Specifying a dataframe’s timezone to ensure .push() yields matching timestamps between Workbench and source dataframe

[Emilio Conde]

Timezone mismatches can oftentimes arise when using the .push() function with a dataframe. To ensure the dataframe’s timezone matches with the source workbench, we can use pandas tz_localize() function. See an example of encountering and addressing this issue while pushing a csv dataset into workbench below.

 

Step 1:

Complete imports

 

Step 2:

Load in csv file as a dataframe. When you want to push data, it must have an index with a datetime data type. That's why we used the parse_dates and index_col arguments for Pandas.read_csv(). Note my csv file’s date/time column is named TIME(unitless), hence the arguments within parse_dates and index_col.

 

*** Note the dates in Out[5] all are -06:00***

 

If I simply moved forward to .push(), I’d see the following results:

 

The original data’s dates are not properly aligned with my worksheet, which is in US/Eastern. Instead, I should use the tz_localize() function on my index before pushing. See Step 3.

 

Step 3:

Use the tz_localize() function on your index to first remove any native timezone from the dataframe, then again to assign the timezone of interest to the dataframe. 

 

*** Note the dates in Out[8] now are all -04:00***

 

Finally, I can proceed to push the data into Seeq. You can now see that the timestamps of my data in workbench matches with their original timestamps.

 

 

 

 

 

Edited August 27, 2021 by Emilio Conde
Included incorrect images