![](https://cdn-images-1.medium.com/max/1600/0*8YAmy3EMd9hR3xXq)

Photo by [Artturi Jalli](https://unsplash.com/@artturijalli?utm_source=medium\&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium\&utm_medium=referral)

It started the night I had three scripts running side-by-side, each trying to solve a problem I didn’t want to touch manually. One script scraped a client’s inventory, another cleaned the data, and the last one generated a PDF report while I made coffee. I realized then, coding like the top 1% isn’t about knowing “more” Python. It’s about knowing the right tools that turn hours of work into seconds of automation.

And here’s the truth: most Python developers never touch these libraries, which is why they stay in the 99%. You’re about to change that.

### 1. Pendulum-lite Alternative

Arrow is the fast, intuitive datetime library you didn’t know you needed. It removes the boilerplate that comes with Python’s `datetime`.

```
import arrowprint(arrow.now().shift(days=+3).format('YYYY-MM-DD'))
```

Perfect for automation scripts that schedule tasks, generate logs, or handle timezone conversions.

### 2. Faker

Why waste time typing fake names and emails? Faker generates them in milliseconds.

```
from faker import Fakerfake = Faker()print(fake.name(), fake.email())
```

It’s a lifesaver when automating tests or mocking datasets.

### 3. TQDM

Automations often run in the background, but sometimes you need visibility. TQDM wraps any iterable with a progress bar.

```
from tqdm import tqdmimport timefor _ in tqdm(range(100)):    time.sleep(0.01)
```

When running big automation jobs, this keeps you from wondering, “Is this still running?”

### 4. YAML

YAML lets you store automation settings in a cleaner, human-readable way.

```
import yamlconfig = yaml.safe_load(open('config.yml'))print(config['api_key'])
```

Instead of hardcoding credentials, you keep them in a `.yml` file and load them dynamically.

### 5. Boto3

From uploading files to S3 to spinning up EC2 instances, Boto3 makes AWS scripting painless.

```
import boto3s3 = boto3.client('s3')s3.upload_file('report.pdf', 'my-bucket', 'report.pdf')
```

You can build full cloud automation workflows without touching the AWS console.

### 6. Airflow

Airflow isn’t just for big companies. It schedules and manages complex automation pipelines.

```
pip install apache-airflow
```

Imagine chaining scripts so they run in perfect order. Airflow does exactly that.

### 7. PyPDF2

Whether it’s merging multiple PDFs or extracting text, PyPDF2 lets you automate the process in seconds.

```
from PyPDF2 import PdfMergermerger = PdfMerger()merger.append('file1.pdf')merger.append('file2.pdf')merger.write('combined.pdf')merger.close()
```

No more dragging files into random online PDF tools.

### 8. FastF1

For automation in niche fields, FastF1 is a Formula 1 data API wrapper. It’s perfect for sports dashboards and analytics.

```
import fastf1fastf1.Cache.enable_cache('cache')session = fastf1.get_session(2023, 'Monaco', 'Q')session.load()print(session.results)
```

Shows how automation isn’t limited to “business” work, it can be for fun too.

### 9. Watchfiles

Think `watchdog`, but faster. Watchfiles runs functions automatically when files change.

```
from watchfiles import watchfor changes in watch('./data'):    print(changes)
```

Great for instant triggers when a file is updated, downloaded, or replaced.

### Closing Words:

Don’t just “collect” libraries, master them. The difference between a good Python developer and a top 1% developer is not knowing what exists, but knowing exactly when to use it.

> **A beginner-friendly Python guide made for non-programmers.** [**Start learning**](https://abdulahad28.gumroad.com/l/irehc) **Python the easy way!**

***Want a pack of prompts that work for you and save hours?*** [***click here***](https://abdulahad28.gumroad.com/l/rwnlrm)

> **Ready to go from Java beginner to confident developer?** [**Start here**](https://gumroad.com/a/1035515539/QqjGH)**.**

*Want more posts like this? Drop a “YES” in the comment, and I’ll share more coding tricks like this one.*

*Want to support me? Give 50 claps on this post and follow me.*

*Thanks for reading!*

### A message from our Founder

**Hey,** [**Sunil**](https://linkedin.com/in/sunilsandhu) **here.** I wanted to take a moment to thank you for reading until the end and for being a part of this community.

Did you know that our team run these publications as a volunteer effort to over 200k supporters? **We do not get paid by Medium**!

If you want to show some love, please take a moment to **follow me on** [**LinkedIn**](https://linkedin.com/in/sunilsandhu)**,** [**TikTok**](https://tiktok.com/@messyfounder) **and** [**Instagram**](https://instagram.com/sunilsandhu).

