Data Exploration and Visualization
By the end of this chapter, you will be able to:
- Create summaries, aggregations, and descriptive statistics from your data
- Reshape pandas DataFrames to detect relationships in data
- Build pivot tables and perform comparative analysis and tests
- Create effective visualizations through Matplotlib and seaborn
This chapter explains how to derive various descriptive statistics and generate insights and visualizations from your data.
In the previous chapter, we saw how to transform data and attributes obtained from raw sources into expected attributes and values through pandas. After structuring data into a tabular form, with each field containing the expected (correct ...