Now, you will work with the data set that doesn't contain missing values. The `NaN` values from the column `'Age'` were replaced with the **mean** of the column, and the `NaN` value from the `'Fare'` column was deleted.
So, now it's time to learn how to manage categorical variables. Categorical means that they have some categories. For instance, in the column `'Sex'`, there is `'male'` and `'female'`; or in the column `'Embarked'`, there is `'Q'`, `'S'`, and `'C'`. 

**What should we do to calculate the number of values in each category or to find out information on them?**

You already know `.loc[]`, `.isin()`, `.between()` and a lot of functions,  but in pandas, there is a more beautiful and convenient way to do this. Use the function `.get_dummies()`. As an example, we will apply it to the column `'Embarked'` for an example. Look at the implementation and the result (we will output random 5 random passengers' names and new columns that we created).

import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic3.csv', index_col = 0)
data = pd.get_dummies(data, columns = ['Embarked'])
print(data[['Name', 'Embarked_C', 'Embarked_Q', 'Embarked_S']].sample(5))

Look at the result:

||**PassengerId**|**Embarked_C**|**Embarked_Q**|**Embarked_S**|
|---|---|---|---|---|
|211|Finoli, Mr. Luigi|0|0|1|
|205|Omont, Mr. Alfred Fernand|1|0|0|
|212|Deacon, Mr. Percy William|0|0|1|
|372|Ismay, Mr. Joseph Bruce|0|0|1|
|308|Hays, Mr. Charles Melville|0|0|1|



**Explanation:**

As a result, our function split the column `'Embarked'` into three columns: `'Embarked_C'`, and `'Embarked_Q'`, `'Embarked_S'`. In total, we have three categories. Each passenger has their category in the `'Embarked'` column. Thus, our function creates three columns corresponding to each category, and in line with each passenger, it fills the row of the column with `1` if the person was initially related to the geography; otherwise, it says `0`. Thus, we get `1` in just one column.

`pd.get_dummies(data, columns = ['Embarked'])`
- `pd.get_dummies()` - this function converts **categorical** variables into **dummy** ones (1 or 0).
- `data` - the data frame that you want to use.
- `columns = ['Embarked']` - columns have categorical variables that you want to transform into dummy ones. Pay attention; it is **obligatory** to put column names into the list.

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows  by indices.

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here. 