Barack Obama, the 44th President of the United States (2009 - 2017), initiated and implemented several key reforms and policy changes during his two-term presidency. His administration saw transformations particularly around healthcare, economic stabilization, environmental regulations, international relations, and social issues.

1. Patient Protection and Affordable Care Act (Obamacare): Arguably his most significant reform, the ACA was enacted in 2010 with the aim of improving access to healthcare, reducing healthcare costs, and offering new protections for people with pre-existing conditions. The ACA has significantly decreased the number of uninsured Americans, though it has also been noted for ongoing controversies regarding its cost and overall efficiency.

2. Economic Stimulus & Financial Reform: In response to the 2008 financial crisis, Obama signed the American Recovery and Reinvestment Act in 2009 which, among other functions, aimed to preserve and create jobs, spur economic activity and invest in long-term growth. Additionally, the Dodd–Frank Wall Street Reform and Consumer Protection Act was passed in 2010 to prevent the recurrence of conditions that led to the 2008 financial crisis.

3. Environmental Policy: Obama’s administration took significant steps to address climate change, such as signing the Paris Agreement in 2015. Other major regulations include the Clean Power Plan, which aimed to reduce carbon pollution from power plants, and the increase of Corporate Average Fuel Economy (CAFE) standards, which aimed to improve the average fuel economy of cars and light trucks produced for sale in the United States.

4. Foreign Policy: The Obama administration saw the normalization of U.S. relations with Cuba, the implementation of the Iran nuclear deal, and the pivotal decision to send Navy Seals to kill Osama bin Laden in Pakistan.

5. Social issues: Obama publicly supported same-sex marriage, leading the repeal of the Defense of Marriage Act and the "Don't ask, don't tell" policy, which prohibited openly gay, lesbian, and bisexual people from serving in the military. His administration also implemented DACA (Deferred Action for Childhood Arrivals), which provided relief from deportation to eligible immigrant youth who came to the U.S. when they were children.

Remember, policy effectiveness and the judgement of positive and negative impact tend to be largely subjective and can depend on an individual's political ideologies and economic beliefs. It's always best to read a variety of sources to get a well-rounded understanding of the complex impacts of these policies.

Python provides several methods to reverse a list. Here are a few common ones, each with their own advantages:

1. **Using the inbuilt `reverse()` function:** This method modifies the original list.

```python
list = [1, 2, 3, 4, 5]
list.reverse()
print(list) # Output: [5, 4, 3, 2, 1]
```

Keep in mind that this changes the original list. If you need to the keep the original list as it is, consider using the second method.

2. **Using slicing:** This technique creates a reversed copy of the list.

```python
list = [1, 2, 3, 4, 5]
reversed_list = list[::-1]
print(reversed_list) # Output: [5, 4, 3, 2, 1]
```

The `[::-1]` slice is a quick and concise way to make a reversed copy of a list. Your original list remains unchanged.

3. **Using the `reversed()` function:** This returns a reverse iterator which is useful if you just need to iterate over the list in reverse order, but don't actually need a reversed list. This also doesn't modify the original list.

```python
list = [1, 2, 3, 4, 5]
for item in reversed(list):
print(item)
```

This will print the list items in reverse order, but if you check your original list, it is still intact.

In terms of efficiency, if you're just trying to iterate through a list in reverse, the `reversed()` function is likely the most efficient as it does not require extra space to store a reversed version of your list. However, keep in mind the functionality you need for your application. If you require a reversed version of your list to use in more than one place in your code, using the `reverse()` function or list slicing may be more efficient to avoid creating multiple reversed iterators.

In Python, exceptions can be handled using the try, except, and finally statements.

Here's how it generally looks:

```python
try:
# code in this block where exception might occur
except SomeException:
# code to handle the exception
finally:
# code that will be executed no matter what, exception or not
```

The code in the `try` block is the action that you want to monitor for exceptions. The code in the `except` block is what will be executed if an exception of the specified type is raised in the try block.

