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Go to credential Manager -> Windown Credential -> Add a generic credential fill up the fields Network address: git:https://bitbucket.org Username: App Password: Solver from Here

You are rendering the navbar outside the routing context. The Router isn't aware of what routes the links are attempting to link to that it is managing. The reason routing works when directly navigating to "/experiences" is because the Router is aware of the URL when the app mounts.

If you are upgrading your Laravel 8 project to Laravel 9 by importing your existing application code into a totally new Laravel 9 application skeleton, you may need to update your application's "trusted proxy" middleware.

Within your app/Http/Middleware/TrustProxies.php file, update use Fideloper\Proxy\TrustProxies as Middleware to use Illuminate\Http\Middleware\TrustProxies as Middleware.

Next, within app/Http/Middleware/TrustProxies.php, you should update the $headers property definition:

// Before...

protected $headers = Request::HEADER_X_FORWARDED_ALL;

// After...

You missed adding this in your eslint.json file.

As suggested here, the following will suffice:

The JNDI feature was added into Log4j 2.0-beta9.

Log4j 1.x thus does not have the vulnerable code.

Although I can't provide a formal definition of why/how the rounding modes were chosen as they were, the citation about compatibility with the % operator, which you have included, does make sense when you consider that % is not quite the same thing in C++ and Python.

In C++, it is the remainder operator, whereas, in Python, it is the modulus operator – and, when the two operands have different signs, these aren't necessarily the same thing. There are some fine explanations of the difference between these operators in the answers to: What's the difference between “mod” and “remainder”?

Now, considering this difference, the rounding (truncation) modes for integer division have to be as they are in the two languages, to ensure that the relationship you quoted, (m/n)*n + m%n == m, remains valid.

Here are two short programs that demonstrate this in action (please forgive my somewhat naïve Python code – I'm a beginner in that language):

C++:

Follow the instructions in the documentation, which specifically calls out the steps for version 9:

1. Install Firebase using npm:

Most of the solutions here would be used with melt, so to know the method melt, see the documentaion explanation

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

And the parameters are:

Parameters

• id_vars : tuple, list, or ndarray, optional

  Column(s) to use as identifier variables.

• value_vars : tuple, list, or ndarray, optional

  Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

• var_name : scalar

  Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.

• value_name : scalar, default ‘value’

  Name to use for the ‘value’ column.

• col_level : int or str, optional

  If columns are a MultiIndex then use this level to melt.

• ignore_index : bool, default True

  If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

  New in version 1.1.0.

Melting merges multiple columns and converts the dataframe from wide to long, for the solution to Problem 1 (see below), the steps are:

1. First we got the original dataframe.

2. Then the melt firstly merges the Math and English columns and makes the dataframe replicated (longer).

3. Then finally adds the column Subject which is the subject of the Grades columns value respectively.

This is the simple logic to what the melt function does.

I will solve my own questions.

Problem 1 could be solve using pd.DataFrame.melt with the following code: