Agfdhyk

i'm new to Pandas, currently i have a series like this one:

import pandas as pd  



index = [x for x in range(75860, 76510, 10)]

# number of occurrence

value = [1, 1, 4, 6, 7, 7, 7, 7, 8, 7, 7, 7, 8, 6, 6, 7, 15, 23, 26, 30, 31, 28, 22, 22, 21, 19, 14, 15, 15, 14, 12, 12, 13, 14, 14, 15, 15, 19, 19, 23, 25, 34, 38, 39, 40, 41, 35, 35, 30, 26, 23, 23, 29, 25, 25, 25, 23, 21, 19, 16, 14, 7, 6, 4, 1]



sample_ser = pd.Series(value, index=index)

This series represent measure and how many time they have been counted.

I'm trying to calculate custom parameters, but i'm using standard python for loops i want to know if there's a better way to accomplish that, here is one of the functions.

Thanks for help.

# return limits where 68% of total count took place

# starting from most_counted length we add the highest count closest to most_counted length

# if 2 count are equal we look for the next label, the one with highest count is choose



def active_area(sample_ser):



    # this is the label we have the most occurrence

    most_counted = 76310



    target = sample_ser.sum()*0.68



    total_count = 0



    high_label = most_counted + 10

    low_label = most_counted - 10



    while total_count < target:

        # index out of bound

        if low_label < sample_ser.index[0]:

            total_count += sample_ser[high_label]

            high_label += 10

            continue

        # index out of bound

        if high_label >= sample_ser.index[-1]:

            total_count += sample_ser[low_label]

            low_label -= 10

            continue



        h_len = sample_ser[high_label]

        l_len = sample_ser[low_label]



        if h_len > l_len:

            total_count += h_len

            high_label += 10

            continue



        if h_len < l_len:

            total_count += l_len

            low_label -= 10

            continue



         if h_len == l_len:

            counter = 10

            while True:



                temp_high = high_label+counter

                temp_low = low_label-counter



                if temp_low < sample_ser.index[0]:

                    total_count += h_len

                    high_label += 10

                    break



                if temp_high >= sample_ser.index[-1]:

                    total_count += l_len

                    low_label -= 10

                    break



                h_len_temp = sample_ser[temp_high]

                l_len_temp = sample_ser[temp_low]



                if h_len_temp > l_len_temp:

                    total_count += h_len

                    high_label += 10

                    break



                if h_len_temp < l_len_temp:

                    total_count += l_len

                    low_label -= 10

                    break



                if h_len_temp == l_len_temp:

                    counter += 10

                    continue



    if low_label < sample_ser.index[0]:

        low_label = sample_ser.index[0]

    if high_label >= sample_ser.index[-1]:

        high_label = sample_ser.index[-1]



    return high_label, low_label

edit: removed 3 of 4 for loop from the starting question, more easy for you to answer

Try the following (in my opinion more pythonic) script.

I added some test printouts. In the final version remove them
and convert the main processintg part into a function.

import pandas as pd



def nxt(ser, kk : int):

    """Get key / value from ser for key == kk. If the given key absent, return (-1, 0)"""

    if kk in ser.index:

        val = ser[kk]

        return (kk, val)

    else:

        return (-1, 0)



# Create test Series

index = range(75860, 76510, 10)

value = [1, 1, 4, 6, 7, 7, 7, 7, 8, 7,

     7,  7,  8,  6,  6,  7, 15, 23, 26, 30,

    31, 28, 22, 22, 21, 19, 14, 15, 15, 14,

    12, 12, 13, 14, 14, 15, 15, 19, 19, 23,

    25, 34, 38, 39, 40, 41, 35, 35, 30, 26,

    23, 23, 29, 25, 25, 25, 23, 21, 19, 16,

    14,  7,  6,  4,  1]

sample_ser = pd.Series(value, index=index)



# Processing

target = sample_ser.sum()*0.68  # Target limit

# Index of the max value. Low / high indices start also from here

idmax = low_ind = high_ind = sample_ser.idxmax()

trg = sample_ser[idmax]    # The max value

while 1:

    # Get index / value for elements before / after the current range

    l_ind, l_val = nxt(sample_ser, low_ind - 10)

    h_ind, h_val = nxt(sample_ser, high_ind + 10)

    # Diagnostic printout - part 1

    print(f'L: {l_ind:5} {l_val:2}   R: {h_ind:5} {h_val:2}', end='    ')

    if (l_ind >= 0) and (l_val > h_val) and (trg + l_val <= target):

        # Previous element found, previous value higher,

        # sum of values within the target limit

        trg += l_val      # Add the current (left) value

        low_ind = l_ind   # Set new lower index

        side = 'Left:'    # For diagnostic printout

    elif (h_ind >= 0) and (trg + h_val) <= target:

        # Next element found, sum of values within the target limit

        trg += h_val      # Add the current (right) value

        high_ind = h_ind  # Set new upper index

        side = 'Right:'   # For diagnostic printout

    else:

        print()           # Diagnostic printout - instead of part 2

        break

    # Diagnostic printout - part 2

    print(f'{side:<6} {low_ind:5} {high_ind:5} {trg:3}')

print(f'Result: {low_ind:5} {high_ind:5} {trg:3}')