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Preprocessing Data
course content

Course Content

Preprocessing Data

Preprocessing Data

1. Data Exploration
Data ExtractingExplore the DatasetData Types
2. Data Cleaning
Missing and Wrong DataWork with NaNsReplace Numerical Missing Data with ValuesDealing with Missing AgesReplace Categorical Missing Data with ValuesReplace Missing Values with InterpolationRemoving DuplicatesRemoving OutliersVisualizing Outliers
3. Data Validation
Types ConversionData Binning
4. Normalization & Standardization
Data NormalizationData Standardization
5. Data Encoding
Label EncodingOne Hot Encoding

bookReplace Missing Values with Interpolation

Another approach to deal with numerical data is using interpolation. Each NaN value will be replaced with the result of interpolation between the previous and the next entry over the column. Let's apply the interpolate() function to numeric column Age by setting the limit direction to forward. This means that linear interpolation is applied from the first line to the last.


              
1

            
data = data.interpolate(method = 'linear', limit_direction = 'forward')
copy

Task

Fill the empty places in the code. Compare the data in Age column before and after using interpolation (look at the last 10 rows).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 6
toggle bottom row

bookReplace Missing Values with Interpolation

Another approach to deal with numerical data is using interpolation. Each NaN value will be replaced with the result of interpolation between the previous and the next entry over the column. Let's apply the interpolate() function to numeric column Age by setting the limit direction to forward. This means that linear interpolation is applied from the first line to the last.


              
1

            
data = data.interpolate(method = 'linear', limit_direction = 'forward')
copy

Task

Fill the empty places in the code. Compare the data in Age column before and after using interpolation (look at the last 10 rows).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 6
toggle bottom row

bookReplace Missing Values with Interpolation

Another approach to deal with numerical data is using interpolation. Each NaN value will be replaced with the result of interpolation between the previous and the next entry over the column. Let's apply the interpolate() function to numeric column Age by setting the limit direction to forward. This means that linear interpolation is applied from the first line to the last.


              
1

            
data = data.interpolate(method = 'linear', limit_direction = 'forward')
copy

Task

Fill the empty places in the code. Compare the data in Age column before and after using interpolation (look at the last 10 rows).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Another approach to deal with numerical data is using interpolation. Each NaN value will be replaced with the result of interpolation between the previous and the next entry over the column. Let's apply the interpolate() function to numeric column Age by setting the limit direction to forward. This means that linear interpolation is applied from the first line to the last.


              
1

            
data = data.interpolate(method = 'linear', limit_direction = 'forward')
copy

Task

Fill the empty places in the code. Compare the data in Age column before and after using interpolation (look at the last 10 rows).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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