Python 3.7.3 : Use BeautifulSoup to parse Instagram account.

This example is a bit more complex because it parses the source code in a more particular way depending on it.
The basic idea of this script is to take the content of an Instagram account in the same way as a web browser.
For my account I found a parsing error, I guess the reason is using the points, see festila.george.catalin.

    scripts_content = json.loads(scripts[0].text.strip())
IndexError: list index out of range

In this case comment this line of code and will work:
For the other accounts I've tried, it works very well with the default script.
This is the script I used:

import requests
from bs4 import BeautifulSoup
import json
import re

from pprint import pprint

instagram_url = 'https://instagram.com'
#example user instagram profile_url = sherwoodseries
profile_url=str(input("name of the instagram user: "))


#UnicodeEncodeError: 'charmap' codec can't encode character '\U0001f48b' in posit ion 5022: character maps to 
#fix write text file with  encoding='utf-8'
file1 = open("_shared_data.txt","w", encoding='utf-8') 

#profile_url = 'festila.george.catalin'
headers = {'User-Agent': 'Mozilla/5.0'}
response = requests.get(f"{instagram_url}/{profile_url}", headers = headers)

if response.ok:
    html = response.text
    bs_html = BeautifulSoup(html, "html.parser")
    print(bs_html)
    # get info from ... type="application/ld+json">{"@context":"http:\/\/schema.org","@type":"Person","name":
    scripts = bs_html.select('script[type="application/ld+json"]')
    #scripts_content = json.loads(scripts[0].text.strip())
    #pprint(scripts_content)

    #print scripts_content like json 
    #print(json.dumps(scripts_content,indent = 4,sort_keys = True))

    #print just part of source code get by 'script' (0 .. n), see n = 6 
    #print(bs_html.find_all('script')[6])
    script_tag = bs_html.find('script', text=re.compile('window\._sharedData'))
    shared_data = script_tag.string.partition('=')[-1].strip(' ;')

    #get item from shared data, see "language_code":"en"
    rex_item  = re.compile('(?<=\"language_code\":\")[a-zA-Z_\- ]+(?=\")')
    rex_get_item = rex_item.findall(shared_data)  
    print(rex_get_item)
    #get url image from shared data
    rex_url  = re.compile('(?<=\"display_url\":\")[^\s\"]+(?=\")')
    rex_get_url = rex_url.findall(shared_data)  
    print(rex_get_url)
 
    # load like a json 
    result_json = json.loads(shared_data)
    pprint(result_json)
    
    data = bs_html.find_all('meta', attrs={'property': 'og:description'})
    bb = data[0].get('content').split()
    user = '%s %s %s' % (bb[-3], bb[-2], bb[-1])
    # get from bb parts 
    posts = bb[4]
    print('all string: ',bb)
    print('number of posts: ',posts)
    print('name and the user: ',user)

    # write any output show by print into _a.txt file, see example
    #file1.write(str(bs_html.find_all('script')[4]))
    #example: write to _shared_data.txt file the shared_data
    #file1.write(str(shared_data))
#after write, close the file 
#file1.close() 

This is a part of the output for sherwoodseries account:

...
all string:  ['95', 'Followers,', '24', 'Following,', '56', 'Posts', '-', 'See',
 'Instagram', 'photos', 'and', 'videos', 'from', 'Sherwood', 'Series', '(@sherwo
odseries)']
number of posts:  56
name and the user:  Sherwood Series (@sherwoodseries)

Python 3.7.3 : The pandas python module.

Since I started learning python programming language I have not found a more complex and complete module for viewing complex data.
The official documentation of this python module tells us:
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open-source data analysis/manipulation tool available in any language. It is already well on its way toward this goal.
The official webpage can be found here.
This python module is one of the most popular Python libraries for Data Science and Analytics.
You can install this python module with pip tool:

C:\Python373\Scripts>pip install pandas
Requirement already satisfied: pandas in c:\python373\lib\site-packages (0.24.2)

You can find many tutorials on web with this python module.
Today I will show you a short tutorial about this python module.
Most users use this both python modules:

import numpy as np
import pandas as pd

Most area of the pandas python module has a target into this list:
Window Functions, Aggregations, Missing Data, GroupBy, Merging/Joining, Concatenation, Date Functionality, Timedelta, Categorical Data,
Visualization, IO Tools, Sparse Data, Caveats & Gotchas, Comparison with SQL
There are two types of data structures in pandas: Series and DataFrames.
The pandas Series is a one-dimensional data structure.
The pandas DataFrame is a two (or more) dimensional data structure, like a table
Pandas provide few variants rolling, expanding and exponentially moving weights for window statistics.
Also have the sum, mean, median, variance, covariance, correlation, etc.
The several methods are available to perform aggregations on data.
Pandas provide functions for missing data like the isnull() and notnull().
Let's test the DataFrames with pandas and the Wikipedia example from my tutorial

