I need to process a huge amount of CSV files where the time stamp is always a string representing the unix timestamp in milliseconds. I could not find a method yet to modify these columns efficiently.
I have strings in YMD hms format that had the timezone stripped. But I know they are in Eastern time with daylight savings time.
I have this timestamp value being return by a web service "2014-09-12T19:34:29Z"
I have a dataframe df and it has a Date column. I want to create two new data frames. One which contains all of the rows from df where the year equals some_year and another data frame which contains all of the rows of df where the year does not equal some_year. I know you can do df.ix['2000-1-1' : '2001-1-1'] but in order to get all of the rows which are not in 2000 requires creating 2 extra data frames and then concatenating/joining them.
I have an array of datetime64 type:
Scenario: I have a dataframe with multiple columns retrieved from excel worksheets. Some of these columns are dates: some have just the date (yyyy:mm:dd) and some have date and timestamp (yyyy:mm:dd 00.00.000000).
I am reading data from excel and manipulating the data using python. But dates are coming as integers. How can I convert the dates back to date format? 5/15/2015 is coming as 42139.00 Answers: Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the … Read more
I’m working with some datetime data in a dataframe. It’s in a format day/month/year Ex: Date —————- 27/06/2021 00:00 27/06/2021 00:00 30/06/2021 00:00 30/06/2021 00:00 30/06/2021 00:00 18/06/2021 00:00 26/06/2021 00:00 28/06/2021 00:00 28/06/2021 00:00 27/06/2021 00:00 28/06/2021 00:00 30/06/2021 00:00 12/06/2021 00:00 28/06/2021 00:00 I want to extract the month and year, so I … Read more
I have a datetime column as below –
I read a csv file containing 150,000 lines into a pandas dataframe. This dataframe has a field, Date, with the dates in yyyy-mm-dd format. I want to extract the month, day and year from it and copy into the dataframes’ columns, Month, Day and Year respectively. For a few hundred records the below two methods work ok, but for 150,000 records both take a ridiculously long time to execute. Is there a faster way to do this for 100,000+ records?