how can I convert a date in day/month/year format to timestamp using micropython on raspberry pico? I can't find the same functions used in python.
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I'm trying to upload a database in anylogic that has dates. After the table is imported the dates are converted to dooble. this probably has to do with java but I'm not sure if there is an easier way to fix this.
I'm trying to import data from a large CSV into MongoDB using MongoImport. The date column type is giving me problems.
The CSV has the date in epoch time, but I want it saved into MongoDB was a normal date type.
I'm using --columnsHaveTypes with --fieldFile, but I can't figure out or find any answers anywhere for how to convert the date format on import.
The documentation makes it seem like I can use the Go Lang format of columnName.date(1136239445) (that's the reference time in the documentation), but that won't work. And I can't find any help in date_ms(<arg>) or date_oracle(<arg>).
As much as possible, this needs to be a hands-off operation because the large DB dump (SQLite3 format) will be automatically converted to CSV and imported to MongoDB without human input.
Thanks in advance!
I'm working with a large CSV file with millions of rows. I'm using OpenRefine to manipulate this large data set.
I have a column with date strings in this format "2017-08-17 04:36:00". And I would like to convert them to Unix time in integer format like 1502944560.
I see many Q&A on converting Unix time to Date String but not the other way around. Is this possible in OpenRefine?
value.toDate().datePart("time")
(see the bottom of this documentation for other conversion strings)
In pySpark need to convert forex trading date time as everyday 16pm as day+1, so I did below code and works well, however when I tried to truncate hour/minute/second and keep up to date only, no matter what I do system always converts to Sydney time (I am in Sydney Australia).
I searched google and know I need to set spark session to GMT, however I am using a system called Palantir, it is different from normal pySpark code editor, when I dump code spark.conf.set("spark.sql.session.timeZone", "UTC") it always gave me error. Is there anyway can round to day in London zone without setting spark timezone? Thank you
.withColumn('test_trade_date', F.from_utc_timestamp(F.from_unixtime(F.unix_timestamp(F.col('trade_date_time'))+8*60*60), 'LONDON'))\
You can write a pandas_udf to convert the datetime to whatever timezone you want.
Coming out of an Oracle background converting dates from any format to any format is really easy.
How is this done in SQLite? I've searched and searched for answers and most of the answers simply say... Save your date/strings in SQLite in one single format which is YYYY-MM-DD HH:MM:SS.SSS. This seems rigid to me.
I don't have that luxury as my data is stored in this format DD/MM/YYYY HH:MI:SS am ex. 3/7/2020 8:02:31 AM.
NOTE: For single days/months my date values do not contain leading zeros and my time is NOT in military time.
How do I tell SQLite what my date format is so that I can correctly convert my stored dates to SQLite datetime formats?
Convert from SQLite Date Format to Oracle Date Example:
In Oracle I would simply use the to_date function like so
to_date('2019-03-07 15:39:34', 'YYYY-MM-DD HH24:MI:SS')
All one needs to do is to tell the function what the date format is... and then it spits out a date.... easy peasy. What this example does is convert a SQLite date formated string to a date that Oracle recognizes as a date. It doesn't matter what the format is in as I tell the function what format to expect.
How do I Convert Dates in SQLite from any format to the SQLite Format?
Converting from SQLite's date format string to ANY date is easy as there are functions built in that do this easily... but how to do this the other way round?