About the course
Our Advanced pandas training course aims to take you beyond the basics and is aimed at helping analysts, researchers, BI experts and developers.
You will learn the more advanced features of the pandas library for Python, and gain best practices in order to slice and dice complex data.
Using interactive examples and hands-on exercises, this immersive short course will help you to develop more efficient pandas solutions so we will assume you have some familiarity with pandas and its core concepts.
Online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
Case study: Python training programme for CERN
CERN in Geneva, the largest particle physics laboratory in the world, selected Framework Training after identifying a need for Python training for a diverse range of staff across the organisation.
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- Deep-dive into pandas data types
- Complex data transformation best practices
- Efficient data modelling, pipelines, slicing & dicing and more...
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Quants, analysts, BI professionals, data scientists, developers and researchers who use the pandas library for data analysis.
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Delegates should already grasp the basics of pandas (for instance, having attended our Python Data Analysis course) and would like to take their knowledge to the next level. This is a course best suited for “advanced beginners” or intermediate pandas users, who want to master the best practices.
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This advanced Data Analysis with pandas course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Efficient Data Modelling
Learn how to structure your datasets for optimal performance and clarity, making downstream analysis faster and more intuitive.Deep-Dive into pandas Data Types
Explore pandas' internal data types (e.g., Categorical, Datetime, Nullable) to understand how they affect performance and behavior.Choosing Data Types When Loading the Data
Discover techniques for selecting the most appropriate data types when reading files to reduce memory usage and improve speed.Data Type Conversions
Master efficient and safe conversions between data types to ensure compatibility and minimize performance bottlenecks.Best Practices to Improve Your Memory Usage When Dealing with Larger Data Sets
Apply memory profiling and optimization strategies to keep large datasets manageable and efficient.Efficient Data Pipelines
Design high-performance data pipelines using pandas for cleaning, transforming, and preparing data at scale.Understanding Immutability
Understand how pandas treats object mutability to avoid unintended side effects and improve code reliability.Method Chaining to Improve Readability
Write cleaner, more readable code by chaining methods into concise, expressive data transformation pipelines.Best Practices to Develop and Maintain Complex Data Transformation and Query Pipelines
Learn how to structure, document, and test advanced pandas workflows for maintainability and collaboration.Efficient Slicing and Dicing
Master advanced indexing techniques to quickly extract, filter, and manipulate specific subsets of your data.Complex Aggregations (groupby) on Multiple Columns
Go beyond basic aggregations—perform multi-level grouping, custom aggregation functions, and post-processing with ease.Mastering the Hierarchical (Multi-level) Index
Harness the full power of pandas’ MultiIndex to manage and analyze complex, high-dimensional datasets.Index Stacking and Unstacking
Learn to pivot between wide and long formats using stack/unstack operations, enabling flexible data reshaping.Pivoting and Reshaping
Transform your data with pivot, melt, and reshape methods to suit different analysis and visualization needs.Beyond pandas – Exploring the pandas Ecosystem
Expand your toolkit with an introduction to libraries like Dask, Vaex, Modin, and more, for handling larger-than-memory data or specialized workflows.Overview and Examples of Usage of Other Libraries Built on Top of pandas to Address Specific Needs in Data Preparation, Data Analysis and Data Visualisation
Get hands-on with libraries that enhance pandas' capabilities in areas like data validation (e.g. Pandera), visualization (e.g. seaborn), and profiling (e.g. pandas-profiling). -
https://pandas.pydata.org/ - Get pandas if you haven't already
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