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Privacy Policy for AnyLeft

Last updated: December 28, 2025

Overview

AnyLeft ("the App") is a personal finance tracking application. Your privacy is important to us, and we are committed to protecting it.

Data Collection

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import os
from datetime import datetime
from time import sleep

import pandas as pd
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.ui import WebDriverWait

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import json
import os
import sys

from Crypto.Cipher import AES

default_paths = [
    '~/Library/DBeaverData/workspace6/General/.dbeaver/credentials-config.json',
    '~/.local/share/DBeaverData/workspace6/General/.dbeaver/credentials-config.json',
    '~/.local/share/.DBeaverData/workspace6/General/.dbeaver/credentials-config.json',

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def solution(A: list) -> int:
    """
    Get list of integers and return the number of trees with aesthetics if remove one element.
    :param A: List of integers
    :return: Number of trees with aesthetics if remove one element from the list
    """
    cutting_way_count = 0

    if is_tree_aesthetics(A):
        # if the original list is a tree with aesthetics return 0

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# pip install docxcompose  
# Combines all word files under docs folder
import os
from pathlib import Path

from docxcompose.composer import Composer

from docx import Document as Document_compose

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import math

import pandas as pd
from trello import TrelloClient

client = TrelloClient(
    api_key='',
    api_secret='',
)

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#!/usr/bin/env python
# coding: utf-8

from time import sleep
import requests

from bs4 import BeautifulSoup
import pandas as pd
from tqdm import tqdm
from unidecode import unidecode

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# docker run -d -p 4444:4444 selenium/standalone-chrome:4.0.0-beta-1-20210215
# python selenium_test.py
import uuid
from datetime import datetime
from multiprocessing import Pool
from random import randint
from time import sleep

import pandas as pd
from selenium import webdriver

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import pandas as pd

def json2df(json_df):
  def normalize_column(dataframe, column):
    df = pd.json_normalize(dataframe[column]) # Normalize column  
    df.columns = [f"{column}.{col}" for col in df.columns]
    return pd.concat([dataframe, df], axis=1).drop(columns=column)

  # id
  json_df['_id'] = json_df['_id'].apply(lambda _id: _id.get('$oid'))

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import os
from urllib.request import urlretrieve

import pandas as pd
import requests
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException
from webdriver_manager.chrome import ChromeDriverManager