Let's demonstrate a simple example:

```python
try:
x = int('five')
except ValueError:
print("That's not a valid number!")
```

In the above example, trying to convert a string that's not a number to an integer raises a `ValueError`. The `except` block will catch this exception and print the message, "That's not a valid number!" You can catch multiple exceptions by using multiple `except` statements.

```python
try:
# some code here
except ValueError:
# handle ValueError exception
except (TypeError, ZeroDivisionError):
# handle multiple exceptions
except:
# handle all other exceptions
```

Note that 'except:' catches all kinds of exceptions which might not be a good practice as you might end up catching unexpected errors. It's generally better to catch and handle exceptions that you understand and anticipate.

A `finally` block can be added at the end which will always execute after try and except blocks, regardless whether an exception has been raised or not. This is often used for cleanup code.

```python
try:
x = int('five')
except ValueError:
print("That's not a valid number!")
finally:
print("Done processing.")
```

In addition to this, there's the `else` clause which executes if the try block does not raise any exception.

```python
try:
x = int('5')
except ValueError:
print("That's not a valid number!")
else:
print("No exception occurred.")
finally:
print("Done processing.")
```

In this code, "No exception occurred." will be printed, because converting '5' to an integer does not raise a ValueError. The `finally` block is executed no matter what and "Done processing." will be also printed.

Understanding these control structures for handling exceptions will allow you to write more robust Python code. It's always good to anticipate and handle possible exceptions, providing thoughtful error messages to help with debugging.

Sure, the difference between lists and tuples in Python is quite central to understanding the language. Both lists and tuples are sequence types that can store a collection of items, but there are critical differences that set them apart.

1. **Mutability**: The most significant difference is that lists are mutable whereas tuples are immutable. This means you can change the contents of a list after it's been created by adding, removing, or changing elements. However, once a tuple is created, you cannot change it. If you need to add or remove elements, you'd have to create a new tuple. Here's an example:

```python
# List -- mutable
my_list = ['apple', 'banana', 'cherry']
my_list[1] = 'blueberry'
print(my_list) # Output: ['apple', 'blueberry', 'cherry']

# Tuple --Immutable
my_tuple = ('apple', 'banana', 'cherry')
my_tuple[1] = 'blueberry' # This will raise a TypeError
```

2. **Purpose and Usage**: Lists are meant to hold homogenous (similar type) items (though Python doesn't enforce this), which means it is often used to store multiple instances of the same kind. On the other hand, tuples are typically used to hold heterogeneous (different types) items — they can be thought of as a single object consisting of multiple parts.

3. **Function and Method Availability**: Since lists are mutable, they have many more built-in functions and methods you can use, such as `.append()`, `.remove()`, `.insert()`, `.sort()`, etc. Tuples have fewer functions, as they don't allow modifications.

4. **Performance**: Tuples can be more efficient than lists. If your data doesn't have to change, you should use tuples over lists because tuples are lighter and more performance-efficient.

Understanding whether you want a mutable or immutable collection can help decide when you'd want to use a list versus a tuple. If you aren't going to modify the list of elements and all you'll be doing is iterating through the items, you can use tuples. For anything where you need to modify the list of items, you'll have to use a list.

Sure, I'd be glad to help. Writing to a file in Python is usually done using the built-in `open()` function with the appropriate mode.

Here's a simple example:

```python
# Open the file with write permission.
f = open('filename.txt', 'w')

# Write some text to the file.
f.write("Hello, world!")

# Close the file.
f.close()
```

In this code:

1. `'filename.txt'` is the name of the file. You can replace this with your file's name.
2. `'w'` stands for 'write'. It's the mode we open the file in.

Here are the different modes you can provide to the `open()` function:

- `'r'` : use for reading
- `'w'` : use for writing
- `'x'` : use for creating and writing to a new file
- `'a'` : use for appending to a file
- `'b'` : use for binary mode
- `'t'` : use for text mode
- `'+'` : use for updating (reading and writing)

Remember to always close the file after you are done to free up system resources. If you want to avoid having to remember to close the file, use the `with` keyword, which will automatically close the file when done:

```python
with open('filename.txt', 'w') as f:
f.write("Hello, world!")
```

In this version of the code, `f.close()` is unnecessary. The file is automatically closed as soon as you leave the `with` block.

Just be aware that when you write to a file using the `'w'` mode, any existing content in the file is deleted. If you want to add to an existing file without deleting its content, use the append (`'a'`) mode instead of the write (`'w'`) mode.