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> # get table from wikipedia
... import requests
>>> from bs4 import BeautifulSoup
>>> website_url = requests.get('https://en.wikipedia.org/w/index.php?title=Table
_of_food_nutrients').text
>>> soup = BeautifulSoup(website_url,'lxml')
>>>
>>> my_table = soup.find('table',{'class':'wikitable collapsible collapsed'})
>>> links = my_table.findAll('a')
>>> Food = []
>>> for link in links:
...     Food.append(link.get('title'))
...
>>> print(Food)
["Cows' milk (page does not exist)", 'Buttermilk', 'Fortified milk (page does no
t exist)', 'Powdered milk', "Goats' milk", 'Malted milk', 'Hot chocolate', 'Yogu
rt', 'Milk pudding (page does not exist)', 'Custard', 'Ice cream', 'Ice milk', '
Cream', 'Cheese', 'Cheddar cheese', 'American cheese', 'Processed cheese', 'Egg
(food)', 'Scrambled', 'Omelet', 'Yolk']
>>> import pandas
>>> import pandas as pd
>>> df = pd.DataFrame()
>>> df['Foods'] = Food
>>> print(df)
                                   Foods
0       Cows' milk (page does not exist)
1                             Buttermilk
2   Fortified milk (page does not exist)
3                          Powdered milk
4                            Goats' milk
5                            Malted milk
6                          Hot chocolate
7                                 Yogurt
8     Milk pudding (page does not exist)
9                                Custard
10                             Ice cream
11                              Ice milk
12                                 Cream
13                                Cheese
14                        Cheddar cheese
15                       American cheese
16                      Processed cheese
17                            Egg (food)
18                             Scrambled
19                                Omelet
20                                  Yolk
>>> df.describe()
              Foods
count            21
unique           21
top     Malted milk
freq              1
>>> df.apply(pd.Series.value_counts)
                                      Foods
Malted milk                               1
Ice milk                                  1
Omelet                                    1
Goats' milk                               1
Custard                                   1
Cheddar cheese                            1
American cheese                           1
Ice cream                                 1
Yolk                                      1
Cream                                     1
Cows' milk (page does not exist)          1
Yogurt                                    1
Fortified milk (page does not exist)      1
Egg (food)                                1
Powdered milk                             1
Milk pudding (page does not exist)        1
Cheese                                    1
Hot chocolate                             1
Buttermilk                                1
Processed cheese                          1
Scrambled                                 1

The last example is to show data with

>>> import numpy as np
>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>> ts = pd.Series(np.random.randn(76), index=pd.date_range('1/1/76', periods=76
))
>>> ts.plot()

>>> plt.show()

Python 3.7.3 : Using the pipenv tool.

The goal of this tutorial is how to use pipenv and manage dependencies and development environments on a collaborative Python project.
The documentation area can be found here.

C:\Python373>cd Scripts

C:\Python373\Scripts>pip install --user pipenv
Collecting pipenv
...
Successfully installed pipenv-2018.11.26 virtualenv-clone-0.5.3

If you see errors like this:
WARNING: The scripts pipenv-resolver.exe and pipenv.exe are installed in 'C:\Users\....'

which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, 
use --no-warn-script-location.

You need to add this path show into the WARNING to the windows path, see command:

setx path "%path%;C:\Users\..\Python37\Scripts"

Let use this tool to install a requests package:

C:\Python373\Scripts>cd ..

C:\Python373>cd my_flask

C:\Python373\my_flask>pipenv install requests
Creating a virtualenv for this project.
Pipfile: C:\Python373\my_flask\Pipfile
Using c:\python373\python.exe (3.7.3) to create virtualenv.
[  ==] Creating virtual environment...Already using interpreter c:\python373\pyt
hon.exe
Using base prefix 'c:\\python373'
New python executable in C:\Users\catafest\.virtualenvs\my_flask-j9hGDZgP\Script
s\python.exe
Installing setuptools, pip, wheel...
done.
Successfully created virtual environment!
Virtualenv location: C:\Users\catafest\.virtualenvs\my_flask-j9hGDZgP
Creating a Pipfile for this project.
Installing requests.
Adding requests to Pipfile's [packages].
Installation Succeeded
Pipfile.lock not found, creating.
Locking [dev-packages] dependencies.
Locking [packages] dependencies.
Success!
Updated Pipfile.lock (444a6d)!
Installing dependencies from Pipfile.lock (444a6d).
  ================================ 5/5 - 00:00:04
To activate this project's virtualenv, run pipenv shell.
Alternatively, run a command inside the virtualenv with pipenv run.

The syntax for the Pipfile is TOML, and the file is separated into sections. [dev-packages] for development-only packages, [packages] for minimally required packages, and [requires] for other requirements like a specific version of Python
If you got this error you need restarts the command prompt:

'pipenv' is not recognized as an internal or external command,
operable program or batch file.

The next command let us see output diagnostic information for use in GitHub issues:

C:\Python373\my_flask>pipenv --support | clip
C:\Python373\my_flask>pipenv --support

Check for security vulnerabilities (and PEP 508 requirements) in your environment with:

C:\Python373\my_flask>pipenv check
Checking PEP 508 requirements.
Passed!
Checking installed package safety.
All good!

