Tutorials: Introduction to Data Analysis Using Pandas

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Section 1: Getting Started With Pandas

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.

Section 2: Data Wrangling

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, fixing data type mismatches), restructure/reshape it, and enrich it (e.g. discretizing columns, calculating aggregations, combining data sources).

Section 3: Data Visualization

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.

Section 4: Hands-On Data Analysis Lab

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.