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NEW QUESTION: 1
What does IBM require to verify a customer's environment before installation of an enterprise tape library?
A. Technical and delivery Assessment
B. Butterfly study
C. Submit a SCORE request
D. Product Installation and Planning guide
Answer: A
Explanation:
Explanation
When you buy an IBM hardware product, the business partner or IBM will generally perform a Technical Delivery Assessment (TDA) meeting with you to insure that the solution you're going to buy is appropriate for your environment and meets your needs.

NEW QUESTION: 2
Regarding the data forwarding between the FP IT AP and the controller, which of the following statements are correct? (Multiple choice)
A. In the local forwarding mode, the STA's DHCP, ARP and other messages are directly converted by the AP to the 802.3 format and sent to the access switch
B. In centralized forwarding mode, STA's DHCP messages will be sent to the controller through the tunnel for centralized processing
C. In the centralized forwarding mode, the outer Ethernet between the AP and the access switch carries the management VLAN tag
D. In the centralized forwarding mode, the wireless data message between the AP and the access switch is in the 802.11 format, and then encapsulated through the tunnel.
Answer: A,B,C,D

NEW QUESTION: 3
次の形式のsalesDataという名前のPythonデータフレームがあります。

データフレームは、次のように長いデータ形式にアンピボットする必要があります。

変換を実行するには、Pythonでpandas.melt（）関数を使用する必要があります。
コードセグメントをどのように完成させる必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

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