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Thursday, August 1, 2019

Python 3.7.3 : Using the flask - part 005.

In the last tutorial, I used the flask-sqlalchemy python module.
Today I will show you how to use the flask_marshmallow python module.
First, let's take a look at this python module, see the official webpage:
Flask-Marshmallow is a thin integration layer for Flask (a Python web framework) and marshmallow (an object serialization/deserialization library) that adds additional features to marshmallow, including URL and Hyperlinks fields for HATEOAS-ready APIs. It also (optionally) integrates with Flask-SQLAlchemy.
The base class User will need to be integrated with this python module:
from flask import Flask
from flask import render_template
from forms import SignUpForm
from flask import request

from flask import jsonify
from flask_sqlalchemy import SQLAlchemy
from flask_marshmallow import Marshmallow
import os
from forms import AddUser

app = Flask (__name__)
app.config['SECRET_KEY'] = 'abcdefg'
# set SQLAlchemy
basedir = os.path.abspath(os.path.dirname(__file__))
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'server.sqlite')
db = SQLAlchemy(app)
ma = Marshmallow(app)
class User(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True)
    email = db.Column(db.String(120), unique=True)

    def __init__(self, username, email):
        self.username = username
        self.email = email
    '''
    def __rep_(self):
        return '' % self.username
    '''
class UserSchema(ma.ModelSchema):
    class Meta:
        model = User
user_schema = UserSchema()
users_schema = UserSchema(many=True)

@app.route("/users/", methods=['GET'])
def users():
    users = User.query.all()
    #return users_schema.jsonify(users)
    all_users = users_schema.dump(users)
    return jsonify(all_users.data)

# the default name main
if __name__ == '__main__':
    app.run(debug=True)
Let's take a look at http://127.0.0.1:5000/users/ and see the result:
[
  {
    "email": "catafest@yahoo.com", 
    "id": 1, 
    "username": "catafest"
  }, 
  {
    "email": "test@test.com", 
    "id": 2, 
    "username": "user_test"
  }
]

Tuesday, July 30, 2019

Python 3.7.3 : Using the flask - part 004.

The goal of this tutorial is to interact with the database in order to use it with flask_sqlalchemy python module.
The db.Model is used to interact with the database.
A database doesn't need a primary key but if you using the flask-sqlalchemy you need to have it for each one table in order to connect it.
Let's see the database:
C:\Python373\my_flask>python
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD6
4)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from server import db
>>> db.create_all()
>>> db.engine.table_names()
['user']
Let's add some data into database on user table:
C:\Python373\my_flask>python
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD6
4)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from server import db
>>> from server import User
>>> first_user = User(username='catafest',email='catafest@yahoo.com')
>>> db.session.add(first_user)
>>> db.session.commit()
>>> test_user = User(username='test',email='test@test.com')
>>> db.session.add(test_user)
>>> db.session.commit()
Update is a simple issue.
Let's update the username test_user from test to user_test:
>>> test_user.username = 'user_test'
>>> db.session.commit()
The delete is simple like the add:
>>> db.session.delete(test_user)
>>> db.session.commit()
Let's use query:
>>> results=User.query.all()
>>> results[0].username
'catafest'
>>> results[0].email
'catafest@yahoo.com'
>>> results
The next step is an important issue because let you to see how result by content and query and filter by first result:
>>> q1 = User.query.filter_by(username='catafest')
>>> q1
...flask_sqlalchemy .basequery= ...
>>> print(q1)
SELECT user.id AS user_id, user.username AS user_username, user.email AS user_email
FROM user
WHERE user.username = ?
>>> q2 = User.query.filter_by(username='catafest').first()
>>> q2
< User 1 >
>>> print(q2)
< User 1 >
>>> print(q1.username)
Traceback (most recent call last):
  File "", line 1, in 
AttributeError: 'BaseQuery' object has no attribute 'username'
>>> print(q2.username)
catafest
>>> print(q2.username,q2.email)
catafest catafest@yahoo.com 
In this case because the first is limited to one result the print of q2 is the correct way.

