Select Pandas dataframe rows based on 'hour' datetime

Question

I have a pandas dataframe 'df' with a column 'DateTimes' of type datetime.time.

The entries of that column are hours of a single day:

00:00:00
.
.
.
23:59:00

Seconds are skipped, it counts by minutes.

How can I choose rows by hour, for example the rows between 00:00:00 and 00:01:00?

---

If I try this:

df.between_time('00:00:00', '00:00:10')

I get an error that index must be a DateTimeIndex.

I set the index as such with:

df=df.set_index(keys='DateTime')

but I get the same error.

I can't seem to get 'loc' to work either. Any suggestions?

Answer 1

Here a working example of what you are trying to do:

times = pd.date_range('3/6/2012 00:00', periods=100, freq='S', tz='UTC')
df = pd.DataFrame(np.random.randint(10, size=(100,1)), index=times)
df.between_time('00:00:00', '00:00:30')

Note the index has to be of type DatetimeIndex.

I understand you have a column with your dates/times. The problem probably is that your column is not of this type, so you have to convert it first, before setting it as index:

# Method A
df.set_index(pd.to_datetime(df['column_name'], drop=True)

# Method B
df.index = pd.to_datetime(df['column_name'])
df = df.drop('col', axis=1)

(The drop is only necessary if you want to remove the original column after setting it as index)

Answer 2

Check out these links:
convert column to date type: Convert DataFrame column type from string to datetime
filter dataframe on dates: Filtering Pandas DataFrames on dates
Hope this helps