Let's use the example from the documentation webpage:

import requests

response = requests.get('https://httpbin.org/ip')

print('Your IP is {0}'.format(response.json()['origin']))

The result works great.

C:\Python373\my_flask>pipenv run python my_ip.py
Your IP is ...

You can print out a tree-like structure showing your dependencies.

C:\Python373\my_flask>pipenv graph
requests==2.22.0
  - certifi [required: >=2017.4.17, installed: 2019.6.16]
...

Using this command will see a reversed tree may be more useful when you are trying to figure out conflicting sub-dependencies.

C:\Python373\my_flask>pipenv graph --reverse
certifi==2019.6.16
  - requests==2.22.0 [requires: certifi>=2017.4.17]
chardet==3.0.4
...

This command installs all the dependencies needed for development:

C:\Python373\my_flask>pipenv install --dev
Installing dependencies from Pipfile.lock (444a6d).
  ================================ 5/5 - 00:00:10
To activate this project's virtualenv, run pipenv shell.
Alternatively, run a command inside the virtualenv with pipenv run.

You can uninstall a python module:

pipenv uninstall numpy

You can replace --all with --all-dev to just remove dev packages.
Let’s start a shell in a virtual environment to isolate the development of this app:

C:\Python373\my_flask>pipenv shell
Launching subshell in virtual environment.
Microsoft Windows [Version 6.3.9600]
(c) 2013 Microsoft Corporation. All rights reserved.

(my_flask-j9hGDZgP) C:\Python373\my_flask>

The package name, together with its version and a list of its own dependencies, can be frozen by updating the Pipfile.lock with the lock keyword.

(my_flask-j9hGDZgP) C:\Python373\my_flask>pipenv lock
Locking [dev-packages] dependencies.
Locking [packages] dependencies.
Success!
Updated Pipfile.lock (444a6d)!

Clears caches (pipenv, pip, and pip-tools) with:

pipenv --clear

Remove the virtualenv created:

(my_flask-j9hGDZgP) C:\Python373\my_flask>pipenv --rm

Close the pipenv shell:

(my_flask-j9hGDZgP) C:\Python373>exit

C:\Python373\my_flask>

This tool requires special attention in developing and programming with Python, so we have only traveled some of its real possibilities.

Python 3.7.3 : Using the werkzeug.

From the official webpage, you can see all the features of this python module:
Werkzeug is a comprehensive WSGI web application library. It began as a simple collection of various utilities for WSGI applications and has become one of the most advanced WSGI utility libraries.
It includes:
An interactive debugger that allows inspecting stack traces and source code in the browser with an interactive interpreter for any frame in the stack.
A full-featured request object with objects to interact with headers, query args, form data, files, and cookies.
A response object that can wrap other WSGI applications and handle streaming data.
A routing system for matching URLs to endpoints and generating URLs for endpoints, with an extensible system for capturing variables from URLs.
HTTP utilities to handle entity tags, cache control, dates, user agents, cookies, files, and more.
A threaded WSGI server for use while developing applications locally.
A test client for simulating HTTP requests during testing without requiring running a server.
The documentation of this python module can be found here.
You can install this python module with the pip tool:

pip install -U Werkzeug

This module can install it if you install the flask python module with python 3.7.3.
Let's test it:

C:\Python373>python
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from werkzeug.wrappers import Request, Response
>>>
>>> @Request.application
... def application(request):
...     return Response('Hello, World!')
...
>>> if __name__ == '__main__':
...     from werkzeug.serving import run_simple
...     run_simple('localhost', 4000, application)
...
 * Running on http://localhost:4000/ (Press CTRL+C to quit)
127.0.0.1 - - [16/Jul/2019 13:29:44] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [16/Jul/2019 13:29:46] "GET /favicon.ico HTTP/1.1" 200 -

If you run this source code and open your browser with this http://localhost:4000/ address then you will see a text with Hello, World!.
The common uses of this python package are:

>>> from werkzeug.wrappers import Request, Response
>>> from werkzeug.routing import Map, Rule
>>> from werkzeug.exceptions import HTTPException, NotFound
>>> from werkzeug.wsgi import SharedDataMiddleware
>>> from werkzeug.formparser import parse_form_data
>>> from werkzeug.utils import redirect
>>> from werkzeug.utils import escape
>>> from werkzeug.serving import run_simple

Let's test another simple application with werkzeug python module:

from werkzeug.serving import run_simple
from werkzeug.wrappers import Request
from werkzeug.wrappers import Response

class Application(object):
    def __init__(self):
        print('init Application class')

    def dispatch_request(self, request):
        return Response("Logged in as %s" % request.authorization)

    def __call__(self, environ, start_response):
        request = Request(environ)
        auth = request.authorization
        response = self.dispatch_request(request)
        return response(environ, start_response)

if __name__ == "__main__":
    application = Application()
    run_simple("localhost", 5000, application)

The result will be this:

C:\Python373>python.exe werkzeug_001.py
init Application class
 * Running on http://localhost:5000/ (Press CTRL+C to quit)
127.0.0.1 - - [16/Jul/2019 14:05:31] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [16/Jul/2019 14:05:32] "GET /favicon.ico HTTP/1.1" 200 -

You can add more features to this source code and check with werkzeug features of Web Server Gateway Interface (WSGI).
A full list with examples can be found here.

