WebApr 10, 2024 · 1 Answer. Sorted by: 1. You can do something similar in Polars to what you are doing in Pandas. However, you can use truncate the extract the day + hour instead of slicing the string. This should be faster, and also easier to read. For rounding down to the nearest decimal, I did not find a Polars method for it. So I kept your logic. WebSep 8, 2024 · In order to round a DateTime object to the nearest week, you need to use a combination of the to_period and start_time operations from Pandas on the DateTime column. It’s not possible to use the round operation as is the case with seconds, minutes, hours, and days as weeks don’t follow the same logical rounding.
Round Pandas date to nearest year/month - Stack Overflow
WebConvert argument to datetime. This function converts a scalar, array-like, Series or DataFrame /dict-like to a pandas datetime object. Parameters argint, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like The object to convert to a datetime. Webimport pandas as pd df = pd.Series (pd.to_timedelta ( [ '0 days +01:01:02.123', '0 days +04:03:04.651'])) df.dt.round ('5s') #Rounds to 5sec Output would be: 0 01:01:00 1 04:03:05 dtype: timedelta64 [ns] Other useful and connected question (timedelta is similar in usage to datetime): How do I round datetime column to nearest quarter hour photography osu
pandas.Timestamp.round — pandas 2.0.0 documentation
WebApr 13, 2024 · Given a dataframe like: import numpy as np import pandas as pd df = pd.DataFrame( {'Date' : pd.date_range('1/1/2011', periods=5, freq='3675S'), 'Num' : np.random.rand ... WebJul 25, 2024 · def first_of_month (date): return date + pd.offsets.MonthEnd (-1) + pd.offsets.Day (1) You can apply this function on pd.Series: df ['month'] = df ['purchase_date'].apply (first_of_month) With that you will get the month column as a Timestamp. If you need a specific format, you might convert it with the strftime () method. WebMay 5, 2024 · If you are specifically interested in rounding (not merely truncating down), your Series to the nearest tenth of a second, then: to Timestamps: df ['timestamp'].dt.round ('100ms') # still a Series of Timestamps To get a Series of strings (with controlled format) instead of Timestamps, then apply one of the other answers to the above, e.g.: photography orkney