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A file is a collection of information or data that is stored on a computer. You are already familiar with several file types, such as your audio, video, and text files.

Text files and binary files are the two categories into which we often split files. Simple text is contained in text files, as opposed to binary data, which can only be read by computers.

A group of files and subdirectories is called a directory or folder. A subdirectory is a directory present inside a directory. Numerous operating system functions can be carried out automatically.

File Operations Using Python

Python provides various methods to perform operations on the files and folders of the underlying operating system.

The OS module in Python has functions for adding and deleting folders, retrieving their contents, changing the directory, locating the current directory, and more. Import this module, we will use the listdir() method of it to fetch the files.

Similarly, the shutil module provides a number of functions for dealing with operations on files and associated collections. It gives users the option to copy and delete files. You can copy the contents of one folder to another using the shutil.copy(), shutil.copy2() and shutil.copytree() methods of this module.

You can include these functions in your file by importing their respective modules as shown below −

import

shutil shutil

submodule_name

(

arguments passed

)

Using shutil.copy() operation

Using this function, the text or content of the source file is copied to the target file or directories. Additionally, the permission mode of the file is preserved, but the file metadata (such as the “Date Creation”, “Date Modification” etc..) is not preserved.

Syntax

Following is the syntax of the shutil.copy() method−

shutil.copy(origin, target)

where

Origin − A string containing the source file’s location or path

Target − A string containing the destination file’s location or path.

Example

Following is an example of to copy files from one folder to other using shutil.copy() operation −

import

shutil files

listdir

(

origin

)

for

file_name

files

shutil

copy

(

origin

file_name

target

file_name

)

(

"Files are copied successfully"

)

Output

Following is an output of the above query:

Files are copied successfully

Note − Both a relative and an absolute path can be used to copy a file. The file’s location on the disc is indicated by the path

Here we are providing the folder path of both the source and the destination of the files.

Using shutil.copy2() operation

First of all, this function is exactly like copy() with the exception that it keeps track of the source file’s metadata.

The execution program for this is exact same as shutil.copy(). The only difference is that while fetching the file to directory, in place of shutil.copy() we write shutil.copy2().

shutil

copy2

(

origin

file_name

target

file_name

)

Syntax

Following is the syntax of the shutil.copy2() method –

shutil.copy2(origin, target)

Origin and target values are same as defined above.

The copy2() function in this code does one additional operation in addition to a copy() which is to keep the metadata.

Using shutil.copytree() method

This function moves a file and any subdirectories it contains from one directory to another.

This indicates that both the source and the destination include the file. The string must contain the names of both parameters.

Syntax

Following is the syntax of the shutil.copytree() method –

shutil.copytree(origin, target)

Origin and target values are same as defined above.

Example

Following is an example of to copy files from one folder to other using shutil.copytree() operation:

import

shutil

(

"File Copied Successfully"

)

Output

Following is an output of the above query:

File Copied Successfully

As an output we will be able to see the changes made after the execution i.e. the ‘Works’ folder gets copied to the ‘Works TP’ folder with the name of ‘newfolder’ as assigned in the code above containing all the files inside it which was there in the Works folder.

In order to obtain a duplicate of that file, we have included the copytree() function in this code.

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How To Copy A File In Python: Shutil.copy()

To copy a file in Python, use the shutil module’s copyfile() function.

from shutil import copyfile copyfile(src, dst)

This piece of code copies the contents of the file src to a destination file dst.

Both src and dst are strings that represent the names of the files (or rather, paths to the files).

If a file dst does not exist, a new file is created. If it does, the file behind that path is overridden.

Real-Life Example of How to Copy a File with Python

To understand how to copy a file in Python, you need to see an example.

Let’s start with a setup like this, where you have two files chúng tôi and chúng tôi in the same folder on your computer:

The chúng tôi is a program that copies the file called chúng tôi to a new file called chúng tôi .

Because you are working in the same folder, you do not need to worry about the full paths of the files.

All you need is the names of the files.

Here is the code:

from shutil import copyfile src = 'Example1.txt' dst = 'Example2.txt' copyfile(src, dst)

After running this piece of code, you should see a file called chúng tôi appear in the same folder:

This is how easy it is to copy a file using Python.

