semvec/pypi-packages huggingface dataset:
Topsis-101917064 0.1
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

Submitted By: **Shikhar 101917064**

***

## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
making method. TOPSIS chooses the alternative of shortest Euclidean distance
from the ideal solution, and greatest distance from the negative-ideal
solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-Shikhar 101917064  can be run as in the following example:


### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```

<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```
<br>
kustopy 1.2.8
Query and Ingestion Client for Azure using Python
License: MIT

# Getting started
kustopy is a Python SDK for Azure Data Explorer (Kusto).

[![PyPI version](https://badge.fury.io/py/kustopy.svg)](https://badge.fury.io/py/kustopy)
[![Downloads](https://pepy.tech/badge/kustopy)](https://pepy.tech/project/kustopy)

Ingestion using pyspark dataframes, is quite slow (compared to ingestion using pandas dataframes). 
But data is immediately in database after the command is run, while for pandas dataframes it can take
while for data to appear.

## Installation and Import
```bash
pip install kustopy
```

```python
from kustopy import KustoPyClient as kpc
```
## Client setup
```python
# Set up the query client
client = kpc.Client(cluster='https://sample.kusto.windows.net/',
                    database='sample',
                    client_id=client_id,
                    client_secret=client_secret,
                    tenant_id=tenant_id,
                    truncation=False)
```
---
### Queries
```python
# Get list of all tables available in database
client.get_table_names()
```
```python
# Get list of all tables available in database
client.get_schema(table_name='SampleTable')
```

```python
# Write standard kusto queries. To get the response as pandas dataframe set dataframe to True.
client.query(user_input='SampleTable | take 100 | where fruit=="apple"', data_frame=True)
```

---
### Ingestions


```python
# Drop table from the database
client.drop_table(table_name='SampleTable')
```

```python
# Drop table from the database
client.drop_dublicates(table_name='SampleTable')
```

```python
# Write pandas or pyspark dataframe to the database. If the table exists you will get an Error.
client.write_table(data_frame=df, table_name='SampleTable', folder='Sample')
```

```python
# Write pandas or pyspark dataframe to the database. If the table exists it will be replaced, otherwise created.
client.write_replace_table(data_frame=df, table_name='SampleTable', folder='Sample')
```

```python
# Write pandas or pyspark dataframe to the database. If the table exists it will be expanded, otherwise created.
client.write_append_table(data_frame=df, table_name='SampleTable', folder='Sample')
```
topsis-Ankita-101903766 1.0
A package for python implemetation of TOPSIS method for multiple criteria decision making
License: MIT
Keywords: SOME,MEANINGFULL,KEYWORDS

Topsis is a multi-criteria decision analysis method. it compares a set of alternatives based on a pre-specified criterion. The method is used in the business across various industries, every time we need to make an analytical decision based on collected data.