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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:
 <a href='http
Step1:  <strong>Now you can use the .groupby() method to group rows together based off of a column name.<br>For instance let's group based off of Company. This will create a DataFrameGroupBy object
Step2:  You can save this object as a new variable
Step3:  And then call aggregate methods off the object
Step4:  More examples of aggregate methods
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Python Code:
import pandas as pd
# Create dataframe
data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
       'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
       'Sales':[200,120,340,124,243,350]}
df = pd.DataFrame(data)
df
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<center><em>Copyright Pierian Data</em></center>
<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>
Groupby
The groupby method allows you to group rows of data together and call aggregate functions
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df.groupby('Company')
Explanation: <strong>Now you can use the .groupby() method to group rows together based off of a column name.<br>For instance let's group based off of Company. This will create a DataFrameGroupBy object:</strong>
End of explanation
by_comp = df.groupby("Company")
Explanation: You can save this object as a new variable:
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by_comp.mean()
df.groupby('Company').mean()
Explanation: And then call aggregate methods off the object:
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by_comp.std()
by_comp.min()
by_comp.max()
by_comp.count()
by_comp.describe()
by_comp.describe().transpose()
by_comp.describe().transpose()['GOOG']
Explanation: More examples of aggregate methods:
End of explanation
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