Key Points

1. Python or R for analytics?  creating models or building models in apps?

References

...

...

Python for Java Developers - quick start

...

https://lobster1234.github.io/2017/05/25/python-java-primer/

https://drive.google.com/open?id=1bSt5RuW8VAGmpE9vYa5x-d02i7TnEyrD

python-4-java-io-lobster1234.github.io-Python Primer for Java Developers.pdf

...

https://realpython.com/oop-in-python-vs-java/

python-4-java-realpython.com-Object-Oriented Programming in Python vs Java.pdf

...

https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis?fbclid=
IwAR3AEPrIfQrpoHPIeymv7zAPhEnrpR1RL0zZ8IS7
9AlaHgyzUPgouZmbBX4

python-vs-R-for-analytics-use-cases-2019.pdf

...

Key Concepts

Python for Java Developers Quickstart

https://medium.com/nestedif/cheatsheet-python-for-java-developers-98f75c94a1a

python-4-java-medium.com-Python for JAVA Developers Basics.pdf

python-4-java-io-lobster1234.

Key Points

1. Python or R for analytics?  creating models or building models in apps?

References

...

Migrating to Python

...

...

...

...

...

...

comp.lang.python

Probably your most valuable Python resource, right after python.org. Spot Python luminaries in their native habitat! Don't be surprised to have your questions answered by the original programmer or the author of the book open on your desk! The best thing about comp.lang.python is how "newbie-friendly" the mail group is. You can ask any question and never get a "RTFM" thrown back at you.

Where is Python's CPAN?

See the Python Package Index (PYPI), Python's centralised database of software. Any developer of Python software packaged using distutils (the built-in packaging system) may easily submit their package information to the index.

Before PYPI, Pythonistas contributed code to The Vaults of Parnassus. Unfortunately, the Vaults are not standardized or automated to the degree that the CPAN is, and the site now appears to be inactive. All hail its many useful years of service.

Tips for Learning Python

The following was taken from a post in comp.lang.python from Mel Wilson, which I thought well summarized good Python programming style for people coming from other languages. I also added some things I would have appreciated knowing during my first 3 days with Python.

• The docs at python.org are very, very good. Hold onto that wallet, you don't need a trip to the bookstore to learn Python!
• Scan the full list of built-in module names early on. Python is advertised as "batteries included", so knowledge of the built-in modules could reduce the lines of code by a factor of ten.
• Learn Python slice notation, you will be using it a lot. I have this chart taped to my monitor:

Python indexes and slices for a six-element list.
Indexes enumerate the elements, slices enumerate the spaces between the elements.

Index from rear:    -6  -5  -4  -3  -2  -1      a=[0,1,2,3,4,5]    a[1:]==[1,2,3,4,5]
Index from front:    0   1   2   3   4   5      len(a)==6          a[:5]==[0,1,2,3,4]
                   +---+---+---+---+---+---+    a[0]==0            a[:-2]==[0,1,2,3]
                   | a | b | c | d | e | f |    a[5]==5            a[1:2]==[1]
                   +---+---+---+---+---+---+    a[-1]==5           a[1:-1]==[1,2,3,4]
Slice from front:  :   1   2   3   4   5   :    a[-2]==4
Slice from rear:   :  -5  -4  -3  -2  -1   :
                                                b=a[:]
                                                b==[0,1,2,3,4,5] (shallow copy of a)

• Lose the braces, as you know them, and most of the semicolons, obviously.
• Backslash can be used to allow continuing the program line past a carriage-return, but you almost never have to use it. Python is smart enough to do the right thing when it sees an open bracket, a comma separated list, and a carriage-return.
• Strings are immutable. Whenever you think you have changed a string, remember that you really created a new string.

• Where you would use <vector T>, use lists, or tuples, that is [] or (). Where you would use <map T1, T2>, use dictionaries, that is {} .

• The semantics of iterators is available, but most of the syntax goes away. for item in alist: iterates over all the items in alist, one by one .. where alist is a sequence, i.e. a list, tuple, or string. To iterate over a sublist, use slices: for item in alist[1:-1]: does as above, but omits the first and last items.

• For trickier iterations, read and re-read the Library doc on the topic of general-purpose functions. There are some functions that apply to sequences: map, filter, reduce, zip. that can work wonders. Hidden somewhere under the documentation for sequences there is a description of string methods that you'll want to read.

• Hidden under the docs for 'Other Types' are the descriptions of all the file methods. There are no iostreams per se, but the class method str can get some of the effect for your own classes, and there are surely other angles I haven't thought of.

