semvec/pypi-packages huggingface dataset:
excellentpandas 0.1.1
Load pandas DataFrames in Excel

# excellentpandas

[![License](http://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/kinverarity1/excellentpandas/blob/master/LICENSE)

Very quickly load pandas DataFrames in Excel

Python is awesome and I love it for doing all sorts of data manipulation. But sometimes Microsoft Excel remains the best place to do quick data exploration and filtering. So thanks to the brilliant [xlwings](xlwings), it's easy to integrate the two. This module has some very simple functions to make this as easy as possible.

![Screen capture showing Jupyter Notebook code cells being run with Excel workbooks being loaded in the background](demo.gif)

Say you have this script:

```python
>>> df = read_data()
>>> result = df.groupby("blah")[vars].agg(something_complex)
```

And you want to explore ``result`` quickly in Excel.

You can use one of the following functions to immediately launch the DataFrame in a new Excel Workbook on your desktop via a non-blocking call:

### ``show_in_excel(df)``

```python
>>> from excellentpandas import show_in_excel
>>> df = read_data()
>>> result = df.groupby("blah")[vars].agg(something_complex)
>>> show_in_excel(result)
>>>
```

### Use ``.pipe(via_excel)`` for a chained version

```python
>>> from excellentpandas import via_excel
>>> df = read_data()
>>> result = df.groupby("blah")[vars].agg(something_complex).pipe(via_excel)
>>>
```

### Show info as well with ``.pipe(via_info_excel)``

```python
>>> from excellentpandas import via_info_excel
>>> df = read_data()
>>> result = df.groupby("blah")[vars].agg(something_complex).pipe(via_info_excel)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 16 entries, 2004 to 2019
Data columns (total 6 columns):
...
dtypes: float64(6)
memory usage: 896.0 bytes
>>>
```

## Install

```
$ pip install -U excellentpandas
```
balancedtime 0.16b0
Convert regular time into 12-hour days and nights

.. balanced-time documentation master file, created by
   sphinx-quickstart on Fri Nov 16 18:55:43 2018.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

balancedtime
=========================================
|pypi_ver|

.. |pypi_ver| image:: https://img.shields.io/pypi/v/balancedtime.svg
    :target: https://pypi.org/project/balancedtime/

Using balancedtime
-------------------
balancedtime has two methods, one to get the current or specified time in a given location, and one to output a handy clock conversion for today in a given location.

convert_time_to_balanced_time(time=None, zipcode=10010, pretty_print=False)
the 'time' argument can be a datetime.datetime object.
'zipcode' can be a contiguous US ZIP code

pretty_print will output a datetime.datetime object if False, and a text representation of the time (in a 12 hour format with AM/PM) if True.

.. note::

   zipcode currently excludes leading 0 ZIPs (mostly in the Northeast.) and all ZIP codes not part of the contiguous United States. This is expected to be fixed in subsequent releases.


Examples
========

The following examples demonstrate the functionality available in the module

convert_time_to_balanced_time
-----------------------------

::

    >>> import balancedtime
    >>> balanced_time.convert_time_to_balanced_time(time=datetime.datetime(2018,5,3,6,30),zipcode=10010,pretty_print=True) #time in NYC on May 3, 2018, at 6:30 AM
    '06:32:06 AM'
    >>> balanced_time.convert_time_to_balanced_time(time=datetime.datetime(2018,5,3,6,30),zipcode=10010,pretty_print=False) #same, but return a datetime.datetime object
    datetime.datetime(2018, 5, 3, 6, 32, 6, 185567)




clock_conversion
----------------

::

