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I have a csv file, that I read into a pandas dataframe. The date and times are listed in a column "DateTime". I want to find the most recent and the least recent date to create an index to create a time series graph. Does pandas have a function that will return the most recent and the least recent date?
Edit:
I already tried using min and max. They give incorrect answers.
>>> f['Start Date'] Trip ID 4576 8/29/2013 14:13 4607 8/29/2013 14:42 4130 8/29/2013 10:16 4251 8/29/2013 11:29 4299 8/29/2013 12:02 4927 8/29/2013 18:54 4500 8/29/2013 13:25 4563 8/29/2013 14:02 4760 8/29/2013 17:01 4258 8/29/2013 11:33 4549 8/29/2013 13:52 4498 8/29/2013 13:23 4965 8/29/2013 19:32 4557 8/29/2013 13:57 4386 8/29/2013 12:31 ... 198757 2/28/2014 20:40 198760 2/28/2014 20:59 198761 2/28/2014 20:59 198763 2/28/2014 21:32 198764 2/28/2014 21:32 198765 2/28/2014 21:34 198766 2/28/2014 21:41 198767 2/28/2014 21:50 198768 2/28/2014 21:54 198770 2/28/2014 22:19 198771 2/28/2014 22:15 198772 2/28/2014 22:38 198773 2/28/2014 22:45 198774 2/28/2014 23:01 198775 2/28/2014 23:20 Name: Start Date, Length: 144015, dtype: object >>> min(f['Start Date']) '1/1/2014 0:14' >>> max(f['Start Date']) '9/9/2013 9:59' 
843
One thing to notice here is our DataFrame gets sorted in ascending order of dates, to sort the DataFrame in descending order we can pass an additional parameter inside the sort_values() function that will set ascending value to False and will return the DataFrame in descending order.
Pandas DataFrame – Get Index To get the index of a Pandas DataFrame, call DataFrame. index property. The DataFrame. index property returns an Index object representing the index of this DataFrame.
First convert your date column in to a datetime column using
>> df['StartDate'] = pd.to_datetime(df['StartDate']) You then can find the oldest date and most recent date using
>> least_recent_date = df['StartDate'].min() >> most_recent_date = df['StartDate'].max() If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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