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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Fire up GraphLab create
Step1:  Load a tabular dataset
SFrame is a tabular, column-mutable dataframe object that can scale to big data. The data in SFrame is stored column-wise, and is stored on persistent storage (e.g. disk) to avoid being constrained by memory size. Each column in an SFrame is a size-immutable SArray, but SFrames are mutable in that columns can be added and subtracted with ease. An SFrame essentially acts as an ordered dict of SArrays.
Step2:  SFrame basics
Step3:  Inspect Dataset
Step4:  Creating new columns
Step5:  Apply Function for Data transformation
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Python Code:
import graphlab as gl
Explanation: Fire up GraphLab create
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sf = gl.SFrame('data/people-example.csv')
Explanation: Load a tabular dataset
SFrame is a tabular, column-mutable dataframe object that can scale to big data. The data in SFrame is stored column-wise, and is stored on persistent storage (e.g. disk) to avoid being constrained by memory size. Each column in an SFrame is a size-immutable SArray, but SFrames are mutable in that columns can be added and subtracted with ease. An SFrame essentially acts as an ordered dict of SArrays.
End of explanation
sf # we can view first few lines of the table
sf.head()
sf.tail()
Explanation: SFrame basics
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sf['Country']
sf['age'].mean()
Explanation: Inspect Dataset
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sf
sf['Full Name'] = sf['First Name'] + ' ' + sf['Last Name']
sf
Explanation: Creating new columns
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sf['Country']
def transform_country(country):
    return 'United States' if country == 'USA' else country
transform_country('USA')
transform_country('India')
sf['Country'] = sf['Country'].apply(transform_country)
sf
Explanation: Apply Function for Data transformation
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