I am having a problem to call out the rows and their corresponding index based on the condition defined in my code. I have tried all the examples found here, but none is exactly the same as the problem I have. The dataset looks like this:

```py
import pandas as pd
import datetime
import numpy as np
from matplotlib import pyplot as plt

df = pd.read_csv('Datasets.csv')
print(df)

Posted_Time Label
07/01/2018  13.01
14/01/2018  9.80
21/01/2018  9.08
28/01/2018  8.64
04/02/2018  8.78
11/02/2018  7.27
18/02/2018  7.16
25/02/2018  7.09
04/03/2018  5.85
11/03/2018  8.71

```

`Posted_Time` is the index column, and it is in Datetime Format. I will like to select all rows that exceed a set threshold defined for the `Label` column as follows.

```
df2 = df.ewm(span=4, adjust=False).mean()
mean = df2['Label'].mean().astype(float)
std = df2['Label'].std().astype(float)
thres = std+mean

i = 0
control = True
for record in df2['Label']:
    if record > thres:
        print(i, 'in position!', i)
        control = False
    i += 1
if control == True:
    print('All points are within control limits.')

```

When I run my code, it returns the record position in the dataframe instead of the actual record and the corresponding index (`Posted_Time`). Here is the result that I get.

```
0 in position! 0
1 in position! 1
19 in position! 19
23 in position! 23

```

What am I doing wrong? Can someone help to correct my iteration code to achieve the desired result below, please?

```
07/01/2018  13.01 in position 0 
14/01/2018  9.80
21/01/2018  9.08
04/02/2018  8.78
11/03/2018  8.71

```

Thanks