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# ggrep is the GNU grep. On macOS, it is installed via `brew install grep`

# Using perl regex for lookahead and lookbehind
# This example greps the EXACT match between quotation marks
aws lambda publish-version --function-name foo --profile bar | ggrep -Po '(?<=")\d+(?=")'

# This example counts the number of occurrences of "prod" that is wedged bewteen string literals
# '"Name": "' and '"'
aws lambda list-aliases --function-name foo --profile bar | ggrep -Pc '(?<="Name":\s")prod(?=")'

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def grouped_barplot(
    ax,
    df,
    xlabel: str = '',
    ylabel: str = '',
    title: str = '',
    width: float = 0.6,
    loc: str = '',
    bbox_to_anchor: List = [],
):

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import timeit


setup = """
import pandas as pd
from dateutil import parser
from datetime import datetime

def test_datetime(timestamp):
    try:

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from argparse import ArgumentParser, RawDescriptionHelpFormatter


ef get_argument_parser() -> ArgumentParser:
    """Acquire command line arguments

    :return: The argument parser
    :rtype: ArgumentParser
    """
    parser = ArgumentParser(

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import unittest
from unittest.mock import MagicMock, patch


class TestFoo(unittest.TestCase):
    """blablabla

    To run all the tests in TestFoo, run command

        python3 -m unittest tests.test_foo.TestFoo

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def chunk_read(f_obj: TextIOBase, sentinel: str, max_sentinel: int):
    """Read a file object in chunks

    Read the file object line by line. Each time a sentinel is detected, we increment
    a count. Once the count reaches max_sentinel, we have gatherered the required
    chunk and yield it.

    The function is inspired by this SO answer:
    https://stackoverflow.com/a/42964612/9723036

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" Set absolute line number for the current line and the rest is relative number
set number
set relativenumber

" Enable monokai theme
syntax enable
set background=dark
colorscheme monokai

" I do not remember what these do

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Regarding Math Notation

You can copy and paste the LaTeX notation in a LaTeX editor to view the math notation. A good online LaTeX editor tool is HostMath.

Definition

Let $w_i$ be the weight of ith random variable $x_i$, then weighted average is expressed as

$$ \mu_x = \frac{\sum_{i=1}^n{w_ix_i}}{\sum_{i=1}^nw_i}

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#! /usr/bin/python

import multiprocessing as mp
import time

'''
A simple benchmark testing while learning about multiprocessing in python (2.7.10)
It is apparent from this test, conducted in MacBook Air OS 10.13.6, that using Pool
class of multiprocessing yields the best runtime performance. My guess is that in
the parallel() function, each new process is spawn up manually via a for loop, whereas

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<!--For fastlane screengrab use-->
package com.your.app;

import androidx.test.rule.ActivityTestRule;

import org.junit.AfterClass;
import org.junit.BeforeClass;
import org.junit.ClassRule;
import org.junit.Rule;
import org.junit.Test;