As an analyst, you may often receive data from multiple sources, and in order to draw a conclusions, you need to combine data in a single dataframe.

If you are familiar with SQL, you may heard about different types of joins (like, left, right, or inner). As an example, we will use two dataframes with pregenerated data (id, name, surname, age, and height (in cm)).

To get started with joins, first we need to note that any kind of join require some key field that will be used for comparison between tables. These values should be unique across each table.

> For instance, it's common practice to join tables by some id field.

This course covers intermediate topics on pandas, a must-have tool for each data analyst. During the course, you will learn how to prepare data for further interactions and how to group it using different techniques. You will learn the easiest data visualization and be acquainted with data joining.

The data received from different sources can be messy, and to use it in the future, you must ensure it is convenient. In the first section, you will learn what data preprocessing is and will deal with some logical inconsistencies.

Missing or NA values, outliers, and inconsistencies are other types of problematic data. Throughout the second section of the course, you will learn how to deal with such issues.

As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

Sometimes one built-in function is not enough to draw a complete conclusion, so you need to use something more complex. This section will teach you how to apply multiple functions while grouping. Also, you will learn to visualize data using the pandas library only and will be acquainted with the main plot types. As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

As mentioned before, sometimes you may need to work with data received from multiple sources. This section will teach you how to join two dataframes using different techniques.

What is Joining Data?