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Exam DP-100 topic 3 question 18 discussion

Actual exam question from Microsoft's DP-100
Question #: 18
Topic #: 3
[All DP-100 Questions]

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.
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Suggested Answer:
by meswapnilspal at May 8, 2020, 7:59 p.m.

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meswapnilspal
Highly Voted 4 years, 7 months ago
if salesData contains data in pivoted form ,the syntax should be, newsalesData = pd.melt(salesData, id_vars = ...............
upvoted 58 times
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FuzzyF
Highly Voted 4 years, 2 months ago
Just tried out: pd.melt(salesData, id_vars=['shop'], value_vars=[2017, 2018], var_name='year') works as desired. [2017, 2018] for value_vars is correct. Side note: without 'var_name' parameter a default name is given to column 2.
upvoted 40 times
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evangelist
Most Recent 7 months ago
salesData = pd.melt(salesData, id_vars='shop', value_vars=['2017', '2018'])
upvoted 1 times
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kay1101
7 months, 2 weeks ago
I think this question is missing a step of: whateverdfname = pd.DataFrame(....) then we can use salesData =pd.melt(whateverdfname, ...,...) so the second and third answer are correct, first box not sure.
upvoted 1 times
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Ahmed_Gehad
1 year, 5 months ago
The answer is wrong. It shall be pd.melt(salesData, id_vars=['shop'], value_vars=['2017', '2018'])
upvoted 2 times
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PI_Team
1 year, 5 months ago
import pandas as pd salesData = pd.DataFrame({ 'shop': ['ShopX', 'ShopY', 'ShopZ'], '2017': [34, 65, 48], '2018': [25, 76, 55] }) meltedData = pd.melt(salesData, id_vars=['shop'], value_vars=['2017', '2018'], var_name='year', value_name='value')
upvoted 2 times
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silva_831
2 years, 2 months ago
Please do not mislead the people. The correct answer should be as below: 1. salesData 2. shop 3. [2017, 2018]
upvoted 14 times
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MohammadKhubeb
2 years, 11 months ago
why not YEAR, because we are giving the values of col YEAR i.e., 2017,... ?
upvoted 1 times
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trickerk
3 years, 5 months ago
Correct answer: salesData shop [2017, 2018]
upvoted 9 times
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ljljljlj
3 years, 5 months ago
On exam 2021/7/10
upvoted 6 times
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ali25
3 years, 9 months ago
import pandas as pd salesData = pd.DataFrame({'shop': {0: 'shopx', 1: 'shopy', 2: 'shopz'}, '2017': {0: '34', 1: '65', 2: '48'}, '2018': {0: '25', 1: '76', 2: '55'}}) salesData salesData = salesData.reset_index() salesData salesData = pd.melt(salesData, id_vars =['shop'], value_vars=['2017', '2018']) salesData
upvoted 8 times
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Raxy
4 years ago
Is this a test question that will be included in real exam?
upvoted 3 times
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alphmzla
4 years ago
Just tried df = pd.melt(df, id_vars= ['Shop'], value_vars=['2017', '2018']), it returns desired outcome
upvoted 1 times
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worker_3141592
4 years, 2 months ago
import pandas as pd salesData = pd.DataFrame({'shop': {0: 'Shop X', 1: 'Shop Y', 2: 'Shop Z'}, '2017': {0: 34, 1: 65, 2: 48}, '2018': {0: 25, 1: 76, 2: 55}}) salesData = pd.melt(salesData,id_vars='shop',value_vars=['2017','2018']) print(salesData) ----------------------------------------------------------------------------------------------------------------------- shop variable value 0 Shop X 2017 34 1 Shop Y 2017 65 2 Shop Z 2017 48 3 Shop X 2018 25 4 Shop Y 2018 76 5 Shop Z 2018 55
upvoted 4 times
dsyouness
4 years, 2 months ago
but if we use dataFrame instead of salesData : dataFrame = pd.DataFrame({'shop': {0: 'Shop X', 1: 'Shop Y', 2: 'Shop Z'}, '2017': {0: 34, 1: 65, 2: 48}, '2018': {0: 25, 1: 76, 2: 55}})
upvoted 1 times
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SN22
4 years, 2 months ago
salesData is correct
upvoted 2 times
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ARC
4 years, 4 months ago
pd.melt(salesData, id_vars=['shop'], value_vars=['year'])
upvoted 2 times
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user11111
4 years, 4 months ago
Box 2 should be year, because that is the ID of the column you wish to unpivot. You cant use shop because shop doesn't have a value ['2017,'2018']
upvoted 2 times
user11111
4 years, 4 months ago
The above is wrong, one is not evaluating columns but rather unpivoting columns therefore Box should be shop. One is unpivoting ['2017,'2018'] relative to shop (column 1)
upvoted 4 times
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