Monday, July 29, 2019

Python 3.7.3 : Using the twitter python module - part 003.

Today I will speak about twitter python module with the new changes of the A.P.I.
This two tutorial will not work now because the twitter A.P.I is changed.
The reason I don't delete it is the similar flow programming and access the A.P.I.:
Let's start with the install of this python module with Python version 3.7.3:
C:\Python373>cd Scripts

C:\Python373\Scripts>pip install python-twitter
...
Installing collected packages: python-twitter
Successfully installed python-twitter-3.5
Let's test the GetSearch.
You need to create a twitter application to have access to the tokens and secret keys:
import os
import json
import twitter
from twitter import *
CONSUMER_KEY=""
CONSUMER_SECRET=""

ACCESS_TOKEN=""
ACCESS_TOKEN_SECRET=""
LANGUAGES="En"
at=input("ACCESS_TOKEN: ", )
ats=input("ACCESS_TOKEN_SECRET: ", )
ck=input("CONSUMER_KEY: ", )
cs=input("CONSUMER_SECRET: ", )
api = Api(ck, cs, at, ats)
def main():
    print("Search by query using the GetSearch ")
    r = api.GetSearch(raw_query="q=twitter%20&result_type=recent&since=1999-03-07&count=100")
    print(r)
if __name__ == '__main__':
    main()
The result will be something like this:
...
@GeorgePapa19 @realDonaldTrump Sure Did https://t.co/GblrSOsaJg'), Status(ID=115
5787444525993984, ScreenName=MD__PCY, Created=Mon Jul 29 10:29:34 +0000 2019, Te
xt='mal ako ni twitter https://t.co/lRA1BOyf6a'), Status(ID=1155787444517838849,
 ScreenName=Elyse95, Created=Mon Jul 29 10:29:34 +0000 2019, Text='????? ????? ?
??? ..??? ???? ????????? ????? ????? ????? ?????? ??? ???????? .. !!    ... http
s://t.co/weChoAPDnC. https://t.co/34D2nmtbOz'), Status(ID=1155787444517781504, S
creenName=chrichacham123, Created=Mon Jul 29 10:29:34 +0000 2019, Text='RT @John
JCrace: Project Blind Faith! https://t.co/2mpB0MC540'), Status(ID=11557874445134
72514, ScreenName=318520_mu, Created=Mon Jul 29 10:29:34 +0000 2019, Text='31-85
20 ????????\n???????? → ???twitter??????????????\n??...?\n\nhttps://t.co/oVET5Z9
wXq')]

Sunday, July 28, 2019

Python 3.7.3 : Using the flask - part 003.

Another tutorial with python 3.7.3 and flask python module.
In the last tutorial, I speak about some tricks and tips.
Today, I will show some steps for fixing and run with flask-sqlalchemy.
The source code from my GitHub account can be updated with this source code.
app = Flask (__name__)
app.config['SECRET_KEY'] = 'abcdefg'
# set SQLAlchemy
basedir = os.path.abspath(os.path.dirname(__file__))
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'server.sqlite')
db = SQLAlchemy(app)
ma = Marshmallow(app)
class User(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True)
    email = db.Column(db.String(120), unique=True)

    def __init__(self, username, email):
        self.username = username
        self.email = email
If you run it then you get this:
C:\Python373\my_flask>python
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD6
4)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from server import db
C:\Python373\lib\site-packages\flask_sqlalchemy\__init__.py:835: FSADeprecationW
arning: SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and will be dis
abled by default in the future.  Set it to True or False to suppress this warnin
g.
  'SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and '
You will have a database file named server.sqlite.
#app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
Let's create and show the database:
C:\Python373\my_flask>python
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD6
4)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from server import db
>>> db.create_all()
>>> db.engine.table_names()
['user']
A good approach is to create a config.py file and used into the application area.
The config.py file will have this source code:
SQLALCHEMY_TRACK_MODIFICATIONS = False
SQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(basedir, 'server.sqlite')
DEBUG=True
The server.py has this source code:
...
app = Flask (__name__)
app.config.from_pyfile('config.py')
db = SQLAlchemy(app)
...

