convert to datetime based on condition - pandas

Question

I want to convert my datetime object into seconds
0 49:36.5
1 50:13.7
2 50:35.8
3 50:37.4
4 50:39.3
...
92 1:00:47.8
93 1:01:07.7
94 1:02:15.3
95 1:05:03.0
96 1:05:29.6
Name: Finish, Length: 97, dtype: object
the problem is that the format changes at index 92 which results in an error: ValueError: expected hh:mm:ss format before .
This error is caused when I try to convert the column to seconds
filt_data["F"] = pd.to_timedelta('00:'+filt_data["Finish"]).dt.total_seconds()
when I do the conversion in two steps it works but results in two different column which I don't know how to merge nor does it seem really efficient:
filt_data["F1"] = pd.to_timedelta('00:'+filt_data["Finish"].loc[0:89]).dt.total_seconds()
filt_data["F2"] = pd.to_timedelta('0'+filt_data["Finish"].loc[90:97]).dt.total_seconds()
the above code does not cause any error and gets the job done but results in two different columns. Any idea how to do this?
Ideally I would like to loop through the column and based on the format i.E. "50:39.3" or "1:00:47.8" add "00:" or "0" to the object.

Answer 1

I would use str.replace:
pd.to_timedelta(df['Finish'].str.replace('^(\d+:\d+\.\d+)', r'0:\1', regex=True))
Or str.count and map:
pd.to_timedelta(df['Finish'].str.count(':').map({1: '0:', 2: ''}).add(df['Finish']))
Output:
0 0 days 00:49:36.500000
1 0 days 00:50:13.700000
2 0 days 00:50:35.800000
3 0 days 00:50:37.400000
4 0 days 00:50:39.300000
92 0 days 01:00:47.800000
93 0 days 01:01:07.700000
94 0 days 01:02:15.300000
95 0 days 01:05:03
96 0 days 01:05:29.600000
Name: Finish, dtype: timedelta64[ns]

Answer 2

Given your data:
import pandas as pd
times = [
"49:36.5",
"50:13.7",
"50:35.8",
"50:37.4",
"50:39.3",
"1:00:47.8",
"1:01:07.7",
"1:02:15.3",
"1:05:03.0",
"1:05:29.6",
]
df = pd.DataFrame({'time': times})
df
You can write a function that you apply on each separate entry in the time column:
def format_time(time):
time = time.split('.')[0]
time = time.split(':')
if(len(time) < 3):
time.insert(0, "0")
return ":".join(time)
df["formatted_time"] = df.time.apply(format_time)
df
Then you could undertake two steps:
Convert column to datetime
Convert column to UNIX timestamp (number of seconds since 1970-01-01)
df["time_datetime"] = pd.to_datetime(df.formatted_time, infer_datetime_format=True)
df["time_seconds"] = (df.time_datetime - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
df