import parsedatetime as pdtcal = pdt.Calendar()examples = [ "2016-07-16", "2016/07/16", "2016-7-16", "2016/7/16", "07-16-2016", "7-16-2016", "7-16-16", "7/16/16", "19 November 1975", "19 November 75", "19 Nov 75", "tomorrow", "yesterday", "10 minutes from now", "the first of January, 2001", "3 days ago", "in four days' time", "two weeks from now", "three months ago", "2 weeks and 3 days in the future",]print('{:30s}{:>30s}'.format('Input', 'Result'))print('=' * 60)for e in examples: dt, result = cal.parseDT(e) print('{:<30s}{:>30}'.format('"' + e + '"', dt.ctime()))Input Result============================================"2016-07-16" Sat Jul 16 16:25:20 2016"2016/07/16" Sat Jul 16 16:25:20 2016"2016-7-16" Sat Jul 16 16:25:20 2016"2016/7/16" Sat Jul 16 16:25:20 2016"07-16-2016" Sat Jul 16 16:25:20 2016"7-16-2016" Sat Jul 16 16:25:20 2016"7-16-16" Sat Jul 16 16:25:20 2016"7/16/16" Sat Jul 16 16:25:20 2016"19 November 1975" Wed Nov 19 08:41:38 1975"19 November 75" Wed Nov 19 08:41:38 1975"19 Nov 75" Wed Nov 19 08:41:38 1975"tomorrow" Tue Jun 21 09:00:00 2016"yesterday" Sun Jun 19 09:00:00 2016"10 minutes from now" Mon Jun 20 08:51:38 2016"the first of January, 2001" Mon Jan 1 08:41:38 2001"3 days ago" Fri Jun 17 08:41:38 2016"in four days' time" Fri Jun 24 08:41:38 2016"two weeks from now" Mon Jul 4 08:41:38 2016"three months ago" Sun Mar 20 08:41:38 2016"2 weeks and 3 days in the future" Thu Jul 7 08:41:38 2016Forwarded from Python Textbooks (Igor A. Kamyshev)
Data Visualization with Python and JS – Kyran Dale (en) 2016
Рассмотрен стек визуализации данных: Python и JS, библиотеки Pandas и D3.
Книга предоставлена @frontend_textbooks.
#middle
Рассмотрен стек визуализации данных: Python и JS, библиотеки Pandas и D3.
Книга предоставлена @frontend_textbooks.
#middle
Forwarded from Frontend Textbooks (Igor A. Kamyshev)
Data Visualization with Python and JS (en).pdf
14.5 MB
Crossfilter - js data-processing libraries, быстрая фильтрация строк/столбцов в датасете
import sqlite3
import requests
from lxml import html
from lxml import etree
from datetime import datetime
def log(*s):
print("{0} [LOG]:".format(datetime.now()), *s)
def main():
r = requests.get("https://www.av ito.ru/krasnodar/avtomobili/vaz_lada?view=list&radius=0&s_trg=3&f=188_893b&i=1")
if r.status_code != 200:
log("Status code check fail: {}".format(r.status_code))
tree = html.fromstring(r.text)
res = []
l = tree.xpath('//div[@itemtype="http://sch ema.org/Product"]')
for p in l:
id = p.attrib.get('id')
price = p.xpath('.//p[@itemprop="price"]')[0].attrib.get('content')
mileage = p.xpath('div[@class="mileage"]')[0].text
desc = p.xpath('meta[@itemprop="description"]')[0].attrib.get('content')
res.append((id, mileage, desc))
if name == "main":
main()
import requests
from lxml import html
from lxml import etree
from datetime import datetime
def log(*s):
print("{0} [LOG]:".format(datetime.now()), *s)
def main():
r = requests.get("https://www.av ito.ru/krasnodar/avtomobili/vaz_lada?view=list&radius=0&s_trg=3&f=188_893b&i=1")
if r.status_code != 200:
log("Status code check fail: {}".format(r.status_code))
tree = html.fromstring(r.text)
res = []
l = tree.xpath('//div[@itemtype="http://sch ema.org/Product"]')
for p in l:
id = p.attrib.get('id')
price = p.xpath('.//p[@itemprop="price"]')[0].attrib.get('content')
mileage = p.xpath('div[@class="mileage"]')[0].text
desc = p.xpath('meta[@itemprop="description"]')[0].attrib.get('content')
res.append((id, mileage, desc))
if name == "main":
main()
Jupyter Notebook
python -m pip install --upgrade pip
python -m pip install jupyter
pip install numpy pandas scikit-learn matplotlib seaborn
jupyter notebook
python -m pip install --upgrade pip
python -m pip install jupyter
pip install numpy pandas scikit-learn matplotlib seaborn
jupyter notebook