Saturday, July 27, 2019

Python 3.7.3 : About pytweening python module.

This is a simple module of tweening and easing functions implemented in Python, see the GitHub webpage.
C:\Python373\Scripts>pip install pytweening
Collecting pytweening
Downloading https://files.pythonhosted.org/packages/b9/f8/c32a58d6e4dff8aa5c27
e907194d69f3b57e525c2e4af96f39c6e9c854d2/PyTweening-1.0.3.zip
Building wheels for collected packages: pytweening
Building wheel for pytweening (setup.py) ... done
Created wheel for pytweening: filename=PyTweening-1.0.3-cp37-none-any.whl size
=3821 sha256=6655c055d779982ff1259a5266bf1300c1cd02046e45e92cf18b20053a326531
Stored in directory: C:\Users\catafest\AppData\Local\pip\Cache\wheels\7b\92\30
\06e21159eed2709436bfb6d7c690959e578cf74f029643866e
Successfully built pytweening
Installing collected packages: pytweening
Successfully installed pytweening-1.0.3
Let's test it:
C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD6
4)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pytweening
>>> dir(pytweening)
['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__',
'__package__', '__path__', '__spec__', '__version__', '_checkRange', 'division',
 'easeInBack', 'easeInBounce', 'easeInCirc', 'easeInCubic', 'easeInElastic', 'ea
seInExpo', 'easeInOutBack', 'easeInOutBounce', 'easeInOutCirc', 'easeInOutCubic'
, 'easeInOutElastic', 'easeInOutExpo', 'easeInOutQuad', 'easeInOutQuart', 'easeI
nOutQuint', 'easeInOutSine', 'easeInQuad', 'easeInQuart', 'easeInQuint', 'easeIn
Sine', 'easeOutBack', 'easeOutBounce', 'easeOutCirc', 'easeOutCubic', 'easeOutEl
astic', 'easeOutExpo', 'easeOutQuad', 'easeOutQuart', 'easeOutQuint', 'easeOutSi
ne', 'getLine', 'getPointOnLine', 'linear', 'math']
>>> pytweening.linear(0.1)
0.1
>>> pytweening.easeInOutSine(0.1)
0.024471741852423234
>>> pytweening.easeInQuad(0.1)
0.010000000000000002
>>> pytweening.getLine(0, 0, 0.1, 0.1)
[(0, 0)]
>>> pytweening.getLine(0, 0, 1.1, 1.1)
[(0, 0), (1, 1)]
For example, this module can help with some math function:
  • simple linear tweening - no easing, no acceleration;
  • quadratic easing in/out - acceleration until halfway, then deceleration;
  • cubic easing in/out - acceleration until halfway, then deceleration;
  • quartic easing in/out - acceleration until halfway, then deceleration;
  • sinusoidal easing in/out - accelerating until halfway, then decelerating;
  • ...

Friday, July 26, 2019

Python 3.7.3 : Tonny I.D.E. for python programmers.

Today I tested the Thonny I.D.E. from thonny.org official webpage.
Yesterday I tried several editors for python programming language and did not work.
One of these is the spyder editor that does not work with python 3.7.3 - we have not discovered why.
The Mu is a simple Python editor for beginner programmers and has a strange working I.D.E. for good and fast development.
The PyCharm, it is developed by the Czech company JetBrains is not fully free ...

The Thonny I.D.E. is fast and comes with two good features:
Code completion and Highlights syntax errors.
The install steps are easy with a wizard application.
After that, you can test with any python source code.
The source code from the image is a simple example from this webpage.
Let's see a screenshot:

This is a video tutorial with this I.D.E. from Aivar Annamaa user:



Thursday, July 25, 2019

Python 3.7.3 : Using the flask - part 002.

