These are the best USB-C power adapters for your new iPhone 12

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October 15, 2020 at 03:36PM

Laravel Admin Panel

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October 14, 2020 at 09:12PM

The Perfect Pair of Budget Earbuds Are Only $20 Right Now

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October 14, 2020 at 09:06AM

A YouTube Channel That Reviews Footwear by Cutting It In Half

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Yesterday we covered a footwear designer showing a Nike cut in half for analysis. Today we’ve discovered a YouTube channel that does nothing but cut footwear in half, to analyze the quality and design.

Here Weston Kay, a man who knows leather–he’s the owner of leather goods company Rose Anvil–shows you the insides of a pair of Carhartt work boots.

He starts off by showing you the red flags that you yourself could spot at the store, without cutting into the boots: Disguising low-quality leather as full-grain; visible manufacturing errors with the welt; a poor-quality outsole. Then he gets into the insides, where things kind of go to hell.

Being generally unfamiliar with boot design, I learned a few things by watching the vid. The necessity of reinforcing the heel slip area, for one. I was also surprised to see that there is a footwear design equivalent to the architectural trick of McMansion foyers.

To explain, McMansions often have front doors that open into a two-story open-air space with an impressive-looking, sweeping staircase. The idea is that this feature impresses you into wanting to buy the home. Only after living there for a while do you realize that the space is useless and the staircase needlessly long.

In the case of the Carhartt boots, it’s the voids in the heel. It makes them comfortable when you try them on in the store, likely leading you to buy them. It’s only a few weeks later, when they’ve already got your money, that the heel compresses into oblivion. A neat, and dirty, trick.

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October 13, 2020 at 02:22PM

ACB Calmly Answers Questions While Typing Up Appellate Court Decision And Cooking Dinner For 9

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October 13, 2020 at 12:02PM

Web Scraping With Beautiful Soup and Python

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October 13, 2020 at 10:23AM

ListenData: Learn Python for Data Science

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This tutorial would help you to learn Data Science with Python by examples. It is designed for beginners who want to get started with Data Science in Python. Python is an open source language and it is widely used as a high-level programming language for general-purpose programming. It has gained high popularity in data science world. In the PyPL Popularity of Programming language index, Python scored second rank with a 14 percent share. In advanced analytics and predictive analytics market, it is ranked among top 3 programming languages for advanced analytics.

Data Science with Python Tutorial

Table of Contents

---

Python 2 vs. 3

Google yields thousands of articles on this topic. Some bloggers opposed and some in favor of 2.7. If you filter your search criteria and look for only recent articles, you would find Python 2 is no longer supported by the Python Software Foundation. Hence it does not make any sense to learn 2.7 if you start learning it today. Python 3 supports all the packages. Python 3 is cleaner and faster. It is a language for the future. It fixed major issues with versions of Python 2 series. Python 3 was first released in year 2008. It has been 12 years releasing robust versions of Python 3 series. You should go for latest version of Python 3.

Python for Data Science : Introduction

Python is widely used and very popular for a variety of software engineering tasks such as website development, cloud-architecture, back-end etc. It is equally popular in data science world. In advanced analytics world, there has been several debates on R vs. Python. There are some areas such as number of libraries for statistical analysis, where R wins over Python but Python is catching up very fast.With popularity of big data and data science, Python has become first programming language of data scientists.
There are several reasons to learn Python. Some of them are as follows –

1. Python runs well in automating various steps of a predictive model.
2. Python has awesome robust libraries for machine learning, natural language processing, deep learning, big data and artificial Intelligence.
3. Python wins over R when it comes to deploying machine learning models in production.
4. It can be easily integrated with big data frameworks such as Spark and Hadoop.
5. Python has a great online community support.

Do you know these sites are developed in Python?

1. YouTube
2. Instagram
3. Reddit
4. Dropbox
5. Disqus

How to install Python?

There are two ways to download and install Python

1. Download Anaconda. It comes with Python software along with preinstalled popular libraries.
2. Download Pythonfrom its official website. You have to manually install libraries.

Recommended : Go for first option and download anaconda. It saves a lot of time in learning and coding Python

Coding Environments

Anaconda comes with two popular IDE :

1. Jupyter (Ipython) Notebook
2. Spyder

Spyder. It is like RStudio for Python. It gives an environment wherein writing python code is user-friendly. If you are a SAS User, you can think of it as SAS Enterprise Guide / SAS Studio. It comes with a syntax editor where you can write programs. It has a console to check each and every line of code. Under the ‘Variable explorer’, you can access your created data files and function. I highly recommend Spyder!

Spyder – Python Coding Environment

Jupyter (Ipython) Notebook Jupyter is equivalent to markdown in R. It is useful when you need to present your work to others or when you need to create step by step project report as it can combine code, output, words, and graphics.

READ MORE »

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October 11, 2020 at 03:51PM

Is Python Becoming More Popular Than Java?

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Python has reached "a new all-time high" on TIOBE’s index of programming language popularity. TechRadar reports:
Java’s days as the world’s second most popular programming language could be numbered according to Tiobe’s latest programming language rankings which show Python is becoming increasingly popular among developers.
The firm’s Index for October 2020 shows that Java has been overtaken by C as the world’s most popular programming language when compared to the same period last year. Python remains in third place but it’s quickly closing the gap between it and Java.
According to Tiobe CEO Paul Jensen, C and Java have held the top two spots consistently for the past two decades. However, the 25-year-old programming language Java is approaching its "all time low" in popularity as it has fallen by 4.32 percentage points when compared to where it stood in October of last year. Tiobe ranks programming languages in its popularity index based on the number of hits each language gets across 25 search engines. RedMonk’s rankings already show Python as more popular than Java — the first time since 2012 that Java isn’t one of their top two most popular languages. And TIOBE’s CEO says "Let’s see what will happen the next few months."
Here’s their October rankings for the top 10 most popular programming languages.
CJavaPythonC++C#Visual BasicJavaScriptPHPRSQL
And coming in at #11 is Perl.

Read more of this story at Slashdot.

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October 11, 2020 at 02:01PM

Hunting Season: Protect Your Guns and Know What to Do If They’re Stolen

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By Richard Hayes

This time of year is a favorite among many gun owners, getting away from the world, hanging out with some close friends, and going on a ritual hunting trip. Perhaps you prefer hiking, fishing, or some other getaway. Unfortunately, the best laid plans can become the worst trip imaginable when you find your truck or your car broken into, windows smashed, and your gun is missing. What should you do right now? Better yet, what could you have done to avoid this, if anything?

If this ever happens to you, you’ll need to protect yourself by talking to an attorney to have their advice on how to handle your police report. It’s hard to believe you were just the victim of a crime, but in many places you may feel like you are being treated as the criminal.

Know the Reporting Requirements for a Stolen Gun

Be aware that some states have mandatory reporting requirements if your gun is stolen. And even if a state doesn’t require it, you’ll want to have a paper trail establishing when the firearms left your possession so that you are not implicated in a future crime. Worse than having your gun stolen is to be falsely accused of a crime. If you fail to report your gun stolen and then find yourself confronted by law enforcement weeks, months, or even years later, you may learn that your stolen gun has been used in a crime such as a murder or a robbery.

Having your U.S. LawShield Independent Program Attorney at the ready becomes even more critical in these cases. Some jurisdictions will send an officer out to take a report, and some will take the report over the phone. But you’ll need to have some information ready in order to make that report, such as the make, model, and serial number of the stolen weapon or weapons.

Dan Z. for TTAG

More importantly, you will want to have the advice of your U.S. LawShield Independent Program Attorney so that when you are talking with the police, you will understand all of the gun laws applicable to the situation and avoid any of the legal “gotcha’s” relating to your possession of the weapon, how it was stored, or even its accessibility to children or others.

After you make your report, police will enter the firearms and flag them as stolen in the National Crime Information Center (“NCIC”). This crucial step will have the biggest impact on being notified if your firearm winds up at a crime scene, or perhaps even at a pawn shop.

Check the State Laws and Check Your Vehicle Before Heading Out

Another consideration is when your getaway travel plans take you beyond the borders of your home state. You must check the laws of any states to where you travel when you’re traveling through them before you hit the road. This is especially true if you plan on hunting, because you’ll want to be aware of all the firearms and wildlife laws before you take aim at that 10-point buck.

And if you stop for a bite to eat or an overnighter on your journey, do not leave your firearms visible in your vehicle. The volume of vehicle break-ins seems to be increasing daily, and firearms are the top prize for a criminal. You also don’t want to accidentally bring a weapon that is legal in your home state, but illegal in the state, county, or city you are visiting. Many jurisdictions require firearms to remain concealed within a vehicle. The last thing you want is to leave for your getaway only to return home on probation.

If you have not already done so, make sure you take photos of your firearm serial numbers and back them up electronically. Of course, if you don’t want to send pictures of your guns to the cloud, keep a written log of the make, model, and serial numbers of your firearms, and keep the logs stored in a separate, secured location away from your gun collection.

It is important to know the laws of the state you find yourself in before traveling and preparing for your getaway hunting, hiking, or fishing trip. And I hope this year’s getaway is your best ever.

 

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October 11, 2020 at 02:05PM

Python 101: An Intro to Working with JSON

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JavaScript Object Notation, more commonly known as JSON, is a lightweight data interchange format inspired by JavaScript object literal syntax. JSON is easy for humans to read and write. It is also easy for computers to parse and generate. JSON is used for storing and exchanging data in much the same way that XML is used.

