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Fill In the Missing Values | Preprocessing Data
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 Learning 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.

Завдання

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'.

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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 Learning 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.

Завдання

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'.

Все було зрозуміло?

Секція 5. Розділ 5
toggle bottom row
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