semvec/pypi-packages huggingface dataset:
rogit 1.3
This is a little git automation for lazy devs. Add, commit and push in one single command.
License: MIT
Keywords: git,automation

# Rogit
This is a little automation for lazy devs like me

## Installation
```bash
pip3 install rogit
```

## Usage
Inside your terminal, at the folder of your code, type
```bash
rogit
```
And it will prompt for the necessary info (mainly the commit message).

Made with �� from ����
excelyzer 0.0.18
Making Sense of Nonsense Excel Files
License: MIT
Requires Python: >=3.5
Keywords: data-analysis,business-intelligence,excel

<div align="center">
  <h1>Excelyzer</h1>
  <i>Making Sense of Unstructured Excel Files</i>
</div>


## Install

To install, use Python's pip via `pip install excelyzer`. It requires a Python version >= 3.5. To build the package locally, enter `python3 setup.py build_ext --inplace`.


## Usage

Excelyzer is quite limited for now, but we hope to change that soon! So far, excelyzer supports finding columns that are the sum or the product of other columns. The input works with a Pandas dataframe and a NumPy array.

### Find Sum of Columns

By default, columns are compared by the ratio of equal values in all rows.
```python

from excelyzer.dependencies import find_sum_columns

dataframe = pd.read_excel('table.xlsx')

find_sum_columns(dataframe, threshold=0.8, tolerance=1e-6)
# returns: [{'Total Year': ['Q1', 'Q2', 'Q3', 'Q4']}]
```

Excelyzer returns a list of dictionaries: The key corresponds to the sum column name, the value to the column names for the summands. The threshold defines the ratio of rows that needs to be equal within a tolerance value (with a default value of `1e-6`).

A second comparison type is based on the **absolute distance**.
```python

dependencies = find_sum_columns(dataframe, threshold=0.001, comparison_type='distance')
```

### Find Product of Columns

Along the lines of finding the sum, excelyzer also supports finding columns that are the product of other columns.
```python

from excelyzer.dependencies import find_product_columns

find_product_columns(dataframe, threshold=0.8)
# returns: [{'Total': ['Quantity', 'Price', 'Exchange Rate']}]
```

## License

Excelyzer is licensed under the MIT License.