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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

Selecting Data Based on Condition

To excel in programming, you always need to practice. So, in this task, you must also memorize functions from the previous chapter. If you aren't solid on the functions from the last chapter, look at the hints to revise them.

Tarea

  1. Extract all rows from the columns 'name' and 'hazardous' using the .loc[] attribute.
  2. Filter data; extract rows where 'hazardous' is True.
  3. Output the first five rows of the data_filtered dataset.

Tarea

  1. Extract all rows from the columns 'name' and 'hazardous' using the .loc[] attribute.
  2. Filter data; extract rows where 'hazardous' is True.
  3. Output the first five rows of the data_filtered dataset.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 2
toggle bottom row

Selecting Data Based on Condition

To excel in programming, you always need to practice. So, in this task, you must also memorize functions from the previous chapter. If you aren't solid on the functions from the last chapter, look at the hints to revise them.

Tarea

  1. Extract all rows from the columns 'name' and 'hazardous' using the .loc[] attribute.
  2. Filter data; extract rows where 'hazardous' is True.
  3. Output the first five rows of the data_filtered dataset.

Tarea

  1. Extract all rows from the columns 'name' and 'hazardous' using the .loc[] attribute.
  2. Filter data; extract rows where 'hazardous' is True.
  3. Output the first five rows of the data_filtered dataset.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 2
toggle bottom row

Selecting Data Based on Condition

To excel in programming, you always need to practice. So, in this task, you must also memorize functions from the previous chapter. If you aren't solid on the functions from the last chapter, look at the hints to revise them.

Tarea

  1. Extract all rows from the columns 'name' and 'hazardous' using the .loc[] attribute.
  2. Filter data; extract rows where 'hazardous' is True.
  3. Output the first five rows of the data_filtered dataset.

Tarea

  1. Extract all rows from the columns 'name' and 'hazardous' using the .loc[] attribute.
  2. Filter data; extract rows where 'hazardous' is True.
  3. Output the first five rows of the data_filtered dataset.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

To excel in programming, you always need to practice. So, in this task, you must also memorize functions from the previous chapter. If you aren't solid on the functions from the last chapter, look at the hints to revise them.

Tarea

  1. Extract all rows from the columns 'name' and 'hazardous' using the .loc[] attribute.
  2. Filter data; extract rows where 'hazardous' is True.
  3. Output the first five rows of the data_filtered dataset.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 2. Capítulo 2
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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