Here you're looping over a list or array unique_models

for models in unique_models:

unique_models = 1 makes no sense at all and should throw an error as you're trying to add an int to a list.

Instead you can get to the next model you can break out of the inner loop by using break instead of unique_models = 1, however that will break out of the loop after the first iteration. What you want to do is loop over BOTH count_of_models and unique_models at the same time, you can use zip for that.

for models, counts in zip(unique_models, count_of_models):

    if counts < 500:

        other_models = np.append(other_models, np.array([[models, counts]]), axis=0)

    else:

        representative_models = np.append(other_models, np.array([[models, counts]]), axis=0)