Let's see some tips for starting any project with flask python module.
Use these python modules to work with databases: flask-sqlalchemy and flask_marshmallow.
The Flask-SQLAlchemy is an extension for Flask that adds support for SQLAlchemy to your application.
The marshmallow is an ORM/ODM/framework-agnostic library for converting complex datatypes, such as objects, to and from native Python datatypes.
Let's install it:
C:\Python373>cd Scripts

C:\Python373\Scripts>pip install flask-sqlalchemy
Collecting flask_sqlalchemy
...
Successfully installed flask-sqlalchemy-2.4.0
C:\Python373\Scripts>pip install flask_marshmallow
Collecting flask_marshmallow
...
Successfully installed flask-marshmallow-0.10.1 marshmallow-2.19.5
C:\Python373\Scripts>pip install marshmallow-sqlalchemy
Collecting marshmallow-sqlalchemy
...
Successfully installed marshmallow-sqlalchemy-0.17.0
Use these python modules for login and authorization issues: flask-login
The Flask-Login provides user session management for Flask. It handles the common tasks of logging in, logging out, and remembering your users’ sessions over extended periods of time.
Flask-login doesn't actually have a user backend, it just handles the session machinery to help you to log in and logout users.
Flask-Login can work with user models that are based on any database system with four required items: is_authenticated, is_active, is_anonymous and get_id.
Let's install it:
C:\Python373\Scripts>pip install flask-login
Collecting flask-login
...
Successfully installed flask-login-0.4.1
This python module named python-dotenv let us to reads the key-value pair from .env file and adds them to an environment variable.
C:\Python373\Scripts>pip install python-dotenv
Collecting python-dotenv
...
Successfully installed python-dotenv-0.10.3
Flask-Login provides a UserMixin which makes it easy to create a user class, see example class User:
from flask_login import UserMixin
...
class User(UserMixin, db.Model):
Another tip is to use the get_or_404 and first_or_404 to avoid show errors on the webpage.
Reduce the passing of multiple variables to render_template, let's see the example:
from flask import render_template
...
return render_template('index.html', fn=name.first, ln=name.last, day=student.day, 
.... ')
This can de easy fix with this code to send value I want to pass:
context = {
'fn': name.first,
'ln': name.last,
'day': student.day,
...
}
return render_template('index.html', **context)
Use query to solve complex tasks.
Let's see a simple example of delete from a Student database class:
...
class Student(db.Model):
id=db.Column(db.Integer, primary_key=True)
...
country=db.Column(db.String(2))
...
Let's delete this with a query and return the number of deleted by delete_count:
del_ids= db.session.query(Student.id).filter(Student.country=='Ro')
delete_count=db.session.query(Student).filter(Student.id.in_(del_ids.subquery())).delete(synchronize_session=False)
I used two files in the main project named .env and .flaskenv:
The file .env comes with:
SQLALCHEMY_DATABASE_URI=sqlite:///db.sqlite3
SECRET_KEY = abcdef
The file .flaskenv has these lines of source code:
FLASK_ENV = development
FLASK_APP = my_flask_app
This can be used into a new file named settings.py:
import os 
SQLALCHEMY_DATABASE_URI = os.environ.get('SQLALCHEMY_DATABASE_URI')
SECRET_KEY = os.environ.get('SECRET_KEY')
SQLALCHEMY_TRACK_MODIFICATIONS = False
Using this the main flask application will start like this:
from flask import Flask
def create_my_app(config_file='settings.py'):
    app = Flask(__name__)
    app.config.from_pyfile(config_file)
    return app
There are plenty of tips and tricks that can be found based on the project in the bottle.
These are the most used.


Wednesday, July 24, 2019

Python 3.7.3 : Testing the timeit and Bokeh python module.

