Working with Tabular Data and DataFrames
In the last chapter, we saw how DataFrames can load and store tabular data sources, such as CSV files, and the results of SQL queries.
DataFrames are the foundation of many common tasks in data analysis and machine learning. As a result, it’s important to be able to work effectively with DataFrames and perform common operations as you explore and transform your data – regardless of the tasks you’re trying to accomplish.
In this chapter, we’ll build a solid foundation in working with DataFrames as we cover the following:
- Understanding data cleaning and data wrangling
- Working with
DataFramesin C# - Working with columns
- Handling missing values
- Sorting, filtering, grouping, and merging data
DataFramesin other languages
