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Advanced Techniques in pandas
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Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
Outputting Columns by TitleOutputting Rows by IndexSelecting Specific Rows and ColumnsLearning More About IndexationGetting Familiar With lambda FunctionsExpanding Functionality of the .iloc[] Attribute
2. Dealing With Conditions
Setting ConditionSelecting Data Based on ConditionDealing With Several ConditionsMaking Your Code BeautifulConditions Quiz
3. Extracting Data
Is Data in ...?Combining Your KnowledgeBetween MethodExtracting Specific DataFinding the Smallest Values of a ColumnFinding the Largest Values of a ColumnFinding the Correlation
4. Aggregating Data
Getting Familiar With the .groupby() MethodGrouping by Several ColumnsComplicated GroupingAdvanced GroupingDealing With Pivot TablesCreating a Pivot Table
5. Preprocessing Data
Checking for Missing ValuesCalculating the Number of Missing ValuesWhat Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing ValuesManaging Categorical VariablesChecking the Column TypeManaging an Incorrect ColumnRenaming the Column

bookExpanding Functionality of the .iloc[] Attribute

We will learn some new features that iloc[] provides. The coolest one is that we can specify indices of both rows and columns. This attribute is similar to .loc[], but the last index of the slicing is exclusive.

Look at the example and the relevant output:

  • data.iloc[1, 2] - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them;
  • data.iloc[:, 3] - extracts all values from the rows of the fourth column 'IMDb-Rating';
  • data.iloc[3, :] or data.iloc[3] - extracts the 4th row and all relevant columns;
  • data.iloc[:2, 1:4] - extracts the first two rows and column with the indices 1, 2, 3;
  • data.iloc[[2,4],[1,3]] - extracts the rows with indices 2,4 and columns with the indices 1, 3.

Tarefa

Your task here is just to practice. Output information on the first 50 rows and the columns with indices 1 and 4.

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Seção 1. Capítulo 6
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bookExpanding Functionality of the .iloc[] Attribute

We will learn some new features that iloc[] provides. The coolest one is that we can specify indices of both rows and columns. This attribute is similar to .loc[], but the last index of the slicing is exclusive.

Look at the example and the relevant output:

  • data.iloc[1, 2] - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them;
  • data.iloc[:, 3] - extracts all values from the rows of the fourth column 'IMDb-Rating';
  • data.iloc[3, :] or data.iloc[3] - extracts the 4th row and all relevant columns;
  • data.iloc[:2, 1:4] - extracts the first two rows and column with the indices 1, 2, 3;
  • data.iloc[[2,4],[1,3]] - extracts the rows with indices 2,4 and columns with the indices 1, 3.

Tarefa

Your task here is just to practice. Output information on the first 50 rows and the columns with indices 1 and 4.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 1. Capítulo 6
toggle bottom row

bookExpanding Functionality of the .iloc[] Attribute

We will learn some new features that iloc[] provides. The coolest one is that we can specify indices of both rows and columns. This attribute is similar to .loc[], but the last index of the slicing is exclusive.

Look at the example and the relevant output:

  • data.iloc[1, 2] - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them;
  • data.iloc[:, 3] - extracts all values from the rows of the fourth column 'IMDb-Rating';
  • data.iloc[3, :] or data.iloc[3] - extracts the 4th row and all relevant columns;
  • data.iloc[:2, 1:4] - extracts the first two rows and column with the indices 1, 2, 3;
  • data.iloc[[2,4],[1,3]] - extracts the rows with indices 2,4 and columns with the indices 1, 3.

Tarefa

Your task here is just to practice. Output information on the first 50 rows and the columns with indices 1 and 4.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

We will learn some new features that iloc[] provides. The coolest one is that we can specify indices of both rows and columns. This attribute is similar to .loc[], but the last index of the slicing is exclusive.

Look at the example and the relevant output:

  • data.iloc[1, 2] - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them;
  • data.iloc[:, 3] - extracts all values from the rows of the fourth column 'IMDb-Rating';
  • data.iloc[3, :] or data.iloc[3] - extracts the 4th row and all relevant columns;
  • data.iloc[:2, 1:4] - extracts the first two rows and column with the indices 1, 2, 3;
  • data.iloc[[2,4],[1,3]] - extracts the rows with indices 2,4 and columns with the indices 1, 3.

Tarefa

Your task here is just to practice. Output information on the first 50 rows and the columns with indices 1 and 4.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 1. Capítulo 6
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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