semvec/pypi-packages huggingface dataset:
llama-index-callbacks-opentelemetry 0.5.0
llama-index instrumentation opentelemetry integration
License: MIT
Requires Python: <4.0,>=3.9

# LlamaIndex Callback Integration: OpenTelemetry

```shell
pip install llama-index-callbacks-opentelemetry
```

## Usage

```python
from llama_index.core import set_global_handler

set_global_handler("opentelemetry")
```

or:

```python
from llama_index.callbacks.opentelemetry import instrument_opentelemetry

instrument_opentelemetry()
```
shockless-hw-5-prj 0.1.0
Репозиторий курса MLOps
License: MIT

fedor-avilov-16
==============================

Репозиторий курса MLOps

Project Organization
------------

    ├── LICENSE
    ├── Makefile           <- Makefile with commands like `make data` or `make train`
    ├── README.md          <- The top-level README for developers using this project.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   └── raw            <- The original, immutable data dump.
    │
    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
    │
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                         the creator's initials, and a short `-` delimited description, e.g.
    │                         `1.0-jqp-initial-data-exploration`.
    │
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    │
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    │
    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
    │                         generated with `pip freeze > requirements.txt`
    │
    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
    ├── src                <- Source code for use in this project.
    │   ├── __init__.py    <- Makes src a Python module
    │   │
    │   ├── data           <- Scripts to download or generate data
    │   │   └── make_dataset.py
    │   │
    │   ├── features       <- Scripts to turn raw data into features for modeling
    │   │   └── build_features.py
    │   │
    │   ├── models         <- Scripts to train models and then use trained models to make
    │   │   │                 predictions
    │   │   ├── predict_model.py
    │   │   └── train_model.py
    │   │
    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
    │       └── visualize.py
    │
    └── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io


--------

<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
EDAeasy 1.2.9
Functions and tools for making Exploratory Data Analysis easy!
License: MIT
Requires Python: <4.0,>=3.9
Keywords: Exploratory Analysis, EDA

# EDAeasy 😀
The package for quick exploratory data analysis


## Instalation 

`pip install EDAeasy`

## Usage
The **dataframe_summary** function have relative simple summary of the columns of your dataframe
for quick look at tabular data

    Generate a summary DataFrame of the input DataFrame 'dataframe'.

    Parameters
    ----------
    dataframe : pandas.DataFrame
        The input DataFrame for which the summary needs to be generated.

    Returns
    -------
    pandas.DataFrame
        A DataFrame containing summary information for each column in 'df':
        - Type: Data type of the column.
        - Min: Minimum value in the column.
        - Max: Maximum value in the column.
        - Nan %: Percentage of NaN values in the column.
        - # Unique Values: Total number of unique values in the column.
        - Unique values: List of unique values in the column.

    Example
    -------
    >>> data = {
            'age': ['[40-50)', '[60-70)', '[70-80)'],
            'time_in_hospital': [8, 3, 5],
            'n_lab_procedures': [72, 34, 45],
            ...
        }
    >>> dataframe = pd.DataFrame(data)
    >>> result = dataframe_summary(df)
    >>> print(result)
               Type       Min        Max  Nan %  # Unique Values                                  Unique values
    Variables                                                                                                              
    age       object   [40-50)    [90-100)    0.0        3      ['[70-80)', '[50-60)', '[60-70)', '[40-50)', '[80-90)', ...
    time_in_hospital  int64    1           14    0.0        3        [8, 3, 5]
    n_lab_procedures  int64    1          113    0.0        3        [72, 34, 45]
    ...

    Note
    ----
    The function uses vectorized operations to improve performance and memory usage.
llama-index-tools-passio-nutrition-ai 0.3.0
llama-index tools passio_nutrition_ai integration
License: MIT
Requires Python: <4.0,>=3.9

# Passio Nutrition AI Tool

This tool connects to a Passio Nutrition AI account and allows an Agent to perform searches against a database of over 2.2M foods.

You will need to set up a search key using Passio Nutrition API,learn more here: https://www.passio.ai/nutrition-ai#nutrition-api-pricing

## Usage

Here's an example usage of the NutritionAIToolSpec.

```python
from llama_index.tools.passio_nutrition_ai import NutritionAIToolSpec
from llama_index.agent import OpenAIAgent

tool_spec = NutritionAIToolSpec(api_key="your-key")

agent = OpenAIAgent.from_tools(tool_spec.to_tool_list())

agent.chat("What is the nutritional value of an apple?")
agent.chat("I had a cobb salad for lunch, how many calories did I eat?")
```

`passio_nutrition_ai`: Search for foods and their micro nutrition results related to a query

This loader is designed to be used as a way to load data as a Tool in a Agent.
ytb-downloader 0.4.0
Download youtube videos and convert them to mp3.
License: MIT
Requires Python: <4.0,>=3.9

Youtube Downloader
==================

Introduction
------------
Download youtube videos and convert them to mp3.

User Guide
----------

How to Install
++++++++++++++

To install `ytb_downloader` by running::

    $ pip install ytb_downloader

How to Use
++++++++++

You can use the command line tool from `ytb_downloader` to download audios or videos
from Youtube.

Download Audio
~~~~~~~~~~~~~~
Start downloading a single audio by calling::

    $ ytb_downloader "<your youtube video url>"

If you want to specify the format of the downloaded video, you can use the option
`--format` or `-f` with the format as string, such as::

    $ ytb_downloader --format "mp3" "https://www.youtube.com/watch?v=nOubjLM9Cbc"

For more details, please execute::

    $ ytb_downloader --help

Download Video
~~~~~~~~~~~~~~
Start downloading a single video by calling::

    $ ytb_downloader --video-only "<your youtube video url>"

Download in bulk
~~~~~~~~~~~~~~~~
If you want to download audios in bulk, please prepare a *csv* file containing all
the urls of the audios. Here is an example about how does this csv file look like.

.. list-table:: example-url-only.csv
   :widths: 25
   :header-rows: 1

   * - url
   * - https://www.youtube.com/watch?v=WqkjYKUXERQ
   * - https://www.youtube.com/watch?v=nOubjLM9Cbc

Here is the content of the example-url-only.csv file::

    url
    https://www.youtube.com/watch?v=WqkjYKUXERQ
    https://www.youtube.com/watch?v=nOubjLM9Cbc

In the csv file, you can also specify the *format*, *time_start*, *time_end* and *bitrate*
for each audio, such as

.. list-table:: example.csv
   :widths: 50 25 25 25 25 25
   :header-rows: 1

   * - url
     - format
     - time_start
     - time_end
     - fps
     - bitrate
   * - https://www.youtube.com/watch?v=WqkjYKUXERQ
     - mp3
     - 0
     -
     - 44100
     - 3000k
   * - https://www.youtube.com/watch?v=nOubjLM9Cbc
     - mp3
     - 3
     - 100
     - 200
     - 500k

Here is the content of the example.csv file::

    url,format,time_start,time_end,fps,bitrate
    https://www.youtube.com/watch?v=WqkjYKUXERQ,mp3,0,,44100,3000k
    https://www.youtube.com/watch?v=nOubjLM9Cbc,mp3,3,100,200,500k

To download all the audios from the urls listed in this file by calling::

    $ ytb_downloader_bulk example.csv

If you want to download the videos only, you need to provide a csv file with
one column *url*, and list all the urls you want to download in that column. Then
execute::

    $ ytb_downloader_bulk --video-only example.csv

Maintainers
-----------

..
    TODO: List here the people responsible for the development and maintaining of this project.
    Format: **Name** - *Role/Responsibility* - Email

* **Zhiwei Zhang** - *Maintainer* - `zhiwei2017@gmail.com <mailto:zhiwei2017@gmail.com?subject=[GitHub]Youtube%20Downloader>`_

.. _bandit: https://bandit.readthedocs.io/en/latest/
.. _mypy: https://github.com/python/mypy
.. _pytest: https://docs.pytest.org/en/stable/