NumPy and Pandas Training Overview
NumPy and Pandas are the most popular Python libraries for data analysis and processing used by machine learning practitioners, data scientists, data engineers, and data analysts. NumPy is a library that allows users to perform numerical calculations quickly. Pandas is a higher-level library that simplifies working with and analyzing large datasets.
Accelebrate's NumPy and Pandas training course teaches attendees how to get the most out of both libraries for their Python data science projects.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, we offer some courses as live, instructor-led online classes for individuals.
Objectives
- Understand the power and efficiencies of NumPy and Pandas
- Use the core and advanced features of both Python libraries
- Navigate the related APIs
Prerequisites
Participants must have a working knowledge of Python and be familiar with core statistical concepts (variance, correlation, etc.).
Outline
Expand All | Collapse All
Introduction
NumPy
- NumPy efficiencies
- Reshaping and flattening ndarrays
- Axis-aware functionality
- Vectorization and broadcasting
- Iterators
- Random number generations
- Statistical distribution functionality
- Linear algebra functions
Pandas
- Series and DataFrame APIs
- Accessing data
- Filtering data
- Data aggregation
- Data descriptive statistics
- Dealing with missing data
- Fine-tuning column data types
- Pivt tables and crosstabs
- Working with time series
Conclusion
Training Materials
All NumPy and Pandas for Python training students will receive comprehensive courseware.
Software Requirements
- Anaconda Python 3.6 or later
- Spyder IDE and Jupyter notebook (Comes with Anaconda)