Python 3.7.3 : Programming Krita.

Today I wrote a python tutorial about Krita software and programming python.
The Krita software use python version 3.6.2.

==== Warning: Script not saved! ====
3.6.2 (v3.6.2:5fd33b5, Jul  8 2017, 04:57:36) [MSC v.1900 64 bit (AMD64)]

The full tutorial can be found at my Blogspot (a Blogspot about the graphics area).

Python 3.7.3 : Simple tests with zipfile python module.

You can read about this python module here.
The ZIP file format is a common archive and compression standard. This module provides tools to create, read, write, append, and list a ZIP file. Any advanced use of this module will require an understanding of the format, as defined in PKZIP Application Note.

This module does not currently handle multi-disk ZIP files. It can handle ZIP files that use the ZIP64 extensions (that is ZIP files that are more than 4 GiB in size). It supports decryption of encrypted files in ZIP archives, but it currently cannot create an encrypted file. Decryption is extremely slow as it is implemented in native Python rather than C.

C:\Python373> python -m zipfile -c test.zip test.html  textalongpath.pdf
C:\Python373> python -m zipfile -c test_folder.zip test.html  temp
C:\Python373>python -m zipfile -l  test.zip
File Name                                             Modified             Size
test.html                                      2019-07-11 10:46:58         6115
textalongpath.pdf                              2019-06-08 22:55:50           84

C:\Python373>python -m zipfile -l  test_folder.zip
File Name                                             Modified             Size
test.html                                      2019-07-11 10:46:58         6115
temp/                                          2019-07-07 21:36:42            0
temp/wlop/                                     2019-07-07 21:36:42            0

This lines of code will create two archives named test.zip and test_folder.zip with the files shown on each command.
For extraction, is need to use the -e argument:

C:\Python373>python -m zipfile -e test.zip zipfiles/

C:\Python373>python -m zipfile -e test_folder.zip zipfiles/

C:\Python373>cd zipfiles

C:\Python373\zipfiles>dir
...

Let's using this python module inside python:

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import zipfile
>>> import datetime
>>> my_zip = zipfile.ZipFile('test.zip','r')
>>> print (my_zip.namelist())
['test.html', 'textalongpath.pdf']
>>> def print_info(archive_name):
...     my_zip = zipfile.ZipFile(archive_name)
...     for info in my_zip.infolist():
...             print (info.filename)
...             print ('Comment: ', info.comment)
...             print ('Modified: ', datetime.datetime(*info.date_time))
...             print ('System: ', info.create_system, '(0 = Windows, 3 = Unix)'
)
...             print ('ZIP version: ', info.create_version)
...             print ('Compressed: ', info.compress_size, 'bytes')
...             print ('Uncompressed: ', info.file_size, 'bytes')
...
>>> print_info('test_folder.zip')
test.html
Comment:  b''
Modified:  2019-07-11 10:46:58
System:  0 (0 = Windows, 3 = Unix)
ZIP version:  20
Compressed:  1679 bytes
Uncompressed:  6115 bytes
temp/
Comment:  b''
Modified:  2019-07-07 21:36:42
...

Extract all files from an archive:

>>> from zipfile import ZipFile
>>> with ZipFile('test.zip','r') as zipObj:
...     zipObj.extractall()

Extract files by extension:

>>> with ZipFile('test.zip', 'r') as zipObj:
...    listOfFileNames = zipObj.namelist()
...    for fileName in listOfFileNames:
...        if fileName.endswith('.html'):
...            zipObj.extract(fileName, 'new.html')
...
'new.html\\test.html'

Create a new arhive named The_new.zip and add the new.html file on it.

>>> zipObj = ZipFile('The_new.zip','w')
>>> zipObj.write('new.html')
>>> zipObj.close()
>>> print_info('The_new.zip')
new.html/
Comment:  b''
Modified:  2019-07-14 22:15:58
System:  0 (0 = Windows, 3 = Unix)
ZIP version:  20
Compressed:  0 bytes
Uncompressed:  0 bytes

Python 3.7.3 : Three examples with BeautifulSoup.

Beautiful Soup is a library that makes it easy to scrape information from web pages. It sits atop an HTML or XML parser, providing Pythonic idioms for iterating, searching, and modifying the parse tree., see the pypi webpage.
This python module was created by Leonard Richardson.
A large definition can be this:
Web Scraping (also termed Screen Scraping, Web Data Extraction, Web Harvesting, etc.) is a technique employed to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer or to a database in table (spreadsheet) format.
This python module can do that but the input format and output format is different.
The input can be a webpage like an URL or webpage with all pieces of information and the output depends by the this and the user choices.
Les's see some examples:
First example show you how to take content of the first row table from a wikipedia webpage.