How to Copy a File to Different Folder

More often than not your files are not going to be in the same folder.

If this is the case, accessing the files with their names is not possible.

If you want to copy a file from one folder to another, you need to specify the path of the source and the destination.

The path obviously depends entirely on your file setup.

Let’s see a simple and demonstrative example.

Let’s say you have a folder called Project. Inside this folder, you have three subfolders called Scripts, Images, and Favorites.

Inside the Scripts folder, you have a file called chúng tôi . This is a program file that copies an image called chúng tôi from Images to Favorites.

Here is an illustration of the folder structure:

Because the code file, the source file, and the destination all lie in different folders, you need to work with paths instead of file names.

Here is how the chúng tôi file looks in the Scripts folder

from shutil import copyfile src = '../Images/zebra.png' dst = '../Favorites/zebra.png' copyfile(src, dst)

Let’s examine the paths to understand how they work.

The ../ refers to the parent folder. In the case of the Scripts folder, this refers to the Project folder in which the Images and Favorites folders live.

The parent folder has to be specified because otherwise, the chúng tôi script would search inside the Scripts folder which has no other files except for the code file.

Inside the Project folder, accessing the subfolders Images and Favorites is trivial:

Images/zebra.png is the path to the image.

Favorites/zebra.png is the desired path to the copied file.

Conclusion

Today you learned how to copy a file in Python using the shutil module’s copyfile() function:

from shutil import copyfile copyfile(src, dst)

Where:

src is the path of the file to be copied as a string.

dst is the destination file’s path as a string.

In case the source file and the destination file lie in the same folder, it is enough to use the names of the files.

Thanks for reading. Happy coding!

How To Copy A List In Python? (Why It Fails With Assignment)

In Python, copying a list can be unintuitive for beginners. This is because you cannot use the assignment operator to take an independent copy of a list. This can be confusing especially for beginner Pythonistas.

To create an independent copy of a list in Python, you need to use the copy module’s deepcopy() function like this:

import copy new_list = copy.deepcopy(old_list)

This comprehensive guide teaches you how to take an independent copy of a list in Python. You will also learn why doing it with an assignment operator doesn’t work.

The Problem with Copying Lists in Python

When dealing with Python variables, you can create a copy of a variable by assigning the existing variable to a new one.

For example:

a = 10 b = a

Now the variable b is a copy of variable a.

But if you copy a Python list in a similar fashion:

new_list = old_list

Any modifications made to the new_list also change the original list old_list.

It happens because new_list is actually not a copy of old_list. Instead, it is a reference to the same object in memory.

“Copying” a list just creates an alias for the same object in memory.

To create a completely independent copy of a list, use the copy module’s deepcopy() function.

import copy new_list = copy.deepcopy(old_list)

This is the only rational way to create a fully independent copy of a list in Python.

But if you don’t care about independent copies, there are many other ways to create a copy of a list in Python. These are:

copy() method. Creates a shallow copy.

[:] slicing operator. Creates a shallow copy.

list() function. Creates a shallow copy.

copy.copy() function. Creates a shallow copy.

copy.deepcopy() function. Creates a deep copy.

The following sections teach you how the assignment operator works, what is a shallow copy, and why copying “fails” with the assignment operator.

Assignment Operator (=) in Python

If you use the assignment operator (=) to copy a list in Python, you are not actually creating a copy of the object. Instead, you just give rise to a new variable that refers to the original list. The new variable is an alias to the original list.

Let’s see an example where we:

Create a list.

Assign or “copy” the list to a new variable.

Change the first number in the original list.

Print both lists.

And see what happens.

Here’s the code:

numbers = [1, 2, 3] new_numbers = numbers # Only change the original list numbers[0] = 100 print(numbers) print(new_numbers)

Output:

[100, 2, 3] [100, 2, 3]

Here you only changed the first element in the original numbers list. But this change also took place in the new_numbers list.

This happens because numbers and new_numbers are actually the very same list object.

Under the hood, both lists point to the same blob in memory.

Another way to verify this is by checking the memory address of these objects.

In Python, you can use the id() method to find out the memory address of any object.

Let’s check the memory addresses of both numbers and new_numbers.

print(id(numbers)) print(id(new_numbers))

Output:

140113161420416 140113161420416

The IDs are the same!

