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NEW QUESTION: 1
You need to generate a list of all customer last names with their credit limits from the CUSTOMERS table. Those customers who do not have a credit limit should appear last in the list. Which two queries would achieve the required result? (Choose two.)
A. SELECT cust_last_name,cust_credit_limit FROM customers ORDER BY cust_credit_limit NULLS LAST;
B. SELECT cust_last_name,cust_credit_limit FROM customers ORDER BY cust_credit_limit DESC;
C. SELECT cust_last_name,cust_credit_limit FROM customers ORDER BY cust_credit_limit;
D. SELECT cust_last_name,cust_credit_limit FROM customers ORDER BY cust_last_name,cust_credit_limit NULLS LAST;
Answer: A,C
NEW QUESTION: 2
Which of the following fields contained in the IP/TCP/UDP headers can be used to make a routing decision when using policy-based routing? (Choose three)
A. Source TCP/UDP ports
B. TCP flags
C. Type of service.
D. Source IP address.
E. Checksum
Answer: A,C,D
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