semvec/pypi-packages huggingface dataset:
common-cols-project 0.1.3
Given a list of data frames, finds common columns between all pairs of data frames.
Requires Python: >=3.6

# Common Columns Package  

This package contains a function called **common_columns** that can be used to find the common columns between all pairs of data frames.

### Installation  
To install this package, you can use pip. Open your terminal and run the command provided above.

### Usage  
To use the common_columns function, you first need to import it from the package:

from common_columns import common_columns  
The function takes in a variable-length argument list of pandas data frames. Here's an example of how to use it:

import pandas as pd  
from common_columns import common_columns

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})  
df2 = pd.DataFrame({'B': [7, 8, 9], 'C': [10, 11, 12]})  
df3 = pd.DataFrame({'B': [13, 14, 15], 'D': [16, 17, 18]})  

common_cols_all = common_columns(df1, df2, df3)

> In this example, we have three data frames (df1, df2, and df3). We call the common_columns function with these data frames as arguments, and store the result in the common_cols_all variable. The common_cols_all variable will contain a set of column names that are common to all three data frames, if any.

### Function Description
The common_columns function takes in a variable-length argument list of pandas data frames, and returns a set containing the names of columns that are common to all data frames, if any. It works as follows:

### Initialize a list of common columns.
Get the names of the data frames, assigning a default name to any data frames without a name attribute.
### Loop over all pairs of data frames.
Find the common columns between the two data frames.
If there are common columns, add them to the list of common columns.
### Find the common columns across all data frames.
Return a set containing the names of columns that are common to all data frames, if any.

## Contributing
If you'd like to contribute to this package, please feel free to fork the repository and submit a pull request. We welcome contributions from the community! You can also contact me on my personal email adress: elenagarciamanes@gmail.com
flamingo-histology 0.1.2
Flamingo: a package for extracting and exporting annotations from H&E images
Requires Python: >=3.10

# flamingo 🦩
<img src="https://github.com/jsmckenzie/flamingo/blob/main/docs/image.jpg" alt="Flamingo" height="150" align="right" caption="Text left hanging">

#### Extract annotations from .ndpa files and map on to the .ndpi image for use in other applications

* * *

[![PyPI version](https://badge.fury.io/py/flamingo-histology.svg)](https://pypi.org/project/flamingo-histology/)


## Install
```Python
conda create --name ndpi python=3.10
conda activate ndpi

conda install -c conda-forge openslide
conda install -c conda-forge openslide-python # probably better than the command above

conda install numpy matplotlib jupyterlab

pip install flamingo-histology

```

## Example image
Example annotations (not corresponding to real regions) are provided in the `example` folder for files that can be downloaded from the following locations:
- [test3-FITC 2 (485).ndpi](https://downloads.openmicroscopy.org/images/Hamamatsu-NDPI/manuel/)


## Usage
```
import sys
from flamingo.Flamingo import HE


```



Openslide documentation can be found here: https://openslide.org
nyaa-timer 2.0.0
A simple nyaa_timer decorator to calculate the elapsed time of a function

Title: Nyaatimer - A Simple Python Timer Decorator with Rich Output

Description:

Nyaatimer is a lightweight Python package that provides a simple timer decorator for measuring the execution time of functions. The package uses the `perf_counter` function from the `time` module to accurately measure the time taken, and the `rich` library to display the results in a visually appealing format.

Features:

- Easy-to-use decorator for timing function execution.
- Accurate time measurement using `perf_counter`.
- Attractive output with `rich` library integration.
- Displays function name, arguments, and keyword arguments in the output.

Example Usage:

To use Nyaatimer, simply import the `nyaatimer` decorator and apply it to any function you want to measure the execution time of:

```
from nyaatimer import nyaatimer


@nyaatimer
def example_function(a, b):
    return a + b


result = example_function(1, 2)
```

This will output the execution time and function details in a visually appealing format:

`Function example_function took 0.00012345 seconds Args=(1, 2), Kwargs={}`

Installation:

To install Nyaatimer, simply run the following command:

`pip install nyaatimer`

Requirements:

- Python 3.6 or later
- rich library (automatically installed as a dependency)

License:

Nyaatimer is released under the MIT License.