This verifies that the numbers and new_numbers are aliases pointing to the same list object in memory.

Think of the list object as a chunk of memory without a name. The numbers and new_numbers are just names via which you can access the list object in memory.

So when you create a new variable and assign a list object to it, you are introducing a new reference label to the original object.

To recap, the assignment operator (=) creates a new reference to an object in memory. It does not copy anything. This applies to lists as well as any other object in Python.

Next, let’s take a look at how you can actually copy list objects in Python.

The Copy Module in Python

As you learned, you cannot use the assignment operator to copy objects in Python. This is why there is a separate module, copy dedicated to copying Python objects.

The two key functions in the copy module are:

copy.copy()

copy.deepcopy()

Let’s take a look at what these functions do and what are the differences.

Shallow Copy: copy.copy()

In Python, a shallow copy can be created using copy.copy() function. A shallow copy solves our problem of copying a list in a way it does not depend on the original list.

For example:

import copy numbers = [1, 2, 3] # Independent copy of 'numbers' list new_numbers = copy.copy(numbers) numbers[0] = 100 print(numbers) print(new_numbers)

Output:

[100, 2, 3] [1, 2, 3]

As you can see, changing the first element in the original list did not change the copied list.

Let’s also verify the objects are not the same by using the id() function:

print(id(numbers)) print(id(new_numbers))

Output:

139764897739904 139764897692480

Horray! Now you know how to create a shallow copy of a list in Python. But mind the word shallow! It’s important to notice that sometimes you might deal with a list of lists.

In this case, the shallow copy does not behave the way you expect. Instead, it creates an independent copy of the outer list, but the inner lists are bound to the original list.

I know it sounds confusing.

Let me show what this means by running a simple experiment in which I:

Create a list of lists.

Create a shallow copy of the list.

Modify the first list’s first object.

import copy numbers = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] new_numbers = copy.copy(numbers) numbers[0][0] = 1000 print(numbers) print(new_numbers)

Output:

[[1000, 2, 3], [4, 5, 6], [7, 8, 9]] [[1000, 2, 3], [4, 5, 6], [7, 8, 9]]

Here changing the first element of the first list affects the copied version of the list even though the new list should be a copy of the original one.

But why does this happen?

Let’s first compare the IDs of the lists to see if they are the same object:

print(id(numbers)) print(id(new_numbers))

Output:

140602923260928 140602923261632

Even the IDs do not match! This means new_numbers should be a true copy of numbers—and it is!

But why do the values still change in the copied list?

This is because copy.copy() creates a shallow copy.

This means the whole list is copied, but the lists inside the list are not. In other words, the inner lists refer to the lists in the original list.

I know this sounds strange, but this is how it works.

Let’s verify this by checking the IDs of the lists inside the list:

print(id(numbers[0]), id(numbers[1]), id(numbers[2])) print(id(new_numbers[0]), id(new_numbers[1]), id(new_numbers[2]))

Output:

140685291558208 140685291498496 140685291708160 140685291558208 140685291498496 140685291708160

As you can see, all the inner list IDs are the same.

So the outer list is copied but the inner lists are still bound to the original list of lists.

To put it together, here is an illustration of how copy.copy() works on a list of lists.

This highlights the behavior of shallow copying in Python.

As stated earlier, to create a completely independent copy, use the copy.deepcopy() function. Let’s take a closer look at this function to see what it does.

Deep Copy: copy.deepcopy()

Another key function in the copy module is the deepcopy() function.

This function creates a completely independent copy of a list or any other compound object in Python.

For example, let’s repeat the example in the previous chapter using deepcopy():

import copy numbers = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] new_numbers = copy.deepcopy(numbers) numbers[0][0] = 1000 print(numbers) print(new_numbers)

Output:

[[1000, 2, 3], [4, 5, 6], [7, 8, 9]] [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

As you can see, changing the first element in the first list did not affect the copied list.

In other words, you have successfully created a completely independent copy of the original list.

No part in the deep-copied list points to the original list. Thus, a deep copy creates a truly independent copy.

I recommend you play with the examples to learn what is happening truly.

Copying a Number Object in Python

This guide would not be complete if we did not talk about copying other objects than lists. It is important to realize everything related to copying lists applies to copying any other Python object.

