Remove noise(hours) for parsing time in Y/M/D format-CodePudding

I am parsing the dates of my dataset, but am encountering a lot of ParserError because the hours are often in the wrong format. I've decided to skip the hours and only focus on Years, Months, Days

These are the variants I have for date:

| Startdate |

| --- |

| March 23, 2022 6:00 |

| March 23, 2022 7:0 |

| March 23, 2022 7: |

| March 23, 2022 7 |

For now, only the first date/row works for parsing data. I currently skip the other rows, however I would want to also include them by just excluding the hours.


for date in df_en['Startdate']:

    try:

        parse(date).date()

    except Exception:

        pass

What is the right way to still parse the other dates without having to bother with hours?

I've tried to convert the time into a valid hours format. using pd.to_datetime did not work because the time format was a str march not number 3. When manually changed towards 3, it still gave the error ValueError: unconverted data remains: :00. Therefore with no relevancy for hours, I just wanted to skip it.

Source: https://serveanswer.com/questions/converting-to-datetime-parsererror-unknown-string-format-2022-02-17-7


dates = ['December 1, 2021 6:00', 'March 23, 2022 6']

for date in dates:

    date.replace(' (\d{1})', ' 0\\1')

    pd.to_datetime(date, format='%m %d, %Y %H')

    print(date)

Endgoal:

| Year | Month | Day |

| --- | --- | --- |

| 2022 | March | 23 |

| 2022 | March | March |

CodePudding user response：

I guess you can just dump the hour part

dates = ['March 23, 2022 6:00', 'March 23, 2022 7:0', 'March 23, 2022 7:', 'March 23, 2022 7']
pd.to_datetime([' '.join(x.split(' ')[:-1]) for x in dates])

DatetimeIndex(['2022-03-23', '2022-03-23', '2022-03-23', '2022-03-23'], dtype='datetime64[ns]', freq=None)

after that you can do df['date'].dt.year to extract year, month, day

CodePudding user response：

If you just need year/month/day columns, there's actually no need to parse to datetime. Just deal with the strings by splitting and rearranging; EX:

import pandas as pd

df = pd.DataFrame({'Startdate': ['December 1, 2021 6:00', 'March 23, 2022 6']})

parts = df['Startdate'].str.split('\ |, ')

df['year'], df['month'], df['day'] = parts.str[2], parts.str[0], parts.str[1]

print(df)
#                Startdate  year     month day
# 0  December 1, 2021 6:00  2021  December   1
# 1       March 23, 2022 6  2022     March  23