In the previous chapter, you dealt with the incorrect values in a column. It is an excellent time to fix any changes. In our case, we can rename the column to pin that it was changed.

To rename a column, use the `.rename()` function. Look at the example where we will rename the column `'Survived'`, and then output the column names.

import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic4.csv', index_col = 0)
data.rename(columns = {'Survived': 'Survived_Passenger'}, inplace = True)
print(data.columns)

The result is: 
```
Index(['PassengerId', 'Survived_Passenger', 'Pclass', 'Name', 'Sex', 'Age',
       'SibSp', 'Parch', 'Ticket', 'Fare', 'Embarked'],
      dtype='object')
```


**Explanation:**

`.rename(columns = {'Survived': 'Survived_Passenger'}, inplace = True)`
- `.rename()` - a function that we apply to the dataset to rename columns name.
- `columns = {'Survived': 'Survived_Passenger'}` - in the curly brackets, you specify all columns and their new names. In this case, we renamed just one column, but you can put several of them here separated by commas.

`.columns` - a function that outputs the column names. In our case, we can no longer see the column name `'Survived'`.

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. 