Introduction to Data Analysis Using Pandas
Working with data can be challenging: it often doesn't come in the best format for analysis, and understanding it well enough to extract insights requires both time and the skills to filter, aggregate, reshape, and visualize it. This session will equip you with the knowledge you need to effectively use pandas – a powerful library for data analysis in Python – to make this process easier.
Pandas makes it possible to work with tabular data and perform all parts of the analysis from collection and manipulation through aggregation and visualization. While most of this session focuses on pandas, during our discussion of visualization, we will also introduce at a high level Matplotlib (the library that pandas uses for its visualization features, which when used directly makes it possible to create custom layouts, add annotations, etc.) and Seaborn (another plotting library, which features additional plot types and the ability to visualize long-format data).
Workshop outline
We will begin by introducing the Series, DataFrame,
and Index classes, which are the basic building blocks of the pandas library, and showing how
to work with them. By the end of this section, you will be able to create DataFrames and perform
operations on them to inspect and filter the data.
To prepare our data for analysis, we need to perform data wrangling. In this section, we will learn how to clean and reformat data (e.g., renaming columns and fixing data type mismatches), restructure/reshape it, and enrich it (e.g., discretizing columns, calculating aggregations, and combining data sources).
The human brain excels at finding patterns in visual representations of the data; so in this section, we will learn how to visualize data using pandas along with the Matplotlib and Seaborn libraries for additional features. We will create a variety of visualizations that will help us better understand our data.
We will practice all that you've learned in a hands-on lab. This section features a set of analysis tasks that provide opportunities to apply the material from the previous sections.
Past sessions
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