Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Pandas 2026 - Mastering Python Data Analysis training are: Basic knowledge of Python: variables, lists, loops, functions, and standard libraries. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Pandas 2026 - Mastering Python Data Analysis training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Pandas 2026 - Mastering Python Data Ana... | Learni

The story of Learni

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.

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Methods, approaches, and teaching resources

The Training Pandas 2026 - Mastering Python Data Analysis 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.

Teaching resources provided

To ensure the quality of the Training Pandas 2026 - Mastering Python Data Analysis training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and 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.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Pandas 2026 - Mastering Python Data Analysis training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

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.

Training access terms and deadlines

Training objectives

Master Pandas data structures for professional analyses

Develop skills in data import and cleaning in a business context

Implement data manipulation and aggregation operations with Pandas 2026

Design certified and efficient data analysis pipelines

Optimize Pandas performance for scalable data projects

Produce actionable visualizations and reports in a professional context

Training program

Module 1Pandas 2026 Fundamentals: Installation and Basic Structures (Series, DataFrames, Initial Tools)

Installation of the Pandas 2026 environment with Anaconda and Jupyter Notebook, creation of first Series and DataFrames from real company data, exploration of indexes and essential attributes via interactive exercises, basic data manipulation with head, tail, and describe, development of a first simple analysis script on a CSV dataset, code review by the trainer to consolidate professional skills.

Module 2Pandas 2026 Import: Reading Various Files (CSV, Excel, SQL, Optimized Methods)

Loading data from multiple sources such as CSV, Excel, and SQL databases with pd.read_*, handling encodings and data types to avoid common errors, practical exercises on real e-commerce datasets, merging files with concat and merge, integrity checks via info and isna, production of a preliminary data quality report, setting up an automated import workflow to save time in a business setting.

Module 3Pandas 2026 Manipulation: Cleaning and Transformation (select, filter, apply, groupby)

Advanced cleaning of missing data with dropna, fillna, and interpolate on real sales cases, selection and filtering with loc, iloc, and query to extract quick insights, application of custom functions via apply and lambda, powerful aggregations with groupby and pivot_table on HR data, collaborative exercises to transform a raw dataset into an exploitable table, script optimization for professional production performance.

Module 4Pandas 2026 Analysis: Advanced Aggregations and Visualizations (Matplotlib, Seaborn Integrated)

Descriptive statistical calculations with describe, value_counts, and corr to detect hidden trends, creation of group summaries via agg and transform on marketing datasets, integration of direct visualizations with plot and hist, combination with Matplotlib for professional charts, exercises on real customer churn analysis, generation of simple interactive dashboards, export of results to PDF and HTML for certified business reports.

Module 5Pandas 2026 Projects: Complete Business Cases and Optimization (Pipelines, Best Practices)

Development of an end-to-end thread project on real business data such as financial forecasting, building modular pipelines with functions and loops for reusability, memory optimization with categorical and chunking for large volumes, basic unit tests and advanced debugging, deployment to production via standalone scripts, presentation of the final deliverable with Q&A, provision of ready-to-use templates to accelerate your future professional analyses.