<img src="https://cdn-images-1.medium.com/max/1600/0*8YAmy3EMd9hR3xXq" alt="">
Photo by <a href="https://unsplash.com/@artturijalli?utm_source=medium&#x26;utm_medium=referral">Artturi Jalli</a> on <a href="https://unsplash.com?utm_source=medium&#x26;utm_medium=referral">Unsplash</a>
It started the night I had three scripts running side-by-side, each trying to solve a problem I didn’t want to touch manually. One script scraped a client’s inventory, another cleaned the data, and the last one generated a PDF report while I made coffee. I realized then, coding like the top 1% isn’t about knowing “more” Python. It’s about knowing the right tools that turn hours of work into seconds of automation.
And here’s the truth: most Python developers never touch these libraries, which is why they stay in the 99%. You’re about to change that.
<h3 id="1-pendulum-lite-alternative">1. Pendulum-lite Alternative</h3>
Arrow is the fast, intuitive datetime library you didn’t know you needed. It removes the boilerplate that comes with Python’s <code>datetime</code>.
<pre><code class="code-highlight">import arrowprint(arrow.now().shift(days=+3).format('YYYY-MM-DD'))
</code></pre>
Perfect for automation scripts that schedule tasks, generate logs, or handle timezone conversions.
<h3 id="2-faker">2. Faker</h3>
Why waste time typing fake names and emails? Faker generates them in milliseconds.
<pre><code class="code-highlight">from faker import Fakerfake = Faker()print(fake.name(), fake.email())
</code></pre>
It’s a lifesaver when automating tests or mocking datasets.
<h3 id="3-tqdm">3. TQDM</h3>
Automations often run in the background, but sometimes you need visibility. TQDM wraps any iterable with a progress bar.
<pre><code class="code-highlight">from tqdm import tqdmimport timefor _ in tqdm(range(100)): time.sleep(0.01)
</code></pre>
When running big automation jobs, this keeps you from wondering, “Is this still running?”
<h3 id="4-yaml">4. YAML</h3>
YAML lets you store automation settings in a cleaner, human-readable way.
<pre><code class="code-highlight">import yamlconfig = yaml.safe_load(open('config.yml'))print(config['api_key'])
</code></pre>
Instead of hardcoding credentials, you keep them in a <code>.yml</code> file and load them dynamically.
<h3 id="5-boto3">5. Boto3</h3>
From uploading files to S3 to spinning up EC2 instances, Boto3 makes AWS scripting painless.
<pre><code class="code-highlight">import boto3s3 = boto3.client('s3')s3.upload_file('report.pdf', 'my-bucket', 'report.pdf')
</code></pre>
You can build full cloud automation workflows without touching the AWS console.
<h3 id="6-airflow">6. Airflow</h3>
Airflow isn’t just for big companies. It schedules and manages complex automation pipelines.
<pre><code class="code-highlight">pip install apache-airflow
</code></pre>
Imagine chaining scripts so they run in perfect order. Airflow does exactly that.
<h3 id="7-pypdf2">7. PyPDF2</h3>
Whether it’s merging multiple PDFs or extracting text, PyPDF2 lets you automate the process in seconds.
<pre><code class="code-highlight">from PyPDF2 import PdfMergermerger = PdfMerger()merger.append('file1.pdf')merger.append('file2.pdf')merger.write('combined.pdf')merger.close()
</code></pre>
No more dragging files into random online PDF tools.
<h3 id="8-fastf1">8. FastF1</h3>
For automation in niche fields, FastF1 is a Formula 1 data API wrapper. It’s perfect for sports dashboards and analytics.
<pre><code class="code-highlight">import fastf1fastf1.Cache.enable_cache('cache')session = fastf1.get_session(2023, 'Monaco', 'Q')session.load()print(session.results)
</code></pre>
Shows how automation isn’t limited to “business” work, it can be for fun too.
<h3 id="9-watchfiles">9. Watchfiles</h3>
Think <code>watchdog</code>, but faster. Watchfiles runs functions automatically when files change.
<pre><code class="code-highlight">from watchfiles import watchfor changes in watch('./data'): print(changes)
</code></pre>
Great for instant triggers when a file is updated, downloaded, or replaced.
<h3 id="closing-words">Closing Words:</h3>
Don’t just “collect” libraries, master them. The difference between a good Python developer and a top 1% developer is not knowing what exists, but knowing exactly when to use it.
<blockquote>
A beginner-friendly Python guide made for non-programmers. <a href="https://abdulahad28.gumroad.com/l/irehc">Start learning</a> Python the easy way!
</blockquote>
Want a pack of prompts that work for you and save hours? <a href="https://abdulahad28.gumroad.com/l/rwnlrm">click here</a>
<blockquote>
Ready to go from Java beginner to confident developer? <a href="https://gumroad.com/a/1035515539/QqjGH">Start here</a>.
</blockquote>
Want more posts like this? Drop a “YES” in the comment, and I’ll share more coding tricks like this one.
Want to support me? Give 50 claps on this post and follow me.
Thanks for reading!
<h3 id="a-message-from-ourfounder">A message from our Founder</h3>
Hey, <a href="https://linkedin.com/in/sunilsandhu">Sunil</a> here. I wanted to take a moment to thank you for reading until the end and for being a part of this community.
Did you know that our team run these publications as a volunteer effort to over 200k supporters? We do not get paid by Medium!
If you want to show some love, please take a moment to follow me on <a href="https://linkedin.com/in/sunilsandhu">LinkedIn</a>, <a href="https://tiktok.com/@messyfounder">TikTok</a> and <a href="https://instagram.com/sunilsandhu">Instagram</a>.

Automation tools so sharp, your scripts will feel like they write themselves

9 Python Libraries That Will Make You Code Like the Top 1%

![](https://cdn-images-1.medium.com/max/2400/0*MS3jsebj8AsD3Gqt)

AI Generated

Let’s face it: Python is a beautiful mess.