    >>> clock_conversion(zipcode=10010)

    balanced calendar for New York, NY, 2018/11/18
      old   :  balanced
    12:00 AM = 12:15:50 AM
    1:00 AM  = 01:06:44 AM
    2:00 AM  = 01:57:38 AM
    3:00 AM  = 02:48:32 AM
    4:00 AM  = 03:39:26 AM
    5:00 AM  = 04:30:20 AM
    6:00 AM  = 05:21:14 AM
    7:00 AM  = 06:17:27 AM
    8:00 AM  = 07:30:36 AM
    9:00 AM  = 08:43:46 AM
    10:00 AM = 09:56:55 AM
    11:00 AM = 11:10:04 AM
    12:00 PM = 12:23:14 PM
    1:00 PM  = 01:36:23 PM
    2:00 PM  = 02:49:32 PM
    3:00 PM  = 04:02:42 PM
    4:00 PM  = 05:15:51 PM
    5:00 PM  = 06:20:08 PM
    6:00 PM  = 07:10:55 PM
    7:00 PM  = 08:01:42 PM
    8:00 PM  = 08:52:29 PM
    9:00 PM  = 09:43:17 PM
    10:00 PM = 10:34:04 PM
    11:00 PM = 11:24:51 PM

Installation
------------

Install balanced_time by running:

    pip install balancedtime


Contribute
----------

- Issue Tracker: https://github.com/Lolologist/balancedtime/issues
- Source Code: https://github.com/Lolologist/balancedtime

Version History
===============

======== =======================================================================
Version  Description
======== =======================================================================
0.1b     First release
======== =======================================================================
basic-ml 1.0.0
A lightweight package for basic machine learning needs
Requires Python: >=3.6

# BasicML

BasicML is a Python package containing implementations of basic machine learning algorithms. Below is a list of all the ML models that come with this package: 

**Regression:** 
- LinearRegression

**Classification:** 
- DecisionTree
- KNearestNeighbors
- LogisticRegression
- NeuralNetwork

**Clustering**
- KMeans

## Installation: 

Install using pip:

`pip install basic-ml`


## Usage

```python
import numpy as np  
from ml import LinearRegression  

# Replace with your own data
trn_X, trn_y, tst_X, tst_y = np.ones(1), np.ones(1), np.ones(1), np.ones(1)  

lr = LinearRegression()  
lr.fit(trn_X, trn_y)  

predictions = lr.predict(tst_X)  
print('Predicted: {}\nActual: {}'.format(predictions, tst_y))
```

To see example code, open/run any of the 6 main Python files in the `ml` folder.

## Contact

Reach out to me at [alan.bi326@gmail.com](mailto:alan.bi326@gmail.com) for questions and feedback!
gemben 0.0.4
Benchmark for Graph Embedding Algorithms

[![Documentation Status](https://readthedocs.org/projects/gemben/badge/?version=latest)](https://gemben.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/gemben.svg)](https://badge.fury.io/py/gemben) [![PyPI license](https://img.shields.io/pypi/l/ansicolortags.svg)](https://pypi.python.org/pypi/ansicolortags/) 

# GEM-benchmark 

## Documentation
[gemben documentation!](https://gemben.readthedocs.io)

## Introduction
Graph embedding, which refers to the task of representing nodes of a graph in a low-dimensional space, has gained significant traction in the past few years, with applications including link prediction, node classification, and graph visualization. Many methods have been proposed for this task which primarily differs in the inherent properties being preserved from the original graph. However, comparing such methods is challenging. Most methods show performance boosts on just a few selected networks. Such performance improvement may be due to fluctuations or specific properties of the networks at hand, thus being often inconclusive when comparing methods on different networks. To conclusively determine the utility and advantages of an approach, one would need to make a comparison on several such networks. In this work, we introduce a principled framework to compare graph embedding methods. We test embedding methods on a corpus of real-world networks with varying properties and provide insights into existing state-of-the-art embedding approaches. We cluster the input networks in terms of their properties to get a better understanding of embedding performance. Furthermore, we compare embedding methods with traditional link prediction techniques to evaluate the utility of embedding approaches. We use the comparisons on benchmark graphs to define a score, called GFS-score, that can apply to measure any embedding method. We rank the state-of-the-art embedding approaches using the GFS-score and show that it can be used to understand and evaluate a novel embedding approach. We envision that the proposed framework may serve as a community benchmark to test and compare the performance of future graph embedding techniques.
bvareader 0.2.0
Package to read and process BVA data from LF Motol