Let’s repeat the very first example in this guide using integers instead of lists.

In other words, let’s:

Create a number variable.

Copy the number to another variable using the assignment operator.

Change the original number.

See what happens to the copy.

a = 10 b = a a = 50 print(a, b)

Output:

50 10

As you can see, changing the original number a did not change the number b. This is probably something you would expect.

But this contradicts what we said earlier about copying Python objects: A Python object cannot be copied using the assignment operator.

However, looking at the above example, it seems b is an independent copy of a because changing a does not change b.

Even though this happens, b is not a copy of a. This is important to understand. You can verify this by checking the IDs of the variables before changing the value in a.

a = 10 b = a print(id(a)) print(id(b))

Output:

9789280 9789280

As you can see, the IDs match. In other words, a and b are both aliases to the same integer object in memory.

But why does changing a not change b then?

It all boils down to mutability.

First of all, you need to recall that a variable is just a label via which you can access an object that lives somewhere in memory. So if you change the value of a variable, you aren’t actually changing the variable itself, but the object in memory that it refers to.

In Python, integer objects are immutable. Immutability means you cannot make direct changes to integer objects. If you assign a new integer to a variable, you create a new integer object in memory and set the existing variable point to that new memory address.

On the other hand, a list is a mutable object. This means you can change the list object directly. So if you assign an existing list to a new variable, you are making the new variable point to the original list.

This describes Python mutability in a nutshell.

Now, let’s go back to the example of copying an integer. Let’s print the IDs of the variables before and after changing the value in a:

a = 10 b = a print(f"Before assignment id(a) = {id(a)}, id(b) = {id(b)}") a = 50 print(f"After assignment id(a) = {id(a)}, id(b) = {id(b)}")

Output:

Before assignment id(a) = 9789280, id(b) = 9789280 After assignment id(a) = 9790560, id(b) = 9789280

The IDs of variables a and b match before assigning a new value to a but not afterward.

In other words, before changing the value in a:

a and b point to the same integer object in memory.

And after changing the value in a:

a points to a new integer object in memory but b still points to where a used to point.

So after assigning a new value to variable a, it points to a new integer object in memory. This happens because of immutability. The integer object 10 cannot directly change. Instead, a new integer object needs to be created.

Here is a quick illustration of how the code works:

Assigning a new integer to a creates a new integer object where the variable a points to.

To recap, the assignment operator (=) cannot be used to copy objects in Python. However, when dealing with immutable objects, it looks as if this was the case. But it is not.

If someone tells you to copy a variable, technically you need to use copy.copy() or copy.deepcopy() instead of the assignment operator.

However, when dealing with immutable objects, this is unnecessary, as the behavior is the same regardless of whether you used copy module or assignment operator.

But with mutable objects, you need to use the copy module to create a real copy of the object.

At this point, you understand why the assignment operator does not copy objects in Python. You also learned how to use the copy module to create copies of Python objects.

Now that you understand what is a shallow copy and a deep copy, let’s put it all together by taking a look at 5 common ways to copy a list in Python.

5 Ways to Copy a List in Python

There are five main ways to copy a list in Python:

Let’s see examples of each of these

1. The copy() Method

As of Python 3.3, a list comes with a built-in copy() method. This method creates a shallow copy of the list.

For example:

numbers = [1, 2, 3] new_numbers = numbers.copy() print(numbers) print(new_numbers)

Output:

[1, 2, 3] [1, 2, 3] 2. The [:] Slicing operator

In Python, slicing means pulling a range of values from an iterable, such as a list.

Slicing goes with the syntax of:

iterable[start:end]

Where start specifies the starting index and end specifies the ending index.

If you do not specify the start parameter, slicing starts from the very first element. If you do not specify the end, the slicing ends at the very last element.

Calling iterable[:] returns a slice that represents the whole iterable. In other words, it returns a copy of a list when called on a list.

Notice that this also creates a shallow copy.

For instance:

numbers = [1, 2, 3] new_numbers = numbers[:] print(numbers) print(new_numbers)

Output:

[1, 2, 3] [1, 2, 3] 3. The list() Function

To convert an object to a list in Python, you can use the built-in list() function. This function creates a new list object for the input argument.

