Advanced Techniques in pandas

Course Content

Advanced Techniques in pandas

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

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:


python

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

data.nlargest(10, ['Price', 'Year'], keep = 'all')

data.nlargest(100, ['Price', 'Year'])

data.nlargest[100, ['Price', 'Year']]

data.nsmallest(100, ['Price', 'Year'])

data.nlargest(['Price', 'Year'], 100)

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How can we improve it?

Thanks for your feedback!

Section 3. Chapter 6

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Course Content

Advanced Techniques in pandas

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

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:


python

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

data.nlargest(10, ['Price', 'Year'], keep = 'all')

data.nlargest(100, ['Price', 'Year'])

data.nlargest[100, ['Price', 'Year']]

data.nsmallest(100, ['Price', 'Year'])

data.nlargest(['Price', 'Year'], 100)

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 6

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