Metadata-Version: 2.1
Name: data-science-kit
Version: 0.0.1
Summary: Data Science Basic Functions
Home-page: https://github.com/KeremDlkmn/data-science-kit
Author: Kerem Delikmen
License: MIT
Project-URL: Source, https://github.com/KeremDlkmn/data-science-kit
Keywords: data science,data,data kit,data science kit,science,science kit,data engineer,data engineering,exploratory data analysis,eda,measurement units,data operations,data preprocessing,preprocessing
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Internet :: WWW/HTTP
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.6
Description-Content-Type: text/markdown

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![pypi: v0.0.1](https://img.shields.io/badge/pypi-v0.0.10-yellow.svg)](https://pypi.org/project/data-science-kit/)
[![Build: Passing](https://img.shields.io/badge/Build-Passing-green.svg)](https://github.com/KeremDlkmn/data-science-kit)

# Data Science Kit - Kitbag
KITBAG is a helpful library where you can find basic data science functions under three main headings. Three main headings are given below. The titles are given below;

* Measurement Units
  * Arithmetic Mean
  * Geometric Mean
  * Harmonic Mean
  * Median
  * Mode
  * Variance
  * Standart Deviation
  * Find Minimum Value In List
  * Find Maximum Value In List
  * Kurtosis
  * Skewnewss
  * Quantiles
  * Range Of Change
  * Covariance
  * Correlation
* Exploratory Data Analysis
  * Copy Dataset
  * Show Head & Tail Values
  * Structural Information
  * Variable Types
  * Object To Categorical
  * Observation Values Size
  * Variable Values Size
  * Size Of Dataset
  * Dimension Of DataFrame 
  * Dimension Of Series
  * Variable Names Of Dataset
  * Descriptive Statistics Of Dataset
  * Descriptive Statistics Of Numerical Variable 
  * Descriptive Statistics Of Categorical Variable  
  * Select Categorical Variables 
  * Select Numerical Variables 
  * Frequency Of Categorical Variables
  * Categorical Variables Percentage Of Dataset
  * Find Unique Categorical Variables
  * Frequency Unique Categorical Variables
  * Measure Operations Of Single Categorical Variables With All Numerical Variables
  * Measure Operations Of Single Categorical Variables Single Numerical Variables
  * Measure Operations Of Multiple Categorical Variables Single Numerical Variables
  * Create And Sort Rank Variables
  * Select Range Row By Index
* Data Operations
  * Missing Values
    * Is There Any NAN Values
    * Total NAN Values
    * Percentage NAN Values
    * Drop NAN Values In Row 
    * Drop NAN Values In Column
    * Fill NAN Values Column
    * Fill NAN Values All Dataset
    * Fil NAN Values KNN
    * Fil NAN Values EM
  * Outlier Values
    * Select Lower Outlier Values - According to the specified Column
    * Select Upper Outlier Values - According to the specified Column
    * Select All Outlier Values - According to the specified Column
    * Delete Outlier Values
    * Fill Mean Value All Outlier Values
    * Fill Suppression Method All Outlier Values
    * Local Outlier Factor
    * Local Outlier Factor Suppression Method
  * Variables Standartization
    * Numerical Variables Standartization  
    * Numerical Variables Normalization  
    * Numerical Variables Transformation  
    * Numerical Variables Binarize  
    * Numerical Variables To Categorical Variables
  * Encoding Operations
    * Ordinal Encoder
    * Label Encoder
    * Where Encoder
    * Onehot Encoder