# get table from wikipedia 
import requests
from bs4 import BeautifulSoup
website_url = requests.get('https://en.wikipedia.org/w/index.php?title=Table_of_food_nutrients').text
soup = BeautifulSoup(website_url,'lxml')

my_table = soup.find('table',{'class':'wikitable collapsible collapsed'})
links = my_table.findAll('a')
Food = []
for link in links:
    Food.append(link.get('title'))

print(Food)

The next example takes all files from a page

# get links using the url
import urllib
from bs4 import BeautifulSoup
page = urllib.request.urlopen('http://____share.net/filmes/').read()
soup = BeautifulSoup(page)
soup.prettify()
for anchor in soup.findAll('a', href=True):
    print (anchor['href'])

The last example takes all images from the search query of imgur website:

# get images from imgur search query
import urllib
from bs4 import BeautifulSoup
url = 'https://imgur.com/search/score?q=cyborg'
with urllib.request.urlopen(url) as f:
    soup = BeautifulSoup(f.read(),'lxml')

a_tags = soup.findAll("a",{"class":"image-list-link"})
img_tags = [a.find("img") for a in a_tags]
print(img_tags)
srcs = []
for s in img_tags:
    src_tags=('http:'+s['src'])
    srcs.append(src_tags)

print(srcs)

As a conclusion, this module will pose problems for those who do not understand how to scroll through the source code, the content of web pages, how to read 'lxml', 'page', etc.
It will greatly help your Chrome F12 key to access parts of web content.

Python 3.7.3 : Testing the Bokeh python module.

This python module has a beautiful website:
Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
Let's install this python module with the pip tool:

C:\Python373>cd Scripts

C:\Python373\Scripts>pip install bokeh
Collecting bokeh
...
Successfully built bokeh
Installing collected packages: PyYAML, tornado, bokeh
Successfully installed PyYAML-5.1.1 bokeh-1.2.0 tornado-6.0.3

Let's test it with a simple example:

from bokeh.plotting import figure, output_file, show

output_file("test.html")
plot = figure()
plot.line([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 7, 6, 1, 5, 6, 7, 9, 1], line_width=2)
show(plot)

This will create a file into my python folder C:/Python373/test.html.
The HTML webpage comes with the graph and additional tool like Pan, Box Zoom, Wheel Zoom, Save, Reset and help.
If you need sample data that is not included in the Bokeh GitHub repository or released packages then you need to download it.

>>> import bokeh.sampledata
>>> bokeh.sampledata.download()
Creating C:\Users\catafest\.bokeh directory
Creating C:\Users\catafest\.bokeh\data directory
Using data directory: C:\Users\catafest\.bokeh\data
Downloading: CGM.csv (1589982 bytes)
   1589982 [100.00%]
Downloading: US_Counties.zip (3171836 bytes)
    229376 [  7.23%]

Bokeh comes with support for working with Geographical data: Mercator, Google Maps, GeoJSON Data.
You can see all example into the webpage gallery.

Python 3.7.3 : About python version 3.7.3.

All versions of python come with many features and changes with every released version.
A full list of these changes can be found at PEP official webpage and this documentation webpage.
The goal of these tutorials is to fix the learning area by each python version and have a good picture of these features.
Let's start with the first step - python modules.
Several of the standard library Python packages have been reorganized or moved with a few notable changes:

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pickle
>>> import profile
>>> import urllib
>>> import urllib3

The division math operation has new features to explicitly convert integers to floats when working with integer variables:

>>> one = 1
>>> two = 2
>>> three = 3
>>> float(three)
3.0
>>> one/two
0.5
>>> one//three
0
>>> one//two
0
>>> float(one)/three
0.3333333333333333
>>>

You can use the pathlib library which provides the Path() object to fulfill all your path manipulation needs.

>>> from pathlib import Path
>>> folder1 = Path('/folder1')
>>> config_path = folder1 / 'subfolder1'
>>> config_path
WindowsPath('/folder1/subfolder1')
>>> str(config_path)
'\\folder1\\subfolder1'
>>> config_path.name
'subfolder1'

You can use operators with matrix:

>>> import numpy as np
>>> x = np.array([[11, 33], [22, 55]])
>>> y = np.array([[1, 3], [2, 5]])
>>> x @ y
array([[ 77, 198],
       [132, 341]])
>>> x * y
array([[ 11,  99],
       [ 44, 275]])

The list and dictionaries can easily be emptied using the .clear method:

>>> my_list = ['a','b','c']
>>> my_list.clear()

The print function is changed.

>>> print('hello')
hello
>>> a = ''
>>> f = open('my_file.txt', 'w')
>>> print(a, file=f)
>>> f.close()

The function annotations can provide information on inputs/outputs:

>>> def a_to_b(x: str) -> str:
...     return x.replace('a','b')
...
>>> a_to_b("abcdcba!?!")
'bbcdcbb!?!'

Fix the sensible comparison:

>>> 'True' > True
Traceback (most recent call last):
  File "", line 1, in 
TypeError: '>' not supported between instances of 'str' and 'bool'

With Python 3.6 we have a new type of strings: f-strings and string interpolation:

>>> var = 76/3
>>> f'The value is {var}.'
'The value is 25.333333333333332.'

You can use underscores in numbers:

>>> int_a = 1_000_000_000
>>> hex_b = 0b_0011_1111_0100_1110
>>> print(int_a,hex_b)
1000000000 16206

The new Unicode strings and variable (including emoji) names to be used.
An LRU cache decorator for your functions: functools.lru_cache.
An enumerated type in the standard library: Enum.
Use the standard ipaddress:

>>> import ipaddress
>>> ipaddress.ip_address('192.168.0.1')
IPv4Address('192.168.0.1')
>>> ipaddress.ip_address('2001:db8::')
IPv6Address('2001:db8::')

In Python 3, decimals are rounded to the nearest even number (.5).
The input() function was fixed in Python 3 so that it always stores the user inputs as str objects.
In Python 3, the range() was implemented like the xrange() in older version.
That range got a new __contains__ method in Python 3.x.
You can simply convert the iterable object into a list via the list() function.

>>> print(range(3))
range(0, 3)
>>> print(type(range(3)))

>>> print(list(range(3)))
[0, 1, 2]

With advanced unpacking and range you can do this:

>>> a, b = range(2)
>>> print(a,b)
0 1
>>> a, b, *rest = range(6)
>>> print(a,b)
0 1
>>> print(rest)
[2, 3, 4, 5]
>>> a, *rest, b = range(6)
>>> print( a,b, rest)
0 5 [1, 2, 3, 4]

Get the first and the last of the open file with:

first, *_, last = f.readlines()

Keyword only arguments can be done with:

def f(a, b, *args, option=True):

The only way to access it is to explicitly call f(a, b, option=True).
If you don't want to collect *args the use this:

def f(a, b, *, option=True):

You can just use os.stat(file, follow_symlinks=False) instead of os.lstat.
The next function can be call only into this way:

>>> my_generator = (letter for letter in 'abcd')
>>> next(my_generator)
'a'
>>> next(my_generator)
'b'

The for-loop variables don’t leak into the global namespace anymore:

>>> i = 1
>>> print('comprehension:', [i for i in range(6)])
comprehension: [0, 1, 2, 3, 4, 5]
>>> print('i is ', i)
i is  1

The async and await are now reserved keywords.
More useful exceptions and also change the comma with the keyword as:

>>> try:
...     f = open('my_file.txt')
... except OSError as e:
...     if e.errno == errno.ENOENT:
...             #

...     else:

...     raise 

In Python 3, the .keys() method instead returns an iterator object instead of a list.

>>> my_dict = {'a': 11, 'b': 12, 'c': 13, 'd': 14}
>>> my_dict.keys()
dict_keys(['a', 'b', 'c', 'd'])
>>> my_dict_keys=list(my_dict.keys())
>>> my_dict_keys[3]
'd'
>>> my_dict.keys()[3]
Traceback (most recent call last):
  File "", line 1, in 
TypeError: 'dict_keys' object is not subscriptable

Keyword-only arguments and positional parameters are valid in Python 3.7.3.
The chained Exceptions provide by python 3.7.3 has more information and the original exception is printed out, along with the original traceback.

>>> raise exception from e
Traceback (most recent call last):
  File "", line 1, in 
NameError: name 'exception' is not defined
>>> raise NotImplementedError from OSError
OSError

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "", line 1, in 
NotImplementedError

These were briefly some information about python 3.7.3 that might be of use to you.

Python 3.7.3 : Testing Wagtail.

The Wagtail is a beautiful project and can be integrated with Django.
In this tutorial, I will show you the steps for the first install of a basic website.

C:\Python373>python -m virtualenv venv_wagtail
Using base prefix 'C:\\Python373'
New python executable in C:\Python373\venv_wagtail\Scripts\python.exe
Installing setuptools, pip, wheel...
done.

C:\Python373>venv_wagtail\Scripts\activate.bat

(venv_wagtail) C:\Python373>pip install wagtail
Collecting wagtail
...
Successfully built django-treebeard draftjs-exporter
Installing collected packages: sqlparse, pytz, Django, django-taggit, chardet, i
dna, urllib3, certifi, requests, django-treebeard, draftjs-exporter, beautifulso
up4, django-modelcluster, djangorestframework, Willow, six, Pillow, webencodings
, html5lib, Unidecode, wagtail
Successfully installed Django-2.2.3 Pillow-5.4.1 Unidecode-1.1.1 Willow-1.1 beau
tifulsoup4-4.6.0 certifi-2019.6.16 chardet-3.0.4 django-modelcluster-4.4 django-
taggit-0.24.0 django-treebeard-4.3 djangorestframework-3.9.4 draftjs-exporter-2.
1.6 html5lib-1.0.1 idna-2.8 pytz-2019.1 requests-2.22.0 six-1.12.0 sqlparse-0.3.
0 urllib3-1.25.3 wagtail-2.5.1 webencodings-0.5.1