When you call the list() function on a list in Python, you force it to create a copy of the original list. The type of this copy is also shallow.

For instance:

numbers = [1, 2, 3] new_numbers = list(numbers) print(numbers) print(new_numbers)

Output:

[1, 2, 3] [1, 2, 3] 4. The copy.copy() Function

As discussed earlier in this guide, there is a dedicated module copy for copying Python objects.

One of the functions in this module is the copy() function. This function creates a shallow copy of a Python object. You can use copy.copy() to create a copy of a list.

For instance:

import copy numbers = [1, 2, 3] new_numbers = copy.copy(numbers) print(numbers) print(new_numbers)

Output:

[1, 2, 3] [1, 2, 3] 5. The copy.deepcopy() Function

The only way to create a truly independent deep copy of a Python object is by using the copy.deepcopy() function.

The difference between a shallow copy and a deep copy is only relevant to objects that consist of objects. This is comprehensively explained earlier in this guide.

You can use copy.deepcopy() to create a deep copy of a list.

For example:

import copy numbers = [1, 2, 3] new_numbers = copy.deepcopy(numbers) print(numbers) print(new_numbers)

Output:

[1, 2, 3] [1, 2, 3] Conclusion

Today you learned how to copy a Python list successfully.

To recap, copying using the assignment operator is not possible. Instead of copying, it creates a new alias to the original object. This means changing the original object changes the “copy” as well.

To truly copy an object in Python, use the copy module’s functions:

copy.copy() for a shallow copy where compound objects are bound to the original object.

copy.deepcopy() for a deep and completely independent copy.

No One Wants Another H

The first atomic test was atmospheric. From that day in 1945 and through the first two decades after the end of World War II, the United States and the Soviet Union conducted around 400 more atmospheric tests in total. France carried out its last atmospheric test in the 1970s, and China conducted the last atmospheric nuclear test to date in October 1980. Over half the population of the world is younger than the last nuclear detonation in the sky, but that might all change as tensions between the United States and North Korea edge toward a modern atomic brinkmanship.

North Korea is the only nation to test nuclear weapons in the 21st century. So far, all of North Korea’s tests were done underground, where the effects of the blast can be better contained. Testing an atmospheric blast means lofting a warhead vertically, above North Korea itself, or it means launching on a more horizontal trajectory, with the missile carrying a nuclear warhead traversing over a nearby country. While within the technical abilities of Kim Jong-un’s state, it took a failure of diplomatic understanding to even put the test on the proverbial negotiating table.

On Friday, Foreign Minister Ri Yong-ho said that, in response to President Trump’s threat at the United Nations to destroy North Korea, Pyongyang may take action, up to and including the “powerful detonation of an H-bomb in the Pacific,” according to Yonhap. The statement followed an escalating war of words that week, as the president of the world’s oldest nuclear power tried to constrain the world’s youngest.

To understand the risk posed by a possible new atmospheric test, it helps to step back to 1963, when the United States and the Soviet Union signed the Partial Test Ban Treaty. And we’ll need to narrow our scope a little—down to baby teeth.

Radioactive baby teeth

“Health concerns in the American public were rising, because of detectable levels of radiation in people’s bones,” says Alex Wellerstein, an assistant professor at the Stevens Institute of Technology who specializes in the history of nuclear weapons. “There was a big event called the baby tooth survey, where people were encouraged to send in their children’s baby teeth after they had fallen out. Scientists could use that in conjunction with the location and the age of the child to track how much Strontium 90 was getting into American bones.”

Strontium 90 is a radioactive isotope produced by nuclear fission. With atmospheric tests, the plume of radioactive by-product would stay in the atmosphere, mix with other clouds, and then come down when it rained, and end up in the ecosystem, like on grass. Cows would eat that grass, and then because strontium acts like calcium chemically, that strontium would end up in the milk, and then end up in human bones.

“Little bits of this stuff aren’t going to cause you lots of trouble,” says Wellerstein, “but as you raise that exposure up higher and higher and over large populations, you’re just adding little bits of uptick to the base chance of fatal cancer, which is already higher than people like to think about. Adding a couple percentage points in there for a population of 300 million starts to add up to thousands of people, even if it’s hard to detect which radioactive source the exposure was from specifically.”

