Preparing Data for EDA
Before exploring and analyzing tabular data, we sometimes will be required to prepare the data for analysis. This preparation can come in the form of data transformation, aggregation, or cleanup. In Python, the pandas library helps us to achieve this through several modules. The preparation steps for tabular data are never a one-size-fits-all approach. They are typically determined by the structure of our data, that is, the rows, columns, data types, and data values.
In this chapter, we will focus on common data preparation techniques required to prepare our data for EDA:
- Grouping data
- Appending data
- Concatenating data
- Merging data
- Sorting data
- Categorizing data
- Removing duplicate data
- Dropping data rows and columns
- Replacing data
- Changing a data format
- Dealing with missing values