The tutorial today has several goals:
  • testing the timeit function to measure the execution time of a line of print code;
  • using python lists to record data;
  • generate lists using the range function;
  • multiplying a number with an entire list of numbers;
  • how to use the bokeh module with CustomJSHover and HoverTool
Let's test with the timeit python module, see the official webpage.
This module provides a simple way to time small bits of Python code. It has both a Command-Line Interface as well as a callable one. It avoids a number of common traps for measuring execution times.
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 timeit
>>> import random
>>> timeit.timeit('print("a")',number=1)
a
0.00013114200010022614
>>> timeit.timeit('print("a")',number=100)
...
a
0.007177434000027461
>>> timeit.timeit('print("a")',number=1000)
...
a
0.07585798000002342
Let make more visible the results with the bokeh python module:
import timeit
import random

# if you want to use numpy 
#import numpy as np

# import bokeh python module 
from bokeh.plotting import figure, show, output_file
# for show values
from bokeh.models.tools import CustomJSHover
from bokeh.models import HoverTool
print ('''
You can tests a number of times named l
with a number of print named i
and see the result value with bokeh python module
timeit.timeit('print("a")',number = i)
''')
i = int(input('The increment number i:'))
l = int(input('The number of test l:'))

# create the list with the output values
timit_list = []
# create the test function for l and i and return the list
def get_timeit(i,l):
 while i < l:
  i += 1
  out=timeit.timeit('print("a")',number = i)
  timit_list.append(out)
 return timit_list

# run the test function with l and i
# this will be coordinate y
yt=get_timeit(i,l)

# show result of the test 
print(yt)

# create the coordinate x
xt = [i for i in range(0, len(yt))]

#xt = np.linspace(0, l, l)
# print the coordinate x
#print(xt)

# create the output HTML file to see the result
output_file("test.html")

# create a figure with a timeit type y-axis
fig = figure(title='timit_list values for print("a")',
             plot_height=400, plot_width=700,
             x_axis_label='x has each incrementation of i', y_axis_label='value of timeit of print the char a',
             x_minor_ticks=3, 
             toolbar_location=None)

# create a circle for each value
# see new multiplication with a list 
# y=[i * 100 for i in yt]
fig.circle(x=xt, y=[i * 100 for i in yt], 
         color='blue', size=5,
         legend='Values')

x_custom = CustomJSHover(code="""
    return '' + special_vars.data_x""")

y_custom = CustomJSHover(code="""
    return '' + special_vars.data_y""")

fig.add_tools(
    HoverTool(
        show_arrow=True, 
        tooltips=[
 ('xt', '$data_x'),
 ('yt', '$data_y')
        ],
        formatters=dict(
            xt=x_custom,
            yt=y_custom
        )
    )
)

# Put the legend in the upper left corner
fig.legend.location = 'top_left'

# Let's check it out
show(fig)
The result of this python source code can be found on my YouTube channel:


Tuesday, July 23, 2019

Python 3.7.3 : Using the flask - part 001.

A short intro into this python module can be found at the PyPI website:
Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy, with the ability to scale up to complex applications. It began as a simple wrapper around Werkzeug and Jinja and has become one of the most popular Python web application frameworks.

Flask offers suggestions but doesn’t enforce any dependencies or project layout. It is up to the developer to choose the tools and libraries they want to use. There are many extensions provided by the community that makes adding new functionality easy.

The reason I used a series of tutorials with this python module is the complexity of the features of this python module.
Let's briefly outline some of the essential aspects of flask programming.
  • Flask is a simple, lightweight, and minimalist web framework;
  • Flask is developed based on the Jinja2 template engine;
  • Flask depends on the Jinja template engine and the Werkzeug WSGI toolkit;
  • Flask does not provide a built-in ORM system (Object Relation Mapping);
  • in Flask web applications to perform CRUD operations on a database can be tedious (see: ORM techniques of Flask-SQLAlchemy);
  • The Flask has a simple and customizable architecture;
  • the Flask to accelerate the development of simple websites that use static content;
  • has the option to extend and customize Flask according to precise project requirements;
A list of companies using the Flask framework - who is using Flask?
The install process is very simple using the pip tool:
C:\Python373>cd Scripts

