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CREATE TABLE tbl AS SELECT strptime(dt_iso, '%x %X %z %Z') AS dt, temp_min, temp, temp_max, feels_like FROM "nyc_weather.csv";
CREATE TABLE averages AS SELECT date_part('month', dt) AS month, date_part('day', dt) AS day, date_part('hour', dt) AS hour, avg(temp_min), avg(temp), avg(temp_max), avg(feels_like) FROM tbl GROUP BY month, day, hour ORDER BY month, day, hour;
SELECT * FROM averages WHERE month = 2 AND day = 9;
┌───────┬───────┬───────┬────────────────────┬────────────────────┬────────────────────┬────────────────────┐
│ month │  day  │ hour  │   avg(temp_min)    │    avg("temp")     │   avg(temp_max)    │  avg(feels_like)   │
│ int64 │ int64 │ int64 │       double       │       double       │       double       │       double       │
├───────┼───────┼───────┼────────────────────┼────────────────────┼────────────────────┼────────────────────┤
│     2 │     9 │     0 │ 28.441458333333333 │  29.88333333333333 │ 31.296875000000004 │            20.9225 │
│     2 │     9 │     1 │ 27.859555555555552 │ 29.6

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#! python
import argparse
import sqlite3
import os
import shutil
import sys
from urllib.parse import unquote

QUERY = """
SELECT ZGAME.ZNAME, ZIMAGE.ZRELATIVEPATH, ZIMAGE.ZSOURCE, ZROM.ZLOCATION

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.DEFAULT: setup

SHELL = /bin/bash

NAME := coreml_stable_diffusion
PYMAJOR := 3
PYREV := 8
PYPATCH := 10
PYVERSION := ${PYMAJOR}.${PYREV}.${PYPATCH}
PYENV := ~/.pyenv/versions/${PYVERSION}

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#!python
"""
extract_and_merge_csv_cols.py is a script to extract a target column from a set
of csvs and merge them into a single csv where each column name is the filename
of the original csv.

Usage:
    extract_and_merge_csv_cols.py <target_col> <csvs>...
"""

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from pathlib import Path
from enum import Enum
from collections import defaultdict

SYSTEM_DICTIONARY = "/usr/share/dict/words"

class Hint(Enum):
    GRAY = 0
    YELLOW = 1
    GREEN = 2

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while true; do { echo -e 'HTTP/1.1 200 OK\r\n'; fortune -s | cowsay; } | nc -l 8080; done

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python3 -c "while 1: print(chr(int(9585.5 + __import__('random').random())), end='')"

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package io.oden.laser.common.transforms;

import org.apache.beam.sdk.transforms.windowing.*;
import org.apache.beam.sdk.transforms.windowing.Window.OnTimeBehavior;
import org.joda.time.Duration;

public class Windows {
  /*
   * The Window described attempts to both be prompt but not needlessly retrigger.
   * It's designed to account for the following cases...

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In [3]: df1 = pd.DataFrame({"start": [1,2,6], "end": [2,6,9]})
   ...: df2 = pd.DataFrame({"foo": [1,2,3,4,5,6,7,8,9]})
   ...: dfm = pd.merge(df1, df2, how="cross")
   ...: dfm.loc[(dfm.foo >= dfm.start) & (dfm.foo < dfm.end)]
Out[3]: 
    start  end  foo
0       1    2    1
10      2    6    2
11      2    6    3
12      2    6    4

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In [1]: import chardet
  
In [2]: s = b'200 \xb5l Ultra Po'
  
In [3]: chardet.detect(s)
Out[3]: {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
  
In [4]: s.decode(chardet.detect(s)['encoding'])
Out[4]: '200 µl Ultra Po'