semvec/pypi-packages huggingface dataset:
msross 0.0.1
package
Requires Python: >=3.6

# lib-name
```
step to commit:
    step rename:
        rename:
        ./
        |__src/
            |__packge_name/
        to your package name
    step edit:
        edit:
            ./
            |__setup.cfg
        name, version, author, author_email
    step build:
        step-1:
            make a pypi account
        step-2:
            run code on command line:
                Unix/macOS:
                    python3 -m pip install --upgrade build
                    python3 -m pip install --upgrade twine
                    python3 -m build
                    twine upload dist/*
                Windows:
                    py -m pip install --upgrade build
                    py -m pip install --upgrade twine
                    py -m build
                    twine upload dist/*
```
Uploading distributions to `https://test.pypi.org/legacy/`

Enter your username: `[your username]`


Enter your password: `[your password, this is hidden]`

Uploading ```package-name-version-py3-none-any.whl```

100%|█████████████████████| 4.65k/4.65k [00:01<00:00, 2.88kB/s]

Uploading ```package-name-version.tar.gz```

100%|█████████████████████| 4.25k/4.25k [00:01<00:00, 3.05kB/s]
seren 0.0.1
package
Requires Python: >=3.6

# lib-name
```
step to commit:
    step rename:
        rename:
        ./
        |__src/
            |__packge_name/
        to your package name
    step edit:
        edit:
            ./
            |__setup.cfg
        name, version, author, author_email
    step build:
        step-1:
            make a pypi account
        step-2:
            run code on command line:
                Unix/macOS:
                    python3 -m pip install --upgrade build
                    python3 -m pip install --upgrade twine
                    python3 -m build
                    twine upload dist/*
                Windows:
                    py -m pip install --upgrade build
                    py -m pip install --upgrade twine
                    py -m build
                    twine upload dist/*
```
Uploading distributions to `https://test.pypi.org/legacy/`

Enter your username: `[your username]`


Enter your password: `[your password, this is hidden]`

Uploading ```package-name-version-py3-none-any.whl```

100%|█████████████████████| 4.65k/4.65k [00:01<00:00, 2.88kB/s]

Uploading ```package-name-version.tar.gz```

100%|█████████████████████| 4.25k/4.25k [00:01<00:00, 3.05kB/s]
mila 0.0.1
package
Requires Python: >=3.6

# lib-name
```
step to commit:
    step rename:
        rename:
        ./
        |__src/
            |__packge_name/
        to your package name
    step edit:
        edit:
            ./
            |__setup.cfg
        name, version, author, author_email
    step build:
        step-1:
            make a pypi account
        step-2:
            run code on command line:
                Unix/macOS:
                    python3 -m pip install --upgrade build
                    python3 -m pip install --upgrade twine
                    python3 -m build
                    twine upload dist/*
                Windows:
                    py -m pip install --upgrade build
                    py -m pip install --upgrade twine
                    py -m build
                    twine upload dist/*
```
Uploading distributions to `https://test.pypi.org/legacy/`

Enter your username: `[your username]`


Enter your password: `[your password, this is hidden]`

Uploading ```package-name-version-py3-none-any.whl```

100%|█████████████████████| 4.65k/4.65k [00:01<00:00, 2.88kB/s]

Uploading ```package-name-version.tar.gz```

100%|█████████████████████| 4.25k/4.25k [00:01<00:00, 3.05kB/s]
dimschema 0.3
dimschema

# Dimemsions GBQ Schema Utility

CLI to retrieve SQL schema information about the [Dimensions on Google BigQuery](https://console.cloud.google.com/bigquery?p=dimensions-ai&page=project) dataset. 

**NOTE** This is the same documentation available from https://docs.dimensions.ai/bigquery/data-sources.html - only it's retrieving from the BigQuery database itself. 


## Requirements 

* Python 
* Personal Google account credentials set up in your local machine. The [BigQueryLAB](https://bigquery-lab.dimensions.ai/tutorials/01-connection/#option-2-using-a-local-jupyter-and-your-personal-credentials) has a tutorial showing how to do that. 

## Examples

* `dimschema publications .` : show all fields from the Publications table

* `dimschema ids` : show fields containing the string 'ids' 

* `dimschema datasets ids` : show fields containing the string 'ids', only in the Datasets table


## Development

```
$ mkvirtualenv dimschema
$ pip install --editable .
```

See https://stackoverflow.com/questions/30306099/pip-install-editable-vs-python-setup-py-develop
imdb-lib 0.3
Web Scraping data from imdb website.
Requires Python: >=3.7

# IMDB_LIB Package
The main purpose of this package is to give acess to imdb data by scraping the website.

## Functionalities

### Import statemant
`httpIMDB` is the main class that you'll be using for now (_version 0.1_). It is responsible for all available data that can be retrieved.

```
from imdb_lib.session import httpIMDB

imdb = httpIMDB()
```
#
### `httpIMDB.getPopularTVShows()` and `httpIMDB.getPopularMovies()`
Scrape the top 100 most popular TV Shows/Movies according to the IMDB ranking

Returns a pandas dataframe with 100 rows and the following columns: `Id,title, poster, rating`

#
### `httpIMDB.getTopRatedTVShows()` and `httpIMDB.getTopRatedMovies()`
Scrape the top 100 most popular TV Shows/Movies according to the IMDB ranking

Returns a pandas dataframe with 250 rows and the following columns: `Id,title, poster, rating`

#
### `httpIMDB.getTitleDetails(id)`
Scrape the given title id for details


Returns a pandas dataframe with one row and the following columns: `title, poster, sinapse, rating, popularity, genres, types, top_cast, runtime, duration`

#
### `httpIMDB.searchTitle(title)`
As the name suggests, it is used to search for a title by its name

Requires 1 parameter, the title to be searched

Returns a pandas dataframe with the following columns: `Id and Title`