C:\Python373\Scripts>pip install flask
Collecting flask
...
Installing collected packages: itsdangerous, Werkzeug, flask
Successfully installed Werkzeug-0.15.4 flask-1.1.1 itsdangerous-1.1.0
Let's start initializing the first flask application into the server.py file:
from flask import Flask 
app = Flask (__name__)
# create a wrarp of localhost
@app.route('/')
def home():
 return 'Hello world'
# the default name main 
if __name__ == '__main__':
 app.run()
About Routing and Variable Rules.
This can change it into the server.py script like this:
@app.route('/about')
def about():
 return 'The about page'
@app.route('/blog')
def blog():
 return 'This is the blog'
The next step is the variable rule issue:
# creaza a regula variabila ( variable rules) 
# the type of the variable can be set, see int 
# @app.route('/blog/')
@app.route('/blog/')
def blogpost(blog_id):
 return 'This is the post '+str(blog_id)
To run it, just use:
C:\Python373\my_flask>python server.py
 * Serving Flask app "server" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
The full source code can be found at my GitHub account.

Monday, July 22, 2019

Python 3.7.3 : The sip python module.

The official webpage pypi.org comes with this intro:
One of the features of Python that makes it so powerful is the ability to take existing libraries, written in C or C++, and make them available as Python extension modules. Such extension modules are often called bindings for the library.

SIP is a tool that makes it very easy to create Python bindings for C and C++ libraries. It was originally developed to create PyQt, the Python bindings for the Qt toolkit, but can be used to create bindings for any C or C++ library.

SIP comprises a code generator and a Python module. The code generator processes a set of specification files and generates C or C++ code which is then compiled to create the bindings extension module. The sip Python module provides support functions to the automatically generated code.

The SIP is copyright (c) Riverbank Computing Limited and its homepage is this webpage.
Support may be obtained from the PyQt mailing list at here.
The SIP is a tool for quickly writing Python modules that interface with C++ and C libraries.
The SIP comprises a code generator and a Python module.
About the code generator:
First, the install:
C:\Python373\Scripts>pip3 install sip
Collecting sip
...
Installing collected packages: sip
Successfully installed sip-4.19.8
If you using the PyQt5 then this version includes a private copy of the module.
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.
>>> from PyQt5 import sip
If you want to see backward compatibility the module then needs to imported and will only work if another PyQt5 module is imported first.
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.
>>> from PyQt5 import QtCore
>>> import sip
A good example of how can you build with SIP can be seen here.
About the SIP Python module:
This provides support functions to the automatically generated code.
The import python module sip let you use all functions build by the code generator:
>>> dir(sip)
['SIP_VERSION', 'SIP_VERSION_STR', '_C_API', '__doc__', '__file__', '__loader__'
, '__name__', '__package__', '__spec__', '_unpickle_enum', '_unpickle_type', 'as
sign', 'cast', 'delete', 'dump', 'enableautoconversion', 'enableoverflowchecking
', 'getapi', 'isdeleted', 'ispycreated', 'ispyowned', 'setapi', 'setdeleted', 's
etdestroyonexit', 'settracemask', 'simplewrapper', 'transferback', 'transferto',
 'unwrapinstance', 'voidptr', 'wrapinstance', 'wrapper', 'wrappertype']
If you want to improve or create python modules with C or C ++, then this tool can help.

Sunday, July 21, 2019

Python 3.7.3 : The IMDbPY python module version 6.8.

