You've solved the first problem with wrong column type. Let's solve the remaining one (with dots). Recall that there are 4 columns with wrong types left (`'morgh', 'valueh', 'grosrth', 'omphtotinch'`). These columns considered to have dots as indicators for 'Not applicable'. For instance, columns `valueh` and `grosrth` are mutually exclusive, since the first one indicates the price of dwelling (i.e., house is owned) and the second one indicates the monthly rent.

The most appropriate way to solve this problem is to replace dots by `NA` values. In that case, we would be able to manipulate column like a numerical one.

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.

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