Summary
In this chapter, we dived deep into the pandas library to learn advanced data wrangling techniques. We started with some advanced subsetting and filtering on DataFrames and round this up by learning about boolean indexing and conditional selection of a subset of data. We also covered how to set and reset the index of a DataFrame, especially while initializing.
Next, we learned about a particular topic that has a deep connection with traditional relational database systems – the group by method. Then, we dived deep into an important skill for data wrangling - checking for and handling missing data. We showed you how pandas help in handling missing data using various imputation techniques. We also discussed methods for dropping missing values. Furthermore, methods and usage examples of concatenation and merging of DataFrame objects were shown. We saw the join method and how it compares to a similar operation in SQL.
Lastly, miscellaneous useful methods on DataFrames, such as randomized...