The GitHub official webpage comes with this intro:
IMDbPY is a Python package for retrieving and managing the data of the IMDb movie database about movies, people and companies.
The last release version 6.8 was at 2019 Jul 20.
The official webpage tells us:
In the release 6.8 (codename "Apollo 11") of IMDbPY, multiple parsers were added and fixes; the new search_movie_advanced method allows advanced movie searches...
The changes of the version 6.8 can be found at GitHub webpage and come with these new features:
#224: introduce the search_movie_advanced(title, adult=None, results=None, sort=None, sort_dir=None) method
#145: names are stored in normal format (Name Surname)
#225: remove the obsolete cookie
#182: box office information
#168: parse series and episode number searching for movies
#217: grab poster from search
#218: extract MPAA rating
#220: extract actor headshot from full credits
The install on Python 3.7.3 is easy with pip3 tool:
C:\Python373\Scripts>pip3 install imdbpy
Collecting imdbpy
...
Installing collected packages: imdbpy
Successfully installed imdbpy-6.8
Let's test the new features:
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 imdb
>>> ia = imdb.IMDb()
>>> movies = ia.search_movie_advanced('debby does dallas', adult=True)
>>> print(movies)
...
>>> people = ia.search_person('Clark Gregg')
>>> print(people)
Let's test it:
import imdb
from imdb import IMDb

ia = imdb.IMDb()

# create a file to put the output 
file1 = open("_imdb_data.txt","w", encoding='utf-8') 

# get movies by movie 
# example: Alien 

def get_by_movie():
 my_movie = str(input('Type the movie name: '))
 movies = ia.search_movie_advanced(my_movie, adult=True)
 print(type(movies))
 return movies

# get filmography by id 
filmography_list = []
def get_filmography_by_id(id):
 actor_results = ia.get_person_filmography(id)
 for item in actor_results['data']['filmography']:
  filmography_list.append(str(item))
 return filmography_list

# the main function 
def main():
 a = get_by_movie()
 for i in a:
  print("________________________")
  print("i: ",i)
  # you can uncomment this to test Movie class functions
  #print("Type:",type(i))
  #print("Summary:",i.summary())
  #print("ID: ",i.getID())
  #print("Smart cannonical title: ",i.smartCanonicalTitle())
  #print("caracters ref: ",i.get_charactersRefs())
  #print("current info: ",i.get_current_info())
  #print("cinematographic process: ",i.get('cinematographic process'))
  #print(i["title"])
  #print informations items from Movie class
  print("~~~~~~~~~~~~~~~~~~~~~~~~")
  for k, v in i.items():
   print(k, v)
   # write to the file the value of a
   txt = str(k)+":"+str(v)+"\n" 
   file1.write(txt)
  print("------------------------")
  # add a new line on each movie
  file1.write('-----^-----\n')
 #get filmography by id 
 id_filmography=get_filmography_by_id('0078748')
 # print the filmography
 for item in id_filmography:
  print(item)

 #after write, close the file 
 file1.close()

if __name__ == '__main__':
    main()
This is the first part of the output file named _imdb_data:
title:Alien
certificates:['R']
runtimes:['117']
genres:['Horror', 'Sci-Fi']
rating:8.5
votes:719508
metascore:89
gross:78900000
plot:After a space merchant vessel perceives an unknown transmission as a distress call, its landing on the source moon finds one of the crew attacked by a mysterious lifeform, and they soon realize that its life cycle has merely begun.
directors:[]
cast:[, , , ]
cover url:https://m.media-amazon.com/images/M/MV5BMmQ2MmU3NzktZjAxOC00ZDZhLTk4YzEtMDMyMzcxY2IwMDAyXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg
year:1979
kind:movie
canonical title:Alien
long imdb title:Alien (1979)
long imdb canonical title:Alien (1979)
smart canonical title:Alien
smart long imdb canonical title:Alien (1979)
full-size cover url:https://m.media-amazon.com/images/M/MV5BMmQ2MmU3NzktZjAxOC00ZDZhLTk4YzEtMDMyMzcxY2IwMDAyXkEyXkFqcGdeQXVyNzkwMjQ5NzM@.jpg
-----^-----
title:Aliens
certificates:['R']
runtimes:['137']
...

Saturday, July 20, 2019

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)

Thursday, July 18, 2019

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()