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NEW QUESTION: 1
A. Option E
B. Option C
C. Option A
D. Option B
E. Option D
Answer: B
NEW QUESTION: 2
Before completing a nursing diagnosis, the nurse must first:
A. Plan interventions
B. Write goals and objectives
C. Perform an assessment
D. Perform evaluation
Answer: C
Explanation:
(A) Goals and objectives are based on a nursing assessment and diagnosis. (B) Assessment is the first step of nursing process. (C) Interventions are nursing actions to meet goals and objectives. (D) Evaluation process follows nursing interventions.
NEW QUESTION: 3
Sie müssen ein Netzwerk erstellen, das die folgenden Anforderungen erfüllt. Anforderungen:
1) Das Remote-Verkaufsteam muss in der Lage sein, eine Verbindung zum Unternehmensnetzwerk herzustellen
2) Jede Abteilung darf NUR zwei PCs und einen Server haben
3) Jede Abteilung muss voneinander segmentiert sein
4) Die folgenden VLANs wurden festgelegt:
a) VLAN 2- Finanzen
b) VLAN 12 - Vertrieb
c) VLAN 22 - Personalwesen (HR)
Vervollständigen Sie das Netzwerk, indem Sie die erforderlichen Objekte in das Diagramm ziehen
ANWEISUNGEN: Objekte können mehrfach verwendet werden und nicht alle Platzhalter müssen ausgefüllt werden
Answer:
Explanation:
Explanation:
NEW QUESTION: 4
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