WittmannF

// Function to automate saving process with dynamic reading list selection
function saveArticles(readingListId) {
    // Find all the "Save" buttons
    const saveButtons = document.querySelectorAll('a.gs_or_sav');

    saveButtons.forEach((saveButton, index) => {
        setTimeout(() => {
            // Click the "Save" button
            saveButton.click();

WittmannF

# General
.DS_Store
.AppleDouble
.LSOverride

# Icon must end with two \r
Icon

# Thumbnails
._*

WittmannF

--1
SELECT * 
FROM "olist_order_payments_dataset" 
WHERE payment_type='voucher' 
  OR payment_type='boleto' 

--2
SELECT *, product_length_cm*product_height_cm*product_width_cm volume
FROM "olist_products_dataset"
LIMIT 5

WittmannF

#!/usr/bin/env python3

# -*- coding: utf-8 -*-
"""
Original Repository with up to date version: https://github.com/WittmannF/sort-google-scholar
This code creates a database with a list of publications data from Google 
Scholar.
The data acquired from GS is Title, Citations, Links and Rank.
It is useful for finding relevant papers by sorting by the number of citations
This example will look for the top 100 papers related to the keyword, 

WittmannF

<link rel="stylesheet" type="text/css" href="https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css">


<div id="ldavis_el65140446592215944742620371"></div>
<script type="text/javascript">

var ldavis_el65140446592215944742620371_data = {"mdsDat": {"x": [-0.13990785077723894, -0.09679313042303961, 0.08372094501590253, 0.07604489996546725, 0.08866863335149071, -0.01173349713258175], "y": [0.02244036859154854, 0.013060650645922876, 0.1393068438230911, -0.08369764087875763, -0.019555346123243667, -0.07155487605856131], "topics": [1, 2, 3, 4, 5, 6], "cluster": [1, 1, 1, 1, 1, 1], "Freq": [24.220427827520453, 20.28266275869824, 19.98643568178107, 14.374536641380327, 10.845324205247051, 10.290612885372857]}, "tinfo": {"Term": ["week", "difficul", "classroom", "act", "oth", "rul", "mood", "repetit", "irrit", "inappropry", "talk", "behavy", "hopeless", "mot", "pleas", "adult", "task", "impuls", "childr", "psychomot", "thing", "hand", "anxy", "ment", "wav", "rock", "body", "skin", "distress", "compuls", "quie

WittmannF

# envolveu a combinação de encoding e o separador:
url_bernardo = 'https://raw.githubusercontent.com/beloureiro/Planning/main/DB11FB06-1447-11EB-AD05-1866DA94328D.csv'
df = pd.read_csv(url_bernardo, sep=';', encoding='latin')

WittmannF

Solution: Deploy an Azure Machine Learning Model

Part 1: Configure deployment settings

1. Create a new Automated ML run

WittmannF

Requires Changes

5 specifications require changes

Hello student,

Well done in your first submission! 👏 👏 A few minor changes are still required in order to meet our rubric. Keep doing this great job!

Cheers,

WittmannF

Instalar o Anaconda com Python 3.7:

• Acessar o link da instalação: https://www.anaconda.com/distribution/#download-section
• Selecionar o seu sistema operacional:

• Abaixo de Python 3.7 Version, clicar em Download:

WittmannF

import re
PATTERN = '(.*), (.*) - (.*): [bB]om dia'

db = re.findall(PATTERN, txt)