On one hand, it’s the glue holding modern AI workflows together. On the other, it’s become a bit of a graveyard for libraries that *should* have retired years ago.

Now, before you raise your pitchfork, I’m *not* saying these libraries were bad. Most of them were brilliant in their prime. But this is 2025, and we need to talk about the tools in your stack that are quietly draining your time, your compute, and sometimes — your will to code.

So, here’s my hit list: 5 Python libraries you *need* to drop this year, and what to use instead.

### 1. Matplotlib

> Why it’s on the chopping block: It’s like drawing with a chisel in the age of iPads.

Matplotlib is the granddaddy of Python plotting libraries. And I respect my elders… but not when they make me write 20 lines of code just to get a bar chart that doesn’t look like it was generated in 2008.

The truth? Matplotlib has become the PowerPoint 2003 of data viz — clunky, verbose, and visually uninspiring unless you *heavily* customize it.

**Use Instead**:

* **Plotly** (for interactivity and modern UI)
* **Altair** (for quick, declarative, grammar-based plots)
* **Polars + hvPlot** (if you’re moving toward faster, Arrow-native workflows)

> Quick fact: Plotly Express can render a polished chart with 1 line of code. Matplotlib? More like 6, plus debugging time.

### 2. Requests

> Why it’s on the chopping block: It’s blocking your progress — literally.

I’ll be honest: I used to swear by `requests`. I still reach for it when I want to fetch a simple webpage or check a status code. But in 2025, speed is the name of the game — and `requests` is slow, synchronous, and not built for concurrent workloads.

If your AI or data pipeline involves scraping, fetching APIs, or any kind of batch HTTP work, `requests` is the bottleneck.

**Use Instead**:

* **httpx** (drop-in replacement with async support)
* **aiohttp** (for full async workflows)
* **trio + asks** (if you’re feeling bold — and okay with learning curve)

> Bonus: `httpx` supports connection pooling and HTTP/2 out of the box. `requests` still thinks it’s 2012.

### 3. pickle

> Why it’s on the chopping block: It’s insecure, opaque, and a pain to debug.

Ah, `pickle`. The duct tape of object serialization in Python. Sure, it works — until it doesn’t.

Let’s count the issues:

* You can’t inspect the output.
* It’s not language-agnostic.
* It can execute arbitrary code (hello, security risks).
* And let’s not even start on version incompatibility between Python 3.10 and 3.12.

**Use Instead**:

* **joblib** (for ML model artifacts)
* **json / orjson** (for structured data)
* **pydantic v2 + dataclasses-json** (for typed, safe serialization)
* **protobuf / msgpack** (if performance and interoperability matter)

> In short: if your object matters, don’t pickle it. You’re better than that.

### 4. BeautifulSoup

> Why it’s on the chopping block: It’s the Swiss army knife you keep using as a chainsaw.

Don’t get me wrong — `BeautifulSoup` is elegant. For small HTML parsing tasks, it’s still handy. But anything more complex than scraping a single webpage and it turns into a brittle mess of `find_all()` spaghetti.

You want speed? It’s not here. You want structured DOM parsing with modern JavaScript rendering? Look elsewhere.

**Use Instead**:

* **selectolax** (ultra-fast parsing via Rust)
* **parsel** (for XPath and CSS queries)
* **Playwright / Selenium** (when JS rendering is required)
* **trafilatura** (for extracting clean article text — a hidden gem)

> TL;DR: If your scraper breaks every time a `<div>` tag changes, you’re doing it wrong.

### 5. TensorFlow 1.x and legacy Keras

> Why it’s on the chopping block: You deserve a better developer experience.

I know there are still tutorials floating around that teach TensorFlow 1.x like it’s 2017. And I get the nostalgia — static graphs, `sess.run()`, and [`tf.placeholder`](https://tf.placeholder) were... character-building.

But at this point, if you’re not using PyTorch or at least TensorFlow 2.x (with `tf.keras`), you're actively making your life harder.

Modern LLM workflows, custom training loops, ONNX export — they’re just smoother in PyTorch. That’s not an opinion. That’s a battle-tested fact.

**Use Instead**:

* **PyTorch** (flexibility and ecosystem)
* **JAX** (for TPU speed demons and pure function lovers)
* **HuggingFace Transformers + Diffusers** (because why reinvent?)

> Also, fun fact: almost every new paper implementation from CVPR/NeurIPS now uses PyTorch or JAX. Not TensorFlow.

***

> [**Master Python Faster! 🚀 Grab Your FREE Ultimate Python Cheat Sheet — Click Here to Download!**](https://abdurrahman12.gumroad.com/l/cheat-sheat)

*If you enjoyed reading, be sure to give it 50 claps! **Follow** and don’t miss out on any of my future posts — **subscribe** to my profile for must-read blog updates!*

***Thanks for reading!***

### Thank you for being a part of the community

*Before you go:***️️**

* Follow us: [**X**](https://x.com/inPlainEngHQ) | [**LinkedIn**](https://www.linkedin.com/company/inplainenglish/) | [**YouTube**](https://www.youtube.com/@InPlainEnglish) | [**Newsletter**](https://newsletter.plainenglish.io/) | [**Podcast**](https://open.spotify.com/show/7qxylRWKhvZwMz2WuEoua0) | [**Twitch**](https://twitch.tv/inplainenglish)
* [**Start your own free AI-powered blog on Differ**](https://differ.blog/) 🚀
* [**Join our content creators community on Discord**](https://discord.gg/in-plain-english-709094664682340443) 🧑🏻‍💻
* For more content, visit [**plainenglish.io**](https://plainenglish.io/) + [**stackademic.com**](https://stackademic.com/)

<img src="https://cdn-images-1.medium.com/max/2400/0*MS3jsebj8AsD3Gqt" alt="">
AI Generated
Let’s face it: Python is a beautiful mess.
On one hand, it’s the glue holding modern AI workflows together. On the other, it’s become a bit of a graveyard for libraries that should have retired years ago.
Now, before you raise your pitchfork, I’m not saying these libraries were bad. Most of them were brilliant in their prime. But this is 2025, and we need to talk about the tools in your stack that are quietly draining your time, your compute, and sometimes — your will to code.
So, here’s my hit list: 5 Python libraries you need to drop this year, and what to use instead.
<h3 id="1-matplotlib">1. Matplotlib</h3>
<blockquote>
Why it’s on the chopping block: It’s like drawing with a chisel in the age of iPads.
</blockquote>
Matplotlib is the granddaddy of Python plotting libraries. And I respect my elders… but not when they make me write 20 lines of code just to get a bar chart that doesn’t look like it was generated in 2008.
The truth? Matplotlib has become the PowerPoint 2003 of data viz — clunky, verbose, and visually uninspiring unless you heavily customize it.
Use Instead:
<ul>
<li>Plotly (for interactivity and modern UI)</li>
<li>Altair (for quick, declarative, grammar-based plots)</li>
<li>Polars + hvPlot (if you’re moving toward faster, Arrow-native workflows)</li>
</ul>
<blockquote>
Quick fact: Plotly Express can render a polished chart with 1 line of code. Matplotlib? More like 6, plus debugging time.
</blockquote>
<h3 id="2-requests">2. Requests</h3>
<blockquote>
Why it’s on the chopping block: It’s blocking your progress — literally.
</blockquote>
I’ll be honest: I used to swear by <code>requests</code>. I still reach for it when I want to fetch a simple webpage or check a status code. But in 2025, speed is the name of the game — and <code>requests</code> is slow, synchronous, and not built for concurrent workloads.
If your AI or data pipeline involves scraping, fetching APIs, or any kind of batch HTTP work, <code>requests</code> is the bottleneck.
Use Instead:
<ul>
<li>httpx (drop-in replacement with async support)</li>
<li>aiohttp (for full async workflows)</li>
<li>trio + asks (if you’re feeling bold — and okay with learning curve)</li>
</ul>
<blockquote>
Bonus: <code>httpx</code> supports connection pooling and HTTP/2 out of the box. <code>requests</code> still thinks it’s 2012.
</blockquote>
<h3 id="3-pickle">3. pickle</h3>
<blockquote>
Why it’s on the chopping block: It’s insecure, opaque, and a pain to debug.
</blockquote>
Ah, <code>pickle</code>. The duct tape of object serialization in Python. Sure, it works — until it doesn’t.
Let’s count the issues:
<ul>
<li>You can’t inspect the output.</li>
<li>It’s not language-agnostic.</li>
<li>It can execute arbitrary code (hello, security risks).</li>
<li>And let’s not even start on version incompatibility between Python 3.10 and 3.12.</li>
</ul>
Use Instead:
<ul>
<li>joblib (for ML model artifacts)</li>
<li>json / orjson (for structured data)</li>
<li>pydantic v2 + dataclasses-json (for typed, safe serialization)</li>
<li>protobuf / msgpack (if performance and interoperability matter)</li>
</ul>
<blockquote>
In short: if your object matters, don’t pickle it. You’re better than that.
</blockquote>
<h3 id="4-beautifulsoup">4. BeautifulSoup</h3>
<blockquote>
Why it’s on the chopping block: It’s the Swiss army knife you keep using as a chainsaw.
</blockquote>
Don’t get me wrong — <code>BeautifulSoup</code> is elegant. For small HTML parsing tasks, it’s still handy. But anything more complex than scraping a single webpage and it turns into a brittle mess of <code>find_all()</code> spaghetti.
You want speed? It’s not here. You want structured DOM parsing with modern JavaScript rendering? Look elsewhere.
Use Instead:
<ul>
<li>selectolax (ultra-fast parsing via Rust)</li>
<li>parsel (for XPath and CSS queries)</li>
<li>Playwright / Selenium (when JS rendering is required)</li>
<li>trafilatura (for extracting clean article text — a hidden gem)</li>
</ul>
<blockquote>
TL;DR: If your scraper breaks every time a <code>&#x3C;div></code> tag changes, you’re doing it wrong.
</blockquote>
<h3 id="5-tensorflow-1x-and-legacykeras">5. TensorFlow 1.x and legacy Keras</h3>
<blockquote>
Why it’s on the chopping block: You deserve a better developer experience.
</blockquote>
I know there are still tutorials floating around that teach TensorFlow 1.x like it’s 2017. And I get the nostalgia — static graphs, <code>sess.run()</code>, and <a href="https://tf.placeholder"><code>tf.placeholder</code></a> were... character-building.
But at this point, if you’re not using PyTorch or at least TensorFlow 2.x (with <code>tf.keras</code>), you're actively making your life harder.
Modern LLM workflows, custom training loops, ONNX export — they’re just smoother in PyTorch. That’s not an opinion. That’s a battle-tested fact.
Use Instead:
<ul>
<li>PyTorch (flexibility and ecosystem)</li>
<li>JAX (for TPU speed demons and pure function lovers)</li>
<li>HuggingFace Transformers + Diffusers (because why reinvent?)</li>
</ul>
<blockquote>
Also, fun fact: almost every new paper implementation from CVPR/NeurIPS now uses PyTorch or JAX. Not TensorFlow.
</blockquote>
<hr>
<blockquote>
<a href="https://abdurrahman12.gumroad.com/l/cheat-sheat">Master Python Faster! 🚀 Grab Your FREE Ultimate Python Cheat Sheet — Click Here to Download!</a>
</blockquote>
If you enjoyed reading, be sure to give it 50 claps! Follow and don’t miss out on any of my future posts — subscribe to my profile for must-read blog updates!
Thanks for reading!
<h3 id="thank-you-for-being-a-part-of-the-community">Thank you for being a part of the community</h3>
*Before you go:*️️
<ul>
<li>Follow us: <a href="https://x.com/inPlainEngHQ">X</a> | <a href="https://www.linkedin.com/company/inplainenglish/">LinkedIn</a> | <a href="https://www.youtube.com/@InPlainEnglish">YouTube</a> | <a href="https://newsletter.plainenglish.io/">Newsletter</a> | <a href="https://open.spotify.com/show/7qxylRWKhvZwMz2WuEoua0">Podcast</a> | <a href="https://twitch.tv/inplainenglish">Twitch</a></li>
<li><a href="https://differ.blog/">Start your own free AI-powered blog on Differ</a> 🚀</li>
<li><a href="https://discord.gg/in-plain-english-709094664682340443">Join our content creators community on Discord</a> 🧑🏻‍💻</li>
<li>For more content, visit <a href="https://plainenglish.io/">plainenglish.io</a> + <a href="https://stackademic.com/">stackademic.com</a></li>
</ul>

They’re slowing you down — and no one’s telling you

Stop Using These 5 Python Libraries in 2025

![](https://cdn-images-1.medium.com/max/2400/0*YJtcMmB1bLvJLFGm)

If you’ve been building software long enough, you know the real horror story isn’t the bug you can’t fix — it’s the one you don’t even know exists. Nothing tanks confidence in a project faster than flaky tests, untraceable crashes, and deployment chaos.

The good news? Python has a treasure chest of tools designed to prevent your next project from becoming a dumpster fire. And I don’t mean the usual suspects like `pytest` or `virtualenv`. I’m talking about tools that even seasoned developers rarely touch — the kind that’ll have you catching issues before they escalate, automating sanity checks, and sleeping like an actual human being at night.

Let’s dive in.

### 1. `bandit` — Security Checks You Wish You Ran Earlier

Imagine shipping code only to realize you hardcoded credentials in a forgotten utility file. Nightmare fuel.

`bandit` scans your Python codebase for common security issues: weak cryptography, unsafe function usage, and other “I’ll fix it later” mistakes.

```
pip install banditbandit -r your_project/
```

> Tip: Run it as part of your CI pipeline. Because nothing feels worse than finding out from a Hacker News post that your app leaked an API key.

### 2. `py-spy` — Your Project’s Black Box Recorder

Performance bugs are the trickiest. They only show up when you’re demoing to your boss, right?

`py-spy` is a sampling profiler that lets you peek into what your Python process is doing *without restarting it*. No code changes, no restarts, no excuses.

```
pip install py-spypy-spy top --pid <your_pid>
```

> Fact: NASA has been using profiler tools like this for decades — because when you’re running code on a rover 200 million miles away, debugging isn’t really an option.

### 3. `hypothesis` — The Test Generator You Didn’t Know You Needed

We humans write boring tests. We check the happy path, maybe throw in a couple of edge cases. But what about the edge cases of edge cases?

Enter `hypothesis`. It generates thousands of randomized test inputs, automatically exploring corners of your code you’d never manually think of.

```
from hypothesis import given, strategies as st@given(st.lists(st.integers()))def test_sorting(nums): assert sorted(nums) == sorted(sorted(nums))
```

This has caught real-world bugs in libraries maintained by teams of PhDs. If they can miss things, so can you.

### 4. `safety` — Dependency Audit on Autopilot

Your code is only as safe as your dependencies — and let’s face it, half of PyPI is one bad day away from CVEs.

> `safety` checks your installed packages against a database of known vulnerabilities.

```
pip install safetysafety check
```

Run this once and you’ll probably discover that your favorite library hasn’t patched a critical issue since 2019.

### 5. `structlog` — Logging That’s Actually Readable

Default Python logs? About as decipherable as hieroglyphics.

`structlog` lets you log in structured JSON, making it painless to search, filter, and debug in production. Pair it with tools like Elasticsearch or Loki, and suddenly tracing that elusive bug isn’t a guessing game anymore.

```
import structloglog = structlog.get_logger()log.info("user_logged_in", user_id=123, feature="checkout")
```

> Because the only thing worse than a production error is a production error with cryptic logs.

### 6. `tox` — Testing in Parallel Universes

You tested locally on Python 3.10. CI runs on 3.9. Production is still on 3.8 because “upgrades break stuff.” Sound familiar?

`tox` runs your test suite across multiple Python environments automatically. One config file, infinite peace of mind.

```
pip install toxtox
```

Think of it as multiverse testing — if your code can survive across dimensions (read: versions), it can survive production.

### 7. `pre-commit` — Your Personal Gatekeeper

You know that one teammate who commits print statements straight to `main`? Yeah.

`pre-commit` lets you enforce hooks that run automatically before every commit — formatting, linting, type checks, even banning TODOs.

```
pip install pre-commitpre-commit install
```

> It’s like giving your repo a bouncer. No nonsense gets in.

### 8. `pdbpp` — Debugging, but Without the Pain

Standard `pdb` works… but it feels like using Notepad when you’ve got VS Code installed.

`pdbpp` is a drop-in replacement that adds syntax highlighting, sticky mode (see code around the current frame), and smarter navigation.

```
pip install pdbpppython -m pdb your_script.py
```

> Once you try it, you’ll wonder how you ever lived without it.

***

If you enjoyed reading this please make sure to give ***50 CLAPS*** and hit ***FOLLOW*** button.

*Thanks for reading :)*

### A message from our Founder

**Hey,** [**Sunil**](https://linkedin.com/in/sunilsandhu) **here.** I wanted to take a moment to thank you for reading until the end and for being a part of this community.

Did you know that our team run these publications as a volunteer effort to over 3.5m monthly readers? **We don’t receive any funding, we do this to support the community. ❤️**

If you want to show some love, please take a moment to **follow me on** [**LinkedIn**](https://linkedin.com/in/sunilsandhu)**,** [**TikTok**](https://tiktok.com/@messyfounder), [**Instagram**](https://instagram.com/sunilsandhu). You can also subscribe to our [**weekly newsletter**](https://newsletter.plainenglish.io/).

<img src="https://cdn-images-1.medium.com/max/2400/0*YJtcMmB1bLvJLFGm" alt="">
If you’ve been building software long enough, you know the real horror story isn’t the bug you can’t fix — it’s the one you don’t even know exists. Nothing tanks confidence in a project faster than flaky tests, untraceable crashes, and deployment chaos.
The good news? Python has a treasure chest of tools designed to prevent your next project from becoming a dumpster fire. And I don’t mean the usual suspects like <code>pytest</code> or <code>virtualenv</code>. I’m talking about tools that even seasoned developers rarely touch — the kind that’ll have you catching issues before they escalate, automating sanity checks, and sleeping like an actual human being at night.
Let’s dive in.
<h3 id="1-banditsecurity-checks-you-wish-you-ranearlier">1. <code>bandit</code> — Security Checks You Wish You Ran Earlier</h3>
Imagine shipping code only to realize you hardcoded credentials in a forgotten utility file. Nightmare fuel.
<code>bandit</code> scans your Python codebase for common security issues: weak cryptography, unsafe function usage, and other “I’ll fix it later” mistakes.
<pre><code class="code-highlight">pip install banditbandit -r your_project/
</code></pre>
<blockquote>
Tip: Run it as part of your CI pipeline. Because nothing feels worse than finding out from a Hacker News post that your app leaked an API key.
</blockquote>
<h3 id="2-py-spyyour-projects-black-boxrecorder">2. <code>py-spy</code> — Your Project’s Black Box Recorder</h3>
Performance bugs are the trickiest. They only show up when you’re demoing to your boss, right?
<code>py-spy</code> is a sampling profiler that lets you peek into what your Python process is doing without restarting it. No code changes, no restarts, no excuses.
<pre><code class="code-highlight">pip install py-spypy-spy top --pid &#x3C;your_pid>
</code></pre>
<blockquote>
Fact: NASA has been using profiler tools like this for decades — because when you’re running code on a rover 200 million miles away, debugging isn’t really an option.
</blockquote>
<h3 id="3-hypothesisthe-test-generator-you-didnt-know-youneeded">3. <code>hypothesis</code> — The Test Generator You Didn’t Know You Needed</h3>
We humans write boring tests. We check the happy path, maybe throw in a couple of edge cases. But what about the edge cases of edge cases?
Enter <code>hypothesis</code>. It generates thousands of randomized test inputs, automatically exploring corners of your code you’d never manually think of.
<pre><code class="code-highlight">from hypothesis import given, strategies as st@given(st.lists(st.integers()))def test_sorting(nums): assert sorted(nums) == sorted(sorted(nums))
</code></pre>
This has caught real-world bugs in libraries maintained by teams of PhDs. If they can miss things, so can you.
<h3 id="4-safetydependency-audit-on-autopilot">4. <code>safety</code> — Dependency Audit on Autopilot</h3>
Your code is only as safe as your dependencies — and let’s face it, half of PyPI is one bad day away from CVEs.
<blockquote>
<code>safety</code> checks your installed packages against a database of known vulnerabilities.
</blockquote>
<pre><code class="code-highlight">pip install safetysafety check
</code></pre>
Run this once and you’ll probably discover that your favorite library hasn’t patched a critical issue since 2019.
<h3 id="5-structloglogging-thats-actuallyreadable">5. <code>structlog</code> — Logging That’s Actually Readable</h3>
Default Python logs? About as decipherable as hieroglyphics.
<code>structlog</code> lets you log in structured JSON, making it painless to search, filter, and debug in production. Pair it with tools like Elasticsearch or Loki, and suddenly tracing that elusive bug isn’t a guessing game anymore.
<pre><code class="code-highlight">import structloglog = structlog.get_logger()log.info("user_logged_in", user_id=123, feature="checkout")
</code></pre>
<blockquote>
Because the only thing worse than a production error is a production error with cryptic logs.
</blockquote>
<h3 id="6-toxtesting-in-parallel-universes">6. <code>tox</code> — Testing in Parallel Universes</h3>
You tested locally on Python 3.10. CI runs on 3.9. Production is still on 3.8 because “upgrades break stuff.” Sound familiar?
<code>tox</code> runs your test suite across multiple Python environments automatically. One config file, infinite peace of mind.
<pre><code class="code-highlight">pip install toxtox
</code></pre>
Think of it as multiverse testing — if your code can survive across dimensions (read: versions), it can survive production.
<h3 id="7-pre-commityour-personal-gatekeeper">7. <code>pre-commit</code> — Your Personal Gatekeeper</h3>
You know that one teammate who commits print statements straight to <code>main</code>? Yeah.
<code>pre-commit</code> lets you enforce hooks that run automatically before every commit — formatting, linting, type checks, even banning TODOs.
<pre><code class="code-highlight">pip install pre-commitpre-commit install
</code></pre>
<blockquote>
It’s like giving your repo a bouncer. No nonsense gets in.
</blockquote>
<h3 id="8-pdbppdebugging-but-without-thepain">8. <code>pdbpp</code> — Debugging, but Without the Pain</h3>
Standard <code>pdb</code> works… but it feels like using Notepad when you’ve got VS Code installed.
<code>pdbpp</code> is a drop-in replacement that adds syntax highlighting, sticky mode (see code around the current frame), and smarter navigation.
<pre><code class="code-highlight">pip install pdbpppython -m pdb your_script.py
</code></pre>
<blockquote>
Once you try it, you’ll wonder how you ever lived without it.
</blockquote>
<hr>
If you enjoyed reading this please make sure to give 50 CLAPS and hit FOLLOW button.
Thanks for reading :)
<h3 id="a-message-from-ourfounder">A message from our Founder</h3>
Hey, <a href="https://linkedin.com/in/sunilsandhu">Sunil</a> here. I wanted to take a moment to thank you for reading until the end and for being a part of this community.
Did you know that our team run these publications as a volunteer effort to over 3.5m monthly readers? We don’t receive any funding, we do this to support the community. ❤️
If you want to show some love, please take a moment to follow me on <a href="https://linkedin.com/in/sunilsandhu">LinkedIn</a>, <a href="https://tiktok.com/@messyfounder">TikTok</a>, <a href="https://instagram.com/sunilsandhu">Instagram</a>. You can also subscribe to our <a href="https://newsletter.plainenglish.io/">weekly newsletter</a>.