Besides the health impact, there was a strategic reason to ban atmospheric tests, too. Testing underground limits the size of the weapons that a nation can develop. Not every nuclear power signed and abided by the Partial Test Ban Treaty: besides North Korea, France and China are still not signatories, though it’s been decades since either tested in a way that would violate the treaty. Should North Korea decide to test a weapon in the Pacific, it would also have the challenge of getting that weapon to the Pacific.

No safe trajectory to the Pacific

“The shortest pathway to open ocean is over Hokkaido the way they’ve gone with the two Hawsong tests,” says Vipin Narang, an associate professor of political science at MIT. “It’d have to be a trajectory that doesn’t look like it’s coming to the continental US or Guam, and gives them open ocean. That’s the Hokkaido trajectory, we’ve gotten used to it, they’ve gotten used it it, I think that’s probably the way they go, and it’s the thinnest part of Japan, so the risk to Japan is minimized.”

Twice in August, North Korea tested missiles in a flight over the southwestern peninsula of the island of Hokkaido in Japan. It was the first and second such launches over Japan in over a decade, and the only ones so far that are explicitly missiles, rather than satellite launch vehicles. The launches passed over only two small parts of the island, but they still passed directly through Japan’s airspace, and prompted text alerts for the public in case the tests were instead an attack. Minimizing risk here does not mean no risk, and that’s assuming the missiles perform as expected and carry on into the Pacific.

“The United States only once tested a live warhead on a ballistic missile on ballistic trajectory,” says Wellerstein. “It was Shot Frigatebird of Operation Dominick, which was a submarine launched missile.”

“In general, testing both at once adds a lot of risk and uncertainty and isn’t safe,” says Wellerstein. “There are worst case scenarios which can you prevent with clever engineering, like putting a sensor on the warhead that says ‘if I’m not where I’m supposed to be, don’t go off’, and hopefully North Korea will build those. Less catastrophic but still not good, the missile blows up on the warhead, which won’t explode ideally but may disperse plutonium, or it may blow up in midair. And missiles sometimes do explode on a launchpad and disperse plutonium all over, which is a contamination problem.”

There is the chance that the missile does not reach its intended spot in the Pacific, and explodes prematurely over land. It could also be targeted by missile defense systems, which have so far never succeeded against a target in realistic conditions (though some systems have had some successes in recent test exercises). A test that fails over land and results in deaths, especially if the warhead goes off.

“If something doesn’t go according to plan, and the warhead detonates at a lower altitude than intended and then there are effects on shipping or civil aviation or loss of life,” says Narang, “that’s a world-changing event, that’s an act of war, and I’m not sure how we’re going to climb down from that.”

“We’ve made it hard for the North Koreans to do that,” says Wellerstein. “If they give us forewarning and say, ‘hey, we’re going to test a missile’, we’ve made it clear we’re going to try and shoot down their missiles. You can’t have both, you can’t tell people you’re going to shoot down their missile, maybe, and tell them you’d like it if you gave them warning before the test.”

There could also be an electromagnetic pulse, though beyond the immediate area experiencing the blast, fire, and radiation, it’d be hard to say how much extra reach that electromagnetic pulse effect would have. Many planes in the United States have some protection against this, which is to say some protection against electrical storms, that might translate into protecting it from the pulse.

Outside the immediate blast area, there’s still that plume of radioactive gas.

“If a weapon goes off in the atmosphere, you’ll detect that radioactivity from huge distances, even distances where that radioactivity doesn’t pose any health threats,” says Wellerstein. “I think that’s going to make people very uncomfortable, as the difference between the radioactivity you can detect and the radioactivity that can hurt you is going to be lost on a lot of Americans.”

That plume, and the deadlier risks it entails, is one possible outcome of tensions and miscommunication between the White House and Pyongyang. Should North Korea be the first nation to conduct an atmospheric nuclear test in this century, it would not be the first nation to surprise the United States with such a test. In 1966, China detonated a nuclear weapon at high altitude while President Lyndon B. Johnson was visiting Thailand.

