My loop only saves the result of last iteration

Question

import numpy as np
import pandas as pd

This is my data:

ts = pd.DataFrame([0,1,2,3,4,5,6,7,8,9,10,11,12])
ts.columns = ["TS"]
start_df = pd.Series([1,3,6])
end_df = pd.Series([2,7,10])

I have created the following function to clean up my loop, and a for loop to iterate over each element in ts and save according to the output of check_if .

def check_if(start, ts, end):
    if start <= ts <= end:
        return 1
    else:
        return 0

ts["Flagg"] = np.nan
for ix, hour in enumerate (ts["TS"]):
    for jx, end in enumerate(end_df):
        ts["Flagg"][ix] = check_if(start_df[jx], hour, end_df[jx])

The problem is that my resulting ts["Flagg"] only saves the result of the last iteration, start_df == 6 and end_df == 10 . Is my logic in the loop completely of?

Edit:
Expected output

[0,1,1,1,1,2,2,1,1,1,0,0] 

in column ts["Flagg"] .

Answer 1

Use between with list comprehension for list of boolean mask and then sum it for count True values (are processes like 1 ), thanks @RafaelC for improvement:

ts['new'] = np.sum([ts['TS'].between(x, y) for x, y in zip(start_df, end_df)], axis=0)
print (ts)
    TS  new
0    0    0
1    1    1
2    2    1
3    3    1
4    4    1
5    5    1
6    6    2
7    7    2
8    8    1
9    9    1
10  10    1
11  11    0
12  12    0

Details :

print ([ts['TS'].between(x, y) for x, y in zip(start_df, end_df)])

[0     False
1      True
2      True
3     False
4     False
5     False
6     False
7     False
8     False
9     False
10    False
11    False
12    False
Name: TS, dtype: bool, 0     False
1     False
2     False
3      True
4      True
5      True
6      True
7      True
8     False
9     False
10    False
11    False
12    False
Name: TS, dtype: bool, 0     False
1     False
2     False
3     False
4     False
5     False
6      True
7      True
8      True
9      True
10     True
11    False
12    False
Name: TS, dtype: bool

Answer 2

you can create column (series, list) and then set it as column as jezrael pointed or create column with some initial values and then change them in loop:

ts["Flagg"] = [0 for _ in range(ts.size)]
for ix, hour in enumerate (ts["TS"]):
    for jx, end in enumerate(end_df):
        ts["Flagg"][ix] = check_if(start_df[jx], hour, end_df[jx])