• Forget overloading. You can define a function, and call it with anything you want, but if it has to behave differently for different type operands, you have to use the run-time type identification type function explicitly within the single definition of the function. Default arguments to functions are just as powerful a tool as in C++. Actually polymorphism does work as expected, it just doesn't require deriving from a base class as in C++ or Java.

• In class definitions the equivalents of operator methods are covered in a chapter in the Python Language Reference. (Look for the double-underscore methods like __cmp__, __str__, __add__, etc.)

• In C, the gotcha for new users is probably about pointers; they're tricky and they can't be avoided. The gotchas in Python are situations when you use different references to a single object, thinking you are using different objects. I believe the difference between mutable and immutable objects comes into play. I have no clear answers here .. I still get caught once in a while .. keep your eyes open.
• Read the Tutorial once, skim the Library Reference .. at least the table of contents, then skim the Language Reference and you will probably have encountered everything you need.

• For reference the excellent Python Quick Reference and the Module Index of the Python docs are generally sufficient.

Python PEP 8: Style Guide for Python Code

Style Guide for Python Code

Basic Python projects

https://morioh.com/p/f7286d9ee563?f=5c224490c513a556c9042463&fbclid=IwAR2wHrK-pi5FQ2-zH9fWgdHbu63UIa5yU0TBCwV59D4HM1HHFRG2Hd7j0so

Python and MySQL Tutorial

https://morioh.com/p/n0aRySfEzqFf?f=5c21fb01c16e2556b555ab32&fbclid=IwAR3_QQT4yej5ns4LXwQQVrT4kH7Gkp44Gx7QvWP4ubxRT-LsvIUXmaHl4Nk

sample code 

https://github.com/hnasr/python_playground/tree/master/mysqldemopython

import mysql.connector

#connecting to a database
con = mysql.connector.connect(
    host = "husseinmac",
    user = "root",
    password = "password",
    database = "husseindb",
    port = 3306
)

print("Hey, I think I'm connected")

#cursor 
cur = con.cursor()
#insert a new row

for i in range(100):
    cur.execute("INSERT INTO employees (ID, NAME) VALUES (%s, %s)", (i+10, f'Mark{i}' ))

#execute the query
cur.execute("SELECT ID,NAME FROM employees")

#cur.execute("SELECT ID,NAME FROM employees where NAME = %s", ("Yara",))

rows = cur.fetchall()

for r in rows:
    print(f" ID = {r[0]} NAME = {r[1]}")

#commit the transaction
con.commit()

#close the cursor
cur.close()
#close the connection
con.close()

Potential Value Opportunities

Potential Challenges

...

Key Concepts

Python for Java Developers Quickstart

https://medium.com/nestedif/cheatsheet-python-for-java-developers-98f75c94a1a

python-4-java-medium.com-Python for JAVA Developers Basics.pdf

python-4-java-io-lobster1234.github.io-Python Primer for Java Developers.pdf

Python for Java Developers - OO Coding Concepts - real python

https://realpython.com/oop-in-python-vs-java/

python-4-java-realpython.com-Object-Oriented Programming in Python vs Java.pdf

Python Programming for Beginners.pdf. link

Python Programming for Beginners.pdf  file

Migrating to Python

https://wiki.python.org/moin/MovingToPythonFromOtherLanguages

comp.lang.python

Probably your most valuable Python resource, right after python.org. Spot Python luminaries in their native habitat! Don't be surprised to have your questions answered by the original programmer or the author of the book open on your desk! The best thing about comp.lang.python is how "newbie-friendly" the mail group is. You can ask any question and never get a "RTFM" thrown back at you.

Where is Python's CPAN?

See the Python Package Index (PYPI), Python's centralised database of software. Any developer of Python software packaged using distutils (the built-in packaging system) may easily submit their package information to the index.

Before PYPI, Pythonistas contributed code to The Vaults of Parnassus. Unfortunately, the Vaults are not standardized or automated to the degree that the CPAN is, and the site now appears to be inactive. All hail its many useful years of service.

Tips for Learning Python

The following was taken from a post in comp.lang.python from Mel Wilson, which I thought well summarized good Python programming style for people coming from other languages. I also added some things I would have appreciated knowing during my first 3 days with Python.

• The docs at python.org are very, very good. Hold onto that wallet, you don't need a trip to the bookstore to learn Python!
• Scan the full list of built-in module names early on. Python is advertised as "batteries included", so knowledge of the built-in modules could reduce the lines of code by a factor of ten.
• Learn Python slice notation, you will be using it a lot. I have this chart taped to my monitor:

Python indexes and slices for a six-element list.
Indexes enumerate the elements, slices enumerate the spaces between the elements.

