Jun-03-2019, 11:02 PM
Hi,
I want to iterate through the "Pandas DataFrame" rows and while the "last_day <=day_set".
Condition1: Iterate over the rows of the first column.
sub_condition: on each iteration, check and break the iteration if day_set<=days_last.
I use below code, but not complete,
Kindly someone help.
I want to iterate through the "Pandas DataFrame" rows and while the "last_day <=day_set".
Condition1: Iterate over the rows of the first column.
sub_condition: on each iteration, check and break the iteration if day_set<=days_last.
I use below code, but not complete,
Kindly someone help.
import pandas as pd import csv input=r'D:\PythonCodes\correctiofactordata.csv' df=pd.read_csv(input) increment=3 prev_day=0 day_set=5 days_last=df[-1:]['Days_elapsed'] for i, row in df.iterrows(): data_catch=df.loc[(df['Days_elapsed'] >= prev_day) & (df['Days_elapsed'] <= day_set)] if((data_catch.shape[0])>0): print('Enough data') else: print('not enough data') print(prev_day),print(day_set) prev_day+=5 day_set+=5 print('-------------------')input data:
Time_golden Golden is_golden Days_elapsed Factor 20-03-2019 10:24 98.6 golden 0.0 1.0 20-03-2019 11:10 97.0 golden 0.15 1.3 20-03-2019 13:13 96.0 golden 0.53 1.5 21-03-2019 13:43 95.0 golden 1.03 0.95 23-03-2019 10:37 94.6 golden 1.2 0.96 23-03-2019 18:43 93.0 golden 1.6 1.0 24-03-2019 22:19 92.0 golden 2.53 1.3 25-03-2019 09:23 90.0 golden 2.69 1.5 26-03-2019 11:42 89.0 golden 2.9 0.95 27-03-2019 20:32 87.3 golden 3.0 0.96 27-03-2019 23:42 86.0 golden 3.21 1.5 28-03-2019 00:52 84.0 golden 3.62 0.95 28-03-2019 03:40 82.3 golden 3.96 0.96 21-03-2019 10:34 83.0 notgolden 4.23 1.0 24-03-2019 16:37 80.0 notgolden 5.2 1.3 24-03-2019 21:42 73.0 notgolden 5.6 0.95 27-03-2019 21:02 60.0 notgolden 5.69 0.96 28-03-2019 02:42 53.0 notgolden 6.1 1.0 20-03-2019 10:24 98.6 golden 6.66 1.3 20-03-2019 11:10 97.0 golden 6.987 1.5 20-03-2019 13:13 96.0 golden 7.03 0.95 21-03-2019 13:43 95.0 golden 7.0236 0.96 23-03-2019 10:37 94.6 golden 7.26 1.5 23-03-2019 18:43 93.0 golden 8.5 0.95 24-03-2019 22:19 92.0 golden 8.62 0.96 25-03-2019 09:23 90.0 golden 9.6 1.0 26-03-2019 11:42 89.0 golden 11.02 1.3 27-03-2019 20:32 87.3 golden 11.63 1.3 27-03-2019 23:42 86.0 golden 12.35 1.0