Python has a built-in library called json that you can use for creating, editing and parsing JSON. You can read all about this library here:

• https://docs.python.org/3/library/json.html

It would probably be helpful to know what JSON looks like. Here is an example of JSON from https://json.org:

{"menu": {
  "id": "file",
  "value": "File",
  "popup": {
    "menuitem": [
      {"value": "New", "onclick": "CreateNewDoc()"},
      {"value": "Open", "onclick": "OpenDoc()"},
      {"value": "Close", "onclick": "CloseDoc()"}
    ]
  }
}}

From Python’s point of view, this JSON is a nested Python dictionary. You will find that JSON is always translated into some kind of native Python data type. In this article, you will learn about the following:

• Encoding a JSON String
• Decoding a JSON String
• Saving JSON to Disk
• Loading JSON from Disk
• Validating JSON with json.tool

JSON is a very popular format that is often used in web applications. You will find that knowing how to interact with JSON using Python is useful in your own work.

Let’s get started!

Encoding a JSON String

Python’s json module uses dumps() to serialize an object to a string. The “s” in dumps() stands for “string”. It’s easier to see how this works by using the json module in some code:

>>> import json
>>> j = {"menu": {
...   "id": "file",
...   "value": "File",
...   "popup": {
...     "menuitem": [
...       {"value": "New", "onclick": "CreateNewDoc()"},
...       {"value": "Open", "onclick": "OpenDoc()"},
...       {"value": "Close", "onclick": "CloseDoc()"}
...     ]
...   }
... }}
>>> json.dumps(j)
'{"menu": {"id": "file", "value": "File", "popup": {"menuitem": [{"value": "New", '
'"onclick": "CreateNewDoc()"}, {"value": "Open", "onclick": "OpenDoc()"}, '
'{"value": "Close", "onclick": "CloseDoc()"}]}}}'

Here you use json.dumps(), which transforms the Python dictionary into a JSON string. The example’s output was modified to wrap the string for print. Otherwise the string would all be on one line.

Now you’re ready to learn how to write an object to disk!

Saving JSON to Disk

Python’s json module uses the dump() function to serialize or encode an object as a JSON formatted stream to a file-like object. File-like objects in Python are things like file handlers or objects that you create using Python’s io module.

Go ahead and create a file named create_json_file.py and add the following code to it:

# create_json_file.py

import json

def create_json_file(path, obj):
    with open(path, 'w') as fh:
        json.dump(obj, fh)

if __name__ == '__main__':
    j = {"menu": {
        "id": "file",
        "value": "File",
        "popup": {
          "menuitem": [
            {"value": "New", "onclick": "CreateNewDoc()"},
            {"value": "Open", "onclick": "OpenDoc()"},
            {"value": "Close", "onclick": "CloseDoc()"}
          ]
        }
      }}
    create_json_file('test.json', j)

In this example, you use json.dump(), which is for writing to a file or file-like object. It will write to the file-handler, fh.

Now you can learn about decoding a JSON string!

Decoding a JSON String

Decoding or deserializing a JSON string is done via the loads() method. loads() is the companion function to dumps(). Here is an example of its use:

>>> import json
>>> j_str = """{"menu": {
...   "id": "file",
...   "value": "File",
...   "popup": {
...     "menuitem": [
...       {"value": "New", "onclick": "CreateNewDoc()"},
...       {"value": "Open", "onclick": "OpenDoc()"},
...       {"value": "Close", "onclick": "CloseDoc()"}
...     ]
...   }
... }}
... """
>>> j_obj = json.loads(j_str)
>>> type(j_obj)
<class 'dict'>

Here you recreate the JSON code from earlier as a Python multi-line string. Then you load the JSON string using json.loads(), which converts it to a Python object. In this case, it converts the JSON to a Python dictionary.

Now you are ready to learn how to load JSON from a file!

Loading JSON from Disk

Loading JSON from a file is done using json.load(). Here is an example:

# load_json_file.py

import json

def load_json_file(path):
    with open(path) as fh:
        j_obj = json.load(fh)
    print(type(j_obj))


if __name__ == '__main__':
    load_json_file('example.json')

In this code, you open the passed in file as you have seen before. Then you pass the file-handler, fh, to json.load(), which will transform the JSON into a Python object.

You can also use Python’s json module to validate JSON. You will find out how to do that next.

Validating JSON with json.tool

Python’s json module provides a tool you can run on the command line to check and see if the JSON has the correct syntax. Here are a couple of examples:

$ echo '{1.2:3.4}' | python -m json.tool
Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
$ echo '{"1.2":3.4}' | python -m json.tool
{
    "1.2": 3.4
}

The first call passes the string, '{1.2:3.4}' to json.tool, which tells you that there is something wrong with the JSON code. The second example shows you how to the fix the issue. When the fixed string is passed in to json.tool, it will “pretty-print” the JSON back out instead of emitting an error.

Wrapping Up

The JSON format is used very often when working with web APIs and web frameworks. The Python language provides a nice tool for you to use to convert JSON to Python objects and back again in the json library.

In this chapter, you learned about the following:

• Encoding a JSON String
• Decoding a JSON String
• Saving JSON to Disk
• Loading JSON from Disk
• Validating JSON with json.tool

You now have another useful tool that you can use Python for. With a little practice, you will be working with JSON in no time!

The post Python 101: An Intro to Working with JSON appeared first on The Mouse Vs. The Python.

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September 15, 2020 at 01:23AM