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xpath find grandparent node contains class name | |
elem_parent_btn_download = driver.find_element_by_xpath("(//span[contains(.,'Tải về danh sách')])[4]/ancestor::div[contains(@class,'download-button-dropdown')]") | |
xpath find chidld node: | |
child_element = element.find_element_by_xpath('./') | |
child_element = element.find_element_by_xpath('.//') | |
https://stackoverflow.com/questions/66166788/get-child-element-using-xpath-selenium-python | |
https://stackoverflow.com/questions/20216541/selenium-find-childs-child-elements | |
https://stackoverflow.com/questions/68385050/selenium-python-xpath-find-element-that-has-a-child-grandchild | |
https://stackoverflow.com/questions/43478965/find-out-all-child-elements-xpath-from-parent-xpath-using-selenium-webdriver-in |
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https://stackoverflow.com/questions/70807705/how-to-use-redis-username-with-password-in-docker-compose | |
https://stackoverflow.com/questions/46569432/does-redis-use-a-username-for-authentication | |
http://antirez.com/news/96 |
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([\w.-]+)@(\[(\d{1,3}\.){3}|(?!hotmail|gmail|googlemail|yahoo|gmx|ymail|outlook|bluewin|protonmail|t\-online|web\.|online\.|aol\.|live\.)(([a-zA-Z\d-]+\.)+))([a-zA-Z]{2,4}|\d{1,3})(\]?) |
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import React, { useRef, useState, useEffect } from "react"; | |
import asset from "plugins/assets/asset"; | |
import CONFIG from "web.config"; | |
const FormData = require("form-data"); | |
const Editor = (props) => { | |
const editorRef = useRef(); | |
const [editorLoaded, setEditorLoaded] = useState(false); | |
const { CKEditor } = editorRef.current || {}; |
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git remote prune origin |
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use basic config: | |
from apscheduler.schedulers.background import BackgroundScheduler | |
from apscheduler.executors.pool import ThreadPoolExecutor, ProcessPoolExecutor | |
executors = { | |
'default': ThreadPoolExecutor(20), | |
'processpool': ProcessPoolExecutor(5) | |
} | |
job_defaults = { | |
'coalesce': False, |
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https://www.baeldung.com/linux/docker-fix-no-space-error |
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db.getCollection("fb_report_detail").aggregate([ | |
{"$match":{ | |
"campaign_id":{"$in":["23852528555800329"]}, | |
"adset_id":{"$in":["23852528555870329","23852661751140329"]}}}, | |
{ '$group': { | |
'_id': '$ad_id', | |
'inline_link_clicks': { | |
'$sum': { | |
'$cond': [ | |
{ '$eq': ['$inline_link_clicks', NaN]}, |
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But MongoDB is a general-purpose database, and not a data analysis tool, so a common pattern when analysing data that's stored within MongoDB is to extract the results of a query into a Numpy array, or Pandas dataframe, and to run complex and potentially long running analyses using the toolkit those frameworks provide. Until recently, the performance hit of converting large amounts of BSON data, as provided by MongoDB into these data structures, has been slower than we'd like. | |
Ref: | |
https://www.mongodb.com/developer/languages/python/pymongoarrow-and-data-analysis/ | |
https://arrow.apache.org/use_cases/ |
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db.getCollection("report_detail").aggregate([ | |
{'$match':{'date':'2023-03-01'}}, | |
{ "$project": { | |
"hashmaps": { "$objectToArray": "$$ROOT" } | |
} }, | |
{ "$group": { | |
"_id": null, | |
"fields": { "$addToSet": "$hashmaps.k" } | |
} }, | |
{ "$project": { |
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