inflect | common reflection operations | Reflection library

When talking about SQL injection the risk is that someone can introduce SQL keywords into the query itself by adding SQL to a data parameter.

This is why separation of data and query is absolutely critical. This normally plays out by using placeholder values, as in:

You need to add the install option --no-build-isolation when installing from source and you want to tell Pip to make use of already installed packages to satisfy the build dependencies. Emphasis on build dependencies.

The most common build dependencies, according to PEP 518, are setuptools and wheel. So much so that, as the "minimum requirements for the build system to execute", build tools are expected to add them tacitly to a build configuration.

When installing a new package from source, packages in the Python environment are ignored as far as build dependencies are concerned. As the Pip documentation explains:

When making build requirements available, pip does so in an isolated environment. That is, pip does not install those requirements into the user’s site-packages, but rather installs them in a temporary directory which it adds to the user’s sys.path for the duration of the build. This ensures that build requirements are handled independently of the user’s runtime environment. For example, a project that needs a recent version of setuptools to build can still be installed, even if the user has an older version installed (and without silently replacing that version).

In your example, that temporary directory seems to be /tmp/pip-build-env/r61p50oe. Instead of the --no-build-isolation option, you could also add the source packages of setuptools and wheel (possibly more, depending on the package you're installing) to the local folder specified via the -f/--find-links option.

Found a solution, I added an asterisk(*) on the end of the search string using the StringBuilder class in my .NET Core app. It solved my problem.

It is explained on this page:

https://social.msdn.microsoft.com/Forums/sqlserver/en-US/17653360-2c72-4647-96f0-d3a2a9e67f0e/searching-partial-and-or-full-word-from-fulltext-search-those-might-be-precise-or-fuzzy-less?forum=transactsql

Thanks anyway @Charlieface

• have synthesized data similar to the data that cannot be used without OCR
• you can calculate the polynomial using numpy then just plot another line

It seems that you are confusing the use-cases for PythonVirtualenvOperator and PythonOperator.

If you simply want to run a Python callable in a task (callable_virtualenv() in your case) you can use PythonOperator. In this case, it does not matter if you installed Airflow in a virtual environment, system wide, or using Docker.

What happens in your code is the following: PythonVirtualenvOperator creates another virtual environment (which is completely unrelated to the one in which you run Airflow), installs Airflow into it, and tries to import Variable. But this another Airflow installation is not configured and that is why you get those exceptions. You could set the AIRFLOW_HOME environment variable for this second Airflow installation to the same directory as used by the first Airflow installation, and this should actually work, but it looks like an overkill to me.

So, what you can do is install colorama into the same environment in which you installed Airflow and replace PythonVirtualenvOperator by PythonOperator.

BTW, those print() inside the callable would be redirected into a log file and not printed to terminal, so it probably does not make much sense to use colorama with them.

Turns out the migration file name db/migrate/20210312_2040_create_converter.rb was erroneous.

The underscore in between the timestamp that was put for readability ended up messing the constant name by appending numbers, hence the Name Error: wrong constant name.

I also:

• pluralized the filename since after removing the underscore, I got another error that asked for the pluralization
• removed the argument :created at after t.timestamps.

In the end, the file name became db/migrate/202103122040_create_converters.rband the migration ran smoothly.

At the end, I ended up solving this by rolling back the changes I made using conda list --revisions to find out until which previous set up I had to roll back to, then afterwards I ran conda install --revision N (where N is the revision you want to trace back to). Suppose the changes you made are rev 4, you want to undo them, and sit back again under rev 3 (your previously "known and working" environment you had), so you run conda install --revision 3 for that case.

Afterwards I re-installed pandas_datareader with python -m pip install pandas-datareader and everything went good again.

Thanks anyways and I hope if someone else runs into this issue, can find this post valuable.

From the documentation, it seems like you ought to pass the query string as unicode if the database's default collation isn't unicode:

When you parse a query string, sys.dm_fts_parser uses the collation of the database to which you are connected, unless you specify the query string as Unicode. Therefore, for a non-Unicode string that contains special characters, such as ü or ç, the output might be unexpected, depending on the collation of the database. To process a query string independently of the database collation, prefix the string with N, that is, N'query_string'.

I therefore suggest changing your query to this:

Here is a way to proceed making an assumption about how you defined your diagonal lines. First create reproducible data and get the quantiles: