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if I run the Python script several times, the 32 bit versions of Python correctly print out the result while the 64 bit version of Python keeps returning the same error. I have had this problem before.

A:

You should never change the default site-packages when working with Python, see here:

Your best bet would be to create a virtual enviroment and install this modules there.

not implicate the Double Jeopardy Clause. See
Manrique, 994 S.W.2d at 628. We therefore hold that because the alleged ineffective
assistance of counsel did not implicate the Double Jeopardy Clause, Heredia was not
entitled to relief.
IV. Conclusion
Having overruled Heredia’s sole point of error, we affirm the judgment of the trial
court.

NELDA V. RODRIGUEZ
Justice

Do not publish.
Tex. R. App. P. 47.2(b).

Memorandum Opinion delivered and
filed this 9th day of March, 2008.
1. See Tex. Penal Code Ann. § 19.02(b)(1) (Vernon 2003).
2. Heredia asserts that he is entitled to a new trial because his counsel was ineffective for (1) failing to
request a probable cause hearing on the charge of intoxication assault, and (2) failing to obtain a complete
deprogramming of the complainant.
3. The arraignment in this case occurred before the reformation of the Texas Rules of Appellate Procedure
and the re-labeling of the Texas Rules of Appellate Procedure as the Texas Rules of Civil Procedure. This
opinion will refer to the rules and documents as filed in their original location in the appellate record as
they existed before their re-designation.
4. On appeal, Heredia makes no reference to the First Amendment. Accordingly, we only address
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About Katie Lauer

Katie Lauer is a writer and editor who spends her days working at the Daily World and nights doing freelance writing and editing. She also has a passion for fashion, design, and self-care. More specifically, she loves making other people look good. As such, she can be found frequenting Husky Soccer games, or running down the street to make sure her wayward dog does not get hit by a car.Q:

Pyspark: Select k rows after a particular time and delete the rest rows

Hi I have a spark dataframe like
+———-+————–+————+
| | event| time |
+———-+————–+————+
| uid2-243 | Posting_New |2018-08-09 |
| uid2-243 | event_liked |2018-08-09 |
| uid2-243 | event_liked |2018-08-09 |
+———-+————–+————+

I need to select the top k “likes” after say 3 hours and delete the rest events.

A:

You can use windowing. Generally, I would keep the entire dataframe and use Window function to calculate the number of likes for each window and filter using on/condition. But in this case we will perform top k for every row in the window.
import pyspark.sql.functions as F
from pyspark.sql.window import Window

df = spark