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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

Calculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Tarefa

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Tarefa

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

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

Tudo estava claro?

Seção 5. Capítulo 2
toggle bottom row

Calculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Tarefa

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Tarefa

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

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

Tudo estava claro?

Seção 5. Capítulo 2
toggle bottom row

Calculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Tarefa

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Tarefa

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

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

Tudo estava claro?

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Tarefa

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 5. Capítulo 2
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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