# BVA reader
Python package which reads and parses xml data from LF in Motol.

## Installation
The project is currently hosted on test PyPi and can be downloaded from such
[https://test.pypi.org/project/bvareader/](https://test.pypi.org/project/bvareader/)


## Use examples
Main use can be through the command line prompts, which doesn't require you to run your own preprocessing scripts. But if you prefer, you can preprocess and save the data in your own way.

```python
from bvareader.reader import read_xml_bva
from bvareader.reader import read_xml_sync
from bvareader.reader import read_sync_file
from bvareader.reader import save_csv

from bvareader.preprocessing import preprocess_bva_data
from bvareader.preprocessing import add_rotation

pd_bva = read_xml_bva("example_data/example.xml")
pd_bva_clean = preprocess_bva_data(pd_bva)
pd_bva = add_rotation(pd_bva)

save_csv(pd_bva_clean, "bva.csv")
save_csv(pd_bva, "bva_full.csv")

bva_sync = read_xml_sync("example_data/example.xml")
save_csv(pd_sync, "bva_sync.csv")

pd_sync = read_sync_file("example_data/example.csv")
save_csv(pd_sync, "sync.csv")
```

#### Timestamp
Bva outputs real timestamp (date and time of each recording). This package converts this datetime number as the POSIX timestamp (number of seconds since 1.1.1970) as per `datetime.timestamp()` function described in [here](https://docs.python.org/3/library/datetime.html#datetime.datetime)

## Command line prompt
By installing the package from PyPI, you will get the python entry point which you can use in the command line. You can investigate each command with the `--help` tag. e.g. `bva-preprocess-xmx --help`. Mostly you will need only the `bva-preprocess-xml` and potentially `bva-settings-to-csv` commands

BVA xml output preprocessing
```bash
bva-preprocess-xml "path_to_xml" -o/--output "output-prefix"
bva-settings-to-csv "path_to_xml"-o/--output "output-prefix"
bva-phases-table "path_to_xml" -o/--output "output-prefix"
bva-measures-start-stop-table "path_to_xml" -o/--output "output-prefix"
```

### FAQ
Q: The command line commands are not found.

A: If you are using conda or virtual envs, make sure you are in a correct environment
Golmorich 1.2.0.1
A simple text editor
Requires Python: >=3.0

# Golmorich - A Simple Text Editor
## Version: 1.2

A simple text editor with the following features:
- Open, save and save as options
- If a file is new, save option works as save as
- Identifies the following file types: C, C Header, C++, Python, Text
- Package used: Tkinter, os

Inspiration from [Zack King's Tutorial](https://www.youtube.com/watch?v=xqDonHEYPgA).

### Installation Instruction:
`pip install Golmorich`

### Running:
In terminal, type the following command: `golmorich.py`

### Screenshot:
![Golmorich Screenshot](https://pbs.twimg.com/media/EDOJxKvVUAAlgTw?format=jpg&name=large)
Promanagement 1.0.2
Process Management GUI App
Requires Python: >=3.0

# Process Management App
This is my second GUI app. A small utility app that shows:
- Process ID
- Process Name
- Username of Process Owner

The app gives you the option to click on a process ID and hit the kill button which kills that process. It auto refreshes the UI so the list of process will also get updated automatically.

### Tools:
- Python 3.6
- PyQt5 [GUI Framework]
- psutil [Python library]

### Test:
If you want to test it and not kill your OS process (or do anything dangerous), test it out by clicking on your browser Process ID and click on the Kill button. This should close your browser.

### How to Run:
``` 
$ pip install Promanagement
$ promanagement.py
```

### Screenshot:
![promanagement Screenshot](https://raw.githubusercontent.com/tiazahmd/promanagement/master/promanshot.png)