It would be great if we add solution to each section that protects your code/server.
For example a PHP script that sanitises request strings against all attacks

I have been testing q and I've found something that could be an issue.

Issue
When I'm doing something like round(avg(c3),3) when querying, is rounding this 32655,5265 to this 32655,526 instead of expected result: 32655,527.

I think that could be an issue, or should be configurable.

Cheers.

Is the README the only documentation available for this project? I find myself having to read the source to understand the interface and features that are available (e.g., context managers, bulk_query, etc.).

In the case of bulk_query, the arguments are simply passed through to the underlying SQLAlchemy engine, with no explanation. I tracked it down in [SQLAlchemy's documentation](https:/

This is a reminder for me or a task if anyone wants :P

Basically, The last two questions aren't really regex's questions.

To do:

• Move said questions to correct place.
• Add new regex questions (Python related!)?
• Maybe add a new ## Regex section, as it is a valuable skill

Describe the problem

We should test on larger datasets that are commonly used in

Steps to reproduce:

1. Create table with some data in Hive:

CREATE TABLE test (v int);

INSERT INTO test VALUES (5),(1),(1),(1),(2),(2),(2),(2),(3),(3),(3),(4),(4),(4),(4),(4),(4),(5),(5),(5),(5),(6),(6),(6),(6);

2. Select test table and choose v column.
   ![Zrzut ekranu 2019-07-9 o 14 36 11](https://user-images.githubusercontent.com/3193882/60888678-b25ffb80-a257-11e9-8c88

http://www.sqlite.org/wal.html can prevent sqlite3 database lock errors in many circumstances, specifically if:

• a database is locked due to writing and another thread tries to read
• there is a long running read operation (preventing any writes from finishing and making them keep a lock until the read operation finishes), crashing later write attempts
• ...

As dataset aims to make data

I think the plugins docs need to extend beyond a single page now. I want to add a whole section on writing tests for plugins, showing how httpx can be used as seen in simonw/datasette-atom#3 and suchlike.

In FAB Security Views we can see some logic related to whitelists.

What we do not see in the Superset or FAB documentation is any mention on how to set them up and what effect they have.

Documentation on this would be useful.

When specifying a model, if I add some attributes that don't exist in my dataset I get no warnings or errors. To reproduce, you can use the sample model.json file in examples/hello_world folder:

            "mappings": {
                        "item.line_item": "line_item",
                        "item.subcategory": "subcategory",
                        "item.subcategory_label": "s

One line description of the issue

Python script

DATABASES = {
    'default': {
        'ENGINE': 'djongo',
        'NAME': 'your-db-name',
        'HOST': 'your-db-host',
    }
}

should be

DATABASES = {
  'default': {  
    'ENGINE':   'djongo',
    'NAME':     'yourmongodb',
    'CLIENT': {
  

The documentation seems fairly limited. Looking for a way to execute a "WHERE EXISTS" or "WHERE NOT EXISTS" type query.

Is the recommendation to extend pypika to include it or is there a way to custom write part of the query?

Not seeing much documentation on extending the module.

Need test cases to cover error handling in batch_work_executor.py

I made a start on some IPython magic for rendering eralchemy generated images in a Jupyter notebook: ipython_magic_eralchemy

It really needs factoring into eralchemy and perhaps ipython-sql somehow so that it could make use of a persistent SQL connection. ([ipython-sql magic](https://github.com/catherinedevlin/ipython-sql/b

Issue description

The original title key translates the title. It should not.

Version of IMDbPY, Python and OS

• Python: 3.6.9
• IMDbPY: 6.9dev20200125 (installed from the repo here)
• OS: uname_result(system='Linux', node='blackfx', release='4.15.0-76-generic', version='#86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020', machine='x86_64', processor='x86_64')

While using alembic I came across two areas that I think could be improved:

• in the template evn.py before setting the log config we should check if it's not already setup.
  I think that we can do it by checking if the root logger has an handler configured.
  This is useful if alembic is used programmatically, since it avoids resetting the program logging config.
  I guess that an option

When merging two or more schemas with merge_schemas() (in schema_transformation.merge_schemas.py), the generated MergedSchemaDescriptor object contains a schema_ast field with an AST for the merged schema. This AST contains uses of the @stitch directive, but no definition for that directive – and is therefore invalid.

Would it be possible to make the type for JSON columns accept Mapping[str, Any] instead of (in addition to?) Dict[str, Any]? My specific use case is I have a fixed JSON schema that I represent with a TypedDict, but this is only compatible with Mapping, not Dict, so I have to either ignore the type or cast it when crea

It's a little annoying that the anonymised variable names sometimes but not always correspond to the table/column name they come from. E.g in some datasets like academic, the variable name is derived from the column name:

        "sql": [
            "SELECT JOURNALalias0.HOMEPAGE FROM JOURNAL AS JOURNALalias0 WHERE JOURNALalias0.NAME = \"journal_name0\" ;"
        ],
        "variables":

• can it help improve test report output?
• am I basically doing something hacky and similar in test_dply_series_methods.py
• will it add a lot of boilerplate?

It can be quicker to type when we want to download the screener data using a single line shell command