fuwiak

string = '11100100’
c = string[0]
lista = []
lista.append(c)
for x in string[1:]:
    if x == c:
        lista.append(x)
    else:
        print(lista)
        c = x

fuwiak

#on yours computer
import pickle
from xgboost import XGBClassifier
from xgboost import Booster
clf = XGBClassifier()
booster = Booster()
booster.load_model('xgbsave_test.model')
clf._Booster = booster

fuwiak

import pickle

pkl_filename = "pickle_model.pkl"

#on server
with open(pkl_filename, 'rb') as file:
    clf = pickle.load(file)

fuwiak

from sklearn.model_selection import train_test_split
from xboost import XGBClassifier

X = #yours data
y = #labels


train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.3, random_state=100)

fuwiak

import requests
import pandas as pd



number_of_pages = 100
#number_of_ads = number_of_pages * per_page

job_title = ["'Data Analyst' and 'data scientist'"]
for job in job_title:

fuwiak

from collections import Counter

vacancy_names = df.name
cloud = Counter(vacancy_names)
from wordcloud import WordCloud, STOPWORDS
stopwords = set(STOPWORDS)
cloud = ''
for x in list(vacancy_names):
    cloud+=x+' '
wordcloud = WordCloud(width = 800, height = 800, 

fuwiak

import ast # run code from string for example ast.literal_eval("1+1") 

salaries = df.salary.dropna() # remove all NA's from dataframe
currencies = [ast.literal_eval(salaries.iloc[i])['currency'] for i in range(len(salaries))]
curr = set(currencies) #{'EUR', 'RUR', 'USD'}

#divide dataframe salararies by currency
rur = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='RUR']
eur = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='EUR']
usd = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='USD']

fuwiak

fuwiak

import numpy as np
import cv2
import imutils
import os
import pytesseract
from PIL import Image
from pytesseract import image_to_string

fuwiak

import numpy as np

def mean(data, x):
  temp = data[::x]
  
  return np.mean(temp)