Working with generated UTF-8 files it’s a challenge to view them properly in Excel. Gladly there is a method how to properly open them in Excel.
In this post we share how to plot distribution histogram for the Weibull ditribution and the distribution of sample averages as approximated by the Normal (Gaussian) distribution. We’ll show how the approximation accuracy changes with samples volume increase.
One may get the full .ipynb file here.
Categories
Invalid data, what it is?
Often we see “invalid data”, “clean data”, “normalize data”. What does it mean as to practical data extraction and how does one deal with that? One shot is better than 1000 words though:
Categories
Simple text analysis with Python
Finding the most similar sentence(s) to a given sentence in a text in less than 40 lines of code 🙂
Working with a Backconnect proxy service (Oxylab.io) we spent a long time looking for a way to authorize it. Originally we used JSoup to get the web pages’ content. The proxy() method can be used there when setting up the connection, yet it only accepts the host and port, no authentication is possible. One of the options that we found, was the following:
function strip_tags(str){
const tags = ['a', 'em', 'div', 'span', 'p', 'i', 'button', 'img' ];
const tagsAndContent = ['picture', 'script', 'noscript', 'source'];
for(tag of tagsAndContent){
let regex = new RegExp( '<' + tag+ '.*?</' + tag + '>', 'gim');
str = str.replace( regex ,"");
}
for(tag of tags){
let regex1 = new RegExp( '<' + tag+ '.*?>', 'gim');
let regex2 = new RegExp( '</' + tag+ '>', 'gim');
str = str.replace(regex1,"").replace(regex2,"");
}
return str;
}
Categories
JavaScript, Regex match groups
Often we want only a certain info from the matched content. So, groups help with that.
The following example shows how to fetch the [duplicate] entry index from the error message. For that we take 1st group, index “1”:
const regex = /Duplicate entry\s'([^']+)+'/gm;
const str = `{ Error: (conn=42434, no: 1062, SQLState: 23000) Duplicate entry '135' for key 'PRIMARY'
sql: INSERT INTO \`test\` (id , string1) values (?,?) - parameters:[[135,'string 756']]
at Object.module.exports.createError (C:\\Users\\User\\Documents\\RnD\\Node.js\\mercateo-`;
let m;
while ((m = regex.exec(str)) !== null) {
// This is necessary to avoid infinite loops with zero-width matches
if (m.index === regex.lastIndex) {
regex.lastIndex++;
}
// The result can be accessed through the `m`-variable.
m.forEach((match, groupIndex) => {
console.log(`Found match, group ${groupIndex}: ${match}`);
});
}As developers scrape data off the web, we use Node.js along with handy Cheerio scraper. When fetching .html() Cheerio parser returns the special symbols as HTML encoded entities, eg.:ä as ä ß as ß
Cheerio developer vindication of the parser action
(1) It’s not the job of a parser to preserve the original document.
(2) .html() returns an HTML representation of the parsed document, which doesn’t have to be equal to the original document.
source.
Imstall mariadb package:
npm i mariadb
The code
const config = require("./config");
const db = config.database;
const mariadb = require('mariadb');
const pool = mariadb.createPool({
host: db.host,
user: db.user,
password: db.password,
database: db.database,
connectionLimit: 5
});
async function asyncSaveDataDB(data) {
let conn;
try {
conn = await pool.getConnection();
const rows = await conn.query("SELECT 1 as val");
console.log(rows); //[ {val: 1}, meta: ... ]
const res = await conn.query("INSERT INTO test (string1) value (?)", [data]);
console.log(res); // { affectedRows: 1, insertId: 1, warningStatus: 0 }
} catch (err) {
throw err;
} finally {
if (conn) return conn.end();
}
}
async function asyncSaveDataBulkDB(arr) {
let conn;
try {
conn = await pool.getConnection();
conn.batch("INSERT INTO `test` (string1) values (?)", arr)
.then(res => {
console.log(res); // 2
});
} catch (err) {
throw err;
} finally {
if (conn) return conn.end();
}
}
if (module.parent) {
module.exports = { asyncSaveDataDB, asyncSaveDataBulkDB }
} else {
asyncSaveDataBulkDB(['tt6', 'test 8']);
}Config.js might look like the following:
module.exports = {
database:{
host: "185.221.154.249",
user: "xxxxxxxxx",
password: "xxxxxxxxx",
database: 'xxxxxxxxx'
}
}
Categories
Centos 7, Node.js, MySQL connect
I’ve mariadb installed on my VDS with Centos 7.
I’ve installed mysql npm package:
npm i mysqlYet requesting that package with
var mysql = require('mysql');
has not provided to the successful connection.
While
var mysql = require('mariadb');
has done.
var mysql = require('mariadb');
var con = mysql.createConnection({
host: "localhost",
user: "admin_default",
password: "xxxxxx",
database: 'admin_default'
}).then(function (){
console.log('connected!');
}, function(err){
console.log(err);
});
//console.log(con);