From the course: Data Analysis with Python and Pandas

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Why use multiple tables?

Why use multiple tables?

From the course: Data Analysis with Python and Pandas

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Why use multiple tables?

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- [Instructor] All right. So in our last section before our final project, we're going to take a look at combining DataFrames. We're going to look at joining data with related fields to add new columns to a DataFrame. And we're also going to look at app pending data from DataFrames that have the same fields to add rows to a DataFrame. We're going to cover combining basics, appending and joining. Short but sweet. Our goals for this section are to append the rows for similar DataFrames, learn the different join types in their behavior, and join DataFrames with single, and multiple related fields. But before we dive into our specific join types, and how to do this, let's go ahead and ask the question, why might we have multiple DataFrames? There are multiple scenarios in working with multiple DataFrames where data files is necessary. Relational databases save space by not repeating redundant data. This means that things like customer demographics will be stored in one table, but they…

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