ickle 1.0.4

🔍 Experimental DataFrame, statistics and analysis library for Python

📈 Ickle - Data Analysis Library

A tiny DataFrame, statistics and analysis library for Python

Installation

Ickle can be installed via pip through PyPi

pip install ickle

Features

• DataFrame along with Visual Representation
• Basic properties (len, columns, shape, etc)
• Subset Selection
• Basic Methods (head, tail)
• Aggregation Methods (min, max, median, sum, etc)
• Non-Aggregation Methods (abs, copy, clip, cummin, etc)
• Additional Methods (isna, count, unique, etc)
• String-Only Methods (capitalize, center, count, find, etc)
• Pivot Table
• CSV
  • read_csv
  • to_csv
• Excel
  • read_excel
  • to_excel

... and more. 🚀 Checkout PATH.md to see the roadmap.

How To Contribute?

See 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.

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 here