멀티캠퍼스 AWS 서버 관련 정리

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

pip install scrapy

$ 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 worldometer www.worldometers.info/world-population/population-by-country
Created spider 'worldometer' using template 'basic' in module:
  multiCam_tutorial.spiders.worldometer

from django.shortcuts import render, redirect
from django.http import HttpResponse
import pandas as pd

# Create your views here.
def calculate(request):
    file = request.FILES['fileInput']
    print("# 사용자가 등록한 파일의 이름: ", file)
    df = pd.read_excel(file, sheet_name="Sheet1", header=0)
    print(df.head())
    # grade별 value 리스트 만들기
    grade_dic = {}
    total_row_num = len(df.index)
    for i in range(total_row_num):
        data = df.loc[i, :]
        if not data.grade in grade_dic.keys():
            grade_dic[data.grade] = [data.value]
        else:
            grade_dic[data.grade].append(data.value)

    # print(grade_dic)
    # grade별 최솟값 최댓값 평균값 구하기
    grade_calculate_dic = {}
    for key in grade_dic.keys():
        grade_calculate_dic[key] = {}
        grade_calculate_dic[key]['min'] = min(grade_dic[key])
        grade_calculate_dic[key]['max'] = max(grade_dic[key])
        grade_calculate_dic[key]['avg'] = float(sum(grade_dic[key])) / len(grade_dic[key])

    # 결과 출력
    grade_list = list(grade_calculate_dic.keys())
    grade_list.sort()
    for key in grade_list:
        print("# grade: ", key)
        print("min:", grade_calculate_dic[key]['min'], end="")
        print("/ max:", grade_calculate_dic[key]['max'], end="")
        print("/ avg:", grade_calculate_dic[key]['avg'], end="\n\n")

    # 아래 코드와 동일
    result = df.groupby('grade')['value'].agg(["min", "max", "mean"])
    print(result)

    # 이메일 주소 도메인별 인원 구하기
    email_domain_dic = {}
    for i in range(total_row_num):
        data = df.loc[i, :]
        email_domain = data['email'].split("@")[1]
        if not email_domain in email_domain_dic.keys():
            email_domain_dic[email_domain] = 1
        else:
            email_domain_dic[email_domain] += 1
    print("## EMAIL 도메인별 사용 인원")
    for key in email_domain_dic.keys():
        print("#", key, ": ", email_domain_dic[key], "명")

    df['domain'] = df['email'].apply(lambda x : x.split("@")[1])
    result2 = df.groupby('domain')['value'].agg("count").sort_values(ascending=False)
    print(result2)

    # 세션 추가
    grade_calculate_dic_to_session = {}
    for key in grade_list:
        grade_calculate_dic_to_session[int(key)] = {}
        grade_calculate_dic_to_session[int(key)]['max'] = float(grade_calculate_dic[key]['max'])
        grade_calculate_dic_to_session[int(key)]['avg'] = float(grade_calculate_dic[key]['avg'])
        grade_calculate_dic_to_session[int(key)]['min'] = float(grade_calculate_dic[key]['min'])
     request.session['grade_calculate_dic'] = grade_calculate_dic_to_session
      request.session['email_domain_dic'] = email_domain_dic

    # return HttpResponse("calculate, calculate function!")
    return redirect('/result')