semvec/pypi-packages huggingface dataset:
auto-notion-py 0.1.4
Notion automation package
Requires Python: >=3.8,<4.0

# Notion automation utilities

**Note: Package in heavy development**

Installation:

```bash
    $ poetry add "auto_notion_py==0.1.4"
```

Quick exmple:

```python

from auto_notion_py.github.api import get_pull_requests_df, GHFetch, GHFetchOrg, GHFetchRepo
from auto_notion_py.notion.api import notion_db_push
from auto_notion_py.zotero.api import get_zotero_publications_df
import asyncio

# Query all Zotero publications and update the Notion database
zotero_df = await get_zotero_publications_df(
    zotero_api_key,
    zotero_db_id,
)
await notion_db_push(notion_token, notion_db_id_zotero_publications, zotero_df)

# Query all PRs and update the Notion database
all_prs_df = await get_pull_requests_df([
    GHFetch(
        token=github_token,
        creators=None,
        organizations=[
            GHFetchOrg(
                name="covid-genomics",
                creators=None,
                repositories=None,
            ),
        ],
    ),

])
await notion_db_push(notion_token, notion_db_id_pull_requests, all_prs_df)
```
sklearnFunctions 0.0.9
module built on needy sklearn preprocessing functions
Requires Python: >=3.6

# sklearnFunctions - eleka12

This package is built on top of functions which are mostly required in a Data processing lifecycle.

As of now This package contains a function which can remove outliers present in the Dataset

More number of functions will be added... 

# How to use sklearnFunctions

* install the latest package 

> * in jupyter notebook -
```
    !pip install sklearnFunctions
```

> * in command prompt -
```bash    
    pip install sklearnFunctions
```

* Now run below snippets of code in your jupyter-notebooks / python project 

## Importing sklearnFunctions

```python

from sklearnFunctions.preprocessing import outliers

```

## The input for the outlier function must be in DataFrame

```python

old_df = pd.DataFrame({"X":xvals,"Y":yvals})


```

## Checking for outliers | .is_present() function will return a percentage of outlier present in each column

```python

outliers().is_present(old_df)

```

## Removing outliers from DataFrame | .outliers_removal() function will remove all the outliers present in DataFrame

```python

new_df = outliers().outliers_removal(old_df)

```

## pypi repo link -

[mongoops - PYPI](https://pypi.org/project/sklearnFunctions/)

## Github repo link - 

[mongoops - Github](https://github.com/Karthik-VG/sklearnFunctions)
google-drive-sheets 0.0.6
Utilities for Google Drive and Google Sheets APIs
Requires Python: >=3.6
Keywords: python,google drive,google sheets,gsuite

# google-drive-sheets
Utilities for Google Drive and Sheets APIs that help you create folders, spreadsheets and read/save data

---
## Prerequisites
Before executing anything, please go through the steps [here](https://developers.google.com/drive/api/v3/quickstart/python). It will kickstart the authorization process for your own app. \
Save the `credentials.json` file (not saved in this repo) generated from the first step, in the root directory of your project to enable Google Drive and Sheets capabilities.
> *Note*: the `credentials.json` file has to be present in order to work with the APIs.

> *Note*: If while creating drive/sheets service objects, you get `"error": "invalid_grant", "error_description": "Bad Request"`, the `credentials.json` might have been expired. 
> Go to the Google Cloud Console and generate a new OAuth Client ID (find more information on the [guide page](https://developers.google.com/workspace/guides/create-credentials#desktop-app) for your app).