The Unexpected History of Common Terminal Commands

New tech jobs, popular resources, and Python concepts for data science.

December 17, 2024

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This week we’re sharing a history lesson, exploring Python for data science, and answering a few popular resources from the community. And we’re including a survey at the bottom to collect your feedback. Please let us know what you need!

This Week’s Resources

Python Concepts for Data Science

There are so many Python concepts you’ll need for a career in data science. I cover 10 of them in my guide to Python for Data Science. But let’s dig into one right here.

Ask any data scientist; they’ll probably all have a tale about challenges with messy or unstructured data. This is where the magical power of those cryptic-looking regular expressions comes into play.

Regex is an invaluable tool for text processing, as we can use it to find, extract, and even replace patterns in strings. And yes, I know that learning regular expressions can seem daunting at first, given the cryptic-looking patterns that they use.

But trust me, when you understand the basic building blocks and rules, it becomes an extremely powerful tool in your toolkit. And hen it comes to Python, the re module provides the interface you need to harness regular expressions. 

You can match and manipulate string data in diverse and complex ways by defining specific patterns.

'''
Hackr.io: 10 Python Concepts I Wish I Knew Earlier
Regular Expressions: General Syntax
'''
import re

# Basic match
re.match(pattern, string)

# Search throughout a string
re.search(pattern, string)

# Find all matches
re.findall(pattern, string)

# Replace patterns
re.sub(pattern, replacement, string)

As a practical example, consider a scenario where you need to extract email addresses from text. Regular expressions to the rescue! These provide a straightforward approach to capturing these patterns, as shown below. 

'''
Hackr.io: 10 Python Concepts I Wish I Knew Earlier
Regular Expressions Example
'''
import re

text = "Contact Alice at [email protected] and Bob at [email protected] for more details."
email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,7}\b'
emails = re.findall(email_pattern, text)

Of course, there are many others (including Pandas). Read the full guide to learn them all.

Foundational Linux Commands that Shaped the Modern Terminal

This new in-depth video explores foundational Linux commands. These shaped modern tools and are no longer widely used. More specifically, we cover commands like ifconfig, netstat, ed, and rcp, outlining their historical significance and how they influenced current technologies and tools, such as ip for network management, ss for network monitoring, modern text editors, and secure copying protocols like scp and rsync.

We also cover commands like fsck for file system integrity, the talk command for early real-time communication, TraceRoute for network diagnostics, and the who command for user monitoring. Though these commands have been largely replaced or surpassed by more modern tools, their contributions are acknowledged as essential to the development of Linux's capabilities today.

These historical Linux commands that have laid the groundwork for current tools and practices, offering viewers a nostalgic journey through the evolution of Linux.

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It’s the end of the year, so we’re taking stock of our most helpful resources. Let’s talk about the community’s most popular tech resources and the resources that have delivered the most value. What did you learn this year?

Here are some of this year’s top languages and fields of study in the tech community:

And if you haven’t already seen it, here’s our most popular new video from the past 12 months.

This Week’s Tech Jobs

As always, we’re proud to share this week’s latest tech jobs. It’s an especially good time to apply, just don’t expect to hear back this week. Many hiring managers reply to end-of-season applications at the start of the new year.

New jobs listed each week for tech workers.

In The News

Google’s new quantum chip, Willow, broke records with a benchmark computation. According to the company:

Willow’s performance on this benchmark is astonishing: It performed a computation in under five minutes that would take one of today’s fastest supercomputers 1025 or 10 septillion years. If you want to write it out, it’s 10,000,000,000,000,000,000,000,000 years. This mind-boggling number exceeds known timescales in physics and vastly exceeds the age of the universe. It lends credence to the notion that quantum computation occurs in many parallel universes, in line with the idea that we live in a multiverse, a prediction first made by David Deutsch.

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