(venv_wagtail) C:\Python373>wagtail start mysite
Creating a Wagtail project called mysite

(venv_wagtail) C:\Python373>cd mysite

(venv_wagtail) C:\Python373\mysite>python manage.py migrate

(venv_wagtail) C:\Python373\mysite>python manage.py createsuperuser
Username (leave blank to use 'catafest'):
Email address: catafest@yahoo.com
Password:
Password (again):
This password is too short. It must contain at least 8 characters.
Bypass password validation and create user anyway? [y/N]: n
Password:
Password (again):
Superuser created successfully.

(venv_wagtail) C:\Python373\mysite>manage.py runserver
Watching for file changes with StatReloader
Performing system checks...

System check identified no issues (0 silenced).
July 09, 2019 - 21:20:46
Django version 2.2.3, using settings 'mysite.settings.dev'
Starting development server at http://127.0.0.1:8000/
Quit the server with CTRL-BREAK.

Success! mysite has been created

Let's see the result of the first Wagtail test on my YouTube channel:

Python 3.7.3 : The python-slugify python module.

This python module named python-slugify can handle Unicode.
You can see this python module source code and examples at GITHUB webpage.
The install step with pip python tool is easy:

C:\Python373>cd Scripts
C:\Python373\Scripts>pip install python-slugify
Collecting python-slugify
...
Successfully built python-slugify
Installing collected packages: text-unidecode, python-slugify
Successfully installed python-slugify-3.0.2 text-unidecode-1.2

Let's see some simple example.

>>> from slugify import slugify
>>> txt = "___This is a test___"
>>> regex_pattern = r'[^-a-z0-9_]+'
>>> r = slugify(txt, regex_pattern=regex_pattern)
>>> print(r)
___this-is-a-test___

Remove an email address from a string:

>>> txt_email = "___My mail is catafest@yahoo.com *&@!@#$$76"
>>> regex_pattern =r'[\w\.]+\@[\w]+(?:\.[\w]{3}|\.[\w]{2}\.[\w]{2})\b'
>>> r = slugify(txt_email, regex_pattern=regex_pattern)
>>> print(r)
___my mail is - *&@!@#$$76

You can write your data into one text file:

>>> ftxt = open('C:\\Python373\\soup.txt','w')
>>> ftxt.write(soup)
Traceback (most recent call last):
  File "", line 1, in 
TypeError: write() argument must be str, not BeautifulSoup
>>> ftxt.write(str(soup))

If you got errors the use the slugify to fix it:

ftxt.write(slugify(str(soup)))

Python 3.7.3 : Using attrgetter from operator python module.

Return a callable object that fetches attr from its operand. If more than one attribute is requested, returns a tuple of attributes. The attribute names can also contain dots. see documentation.
The attrgetter operator works more or less similar to itemgetter, except that it looks up an attribute instead of an index.
For example to test this operator we need to create a class to access the attribute on the class.
I create a class named Person with name and surname initialization.
I will get the attributes from this class and show it.

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import operator
>>> class Person:
...     def __init__(self, name, surname):
...             self.n = name
...             self.s = surname
...
>>> my_per = Person('Festila', 'Catalin George')
>>> getter = operator.attrgetter("n")
>>> print(getter)
operator.attrgetter('n')
>>> print(getter(my_per))
Festila
>>> another_getter = operator.attrgetter("s")
>>> print(another_getter)
operator.attrgetter('s')
>>> print(another_getter(my_per))
Catalin George
>>> all = operator.attrgetter("n","s")
>>> print(all)
operator.attrgetter('n', 's')
>>> print(all(my_per))
('Festila', 'Catalin George')

To restrict setting an attribute outside of constructor you need to use a private attribute starting with an underscore, and a read-only property for public access.
See the new class with the attribute s restricted:

class Person:
    def __init__(self, name, surname):
            self.n = name
            self.s = surname
s = property(operator.attrgetter("_s"))

Let's test it in another way:

>>> from operator import attrgetter
>>> test=attrgetter('something')
>>> print(test)
operator.attrgetter('something')
>>> print(type(test))

>>> print(test(something))
Traceback (most recent call last):
  File "", line 1, in 
NameError: name 'something' is not defined
>>> print(type(test(something)))
Traceback (most recent call last):
  File "", line 1, in 
NameError: name 'something' is not defined

Because something is not initialized correctly the error is expected for both: test(something) and type(test(something)

>>> something = 'all'
>>> print(type(test))

>>> print(type(test(something)))
Traceback (most recent call last):
  File "", line 1, in 
AttributeError: 'str' object has no attribute 'something'
>>> something = 1
>>> print(type(test(something)))
Traceback (most recent call last):
  File "", line 1, in 
AttributeError: 'int' object has no attribute 'something'

Python 3.7.3 : Use python with MySQL and MariaDB.

