Pandas - Convert Strings to Time Without Date

Pandas - convert strings to time without date

After performing the conversion you can use the datetime accessor dt to access just the hour or time component:

In [51]:

df['hour'] = pd.to_datetime(df['time'], format='%H:%M').dt.hour
df
Out[51]:
        time  hour
index             
1      10:53    10
2      12:17    12
3      14:46    14
4      16:36    16
5      18:39    18
6      20:31    20
7      22:28    22

Also your format string H%:M% is malformed, it's likely to raise a ValueError: ':' is a bad directive in format 'H%:M%'

Regarding your last comment the dtype is datetime.time not datetime:

In [53]:
df['time'].iloc[0]

Out[53]:
datetime.time(10, 53)

Python/Pandas convert string to time only

These two lines:

dfc['Time_of_Sail'] = pd.to_datetime(dfc['Time_of_Sail'])
dfc['Time_of_Sail'] = [time.time() for time in dfc['Time_of_Sail']]

Can be written as:

dfc['Time_of_Sail'] = pd.to_datetime(dfc['Time_of_Sail'],format= '%H:%M:%S' ).dt.time

How to convert a column in pandas data frame as Time object

We can make use of to_timedelta function in pandas.

df['Time'] = pd.to_timedelta(df['Time'])

It will create time format of `timedelta64[ns]

Python pandas - Convert string to datetime without a year using pandas.to_datetime()

You can get the time part of a datetime series with Series.dt.time

print(data_log['LocalTime'].dt.time)

This series will consist of Python standard library datetime.time objects.

Convert string to dateTime without the date information Python

I think this is because you are using datetime module so it is by default adding the starting date to it. you can extract just the time using

datetime.strptime("0:00:01.138000", "%H:%M:%S.%f").time()

Convert strings to pandas timestamps without date

If you only need to compare the them , you can do with

s=pd.Series(['14:12:2006' , '1200' , '1500']).str.replace(':','').astype(str)
s=s.str.ljust(s.str.len().max(),'0').astype(int)
s.iloc[1]<s.iloc[0]<s.iloc[2]
True

---

s
0    14122006
1    12000000
2    15000000
dtype: int64

Pandas Dataframe convert string to data without time

I would start by putting your dates in pd.datetime:

df['date'] = pd.to_datetime(df.date)

Now, you can see that the time component is still there:

df.date.values

array(['2014-06-28T19:00:00.000000000-0500'], dtype='datetime64[ns]')

If you are ok having a date object again, you want:

df['date'] = [x.strftime("%y-%m-%d") for x in df.date]

Here would be ending with a datetime:

df['date'] = [x.date() for x in df.date]
df.date

datetime.date(2014, 6, 29)

Converting Pandas Object to minutes and seconds

I guess what you're looking for is pd.to_timedelta (https://pandas.pydata.org/docs/reference/api/pandas.to_timedelta.html). to_datetime operation which will of course always try to create a date.

What you have to remember about though is that pd.to_timedelta could raise ValueError for your column, as it requires hh:mm:ss format. Try to use apply function on your column by adding '00:' by the beginning of arguments of your column (which I think are strings?), and then turn the column to timedelta. Could be something like:

pd.to_timedelta(perf_dfExtended['Stop_Time'].apply(lambda x: f'00:{x}'))

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