mpolatcan

import base64
import json
from google.oauth2.service_account import Credentials
from google.cloud.container_v1 import ClusterManagerClient
from kubernetes.client import ApiClient, CoreV1Api
from kubernetes.client.configuration import Configuration as KubernetesClientConfiguration
from tempfile import NamedTemporaryFile


class KubernetesApiClient:

mpolatcan

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import six
import random 


df = pd.DataFrame()
df['date'] = ['2016-04-{}'.format(day) for day in range(1, 51)]
df['calories'] = [random.randint(1500, 2000) for i in range(50)]

mpolatcan

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy.spatial.distance import cosine


def get_best_n_match(external_products_df, internal_products_dfs, count=3):
    # Create vectorizer according to internal product space
    vectorizer = TfidfVectorizer()
    vectorizer.fit(internal_products_dfs.name)

mpolatcan

# Written by Mutlu Polatcan
# 09.07.2020
# =============================================
import requests
import re
from datetime import datetime, timedelta


class ShellOilTurkeyScraper:
    URL = "https://www.turkiyeshell.com/pompatest/History.aspx"