Conteúdo do Curso

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data

Outputting Columns by Title Outputting Rows by Index Selecting Specific Rows and Columns Learning More About Indexation Getting Familiar With lambda Functions Expanding Functionality of the .iloc[] Attribute

2. Dealing With Conditions

Setting Condition Selecting Data Based on Condition Dealing With Several Conditions Making Your Code Beautiful Conditions Quiz

3. Extracting Data

Is Data in ...?Combining Your Knowledge Between Method Extracting Specific Data Finding the Smallest Values of a Column Finding the Largest Values of a Column Finding the Correlation

4. Aggregating Data

Getting Familiar With the .groupby() Method Grouping by Several Columns Complicated Grouping Advanced Grouping Dealing With Pivot Tables Creating a Pivot Table

5. Preprocessing Data

Checking for Missing Values Calculating the Number of Missing Values What Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing Values Managing Categorical Variables Checking the Column Type Managing an Incorrect Column Renaming the Column

Managing an Incorrect Column

So, you received the result object. This means that the type of the column is non-numerical, but to calculate necessary values, the column need to be numerical. Let's change that.

Firstly, we need to replace - with .. To do so, you will apply the method .str.replace() to replace the character in the string in the dataset column. The syntax is
data['column_name'].str.replace('old_symbol','new_symbol')
In our case, old_symbol is -, and . is the new_symbol;
Then, convert the column to the float data type. To do so, use .astype() method. The syntax is data['column_name'].astype('type').
In our case, the type is 'float'.

Tarefa

Your task is to:

Follow the algorithm above and firstly replace - with . in the column 'Fare'.
Convert the column 'Fare' to the 'float' data type.
Output the type of the column 'Fare'.

Tarefa

Your task is to:

Follow the algorithm above and firstly replace - with . in the column 'Fare'.
Convert the column 'Fare' to the 'float' data type.
Output the type of the column 'Fare'.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 5. Capítulo 8

Managing an Incorrect Column

So, you received the result object. This means that the type of the column is non-numerical, but to calculate necessary values, the column need to be numerical. Let's change that.

Firstly, we need to replace - with .. To do so, you will apply the method .str.replace() to replace the character in the string in the dataset column. The syntax is
data['column_name'].str.replace('old_symbol','new_symbol')
In our case, old_symbol is -, and . is the new_symbol;
Then, convert the column to the float data type. To do so, use .astype() method. The syntax is data['column_name'].astype('type').
In our case, the type is 'float'.

Tarefa

Your task is to:

Follow the algorithm above and firstly replace - with . in the column 'Fare'.
Convert the column 'Fare' to the 'float' data type.
Output the type of the column 'Fare'.

Tarefa

Your task is to:

Follow the algorithm above and firstly replace - with . in the column 'Fare'.
Convert the column 'Fare' to the 'float' data type.
Output the type of the column 'Fare'.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 5. Capítulo 8

Managing an Incorrect Column

So, you received the result object. This means that the type of the column is non-numerical, but to calculate necessary values, the column need to be numerical. Let's change that.

Firstly, we need to replace - with .. To do so, you will apply the method .str.replace() to replace the character in the string in the dataset column. The syntax is
data['column_name'].str.replace('old_symbol','new_symbol')
In our case, old_symbol is -, and . is the new_symbol;
Then, convert the column to the float data type. To do so, use .astype() method. The syntax is data['column_name'].astype('type').
In our case, the type is 'float'.

Tarefa

Your task is to:

Follow the algorithm above and firstly replace - with . in the column 'Fare'.
Convert the column 'Fare' to the 'float' data type.
Output the type of the column 'Fare'.

Tarefa

Your task is to:

Follow the algorithm above and firstly replace - with . in the column 'Fare'.
Convert the column 'Fare' to the 'float' data type.
Output the type of the column 'Fare'.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

So, you received the result object. This means that the type of the column is non-numerical, but to calculate necessary values, the column need to be numerical. Let's change that.

Firstly, we need to replace - with .. To do so, you will apply the method .str.replace() to replace the character in the string in the dataset column. The syntax is
data['column_name'].str.replace('old_symbol','new_symbol')
In our case, old_symbol is -, and . is the new_symbol;
Then, convert the column to the float data type. To do so, use .astype() method. The syntax is data['column_name'].astype('type').
In our case, the type is 'float'.

Tarefa

Your task is to:

Follow the algorithm above and firstly replace - with . in the column 'Fare'.
Convert the column 'Fare' to the 'float' data type.
Output the type of the column 'Fare'.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Seção 5. Capítulo 8

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo