In a previous article, I defended the idea that mastery of SQL should be part of the skill base of every modern professional. Today, I’m taking it a step further: beyond data querying, we should all be able to automate our repetitive tasks using Python. These two skills are complementary and, together, form the new essential duo of professional productivity.

A logical follow-up to SQL

If SQL lets you access and query data, Python lets you act on it, transform it and automate its processing. This is the natural progression: first access information, then manipulate it efficiently. Imagine a sales rep who can not only extract his sales data in SQL, but also automate the generation of his weekly reports with Python.

A global productivity challenge

The democratization of Python is not just a business issue – it’s a social issue. Think about it for a moment: how many hours are wasted every day around the world on :

• Copy and paste data between files
• Manually rename hundreds of documents
• Compile reports by hand
• Formatting data in Excel

If every worker in the service sector could save just one hour a week thanks to automation, the impact on global productivity would be colossal. It’s not just a question of efficiency, it’s also a question of well-being at work: fewer tedious tasks, more time for reflection and value creation.

Why Python?

In the sea of programming languages, Python stands out for three essential qualities:

• Readability: Python code is almost as readable as plain English (as I’ve already mentioned for SQL).
• Ease of learning: no need to understand object-oriented programming to get started
• Its ecosystem: thousands of ready-to-use libraries for almost any need

Everyday use cases

Let’s look at some concrete examples that I see regularly in companies:

• An administrative assistant who spends hours renaming files according to a precise nomenclature
• A salesperson who has to manually merge data from several Excel files
• A marketing manager who copies and pastes data between different reports
• A project manager who manually updates tracking tables

All these tasks could be automated with a few lines of Python. For example, renaming all files in a :

import os

for filename in os.listdir("documents"):
    if filename.startswith("facture_"):
        nouveau_nom = filename.replace("facture_", "FAC_2024_")
        os.rename(f"documents/{filename}", f"documents/{nouveau_nom}")

This simple script does in 5 seconds what would take hours to do manually.

Really useful basics

To be autonomous with Python, you just need to master a few basic concepts :

1. Basic variables and types:name = "Dupont" # text
age = 42 # number
est_actif = True # Boolean

2. Lists and dictionaries:collaborators = ["Alice", "Bob", "Charlie"]
infos_projet = {
"nom": "Transformation digitale",
"budget": 50000,
"deadline": "2024-06-30"
}

3. Loops for repeating actions:for collaborator in collaborators:
print(f "Send email to {collaborator}")

4. Conditions for making decisions:if infos_projet["budget"] > 40000:
print("Major project - management approval required")

With these basics and a few essential libraries (pandas for data, os for files), you can already perform 80% of current automations.

A new context: the era of generative AI

The emergence of AI assistants such as Claude and ChatGPT has completely changed the way Python is learned and used. Once the fundamentals have been mastered, these tools become real productivity gas pedals:

• They can suggest approaches to solving a problem
• They help debug code
• They propose optimizations
• They explain the code in an educational way

AI will provide you with functional code that you can understand and adapt to your needs, because you’ve mastered the fundamentals. It’s like having a fellow developer always available to help you (I’m repeating myself, I already mentioned this logic in my article on SQL).

Organizational benefits

The democratization of Python in the enterprise brings considerable advantages:

1. Liberating IT teams

   • Fewer time-consuming requests
   • Focus on high value-added projects
   • Better allocation of technical resources

2. Empowering business teams

   • Faster processing of daily tasks
   • Less dependence on other services
   • More time for value-added tasks

3. Improving collaboration

   • Better understanding between technical and business teams
   • Easy sharing of scripts and solutions
   • A shared culture of automation

Integrating Python into the company

The introduction to Python should be structured but not unnecessarily complex:

1. Initial training

   • Basic module for onboarding
   • Focus on practical use cases
   • Creating a library of examples

2. Secure environment

   • Standardized installation (e.g. Anaconda)
   • Shared folders for common scripts
   • Documentation of best practices

3. Continuous support

   • Identification of “champions” in each department
   • Experience-sharing sessions
   • Online learning resources

Practical issues and safety

Obviously, the democratization of Python raises legitimate questions:

1. Security

   • Clear definition of scope of action
   • Use of views and restricted access
   • Validation of critical scripts

2. Maintenance

   • Minimum documentation required
   • Basic tests
   • Review process for shared scripts

But let’s be realistic: in most SMEs, users are already circumventing processes with sometimes risky solutions (uncontrolled Excel macros, unvalidated online tools). We might as well frame these practices with Python.

Beyond automation

Learning Python develops valuable skills:

• Logical, structured thinking
• Methodical problem solving
• Understanding technical issues
• Culture of continuous improvement

Conclusion

Python is no longer a tool reserved for developers – it’s a fundamental skill for the modern worker, the natural complement to SQL in the 21st century professional’s toolbox. Learning Python doesn’t require years of study, just a few hours of training focused on real needs.

On a company scale, the democratization of Python could transform productivity. On a national or global scale, it could unleash immense potential for innovation and efficiency. With the help of generative AI, the barrier to entry has never been lower, and the benefits never more obvious.

We’re at a turning point where basic programming skills are becoming as essential as reading or writing. The question is no longer if, but when and how. For companies, especially SMEs, it’s a minimal investment for maximum efficiency gains. It’s also a way of preparing for the future, as the ability to automate and manipulate data becomes more crucial every day.