 8 Python Tools That Stop Projects From Breaking

 Fewer fires, more shipping.

![](https://cdn-images-1.medium.com/max/2600/0*5AYlaizIxHftu3oE)

Photo by [Boitumelo Phetla](https://unsplash.com/@writecodenow?utm_source=medium\&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium\&utm_medium=referral)

Today we will learn how to send desktop notifications with Python. For this we will just need a computer with Python installed.

### Packages and Setup

You’ll need Plyer’s notification for this project:

```
# In CMD or terminal
``````
pip install plyer
``````
# In project
``````
from plyer import notification
```

You can send notifications by calling the `notify()` function from notification.

It takes four parameters:

`title` The large header text at the top of a notification.

`message` The longer, smaller text where you put more detailed information.

`app_icon` The image that appears next to the title and message.

`timeout` How long the message should show on screen.

If we run it right now, our notification should show up!

![](https://cdn-images-1.medium.com/max/1600/1*Au9m7XZZFQ083Dh3xvy0GQ.png)

**Figure 1:** Notification!

***

> *💡* Speed up your blog creation with [***DifferAI***](https://differ.blog/differ-ai).

> Available for ***free******* exclusively on the ***free and open blogging platform,*** [***Differ***](https://differ.blog/)**.**

[**Try DifferAI — Your Free Writing Assistant**
*DifferAI is your writing co-pilot — seamlessly integrated within Differ’s text editor. Powered by an LLM specifically built for writers and bloggers.*&#x64;iffer.blog](https://differ.blog/differ-ai "https://differ.blog/differ-ai")

***

### Improving the design

Let’s add an image. All we have to do for this part is specify the file path of the image!

***Note:** You have to use a `.ico` file.*

```
app_icon = "image.ico"
```

![](https://cdn-images-1.medium.com/max/1600/1*qzaAdPa20Svgt6j_5oaxtA.png)

**Figure 2:** Notification with image!

### Delay

You can enforce a delay by using Python’s `time.sleep()`:

And it shows up every 30 seconds!

![](https://cdn-images-1.medium.com/max/1600/1*qzaAdPa20Svgt6j_5oaxtA.png)

**Figure 3:** Notification with delay!

### 24/7 365

You can run this file forever with `pythonw`.

`cd` into the directory your `.py` file is, and then execute the command below

```
pythonw filename.py
```

It should execute without any errors.

We can confirm it works by going to our task manager and see the active tasks.

![](https://cdn-images-1.medium.com/max/1600/1*bikEtLQFg6b2vDCD6xHUKQ.png)

**Figure 4:** Python file running in background.

To kill the process, end the task.

If that doesn’t work, and you only have one `pythonw` task running, you can use this command.

```
taskkill /IM pythonw.exe /F
```

### Conclusion

I hope you enjoyed reading this, and that you find a good use for this tool! If you have any questions, suggestions, or general feedback, put it in the comments! Thanks!

<img src="https://cdn-images-1.medium.com/max/2600/0*5AYlaizIxHftu3oE" alt="">
Photo by <a href="https://unsplash.com/@writecodenow?utm_source=medium&#x26;utm_medium=referral">Boitumelo Phetla</a> on <a href="https://unsplash.com?utm_source=medium&#x26;utm_medium=referral">Unsplash</a>
Today we will learn how to send desktop notifications with Python. For this we will just need a computer with Python installed.
<h3 id="packages-andsetup">Packages and Setup</h3>
You’ll need Plyer’s notification for this project:
<pre><code class="code-highlight"># In CMD or terminal
</code></pre>
pip install plyer
<pre><code class="code-highlight"># In project
</code></pre>
from plyer import notification
<pre><code class="code-highlight">
You can send notifications by calling the `notify()` function from notification.

It takes four parameters:

`title` The large header text at the top of a notification.

`message` The longer, smaller text where you put more detailed information.

`app_icon` The image that appears next to the title and message.

`timeout` How long the message should show on screen.

If we run it right now, our notification should show up!

![](https://cdn-images-1.medium.com/max/1600/1*Au9m7XZZFQ083Dh3xvy0GQ.png)

**Figure 1:** Notification!

***

> *💡* Speed up your blog creation with [***DifferAI***](https://differ.blog/differ-ai).

> Available for ***free******* exclusively on the ***free and open blogging platform,*** [***Differ***](https://differ.blog/)**.**

[**Try DifferAI — Your Free Writing Assistant**
*DifferAI is your writing co-pilot — seamlessly integrated within Differ’s text editor. Powered by an LLM specifically built for writers and bloggers.*&#x26;#x64;iffer.blog](https://differ.blog/differ-ai "https://differ.blog/differ-ai")

***

### Improving the design

Let’s add an image. All we have to do for this part is specify the file path of the image!

***Note:** You have to use a `.ico` file.*

</code></pre>
app_icon = "image.ico"
<pre><code class="code-highlight">
![](https://cdn-images-1.medium.com/max/1600/1*qzaAdPa20Svgt6j_5oaxtA.png)

**Figure 2:** Notification with image!

### Delay

You can enforce a delay by using Python’s `time.sleep()`:

And it shows up every 30 seconds!

![](https://cdn-images-1.medium.com/max/1600/1*qzaAdPa20Svgt6j_5oaxtA.png)

**Figure 3:** Notification with delay!

### 24/7 365

You can run this file forever with `pythonw`.

`cd` into the directory your `.py` file is, and then execute the command below

</code></pre>
pythonw filename.py
<pre><code class="code-highlight">
It should execute without any errors.

We can confirm it works by going to our task manager and see the active tasks.

![](https://cdn-images-1.medium.com/max/1600/1*bikEtLQFg6b2vDCD6xHUKQ.png)

**Figure 4:** Python file running in background.

To kill the process, end the task.

If that doesn’t work, and you only have one `pythonw` task running, you can use this command.

</code></pre>
taskkill /IM pythonw.exe /F
<pre><code class="code-highlight">
### Conclusion

I hope you enjoyed reading this, and that you find a good use for this tool! If you have any questions, suggestions, or general feedback, put it in the comments! Thanks!
</code></pre>

How to Send Desktop Notifications with Python

No experience required!

Recommender Systems in Python: Bridging E-Commerce and Gaming Worlds

Learn how to use Python to design and implement commodity trend-following strategies like the MACD.

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9 Python Libraries That Will Make You Code Like the Top 1%

Stop Using These 5 Python Libraries in 2025

8 Python Tools That Stop Projects From Breaking

How to Send Desktop Notifications with Python

Recommender Systems in Python: Bridging E-Commerce and Gaming Worlds

Implementing Commodity Trend Following Strategies in Code

20 Python Command-Line Hacks I Wish I Knew Sooner

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How to Use a Proxy with Python Requests in 2025

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Python

9 Python Libraries That Will Make You Code Like the Top 1%

Stop Using These 5 Python Libraries in 2025

8 Python Tools That Stop Projects From Breaking

How to Send Desktop Notifications with Python

Recommender Systems in Python: Bridging E-Commerce and Gaming Worlds

Implementing Commodity Trend Following Strategies in Code

20 Python Command-Line Hacks I Wish I Knew Sooner

5 AI Projects You Can Build This Weekend (with Python)

How to Use a Proxy with Python Requests in 2025

How to Build a Machine Learning Model from Scratch with Python