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Advanced Techniques in pandas
course content

Kursusindhold

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

book
Finding the Largest Values of a Column

In the last chapter, you learned how to output the smallest values; you can definitely do the same with the largest ones. To do so, you will use a very similar method with the same argument as the previous one: .nlargest(). Look at the example of how to output the 10 newest cars, also sorted by 'Engine_volume' - all duplicates included:

data.nlargest(10, ['Year', 'Engine_volume'], keep = 'all')
question mark

Choose the CORRECT way to get the top 100 the most expensive cars sorted, firstly by 'Price' and then by 'Year', not including duplicates - just the first 100 values.

Select the correct answer

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Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 6

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course content

Kursusindhold

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

book
Finding the Largest Values of a Column

In the last chapter, you learned how to output the smallest values; you can definitely do the same with the largest ones. To do so, you will use a very similar method with the same argument as the previous one: .nlargest(). Look at the example of how to output the 10 newest cars, also sorted by 'Engine_volume' - all duplicates included:

data.nlargest(10, ['Year', 'Engine_volume'], keep = 'all')
question mark

Choose the CORRECT way to get the top 100 the most expensive cars sorted, firstly by 'Price' and then by 'Year', not including duplicates - just the first 100 values.

Select the correct answer

Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 6
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