clair | Vulnerability Static Analysis for Containers | Continuous Deployment library

I am trying to convince myself of the Central Limit Theorem as applies to proportions.

Consider the following generated data:

Consider arbitrary strings in which the character @ represents a placeholder char.

For example:
"@ has bought 8 apples today"
"@ and @ are together for 10 years"
"If @ wouldn't have told me that, I would have never known that you got in touch with @.

Now, in addition I have a list: names = ['Peter', 'James', 'Claire', 'Julia']

Finally, I want to iterate over a random string from my string list and replace every @ with a random element from the names list. Although, it should not happen that the same name is picked twice.

Ugly solution (Pseudo-Code):

I wrote a Python script to perform calculations which I want to run via Excel VBA and display the results in Excel. To be usable for different users I am trying to write a code that will generate the paths to the Python execution and Python scripts based on the usernames they enter since that is the only thing that will change in the path.

Some people have a space in their username which causes problems when trying to run Python in the command prompt.

There are similar questions but I have not found a solution. I tried adding triple double quotations at the beginning and end of the execution path e.g.

I need to duplicate a row based on a column (a number).

I have this dataframe:

I need to split the words based on the character '/' and reform the words in this way:

This dataframe contains some kids and their presents for Easter. Some kids have two presents, while some have only one.

I have a very basic sql file, the code:

I need to delimit the letters and the numbers from a string in an array in Python.

My array is this one:

I need to copy a column's field into a variable, based on a specific condition, and then delete it.

This dataframe contains data of some kids, that have their favourite toy and colour associated:

I need to fill a column's fields, based on the other columns, in this way:

The first df has two columns - names and ages of the kids, but some rows are NaNs, however nothing should be done with the NaNs, just ignore them. But the ages should be filled in, based on the second df.

First df: