The pipeline – From craft to industry

In the previous episode, we finalized our interactive diagnostic cockpit. It’s a victory: we analyze an engine test with surgical precision. But in real life, the engineer doesn’t process just one test.

Imagine this scenario: you return from vacation and a colleague drops off a folder containing 150 CSV files. They need the summary by tomorrow morning. Are you going to open your script 150 times, rename the files, and copy and paste the results into Excel?

This is where we move from craftsmanship to industry. Today, we’re assembling the building blocks from previous days to create a continuous flow: the Pipeline. You drop in your files, click “Run,” and go grab your coffee while Python works for you.

The concept: The industrialization loop

To automate this batch processing, our script gains two new superpowers:

• The Scanner (pathlib) : Instead of targeting a single file (essai_01.csv), you provide the path to a folder. Python instantly lists all the .csv files present.
• The Loop (for file in list_files) : This is the heart of the system. For each file found, Python automatically applies all the logic developed from the beginning:
  Import $rightarrow$ Cleaning $rightarrow$ Calculation $rightarrow$ Graphics.

The structure of a “pro” script: main.py

Before writing the first line of code, you need to prepare the folders. This allows the script to know where to look and where to store the files:

• Root folder: This is your project folder (e.g., MyProjectTests/).
• Data folder: This is the “input bin,” where you place your raw .csv files.
• Output folder: This is the “output bin.” This is where Python will automatically create the reports and graphs for each test.
• The main.py file: This is the “conductor” that controls the operations. It is located in the root directory.

The “Conductor” Code (main.py)

Here is the complete, commented code. Notice how each line calls a key step from the previous days:

from pathlib import Path
import pandas as pd

# 1. We define the folders
dossier_entree = Path('data/')
dossier_sortie = Path('output/')

# We make sure that the exit exist, else we create the exit

dossier_sortie.mkdir(exist_ok=True)

# 2. We scan all CSV files

fichiers = list(dossier_entree.glob('*.csv'))
synthese_globale = [] 

for fichier in fichiers:
    # --- STEP A : The name ---
    # .stem allows to retrieve "Essai_01" from "data/Essai_01.csv"
    nom_essai = fichier.stem
   
    # --- STEP B : The processing ---
    df = pd.read_csv(fichier)
    # (Here we place our cleaning and calculation functions...) 
    df = nettoyer_et_calculer(df) # Your custom function
     
   
    # --- STEP C : Exporting to 'output' ---
    # This is where we use the output folder!
    chemin_rapport = dossier_sortie / f"Rapport_{nom_essai}.html"
   
    # We call our visualization function (see below)
    creer_graphique_plotly(df, nom_essai, chemin_rapport)

    # We store the stats for the Killer Feature
    synthese_globale.append({
        'Fichier': nom_essai,
        'P_Max': df['Puissance'].max()
    })
  
    print(f"✅ Analyse terminée pour {nom_essai}")

Note the use of .stem to clean up filenames and send to the output folder.

The Consolidated Report

The ultimate goal is not just to generate 150 mini-reports, but to create ONE single summary file of the entire test campaign.

At the end of the loop, only one line is needed:

pd.DataFrame(synthese_globale).to_excel("Synthese_Campagne.xlsx")

Imagine this final table: each line represents a test, with its maximum power and status. It’s the ultimate project management tool for a project manager. You’ve just transformed an afternoon of manual work into 10 seconds of execution.

Concrete example

Let’s take the example of 3 engine tests:

Here is the table generated by the script:

File	P_Max	Test status
Essai_Moteur_01	28.18	Compliant
Essai_Moteur_02	38.56	Surcharge
Essai_Moteur_03	28,30	Compliant

In less than a second, Python scanned all the files in the folder and identified the single anomaly. Without this script, you would have had to manually open each curve to discover that engine #2 nearly burned out.

Conclusion: Becoming an Architect of Intelligence

With Batch Processing, you’ve gone from being an operator to an architect. You now control the volume: whether you have 1 or 150 files, your effort remains the same. You’ve built the “engine” of your automation.

But an engine without a body is unsaleable. For now, your results are still hidden away in files or a raw Excel spreadsheet. To truly finalize your transformation into an “Augmented Engineer,” there’s one last step: valuation.

How can you transform these calculations into a clean, professional, official document without spending two hours on formatting? How can you finally break free from the “prison” of Excel to generate summaries that impress your clients and superiors?

Next step: The automatic report

For the final article in this series, we will learn how to generate the ideal “backup”: a consolidated document and a PDF report generated with a single click.

➡️ Read the article: From raw data to official document