<h1 align="center"> 📈 Ickle - Data Analysis Library</h1>
<h3 align="center">
A tiny DataFrame, statistics and analysis library for Python
</h3>
<div align="center">
[](https://badge.fury.io/py/ickle)
[](https://pepy.tech/project/ickle)
[](https://pypi.org/project/ickle/)
</div>
## Installation
Ickle can be installed via pip through PyPi
```
pip install ickle
```
## Features
- [x] DataFrame along with Visual Representation
- [x] Basic properties (len, columns, shape, etc)
- [x] Subset Selection
- [x] Basic Methods (head, tail)
- [x] Aggregation Methods (min, max, median, sum, etc)
- [x] Non-Aggregation Methods (abs, copy, clip, cummin, etc)
- [x] Additional Methods (isna, count, unique, etc)
- [x] String-Only Methods (capitalize, center, count, find, etc)
- [x] Pivot Table
- [ ] CSV
- [x] read_csv
- [ ] to_csv
- [ ] Excel
- [ ] read_excel
- [ ] to_excel
... and more. 🚀 Checkout [PATH.md](PATH.md) to see the roadmap.
## How To Contribute?
See [CONTRIBUTION.md](CONTRIBUTION.md) to know more.
## Getting Started
### DataFrame
A DataFrame holds two dimensional heterogenous data. It accepts dictionary as input, with Numpy arrays as values and strings as column names.
```py
import numpy as np
import ickle as ick
name = np.array(['John', 'Sam', 'Tina', 'Josh', 'Jack', 'Jill'])
place = np.array(['Kolkata', 'Mumbai', 'Delhi', 'Mumbai', 'Mumbai', 'Mumbai'])
weight = np.array([57, 70, 54, 59, 62, 70])
married = np.array([True, False, True, False, False, False])
data = {'name': name, 'place': place, 'weight': weight, 'married': married}
df = ick.DataFrame(data)
```
## Documentation
Read the documentation <a href="https://nbviewer.org/github/karishmashuklaa/ickle/blob/master/Ickle%20Documentation.ipynb">here</a>
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