Relearning how to win another Cold War

“All the same rhetoric we used for Kim Jong-un, we used for Mao,” says Narang. “We said ‘he’s a madman, he can’t be trusted with nuclear weapons’. But we managed. Deterrence has a logic of its own, it’s a universal language. The reality is, Kim Jong-un has bought himself insurance against external regime change, invasion, probably efforts at disarmament. Should the United States attempt it, there’s a possibility of Guam or Japan or even the continental United States eating a nuke. This reality is why he bought himself nuclear weapons in the first place, so he didn’t meet the fate of Saddam and Gaddafi. He’s not going to give them up.”

For decades, the United States maintained a policy of denuclearization for North Korea, hoping some combination of sanctions and diplomatic pressure would convince the pariah state to abandon its nuclear ambitions. Despite the sanctions, despite attempts to isolate North Korea diplomatically, the country developed its own nuclear weapons. This progress capped off this summer with the tests of two intercontinental ballistic missiles and a thermonuclear bomb.

“And the fact is, he’s a nuclear weapons power at this point. We have to get out of the frame of trying to denuclearization, because that’s probably not going to happen,” says Narang. “It means learning how to practice deterrence, like we did with the Chinese and the Russians. It means dialogue and diplomacy at some level. We don’t have to like it, but this is the reality right now. Once you pass the threshold, the cost of denuclearizing is higher than practicing deterrence, which is something the United States is actually pretty good at.”

How To Migrate From Office 365 Tenant To Another Office 365 Tenant?

In this current business scenario, there are many organizations that are moving to the cloud. Most organizations prefer Microsoft Office 365 as it provides multiple functionalities for businesses like Outlook, Exchange Server, OneDrive, Skype for business server, etc.

Sometimes, migration need arise because of mergers, acquisitions, or divestment. Therefore, here we will disclose how you can migrate from Office 365 tenant to another Office 365 tenant?

But, first, you have to know there is no manual solution available to do this task, Microsoft also suggests their users to use third-party software to perform Office 365 tenant to another Office 365 migration.

So, here we will use the best automated solution to do this migration task. Let’s start the migration…

How to Migrate from Office 365 Tenant to another Office 365

When we talk about migration, the first thing that comes into the mind that is data consistency. However, there is multiple migration tool are available to do this task, but we have to select the right tool that will migrate Office 365 tenant to another Office 365 tenant without any type of data loss.

So, we would like to suggest SysTools Office 365 to Office 365 migration tool. After testing the tool, we have found this is the best tool to do this migration task. It comes with multiple which all are help you in Office 365 tenant to Office 365 tenant migration process. Some highlighted features are given below –

1 – Migrate emails, contacts, calendars, documents from Office 365 tenant to another Office 365.

2 – Import CSV option to create the mapping between Office 365 source & destination users.

3 – Date-based filter option to migrate only selective data from one Office 365 to another Office 365.

4 – Capable to migrate data from Office 365 domain.

5 – Delta migration option to migrate newly arrived data.

6 – Option to set Account-based priority to migrate data from these accounts.

7 – Provides multiple project migration options.

8 – Three options for re-run migration.

9 – Create a summary report of the entire Office 365 tenant to Office 365 tenant migration.

Procedure to Perform Office 365 tenant to Office 365 Tenant Migration

Get to know the working procedure of the tool to migrate data from Office 365 tenant to another Office 365 tenant. Follow the given steps –

1 – Select Office 365 as a source & as a destination.

3 – If you need to migrate only selective data you can apply Date-based filter.

4 – After applying all required options you need to login to Office 365 source tenant with Admin ID & Application ID.

8 – Now, browse the CSV file to upload that mapping CSV file into the software.

10 – Software will take few minutes to migrate from Office 365 tenant to another Office 365 tenant.

We have successfully performed Office 365 tenant to Office 365 tenant migration. Now, login Office 365 destination Office 365 tenant to access data.

Conclusion

To migrate data from Office 365 tenant to another Office 365 isn’t complicated if you know the right solution. So in the above article we have explained the right & easy solution to perform Office 365 tenant to Office 365 tenant migration it will help you to do this task easily.

Venn Torr

How To Copy Icloud Contacts To Google Contacts Using Iphone, Ipad, Mac, Or Pc

This handy tutorial shows you how to copy iCloud contacts on your iPhone or Mac to Google Contacts.

Why export iCloud contacts to Google/Gmail?

