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NEW QUESTION: 1
이해 관계자는 종종 새로운 기능이 프로젝트에 포함되도록 요청합니다. 이해 관계자는 프로젝트 예산 및 일정에 영향을 줄 새로운 기능 향상을 제출했습니다. 프로젝트 관리자는 어떻게 해야 합니까?
A. 프로젝트 팀이 일정에 따라 프로젝트 목표를 신속하게 전달하도록 요청
B. 변경 관리 계획 업데이트
C. 변경 제어 보드 (CCB)에 변경 요청 제출
D. 이해 관계자와 회의를 열고 개시일을 재협상
Answer: C
NEW QUESTION: 2
開発者は、Adobe Dynamic TagManagementを使用して単一ページアプリケーションにタグを付ける必要があります。ユーザーが「ページ」から「ページ」に移動すると、アプリケーションのURLが変更されます。
イベントベースのルールにはどのイベントタイプを使用する必要がありますか？
A. ページが読み込まれました
B. カスタム
C. フォーカス
D. pushStateまたはhashchange
Answer: D
Explanation:
Explanation
https://marketing.adobe.com/resources/help/en_US/dtm/t_rules_event_conditions.html
NEW QUESTION: 3
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.
Answer:
Explanation:
Explanation:
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
References:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html
