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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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The Training: Mastering Data Analysis with DataFrames in Python (Pandas) 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 Training: Mastering Data Analysis with DataFrames in Python (Pandas) 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 Training: Mastering Data Analysis with DataFrames in Python (Pandas) training is carried out through:
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Presentation of the Pandas module, installation, and getting started. Reading and writing files (CSV, Excel, JSON). Creation and exploration of DataFrames. Indexing concepts, column and row selection, data types. Practical exercises on simple datasets.
Handling missing values and duplicates, data type conversion. Merging and concatenating DataFrames. Applying functions (apply, map, lambda). Conditional selection, sorting, advanced filtering, and grouping with aggregation (groupby), reshaping (pivot, melt). Intensive application exercises with case studies.
Descriptive statistics: means, medians, variance, correlations. Multi-index operations. Exporting results. Introduction to visualizing DataFrames with matplotlib and seaborn: graphs, curves, histograms, and heatmaps for visual exploration. Mini-project analyzing a real dataset in professional conditions.
Target audience
Analysts, data managers, professionals wishing to structure and effectively analyze tabular data with Python.
Prerequisites
Computer proficiency, basic understanding of Python or another programming language.
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