Index from rear:    -6  -5  -4  -3  -2  -1      a=[0,1,2,3,4,5]    a[1:]==[1,2,3,4,5]
Index from front:    0   1   2   3   4   5      len(a)==6          a[:5]==[0,1,2,3,4]
                   +---+---+---+---+---+---+    a[0]==0            a[:-2]==[0,1,2,3]
                   | a | b | c | d | e | f |    a[5]==5            a[1:2]==[1]
                   +---+---+---+---+---+---+    a[-1]==5           a[1:-1]==[1,2,3,4]
Slice from front:  :   1   2   3   4   5   :    a[-2]==4
Slice from rear:   :  -5  -4  -3  -2  -1   :
                                                b=a[:]
                                                b==[0,1,2,3,4,5] (shallow copy of a)

• Lose the braces, as you know them, and most of the semicolons, obviously.
• Backslash can be used to allow continuing the program line past a carriage-return, but you almost never have to use it. Python is smart enough to do the right thing when it sees an open bracket, a comma separated list, and a carriage-return.
• Strings are immutable. Whenever you think you have changed a string, remember that you really created a new string.

• Where you would use <vector T>, use lists, or tuples, that is [] or (). Where you would use <map T1, T2>, use dictionaries, that is {} .

• The semantics of iterators is available, but most of the syntax goes away. for item in alist: iterates over all the items in alist, one by one .. where alist is a sequence, i.e. a list, tuple, or string. To iterate over a sublist, use slices: for item in alist[1:-1]: does as above, but omits the first and last items.

• For trickier iterations, read and re-read the Library doc on the topic of general-purpose functions. There are some functions that apply to sequences: map, filter, reduce, zip. that can work wonders. Hidden somewhere under the documentation for sequences there is a description of string methods that you'll want to read.

• Hidden under the docs for 'Other Types' are the descriptions of all the file methods. There are no iostreams per se, but the class method str can get some of the effect for your own classes, and there are surely other angles I haven't thought of.

• Forget overloading. You can define a function, and call it with anything you want, but if it has to behave differently for different type operands, you have to use the run-time type identification type function explicitly within the single definition of the function. Default arguments to functions are just as powerful a tool as in C++. Actually polymorphism does work as expected, it just doesn't require deriving from a base class as in C++ or Java.

• In class definitions the equivalents of operator methods are covered in a chapter in the Python Language Reference. (Look for the double-underscore methods like __cmp__, __str__, __add__, etc.)

• In C, the gotcha for new users is probably about pointers; they're tricky and they can't be avoided. The gotchas in Python are situations when you use different references to a single object, thinking you are using different objects. I believe the difference between mutable and immutable objects comes into play. I have no clear answers here .. I still get caught once in a while .. keep your eyes open.
• Read the Tutorial once, skim the Library Reference .. at least the table of contents, then skim the Language Reference and you will probably have encountered everything you need.

• For reference the excellent Python Quick Reference and the Module Index of the Python docs are generally sufficient.

Python PEP 8: Style Guide for Python Code

Style Guide for Python Code

Sample Python Projects

Basic Python projects

https://morioh.com/p/f7286d9ee563?f=5c224490c513a556c9042463&fbclid=IwAR2wHrK-pi5FQ2-zH9fWgdHbu63UIa5yU0TBCwV59D4HM1HHFRG2Hd7j0so

Python and MySQL Tutorial

https://morioh.com/p/n0aRySfEzqFf?f=5c21fb01c16e2556b555ab32&fbclid=IwAR3_QQT4yej5ns4LXwQQVrT4kH7Gkp44Gx7QvWP4ubxRT-LsvIUXmaHl4Nk

sample code 

https://github.com/hnasr/python_playground/tree/master/mysqldemopython

import mysql.connector

#connecting to a database
con = mysql.connector.connect(
    host = "husseinmac",
    user = "root",
    password = "password",
    database = "husseindb",
    port = 3306
)

print("Hey, I think I'm connected")

#cursor 
cur = con.cursor()
#insert a new row

for i in range(100):
    cur.execute("INSERT INTO employees (ID, NAME) VALUES (%s, %s)", (i+10, f'Mark{i}' ))

#execute the query
cur.execute("SELECT ID,NAME FROM employees")

#cur.execute("SELECT ID,NAME FROM employees where NAME = %s", ("Yara",))

rows = cur.fetchall()

for r in rows:
    print(f" ID = {r[0]} NAME = {r[1]}")

