ssh [계정]@[ip주소]
ssh aa@1.1.1.1 

function myFunction_01() {
  let number=9;
  if(number > 10) {
    console.log("큰 수입니다!")
  } else {
    console.log("작은 수입니다.")
  }
}

function myFunction_02() {
  var currentTemperature = 25; 
  var isWeekend = true; 
  var thresholdTemperature = 35; 

  if (currentTemperature > thresholdTemperature && !isWeekend) {
    console.log("집에 계세요!")
  } else if (currentTemperature > thresholdTemperature || isWeekend) {
    console.log("외출하세요!")
  } else {
    console.log("판단을 보류합니다!")
  }
}

function myFunction_03() {
  var numRows = 9; // Number of rows in the table
  var numColumns = 9; // Number of columns in the table

  for (var i = 2; i <= numRows; i++) {
    var row = '';
    for (var j = 1; j <= numColumns; j++) {
      var result = i * j;
      row += result + '\t'; // Use '\t' to separate columns
    }
    Logger.log(row); // Log the row to the Apps Script log
  }
}

function myFunction() {
  Logger.log("Hello World");
}

function myFunction01_1() {
  Logger.log("Hello World");
}

function myFunction01_2() {
  console.log("Hello GAS!")

  // 주석 입력
  /*
  여러 행에 걸쳐 주석을 입력한다. 
  */
}

evan$ git clone https://github.com/yourname/m1_streamlit.git
Cloning into 'm1_streamlit'...
remote: Enumerating objects: 4, done.
remote: Counting objects: 100% (4/4), done.
remote: Compressing objects: 100% (3/3), done.
remote: Total 4 (delta 0), reused 0 (delta 0), pack-reused 0
Receiving objects: 100% (4/4), done.

evan$ cd m1_streamlit
m1_streamlit evan$ conda create -n mulcamp python=3.10.3
m1_streamlit evan$ conda activate mulcamp
(mulcamp) m1_streamlit evan$ conda install numpy scipy scikit-learn pandas

# kaggle.json 파일을 업로드하세요.
from google.colab import files
files.upload()

!mkdir ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json

!kaggle competitions download -c dogs-vs-cats

import plotly 
plotly.__version__
# 5.13.1

import plotly.graph_objects as go
import pandas as pd 

temp = pd.DataFrame({
  "Fruit": ["Apples", "Oranges", "Bananas", "Apples", "Oranges", "Bananas"],
  "Contestant": ["Alex", "Alex", "Alex", "Jordan", "Jordan", "Jordan"],
  "Number Eaten": [2, 1, 3, 1, 3, 2],
})

fig = go.Figure()
fig.add_trace(go.Bar(name = "Alex", x = temp['Fruit'], y = temp[temp['Contestant'] == 'Alex']['Number Eaten'].values))
fig.add_trace(go.Bar(name = "Jordan", x = temp['Fruit'], y = temp[temp['Contestant'] == 'Jordan']['Number Eaten'].values))
fig.update_layout(barmode='group')
print(type(fig))
fig.show()

$ scrapy startproject multiCam_tutorial
New Scrapy project 'multiCam_tutorial', using template directory 'C:\Users\j2hoo\OneDrive\Desktop\your_project_folder\venv\Lib\site-packages\scrapy\templates\project', created in:
    C:\Users\j2hoo\OneDrive\Desktop\your_path\multiCam_tutorial

You can start your first spider with:
    cd multiCam_tutorial
    scrapy genspider example example.com

$ scrapy genspider audible www.audible.com/search
Created spider 'audible' using template 'basic' in module:
  multiCam_tutorial.spiders.audible