Regular expressions (also known as RegEx) are essentially sequences of characters that form a search pattern. These can be utilized for string matching and "find" or "find and replace" operations.

In Python, the `re` module provides support for regular expressions. Here's how to use it:

1. Import the `re` module using: `import re`.
2. After importing the module, you can use the `re.match()`, `re.search()`, `re.findall()`, `re.split()`, and `re.sub()` functions.

Here are some examples:

1. **re.match()**: This function will search the regular expression pattern and then return the first occurrence. The Python RegEx Match method checks for a match only at the beginning of the string.

```python
import re
result = re.match(r'AV', 'AVAnalytics VidhyaAV')
print(result.group(0))
```

2. **re.search()**: This method is similar to `re.match()` but it doesn't limit us to find matches at the beginning of the string only.

```python
import re
result = re.search(r'Analytics', 'AVAnalytics VidhyaAV')
print (result.group(0))
```

3. **re.findall()**: Returns all non-overlapping matches of the pattern in the string as a list of strings.

```python
import re
result = re.findall(r'AV', 'AV Analytics Vidhya AV')
print(result)
```

4. **re.split()**: This splits the string where there is a match and returns a list of strings where the splits have occurred.

```python
import re
result=re.split(r'y','Analytics')
print(result)
```

5. **re.sub()**: This method replaces all occurrences of the RE pattern in the string with a replacement string, and returns the resulting string.

```python
import re
result=re.sub(r'India','the World','AV is largest Analytics community of India')
print (result)
```

A quick note on metacharacters. These are characters that have a special meaning: . ^ $ * + ? { } [ ] \ | ( )

Also, remember to always incorporate error handling when working with regular expressions to mitigate any potential errors in your code.

Overall, regular expressions are a powerful tool for manipulating text and data. They can be a bit tricky to get the hang of initially, but with practice, they can greatly streamline your coding, especially for text processing tasks.

Python decorators are a significant part of Python programming. In simple terms, a decorator in Python is a function that takes another function as its argument, and extends or modifies the behavior of this input function without explicitly modifying it. They are represented by the @decorator_name in Python and are called in a bottom-up manner.

Consider an example of a simple Python function:

def simple_function():
print("Hello, World!")

Now, suppose we want to extend this function so that it not only prints "Hello, World!", but also tells us the time at which it was printed. One way to do this would be to modify the original function like so:

import time

def simple_function():
print(time.ctime())
print("Hello, World!")

However, modifying the original function is not always desirable or practical, especially if the function is complex or used in multiple places. This is where decorators come into play.

Instead of modifying the original function, we can create a decorator that adds the desired functionality:

import time

def time_decorator(func):
def wrapper():
print(time.ctime())
func()
return wrapper

@time_decorator
def simple_function():
print("Hello, World!")

In this case, the `@time_decorator` before the `simple_function()` is Python's decorator syntax. By using this syntax, we are wrapping `simple_function()` inside the `wrapper()` function inside `time_decorator()`. This extends `simple_function()` to first print the current time, and then to print "Hello, World!", all without modifying the original `simple_function()`.

Python decorators can be a bit tricky to understand at first, especially since they involve some complex concepts like functions as arguments and closure. However, they are a very powerful tool that allows for more readable and maintainable code once you get the hang of it.

A virtual environment in Python is an isolated environment where you can have a specific version of Python and its packages, separate from the global Python installed on your system. This can be very useful when you have different projects requiring different versions of Python and its packages.

Now, let's walk you through the process of creating a virtual environment in Python:

You can create a virtual environment using `venv`, which is a standard module in Python 3. It does not come with Python 2.x versions.

Here are the steps to follow:

1. First, open a terminal.
2. Use the `cd` command to navigate to the directory where you want to create the virtual environment. For example:

```bash
cd /path/to/your/project
```

3. Run the following command to create a new virtual environment. Replace `myenv` with whatever name you want to give to your virtual environment.

```bash
python3 -m venv myenv
```

This command will create a new directory called `myenv` (or whatever name you provided) that contains the virtual environment. This includes a fresh copy of Python interpreter, the Pip package manager, standard python library and other supporting files.

