Course Content

# Advanced Techniques in pandas

1. Get Familiar With Indexing and Selecting Data

2. Dealing With Conditions

Advanced Techniques in pandas

## Fill In the Missing Values

Deleting missing values is not the only way to get rid of them. You can also replace all NaNs with a defined value, for instance, with the mean value of the column or with zeros. It can be useful in a lot of cases. You will learn this in the course Statistics with Python. Look at the example of filling missing values in the column `'Age'`

with the median value of this column.

**Explanation:**

`value = data['Age'].median()`

- using the argument`value`

, we tell the`.fillna()`

function what to do with the`NaN`

values. In this case, we applied the`.fillna()`

function to the column`'Age'`

and replaced all missing values with the**median**of the column.`inplace=True`

- the argument we can use for saving changes.

# Task

One of the most common ways of filling missing values is replacing them with the mean value of the column. So, your task here is to replace the `NaN`

values in the column `'Age'`

with the **mean** value of the column (using the `inplace = True`

argument). Then output the sum of the missing value in the column `'Age'`

.

Everything was clear?