HOTSPOT -
You have a Python data frame named salesData in the following format:
 
The data frame must be unpivoted to a long data format as follows:
 
You need to use the pandas.melt() function in Python to perform the transformation.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
 
















 

Suggested Answer: 
    

Box 1: dataFrame -
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source]
Where frame is a DataFrame -
Box 2: shop -
Paramter id_vars id_vars : tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
Box 3: ['2017','2018']
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
Example:
df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})
pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])
A variable  value -
0  a        B      1
1  b        B      3
2  c        B      5
3  a        C      2
4  b        C      4
5  c        C      6
Reference:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

This question is in DP-100 Exam
For getting Microsoft Azure Data Scientist Associate Certificate


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