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DataFrame, analysis & manipulation library for tiny labeled datasets

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📈 Ickle - Data Analysis Library

A tiny DataFrame, statistics and analysis library for Python

PyPI version Downloads Package Status

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

Authors

@karishmashuklaa

@psy-pri