To start using this virtual environment, you need to activate it:

- On macOS/Linux, you can do this by running this command:

```bash
source myenv/bin/activate
```

- On Windows, use this command:

```bash
.\myenv\Scripts\activate
```

Once your virtual environment is activated, the name of your virtual environment will appear on the left side of the terminal prompt. This is an indication that the environment is active. Now you can install packages into the isolated environment. For example, to install the latest version of django, you can type `pip install django`.

To stop using the virtual environment and return to the normal environment, you simply need to deactivate it by running:

```bash
deactivate
```

The name of the virtual environment should now be gone from the terminal prompt indicating that you are no longer in an activated environment.

Remember, a virtual environment is a tool to keep the dependencies required by different projects in separate places by creating isolated Python environments for them. This is one of the many ways to manage dependencies in Python and it's a great way to ensure that your Python environment stays clean and manageable.

In Python programming, `__name__ == "__main__"` serves as an entry point to run scripts. Understanding it requires some familiarity with two special variables in Python: `__name__` and `__main__`.

The `__name__` variable: Python files can act as reusable modules or as standalone programs. When you run a Python file, Python creates a special built-in variable called `__name__` for any module. If the module is the main program (i.e., the script you are directly running), Python sets this variable to `__main__`. If the file is being imported from another module, `__name__` gets set to the module's name.

So, what does `if __name__ == "__main__": `do? This line checks if the module is being run directly or being imported. If the code is being run directly, then `__name__` is set to `__main__`, and the code under this `if` statement will get executed. If the file is being imported as a module to another program, then the `if` condition will be false and the code under this block won't get executed.

This feature is particularly useful when developers write modules as it allows the module to be imported by other modules without running the entire script, but still permitting the script to be run independently for testing purposes.

To illustrate, consider the following script `sample.py`:

```python
def function():
print("Function executed.")

print("Module name: " + __name__)

if __name__ == "__main__":
function()
```

When run directly with a command such as `python sample.py`, the output will be:

```
Module name: __main__
Function executed.
```

If `sample.py` is imported into another script, `function()` won't be called:

```python
import sample
```

Over here the output will be:

```
Module name: sample
```

The output "Function executed." is omitted because `__name__` is not `__main__` in this case, indicating that `sample.py` is not the main program, but rather a module being imported by the main program.

In conclusion, the use of `__name__ == "__main__"` acts as a Python idiom to identify whether the module is being used standalone or being imported elsewhere. This conditional allows certain code to be run when the module is being run standalone, primarily for tests, but prevents this code from being run when the module is imported from other scripts.

BitConnect was an open-source cryptocurrency which was associated with an investment lending platform. However, it was ultimately found to be a Ponzi scheme, a fraudulent investing scam promising high rates of return with little risk to investors. The BitConnect Scheme was reminiscent of classic Ponzi Schemes, where those who invested early were paid returns using the investments of newer entrants.

As for what happened to the people behind the scams, the situation is complex:

1. Although the detailed identity of many people involved in BitConnect is still unknown, some arrests have been made.
2. The most named individual associated with BitConnect is Divyesh Darji, who was reported as the head of BitConnect in India. Darji was arrested in 2018 in connection to the scam, and was later reported to be involved with other similar scams.
3. Carlos Matos, a notable figure for the BitConnect scheme because of his enthusiastic speeches at BitConnect gatherings, hasn't been reported arrested or legally penalized, as there's no confirmed evidence of him being a part of the actual scam. He might have been a victim himself or simply a promotional figure, but the exact relationship between Matos and BitConnect organizers is unclear.

In January 2018, after receiving cease-and-desist letters from the Texas State Securities Board and the North Carolina Secretary of State Securities Division, BitConnect formally announced its shutdown. Several other lawsuits were subsequently filed.

As for investors' funds, most were never recovered. Cryptocurrency, by nature, is a decentralized and unregulated form of currency, so pursuing action against these frauds can be complicated.

This case underscores the importance of thorough research and caution when dealing with cryptocurrency and investment platforms, especially those promising high returns. It's always a good idea to remember the old saying: "If something sounds too good to be true, it probably is."