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What Will We Do With the NaN Values? | Preprocessing Data
Advanced Techniques in pandas

What Will We Do With the NaN Values?

In the previous chapter, you received the result:

PassengerId0
Survived0
Pclass0
Name0
Sex0
Age86
SibSp0
Parch0
Ticket0
Fare1
Cabin327
Embarked0

The dataset has 418 rows. Look at the column Cabin, where we have 327 missing values. There is no sense filling them in because we have minimal information here. So, in this case, the best solution is to delete the column that is senseless to us. One of the reasons is that we can delete only the rows that contain missing values, but we can't delete 327 rows out of 418. So, let's figure out how to do this.

To delete a column, you must apply the function .drop() to the data set. The syntax is the following:

Explanation:

  • .drop() - a function that deletes columns;
  • columns = 'column_name' or columns = ['column_1', 'column_2'] - argument of the function, where you specify the name or names of columns that you want to delete;
  • inplace = True - useful argument of pandas that allows us to save all changes. You can use it in other functions too; we will learn some of them later on.

Tarefa

Your task is to delete the column with the greatest number of NaN values. Follow the algorithm:

  1. Drop the column 'Cabin' using the inplace = True argument.
  2. Output the random 5 rows of the data set.

Tudo estava claro?

Seção 5. Capítulo 3
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Conteúdo do Curso

Advanced Techniques in pandas

What Will We Do With the NaN Values?

In the previous chapter, you received the result:

PassengerId0
Survived0
Pclass0
Name0
Sex0
Age86
SibSp0
Parch0
Ticket0
Fare1
Cabin327
Embarked0

The dataset has 418 rows. Look at the column Cabin, where we have 327 missing values. There is no sense filling them in because we have minimal information here. So, in this case, the best solution is to delete the column that is senseless to us. One of the reasons is that we can delete only the rows that contain missing values, but we can't delete 327 rows out of 418. So, let's figure out how to do this.

To delete a column, you must apply the function .drop() to the data set. The syntax is the following:

Explanation:

  • .drop() - a function that deletes columns;
  • columns = 'column_name' or columns = ['column_1', 'column_2'] - argument of the function, where you specify the name or names of columns that you want to delete;
  • inplace = True - useful argument of pandas that allows us to save all changes. You can use it in other functions too; we will learn some of them later on.

Tarefa

Your task is to delete the column with the greatest number of NaN values. Follow the algorithm:

  1. Drop the column 'Cabin' using the inplace = True argument.
  2. Output the random 5 rows of the data set.

Tudo estava claro?

Seção 5. Capítulo 3
toggle bottom row
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