Pandas Dataframe inefficient for loop through columns

I have Precipitation Data (1800 rows and 15k columns) for each cell and date. I want to find dates where a specific amount of rain (>15mm) was reached and count the days after this event were less rain (<1,1mm) occurred. Together with the amount of rain, start and end period, cell and other information stored in a new DataFrame.

I write a for loop that does this job but it took several days to finish ;(. I am a beginner of python so maybe there are some tips for other methods.

from datetime import datetime, timedelta, date
import datetime
import pandas as pd

#Existing Data
index_dates =  pd.date_range(pd.datetime.today(), periods=10).tolist()
df = pd.DataFrame({'486335':[0,0,16,0,0,0,2,1,8,2],'486336':[2,1,8,0,11,16,0,1,6,8],'486337':[22,1,22,0,0,0,5,3,6,1]},index=index_dates)
columns = df.columns 
counter_columns = 0

iteration = -1 #Iterations Steps
counter = 10 #10 precipitation values per column
duration = 0 #days with no or less than pp_max_1 rain 
count = False

index_list = df.index #Index for updating df / Integear
period_range = 0  #Amount of days after Event without much rain Integear
period_amount = 0 #Amount of PP in dry days except event Integear
event_amount = 0.0  #Amount of heavy rainfall on the event date Float
pp = 0 #actual precipitation
pp_sum = 0.0 #mm
pp_min = 15.0 #mm min pp for start to count dry days until duration_min_after
pp_max_1 = 0.11 #max pp for 1 day while counting dry days
dry_days = 0 #dry days after event

for x in df:
    for y in df[x]:
        iteration = iteration + 1
        if iteration == counter:
            iteration = 0
            counter_columns = counter_columns + 1
            print("column :",counter_columns, "finished")
        if y >= pp_min and count == False:
            duration = duration + 1
            count = True
            start_period = index_list[iteration]
            event_amount = y
            index = iteration
            pp_sum = pp_sum + y
        elif y >= pp_min and count == True or y >= pp_max_1 and count == True:
            end_period = index_list[iteration]
            dry_periods = dry_periods.append({"start_period":start_period ,"end_period":end_period,"period_range":duration,"period_amount":pp_sum ,"event_amount":event_amount, "cell":columns[counter_columns]},ignore_index=True).sort_values('period_range',ascending=False)
            duration = 0
            count = False
            pp_sum = 0
        elif pp <= pp_max_1 and count == True:
            duration = duration + 1
            pp_sum = pp_sum + y
        else:
            continue
print(dry_periods)

The output looks like this

start_period              end_period period_range  \
0  2019-07-05 13:15:05.545 2019-07-09 13:15:05.545            4   
1  2019-07-05 13:15:05.545 2019-07-09 13:15:05.545            4   
2  2019-07-05 13:15:36.569 2019-07-09 13:15:36.569            4   
3  2019-07-05 13:15:36.569 2019-07-09 13:15:36.569            4   
4  2019-07-05 13:16:16.372 2019-07-09 13:16:16.372            4   
5  2019-07-05 13:16:16.372 2019-07-09 13:16:16.372            4   
6  2019-07-05 13:16:38.461 2019-07-09 13:16:38.461            4   
15 2019-07-05 13:16:38.461 2019-07-09 13:16:38.461            4   
7  2019-07-08 13:15:05.545 2019-07-10 13:15:05.545            2   
8  2019-07-08 13:15:36.569 2019-07-10 13:15:36.569            2   
9  2019-07-08 13:16:16.372 2019-07-10 13:16:16.372            2   
10 2019-07-08 13:16:38.461 2019-07-10 13:16:38.461            2   
11 2019-07-03 13:15:05.545 2019-07-04 13:15:05.545            1   
12 2019-07-03 13:15:36.569 2019-07-04 13:15:36.569            1   
13 2019-07-03 13:16:16.372 2019-07-04 13:16:16.372            1   
14 2019-07-03 13:16:38.461 2019-07-04 13:16:38.461            1   

    period_amount event_amount    cell  
0            16.0           16  486335  
1            22.0           22  486337  
2            16.0           16  486335  
3            22.0           22  486337  
4            16.0           16  486335  
5            22.0           22  486337  
6            16.0           16  486335  
15           22.0           22  486337  
7            16.0           16  486336  
8            16.0           16  486336  
9            16.0           16  486336  
10           16.0           16  486336  
11           22.0           22  486337  
12           22.0           22  486337  
13           22.0           22  486337  
14           22.0           22  486337