Loading...
Please wait a moment
Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
Which format do you prefer?
30 free minutes with a training advisor — no commitment.
Loading available slots...
Discover top hospitality management training options for hotel professionals targeting April 2026. Explore trends, key skills, and programs to boost careers in a recovering industry.
Discover step-by-step methods to master bookkeeping and accounting fundamentals in April 2026. Explore online courses, tools, practice tips, and future trends like AI integration for aspiring professionals.
Professional Training training in Memphis in October 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Artificial Intelligence training in Raleigh in June 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Don't let this gap widen
Sans barres de progression performantes en Python, vos scripts de traitements de données paraissent figés, frustrant utilisateurs et développeurs.
Résultat : 35 % du temps de calcul est perdu en monitoring manuel, avec 28 % des tâches longues abandonnées prématurément selon des études sur les workflows data.
Pour l'entreprise, cela génère des retards cumulés à 400 € par jour de projet bloqué, exposant à des pertes clients et une concurrence impitoyable.
Chaque mois sans cette maîtrise aggrave les inefficacités, menaçant directement votre progression de carrière dans un secteur où l'agilité code est reine.
The Maîtriser TQDM : Créez des Barres de Progression Performantes en Python training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Maîtriser TQDM : Créez des Barres de Progression Performantes en Python training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Maîtriser TQDM : Créez des Barres de Progression Performantes en Python training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Présentation de tqdm, cas d'usage récents, installation avec pip/conda. Tour d'horizon des alternatives. Utilisation de tqdm pour les boucles for simples. Paramétrage des options de base (description, unités, total, etc.). Exercices : création d'une barre de progression pour un traitement de fichiers volumineux.
Intégration de tqdm aux scripts existants. Utilisation dans les environnements Jupyter, adaptation au streaming et itérateurs personnalisés. Barres de progression imbriquées. Customisation de l’apparence et des messages. Exercices pratiques : suivi de progression sur plusieurs tâches simultanées.
Introduction aux problématiques de visualisation de progression lors de traitements parallèles. Utilisation de tqdm avec map, threads et multiprocessing. Limites et astuces pour éviter des comportements inattendus. Étude de cas : téléchargement de multiples fichiers avec gestion de progression avancée. Questions/réponses et revue de cas d’utilisation des participants.
Target audience
Développeurs et scientifiques débutants utilisant Python souhaitant améliorer l'expérience utilisateur lors de traitements de données ou de scripts longs
Prerequisites
Connaissance de base de Python et des structures de contrôles (boucles, fonctions)
Loading...
Please wait a moment





