If you want to use MariaDB databases with python then you need to install the MySQL.
Use the pip tool to install the mysql-connector-python python module:

C:\Python373\Scripts>pip install mysql-connector-python
Collecting mysql-connector-python
...
Installing collected packages: mysql-connector-python
Successfully installed mysql-connector-python-8.0.16

The information about python module named mysql-connector-python version 8.0.16 can be found at this web page.
For the test, I install MariaDB from the official website with user root and my password.
Let's test the python module for MariaDB:

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import mysql
>>> import mysql.connector as mariadb

After install, start the command prompt of MariaDB and test it:

C:\Windows\System32>mysql -u root -p
Enter password: ****
Welcome to the MariaDB monitor.  Commands end with ; or \g.
Your MariaDB connection id is 9
Server version: 10.4.6-MariaDB mariadb.org binary distribution

Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

MariaDB [(none)]> SHOW DATABASES;
+--------------------+
| Database           |
+--------------------+
| information_schema |
| mysql              |
| performance_schema |
| test               |
+--------------------+
4 rows in set (0.001 sec)
MariaDB [information_schema]> USE information_schema;
Database changed
MariaDB [information_schema]> SHOW TABLES;
...
| INNODB_TABLESPACES_SCRUBBING          |
| INNODB_SYS_SEMAPHORE_WAITS            |
+---------------------------------------+
77 rows in set (0.001 sec)

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import mysql.connector as mariadb
>>> mariadb_connection = mariadb.connect(user='root', password='test', database=
'information_schema')
>>> cursor = mariadb_connection.cursor()
>>> cursor.execute("USE information_schema")
>>> cursor.execute("SHOW TABLES")
>>> result = cursor.fetchall()
>>> print(result)
[('ALL_PLUGINS',), ('APPLICABLE_ROLES',), ('CHARACTER_SETS',), ('CHECK_CONSTRAIN
TS',), ('COLLATIONS',), ('COLLATION_CHARACTER_SET_APPLICABILITY',), ('COLUMNS',)
, ('COLUMN_PRIVILEGES',), ('ENABLED_ROLES',), ('ENGINES',), ('EVENTS',), ('FILES
',), ('GLOBAL_STATUS',), ('GLOBAL_VARIABLES',), ('KEY_CACHES',), ('KEY_COLUMN_US
AGE',), ('OPTIMIZER_TRACE',), ('PARAMETERS',), ('PARTITIONS',), ('PLUGINS',), ('
PROCESSLIST',), ('PROFILING',), ('REFERENTIAL_CONSTRAINTS',), ('ROUTINES',), ('S
CHEMATA',), ('SCHEMA_PRIVILEGES',), ('SESSION_STATUS',), ('SESSION_VARIABLES',),
 ('STATISTICS',), ('SYSTEM_VARIABLES',), ('TABLES',), ('TABLESPACES',), ('TABLE_
CONSTRAINTS',), ('TABLE_PRIVILEGES',), ('TRIGGERS',), ('USER_PRIVILEGES',), ('VI
EWS',), ('GEOMETRY_COLUMNS',), ('SPATIAL_REF_SYS',), ('CLIENT_STATISTICS',), ('I
NDEX_STATISTICS',), ('INNODB_SYS_DATAFILES',), ('USER_STATISTICS',), ('INNODB_SY
S_TABLESTATS',), ('INNODB_LOCKS',), ('INNODB_MUTEXES',), ('INNODB_CMPMEM',), ('I
NNODB_CMP_PER_INDEX',), ('INNODB_CMP',), ('INNODB_FT_DELETED',), ('INNODB_CMP_RE
SET',), ('INNODB_LOCK_WAITS',), ('TABLE_STATISTICS',), ('INNODB_TABLESPACES_ENCR
YPTION',), ('INNODB_BUFFER_PAGE_LRU',), ('INNODB_SYS_FIELDS',), ('INNODB_CMPMEM_
RESET',), ('INNODB_SYS_COLUMNS',), ('INNODB_FT_INDEX_TABLE',), ('INNODB_CMP_PER_
INDEX_RESET',), ('user_variables',), ('INNODB_FT_INDEX_CACHE',), ('INNODB_SYS_FO
REIGN_COLS',), ('INNODB_FT_BEING_DELETED',), ('INNODB_BUFFER_POOL_STATS',), ('IN
NODB_TRX',), ('INNODB_SYS_FOREIGN',), ('INNODB_SYS_TABLES',), ('INNODB_FT_DEFAUL
T_STOPWORD',), ('INNODB_FT_CONFIG',), ('INNODB_BUFFER_PAGE',), ('INNODB_SYS_TABL
ESPACES',), ('INNODB_METRICS',), ('INNODB_SYS_INDEXES',), ('INNODB_SYS_VIRTUAL',
), ('INNODB_TABLESPACES_SCRUBBING',), ('INNODB_SYS_SEMAPHORE_WAITS',)]