Most people moving from Android to iPhone look for ways to get Google contacts on iPhone. However, if you’re planning to sell your iPhone and move to Android, you must know how to have your iCloud contacts on Google so that they can appear on your Android phone.

Secondly, if you use Google Contacts for work or business, you might also want to have your iCloud contacts on it. This ensures everything is in one place.

Finally, your contacts are safely backed up and stored in iCloud. But to have them stored in a second location as well, you can copy iCloud contacts to Gmail. This way, if you accidentally delete one or all contacts from iCloud and can’t restore them, you still have a copy on Google.

How to copy iCloud contacts to Google

You have the same iCloud Contacts on all your Apple devices as they sync via iCloud. Therefore, depending on your choice, you can use your iPhone, iPad, or Mac to transfer them to Google Contacts. If you ask me, I prefer using my Mac.

On iPhone or iPad

Here’s how to copy iCloud contacts to Google on iPhone or iPad:

1) Get the Exports Contacts app and allow it to access your contacts.

2) Make sure vCard is selected, and tap Continue.

3) Once the app gets the file ready, tap Export.

4) Choose Save to Files from the iOS Share Sheet, select a folder, and tap Save.

5) Go to chúng tôi and sign in with your Google account if you aren’t already.

6) Tap the three lines button from the top left and choose Import.

7) Choose Select file and pick the chúng tôi file you saved in step 4 above.

8) Select Import.

Google Contacts will now import your iCloud Contacts and save them safely here. You can access these contacts anytime on chúng tôi Alternatively, if you add this Google account to your iPhone or Android phone, these contacts will automatically appear in the iOS or Android Contacts app.

On Mac

Copying your iCloud contacts to Google is effortless on your Mac, and you have two quick ways to do that.

Drag & drop inside the macOS Contacts app

Here’s how to have your iCloud contacts on Google using the Mac’s Contacts app:

1) Open the Contacts app and press Control + Command + S to show the groups sidebar. Here, do you see your Google account? If yes, move to step 3. If not, follow step 2.

3) From the left sidebar, select All iCloud, which will show only your iCloud contacts.

5) Now, drag the selected iCloud contacts onto your Google account shown in the left sidebar.

If you don’t want to add your Google account to your Mac, you can use the Contacts app to create a vCard (VCF file) of your iCloud contacts and then import that file to Google Contacts. Here’s how:

1) Open Mac’s Contacts app and press Command + A to select all your iCloud Contacts.

4) Visit chúng tôi and make sure you’re signed in using the Google account of your choice.

Google Contacts will upload the VCF file, read all the contacts in the file, and save them to your Google account.

Note that Google Contacts may fail to import all contacts if you have a huge number of contacts in a single VCF file (like above 700 contacts). In that case, try again or create several smaller vCard files.

On Windows PC

If you use a Windows PC, simply head over to chúng tôi in Microsoft Edge or Chrome and sign in with your Apple ID. After that, follow these steps to export iCloud contacts and import them to Google:

1) Inside iCloud, pick Contacts.

2) Select all your iCloud contacts by pressing Control + A.

4) Once you have your iCloud contacts downloaded to your PC, follow the same steps as above to import them to Google Contacts.

How to use only Google Contacts on iPhone (and not iCloud)

If you often switch mobile phones across platforms (iOS and Android), it would help to sync your contacts only to your Google account, which can be used on both platforms with ease.

Follow these steps to stop using iCloud Contacts on your iPhone and sync your contacts only to Google:

1) First, follow one of the above methods to export your iCloud contacts to Google.

2) After that, open iPhone Settings and tap your name from the top.

3) Tap iCloud and switch off Contacts. From the slide-up alert that appears, you can tap Delete from My iPhone. This will only remove the contacts from your device but keep them saved to iCloud and your other Apple devices. Plus, as these contacts are already in your Google account (step 1), you will see them on your iPhone again in step 5 below.

5) Once everything is set, open the Contacts app, and you should see all your phone numbers here.

You’re now only using Google Contacts on your iPhone and not iCloud Contacts. Any new contact you save will be stored in your Google account. Plus, if you delete a contact from your iPhone, the same will also disappear from Google and your Android phone or iPhone where you’re using this Google account.

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