import os
import glob
%matplotlib inline
from skimage.measure import compare_ssim
import imutils
import cv2
from PIL import Image
import matplotlib.pyplot as plt
FILE = os.path.join('./', '5dcd4625baa03265637591.png')
imageA = cv2.imread(FILE)
fig, axs = plt.subplots(nrows=10, ncols=1, figsize=(30, 10))
img1 = imageA[60:100]
axs[0].imshow(img1)
step = 60
curr = 100
for i in range(1,10):
img = imageA[curr:curr + step]
axs[i].imshow(img)
curr += step
from lxml import etree
str1 = """<div class="cl-pagination">
...
</div>"""
root = etree.fromstring(str1)
print(root.xpath('.//ul/li[last()-1]/a')[0].text)print(root.xpath('.//ul/li[not(contains(@class, "next-page")) and not(contains(@class, "previous-page"))]/a')[-1].text)import operator
data = {'Улан-Удэ': 65, 'Иркутск': 2, 'Шаралдай': 1, 'Санкт-Петербург': 1, 'Москва': 5, 'New York City': 1, 'Южно-Сахалинск': 1, 'Ростов-на-Дону': 1, 'Улюкчикан': 1, 'Мухоршибирь': 1, 'Masaya': 1, 'Barcelona': 1, 'Новосибирск': 1, 'Chicago': 1, 'Taoyuan': 1, 'Madrid': 1, 'München': 1, 'Ōsaka': 1, 'Киев': 1, 'Рай (деревня)': 1}
max(data.items(), key=operator.itemgetter(1))[0]Django officially supports the following databases:
PostgreSQL
MySQL
Oracle
SQLite
var str1 = "M1,60 0,0,7.332538412e-8,-90"
str1.split(' ')[0] + ' ' + str1.split(' ')[1].split(',').map(function(item){return parseFloat(item).toFixed(2)}).join(',')str1.split(' ')[0] + ' ' + str1.split(' ')[1].split(',').map(function(item){return parseFloat(item).toFixed(0)}).join(',')str1 = ('\n\nACES Direct | zakelijk\n Gratis retour binnen 60 dagen.\n', '\n€ 1.700,05')
print(str1[0].replace('\n',''), '-', str1[1].replace('\n',''))... -p 127.0.0.1:3306:3306 ...... -p 0.0.0.0:3306:3306 ...