New Python programmable calculators 💻

♦️Dallas-based Texas Instruments’ latest generation of calculators is getting a modern-day update with the addition of programming language Python. The goal is to expand students’ ability to explore science, technology, engineering and math through the device that’s all-but-required in the nation’s high schools and colleges.

♦️Along with the addition of Python, the new model of calculator will also have familiar features like durability, a long-lasting battery and full-color screen. The device is also approved to use on college entrance exams as well as Advanced Placement and International Baccalaureate tests.

🔗Source

#news

Assert Statements in Python

♦️Python’s assert statement is a debugging aid that tests a condition. If the condition is true, it does nothing and your program just continues to execute. But if the assert condition evaluates to false, it raises an AssertionError exception with an optional error message.

♦️The goal of using assertions is to let developers find the likely root cause of a bug more quickly. An assertion error should never be raised unless there’s a bug in your program. They’re not intended to signal expected error conditions, like “file not found”, where a user can take corrective action or just try again.

#tips

Speech Recognition with Python 🗣

🔸Have you ever wondered how to add speech recognition to your Python project? It’s easier than you might think. SpeechRecognition is a Python library for performing speech recognition, with support for several engines and APIs, online and offline.

Speech recognition engine/API support:
▪️CMU Sphinx (works offline)
▪️Google Speech Recognition
▪️Google Cloud Speech API
▪️Wit.ai
▪️Microsoft Bing Voice Recognition
▪️Houndify API
▪️IBM Speech to Text
▪️Snowboy Hotword Detection (works offline)

⚙️Installation
pip install SpeechRecognition

🔗GitHub
🔗Examples
🔗Speech Recognition in Python- The Complete Beginner’s Guide

#speechrecognition

Complex Numbers in Python 🐍

Most general-purpose programming languages have either no support or limited support for complex numbers. Python is a rare exception because it comes with complex numbers built in.

The quickest way to define a complex number in Python is by typing its literal directly in the source code:
>>> z1 = 1 + 2j

You can also use a built-in Python function, complex(). It accepts two numeric parameters. The first one represents the real part, while the second one represents the imaginary part denoted with the letter j in the literal you saw in the example above:

>>> z2 = complex(1, 2)
>>> z1 == z2
True

To get the real and imaginary parts of a complex number in Python, you can reach for the corresponding .real and .imag attributes:

>>> z2.imag
2.0

Note that both properties are read-only because complex numbers are immutable, so trying to assign a new value to either of them will fail.

🔗In-depth tutorial on complex numbers by RealPython

#tutorial #complex

10 Ways to Speed Up Your Python Code ⚡️

1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers

= []
for x in range(100000):
    if x % 2 == 0: 
        numbers.append(x**2)

2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.

3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.

4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.

5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.

6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.

7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.

8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.

9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.

10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.

🔗Source

#tips

Working With Zip Files in Python 📕

The ZIP file format is a common archive and compression standard. The in-built zipfile module provides tools to create, read, write, append, and list a ZIP file.

The most common class which is used to work with Zip Files is ZipFile class. It is used to write and read the Zip files and also has some methods which are used to handle the them.

🔗Gees For Geeks tutorial

#zipfile