#commit the transaction
con.commit()

#close the cursor
cur.close()
#close the connection
con.close()

Simplest Neural Network in Python - Perceptron

https://morioh.com/p/512d41219fda?f=5c21fb01c16e2556b555ab32&fbclid=IwAR34w7O9dK5RtqoTryI9QI5op7UUfNXNHJ6wMz7Uenr83DdfgXbdz0Ng3Pg

In this video I'll show you how an artificial neural network works, and how to make one yourself in Python. In the next video we'll make one that is usable, but if you want, that code can already be found on github. I recommend watching at 1.5x speed, unless you're coding along.

video

video 2 

https://morioh.com/p/512d41219fda

Image Added

Github code

https://github.com/jonasbostoen/simple-neural-network

reating a simple neural network in Python with one input layer (3 inputs) and one output neuron. A neural network with no hidden layers is called a perceptron. In the training_version.py I train the neural network in the clearest way possible, but it's not really useable. The outputs of the training can be found in outputs.txt . neural_network.py is an object and can be used by giving in different inputs.

Thanks to Milo Spencer-Harber for this: https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1

And to Andrew Trask for this: https://iamtrask.github.io/2015/07/12/basic-python-network/

What does it do?

The neural_net.py tries to predict the output given 3 binary inputs. If the first input is 1, the output should be one. Otherwise the output should be 0.

Build Amazon Price Checker with Python, Selenium

https://morioh.com/p/9cb73e75c41b?f=5c224490c513a556c9042463&fbclid=IwAR108UiH8umne49I1rjG1AsfLtwIQiFBzu3_ImlKWEnZgHPns5922WNDfDY

In this FREE LIVE training, Qazi and Jakob will show you how to build an AMAZON Price Tracker with Python and Selenium 🚀

Github Repo 👉 https://github.com/jakobowsky/AmazonPriceTracker

Screen Scrape to create Web dataset with Python, SOAP library 

https://morioh.com/p/876f3464c2d6?f=5c21f93bc16e2556b555ab2f&fbclid=IwAR3WtaWoQFVOp4Ch0_WftXHWytLd1H9yzPQMuarRWr7YgsvvZhVC9LTz1jU

Web Scraping using Python To Create a Dataset | Data Science | Machine Learning | Python
In this article I will show you how you can create your own dataset by Web Scraping using Python. Web Scraping means to extract a set of data from web. If you are a programmer, a Data Scientist, Engineer or anyone who works by manipulating the data, the skills of Web Scrapping will help you in your career. Suppose you are working on a project where no data is available, then how you are going to collect the data. In this situation Web Scraping skills will help you.

Web Scraping with Beautiful Soap
Beautiful Soap is a Library in Python which will provide you some flexible tools to for Web Scraping. Now let’s import some necessary libraries to get started with with our task:

https://thecleverprogrammer.com/2020/07/18/web-scraping-using-python-to-create-a-dataset/?utm_source=rss&utm_medium=rss&utm_campaign=web-scraping-using-python-to-create-a-dataset

Logistic Regression Using Python 

Python Libraries

https://blog.hubspot.com/website/python-ai-libraries

Introduction-To-Python.pdf.  link

Python Programming for Beginners - Bryan Cafferky

series

https://www.youtube.com/channel/UCEdMzQ0m9WcZQepgizrHpMw

p1 -get started

https://www.youtube.com/watch?v=iL1DJSpRxaM&t=7s

p2 - syntax

https://www.youtube.com/watch?v=Y69OtFzeY-Y&t=32s

p3 - variables

https://www.youtube.com/watch?v=oLYJIBOLfGI&t=2s

Python and SQL series - Bryan Cafferky

Python + SQL: Part 1 - The Easiest Way

https://www.youtube.com/watch?v=xY54Emo8rQM

Why use SQLite ?

https://www.youtube.com/watch?v=E7aY1XJX1og&t=147s

Using SQLite Studio

https://www.youtube.com/watch?v=dugUk893gxQ

Python and Postgres Video Series

https://www.youtube.com/watch?v=u-c6rhd8MJc

https://github.com/bcafferky/shared/tree/master/PythonPostgreSQLUpsert

Potential Value Opportunities

Potential Challenges

Candidate Solutions

The Quick Python Book, Fourth Edition“ by Naomi Ceder - review

Dunja Nikitovic <duni@manning.com>

Thank you for joining us in this review, your support means a lot to us.

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Step-by-step guide for Example

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