Pandas, Parsing Timestamps

Pandas, Parsing Timestamps Due to the numerous time data formats and resolutions, dealing with timestamps can be difficult. Several time data types, structures, and operations will be covered in this video. Youwill be able to comprehend and articulate POSIX time, epoch time, and Unix time by the endof this video. You will also be able to specify the various date-and-time data types, chooserows based on timestamps, and chronologically order tables. Unix time counts the number of seconds that have passed since January 1st of the year 1970 in the UTC time zone. We must format this integer into a date and time that can beread by humans. Depending on the machine's specs, the general datetime data typeDatetime64 [Ns] converts to specialized data types termed M8. The conversion of theoriginal instant since 1970 UTC time, int64, into one of the datetime forms described beforeis our main objective. The to datetime function makes it simple to convert a timestamp toPython representation. This function transforms the input into a date-time format that can beread by humans. We can then put the results in a new column we've termed parsed time. We can use time once it has been converted to Python representation to build filters. Just the rows that meet particular criteria are chosen by the filter. To pick just the cells in thedataframe that satisfy this requirement, for example, we can define a boolean filter calledgreater than t where the date is after 2015, February 1, or 02-01. Moreover, we may usePanda's dataframes' sort values function to use timestamps to sort data in ascending ordescending order. By delivering assorted data, sorting time series data facilitates the creation of excellent visualizations. Panda's ability to categorize data based on timestamps can therefore almostimmediately provide us with a comprehensive view. By examining the evolution from the topto the bottom rows of the dataframe, we can also study data over time. In conclusion, because Unix time is an integer, it can be challenging for consumers to read. But, transforming it to Python datetime format provides us with useful means of selecting thedata and providing timestamps that are human-readable. Timestamps are a great place tostart for our live coding session because they provide so many opportunities for insights. Wecan learn a lot about that moment in time by examining Panda's capacity for observing andutilizing timestamps.