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pshichka [43]
3 years ago
9

:P 80 points to spare so i guess its free!!!!!!

Computers and Technology
2 answers:
erica [24]3 years ago
8 0

Answer:

Thanks!

Explanation:

Naddik [55]3 years ago
5 0

Answer:

Thank you! can i have brainliest please?

Explanation:

You might be interested in
In a PERT network, non-critical activities that have little slack need to be monitored closely
kozerog [31]

Answer: b)because near-critical paths could become critical paths with small delays in these activities

Explanation: Program evaluation and review technique(PERT) technique is used for management of the project .The organizing, maintenance,coordination of any task is done by this chart. The non-critical activities need to be observed carefully because they may create near-critical methods which can lead to complexity.

Other options are incorrect because not all activities are equally rather monitoring is done to avoid errors and slack can occur at any path . Small delays can be caused but not complete project can get delayed and there can be risk of complexity rather than being uncompleted.Thus, the correct option is option(b).

6 0
3 years ago
Un comerciante de régimen código para comprar mercancías gravadas a un comerciante de régimen simplificado precio de venta al pú
Genrish500 [490]

Answer:

El valor sin impuestos de las mercancías era de $1.512.605.

Explanation:

Dado que un comerciante de régimen código para comprar mercancías gravadas a un comerciante de régimen simplificado pagó un precio de venta al público 1.800.000 incluido el IVA tarifa general (19%), para determinar el valor de las mercancías sin el impuesto incluido se debe realizar el siguiente cálculo:

1.19 = 1.800.000

1 = X

1.800.000 / 1.19 = X

1.512.605 = X

Por lo tanto, el valor sin impuestos de las mercancías era de $1.512.605.

5 0
3 years ago
Answer the following Python Interview questions • How is Python an interpreted language? • What is the difference between Python
8_murik_8 [283]

Answer:

1. Python is called as interpreted language. However as a programming language it is not fully compiled nor interpreted language. Python program runs from direct source code which makes it byte code interpreted.

An Interpreter is the one which takes the code and performs the actions specified in the code. It turns the code into intermediate language which is again translated to machine language understood by the processor. Interpreted or compiled are the property of the implementation and not of the language.

2. Array : It is a collection of same data type elements stored at contagious memory location. It is handled in python with module array. All the elements of array must be of same data type. In order to manipulate same data types arrays are used.

Ex: array1 = a.array ('i', [10, 20, 30]) this is a array of integer type.

Lists : Python lists are ordered data structure and are like non homogeneous dynamic sized arrays. It may contain integers, strings, boolean, or objects.

Ex: List1 = [70, 89, 98] , List2 = ["Rose", "Lilly", "Jasmine"]

List3 = [1, 10, 100, 'Test', 'Test1']

Tuple : It is a collection of objects separated by commas and they are immutable. It is static which makes them faster.

Ex: tupule1 = ('element1', 'element2')

List and Tuple in Python are the class of data structure. The list is dynamic, whereas tuple has static characteristics.

Lists are mutable but tuples are not.

tuples are mainly used to access the elements where as lists are used in operations like insertion and deletion.

Iterations are time consuming in list where as it is faster in tuples.

tuples don't have inbuilt methods but list has many builtin methods.

tuples takes slightly less memory that lists in Python

Records: Records data structure will have fixed number of fields and each field may have a name and different type. Immutable records are implemented using named tuple.

3. Syntax of slice in python is list[<start>:<stop>:<step>] and it can be used on tuples and lists.

so X [::-1] means that it will start from the end towards the first element by taking each of the elements.

for ex: X = '9876'

X [::-1] will result in '6789'.

Means it will reverse all the elements in the array.

4. Items of list can be shuffled with the random.shuffle() function of a random module.

Syntax is : random.shuffle(x, random)

x- It is a sequence to shuffle and can be list or tuple.

random- it is a optional argument which returns random float number between 0.1 to 1.0.

5. range() – Range function returns a range object which is a type of iterable object.

xrange() – xrange function returns the generator object that can be used to display numbers only by looping. Only particular range is displayed on demand and hence called “lazy evaluation“.

• Return type of range () is range object whereas that of xrange() is xrange object.

• Variable to store the range using range () takes more memory but xrange takes comparative less memory.

• Range returns the list but xrange returns the xrange object. Hence operations on list can be applied for range but not on xrange.

• Xrange is faster to implement than range as xrange evaluates only generator objects.

• Xrange is depreciated in Python 3 and above.

For ex :

x = range (10, 100)

y= xrange (10, 100)

#To know the return type we can print it

print ( return type of range () is : “)

print (type (x))

print ( return type of xrange () is : “)

print (type (y))

Output will be list and xrange respectively.

6. NumPy's arrays are more compact than Python lists

reading and writing items is also faster with NumPy.

Memory taken by python lists are way higher than NumPy arrays.

Python lists don’t support vectorized operation.

Since lists can contain objects of different types its type information must be stored and executed every time operation is performed on it.

Memory taken by python lists are a lot higher than that of NumPy Arrays.

Reading and writing of elements in NumPy arrays are faster than lists.

NumPy arrays are compact and accumulate lesser storage.

Numpy is convenient and efficient.

For ex :

Metrics operations are easy in NumPy.

Checkerboard pattern can be done using NumPy.

7. Attached as Image

8. split() method returns a list of strings after breaking the given string by the specified separator. It is splitting of string into list with each word is a list item.

Syntax : str.split ( separator, maxsplit)

Separator : its is delimiter used and by default whitespace is used as separator.

Maxsplit : Maximum number of times to split the string. By default it has no limit.

For ex:

text = 'apples and oranges are different '

print(text.split())

output will be : ['apples', 'and', 'oranges', 'are', 'different' ]

Explanation:

7 0
3 years ago
Please!! I need help, this is due by tonight!!!
lyudmila [28]

Answer:

sorry if it is too late but I think it is d

Explanation:

4 0
3 years ago
Consider the following two tables where EmployeeNum is primary key in both tables. What is the result of combining the two table
oee [108]

Answer:

The result of combining the both tables is:

Employee(EmployeeNum, LastName, FirstName, WageRate, SocSecNum, DepartmentNum, Street, City, State, PostalCode)

Explanation:

Since the EmployeeNum is the primary and it is available in both tables, what happens is that, it will not list the primary key column twice. It will list the primary key first then all the other attribute in the first table followed by the attribute in the second table. And it will take note so as not to repeat attribute that already occur in the first table.

For instance; in this case, besides EmployeeNum, LastName and FirstName also appear in the both tables but only one instance of them were listed in the resulting table.

4 0
3 years ago
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