selecting specific rows based on timestamp in pandas

Question

I have a dataframe with a DateTime index:

>>>df.head()
Out:
                        Conn_ses
    DateTime                     
    2018-07-02 14:46:08       332
    2018-07-02 15:00:53       328
    2018-07-02 15:05:53       324
    2018-07-02 15:10:53       326
    2018-07-02 15:15:53       326

I now want to select the rows every 30 minutes (so starting from 15.00) So, i tried df.resample but it gives me the warning that only resample.mean() or resample.sum() can be used. However, I don't need that, i want to retain my original values. This is my result when using resample:

>>> df1=df['Conn_ses'].resample('30Min')
>>> df1.head()
/.../W10 data analysis.py:1: FutureWarning: .resample() is now a deferred operation
use .resample(...).mean() instead of .resample(...)
  from datetime import datetime
DateTime
2018-07-02 14:30:00    332.000000
2018-07-02 15:00:00    323.333333
2018-07-02 15:30:00    314.000000
2018-07-02 16:00:00    296.666667
2018-07-02 16:30:00    248.833333
Freq: 30T, Name: Conn_ses, dtype: float64

Is the resample method the right one in this case? If not, how can I approach this issue?

Answer 1

I believe you need:

df1 = df['Conn_ses'].resample('30Min').first()
print (df1)
DateTime
2018-07-02 14:30:00    332
2018-07-02 15:00:00    328
Freq: 30T, Name: Conn_ses, dtype: int64